Microbial Cell Factories Engineering for Production of Biomolecules [1 ed.] 0128214775, 9780128214770

Table of contents :
Front Matter
Copyright
Dedication
Contributors
About the editor
Foreword
Preface
Acknowledgments
An introduction to microbial cell factories for production of biomolecules
Introduction
Microbial hosts as microbial cell factories
Escherichia coli as a cell factory
Corynebacterium glutamicum as a cell factory
Lactococcus lactis as a cell factory
Bacillus subtilis as a cell factory
Pseudomonas putida as a cell factory
Saccharomyces cerevisiae as a cell factory
Pichia pastoris as a cell factory
Hansenula polymorpha as a cell factory
Yarrowia lipolytica as a cell factory
Cyanobacteria as a cell factory
Design and optimization of microbial cell factories
Conclusion and future remarks
References
Advances in long DNA synthesis
Introduction, history, and evolution of gene synthesis
Technological developments
Oligo synthesis
Column-based oligo synthesis
Array-based oligo synthesis
Gene synthesis
Array-based gene synthesis
Larger DNA assemblies
Whole-genome synthesis
Assembly of genetic parts for biosynthetic pathway building and its limitation
Applications of synthetic genes
Biosensing
Therapeutics
Blood glucose homeostasis
Cancer
Disease mechanisms and prevention
Novel treatments for bacterial infections
Biofuels and biomaterials
Current challenges in DNA synthesis
Biosynthetic pathway for the development of microfactories
Future developments
References
Discovery of enzymes responsible for cyclization and postmodification in triterpenoid biosynthesis
Introduction
Enzymes responsible for cyclization and postmodification of triterpenoid are crucial for triterpenoid biosynthesis
Approaches for discovery of enzymes in cyclization and postmodification of triterpenoid
Traditional approaches
Gene deletion
Gene silencing
Enzymatic activity guided protein purification
Mutation-based approaches
Synthetic biology approaches
Genomic-guided heterologous expression
Transcriptomic-guided heterologous expression
Combinatorial approaches
Concluding remarks and future perspectives
References
Synthetic biology approaches for secondary metabolism engineering
Introduction
Secondary metabolism
Biosynthetic clusters for secondary metabolites
Top-down strategies: The known biology
Characterization of the biological modules in secondary metabolism
Functional assembly of modules
Functional testing of the customized biosynthetic clusters
Bottom-up strategies: De novo systems
Conclusions and further perspectives
References
Synthetic biology design tools for metabolic engineering
Introduction
Tools for metabolic modeling
Computer models of metabolism
Trade-off between growth and production, and consideration of productivity
Addressing the interaction between synthetic gene circuits and host
Tools for metabolic pathway design
Metabolic pathway selection
Enzyme sequence selection
Tools for metabolic pathway experimental design
Bottom-up synthetic biology design of experiments
Optimal design of experiments in metabolic engineering
Tools for metabolic pathway dynamic regulation
Biosensors design for metabolic engineering
Metabolic pathway dynamic regulation
Machine learning tools for metabolic pathway design
Automated design workflows and standardization
Standardized information representation in synthetic biology
Workflow development for metabolic engineering
Integration with lab management software
Conclusions and future perspectives
References
Metabolic engineering for microbial cell factories
Introduction
Metabolic engineering approaches
Emergence of systems metabolic engineering
Systems metabolic engineering strategies
Design of systems metabolic engineering project
Selection of appropriate host platform
Construction of synthetic metabolic pathways
Optimization of metabolic fluxes
The principles and tools for pathway prediction and design
The pathways constructed using rational and computational strategies
De novo pathway design
In silico pathway prediction
Enzyme engineering and creation for synthetic pathways
Metabolic flux analysis
Enhancing tolerance against products and inhibitors
Scale up and industrial production
Conclusion and future perspectives
References
CRISPR-based tools for microbial cell factories
Introduction
CRISPR-Cas editing at the single gene level
Gene editing with CRISPR-Cas
CRISPR-Cas editing of single genes in bacteria
CRISPR-Cas editing of single genes in yeast
CRISPR-Cas editing at the genome level
CRISPR-optimized MAGE recombineering
CRISPR-EnAbled Trackable genome Engineering (CREATE)
Curing strategies for editing (gRNA) plasmids facilitate iterative CRISPR editing
CRISPR-Cas9 genome engineering tools in yeast
Gene regulation tools
CRISPR interference
CRISPR activation
Conclusions
References
Escherichia coli, the workhorse cell factory for the production of chemicals
Introduction: Escherichia coli, a model microorganism for basic and applied research
Precursor biotransformation
Whole-cell biocatalysis via single-step ``pathways´´: Harnessing enzymatic promiscuity
Protein engineering: Modification of enzymes by directed evolution
Cofactors pools and regeneration
Multistep biosynthesis pathways: One-pot multicatalysis
De novo biotransformations and metabolic engineering
Metabolites of industrial interest
d-Lactate
Dicarboxylic acids (succinate and malate)
Pyruvate
Amino acids
Biofuels
Biohydrogen
Bioethanol
Coproduction of biohydrogen and bioethanol
1-Butanol, 1-propanol, isopropanol and isobutanol
Biopolymers
Diols: 1,3-PDO, 1,2-PDO, and 1,4-BDO
PHA
Concluding remarks
References
Bacillus subtilis-based microbial cell factories
Introduction
Bacillus as a workhorse for high-value compound biosynthesis
Pathways involved in enzymes and primary metabolites biosynthesis
Gene clusters involved in secondary metabolites biosynthesis
Engineered Bacillus subtilis
Metabolic modeling for strains optimization of B. subtilis
Genome editing
Homologous recombination-based modification
CRISPR/Cas9-mediated genome editing
Transcriptional engineering to overproduce biomolecules or proteins of interest by B. subtilis
Promoter exchange in B. subtilis
Promoter engineering in B. subtilis
Expression of cryptic biosynthetic gene clusters
Engineered Bacillus on the translational level
Factors affecting translation rate
Regulation
Stability-enhancing sequences
Ribosome-binding site (RBS)
Other factors
Engineered Bacillus on transport level
Use of coproducts for the synthesis of high-value chemicals
Use of original substrates for B. subtilis culture media
Use of renewable resources for the production of secondary metabolites by Bacillus
Use of organic waste agroresidue and wastewater for the production of enzyme by Bacillus
Use of organic waste agroresidue for the production of biomolecule during solid-state fermentation of Bacillus
Minibacillus
Conclusion and future remarks
References
Pseudomonas putida-based cell factories
Introduction
Pseudomonas putida as a host for the production of natural products
Polyhydroxyalkanoates
Surfactants
Terpenoids
Prodigiosin
Concluding remarks
References
Further reading
Streptomyces-based cell factories for production of biomolecules and bioactive metabolites
Introduction
Streptomyces habitats
General characteristics of the genus Streptomyces
Growth requirements of Streptomyces species
Production of secondary metabolites
Streptomyces species as cell factories for production of antibiotics
Definition of antibiotics
The antibiotics derived from Streptomyces strains
Anticancer, immunostimulatory, immunosuppressive, and antioxidative agents produced by Streptomyces species
Streptomyces species as cell factories for production of active metabolites applied against causative agents of a numb ...
Kasugamycin
Polyoxin
Azalomycin
Validamycin
Geldanamycin and nigericin
Streptomyces species as cell factories for production of insecticides and antiparasitic agents
Tetranactin
Ivermectin
Streptomyces species as cell factories for production of a variety of enzymes
L-asparaginase
Cholesterol oxidase
Uricase (gout treatment enzyme)
Antidiabetic produced by Streptomyces species
Cholesterol synthesis inhibitors produced by Streptomyces species
Lytic enzymes
Cellulases
Amylase
Proteases and keratinases
Chitinolytic enzymes (chitinases)
Chitosanase
Streptomyces species as cell factories for production of lipases
Streptomyces species as cell factories for production of bioemulsifiers and biosurfactants
Streptomyces species as cell factories for production of pigments
Streptomyces species as cell factories for synthesis of nanoparticles
Production of vitamins
Production of odors
Conclusion and future perspective
References
Further reading
Corynebacterium glutamicum as a robust microbial factory for production of value-added proteins and small mol ...
Introduction
Protein secretion system in C. glutamicum
The Sec-dependent pathway
The Tat-dependent pathway
C. glutamicum protein expression system
Expression plasmid vectors of C. glutamicum
Promoters for optimized gene expression in C. glutamicum
Classification of promoters in C. glutamicum expression system
Sources of engineered promoters in C. glutamicum expression system
Sequence optimization of ribosome binding sites (RBS) for gene expression in C. glutamicum
Signal peptide applied in C. glutamicum expression vectors
Other expression elements
Replicons
Resistance markers
Gene editing tools applied in C. glutamicum
pKl8mobsacB and pKl9mobsacB based on homologous recombination technology
Gene knockout system based on Cre/loxP site-specific recombination technology
C. glutamicum as a major workhorse for production of small molecules
Rational metabolic engineering
2OG-derived chemicals
OAA-derived chemicals
Various more chemicals derived from central metabolism of C. glutamicum
Directed evolution
Adaptive laboratory evolution
Biosensors and high-throughput engineering
Conclusions
References
Production of high value-added chemicals by engineering methylotrophic cell factories
Introduction
New progress in genetic manipulation tools for engineering of MeCFs
Advances in engineering of the metabolic pathway in/from methylotrophs
Improvement of methylotrophic phenotypes via evolution
Producing high value-added chemicals by engineering MeCFs
Metabolic potential of native methylotrophs for synthesizing secondary metabolites
Conclusions and perspectives
References
Cyanobacteria-based microbial cell factories for production of industrial products
Introduction
Bioremediation
Biodiesel
Biohydrogen
Bioplastic
Microbial fuel cell
Nanoparticle synthesis by cyanobacteria
Exopolysaccharides producing cyanobacteria
Pigments producing cyanobacteria as microbial fuel cell
Carotenoids
3-Phycobiliproteins
Phycocyanin
Phycoerythrin
Antiviral, antibacterial, antifungal, and anticancer compounds obtain by cyanobacteria
Conclusion and future perspectives
References
Integrated omics perspective to understand the production of high-value added biomolecules (HVABs) in microal ...
Introduction
Unraveling biosynthetic pathways for the production of HVABs
Genomic and phylogenomic analysis
Transcriptomics and proteomics
Metabolomics
Metabolic flux analysis
Integrated omics for the redesigning/remapping metabolic pathways for enhanced HVAB production
Top-down approach (gene to metabolite)
Genome-scale mEtabolic models (GEMs)
Microalgal cell factories: An overview
Conclusions and future remarks
References
Saccharomyces cerevisiae as a microbial cell factory
Introduction
Methods and applications for yeast transformation
Transformation methods
DNA vectors for expressing genes
Promoters
Engineering of S. cerevisiae
CRISPR system-mediated engineering
Surface-display engineering
Metabolic engineering of S. cerevisiae
Cellulose degradation
Xylose utilization
LA production
Production of fine chemicals
Production of recombinant protein
Conclusion
References
Pichia pastoris-based microbial cell factories
Introduction
Genetic engineering tools for P. pastoris
Integrative expression of genes
Episomal expression of genes
Promoter engineering
Genome editing by CRISPR
Protein production by P. pastoris
Useful proteins produced by P. pastoris
Strategies for enhancing protein production
Fermentative chemical production by P. pastoris
Fermentative chemicals produced by P. pastoris
Strategies for enhancing fermentative chemical production
Chemical production by P. pastoris whole-cell biocatalyst
Oxidation reaction
Reduction reaction
ATP-dependent reaction
Cell surface-displayed enzyme reaction
Conclusions
References
Yarrowia lipolytica engineering as a source of microbial cell factories
Introduction
Main characteristics of Yarrowia lipolytica
A short history of Yarrowia lipolytica use
Overview of basic tools for Yarrowia lipolytica engineering
Expression/secretion vectors and transcription unit components
Choosing a replicative or an integrative vector
Vectors carrying multiple transcription units
Regulatory components of transcription units
Promoters
Terminators
Targeting components of transcription units
Cellular organelles targeting and compartmentalization
Signal sequences for secretion
Signal sequences for surface display
Strains and selection markers
Most commonly used recipient strains
Recipient strains with increased homologous recombination efficiency
Glycoengineered strains and their interest for therapeutic applications
Selection marker genes
A post-2010 era of new engineering technologies
New DNA assembly methods
In vivo assembly of metabolic pathways
Design of artificial chromosomes
In vitro DNA assembly methods
Genome editing technologies
CRISPR tools for gene editing
CRISPR tools for repression or activation of transcription
CRISPR tools for base editing
CRISPR tools for whole genome analysis
Other gene editing and transposomics tools
Sexual hybridization through mating-type switching
Yarrowia lipolytica developing applications and future prospects
High-throughput expression platforms for protein engineering and more
Obese Yarrowia lipolytica strains
Whole-cell Yarrowia lipolytica factories for single-cell oil production
Use of waste or renewable resources for production of biofuels and more
Whole-cell factories producing carotenoids, other terpenoids, and polyketides
Whole-cell factories for organic acid production
Bioengineered hybrid materials for environmental applications
What future prospects?
References
Engineering of microbial cell factories for production of plant-based natural products
Introduction
Host microorganisms
Metabolic engineering strategies in microorganisms for production of PNPs
Increasing precursor availability
Biosynthetic pathway engineering
Enzymatic engineering
Terpenoids
Lycopene
Taxol
Alkaloids
Benzylisoquinoline alkaloids
Polyphenols
Resveratrol
Naringenin
Conclusion and future challenges
Funding
References
Engineering of microbial cell factories for omics-guided production of medically important biomolecules
Introduction
Omics-driven microbial chassis development
Design, build, test, and learn of natural product synthesis in engineered microbes
Parts discovery for natural and new-to-nature natural products using systems biology platform
Omics-driven bioproduction of medically important biomolecules and natural products
Terpene synthase for plug-and-play terpene production
Strictosidine biosynthetic genes for refactoring of monoterpene indole alkaloid biosynthesis
Transcriptome-enabled discovery and microbial expression flavonoid biosynthetic genes
Conclusions and perspectives
References
Advances and applications of cell-free systems for metabolic production
Introduction
In vitro transcription-translation (TX-TL) systems
E. coli lysate-based TX-TL system
Other prokaryotic and eukaryotic lysate-based cell-free systems
PURE cell-free system
In vitro systems using purified enzymes
Glycolysis
Terpene production
Hydrogen production
n-Butanol production
CO2 fixation
Photobiocatalysis
Enzymatic electrocatalysis
Applications of TX-TL systems for bioproduction
Bioproduction and rapid prototyping
Engineering and evolution of enzymes
Bottom-up development of synthetic cells with cell-free systems
Conclusions and perspectives
References
Microbial biosensors for discovery and engineering of enzymes and metabolism
Introduction
Types and construction of microbial biosensors
Allosteric transcription factor-based biosensors
Riboswitch-based biosensors
Application of biosensors for the engineering of enzymes and metabolic pathways
Screening and selection
Dynamic regulation
Application of biosensors for the discovery of novel enzymes and metabolic pathways
The necessity of high-throughput screening approaches for discovery of novel gene and operon functions
Functional screening for novel enzymes with desired activities
Altering transcription factor expression to study gene and operon function
Methods to characterize transcription factor-DNA interactions
Toward high-throughput systems to characterize transcription factor-effector interactions
Conclusions and perspectives
References
Manipulation of global regulators in Escherichia coli for the synthesis of biotechnologically relevant products
Introduction of Escherichia coli metabolism
Global regulation of metabolism
Signal transduction
Control of the central carbon metabolism by global regulation
Global regulation in response to the carbon source: Crp and Cra
Crp
Cra
Regulation of intermediary metabolism: CreBC and Rob
CreBC
Rob
Regulation of central carbon metabolism in response to oxygen availability: ArcAB and FNR
ArcAB
FNR
Manipulation of global regulators: Its effect on the synthesis of biotechnological compounds
Biofuels
Ethanol
Butanol and higher alcohols
Polyhydroxyalkanoates
1,3-propanediol
Succinic acid
Concluding remarks
References
Index
A
B
C
D
E
F
G
H
I
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z

Citation preview

Microbial Cell Factories Engineering for Production of Biomolecules

Microbial Cell Factories Engineering for Production of Biomolecules

Edited by

Vijai Singh Indrashil University Rajpur, Mehsana, Gujarat, India

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN 978-0-12-821477-0 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Stacy Masucci Acquisitions Editor: Elizabeth Brown Editorial Project Manager: Billie Jean Fernandez Production Project Manager: Omer Mukthar Cover Designer: Mark Rogers Typeset by SPi Global, India

Dedication Dedicated to Dr. Jhillu Singh Yadav on his 70th birthday (August 4, 2020) for being the source of my inspiration and strength. Vijai Singh

Contributors Numbers in parentheses indicate the pages on which the authors’ contributions begin.

Ali Samy Abdelaal (79), Microbial Engineering Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India; Department of Genetics, Faculty of Agriculture, Damietta University, Damietta, Egypt David Adam (407), Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany Hiyam Adil Altaii (21), Faculty of Sciences, Mosul University, Mosul, Iraq Zhong-Hu Bai (235), National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, Wuxi, China Upasana Basu (21), Indian Institute of Science Education and Research, Bhopal, India Gargi Bhattacharjee (1), Department of Biosciences, School of Science, Indrashil University, Mehsana, Gujarat, India Jorge Bolı´var (115), Department of Biomedicine, Biotechnology and Public Health-Biochemistry and Molecular Biology, Campus Universitario de Puerto Real; Institute of Biomolecules (INBIO), University of Cadiz, Puerto Real, Cadiz, Spain Olivier Borkowski (407), Inria Paris; Institut Pasteur, Paris, France Siqin Cai (37), State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, and Laboratory of Molecular Biochemical Engineering, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China Pablo Carbonell (65), ai2, Polytechnic University of Valencia (Universitat Polite`cnica de Vale`ncia), Valencia, Spain

Franc¸ ois Coutte (139), Lipofabrik, Polytech-Lille, Cit
e scientifique, Villeneuve d’Ascq; UMR Transfrontalie`re BioEcoAgro N° 1158, Univ. Lille, INRAE, Univ. Lie`ge, UPJV, YNCREA, Univ. Artois, Univ. Littoral C^ ote d’Opale, ICV – Institut Charles Viollette, Lille, France Matthieu Da Costa (421), Center for Synthetic Biology, Unit for Biocatalysis and Enzyme Engineering, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium Teresa de Diego Puente (381), Department of Biochemistry and Molecular Biology (B) and Immunology, Faculty of Chemistry, University of Murcia, Campus de Espinardo, Regional Campus of International Excellence “Campus Mare Nostrum”, Murcia, Spain Debarun Dhali (21, 139), University of Lille; Lipofabrik, Polytech-Lille, Cit
e scientifique, Villeneuve d’Ascq, France Manuel Ca´novas Dı´az (381), Department of Biochemistry and Molecular Biology (B) and Immunology, Faculty of Chemistry, University of Murcia, Campus de Espinardo, Regional Campus of International Excellence “Campus Mare Nostrum”, Murcia, Spain Christoph Diehl (407), Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany Carrie A. Eckert (95), Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, Boulder; National Renewable Energy Laboratory (NREL), Golden, CO, United States Diego E. Egoburo (437), Departamento de Quı´mica Biolo´gica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, IQUIBICEN-CONICET, Buenos Aires, Argentina Noura El-Ahmady El-Naggar (183, 277), Department of Bioprocess Development, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria, Egypt

xv

xvi

Contributors

Francisco J. Enguita (51), Instituto de Medicina Molecular Joa˜o Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal

Liya Liang (95), Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, Boulder, CO, United States

Emily F. Freed (95), Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, Boulder, CO, United States

Rongming Liu (95), Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, Boulder, CO, United States

Julia Gallego-Jara (381), Department of Biochemistry and Molecular Biology (B) and Immunology, Faculty of Chemistry, University of Murcia, Campus de Espinardo, Regional Campus of International Excellence “Campus Mare Nostrum”, Murcia, Spain

Xiu-Xia Liu (235), National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, Wuxi, China

Nisarg Gohil (1), Department of Biosciences, School of Science, Indrashil University, Mehsana, Gujarat, India Ragaa A. Hamouda (277), Department of Biology, Faculty of Sciences and Arts Khulais, University of Jeddah, Jeddah, Kingdom of Saudi Arabia; Microbial biotechnology department, Genetic Engineering and Biotechnology Research Institute, University of Sadat City, Sadat City, Egypt Philippe Jacques (139), Lipofabrik, Polytech-Lille, Cit
e scientifique, Villeneuve d’Ascq, France; Microbial Processes and Interactions (MiPI) TERRA Teaching and Research Centre, BioEcoAgro Joint Research Unit (UMRt 1158), Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium M. Julia Pettinari (437), Departamento de Quı´mica Biolo´gica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, IQUIBICEN-CONICET, Buenos Aires, Argentina Pannaga P. Jutur (303), Omics of Algae Group and DBTICGEB Centre for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, India

Zengxin Ma (265), School of Life Sciences, Shandong Province Key Laboratory of Applied Mycology, and Qingdao International Center on Microbes Utilizing Biogas Qingdao Agricultural University, Qingdao, Shandong Province, People’s Republic of China Catherine Madzak (345), Paris-Saclay University, INRAE†, AgroParisTech, UMR SayFood, Thiverval-Grignon, France Rosa Alba Sola Martı´nez (381), Department of Biochemistry and Molecular Biology (B) and Immunology, Faculty of Chemistry, University of Murcia, Campus de Espinardo, Regional Campus of International Excellence “Campus Mare Nostrum”, Murcia, Spain Ryosuke Mitsui (319), Department of Chemical Engineering, Osaka Prefecture University, Sakai, Japan Xuhua Mo (265), School of Life Sciences, Shandong Province Key Laboratory of Applied Mycology, and Qingdao International Center on Microbes Utilizing Biogas Qingdao Agricultural University, Qingdao, Shandong Province, People’s Republic of China Charles Moritz (407, 421), Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany

Mukul S. Kareya (303), Omics of Algae Group and DBT-ICGEB Centre for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, India

Justyna Mozejko-Ciesielska (165), Department of Microbiology and Mycology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland

Val
erie Lecle`re (139), UMR Transfrontalie`re BioEcoAgro N° 1158, Univ. Lille, INRAE, Univ. Lie`ge, UPJV, YNCREA, Univ. Artois, Univ. Littoral C^ ote d’Opale, ICV – Institut Charles Viollette, Lille, France

Asha A. Nesamma (303), Omics of Algae Group and DBTICGEB Centre for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, India

Ana Lu´cia Leita˜o (51), Faculdade de Ci^encias e Tecnologia; MEtRICs, Faculdade de Ci^encias e Tecnologia, Universidade Nova de Lisboa, Campus da Caparica, Caparica, Portugal

Chetan Paliwal (303), Omics of Algae Group and DBTICGEB Centre for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, India

Ye Li (235), National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, Wuxi, China

Amir Pandi (407, 421), Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany

Contributors

Subha Sankar Paul (21), Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore, Singapore Ahmad Bazli Ramzi (393), Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia Lennart Schada von Borzyskowski (421), Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany

xvii

Biogas Qingdao Agricultural University, Qingdao, Shandong Province, People’s Republic of China Han Xiao (37), State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, and Laboratory of Molecular Biochemical Engineering, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China

Rabinder Singh (303), Omics of Algae Group and DBTICGEB Centre for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, India

Xinhui Xing (265), MOE Key Lab of Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering; Center for Synthetic and Systems Biology, Tsinghua University, Beijing, People’s Republic of China

Vijai Singh (1), Department of Biosciences, School of Science, Indrashil University, Mehsana, Gujarat, India

Ryosuke Yamada (319, 335), Department of Chemical Engineering, Osaka Prefecture University, Sakai, Japan

Sean Stettner (95), Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, Boulder, CO, United States

Song Yang (265), School of Life Sciences, Shandong Province Key Laboratory of Applied Mycology, and Qingdao International Center on Microbes Utilizing Biogas Qingdao Agricultural University, Qingdao, Shandong Province; Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin University, Tianjin, People’s Republic of China

Yuman Sun (265), School of Life Sciences, Shandong Province Key Laboratory of Applied Mycology, and Qingdao International Center on Microbes Utilizing Biogas Qingdao Agricultural University, Qingdao, Shandong Province, People’s Republic of China Srividhya Sundaram (407), Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany Gema Lozano Terol (381), Department of Biochemistry and Molecular Biology (B) and Immunology, Faculty of Chemistry, University of Murcia, Campus de Espinardo, Regional Campus of International Excellence “Campus Mare Nostrum”, Murcia, Spain Heykel Trabelsi (21, 139), Micalis Institute, INRAE, Universit
e Paris-Saclay, Jouy-en-Josas; Lipofabrik, Polytech-Lille, Cit
e scientifique, Villeneuve d’Ascq, France Antonio Valle (115), Department of Biomedicine, Biotechnology and Public Health-Biochemistry and Molecular Biology, Campus Universitario de Puerto Real; Institute of Viticulture and Agri-Food Research (IVAGRO) International Campus of Excellence (ceiA3), University of Cadiz, Puerto Real, Cadiz, Spain Mengying Wang (265), School of Life Sciences, Shandong Province Key Laboratory of Applied Mycology, and Qingdao International Center on Microbes Utilizing

Yazen Yaseen (21, 139), Faculty of Sciences, Mosul University, Mosul, Iraq; Lipofabrik, Polytech-Lille, Cit
e scientifique, Villeneuve d’Ascq, France Syed Shams Yazdani (79), Microbial Engineering Group; DBT-ICGEB Centre for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, India Guihong Yu (265), School of Life Sciences, Shandong Province Key Laboratory of Applied Mycology, and Qingdao International Center on Microbes Utilizing Biogas Qingdao Agricultural University, Qingdao, Shandong Province, People’s Republic of China Changtai Zhang (265), School of Life Sciences, Shandong Province Key Laboratory of Applied Mycology, and Qingdao International Center on Microbes Utilizing Biogas Qingdao Agricultural University, Qingdao, Shandong Province, People’s Republic of China Hui Zhang (265), School of Life Sciences, Shandong Province Key Laboratory of Applied Mycology, and Qingdao International Center on Microbes Utilizing Biogas Qingdao Agricultural University, Qingdao, Shandong Province, People’s Republic of China

About the editor Vijai Singh is an associate professor and head of the Department of Biosciences, School of Science at Indrashil University, Mehsana, Gujarat, India. He was an assistant professor in the Department of Biological Sciences and Biotechnology at Institute of Advanced Research, Gandhinagar, India, and also an assistant professor in the Department of Biotechnology at the Invertis University, Bareilly, India. Prior to that, he was a postdoctoral fellow in the Synthetic Biology Group at the Institute of Systems and Synthetic Biology, Paris, France, and School of Energy and Chemical Engineering at the Ulsan National Institute of Science and Technology, Ulsan, South Korea. He received his Ph.D. in Biotechnology (2009) from the National Bureau of Fish Genetic Resources, Uttar Pradesh Technical University, Lucknow, India, with a research focus on the development of molecular and immunoassays for diagnosis of Aeromonas hydrophila. He has extensive experience in synthetic biology including MAGE, small regulatory RNAs, pathway designing, CRISPR/Cas systems, and microfluidics. His research interests are focused on building of a novel biosynthetic pathway for production of medically and industrially important biomolecules. Additionally, his laboratory is working on CRISPR/ Cas9 tools for genome editing. He has more than 8 years of research and teaching experience in synthetic biology, microbiology, and industrial microbiology. He has published 75 articles, 30 chapters, and 5 books. He serves as a member of editorial board and reviewer of a number of peer-reviewed journals. He is also a member of the Board of Study and Academic Council of Indrashil University and is the Member Secretary of Institutional Biosafety Committee (IBSC), Indrashil University.

xix

Foreword I am delighted to write the introductory statements to Microbial Cell Factories Engineering for Production of Biomolecules, a timely volume on the rapidly growing field. Of late, global warming, climate changes, and the nondegradable nature of chemicals and their derivatives used in daily life have left serious repercussion on animal health including humans. Several molecules that are chemically synthesized involve cumbersome and laborious steps. In addition to that, even after a series of arduous steps, sometimes the end products are still low potency molecules owing to its complex chemical structure. Also the use of many of the petrochemical-based chemicals and their products has been banned in many countries. Therefore a pressing need has arisen to find an alternative and green approach for the production of value-added biomolecules to fulfill the current global demands. Bio-based products are currently in high demand and attracting much scientific attention due to its cost-effective and biodegradable nature. However, a major challenge with bio-based products produced using natural microorganisms is the low yield, which is not feasible to be used for industrial applications and to fulfill current market demands. Microbial cell factories engineering offers the production of a wide range of value-added biomolecules through the extension or modification of biosynthetic pathways in microorganisms in a cost-effective, renewable, and eco-friendly manner. State-of-the-art tools and technologies including synthetic biology toolbox, genome editing, biosensors, and cell-free systems are currently being developed that can help to produce, optimize, and fine-tune the production of biomolecules for industrial applications. This book covers predominantly used engineered microbial cell factories such as Escherichia coli, Bacillus subtilis, Pseudomonas putida, Lactobacillus lactis, Streptomyces, Corynebacterium glutamicum, Saccharomyces cerevisiae, Pichia pastoris, Yarrowia lipolytica, Cyanobacteria, and algae for the production of biomolecules. This book covers basic understanding of advanced and applied applications of different areas of microbial cell factories. It is a collection of 23 chapters written by eminent scientists who have well-established research on microbial cell factories for production biomolecules. All the chapters in this book have been written in an easy-to-understand language and designed to cover in-depth knowledge of the subject. I am happy to recognize the valuable efforts of Dr. Vijai Singh, who brought out an excellent volume through the support of Elsevier. I believe that this book is a good source for students, researchers, scientists, policymakers, stakeholders synthetic biologists, metabolic engineers, and biotechnologists interested in the potential of microbial cell factories in many areas.

Dr. Jhillu Singh Yadav, FNA, FTWAS JC Bose Fellow, CSIR-Bhatnagar Fellow Provost and Director, Indrashil University, Rajpur, Mehsana, Gujarat, India Former Director, CSIR-Indian Institute of Chemical Technology, Hyderabad, India

xxi

Preface The awareness of natural products has gained more public attention for its use in our daily life. Many of the products are produced or manufactured from a petrochemical resource that is depleting at a lightning speed. Currently, global warming, climate changes, and the nondegradable nature of chemicals and its derivatives used in daily life have also harmed health. The use of many of the petrochemical-based chemicals and their products has been banned in many countries. Therefore, a pressing need has arisen to find alternative and green approaches for the production of value-added biomolecules to fulfill the current global demands. Microbial cell factories (MCF) are an alternative solution for the production of biomolecules through extension or modification of biosynthetic pathways in microorganisms. Microbial cell factories are rapidly growing and gaining much scientific attention due to their high potential in the future market that can fulfill current demands. MCF considers microbial cells as a production facility in which the optimization process largely depends on metabolic engineering and synthetic biology. In the past few decades, several natural microorganisms have been identified and used for the production and optimization of value-added biomolecules for therapeutic, biotechnological, pharmaceutical, and industrial applications. However, these microorganisms have faced many challenges and issues in the production of biomolecules, which is a major limitation for high production due to less efficiency; lack of genetic engineering, synthetic biology, and metabolic engineering toolboxes; and poor understanding of genetics, physiology, and many more. This book covers state-of-the-art tools and technologies of DNA synthesis, design of biosynthetic pathways, microbial biosensors, cell-free systems, CRISPR/Cas systems, synthetic biology, and metabolic engineering approaches for engineering of microbial cell factories. Currently, only few microorganisms have been employed as cell factories for the production of biomolecules. This book covers microorganisms including Escherichia coli, Bacillus subtilis, Pseudomonas putida, Lactobacillus lactis, Streptomyces, Corynebacterium glutamicum, Saccharomyces cerevisiae, Pichia pastoris, Yarrowia lipolytica, Cyanobacteria, and algae-based microbial cell factories. These cell factories offer a wide range of biomolecules that have been produced by installing a novel biosynthetic pathway or modifying existing biosynthetic pathways for industrial, therapeutic, pharmaceutical, industrial, and biotechnological applications. This book provides a basic understanding of advanced and applied knowledge of different areas of microbial cell factories and covers a great range of topics. It covers a rich literary text of excellent depth, clarity, and coverage, which allows us to easily understand and start research in the area of microbial cell factories. This book is a collation of 23 chapters written by distinguished scientists from 16 countries including Argentina, Belgium, China, Egypt, France, Germany, India, Japan, Poland, Portugal, Saudi Arabia, Singapore, Spain, United Kingdom, and the United States. I hope that this book provides a better understanding of microbial cell factories engineering toward production, optimization, and large scale-up of valuable biomolecules. I strongly believe that the present book would strengthen scientific understanding of microbial cell factories and primer for researchers, students, scientists, stakeholders, policymakers, synthetic biologists, metabolic engineers, biotechnologists, and many more. Although enormous efforts were invested to make this book user-friendly, I am aware that the first version always comes with bugs. I would be happy to receive feedback for improving this book further. Vijai Singh

xxiii

Acknowledgments I am happy to express my sincere gratitude and deep appreciation to Dr. J.S. Yadav, Provost and Director (Research), Indrashil University, India, who gave me outstanding personal and professional support and inspiration to complete this book. I am happy to express my thanks to all the authors for their excellent contributions to this book. I would like to thank Ms. Elizabeth Brown (Senior Acquisitions Editor), Ms. Billie Jean Fernandez (Editorial Project Manager), and Ms. Sheela Bernardine Josy (Copyrights Coordinator) from Elsevier for their support and exceptional management of this project. I greatly appreciate the support of my students Mr. Nisarg Gohil and Ms. Gargi Bhattacharjee, whose discussion and comments helped to shape this book. I thank Dr. Rajesh Bhosale, Dr. Sharan Sharma, Dr. Satya Prakash, and those whose names do not feature here but have directly or indirectly contributed in shaping this project. I wish to express my gratitude to my beloved wife Pritee Singh for her endless support, patience, and inspiration. Lots of affection for my kids Aaradhya and Ayush who missed me during this project. I would like to warmly thank the faculty and staff of Indrashil University for providing a great working environment. I take this opportunity to sincerely thank GOD for his supreme POWER and giving me strength, courage, and wisdom in the shape of this book. Vijai Singh

xxv

Chapter 1

An introduction to microbial cell factories for production of biomolecules Nisarg Gohil, Gargi Bhattacharjee, and Vijai Singh∗ Department of Biosciences, School of Science, Indrashil University, Mehsana, Gujarat, India ∗

Corresponding author: E-mail: [email protected]

1 Introduction Right from its use in traditional fermentation products (bread, cheese, wine, and beer), microorganisms have extensively featured in industries for bulk production of biopharmaceuticals (hormones, enzymes, antibiotics, vitamins, and vaccines), polymers, monomers, high-value chemicals, and other bioproducts. Advances in molecular biology toolboxes, recombinant DNA technology, metabolic engineering, systems biology tools, CRISPR-Cas9 (clustered regularly interspaced short palindromic repeats [CRISPR] associated protein 9), etc. as genome editing techniques along with the availability of complete genome sequencing data have enabled easy genetic manipulation and pathway engineering for transforming microorganisms into microbial cell factories (Zhang et al., 2012; Gohil et al., 2017; Singh et al., 2017, 2018; Choi et al., 2019; Calero and Nikel, 2019; Bhattacharjee et al., 2020a). Furthermore, availability of designed and characterized synthetic biological parts that include synthetic promoters, riboregulators, riboswitches, ribosome binding sites (RBS), terminators, scaffolds, and devices such as synthetic oscillators, biologic gates, and toggle switches have facilitated construction and fine-tuning of the engineered pathway very conveniently (Singh, 2014; Marx et al., 2016; Jin et al., 2017; Patel et al., 2018; Bhattacharjee et al., 2020b). In this revolutionized era, various expression systems have flourished to produce valuable nonmicrobial metabolites from renewable resources. Production of artemisinic acid, a precursor of the antimalarial drug artemisinin (Ro et al., 2006; Paddon et al., 2013), and synthetic moth pheromones, an alternative to insecticides (Hagstr€om et al., 2013), in engineered Saccharomyces cerevisiae yeast are the best examples of important nonmicrobial metabolites. Apart from that, microbial factories have been used to produce advanced biofuels (e.g., pinene, biohydrogen, and biomethanol), commodity chemicals (e.g., adipic acid, 2-pyrrolidone, precursors of polymers, plastic, and surfactant), and biopesticides and even for bioremediation purpose (Adkins et al., 2012; Chubukov et al., 2016; Meadows et al., 2018; Ramı´rez-Garcı´a et al., 2019; Dangi et al., 2019; Das et al., 2020). In this chapter, various microbial hosts as microbial cell factories, its pathway designing, and optimization of microbial factories have been explored.

2 Microbial hosts as microbial cell factories 2.1 Escherichia coli as a cell factory Escherichia coli is the most exploited host organism for designing cell factories for production of valuable chemicals and other important metabolites. A comprehensive insightful physiological and genetic background, rapid growth, ease of precise genetic modification, fast growth in minimal salt medium, growth compatibility in cheaper substrates, and capability to accumulate heterologous proteins up to 50% of its dry cell weight make it true “industrial workhorse” (Demain and Adrio, 2008; Liu et al., 2015a). Even the first protein-biopharmaceutical product approved by the Food and Drug Administration (FDA), Humulin, a recombinant human insulin, was produced in E. coli (Sanchez-Garcia et al., 2016). Nearly 30% of the approved recombinant protein-based biopharmaceutical products are produced in E. coli (Baeshen et al., 2015). Among the approved protein drugs that were being used for cancer treatment till 2015, 69% were fabricated in E. coli (Sanchez-Garcia et al., 2016). Many research groups have extended the native pathway present in E. coli by introducing heterologous gene(s) to produce different metabolites and chemicals. Majorly the native 2-methyl-(D)-erythritol-4-phosphate (MEP) and fatty acid Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00021-0 © 2021 Elsevier Inc. All rights reserved.

1

2 Microbial cell factories engineering for production of biomolecules

biosynthesis pathways have been engineered in E. coli for production of different biomolecules. The MEP pathway has been extended to produce different isoprenoids including terpenoids, terpene, and carotenoids (e.g., bisabolene, lycopene, amorphadiene, farnesene, myrcene, limonene, squalene, pinene, crocin, casbene, b-phellandrene, and taxadiene) (Martin et al., 2003; Ro et al., 2006; Ghimire et al., 2009; Ajikumar et al., 2010; Alonso-Gutierrez et al., 2013; Paddon et al., 2013; Sarria et al., 2014; Wong et al., 2017; Zada et al., 2018; Gohil et al., 2019a; Wang et al., 2019). E. coli has also been used for production of organic acids such as D-lactate (Zhou et al., 2012a), 3-hydroxypropionate (Kim et al., 2014), succinate (Vemuri et al., 2002), L-malate (Zhang et al., 2011), fumarate (Song et al., 2013), glucaric acid (Moon et al., 2009), muconic acid (Niu et al., 2002), and adipic acid (Yu et al., 2014); biofuels such as butanol (Dong et al., 2016; Panchasara et al., 2018), sabinene (Zhang et al., 2014), bioethanol (Ingram et al., 1987), propanol (Atsumi et al., 2008), isopentenol (Li et al., 2018), geraniol (Liu et al., 2016a), farnesol (Zada et al., 2018), isoprene (Kim et al., 2016), levopimaradiene (Leonard et al., 2010), polyhydroxybutyrate (Rahman et al., 2013), and bisabolene (Peralta-Yahya et al., 2011); pigments such as violacein (Fang et al., 2015) and staphyloxanthin (Kim and Lee, 2012); and dicarboxylic acids (Haushalter et al., 2017) and oleochemicals (Haushalter et al., 2015) (refer Table 1). These biosynthetic pathways in E. coli can be further extended to produce heterologous biomolecules for a wide range of applications.

TABLE 1 Important biomolecules produced by Escherichia coli cell factory. Biomolecules

References

Squalene

Ghimire et al. (2009) and Gohil et al. (2019a)

Amorphadiene

Ro et al. (2006)

D-Lactate

Zhou et al. (2012a)

3-Hydroxypropionate

Kim et al. (2014)

Succinate

Vemuri et al. (2002)

L-Malate

Zhang et al. (2011)

Fumarate

Song et al. (2013)

Glucaric acid

Moon et al. (2009)

Muconic acid

Niu et al. (2002)

Adipic acid

Yu et al. (2014)

Butanol

Dong et al. (2016) and Panchasara et al. (2018)

Sabinene

Zhang et al. (2014)

Bioethanol

Ingram et al. (1987)

Propanol

Atsumi et al. (2008)

Isopentenol

Li et al. (2018)

Geraniol

Liu et al. (2016a)

Farnesol

Zada et al. (2018)

Isoprene

Kim et al. (2016)

Levopimaradiene

Leonard et al. (2010)

Polyhydroxybutyrate

Rahman et al. (2013)

Bisabolene

Peralta-Yahya et al. (2011)

Violacein

Fang et al. (2015)

Staphyloxanthin

Kim and Lee (2012)

Dicarboxylic acids

Haushalter et al. (2017)

Oleochemicals

Haushalter et al. (2015)

An introduction to MCF Chapter

1

3

2.2 Corynebacterium glutamicum as a cell factory Since more than half a century, Corynebacterium glutamicum has been immensely used in industries for the production of amino acids, especially L-glutamate and L-lysine with annual production up to 2.16 and 1.48 million tons, respectively (Zahoor et al., 2012). The rising global demand for amino acids and the capability of C. glutamicum to synthesize amino acids have caught the attention of many researchers to overproduce amino acids significantly. As a consequence, firstly Lglutamate and L-lysine (Schneider et al., 2011; Ma et al., 2017) and subsequently L-valine (Hasegawa et al., 2012), Lisoleucine (Kelle et al., 1996), L-serine (Peters-Wendisch et al., 2005), L-arginine (Schneider et al., 2011), L-methionine (Park et al., 2007), L-cysteine (Wei et al., 2019), L-ornithine (Hwang et al., 2008; Schneider et al., 2011), 5-aminovaleric acid (Shin et al., 2016), g-aminobutyric acid (Shi and Li, 2011), and D-amino acids (St€abler et al., 2011) have been overproduced in C. glutamicum by optimizing different endogenous substrate utilization pathways (e.g., glucose and lactic acid), expanding access to alternative sources (e.g., starch, lactose, galactose, arabinose, xylose, cellobiose, dicarboxylic acids, glycerol, coutilization, levoglucosan, glucosamine, and N-acetylglucosamine), overexpressing rate-limiting enzymes, knocking out nonessential pathway genes and employing synthetic biology tools (Zahoor et al., 2012; Wendisch et al., 2016; Baritugo et al., 2018) (refer Table 2). Not only it is solely constricted to amino acids, but also some diamines such as putrescine (Schneider and Wendisch, 2010) and cadaverine (Mimitsuka et al., 2007); organic acids including succinic acid (Okino et al., 2008a), pyruvic acid (Wieschalka et al., 2012), lactic acid (Okino et al., 2008b), a-ketoglutaric acid ( Jo et al., 2012), 2-ketoisovaleric acid (Krause et al., 2010), and D-pantothenic acid (H€user et al., 2005); alcohols such as ethanol (Inui et al., 2004), isobutanol (Blombach et al., 2011), 1,2-propanediol (Niimi et al., 2011), and isopentenol (Sasaki et al., 2019); sugar alcohols such as Dmannitol (B€aumchen et al., 2009) and xylitol (Sasaki et al., 2010); food colorants such as 3-O-glucoside (Zha et al., 2018); polyamides such as 1,5-diaminopentane (Matsuura et al., 2019); and D-( )-acetoin (Mao et al., 2017) have also been produced.

2.3 Lactococcus lactis as a cell factory Just as E. coli and C. glutamicum, Lactococcus lactis has been granted as generally recognized as safe (GRAS) organism by the FDA and used largely over the centuries for preservation and fermentation of food. Achievements over the last two decades regarding the production of different fermentation products, industrial metabolites, enzymes, recombinant proteins, therapeutics, and vaccine delivery systems have transformed L. lactis to an efficient and potent microbial cell factory (Morello et al., 2008; Song et al., 2017). This transformation has been credible due to the availability of profound genetic knowledge obtained from the four fully sequenced Lactococcus strains, identified constitutive and inducible promoters, protein secretion strategies, and development of various Lactococcus expression systems (Morello et al., 2008). Examples pertaining to important industrial products include lactic acid (Kandler, 1983), acetoin (Garcı´a-Quinta´ns et al., 2008), linalool (Herna´ndez et al., 2007), and hyaluronic acid (Rajendran et al., 2016); vitamins such as folate (Sybesma et al., 2004) € u et al., and riboflavin (Sybesma et al., 2004); biofuels such as ethanol (Liu et al., 2016b); peptides such as bacteriocin (Unl€ 2016), brazzein (Berlec et al., 2012), and nisin Z (Zhang et al., 2015); enzymes such as alcohol acyltransferase (Herna´ndez et al., 2007), sesquiterpene synthase (Herna´ndez et al., 2007), bile salt hydrolase (Dong et al., 2015), and acid urease (Yang et al., 2015); and different recombinants cytokines and antigens (Song et al., 2017) (refer Table 3). Apart from that, many plant- and membrane-based proteins have also been expressed successfully (Song et al., 2017). Lactococcus can be further used for many new therapeutics, vaccines and drugs, nutraceuticals and many more for treatment and control of infections as well as diseases.

2.4 Bacillus subtilis as a cell factory Despite numerous advantages of exercising E. coli as host over others for the production of a variety of industrial products, it cannot be remarked as the gold standard for the production of heterologous proteins as it tends to accumulate proteins within the cells, resulting in the generation of inclusion bodies, which makes extraction of proteins cumbersome. Over and above that the presence of pyrogenic lipopolysaccharide layer over the outer membrane of E. coli makes it even more challenging as it needs to be separated before the utilization of proteins (Westers et al., 2004; Zweers et al., 2008). This is where gram-positive Bacillus subtilis outperforms E. coli by exhibiting exceptionally high protein secretion capability and that too even directly in fermentation broth without any toxic by-products (van Dijl and Hecker, 2013).

4 Microbial cell factories engineering for production of biomolecules

TABLE 2 Important biomolecules produced by Corynebacterium glutamicum cell factory. Biomolecules

References

L-Glutamate, L-lysine, L-arginine

Schneider et al. (2011)

L-Ornithine

Hwang et al. (2008) and Schneider et al. (2011)

L-Valine

Hasegawa et al. (2012)

L-Isoleucine

Kelle et al. (1996)

L-Serine

Peters-Wendisch et al. (2005)

L-Methionine

Park et al. (2007)

L-Cysteine

Wei et al. (2019)

5-Aminovaleric acid

Shin et al. (2016)

g-Aminobutyric acid

Li et al. (2011) and Shi and Li (2011)

D-Amino

St€abler et al. (2011)

acids

Putrescine

Schneider and Wendisch (2010)

Cadaverine

Mimitsuka et al. (2007)

Succinic acid

Okino et al. (2008a)

Pyruvic acid

Wieschalka et al. (2012)

Lactic acid

Okino et al. (2008b)

a-Ketoglutaric acid

Jo et al. (2012)

2-Ketoisovaleric acid

Krause et al. (2010)

D-Pantothenic

H€ user et al. (2005)

acid

Ethanol

Inui et al. (2004)

Isobutanol

Blombach et al. (2011)

1,2-Propanediol

Niimi et al. (2011)

Isopentenol

Sasaki et al. (2019)

D-Mannitol

B€aumchen et al. (2009)

Xylitol

Sasaki et al. (2010)

3-O-Glucoside

Zha et al. (2018)

1,5-Diaminopentane

Matsuura et al. (2019)

D-(

Mao et al. (2017)

)-Acetoin

However, use of B. subtilis is still limited for production of recombinant proteins at the industrial scale, which might be because of shortcoming in expression vectors, instability of plasmid, and protease activity. To restrain protease activity, numerous B. subtilis strains have been constructed (Westers et al., 2004). It is noteworthy that B. subtilis has been extensively utilized for overproduction of many enzymes such as a-amylase, protease, lipase A, B. subtilis natto nattokinase, endoglucanase, and endocellulase and recombinant proteins such as streptavidin, single-chain variable fragment (scFv), human epidermal growth factor (hEGF), interferons (INF)-alpha 2, Streptococcus pyogenes pneumolysin, and subtilisin (Westers et al., 2004; Zhang et al., 2019) via highly conserved secretory (Sec) pathway and the twin-arginine translocation (Tat) pathway (van Dijl and Hecker, 2013; Zhang et al., 2019) (refer Table 4). Bacillus ranks among the preferred microbial cell factories and has already been used in many biomolecule synthesis and scale-up. It can be further explored in the near future given that the aforementioned limitations are resolved.

An introduction to MCF Chapter

1

5

TABLE 3 Important biomolecules produced by Lactococcus lactis cell factory. Biomolecules

References

Lactic acid

Kandler (1983)

Acetoin

Garcı´a-Quinta´ns et al. (2008)

Linalool

Herna´ndez et al. (2007)

Hyaluronic acid

Rajendran et al. (2016)

Folate

Sybesma et al. (2004)

Riboflavin

Sybesma et al. (2004)

Ethanol

Liu et al. (2016b)

Bacteriocin

€ u et al. (2016) Unl€

Brazzein

Berlec et al. (2012)

Nisin Z

Zhang et al. (2015)

Alcohol acyltransferase, sesquiterpene synthase

Herna´ndez et al. (2007)

Bile salt hydrolase

Dong et al. (2015)

Acid urease

Yang et al. (2015)

TABLE 4 Important biomolecules produced by Bacillus subtilis cell factory. a-Amylase, protease, lipase A, Bacillus subtilis natto nattokinase, endoglucanase, endocellulase

van Dijl and Hecker (2013) and Zhang et al. (2019)

Streptavidin, scFv, hEGF, interferons (INF)-alpha 2, Streptococcus pyogenes pneumolysin, subtilisin

Westers et al. (2004)

scFv, single-chain variable fragment; hEGF, human epidermal growth factor.

2.5 Pseudomonas putida as a cell factory Pseudomonas putida is a versatile, ubiquitous, amenable, genetically accessible alternative potent strain having the capacity to be used in industries for large-scale production of diverse compounds due to its oxidative stress resistance, low nutritional requirement, and high tolerance to toxins, high temperature, extreme pH, and solvents (Kim and Park, 2014; Lemire et al., 2017; Hernandez-Arranz et al., 2019). It is a promising alternative to E. coli for isoprenoid production (Hernandez-Arranz et al., 2019). Besides this, it is also known for its ability to degrade aromatic compounds significantly (Nogales et al., 2017). To date, it has been used to produce isoprenoids such as limonene (Loeschcke and Thies, 2015), geranic acid (Mi et al., 2014), zeaxanthin (Beuttler et al., 2011), and b-carotene (Loeschcke et al., 2013; Sa´nchezPascuala et al., 2019); aromatic compounds such as trans-cinnamate, L-phenylalanine, 2-phenylethanol, p-coumarate, p-hydroxybenzoate, and phenol (Molina-Santiago et al., 2016; Calero et al., 2018); amino acids such as N-methylglutamate (Mindt et al., 2018); polyhydroxyalkanoates (Wang et al., 2011a); D-glucosaminic acid (Wu et al., 2011); and amino acidderived compounds such as phenol (Wierckx et al., 2005), p-coumarate (Nijkamp et al., 2007), catechol (Prakash et al., 2010), and aliphatic alcohols (Bosetti et al., 1992) (refer Table 5).

2.6 Saccharomyces cerevisiae as a cell factory Saccharomyces cerevisiae, a baker’s yeast, is a eukaryotic well-studied model organism that has been used since ancient times in baking and brewing industries. Similar to E. coli, S. cerevisiae has a good source of genetic engineering toolboxes, ease of engineering, robustness, decent temperature, pH, and salt tolerance, all of which allow the production of proteins,

6 Microbial cell factories engineering for production of biomolecules

TABLE 5 Important biomolecules produced by Pseudomonas putida cell factory. Limonene

Loeschcke and Thies (2015)

Geranic acid

Mi et al. (2014)

Zeaxanthin

Beuttler et al. (2011)

b-Carotene

Loeschcke et al. (2013) and Sa´nchez-Pascuala et al. (2019)

Trans-Cinnamate, L-phenylalanine, 2-phenylethanol, p-coumarate, p-hydroxybenzoate

Molina-Santiago et al. (2016) and Calero et al. (2018)

N-Methylglutamate

Mindt et al. (2018)

Polyhydroxyalkanoates

Wang et al. (2011a)

D-Glucosaminic

Wu et al. (2011)

acid

Phenol

Wierckx et al. (2005)

p-Coumarate

Nijkamp et al. (2007)

Catechol

Prakash et al. (2010)

Aliphatic alcohols

Bosetti et al. (1992)

metabolites, and other compounds that cannot be produced at the prokaryotic level at a great extent in terms of titer, yield, and rate (Ferrer-Miralles et al., 2009; Nielsen, 2019). For implementing the oldest industrial workhorse to modern cell factory, tremendous developments have been made, including the establishment of S. cerevisiae-compatible genome editing tools, the establishment of orthogonal systems, improvement in robustness and stress tolerance, exploration of substrate sources, and enhancement of product spectrum (Kavsˇcek et al., 2015). Started from the production of bread, wine, and beer, it has been now expanded into the production of different chemical classes such as alcohols (e.g., bioethanol, biobutanol, biodiesel, and bisabolene), fatty acids (e.g., oleic acid and octanoic acid), organic acids (e.g., propionic acid, succinic acid, malic acid, lactic acid, and pyruvic acid), sesquiterpenes (e.g., artemisinic acid, bisabolene, santalene, and valencene), flavonoids (e.g., naringenin), steroids (e.g., hydrocortisone), enzymes (e.g., amylase), blood proteins (e.g., human albumin and hemoglobin), alkanes, fatty alcohols (e.g., stearyl alcohol), aromatics (e.g., coumaric acid), amino acids (e.g., ornithine), stilbenoids (e.g., resveratrol), opioids (e.g., thebaine and noscapine), fine chemicals (b-amyrin, b-carotene, valencene, amorphadiene, casbene, cinnamoyl anthranilates, cubebol, eicosapentaenoic acid, farnese, geranylgeraniol, linalool, vanillin, resveratrol, and patchoulol), and protein drugs (insulin, glucagon, scFv, immunoglobulin G, parvovirus B19 VP2, hormones, and vaccines) (Hong and Nielsen, 2012; Kavsˇcek et al., 2015; Coumou et al., 2017; Nielsen, 2019) (refer Table 6). S. cerevisiae is an important organism that plays a vital role in synthesis of a wide range of biomolecules at industrial scale in a renewable and sustainable manner by utilizing cheaper carbon and nitrogen sources.

2.7 Pichia pastoris as a cell factory Although S. cerevisiae has strong fermentation metabolism, it is often delimited by the productivity of recombinant proteins. On the other hand, Pichia pastoris has emerged as an alternative cell factory as it became the most commonly used TABLE 6 Important biomolecules produced by Saccharomyces cerevisiae cell factory. Resveratrol, vanillin, 2,3-butanediol, lactate

Kavsˇcek et al. (2015)

Ethanol, biobutanol, bisabolene, pyruvate, succinate, scFv

Hong and Nielsen (2012)

Oleic acid, octanoic acid, malate, coumaric acid, insulin, human albumin, hemoglobin

Nielsen (2019)

Artemisinic acid

Ro et al. (2006)

scFv, single-chain variable fragment.

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protein expression system after the dominating E. coli, due to its very robust protein secretory capacity and easy downstream processing (Zhu et al., 2019). Pichia factory has all the properties that are expected in an ideal protein expression system, such as cheaper substrate utilization, high stress tolerance, rapid growth, stable expression, high-density mass accumulation, and simple and economic downstream processing (Pen˜a et al., 2018; Yang and Zhang, 2018; Zhu et al., 2019). Examples of important proteins synthesized in P. pastoris include human granulocyte colony-stimulating factor (hGCSF) (Maity et al., 2016), human hormone irisin (Duan et al., 2015), rabies virus glycoprotein (Azoun et al., 2016), human interleukin-25 (IL25) (Liu et al., 2013), human interferon-gamma (hIFNg) (Razaghi et al., 2017), P-glycoprotein (Bai et al., 2011), porcine circovirus type 2 capsid protein (Tu et al., 2013), xylanase (Spohner et al., 2015), glucose isomerase (Ata et al., 2015), lipase (He et al., 2015), and phytase (Spohner et al., 2015). Moreover, P. pastoris have also successfully produced isobutanol and isobutyl acetate from glucose and glycerol (Siripong et al., 2018) (refer Table 7).

2.8 Hansenula polymorpha as a cell factory Over the past three decades, Hansenula polymorpha, a methylotrophic nonconventional yeast, has emerged as a potent microbial cell factory for the production of heterologous recombinant proteins (van Dijk et al., 2000). Advantages that make Hansenula an attractive alternative factory are accessible genetic tools, high cell density fermentation, thermotolerance (ranging from 30 to 50°C), ability to uptake multiple carbon sources (e.g., glycerol, glucose, xylose, and cellobiose), and less hypermannosylation (Ryabova et al., 2003; Manfra˜o-Netto et al., 2019). It has been used for the development of hepatitis B vaccines by producing antigens, namely, Hepavax-Gene (Johnson & Johnson), GeneVac-B (Serum Institute of India), PreS2-S, and Biovac-B (Wockhardt), derived from H. polymorpha (Xu et al., 2014; Manfra˜o-Netto et al., 2019). Majorly, H. polymorpha has been exercised for production of human parathyroid hormone (Mueller et al., 2013), staphylokinase (Moussa et al., 2012), human serum albumin (Youn et al., 2010), heat shock protein gp96 (Li et al., 2011), ferritin (Eilert et al., 2012), bacteriocin enterocin A (Borrero et al., 2012), streptavidin (Wetzel et al., 2016), penicillin (Gidijala et al., 2009), rotavirus VP6 (Bredell et al., 2016), uricase (Chen et al., 2008), lipase (Kumari et al., 2015), streptokinase (Moussa et al., 2012), T4 lysozyme (Wang et al., 2011b), GCSF (Talebkhan et al., 2016), human papillomavirus type 16 L1 (HPV 16 L1) (Li et al., 2009), HPV type 16 L1-L2 chimeric protein (Bredell et al., 2018), HPV type 52 L1 (HPV 52 L1) (Liu et al., 2015b), and hepatitis E virus-like particles (HEV VLPs) (Su et al., 2017) (refer Table 8). H. polymorpha is extensively used for therapeutics and antiviral production, and in future, it may serve as a potential candidate to produce drugs and vaccines against SARS-CoV-2, Ebola, Zika, and many other noxious pathogens.

TABLE 7 Important biomolecules produced by Pichia pastoris cell factory. hGCSF

Maity et al. (2016)

Human hormone irisin

Duan et al. (2015)

Rabies virus glycoprotein

Azoun et al. (2016)

IL25

Liu et al. (2013)

hIFNg

Razaghi et al. (2017)

P-glycoprotein

Bai et al. (2011)

Porcine circovirus type 2 capsid protein

Tu et al. (2013)

Xylanase

Spohner et al. (2015)

Glucose isomerase

Ata et al. (2015)

Lipase

He et al. (2015)

Phytase

Spohner et al. (2015)

Isobutanol, isobutyl acetate

Siripong et al. (2018)

hGCSF, human granulocyte colony-stimulating factor; IL25, human interleukin-25; hIFNg, human interferon-gamma.

8 Microbial cell factories engineering for production of biomolecules

TABLE 8 Important biomolecules produced by Hansenula polymorpha cell factory. Human parathyroid hormone

Mueller et al. (2013)

Staphylokinase

Moussa et al. (2012)

Human serum albumin

Youn et al. (2010)

Heat shock protein gp96

Li et al. (2011)

Ferritin

Eilert et al. (2012)

Bacteriocin enterocin A

Borrero et al. (2012)

Streptavidin

Wetzel et al. (2016)

Penicillin

Gidijala et al. (2009)

Rotavirus VP6

Bredell et al. (2016)

Uricase

Chen et al. (2008)

Lipase

Kumari et al. (2015)

Streptokinase

Moussa et al. (2012)

T4 lysozyme

Wang et al. (2011b)

GCSF

Talebkhan et al. (2016)

HPV 16 L1

Li et al. (2009)

HPV type 16 L1–L2 chimeric protein

Bredell et al. (2018)

HPV 52 L1

Liu et al. (2015b)

HEV VLPs

Su et al. (2017)

GCSF, granulocyte colony-stimulating factor; HPV 16 L1, human papillomavirus type 16 L1; HPV 52 L1, HPV type 52 L1; HEV VLPs, Hepatitis E virus-like particles.

2.9 Yarrowia lipolytica as a cell factory Yarrowia lipolytica, an oleaginous yeast, has an amazing ability to accumulate huge amounts of lipid, making it an eminent choice for the production of polyunsaturated fatty acids (PUFAs), biofuels, secondary metabolites, and other bioproducts (Beopoulos et al., 2010; Abghari and Chen, 2014). Major advantages of using Yarrowia factory are its ability to consume wide range of substandard substrates, which include molasses, glycerol, alkanes, ethanol, sewage sludge, oil mill wastewater, and saturated spend fats (Abghari and Chen, 2014); its GRAS status; oleaginous nature; lipid metabolism affinity with the glycerol pathway (Beopoulos et al., 2008); strong ex novo pathway for degradation and hydrolysis of hydrophobic substrates (Beopoulos et al., 2011); various ABC transporters involved in the transportation of alkanes and fatty acids (Fukuda and Ohta, 2013); and bioremediation capability (Zinjarde and Pant, 2002a). It has been majorly used for the sustainable production of PUFAs such as linoleic acid, docosahexaenoic acid, desaturase, elongase, and acyltransferase (Damude et al., 2006; Beopoulos et al., 2010); nutraceuticals such as sterols, carotenoids, and antioxidants (Bailey et al., 2013; Sharpe et al., 2014); fine chemicals such as dicarboxylic acid and diacid; enzymes such as isocitrate lyase and polyhydroxyalkanoates; secondary metabolites such as citric acid, erythritol, and 2-oxoglutaric acid; g-decalactone; g-dodecalactone; pyruvic acid (Thevenieau et al., 2009); and biosurfactants (Zinjarde and Pant, 2002b) (refer Table 9). It is hard to find strains that can synthesize and accumulate lipids to such high amounts as Y. lipolytica does, and it surely is a promising strain to produce lipids in a sustainable manner, given its frugal choices of substrates.

2.10 Cyanobacteria as a cell factory Cyanobacteria are the only photosynthetic microorganisms that can be used to produce numerous alcohols, diols, fatty acids, fatty alcohol, acids, ketones, and plant secondary metabolites by utilizing simple growth requirements such as CO2, solar energy, and water (Xue and He, 2015; Lau et al., 2015; Luan and Lu, 2018). Interestingly, it can express plant P450 proteins and produce plant secondary metabolites such as ethylene (Takahama et al., 2003), isoprene (Bentley et al.,

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TABLE 9 Important biomolecules produced by Yarrowia lipolytica cell factory. Linoleic acid, docosahexaenoic acid, desaturase, elongase, acyltransferase

Damude et al. (2006) and Beopoulos et al. (2010)

Dicarboxylic acid, diacid, isocitrate lyase, polyhydroxyalkanoates, citrate, erythritol, 2-oxoglutaric acid; g-decalactone, g-dodecalactone, pyruvate

Thevenieau et al. (2009)

Biosurfactants

Zinjarde and Pant (2002b)

2014), caffeic acid (Xue et al., 2014), r-coumaric acid (Xue et al., 2014), mannitol ( Jacobsen and Frigaard, 2014), limonene (Kiyota et al., 2014), and carotenoid (Kudoh et al., 2014; Xue and He, 2015). Along with that, cyanobacteria have also been exercised to synthesize ethanol (Gao et al., 2012), lactic acid (Angermayr et al., 2012), ethylene (Xiong et al., 2015), acetone (Zhou et al., 2012b), isopropanol (Kusakabe et al., 2013), butanol (Panchasara et al., 2018), 2,3-butanediol (Oliver et al., 2013), isobutyraldehyde (Atsumi et al., 2009), D-lactate (Varman et al., 2013), and sucrose (Ducat et al., 2012) (refer Table 10). Many studies have also shown the capability of cyanobacteria to degrade pollutants and remove heavy metals, suggesting an alternative for bioremediation and wastewater treatment (Huertas et al., 2014; Fawzy and Mohamed, 2017). Further exploration of cyanobacterial metabolism, expansion of cyanobacterial toolboxes, improvement in carbon fixation pathways, and upgradation of energy supply may provide a more stable cyanobacterial platform for sustainable production of different valuable bioproducts (Zhou et al., 2016).

3 Design and optimization of microbial cell factories Biological entities are fundamentally different from physical ones, and therefore designing and engineering the biological systems to deliver the designated functions often comes with challenges and gaps. Right from the conceptualization to

TABLE 10 Important biomolecules produced by cyanobacteria cell factory. Ethylene

Takahama et al. (2003)

Isoprene

Bentley et al. (2014)

Caffeic acid

Xue et al. (2014a)

r-Coumaric acid

Xue et al. (2014b)

Mannitol

Jacobsen and Frigaard (2014)

Limonene

Kiyota et al. (2014)

Carotenoid

Kudoh et al. (2014) and He et al. (2015)

Ethanol

Gao et al. (2012)

Lactic acid

Angermayr et al. (2012)

Ethylene

Xiong et al. (2015)

Acetone

Zhou et al. (2012b)

Isopropanol

Kusakabe et al. (2013)

Butanol

Panchasara et al. (2018)

2,3-Butanediol

Oliver et al. (2013)

Isobutyraldehyde

Atsumi et al. (2009)

D-Lactate

Varman et al. (2013)

Sucrose

Ducat et al. (2012)

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Microbial cell factories engineering for production of biomolecules

commercialization of a metabolite, it requires a meticulous plan along with a set of tools that can effortlessly resolve any challenges that may arise while executing the theoretical knowledge into a practical product. Engineering microbes to improve the titer or yield to hit the commercial scale requires a detailed understanding of the growth conditions, physiology, energy needs and metabolism, and stress responses of the particular microbe. When synthesizing commodities at bulk, it is important to consider making use of every available carbon and reducing the loss of ATP in unneeded processes. That is where the knowledge of systems biology in metabolic engineering may aid in better understanding of the interaction of microbial hosts and its metabolism with the heterologous pathway. The feasibility of producing chemicals using microbial cell factories comes from two notable facts: First, these microbes can utilize sustainable carbon sources as substrates to carry out various microbial processes, and, second, the functionality and specificity of the biologically derived molecules often surpass that of the chemically synthesized compounds owing to the tight regulation of the biological systems over the intricate chemical features such as the arrangement of the functional groups and chirality (Morrow and Felcone, 2004). Though the current focus of the scientific community is to synthesize well-characterized and industrially important chemicals having large markets, these features that biological systems offer would be of great importance while working on newer molecules with unique functions and properties to fill in the gaps of novel compounds. The first step in synthesizing a compound through the biological route is to select the molecule of choice, which often depends on what entices the markets. During the initial years, the focus was to target products with high price and low volume, as in pharmaceutical industries. With time the focus shifted toward synthesizing chemicals of economic importance, such as biofuels, which are required in high volume but should have a low cost. However, above all, the feasibility of producing any chemical biologically must always be assessed. While proceeding with engineering a microbe to produce a certain metabolite, the ensuing expenses that drive for scale-up production and downstream processing such as its extraction and purification must be pondered (Chubukov et al., 2016). Once a researcher is settled on a target, the next step is to design an efficient pathway. With advancements in computational techniques, availability of various biological databases, automated processes and scoring methods, and genetic makeup of microbes, the pathway selection process has become much easier than ever. Moreover, the nextgeneration sequencing (Gohil et al., 2019b) along with various other bioinformatics tools has immensely contributed to discovering gene targets for metabolic engineering by providing an array of genomic data. Many online tools are now available that identify suitable biosynthetic gene clusters (Medema et al., 2014; Chen et al., 2019) or simplify the task of choosing a particular pathway based on homology-predicted enzyme function (Vyas et al., 2012; Calhoun et al., 2018). Over the years, the accessibility and affordability to de novo DNA synthesis and standardized cloning and expression vectors along with efficient genome integration techniques have greatly improved the contemporary pathway construction strategies. These techniques have revolutionized the approach to construct combinatorial pathways, thus building libraries with enormous information about hosts’ genetic framework, its open reading frames, and details about protein expression. This has not only simplified the selection of a strain based on library screening or using design-of-experiment methods for further analysis but also readily reduced time and effort that goes in the testing of individual strains to derive the best-suited strain. These data-driven approaches for stain manipulation or pathway construction can be employed for industrial workhorses such as E. coli and S. cerevisiae, as well as a number of other microbes whose genomes can be easily tweaked using synthetic biology tools. Computational- and mathematical-based systems biology plays a key role in optimization of the cell factory by predicting the best model that suits for overproduction of targeted compounds (Garcı´a-Granados et al., 2019). Generally, researchers follow the most systematic and efficient design-build-test-learn (DBTL) cycle, a conceptual model widely adopted for metabolic engineering framework (Fig. 1). In the model, four different components work together to provide the best-suited pathway, host, and parts with the validation of the engineered strain (Petzold et al., 2015). Large-scale analysis includes characterization of enzyme expression by proteomics, optimization of expression levels, toxicity level of pathway intermediates, robustness of cell-to-cell viability, and flux-balance analysis by computational toolbox such as COBRA (COnstraint-Based Reconstruction and Analysis) (Chubukov et al., 2016; Gohil et al., 2017). The design component is very crucial for construction of a novel and productive pathway as it requires very optimal construct by keeping in mind the required precursors, optimal conditions, toxicity of the targeted compound, synthesis rate, mRNA stability, required enzymes, and transcriptional factors (Petzold et al., 2015; Chao et al., 2017). Nowadays, advanced computational and mathematical model developing softwares (such as BioCAD) have made it more convenient to build accurate designs (Nielsen and Keasling, 2016). Following the design component the build component is presently boosted by the genome editing tools, namely, CRISPR-Cas9 and TALEN. The test and learn components deal with extensive analysis, verification, and measurements of the designed constructs (Chao et al., 2017).

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FIG. 1 Schematic representation of design-build-test-learn cycle for improving the production of biomolecules. The first component of the DBTL model is design, which helps in identification of the problem or target and subsequently determines and directs toward the best-suited pathway or host; the build component sorts, formulates, and arranges various parts and tools to integrate within the host system; test component validates performance of the engineered strains; and lastly the learn component analyzes the data of the outcomes and suggests further amelioration of the process. (Adopted from Petzold, C. J., Chan, L.J.G., Nhan, M., Adams, P.D., 2015. Analytics for metabolic engineering. Front. Bioeng. Biotechnol. 3, 135 © Frontiers Media S.A.)

4 Conclusion and future remarks The reach of MCFs is not just limited to producing commodities that entice pharmaceutical, nutraceutical, and/or biofuelproducing industries. The knowledge of manipulating microbial genomes can be invested in biofabricating nanoobjects or to design bifunctional macromolecules to manipulate nanoobjects. Owing to their miniature size, diverse physiology, and genome flexibility, microbial cells can emerge as ideal producers of nanomaterials and structures to construct a wide range of polymers and magnetosomes and viruses or to engineer proteins to form virus-like particles (VLPs) and to tailor peptidedisplaying phages (Villaverde, 2010). When it comes to synthesizing proteins at laboratory or industrial scale, E. coli emerges as the leading candidate for a large variety of proteins due to its rapid growth cycle and simple culture conditions. However, events such as loss of plasmid and antibiotic-based maintenance, use of chemical-based inducers to regulate gene function, metabolic burden on the cell due to large plasmids or protein accumulation, poor secretion, endotoxicity, and most importantly the lack of posttranscriptional machinery are few of the complications encountered when E. coli is used to produce proteins. From the pharmaceutical point of view, companies often insist on achieving efficient glycosylation of proteins and convenient downstream process and purification that can be addressed by using yeast S. cerevisiae or diverse mammalian cell lines. Lately, several other bacterial hosts other than E. coli have gained recognition as impressive cell factories considering their biosynthetic potential, metabolic diversity, and ability to adapt to extremely diverse conditions or environments. Though several limitations are encountered when adopting prokaryote-based production, it is advisable to explore different bacterial strains other than E. coli and not abandon them altogether, since, in future, this not only will facilitate the expansion

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Microbial cell factories engineering for production of biomolecules

of existing catalogue of microbial cell factories but also will aid the development of novel pathways and processes for forthcoming recombinant compounds (Calero and Nikel, 2019). And though recombinant technologies may have completely exploited and exhausted the conventional cell factories, there always remains a chance to explore other microbial factories to keep up with the current demands. The scientific world has evolved to quite an extent from where it was a few decades ago. With the advent of newer and extraordinary technologies, producing compounds biologically is getting easier and straightforward every day. In times when even the 20 standard amino acids fall short to meet the needs to form novel proteins with remarkable features, scientists have gone for quadruplet codons that can be intricately woven within microbial cells to produce novel proteins to accomplish astounding functions (Gohil et al., 2020). The cell extracts of microbes are now being used to produce umpteen chemicals to circumvent the limitation arising due to impermeability of membranes, obstruction of channels, etc. It is now possible to synthesize even toxic proteins or toxoids, which may prove to be lethal for a microbe in vitro. The complication can be sorted by making use of the cell-free protein synthesis system, which is a technique that is essentially devoid of any membrane-bound barriers and yet possesses all the necessary biomolecules, substrates, and transcription-translation machinery. In a similar manner, many other nonprotein compounds can also be synthesized using cell-free synthesis tools by making use of the cell extract of the microbe of choice. So, depending upon the nature of the desired product, a variety of microbial strains can be considered (Khambhati et al., 2019). Synthetic biology has been instrumental in addressing fundamental dilemmas into practical applications. However, once the biological systems are stretched to their limits, intervention of systems biology becomes obligatory to understand and predict the consequences or end results. Implementing the ideas in bioengineering takes relatively more time and effort than any other physical engineering fields. This calls for multidisciplinary efforts and collaborations between biologists, chemists, engineers, software developers, and personnel of different fortes to unite and pool together a myriad set of knowledge and fulfill the global demands of commodities in a sustainable way.

Acknowledgments G.B. and V.S. gratefully acknowledge the Gujarat State Biotechnology Mission (GSBTM Project ID: 5LY45F) for financial support. The authors thank Indrashil University for providing infrastructure and support.

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Chapter 2

Advances in long DNA synthesis Subha Sankar Paula,∗, Heykel Trabelsib, Yazen Yaseenc, Upasana Basud, Hiyam Adil Altaiic, and Debarun Dhalie a

Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore, Singapore, b Micalis Institute, INRAE,

Universit
e Paris-Saclay, Jouy-en-Josas, France, c Faculty of Sciences, Mosul University, Mosul, Iraq, d Indian Institute of Science Education and Research, Bhopal, India, e University of Lille, Villeneuve d’Ascq, France ∗

Corresponding author: E-mail: [email protected]

1 Introduction, history, and evolution of gene synthesis The current developments in the field of gene synthesis are the fruits of decades of gradual improvements following a complex learning curve. Over the last century, scientists have focused their efforts on understanding the deoxyribonucleic acid (DNA) to unravel the “code of life.” The synthesis of DNA molecules occurs naturally in all living organisms by producing two identical strands. Each DNA strand serves as a template for the production of its copy. This biological process named DNA replication is a key player in biological inheritance and involves a cascade of chemical reactions. Inspired by this complex process, researchers have taken advantage of the tremendous progress in genetic engineering and enzyme chemistry to create artificial techniques for DNA synthesis. Historically, it all started with the discovery of nuclein by Friedrich Miescher in 1869 (Dahm, 2008). After purifying the substance, its highly acidic nature was revealed by Albrecht Kossel and Richard Altmann, which gave birth to the term nucleic acid in 1889 (Gribbin, 2004). Later, in 1938, Astbury and Bell published the first x-ray diffraction pattern of DNA and speculated that the molecule had a regular periodic structure (Hall, 2011). Misled by the erroneous theory of gene structure, Atsbury failed to determine the structure of DNA. The scientific community had to wait until 1953 for Francis Crick and James Watson to propose the first double-helical structure of DNA using the X-ray crystallography data generated by Rosalind Franklin (Watson and Crick, 1953). This event marks an unprecedented milestone in biological research. In the 1950s a new breakthrough was recorded with the isolation and purification of the first DNA polymerase from Escherichia coli by the team of Arthur Kornberg, which was later described as DNA polymerase I (Kornberg, 1957; Kresge et al., 2005). Meanwhile, Sir Alexander Todd became the first person to use H-phosphonates in nucleotide chemistry. His team demonstrated that the treatment of benzyl H-phosphonate monoester with diphenyl phosphorochloridate led to the putative creation of a subsequent activated combined anhydride. They also succeeded in synthesizing the first DNA molecule by joining two thymidine nucleosides by a phosphate link (Corby et al., 1952; Michelson and Todd, 1955; Hall et al., 1957). The 1960s marked the discovery of the enzyme DNA ligase that catalyzed the formation of a phosphodiester bond and the elucidation of its role in the repair and replication of DNA in all organisms (Lehnman, 1974). Research conducted in the laboratories of Gellert, Lehman, Richardson, and Hurwitz paved the way for the use of DNA ligases as a key enzyme in molecular cloning and many successive aspects of DNA biotechnology. These versatile enzymes have facilitated the synthesis of double-helical DNAs from chemically synthesized deoxyribopolynucleotides. By adopting this chemicalenzymatic technique, the genes encoding yeast tRNA alanine and E. coli tRNA tyrosine precursors were synthesized by Khorana and his colleagues (Khorana et al., 1972; Kleppe et al., 1976). During that period the progress recorded in DNA research boosted the emergence of genetic engineering tools, and the field took off progressively. The understanding of how restriction enzymes cut DNA and how host DNA works to defend itself promoted innovation in this field to a higher level (Smith and Welcox, 1970; Danna et al., 1973). The next remarkable milestone was reached with the creation of recombinant DNA (rDNA). The importance of this invention was that the recombinant DNA was able to replicate naturally and could even be artificially introduced into other organisms ( Jackson et al., 1972; Cohen et al., 1973; Berg et al., 1974). The accumulation of all this knowledge led to the biggest achievement in genetic engineering in 1978, when the first recombinant DNA human insulin was prepared by David Goeddel and his colleagues (Genentech) by utilizing and combining the insulin A and B chains expressed in E. coli (Quianzon and Cheikh, 2012). Simultaneously, research scientists working on nucleotide chemistry were trying to solve major problems related to the instability of nucleoside intermediates caused by the phosphite-triester method. Among other researchers, Mattucci and Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00014-3 © 2021 Elsevier Inc. All rights reserved.

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Caruthers were successful in the development of the phosphoramidite approach in which the nucleoside phosphoramidites were used as intermediates instead of phosphites. The phosphoramidite chemistry eventually turned out to be the most widely used technology for DNA synthesis, and the foundation barely changed over the years (Beaucage and Caruthers, 1981; Matteucci and Caruthers, 1981; Caruthers, 2013). In 1983 the invention of polymerase chain reaction (PCR) (a procedure for synthesizing DNA) by Kary Mullis dramatically reduced the time required to produce multiple copies of DNA (Mullis et al., 1986). By implementing the chain reaction, any copy of a DNA sequence could be amplified to generate millions of replicas. This invention made DNA far more approachable to researchers and catalyzed the boom of cloning tools and techniques by reducing the time and the cost. The evolution of assembly methods has promoted the synthesis of longer genes. Several techniques for assembling oligos into complete genes or larger genome building blocks have been subsequently developed (Sandhu et al., 1992; Horton, 1993; Wooddell and Burgess, 1996). In the 1990s gene synthesis became more realizable when the first automated oligo synthesizers became commercially available. The development of optical deprotection chemistries had announced a new imagination of analogous synthesis methods on microbiochips that could be used for both oligo and peptide synthesis. Depending on the chip platform, hundreds of thousands of separate oligos could be synthesized theoretically on a single chip (Fodor et al., 1991). The fruit of all the aforementioned innovations at both theoretical and technical levels was harvested in 2003, with the synthesis of the entire viral genome of the phiX174 bacteriophage (Smith et al., 2003). After that a 1.08-Mbp (mega base pair) Mycoplasma mycoides JCVI-syn1.0 genome was created and successfully transplanted into the M. capricolum recipient cell to produce a new M. mycoides cell, which was completely controlled by the synthetic chromosome (Gibson, 2010; Gibson et al., 2010). In the last decade the world’s first operational synthetic eukaryotic genome has become a reality. The Sc2.0 Project, initiated by Dr. Jef Boeke, at Johns Hopkins University, carried out in 2014, is considered as the first attempt toward the synthesis of a eukaryotic cell genome. The selected organism Saccharomyces cerevisiae has 16 linear chromosomes, about 6000 genes, and 12 Mbp of nonredundant DNA (Dymond et al., 2011; Dymond and Boeke, 2012; Pretorius and Boeke, 2018). This serves as the groundwork for future studies on the effect of genome-wide engineering approaches of essential features of living systems.

2

Technological developments

2.1 Oligo synthesis The synthesis of whole-genome constructs begins with the synthesis of individual oligos, followed by the assembly of longer-length constructs and finally clonal selection and sequencing (Smith et al., 2003). Herein, we discuss the advantages and limitations of the current approaches with respect to their effect on the downstream gene synthesis process. Initially the process of synthesis of high-quality and sequence-verified constructs was labor intensive and was limited by errors during the oligo synthesis and assembly stages (Hughes and Ellington, 2017). This led to the development of solid-phase phosphoramidite chemistry in the early 1980s, which resulted in substantial improvements in oligo synthesis methods (Caruthers, 2011; Roy and Caruthers, 2013) and paved the way for automated methods used currently for the commercial synthesis of oligos. Column-based synthesis or microarray-based synthesis utilizes variations of the phosphoramidite chemistry methods (Ma et al., 2012).

2.1.1 Column-based oligo synthesis The phosphoramidite synthesis pathway consists of a chain elongation cycle (Russell et al., 2008), followed by the removal of dimethoxytrityl (DMT) using trichloroacetic acid, resulting in the deprotection of the DMT-protected nucleoside phosphoramidite (Sierzchala et al., 2003). The 50 -hydroxyl group of the growing oligo chain is coupled to the new DMTprotected phosphoramidite to form a phosphite triester. To alleviate deletion errors the capping step is performed, which acetylates unreacted 50 -hydroxyl groups, rendering the unreacted oligo chains inert to further nucleoside additions. In the next step the cyanoethyl-protected phosphate backbone is generated by iodine oxidation of phosphite to a phosphate. For the continuation of the cycle, the DMT protecting group is removed, and this step is monitored to track coupling efficiencies. In the last step the protective groups attached to the bases and the phosphate backbone are removed, and the synthesized DNA oligo (30 to 50 sequence) is cleaved from the solid support (Fig. 1) (Hao et al., 2020). Although column-based coupling yields are generally 99% per coupling, the yield, however, decreases with increasing oligo length (Cheng et al., 2002) (Leproust et al., 2010). One of the limitations of this technique is that the amount of oligo required for the synthetic cycle is very high, making it unsuitable for generating long oligos (Wang et al., 2011). In addition,

Advances in long DNA synthesis Chapter

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FIG. 1 Column-based synthetic oligo synthesis based on phosphoramidite chemistry.

Couple spacer+ 1st neucleo de linked to the coloumn

Detrityla on By removal of DMT from 5’-OH-

Removal of all other protec ng group

Phosphoryla on of 5’ end of the oligoneucleo de

Removing oligoneucleo de from the column

Ac va on and coupling by addi on of Phosphoramidite

Capping of unreacted neucleo des

n cycles

Capping of unreacted neucleo des

Phosphodiester genera on by oxida on of phospho triester Purifica on of the oligoneucleo de

during acidic detritylation, there is a chance of depurination of adenosine, which becomes problematic during the production of long oligos (Septak, 1996; Itakura et al., 1984; Yantsevich et al., 2019). This, in turn, generates abasic sites, resulting in premature cleavages and reduced yields of long oligos (Banerjee et al., 2017). Improved processes continue to be developed, resulting in further improvements in oligo length and quality (Goldman et al., 2013).

2.1.2 Array-based oligo synthesis In spite of a gradual drop in the cost of gene synthesis, the price of precursors required for the synthesis of column-based oligos has not decreased equivalently. Hence, techniques that use DNA microarray-based oligos to assemble the genes may prove to be efficacious in reducing the cost (Bumgarner, 2013; Kothiyal et al., 2009). Microarray oligo synthesis by Affymetrix (Dalma-Weiszhausz et al., 2006; Heber and Sick, 2006) consists of the photoactivation-based deprotection method for the synthesis of spatially located oligos, which makes it a promising and cheaper alternative to column-based oligo synthesis, with the price for synthesis lying in the range of $0.00001–$ 0.001 per base (in 2014 US dollars) (Kaller et al., 2007). Additionally, a chip-based synthesis technology has also been developed to replace the photoactivation-based chemical reaction on the surface array (Sun, 2015). Modifications of this technology have helped Agilent and Twist Bioscience to develop inkjet printing technology (Wolber et al., 2006; Dastjerdi et al., 2014), which has enabled them to synthesize 244,000 sequences of 20–230 nucleotide (nt) length along with 2000–696,000 sequences of 120–300 nt length. Furthermore, Custom Array has developed a technology that has been reported to synthesize 12,000–90,000 chip sequences of 10–170 nt length, in a single chip (Kuhn et al., 2017). In the area of diagnostics, array-based oligo synthesis has been utilized in combination with photolithography computer chip technology to produce approximately 400,000 oligos, which has led to the detection of approximately 9000 genes on a 1.6-cm2 glass surface (Miller and Tang, 2009). In spite of superior synthesis capabilities and lower cost, the product yields of this platform lie in the femtomolar scale, which is two- to fourfold lower than the yields obtained through column synthesis (Klein et al., 2016). Since the oligo yield is very low in this method, amplification techniques need to be developed to make them suitable for assembly purposes (Borovkov et al., 2010). DNA proofreading strategies are essential for this technique owing to its higher error rate compared with the column-based synthesis method. Additionally, the risk of cross-hybridization of oligos during assembly has also been reported in the large-scale synthesis of microarray-based oligos (>1000 sequences) (Dastjerdi et al., 2014). Together, these issues limit both scale and potential applications of this method ( Jaksik et al., 2015). Owing to the fact that array design is being continually improved and the reagents required for the synthesis and the techniques associated with it are under continued optimization, it is hypothesized that platforms wherein synthesis of

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Microbial cell factories engineering for production of biomolecules

high-quality oligo sequences from arrays can be performed will be launched soon, making this technique popular for the generation of oligos for gene synthesis applications (H€olz et al., 2018).

2.2 Gene synthesis The current technologies used for gene synthesis cannot synthesize oligos over 200 nts; as a result, full-length genes are synthesized by alternative methods by stitching together overlapping groups of oligos (Cavaleiro et al., 2015; Fakruddin et al., 2013). Tian et al. synthesized the E. coli 30S ribosomal subunit genes using the multiplexed gene synthesis method (Tian et al., 2004). They minimized the limitations of the process, namely, mishybridization, low concentration, and high error rate, by amplifying the oligos before assembling them for gene synthesis and employing proofreading by hybridization to minimize the error rate. In spite of these modifications, their protocol was limited by scalability. Later on, Quan et al. bypassed these limitations by isolating the oligos for each assembly in separate chambers of an inkjet synthesizer, thereby limiting the potential for mishybridization of the sequences, amplifying oligos by single-primer strand displacement, and using polymerase cycling assembly (PCA) to assemble the genes (Quan et al., 2011). In a more traditional strategy, Kosuri et al. tried to solve the limitations pertaining to low concentrations of the oligos and mishybridization during gene synthesis by barcoding the priming sequences and inserting them into oligos resulting in amplification of those oligos that were needed for a given assembly (Kosuri et al., 2010; Kosuri and Church, 2014). These barcoded priming sequences were further followed through with restriction digestion being utilized for the assembly of genes. Although these techniques were successful in minimizing the error rate, they are still very expensive at larger scales. In order for widerange adoption of these large-scale gene synthesis techniques, efforts required for assembling the genes need to be improved, and the method needs to be cost-effective.

2.2.1 Array-based gene synthesis The recent innovations in microarray-based oligo synthesis and gene assembly have substantially improved the quality, efficiency, and scalability of the process (Yeom et al., 2020; Chan et al., 2017; Khilko et al., 2018). For reliable larger assemblies the requirement of DNA in picomoles (pmol) is higher than that produced in femtomoles (fmol). Due to the interference between oligos, there can be problems in assembling them into larger constructs from an array (Ellis et al., 2011). Since array-synthesized oligos usually end up with errors in the sequence of the synthetic DNA assembled from them, further proofreading strategies are required. Toward this aim, various protocols have been carried out for enriching the effective concentration of the oligo pool so that they can be assembled and reproduced reliably (Tian et al., 2004; Kosuri et al., 2010; Quan et al., 2011). In this direction, Tian et al. have used the primer extension reaction, in which they have utilized an array-synthesized oligo incorporating a priming sequence and a site recognized by an endonuclease on the 30 -end as a template strand (Tian et al., 2004). The common primer sequences are removed using nicking endonuclease, leading to the release of the complementary single-stranded DNA oligos, thus resulting in the amplification of the oligo pool required for gene synthesis. Quan et al. introduced the chip-based oligo synthesis to further improve the multiplexing capabilities of this method (Quan et al., 2011). In this method, assembly wells are microcompartmentalized in specialized chips, and custom inkjet synthesizers are used within individual microwells, leading to an increase in the robustness of the assembly of oligos for gene synthesis. Kosuri et al. alternatively barcoded the sequences, which were used for priming, into each of the array oligos, thereby amplifying the oligo pool, after detaching from the chip surface (Kosuri et al., 2010). It was followed by the removal of the priming sequences by digesting the ds-oligos by restriction endonucleases. This scheme is promising as it does not require specialized chips or array synthesizers, it simultaneously resolves the problems associated with the oligo concentration and pool complexity, and it is also highly compliant to automation.

2.3 Larger DNA assemblies Rapid advances have been made in techniques to produce larger assemblies of DNA from de novo synthesized or amplified gene-length fragments ( Juhas and Ajioka, 2017; Eriksen et al., 2018; Casini et al., 2015; Chao et al., 2015). Gibson assembly (Gibson et al., 2008; Gibson, 2009; Thomas et al., 2015), in vivo yeast assembly (Muller et al., 2012; Lin et al., 2015), and Golden Gate assembly (Engler and Marillonnet, 2014; Marillonnet and Gr€utzner, 2020) are some of the techniques used nowadays for generating large multicomponent gene libraries. Other less frequently used methods utilize ligation-independent cloning (Stevenson et al., 2013), ligase cycling reaction (de Kok et al., 2014), and circular polymerase extension cloning (Quan and Tian, 2009), among other methods. Of these methods, Gibson assembly utilizes the PCR master-mix with DNA polymerase (thermostable), DNA ligase, and exonuclease to assemble the required

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combinatorial assembly of large DNA sequences (Gibson, 2009; Guye et al., 2013; Torella et al., 2014). Most of the aforementioned assembly methods have been used to assemble multiple kilobases (kb) long oligosequences. Gibson assembly has been reported to be used for assembling 60-mer oligos into the 16.3-kb mouse mitochondrial genome (Gibson, 2010; Gibson et al., 2010). Even larger DNA segments have been efficiently synthetically assembled in vivo by utilizing homologous recombination in S. cerevisiae (Finnigan and Thorner, 2015; Muller et al., 2012). Multiple 0.5- to 1-Mbp bacterial genomes have been successfully assembled utilizing homologous recombination within yeasts (Kuijpers et al., 2013; Hutchison et al., 2016). Further automation of these assembly techniques can increase larger synthetic DNA throughput and enable research on large biological hypotheses.

2.4 Whole-genome synthesis The assembly of synthetic genomes from synthesized oligos utilizes both in vitro and in vivo methods (Zampini et al., 2015; Schindler et al., 2018). While in vitro assembly methods are preferred for small DNA fragments (Yamamoto et al., 2020; Yu et al., 2017), in vivo recombination is useful for larger assemblies (Watson and Garcı´a-Nafrı´a, 2019; King et al., 2016; Finnigan and Thorner, 2015). The two main factors that determine the choice of the recipient strain for the assembly of the synthetic genome include the toxicity of the synthetic sequence and the carrying capacity of the exogenous DNA (Go´mezTatay and Herna´ndez-Andreu, 2019; Musiol-Kroll et al., 2019). Since yeast has been reported to have the ability to carry exogenous DNA sequences of large size (Zhang et al., 2019; Brown et al., 2017) and new genetic tools are easily accessible for its manipulation, yeast has been increasingly used as a host platform for in vivo assembly of synthetic genomes (Blount et al., 2018; Walker and Pretorius, 2018; Lee et al., 2015). For in vitro assembly from short synthetic oligos, PCA without restriction digestion has yielded promising results (Roth et al., 2014; Tian et al., 2004). Golden Gate assembly and Gibson assembly techniques assemble the whole-genome in vitro by the ligation-dependent assembly (Essani et al., 2015). These techniques utilize different types of restriction enzymes, namely, type II restriction enzymes recognizing eight base sites, highly specific endonucleases, and sticky end generating type II restriction enzymes (Marillonnet and Gr€utzner, 2020; Kuijpers et al., 2013; Gibson et al., 2008; Gibson, 2009; Gibson, 2010; Gibson et al., 2010). For these techniques to be useful, the standard reagents need to be well characterized and readily available (Casini et al., 2015; Agmon et al., 2015; Lin et al., 2015). Gibson et al. reported the assembly of the whole genome of M. genitalium by combining both in vivo recombination methods using S. cerevisiae and in vitro techniques (Gibson, 2009, 2010, Gibson et al., 2008, 2010). This technique can be time-consuming since multiple assemblies and debugging steps are required (Thomas et al., 2015). To expedite the synthesis the hierarchical assembly is followed for the synthesis of synXII, the longest eukaryotic chromosome constructed, wherein the meiotic recombination-mediated assembly (MRA) method is utilized (Zhang et al., 2017).

2.5 Assembly of genetic parts for biosynthetic pathway building and its limitation BioBricks is an assembly technique that uses type II restriction enzymes for the assembly and is used for sequential assembly of the genetic parts. The presence of a scar sequence at every assembly point is the disadvantage of this approach (Ellis et al., 2011). Like BioBricks, BglBricks also utilizes type II restriction enzyme for the assembly and is used for sequential assembly of the genetic parts. BglBricks results in smaller scar sequences, as well as the restriction used, and is not DNA methylase sensitive in comparison with BioBricks (Anderson et al., 2010). Golden Gate assembly is a one-pot assembly technique that utilizes type IIS restriction enzymes. These enzymes cut outside their recognition site, generating overhangs that can be used for the assembly (Engler et al., 2008). Overlapping polymerase chain reaction (O-PCR) can be considered to have a scarless assembly. This method generally constitutes of designing long oligos (45 + bases long) to create homology between fragments and then normal extension PCR to generate the final long fragment. This method is better than the previous methods, that is, BioBricks and BglBricks, as it can assemble genes up to 5 kb in vitro and the construction of an entire plasmid is possible without any scar. The only limitation for this PCR technique is the assembly of sequences that are GC rich or the ones that contain repeats (Horton et al., 1989). Uracil-specific excision reagent (USER) is a method in which uracil is incorporated within the sequence through primer design and PCR amplification. The incorporated site is cleaved using uracil DNA glycosylase, and the resultant sequence is then excised using AP-lyase, generating overhangs. These overhangs can be assembled through an overlapping mechanism without the requirement of ligase. This method does not develop scars, but it requires one thymidine at the terminal region of the sequence (Nour-Eldin et al., 2010). Sequence- and ligation-independent cloning (SLIC) is a technique that uses both exonuclease activity of T4 DNA polymerase and annealing activity of RecA for the assembly of fragments that can be used for pathway building. The 30 –50 exonuclease activity of T4 DNA polymerase creates overhangs

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that are annealed with the homologous fragments in the presence of RecA, which stabilizes the annealed fragments (Li and Elledge, 2007). In addition to the assembly of genetic circuits, Gibson isothermal assembly can also be used for the assembly of whole genome in vitro from fragments having overlapping sequences from 40 to 400 bp. This method has been used successfully in the in vitro assembly of the 583-kb genome of M. genitalium, utilizing four 100-kb sequences (Gibson et al., 2009). The assembly of the genetic parts can also be performed in vivo through recombination mechanisms. The recombination mechanism can either be RecA dependent or RecA independent. Yeast transformation-assisted recombination (TAR) was first used to assemble 25 fragments of 24-kb size to synthesize the complete synthetic genome of M. genitalium. This genome was assembled in vivo utilizing S. cerevisiae homologous recombination. This method is based on the digestion and annealing of overlapping sequences. The recombination occurs during yeast spheroplast transformation (Benders et al., 2010). The overlapping sequence requirement of this method is as short as 40 bp (Noskov et al., 2001). First reiterative method for multigene constructs was developed in S. cerevisiae utilizing the recombination mechanism (Wingler and Cornish, 2010). This robust method can be used to assemble genetic libraries leading to the development of biosynthetic pathways. Each set of assembly is accomplished by recombination between the acceptor module (chromosomal DNA) and the donor module (plasmid). This method is based on the endonuclease activity and an alternative marker selection. Both modules have endonuclease active sites adjoining different selective markers. Endonuclease activity promotes the recombination of genetic parts between two modules. Both selective markers are placed downstream of the homology region between the two modules. The selective marker in the acceptor module is under the control of a promoter. Once the recombination has occurred, the selectable marker of the donor module is transferred to the acceptor module, leading to the selection of the positive clones. Replicating this method with the donor module with opposite polarity yields the acceptor module in its initial condition. In this manner, this reiterative system can be used for the assembly and integration of large DNA constructs. Besides yeast, Bacillus has also been used as a chassis for genomic scale assembly. This method utilizes specific Bacillus subtilis strains known as Bacillus genome (BGM) vectors for genomic integration. The assembly is sequential, wherein the target sequence is chopped down into 5-kb sequences with overlapping regions. The assembled vectors are cloned into E. coli with different selective markers. The first plasmid is transformed into Bacillus, which is integrated within the genome at the BGM vector site along with the selective marker. The integration of the assembly within the genome is based on a recombination mechanism. After this the resultant Bacillus strain is transformed with the second plasmid with a different selective marker. The second plasmid through recombination facilitates the integration of the next part of the assembly and the replacement of the first selective marker. Thus the sequential addition of genetic parts and alternative selection through selective markers lead to the development of a desired pathway within the Bacillus genome. Besides being a powerful tool for assembly, it has a few drawbacks. The process is laborious, requires long overlapping sequences, and cannot be used for Bacillus sequences (Itaya et al., 2008). Additional recombination strategies have been developed wherein the recombination is mediated by RecE/RecT from the Rac phage or Reda/Redb from the l phage (Muyrers et al., 1999). Both these Rec proteins from the phages are functionally and operationally the same. RecE and Reda have 50 –30 exonuclease activity, whereas RecT and Redb are annealing proteins (Kolodner et al., 1994). For successful homologous recombination, there should be a functional interaction between the exonuclease and the functional protein. In the hosts in which this recombination mechanism is inherent, the indigenous exonuclease RecBCD needs to be inactivated to ensure the activity of these recombinase protein pairs. The homology sequence requirement for both RecE/RecT and Reda/Redb range from 35 to 60 nucleotides, which can be easily generated through oligo synthesis (Muyrers et al., 2000).

3

Applications of synthetic genes

Synthetic biology is the science of designing and assembling biological components or redesigning existing ones to generate systems or organisms with predictable and novel functions. It is an engineering discipline, emerging from biology, based on quantification, that has evolved to generate an intriguing genetic toolbox, enabling us to investigate natural processes and reprogram cells to produce desired genetic outputs. Synthetic gene circuits composed of logic gates, oscillators, and biostable switches can adjust gene expression in response to exogenous or endogenous cues (Kaznessis, 2007; Endy, 2005; Kobayashi et al., 2004). The application of engineering-based methodologies to biology indeed helps to build computing-like behavior in biological systems. The advancements made in synthetic biology provide innovative solutions in the field of healthcare, biofuels, biomaterials, biosensing, etc. (MacDonald and Deans, 2016; Weber and Fussenegger, 2012; Khalil and Collins, 2010). Many of the applications do not fit exclusively in only one of the general classes, but it is this multivalency of synthetic biology that makes it such a powerful tool.

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3.1 Biosensing In this field, efforts are being made to generate tools that can sense cellular events and respond to specific cues, having the potential to diagnose or treat diseases with next-generation therapeutics. Biosensing circuits have been designed and created that offer dynamic reporting and real-time progression of diseases. Such synthetic genetic circuits can sense the typical signals emitted by the aberrant cells and serve as potential diagnostic tools. Programmed engineered cells can be used that turn on or off depending on the pathological state, and thus this approach can be used to target drugs to specific sites and deliver it in a concentration-dependent manner in response to the degree of damage/disease (Nandagopal and Elowitz, 2011; Slomovic et al., 2015; Lu et al., 2009; Purnick and Weiss, 2009). Studies have shown that bacteria preferentially colonize and grow in tumor microenvironments when injected into the body. For example, upon administration of engineered bioluminescent bacteria to mice, the bacterial bioluminescence signal colocalizes within the tumor in the mice, thus providing visualization of tumor progression (Yu et al., 2008; Forbes, 2010). Similarly, some engineered bacteria were created to serve as a diagnostic tool for liver cancer (Danino et al., 2015). Synthetic biologists have designed cells where the conditional expression of microRNAs drives the expression of a reporter gene that helps us to identify cancerous cells against healthy cells (Xie et al., 2011). In addition to this, biologists have designed cells using memory genetic circuits that can record the changes during disease progression. Recently, in a study, bacteria were engineered to remember exposure to the chemical anhydrotetracycline in the mouse gut. This synthetic system lays the foundation for its use in the development of living diagnostics and therapeutics (Kotula et al., 2014). Promoters and their respective transcription factors are often chosen for engineering. In a study the authors designed a system that resulted in the splicing of a synthetic ZF-transcription factor and subsequent alteration of the desired gene expression upon recognition of a particular DNA sequence. The system can be engineered to produce required outputs, ranging from therapeutic enzymes to proteins causing apoptosis of cells (Slomovic et al., 2015; Schwartz et al., 2007). Synthetic biology finds applications in the designing of cell-free detection systems or sensors. Most remarkably, this system has been used to design detection sensors for the Ebola virus. In this process the crude cell-free extract was freezedried onto 2-mm paper discs. Toehold riboregulators complimentary to 36 regions of Ebola mRNA were used. The binding resulted in a LacZ-mediated color change on the paper disc, thus providing a robust, low-cost, and easy-to-use and analyzed diagnostic tool (Pardee et al., 2014).

3.2 Therapeutics 3.2.1 Blood glucose homeostasis The ability of synthetic circuits to sense any change is often coupled to a response so that it can be used for the controlled delivery of biomolecules, thus reducing the side effects and gaining therapeutic advantages. For example, light is being used as an input signal to control gene expression. Recently, researchers devised a genetic circuit where the secretion of glucagon-like peptide 1 (GLP-1, a hyperglycemic hormone) could be controlled using blue light. This system was able to maintain glucose homeostasis in the mouse model for type 2 diabetes (Ye et al., 2011). In another study in mice, insulin was produced in response to decreasing pH levels by using a cAMP-responsive promoter, which was used to treat type 1 diabetic mice (Ausl€ander et al., 2014).

3.2.2 Cancer Cancer is characterized by uncontrolled cellular proliferation, which has the potential to invade nearby tissues and spread to other organs (metastasis). Tremendous progress has been made in the field of cancer therapy in the past, but one major challenge faced is how to distinguish between cancerous and noncancerous cells and to selectively kill the neoplastic cells in their native environment. Therefore developing new therapies should focus on identifying the unique hallmarks of cancer, such as tumor microenvironment, unique mRNA or gene expression patterns, and different metabolic states. Synthetic biologists have developed few anticancer devices and therapies whose location, timing, and dose can be controlled by external signals. For example, treatments have been developed that can identify the hypoxic condition in the tumor microenvironment and act by lysing the cancer cells (Anderson et al., 2006). In a study a genetic circuit miRNA classifier was constructed to detect high or low miRNA levels, markers of HeLa cells, so that it could distinguish it from normal cells. This classifier was useful as it could induce apoptosis in the cancer cells by producing hBax proteins (Xie et al., 2011). Microorganisms like bacteria and viruses have been successfully engineered for use in cancer therapy. Many bacterial species like E. coli and Salmonella naturally sense and move toward tumors. Such bacteria have been engineered to produce cytotoxic compounds, reporter proteins, proapoptotic factors or prodrugs, etc., for noninvasive monitoring of tumor growth and other

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therapeutic purposes. In a study, nonpathogenic E. coli bacteria have been engineered to express RNA hairpins that will set off RNAi, resulting in the knockdown of catenin b-1, a colon cancer oncogene (Xiang et al., 2006). In another study, synthetic viral particles have been devised that exclusively package therapeutic proteins, for example, linamarase. Upon injecting such viruses in human breast xenografts in mice administered with the nontoxic linamarin, cyanide production occurs, resulting in tumor regression (Link et al., 2006). Similarly, viral nanoparticles are being used to deliver site-specific DNA recombinases to host cells for excision or integration of genetic regions on the host chromosome (Voelkel et al., 2010). They are also being used to inject transcription factors that control gene expression of specific genes (Grau et al., 2013).

3.2.3 Disease mechanisms and prevention Synthetic biology provides us the potential to rapidly synthesize and analyze sequences of bacterial or viral genomes at affordable costs (Lartigue et al., 2009). For example, the genome of the H1N1 virus that had caused the 1918 Spanish influenza pandemic was reconstructed using the sequence information obtained from some permafrost-conserved tissue. This study has shown that eight genes are critical for the virulence of this strain and thus has dramatically improved our ability to understand the mechanisms underlying infectious diseases (Tumpey et al., 2005). The characterization of the SARS coronavirus was challenging, and, therefore, a chimeric SARS-like coronavirus was designed containing the spike protein of its human homolog. Such synthesis and analysis enabled us to study the infection-enhancing mutations of this virus and showed that its surface protein is essential for causing infections (Becker et al., 2008). Most recently, synthetic biology has been of prime importance for the characterization of the novel coronavirus that is the cause of the COVID pandemic. In December 2019 the full-length genome of 2019-nCoV was constructed from the partial genomes obtained from the alveolar fluid of the patients presenting symptoms of viral pneumonia. Sequence comparison showed that this virus shares 88% sequence identity with the SARS-like coronavirus (Lu et al., 2020). Synthetic biology and genome engineering provide a strategy for making live vaccines against infectious diseases. For example, the poliovirus was attenuated by converting its capsid’s overrepresented codons into underrepresented codons that diminished translation and reduced its infectivity (Coleman et al., 2008). Similarly, synthetic viruslike particles created by selective expression of viral structural proteins of the chikungunya virus lead to the production of antibodies and protected against viral infection in primates (Akahata et al., 2010). For drug discovery, synthetic transcription circuits have been designed consisting of a pharmacologically active compound-responsive transcription factor and its cognate promoter that regulates gene expression in response to the compound. When such cells are exposed to some compound library, the potential nontoxic and cell-permeable drug candidate will affect the gene expression of the reporter gene (Fussenegger et al., 2000; Karlsson et al., 2011). Synthetic pathways made up of a series of enzymatic reactions are also involved in the large-scale production of secondary metabolites, drugs, and drug precursors (e.g., the precursor of the antimalarial drug artemisinin and halogenated alkaloids) (Ro et al., 2006).

3.2.4 Novel treatments for bacterial infections Biofilms are bacterial communities embedded in a self-produced exopolysaccharide (EPS) matrix that depict enhanced antimicrobial resistance and are instrumental for pathogenesis caused by several clinically relevant strains. In a study, T7 bacteriophage has been engineered to express an enzyme DspB constitutively that hydrolyzes the adhesion protein required for biofilm formation, thus reducing biofilm production upon using this manipulated phage (Lu and Collins, 2007). In another study, E. coli could be sensitized to quinolone antibiotics upon infection with M13 bacteriophage engineered to express LexA, a suppressor of SOS DNA repair system in bacteria that is needed by them to withstand antibioticinduced stress (Kohanski et al., 2010). Studies have shown that feeding probiotic E. coli engineered to express the cholera autoinducer 1 (CAI-1) to mice decreases infection by Vibrio cholerae (Duan and March, 2010).

3.3 Biofuels and biomaterials Biofuels are a potential source of renewable energy, and therefore microbial cells are being optimally manipulated to convert biomass into biofuels efficiently. For example, the production of biobutanol, a higher energy density alternative to ethanol, is attained through the biosynthetic engineering of microorganisms. This process utilizes the host’s amino acid biosynthesis pathway to redirect its 2-keto acid intermediates to accomplish high yields of isobutanol from glucose (Atsumi et al., 2008). Another objective of synthetic biology is to design “smart” systems that can sense signals and respond by switching metabolic states accordingly. Synthetic circuits called “biological timers” have been designed that are able

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to shut down metabolic activities after a particular time. One such example is the time-reliant flocculation of yeast that simplifies the separation of cells from the alcohol produced during industrial fermentation (Ellis et al., 2009). Systems have been engineered with the aim of producing useful recombinant biomaterials. Cells were engineered during a study where synthetic recombinant silk-producing genes were cloned into the innate type III secretion control system to obtain efficient secretion of silk monomers. Synthetic biology also aims to regulate the coordinated behavior of a population of cells by altering cell-to-cell communication and its associated signaling pathways (Widmaier et al., 2009). In a study a synthetic bacterial multicellular system was designed consisting of engineered “receiver” cells that formed patterns on cellular lawns in response to chemical gradients created by “sender” cells, thus serving as scaffolds for tissue engineering, biofabrics, etc. (Basu et al., 2004; Basu et al., 2005). In another study, drug depots were constructed by embedding the biopharmaceutical vascular endothelial growth factor (VEGF) into the hydrogel. Upon subcutaneous implantation in mice and consumption of the trigger molecules at a concentration, VEGF was released in a dose-adjustable manner (K€ampf et al., 2010). This exemplifies how synthetic biology can be integrated with materials sciences to produce gene therapy techniques. Synthetic biology has tremendous potential to provide humankind with major breakthroughs, but there are many challenges that need to be overcome before that. Collaborations between dry lab and wet lab experimentalist are required for improving our understanding of the regulatory mechanisms of complex cellular systems. This will enable researchers to engineer and manipulate cells easily and expand the toolbox required for making genetic manipulations. A robust and reliable method of integrating individual genetic circuits into biological systems with predictable behavior has to be developed. Currently, synthetic biology is majorly used in microbes, but efforts are being made to realize these technologies in mammalian cells as well so that it could equip us with next-generation therapeutic solutions in this century, like stem cell therapies. With constant research, we can overcome the technical shortcomings in this field and genuinely appreciate the robustness and sophistication of the world of synthetic genes.

4 Current challenges in DNA synthesis The main bottleneck in the advancement of synthetic biology is the assembly of DNA to form the genetic circuit and finally building the in vivo pathway for the desired outcome. Pathway is an array of genes with multiple promoters of open reading frames (ORF) performing related functions. The preliminary requirement for pathway building is the assembly of genetic parts, that is, promoter, ribosome binding site, open reading frame, and terminator in a defined sequence. These elements constitute the most essential part of the genetic circuit. Besides these a functional gene also depends on the regulatory elements present upstream and downstream of the gene of interest and the RNA binding motifs. Apart from the ordered assembly, another important parameter that needs to be taken care of is the scar sequence (residual sequence during assembly of the genetic parts), which might have an impact on the genetic function. For example, although the length of the RBS is only six base pairs, it needs to be positioned just before the ORF, and its efficiency is regulated by 50 bases upstream and downstream of the RBS region, which includes the ORF (Salis et al., 2009). Several techniques, as mentioned before, have been developed for the assembly with minimum or no scar. There are several mechanisms of DNA assembly ranging from restriction enzyme assembly to sequence overlap techniques and finally recombination mechanisms. The restriction enzyme assembly techniques include BioBricks, BglBricks, pairwise selection, and Golden Gate assembly. All these techniques utilize type II restriction enzymes for the assembly mechanism. The sequence overlap techniques include InFusion, isothermal assembly, SLIC, USER, and O-PCR. The only difference between O-PCR and the other overlap techniques is the utilization of PCR with overlapping sequences. The final recombination assembly techniques include Bacillus domino and yeast transformation-associated recombination. All the techniques can be used for the assembly of genes and can be scaled up to pathways. These techniques are widely favored for in vitro and in vivo DNA assembly in yeast and bacterial strains (Ellis et al., 2011).

4.1 Biosynthetic pathway for the development of microfactories DNA assembly techniques have been used to produce various compounds, thus implementing this technology for the advancement of microfactories. Streptavidin is a useful molecule used for various in vitro and in vivo applications. The gene responsible for producing this molecule was transferred into B. subtilis with the aid of an expression vector. In this study the gene was assembled in the expression vector using restriction enzymes (Wu et al., 2002). Iturin A is a lipopeptide produced by B. subtilis through the nonribosomal peptide synthesis (NRPS) mechanism. To study the lipopeptide production, a 42-kb region of the B. subtilis RB14 genome consisting of a 38-kb iturin A operon was transferred to a noniturin A producer strain, B. subtilis 168. The method of assembly and integration of this operon within the genome

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of B. subtilis 168 was double-crossover homologous recombination with two short landing pad sequences (Tsuge et al., 2005). The Red/ET recombination and gene complementation approach was used to transfer the geldanamycin biosynthetic genes into Streptomyces. Firstly, genetic engineering of the geldanamycin biosynthetic genes was carried out in E. coli through Red/ET recombination using a complementation plasmid. After that, it was transferred into Streptomyces. This approach made the manipulation more rapid and adaptable (Vetcher et al., 2005). Amicoumacin is a class of inhibitor that, upon binding to the ribosome, inhibits protein synthesis. The amicoumacin biosynthetic gene cluster was first transferred to S. cerevisiae to yield a circular plasmid through TAR. The circular gene cluster was then cloned into E. coli, and the cloned pathway was engineered using the l-Red recombination-mediated PCR targeting. Finally the naturally competent B. subtilis JH642 was transformed with the cloned and engineered pathway genes, which were integrated into the chromosome. This strain was used for further studies (Li et al., 2015). Heterologous expression of biosynthetic pathways for edeine (43-kb) production from Brevibacillus brevis and bacillomycin (37.2 kb) production from B. amyloliquefaciens FZB42 was also carried out in B. subtilis. The integration of the gene cluster was carried out using the Red/ET recombination mechanism. The cloning vector used consisted of homologous sequences of amyE gene of B. subtilis and CcdB as the selection marker (Liu et al., 2016). A combinatorial approach using the RNA-guided Cas9 system was utilized for building the beta-carotene biosynthetic pathway in S. cerevisiae. Direct assembly and chromosomal integration were carried out using up to 17 overlapping DNA fragments encoding the pathway (Eau Claire et al., 2016). Anthocyanins (ACNs) are plant secondary metabolites accountable for most of the red, purple, and blue pigments present in flowers, fruits, and vegetables. De novo biosynthesis of this metabolite was carried out in S. cerevisiae using enzymes from plant sources. The codonoptimized genes were assembled into plasmids, after which they were transferred into S. cerevisiae for chromosomal integration through homologous recombination (Eichenberger et al., 2018). A similar study was carried out by Levisson and coworkers to produce pelargonidin 3-O-glucoside from glucose using engineered S. cerevisiae (Levisson et al., 2018). So far the results are very much encouraging in both bacterial and yeast systems, but it requires more fine-tuning to be used as a chassis for all possible metabolites.

5

Future developments

An important requirement for the building and optimization of pathways in vivo is the advancement of DNA assembly methods. The assembly method needs to be robust and highly efficient, leading to a scarless assembly of the DNA fragments. The development of a set of regulated overlap sequences is required, which will be compatible with different assembly techniques (MacDonald et al., 2011). Future research should focus on assembly protocols that are facilitated by computational tools to assist the construction of the assembly and to automatize the liquid-handling methods. Various synthetic tools are in the pipeline and are being used for the design of the assembly. High-throughput methodology for the assembly, as well as screening, should also be investigated. Design softwares including model-based approaches should be developed giving an insight of the environmental needs of neighboring regions, which will in turn notify the assembly strategy required. Software programs need to be designed in such a way that will facilitate the use of combinatorial workflows using multiple assembly techniques. The software package J5 developed by Joint BioEnergy Institute (JBEI) is an example of such a tool (Ellis et al., 2011). Finally, efforts should be taken in making the gene synthesis inexpensive. These developments will revolutionize the utilization of synthetic genes for the construction of genetic libraries in building pathways and genomes to create tailored pathways (Kosuri and Church, 2014).

References Agmon, N., Mitchell, L.A., Cai, Y., Ikushima, S., Chuang, J., Zheng, A., Choi, W.-J., Martin, J.A., Caravelli, K., Stracquadanio, G., Boeke, J.D., 2015. Yeast Golden Gate (yGG) for the efficient assembly of S. cerevisiae transcription units. ACS Synth. Biol. 4, 853–859. https://doi.org/10.1021/ sb500372z. Akahata, W., Yang, Z.Y., Andersen, H., Sun, S., Holdaway, H.A., Kong, W.P., Lewis, M.G., Higgs, S., Rossmann, M.G., Rao, S., Nabel, G.J., 2010. A virus-like particle vaccine for epidemic Chikungunya virus protects nonhuman primates against infection. Nature Med. 16 (3), 334. Anderson, J.C., Clarke, E.J., Arkin, A.P., Voigt, C.A., 2006. Environmentally controlled invasion of cancer cells by engineered bacteria. J. Mol. Biol. 355 (4), 619–627. Anderson, C., Dueber, J., Leguia, M., et al., 2010. Bgl bricks: a flexible standard for biological part assembly. J. Biol. Eng. 4, 1. Atsumi, S., Hanai, T., Liao, J.C., 2008. Non-fermentative pathways for synthesis of branched-chain higher alcohols as biofuels. Nature 451 (7174), 86. Ausl€ander, D., Ausl€ander, S., Charpin-El Hamri, G., Sedlmayer, F., M€uller, M., Frey, O., Hierlemann, A., Stelling, J., Fussenegger, M., 2014. A synthetic multifunctional mammalian pH sensor and CO2 transgene-control device. Mol. Cell 55 (3), 397–408.

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Chapter 3

Discovery of enzymes responsible for cyclization and postmodification in triterpenoid biosynthesis Siqin Cai and Han Xiao* State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, and Laboratory of Molecular Biochemical Engineering, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China *Corresponding author: e-mail: [email protected]

1 Introduction Triterpenoids are a group of C30 compounds, which are the most widely found natural products in fungi and plants. So far, almost 200 triterpenoid skeletons have been identified (Xu et al., 2004). According to the number of rings contained in the skeleton, triterpenoids can be divided into linear triterpenoids, monocyclic triterpenoids, bicyclic triterpenoids, tricyclic triterpenoids, tetracyclic triterpenoids, and pentacyclic triterpenoids. Most triterpenoids belong to tetracyclic or pentacyclic triterpenoids. Triterpenoids exhibit a lot of important biological activities, including antitumor, antibacterial, antiviral, and antioxidant (Table 1). Due to the significant bioactivities of triterpenoids, their biosynthesis is attracting great interest. Cyclization and postmodification are two important steps in triterpenoid biosynthesis. In this chapter, we emphatically summarize the approaches for discovery of enzymes in cyclization and postmodification of triterpenoid. We also discuss some new ideas for enzyme discovery in triterpenoid biosynthesis.

2 Enzymes responsible for cyclization and postmodification of triterpenoid are crucial for triterpenoid biosynthesis Biosynthesis of triterpenoid usually includes three stages: formation of the C30 core structure, cyclization, and postmodification. As a typical linear triterpenoid, squalene is also considered as the core structure compound for biosynthesis of other types of triterpenoids. Squalene can be biosynthesized from acetyl-CoA via two well-characterized pathways: mevalonate (MVA) pathway or 2-C-methyl-D-erythritol-4-phosphate (MEP) pathway (Tetali, 2019; Kuzuyama and Seto, 2012). The core structure of triterpenoid undergoes cyclization and postmodification to form cyclic triterpenoids (Fig. 1). During these stages, squalene cyclase (SC) and oxidosqualene cyclase (OSC) are mainly responsible for cyclization, while cytochrome P450 (CYP), glycosyltransferase, and acyltransferase are mainly responsible for postmodification (Fig. 1). Compared with core structure formation, cyclization and postmodification are more crucial for triterpenoid biosynthesis, because generating structural diversity and the corresponding bioactivity of triterpenoids rely on the enzymes in the latter two stages (Bhandari et al., 2020; Ca´rdenas et al., 2019). To date a variety of cyclases have been characterized, while the products of most cyclases are tetracyclic and pentacyclic triterpenoids (Fig. 1). For one organism, it may harbor multiple cyclases to generate various types of triterpenoids. For example, there are four cyclases (OSC1, OSC2, OSC3, and OSC4) in Terminalia arjuna. For OSC1, OSC3, and OSC4, they are monofunctional enzymes responsible for biosynthesis of oleanane triterpenoids, sterol scaffold, and lupane triterpenoids, respectively. For OSC2, it is a multifunctional cyclase that generates precursors for ursane- and oleanane-type triterpenoids (Srivastava et al., 2020). On the other hand, for one cyclase, it could also generate multiple triterpenoids with different skeletons. For example, cyclase CAMS1 in Arabidopsis thaliana can produce monocyclic triterpenoids camelliol C, achilleol A, and pentacyclic triterpenoid b-amyrin (Kolesnikova et al., 2007). Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00028-3 © 2021 Elsevier Inc. All rights reserved.

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TABLE 1 Bioactivities of some triterpenoids. Species

Triterpenoids

Bioactivities

References

Fructus Ligustri Lucidi

Oleanolic acid

Antitumor

Zhang et al. (2007)

Oleanolic acid and ursolic acid

Antivirus

Kong et al. (2013)

Sonneratia paracaseolaris

Paracaseolin D

Antitumor

Gong et al. (2017)

1b,3b-Dihydroxy betulin

Anti-H1N1 virus

Salvia argentea var. aurasiaca

1b,3b,15a,28-Tetrahydroxy-urs-9(11),12-diene, 1b,3bdihydroxy-urs-9(11),12-dien-28-al, 1b,3b,15a-trihydroxyurs-11-en-28-al

Antitumor

1b,3b,7b,15a,28-Pentahydroxy-urs-12-ene

Antibacterial

Gynostemma pentaphyllum

Gypsapogenin A, (20S,24S)-3b,20,21b,23b,25pentahydroxy-21,24-cyclodammarane and (23S)-3bhydroxydammar-20,24-dien-21-oic acid 21,23-lactone

Antitumor

Shi et al. (2018)

Salvia buchananii

Salvibuchanic acid

Antitumor

Beladjila et al. (2018)

Psolus patagonicus

Patagonicoside A and its desulfated derivative

Antitumor

Careaga et al. (2009)

Ganoderma lucidum

Ganoderic acid DM and ganoderic acid T

Antitumor

Wu et al. (2012), Tang et al. (2006)

Salvia barrelieri Etl

Epigermanidiol and ursolic acid

Antibacterial

Lehbili et al. (2018)

Pseudocedrela kotschyi

4-Hydroxy-3,4-secotirucalla-7,24-dien-3,21-dioic acid, 3,4-secotirucalla-4(28),7,24-trien-3,21-dioic acid, and 3-methyl ester 3,4-secotirucalla-4(28),7,24-trien-3,21dioic

Antibacterial

Mambou et al. (2018)

Calothamnus quadrifidus

2,23-Dihydroxy betulinic acid, 2,21,23-trihydroxy betulinic acid, 3-acetyl-23-hydroxy betulinic acid, and 2,23-dihydroxy betulinic acid

Antibacterial

Ibrahim et al. (2019)

Gloeophyllum odoratum

Trametenolic acid B

Antiinfluenza activity

Grienke et al. (2019)

Crataeva nurvala

Lupeol and its ester derivative

Ameliorating the renal injury associated with hypercholesterolemia

Sudhahar et al. (2008)

Ilex rotunda

Pedunculoside

Lipid-lowering

Liu et al. (2018), Sudhahar et al. (2008)

Centella asiatica

Madecassoside and asiaticoside

Antioxidant

Hashim et al. (2011), Liu et al. (2018)

Rosmarinus officinalis

Ursolic and oleanolic

Antioxidant

Bernatoniene et al. (2016), Hashim et al. (2011)

Syzygium aromaticum

Oleanolic acid and its derivatives

Analgesic

Rali et al. (2016), Bernatoniene et al. (2016)

Bechkri et al. (2019)

Discovery of key enzymes in triterpenoid biosynthesis Chapter

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FIG. 1 Cyclization and postmodification in triterpenoid biosynthesis. Single bold arrow represents one step reaction, and red circle represents the site of postmodification. AT, acetyltransferase; CYP, cytochrome P450; DOSC, dioxidosqualene cyclase; OSC, oxidosqualene cyclase; SC, squalene cyclase; UGT, UDP-glycosyltransferase.

The heme monooxygenases CYPs, which belong to families of CYP51, CYP71, CYP72, CYP87, CYP88, CYP93, and CYP716, are believed to be highly involved in postmodification of triterpenoids (Miettinen et al., 2017). Heretofore, CYPs have been reported to modify multiple carbon sites, including C-3, C-6, C-12, C-23, and C-26 sites of tetracyclic triterpenoids and C-2, C-3, C-11, C-16, C-22, C-23, C-24, C-28, and C-30 sites of pentacyclic triterpenoids (Table 2). For one site, single or multiple oxidations could be achieved by one CYP (Han et al., 2018; Huang et al., 2012; Seki et al., 2008). In addition, owing to the broad substrate range, some CYPs are able to produce different products by converting different substrates (Huang et al., 2012, 2019a). Similar to CYPs, glucosyltransferases also exhibit promiscuity in transferring different sugar donors to different sugar acceptors (He et al., 2018; Xu et al., 2016; Shibuya et al., 2010), leading to the great changes on the chemical structures and bioactivities of triterpenoids (Rahimi et al., 2019). As for another important group of enzymes, acyltransferase usually transfers acyl group to the hydroxyl group of the substrate. Although triterpenoids with multiple acetylation sites are identified (Baby et al., 2015), limited acetyltransferases have been characterized for triterpenoid biosynthesis (Cao et al., 2020; Lv et al., 2017). In contrast to ample researches on discovery of enzymes responsible for core structure formation (Miziorko, 2011; Berthelot et al., 2012; Laskovics and Poulter, 1981), discovery of enzymes responsible for cyclization and postmodification

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TABLE 2 Examples for enzyme discovery in triterpenoid biosynthesis. Approach for enzyme discovery

Enzyme and its origin

Heterologous expression host

Enzyme function

References

Ma et al. (2019)

Traditional approaches AfumA from Aspergillus fumigatus

–

Cyclization of 2,3-oxidosqualene to 21bHhopane-3b,22-diol

AfumB from A. fumigatus

–

Oxidation of 21bH-hopane-3b,22-diol at C-24 and C-30

AfumC from A. fumigatus

–

Glycosylation of 21bH-3b,22,24-trihydroxyhopane-30-oic at C-24

Gene silencing

CYP716A53v2 from P. ginseng

–

Hydroxylation of protopanaxadiol at C-6

Park et al. (2016)

Enzymatic activity– guided protein purification

UGRdGT from P. notoginseng

–

Glycosylation of ginsenoside Rd at the hydroxyl of C-20

Yue and Zhong (2005)

Mutation-based approaches

S728F SAD1 from Avena species

S. cerevisiae

Cyclization of oxidosqualene and dioxidosqualene to form dammaranediol-II and epoxydammarane, respectively

Salmon et al. (2016)

At1g78955 from A. thaliana

S. cerevisiae

Cyclization of oxidosqualene to form camelliol C and achilleol A

Kolesnikova et al. (2007)

At4g15340 from A. thaliana

S. cerevisiae

Cyclization of oxidosqualene and dioxidosqualene to form arabidio and arabidiol 20,21-epoxide, respectively

Xiang et al. (2006)

AK070534 from O. sativa

S. cerevisiae

Cyclization of oxidosqualene to form achilleol B

Ito et al. (2011)

AK121211 from O. sativa

S. cerevisiae

Cyclization of oxidosqualene to form cycloartenol

AK066327 from O. sativa

S. cerevisiae

Cyclization of oxidosqualene to form parkeol

CYP716A2 from A. thaliana

S. cerevisiae

Hydroxylation of a-amyrin at C-22

CYP716A1 from A. thaliana

S. cerevisiae

Successive oxidation of b-amyrin at C-28

CYP716AL1 from Catharanthus roseus

S. cerevisiae

Successive oxidation of a-amyrin, b-amyrin, and lupeol at C-28

Huang et al. (2012)

CYP72A63 from M. truncatula

S. cerevisiae

Successive oxidation of b-Amyrin at C-30

Seki et al. (2011)

UGTPg1 from P. ginseng

S. cerevisiae

Hydroxylation of protopanaxadiol at C-20

Wang et al. (2015)

Shionone synthase from A. tataricus

S. cerevisiae

Cyclization of oxidosqualene to form shionone

Sawai et al. (2011)

CYP71A16 from A. thaliana

S. cerevisiae

Hydroxylation of marnerol at C-23

Field et al. (2011)

CYP716A53v2 from P. ginseng

S. cerevisiae

Hydroxylation of protopanaxadiol at C-6

Han et al. (2012)

BmeTC from Bacillus megaterium

E. coli

Cyclization of squalene to form 8ahydroxypolypoda-13,17,21-triene

Ueda et al. (2013)

Gene deletion

Synthetic biology approaches Genomic-guided heterologous expression

Yasumoto et al. (2016)

Discovery of key enzymes in triterpenoid biosynthesis Chapter

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TABLE 2 Examples for enzyme discovery in triterpenoid biosynthesis—cont’d Approach for enzyme discovery

Transcriptomicguided heterologous expression

Enzyme and its origin

Heterologous expression host

Enzyme function

References

UGTPg71A29 from P. ginseng

E. coli

Glycosylation of ginsenoside Rh1, Rd at the hydroxyl of C-20

Lu et al. (2018)

GmSGT2 from Glycine max

E. coli

Galactosylation of soyasapogenol B monoglucuronide at C-3

Shibuya et al. (2010)

GmSGT3 from G. max

E. coli

Rhamnosylation of soyasaponin III at C-3

Bs-YjiC from Bacillus subtilis 168

E. coli

Glycosylation of protopanaxatriol at the hydroxyls of C-3, C-6 and C-12

Dai et al. (2018)

BsGT110 from B. subtilis ATCC 6633

E. coli

Glycosylation of ganoderic acid A at the carboxyls of C-15 and C-26

Chang et al. (2019)

CepD2 in Acremonium chrysogenum ATCC 11550

Aspergillus oryzae

Acetylation of 6-deacetyl cephalosporin P1 at the hydroxyl of C-6

Cao et al. (2020)

CYP72A397 from Kalopanax septemlobus

S. cerevisiae

Hydroxylation of oleanolic acid at C-23

Han et al. (2018)

CYP716A94 from K. septemlobus

S. cerevisiae

Successive oxidation of b-amyrin at C-28

CYP716A155 from Rosmarinus officinalis

S. cerevisiae

Successive oxidation of lupeol, a-amyrin and bamyrin at C-28

Huang et al. (2019a)

CYP716A47 from P. ginseng

S. cerevisiae

Hydroxylation of dammarenediol II at C-12

Han et al. (2011)

CYP93E1 from G. max

S. cerevisiae

Hydroxylation of b-amyrin and sophoradiol at C24

Shibuya et al. (2006)

CYP87D18 from Siraitia grosvenorii

S. cerevisiae

Successive oxidation of cucurbitadienol at C-11

Zhang et al. (2016)

CYP716Y1 from Bupleurum falcatum

S. cerevisiae

Hydroxylation of a-amyrin and b-amyrin at C-16

Moses et al. (2014)

CYP512U6 from G. lucidum

S. cerevisiae

Hydroxylation of ganoderic acid DM and ganoderic acid TR at C-23

Yang et al. (2018)

CYP5150L8 from G. lucidum

S. cerevisiae

Successive oxidation of lanosterol at C-26

Wang et al. (2018)

TaOSC1from Terminalia arjuna

S. cerevisiae

Cyclization of oxidosqualene to form b-amyrin

Srivastava et al. (2020)

TaOSC2 from T. arjuna

S. cerevisiae

Cyclization of oxidosqualene to form a- and bamyrin

TaOSC3 from T. arjuna

S. cerevisiae

Cyclization of oxidosqualene to form cycloartenol

TaOSC4 from T. arjuna

S. cerevisiae

Cyclization of oxidosqualene to form lupeol

CYP72A154 from G. uralensis

S. cerevisiae

Successive oxidation of 11-oxo-b-amyrin at C-30

Seki et al. (2011)

CYP716A263 from Taraxacum koksaghyz

S. cerevisiae and Nicotiana benthamiana

Dehydrogenation of taraxasterol, a-amyrin, b-amyrin, and lup-19(21)-en-3-ol at C-3

P€ utter et al. (2019) Continued

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TABLE 2 Examples for enzyme discovery in triterpenoid biosynthesis—cont’d Approach for enzyme discovery

Enzyme and its origin

Heterologous expression host

Enzyme function

References

GuUGT from G. uralensis

S. cerevisiae and E. coli

Glycosylation of glycyrrhetinic acid at the hydroxyl of C-3 and the carboxyl of C-30

Huang et al. (2019b)

UDPglucosyltransferase from Siraitia grosvenorii

E. coli

Glycosylation of mogrol at the hydroxyls of C-3 and C-24

Tang et al. (2011)

UGT74AC1 from S. grosvenorii

E. coli

Glycosylation of mogrol at the hydroxyl of C-3

Dai et al. (2015)

UGT74M1 from Saponaria vaccaria

E. coli

Glycosylation of gypsogenic acid, 16ahydroxygypsogenic acid, quillaic acid, gypsogenin, hederagenin, echinocystic acid, and betulinic acid at the hydroxyls of C-3 and the carboxyls of C-28

FiallosJurado et al. (2016)

UGT73F17 from G. uralensis

E. coli

Glycosylation of oleanane-type triterpenoids at the carboxyls of C-29 and C-30

He et al. (2018)

UGT73F3 from M. truncatula

E. coli

Glycosylation of hederagenin, medicagenic acid and bayogenin at the hydroxyls of C-3 and the carboxyls of C-28, and glycosylation of soyasapogenols A and B at the hydroxyls of C-3

Naoumkina et al. (2010)

GuUGAT from G. uralensis

E. coli

Glycosylation of glycyrrhetinic acid at the hydroxyl of C-3

Xu et al. (2016)

UGT73K1 from M. truncatula

E. coli

Glycosylation of soyasapogenols B and E at the hydroxyls of C-3, glycosylation of hederagenin at the hydroxyl of C-3 and the carboxyl of C-28

Achnine et al. (2005)

UGT71G1 from M. truncatula

E. coli

Glycosylation of medicagenic acid at the hydroxyl of C-3 and the carboxyl of C-28

CYP88D6 from G. uralensis

S. cerevisiae

Successive oxidation of b-amyrin at C-11

Seki et al. (2008)

Dammarenediol synthase from P. ginseng

S. cerevisiae

Cyclization of oxidosqualene to form dammarenediol II

Han et al. (2006)

OSC2 from A. annua

S. cerevisiae and Nicotiana benthamiana

Cyclization of oxidosqualene to form a-, b-, and d-amyrin

Moses et al. (2015)

CYP716A14v2 from A. annua

S. cerevisiae and Nicotiana benthamiana

Dehydrogenation of a-, b-, and d-amyrin at C-3

CYP716A12 from M. truncatula

S. cerevisiae

Successive oxidation of b-amyrin and erythritol at C-28

Combinatorial approaches Transcriptomicguided heterologous expression and gene silencing

Gene disruption and heterologous expression –: Not applicable.

Carelli et al. (2011)

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in triterpenoid biosynthesis remains a fertile area to explore. It may not only provide insights into elucidation of the biosynthetic route of triterpenoid, but also lay the foundation for efficient biosynthesis of triterpenoid.

3 Approaches for discovery of enzymes in cyclization and postmodification of triterpenoid To discover enzymes in cyclization and postmodification of triterpenoid, traditional approaches, including gene deletion, gene silencing, and enzymatic activity–guided protein purification, mutation-based approaches, synthetic biology approaches, and combinatorial approaches will be discussed in this section (Table 2).

3.1 Traditional approaches For gene deletion and gene silencing, they are widely adopted to discover almost all kinds of enzymes for triterpenoid biosynthesis. For enzymatic activity–guided protein purification, it particularly applies to discover robust enzymes that are stable and easy to purify.

3.1.1 Gene deletion Gene deletion is applied to deduce the enzyme function according to the decrease of product and the increase of substrate in the gene knockout mutant. Genomic analysis of Aspergillus fumigatus revealed that a gene cluster may be responsible for the formation of hopane-type glycosides. In this cluster, afumA, afumB, and afumC are predicted to encode cyclase, CYP, and UDP-glycosyltransferase, respectively. In DafumA strain, no cyclic triterpenoid but 2,3-oxidosqualene was detected. In DafumB strain, a new cyclic product 21bH-hopane-3b,22-diol was identified. These data indicated that AfumA is an OSC. When afumC was deleted, the DafumC strain failed to produce fumihopaside A and generated more amount of an intermediate similar to fumihopaside A, which lacks the UDP-glycosyl at C-24. It suggested that AfumC is a C-24 glycosyltransferase. Compared with the chemical structure of 21bH-hopane-3b,22-diol produced by DafumB strain, the intermediate produced by DafumC strain has one more hydroxyl group at C-24 and one more carboxyl group at C-30, which indicates that AfumB is a C-24 and C-30 oxidase (Ma et al., 2019). However, prior to obtaining the gene deletion mutant, mature genetic manipulation tools should be established in the host.

3.1.2 Gene silencing Gene silencing is to reduce gene expression by specific degradation of its RNA at the posttranscriptional level, which is widely accepted for investigating the function of essential gene. When cyp716a53v2 was silenced by RNA interference (RNAi) in Panax ginseng, the production of PPT-group ginsenosides was decreased, while the production of PPD-group ginsenosides was increased, as compared with those produced from the wild-type strain. Thus, CYP716A53V2 may be the C-6 hydroxylase of PPD-group ginsenosides (Park et al., 2016). It should be noted that if an isozyme exists, phenotypic variation may hardly be observed between the gene silenced/deleted strain and the wild-type strain, due to the functional redundancy.

3.1.3 Enzymatic activity guided protein purification This method is to separate target protein from native host as guided by the in vitro enzyme activity. It does not require the gene information or the genetic manipulation of the native strain. However, the product of the enzymatic reaction should be identified to determine the activity. In one example, UGRdGT was partially purified from the intracellular soluble enzymes of Panax notoginseng. During the purification process, the substrate ginsenoside Rd was mostly converted into the product Rb1 to ensure the enzyme activity (Yue and Zhong, 2005). As for the membrane-bounded CYPs, they usually exhibit poor solubility or aggregation during in vitro purification, which may further affect enzyme activity (Sˇrejber et al., 2018). It should be noted that some CYPs were successfully identified by using this approach, albeit with other functions not related to triterpenoid biosynthesis (Sibbesen et al., 1994, 1995).

3.2 Mutation-based approaches For many enzymes (e.g., SC and CYP) in triterpenoid biosynthesis, small changes in key residues may alter the enzyme function, leading to the formation of a new product. Thus mutation-based approach is used to discover new function of the known enzyme. In one study the S-to-F mutation at amino acid #728 in b-amyrin synthase SAD1 led to the formation of

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dammaranediol-II rather than b-amyrin in Avena species. Meanwhile, heterologous expression of this mutated SAD1 in yeast confirmed that it prefers cyclization of dioxidosqualene into epoxydammarane (Salmon et al., 2016).

3.3 Synthetic biology approaches For most of the traditional approaches (e.g., gene deletion and gene silencing), they heavily rely on the host genetic manipulation tools. Thus it is unfavorable to execute such strategies in genetically intractable species including many triterpenoids producing mushrooms. To circumvent this difficulty, synthetic biology approach is to screen candidate genes in a genetically tractable host (e.g., Escherichia coli and Saccharomyces cerevisiae), while multilevel omics information is usually required to determine the screening targets for heterologous expression.

3.3.1 Genomic-guided heterologous expression Genomic study allows prediction of gene function and cloning of the unknown gene cluster, which ultimately reveal the targets for heterologous expression. According to the genome information, six genes were predicted to encode triterpenoid cyclase in Oryza sativa. For identification of cyclase, S. cerevisiae is an ideal host because it can naturally produce squalene and oxidosqualene as substrates for most cyclases. Therefore those genes were expressed in a lanosterol synthase-disrupted S. cerevisiae. As a result, AK121211, AK066327, and AK070534 were identified as cycloartenol synthase, parkeol synthase, and achilleol B synthase, respectively (Ito et al., 2011). In terpenoid biosynthesis, cyclase and enzymes responsible for postmodification are usually in a gene cluster (Field et al., 2011). The candidate CYPs were accordingly found in A. thaliana and overexpressed in yeast. Consequently, CYP708A2 is a thalianol C-7 hydroxylase, and CYP71A16 is a marneral C-23 hydroxylase (Wada et al., 2012; Osbourn et al., 2012; Castillo et al., 2013). In addition, the unknown gene cluster can be cloned by adopting the degenerate primers in the highly conserved region of the candidate enzymes as determined by genomic study. In Aster tataricus, about 1.2-kb fragments were obtained by using nested degenerate primers in the conserved region of known OSCs from plant. With the aid of 50 - and 30 -RACE PCRs, a full length of cDNA was cloned and transferred into yeast, and the OSC-expressing yeast was able to cyclize oxidosqualene to form shionone (Sawai et al., 2011).

3.3.2 Transcriptomic-guided heterologous expression For biosynthesis of triterpenoid, the cyclases and their tailoring enzymes (CYPs) are often coexpressed (Naoumkina et al., 2010). Since triterpenoids are typical secondary metabolites, their biosynthesis are strongly affected by growth condition (Nasrollahi et al., 2014). Thus coexpression analysis and comparative transcriptomic study allow the discovery of candidate genes for cyclization and postmodification in triterpenoid biosynthesis. In Kalopanax septemlobus, several cyp genes were abundantly transcribed when methyl jasmonate was added to the medium. Two of them exhibited a coexpression pattern with the cyclase. These two CYPs were further expressed in yeast to confirm their functions. As a result, CYP716A94 and CYP72A397 were involved in biosynthesis of oleanolic acid from b-amyrin and biosynthesis of hederagenin from oleanolic, respectively (Han et al., 2018). In Glycyrrhiza uralensis roots, high concentrations of glycyrrhizin were accumulated under drought or salt stress conditions. Transcriptomics study discovered that gene GuUGAT was upregulated and coexpressed with key genes in glycyrrhizin biosynthesis in the aforementioned conditions. Heterologous expression of GuUGAT in E. coli showed that it can convert glycyrrhetinic acid into glycyrrhizin (Xu et al., 2016).

3.4 Combinatorial approaches In addition to solely adoption of the aforementioned approaches, combinatorial approach, a combination of traditional approach and synthetic biology approach, is widely applied for enzyme discovery. In one example, transcriptome analysis revealed that contigs related to triterpenoid biosynthesis were overexpressed in the filamentous trichomes of Artemisia annua. A predicted oxidosqualene cyclase OSC2 is contained in a highly expressed contig comp7642. Heterologous expression of OSC2 in S. cerevisiae and Nicotiana benthamiana showed that OSC2 can cyclize 2,3-oxidosqualene to form a-amyrin, b-amyrin, and d-amyrin. Moreover, cyp716a14v2 was coexpressed with osc2 in the trichomes. Heterologous expression of CYP716A14v2 and OSC2 in both yeast and tobacco led to the formation of a-amyron and b-amyron. To characterize the enzyme function of OSC2 and CYP716A14v2 in the native host, transcription of osc2 and cyp716a14v2 was individually silenced by RNAi. Significant decrease of a-amyron and b-amyron was observed in cyp716a14v2silenced A. annua, while production of a-amyrone was decreased in os2-silenced plants. Taken together, these informations indicated that CYP716A14v2 is a C-3 dehydrogenase of a-amyrin and b-amyrin (Moses et al., 2015). In another example

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the retrotransposon Tnt was used for insertional mutagenesis in Medicago truncatula (D’Erfurth et al., 2003). CYP716A12 was disrupted in a mutant that was not able to produce hemolytic saponins. To verify the enzyme function, CYP716A12 was expressed in S. cerevisiae. The CYP716A12 containing microsomes from the engineered yeast strain could catalyze b-amyrin or erythritol to form oleanolic acid, indicating that CYP716A12 catalyzes a successive oxidation to form oleanolic acid at C-28 (Carelli et al., 2011).

4 Concluding remarks and future perspectives Owing to the discussed approaches, many enzymes have been identified for triterpenoid biosynthesis. Gene deletion and gene silencing have been successfully applied to species including A. fumigatus, P. ginseng, and A. annua to discover cyclase, CYP, and transferase. As for the method of enzymatic activity–guided protein purification, it usually contains multiple purification procedures, and sadly, it has seen limited application in discovery of key enzymes in triterpenoid biosynthesis. The mutation-based strategy is to discover the new function of a known enzyme, facilitating biosynthesis of triterpenoid that would not exist in nature. For synthetic biology approaches, S. cerevisiae is the best choice for heterologous expression of eukaryotic enzymes. This significant host is able to produce multiple substrates for CYP or cyclase, and it also has endoplasmic reticulum and posttranslational modification system to support the function of CYPs (Kampranis and Makris, 2012; Vieira Gomes et al., 2018). However, the omics study guided the enzyme discovery that may cause the leaving out of potential targets. Combinatorial approach facilitates identification of the enzyme function in vivo and in vitro that may give more insights into the biosynthesis of triterpenoid in the native host. It’s not difficult to see that each method has its own limitations, which would affect the enzyme discovery in turn. However, with the aid of some emerging technologies, the limitations of the existing approach could be overcome. For example, various gene editing technologies (e.g., CRISPR-Cas) allow target gene editing in species with immature genetic manipulations (Wang et al., 2020) and simultaneous deletion of multiple genes at high efficiency (Bao et al., 2015). In such a scenario, the mutant with all isoenzymes disrupted could be easily obtained to observe the phenotype variation. A highthroughput screening method is highly required in mutation-based approaches and synthetic biology approaches. Although color-based screening method provides high throughput in enzyme prototyping, it cannot be generally applied to enzymes particularly responsible for cyclization and postmodification in triterpenoid biosynthesis. However, establishing platforms for automated gene cloning, enzyme expression and activity screening, as reported in many recent works (Si et al., 2017; Chao et al., 2017), would be applicable to save labor and improve throughput for enzyme discovery in triterpenoid biosynthesis. In addition, the deep learning–based computational framework enables prediction of enzymes in a highthroughput manner (Ryu et al., 2019), which would help discover more potential candidates without prejudice. Thanks to the advances in enzyme engineering and metabolic engineering, efficient bioproduction of many triterpenoids have been achieved after the discovery of key enzymes in triterpenoid biosynthesis. Overexpression of the wild-type b-amyrin cyclase from Euphorbia tirucalli allowed production of a large amount of b-amyrin and negligible amount of tetracyclic triterpenoids in yeast (Ito et al., 2013). However, when the mutated b-amyrin cyclase harboring a F-to-G or F-to-A mutation at amino acid #474 was expressed, the recombinant yeast was able to produce a large amount of dicyclic products and a small amount of b-amyrin (Ito et al., 2014). In another example, ganoderic acid 3-hydroxy-lanosta-8, 24-dien-26 oic acid (GA-HLDOA), an important antitumor triterpenoid from Ganoderma lucidum, was produced in S. cerevisiae by expressing a G. lucidum CYP5150L8 (Wang et al., 2018). Then, production of GA-HLDOA was increased into 154 mg/L by optimizing the expression of CYP5150L8 and the Ganoderma P450 reductase iGLCPR, which was 10.7-fold higher than previous report (Lan et al., 2019). In the future, we believe that more triterpenoids will be efficiently produced by accelerating the discovery rate of key enzymes in triterpenoid biosynthesis.

Acknowledgments This work was supported by the National Natural Science Foundation of China (no. 31971344) and Shanghai Municipal Natural Science Foundation (no. 18ZR1420300). We thank Prof. Jian-Jiang Zhong (Shanghai Jiao Tong University) for his suggestion on this manuscript. Xiao H. thanks the DaSilva Award (The Society for Biotechnology, Japan) for financial support.

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Chapter 4

Synthetic biology approaches for secondary metabolism engineering Ana Lu´cia Leita˜oa,b and Francisco J. Enguitac,∗ a

Faculdade de Ci^ encias e Tecnologia, Universidade Nova de Lisboa, Campus da Caparica, Caparica, Portugal b MEtRICs, Faculdade de Ci^ encias e Tecnologia, Universidade Nova de Lisboa, Campus da Caparica, Caparica, Portugal c Instituto de Medicina Molecular Joa˜o Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal ∗

Corresponding author: E-mail: [email protected]

1 Introduction Modern biology is founded on integrative and systemic approaches that can be applied to the study of cells and their components (Westerhoff and Palsson, 2004). On its origins, molecular biology grew using a reductionist approach that studied very specific biological processes and mechanisms with a lot of detail. However, the accumulation of isolated units of information required an integrative point of view that was the origin of the science of the systems biology (Deamer, 2009). The concept of “synthetic biology” was firstly employed by Szybalksi and Skalka in the last years of the 1970s to conceptualize an upcoming era in the molecular biology that will allow the use of the recombinant DNA technologies to construct and design new biological systems (Szybalski and Skalka, 1978). As a research field, synthetic biology is difficult to define due to its multidisciplinary nature and the inherent complexity of life (Endy, 2005). Synthetic biology is founded on the idea that biological systems can be characterized as a junction of functional modules connected to form circuits (Fig. 1). These modules are responsible for specific biological functions organized in different levels of complexity and can be isolated and rationally combined to generate new systems. The less complex module harbors the cellular biomolecules, including the genetic material and its functional output (protein and RNA molecules). Biomolecules are structured into metabolic pathways, which are associated to cell compartments (organelles in eukaryotic cells and cellular territories in prokaryotic cells). The interconnection and compartmentalization in time and space of the metabolic pathways constitutes a core topic of synthetic biology, because it is directly related with cell physiology ( Jakobson et al., 2018). Synthetic biology not only combines the principles of engineering and computer science to understand biological functions and predict systemic responses upon external stimuli but also intends to derive common principles that allow to construct new living systems that otherwise will not be easily generated by natural evolution (Elowitz and Lim, 2010). Following these postulates the area of synthetic biology should include all the methods and protocols devoted to the design and construction of new biological systems, as well as the reconstruction or redesign of the existing ones using strategies based on a modular conception of the living systems (DeNies et al., 2020). The accomplishment of the synthetic biology goals is supported by three pillars: the knowledge of the molecular background of living organisms to characterize their functional modules (Beites and Mendes, 2015), the development of bioinformatic models for the integration and analysis of these functional blocks to design new living systems (Khater et al., 2016), and the availability of wet lab protocols to construct and validate the new synthetic organisms (O’Connor, 2015; Quin et al., 2014; Seyedsayamdost and Clardy, 2014). The principles of synthetic biology have been illustrated in recent publications showing the production of biologically active compounds by heterologous expression of genes encoding a whole biosynthetic pathway (Paddon et al., 2013) and pushed to the limits with the creation of whole functional cells from a completely synthetic genome (Gibson et al., 2010; Suzuki et al., 2015).

2 Secondary metabolism Cellular metabolism integrates a complex set of biochemical pathways devoted to the maintenance of the cell functions, including the production of energy by the degradation of chemical compounds (catabolism) and the construction of cellular Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00022-2 © 2021 Elsevier Inc. All rights reserved.

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FIG. 1 Organization of biological systems within the framework of synthetic biology concepts compared with computers. The fundament of synthetic biology relies on the idea that biological systems are composed by “modules” interconnected among them forming “circuits.” These modules can be considered as functional units and characterized at different levels of complexity and organization. As in computers, biological modules can be exchanged between living organisms to generate new engineered biological systems, expecting a different performance and output.

structures using precursors (anabolism). Some metabolic processes are dispensable or not essential for the short-term survival of cells, constituting the secondary metabolism. The function of the secondary metabolism has been a subject of controversy among the scientific community (Leitao and Enguita, 2015). Taking into consideration the secondary metabolic processes observed in microorganisms such as filamentous bacteria and fungi, there are three different models about its possible function (Roze et al., 2011). Some scientists considered the secondary metabolism as a way to control, store, or eliminate the metabolic products generated from the primary metabolism (Pott et al., 2019). In genetic terms the secondary metabolism is a protected niche for evolution and natural selection of genetic information that could contribute to the fixation of beneficial traits without compromising the essential cell functions (Wisecaver et al., 2014). However, the most general view considers the secondary metabolism and its products as an essential part of the biochemical cell processes, since it depends on the primary metabolism for supplying the enzymes, energy, and substrates (Seyedsayamdost, 2019). The integration of primary and secondary metabolism is favored by the presence of common features between them. Secondary metabolites are generated by specific metabolic pathways that use substrates formed during the primary metabolism (e.g., acetyl-CoA, amino acids, nucleotides, and sugars). The chemical flow from the primary to the secondary metabolism is supported by several experimental observations. In the filamentous fungi Aspergillus parasiticus, blocking the secondary metabolism by knocking out specific regulatory genes results in a redirection of carbon flow from the primary metabolism (Roze et al., 2010). In strains of Penicillium chrysogenum, the primary carbon sources are related with the yield and efficiency of the production of the secondary metabolite penicillin (Ferreira-Guedes and Leitao, 2018). Moreover, experimental evidence also showed that the enzymatic activities involved in secondary metabolism evolved from primary metabolic enzymes (Firn and Jones, 2000). Random mutations and natural selection of genes encoding primary metabolic enzymes resulted in new enzymatic activities, substrate specificities, and product generation in the secondary metabolism (Carrington et al., 2018). Secondary metabolites are a very diverse family of chemical compounds that are generally synthesized by coordinated action of two different families of enzymes (Fig. 2). First a limited number of precursors from the primary metabolism are used as substrates by condensing enzymes to produce the main scaffold of the metabolite (Hansen et al., 2015; Kwan and Leadlay, 2010). Another family of modifying enzymes will add different chemical groups to the main skeleton, generating the final metabolite (Coque et al., 1995a,b). Secondary metabolites derived from microorganisms and plants tend to have significantly greater chemical complexity than synthetic drugs, harboring an important variety of structures and biological activities. Using a classification based on the chemical structure, we can distinguish the following families of secondary metabolites: terpenoids, alkaloids, fatty acid derivatives and polyketides, nonribosomal peptides, and enzyme cofactors (Fig. 2). Terpenoids belong to the biggest class of secondary metabolites and are modified polymers generated by the assembly of the five-carbon isoprene unit, which is synthesized from the acetyl-CoA catabolite via mevalonate (Quin et al., 2014). Some terpenoid secondary metabolites can have nonterpenoid chemical appendages such as phenolic compounds generating the so-called hybrid terpenoids (Gallagher et al., 2010). The building blocks of terpenoids are assembled by high

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FIG. 2 Relationships between primary and secondary metabolism showing the chemical families of secondary metabolites and the corresponding group of precursors. The building blocks for secondary metabolites are products of the primary metabolism that can be combined and modified to produce a wide range of compounds. The enzymatic activities involved in secondary metabolism can be divided in two different families: the condensing enzymes, responsible for the combination of different precursors to generate the skeleton of the secondary metabolites, and the modifying enzymes that will introduce additional chemical groups to the core of the metabolites increasing their chemical and biological diversity. One of the most relevant primary precursors is acetyl-coA, which can be the source of secondary metabolites as terpenoids, fatty acids, and polyketides. Alkaloids, a diverse family of nitrogencontaining compounds, are mainly synthesized using amino acids or their metabolites as precursors. Proteogenetic or nonproteogenetic amino acids can also be precursors for the biosynthesis of nonribosomal peptides.

molecular weight enzymes designated as terpene synthases and widely distributed among bacteria, fungi, and plants (Yamada et al., 2015). Biological activities of terpenoids are diverse and include antibiotics (Komaki et al., 1999; Soria-Mercado et al., 2005), antitumorals (Li et al., 2016), and free-radical scavengers (Fiorentino et al., 2007). Chemically related with terpenoids due to their common origin, the polyketides and fatty acid derivatives are synthesized by using acetyl-CoA as precursor. Polyketide synthases are the main enzymes responsible for the synthesis of the carbon scaffold of these metabolites, which will be further modified by tailoring enzymes. The coordinated action between synthases and modifying enzymes, mainly oxidases, is responsible for the structural complexity and biological activities of different polyketides (Hang et al., 2016; Maloney et al., 2016). Another group of secondary metabolites to be considered under the point of view of synthetic biology is constituted by nonribosomal peptides (Butz et al., 2008). These compounds are peptides composed by proteogenetic or nonproteogenetic amino acids that are covalently linked by the help of peptidyl synthetases and could be further modified by chemical branching or covalently closed (Phonghanpot et al., 2012; Challis and Ravel, 2000). Peptidyl synthetases are multidomain enzymes composed of modules that activate and link individual amino acids by peptide bonds (Platter et al., 2011; Stachelhaus and Marahiel, 1995) being a very interesting target for applying the principles of synthetic biology ( Jaremko et al., 2020; Yonus et al., 2008). Finally the alkaloids are natural organic compounds containing heterocycles with basic nitrogen atoms in an aromatic or nonaromatic arrange. They are secondary metabolites originated either from amino acids or their metabolites and constitute a very heterogeneous group of chemical compounds in terms of structure and properties (Fraley and Sherman, 2020; Klapper et al., 2018). They can be isolated from bacteria (Klapper et al., 2018), fungi (Schardl et al., 2013; Shweta et al., 2013), and plants (Nair and van Staden, 2019; Singh et al., 2019) and show very wide biological and pharmacological activities.

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Biosynthetic clusters for secondary metabolites

The genes encoding for the enzymes involved in secondary metabolism are frequently clustered showing a modular organization and are usually transcriptionally coregulated by a limited number of promoters per biosynthetic cluster ( Jensen, 2016). Clustering is a smart strategy to regulate gene expression in a coordinated manner (Enguita et al., 1998) that often involves common regulatory mechanisms as transduction pathways (Shu et al., 2009), small molecular weight regulators as cyclic AMP (Tata and Menawat, 1994), and specific transcription factors (Cao et al., 2020; Thieme et al., 2018). The increasing number of available complete genomes arising from the application of next generation sequencing techniques in the last decade allowed to discover a panoply of biosynthetic clusters for secondary metabolites in actinomycetes and fungi (Levy and Myers, 2016), some of them not expressed under laboratory conditions and further designated as “cryptic” or “silent” clusters (Unno et al., 2020; Hashimoto et al., 2020). The secondary metabolic enzymes are usually active over a limited number of metabolite precursors arising from the anabolic or catabolic branches of the primary metabolism such as amino acids, lipids, sugars, nucleotides, acyl-CoA, and other low molecular weight compounds (Firn and Jones, 2000; Cimermancic et al., 2014). The vast majority of secondary metabolism gene clusters are organized around a central gene that encodes for a high molecular weight condensing enzyme responsible for the assembly of the metabolic building blocks (Hansen et al., 2015; Villebro et al., 2019) and other genes encoding for auxiliary enzymes that will be involved in the chemical modifications of the core of the secondary metabolite (Coque et al., 1995a,b). The clusters often include additional regulatory genes involved in gene expression and transport of the metabolite across the cell membrane (Coque et al., 1993; Xu et al., 2017). The intrinsic characteristics of secondary metabolism (dispensability for cell survival) and its genetic background (clustered biosynthetic genes) make it a perfect target for the application of synthetic biology principles for the construction of new cell factories and diverse bioactive compounds (Lee et al., 2019; Nguyen et al., 2012). However, the engineering of secondary metabolic pathways is constrained by the knowledge of the function and working rules of the functional blocks governing the biosynthesis of a particular metabolite and also by the availability of specific techniques of genetic manipulation (Leitao and Enguita, 2015; Leitao and Enguita, 2014).

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Top-down strategies: The known biology

Synthetic biology approaches can be used in top-down strategies with the main objective of dissect a particular biological process and generate a predictable response from an engineered biological system (DeNies et al., 2020). Prior to the systems engineering, a detailed knowledge of the biological background behind the selected organism is needed. Once the structural modules are characterized, they can be rationally combined to generate a modified system that could produce different metabolites when compared with those obtained in the original cell (Beites and Mendes, 2015; Leitao and Enguita, 2014). This strategy could be achieved by following three sequential steps: characterization, assembly, and analysis (Fig. 3).

4.1 Characterization of the biological modules in secondary metabolism Following a top-down strategy the main source of information for the characterization of the biosynthetic modules involved in secondary metabolism is the genomic sequence and structure. The field of genomics is a branch of the modern genetics devoted to the study of the sequence, organization, and structure of the genetic material. Due to the development of parallel sequencing techniques, the field of genomics has evolved rapidly in the last decade (Kumar et al., 2019). Compared with the classic Sanger sequencing protocol (Bibb et al., 1994), the next-generation sequencing (NGS) techniques provide a quick and cost-effective system for the determination of genomic sequences in different organisms (Kumar et al., 2019). NGS techniques, protocols, and general applications have been extensively reviewed in the literature (Levy and Myers, 2016; Kumar et al., 2019; Ware et al., 2012). For the characterization of genes directly involved in secondary metabolism from isolated organisms, the most widely employed NGS protocols were those depending on a previous sample amplification in solid phase (Illumina system) or in liquid droplets (454 Roche) (Ogasawara et al., 2004; Vinuesa and Ochoa-Sanchez, 2015; Choque et al., 2018). NGS protocols can be also applied for metagenomics studies to environmental samples for the characterization of living systems that otherwise will remain unknown (Cuadrat et al., 2018). For instance the combination of culture-independent metagenomics with functional analysis has been successfully employed for the discovery of new clusters of secondary metabolites in environmental samples obtained from the marine ecosystem (Mahapatra et al., 2020). The exponential growth of data generated from NGS techniques required a parallel design of bioinformatic tools for data storage, information handling, and analysis (Shi et al., 2019). A special mention should be given to the platforms designed by the European Molecular Biology Laboratory (EMBL) within the framework of the Elixir-Europe infrastructure. The

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FIG. 3 Strategies and steps for the rational use of molecular biology tools in the design of new biologically active metabolites in a top-down approach. In the first step, genomic analysis of microorganisms will allow to characterize biosynthetic clusters for secondary metabolites. The genomic information within the clusters can be divided into functional elements, which include the biosynthetic genes and the regulatory functional elements such as promoters, enhancers, and terminators. Isolated functional elements can be combined to design customized clusters allowing enhanced biosynthetic properties or the production of hybrid metabolites. The final step will involve the expression of the customized biosynthetic cluster in suitable hosts, which can be homologous or heterologous to the parental species, and the characterization by metabolomic analysis of the generated bioactive compounds. All the design pipeline must be complemented by biological assays to demonstrate the activity of the engineered metabolites.

ENSEMBL database is a general-purpose platform for the storage and retrieval of genomic data, which includes two subplatforms, the bacteria@ENSEMBL and the fungi@ENSEMBL devoted to genomes from bacteria and fungi, respectively (Howe et al., 2020). Also within the EMBL the MGNify database is a metagenomics portal that stores information about DNA sequences of samples obtained from several environments and allows analysis of external metagenomic datasets (Mitchell et al., 2020). Moreover, several databases storing DNA sequences of secondary metabolite clusters are also available in combination with analysis tools that allow to compare experimental data with the database information (Villebro et al., 2019). The most complete database of secondary metabolite clusters is antiSmash, currently storing sequences of more than 32,000 biosynthetic clusters for secondary metabolites (Villebro et al., 2019). antiSmash 5.0 also contains a mining pipeline for detection of new biosynthetic clusters from user’s data (Blin et al., 2019). Other databases as ClusterMine360 (Tremblay et al., 2016) and DoBISCUIT (Ichikawa et al., 2013) contain genomic information about biosynthetic clusters centered around genes encoding high molecular weight condensing enzymes such as polyketide synthases. Also centered in polyketide synthases and nonribosomal peptide synthases, Clustscan database (CSDB/r-CSDB) is a unique useful example of computer tool that could have direct applications in synthetic biology (Diminic et al., 2013). The r-CSDB branch of the database contains sequences and experimental design of more than 20,000 engineered synthetic clusters for the biosynthesis of new secondary metabolites (Diminic et al., 2013).

4.2 Functional assembly of modules The most ambitious enterprise of functional assembly of genetic modules is the creation of a whole artificial genome (Gibson et al., 2010). Metabolic applications related with the production of engineered secondary metabolites could use a smaller scale, which includes only the genes of the secondary metabolism organized in biosynthetic clusters (Komaki et al., 2020; Zhang et al., 2019a).

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The building blocks for a functional assembly of elements from biosynthetic clusters are DNA fragments, and in consequence, synthetic biology applications are heavily dependent on a robust and cheap protocol for the synthesis of long DNA fragments. Classical DNA synthesis protocols based on solid-phase catalysis (column-based methods) were limited by the size of the DNA fragment to be produced. These constraints were circumvented by the rise of the gene synthesis protocols and more recently by the combination of chemical and enzymatic synthesis based on PCR reactions that allow the synthesis of DNA fragments within the kilobase range (Kosuri and Church, 2014). Once the synthetic DNA fragments are available, they would need to be combined into a customized design. Assembly of complex genetic arrangements would be a very difficult task when depending on classical cloning and subcloning protocols (Servick, 2017). Conceptually the DNA assembly process can be achieved at four scales: kilobase-scale, megabase-scale, chromosome-scale, and genome-scale assemblies (Wang et al., 2018). For the engineering of clusters involved in the biosynthesis of secondary metabolites, the most adequate scale is in the range of kilo- to megabases. Synthetic biology requirements have empowered the development of new in vitro assembly protocols that do not depend on multiple subcloning steps. Two recently developed methods have been employed for the construction of gene assemblies: the Gibson and the Golden Gate protocols. The Gibson reaction resembles the natural mechanism of the homologous recombination, being useful for the assembly of regular DNA sequences with a maximum of five fragments (Gibson et al., 2009). The Gibson assembly protocol in combination with bacterial and yeast subcloning was employed even for the assembly of whole chromosomes (Gibson et al., 2010; Gibson, 2011). Golden Gate assembly uses a restriction-ligation combined system for DNA assembly based on a type IIS restriction enzyme (Engler et al., 2009), which is adequate for highly repetitive sequences, and usually up to nine DNA fragments can be assembled in one single reaction (Engler and Marillonnet, 2011). A quantum leap in the assembly scale to the chromosome or genome level will require the involvement of cells for subcloning, usually bacteria or yeast. Recent improvements in genome assembly described the use of nanostructures constructed by DNA molecules that function as molecular scaffolds and improve the yield of the reaction (Manuguerra et al., 2018). At a much smaller scale than assembly reactions, genome editing techniques could be used for the selective modification of specific regions of biosynthetic clusters. Genome editing is defined as the group of techniques devoted to the selective modification of specific loci within a genome. Modern genomic edition is based on the use of targeted endonucleases that will produce double-strand breaks (DSBs) in a specific locus within a DNA molecule (Gaj et al., 2013). These breaks will be recognized by the cell and repaired using two mechanisms: the nonhomologous end joining (NHEJ), an error prone mechanism that will join both the cleaved DNA strands introducing nucleotide deletions, and the homologous recombination (HR), an error-free mechanism that requires the presence of a homologous DNA molecule that acts as template for repair. If the DSBs are produced within a coding sequence and repaired by NHEJ mechanism, the consequence will be the induction of a frameshift and typically a knockout of the coding sequence by triggering the mRNA degradation via nonsense-mediated decay (Leitao et al., 2017). For insertion or replacement of DNA fragments in specific genome locations, the targeted endonucleases will be accompanied by an insertion vector with the desired DNA sequence flanked by homologous arms that will trigger the HR mechanism of DNA repair (Bellin, 2018). Targeted endonucleases belong to two different families: the meganucleases and TALENs, which are directed to genomic loci by using a DNA-binding domain that recognizes a specific nucleotide sequence (Chen et al., 2019), and the Cas9-like nucleases, which are directed by a guide RNA molecule complementary to a specific genomic sequence (Cho et al., 2018). Cas9-like endonucleases, namely, those isolated from the Streptococcus pyogenes CRISPR-Cas9 system, are more flexible than meganucleases and allow a panoply of applications in synthetic biology and metabolic engineering (Roointan and Morowvat, 2016). The CRISPR-Cas9 system has been also tuned and designed for specific applications, namely, those using the inactive mutants of Cas9 protein (dCas9). The dCas9 nuclease can be targeted to specific regions of the genome to repress transcription of regulatory units (CRISPR-mediated interference, iCRISPR) (Sung et al., 2019). This strategy has been successfully used in Corynebacterium glutamicum to repress specific targets such as pgi, pck, and pyk genes, resulting in titer enhancement ratios of L-lysine and L-glutamate production comparable with levels achieved by gene deletion (Cleto et al., 2016). The CRISPR-Cas9 system has been also employed for combinatorial metabolic engineering of C. glutamicum to produce butyrate by introducing a synthetic butyrate pathway and repressing the essential acn gene-encoding aconitase to direct the metabolic flow to the production of butyrate (Yoon and Woo, 2018). A similar strategy was used to generate recombinant Escherichia coli strains expressing the gene cluster for the production of 1,4butanediol, where specific pathway genes were substituted by improved recombinant versions to achieve a high yield of butanediol biosynthesis (Wu et al., 2017).

4.3 Functional testing of the customized biosynthetic clusters Top-down strategies are completed with the functional testing of the assembled biosynthetic clusters. The selection of a native or heterologous host for the expression is conditioned by two factors: first the characteristics of the cells (culture

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conditions, growth rate, efficiency for metabolite production, and possibilities of further genetic manipulation) and second the protocols employed for the design and construction of the customized cluster. Native hosts are mainly used when the genetic manipulation for secondary metabolism production does not involve high-order arrangements. Following this approach, Jia and coworkers performed point mutations that increased the native production of the antibiotic oxytetracycline in Streptomyces rimosus ( Jia et al., 2017). Precise insertion of single or bidirectional heterologous promoters was also used for in loco activation of silent biosynthetic clusters for the biosynthesis of secondary metabolites in Streptomyces albus, Streptomyces lividans, Streptomyces roseosporus, Streptomyces venezuelae, and Streptomyces viridochromogenes (Zhang et al., 2017). Expression of genes and biosynthetic clusters in heterologous hosts has the advantage of an increased control out of the natural environment where the bioactive metabolite is produced (Moser and Pichler, 2019; Chen et al., 2020). Many heterologous hosts can be employed, but those with clear metabolic background are preferred to avoid problems in the downstream process of the secondary metabolite (Collins and Young, 2018). However, this strategy still has technical limitations. The most obvious is related with the different genetic backgrounds between the native and the host cells that could require a codon optimization of the engineered biosynthetic cluster (Tokuoka et al., 2008; Tanaka et al., 2014). Moreover the introduction of a heterologous biosynthetic gene cluster may disturb the original metabolic homeostasis of the host due to the production of toxic metabolites or to the imbalance between primary and secondary metabolism (Ma et al., 2019). The engineering of the host often involves the use of inducible promoters to facilitate the heterologous expression, minimizing the disturbance of its metabolism (Gao et al., 2010). Finally the heterologous expression is always an empirical process depending on the selection of the minimal set of genes and regulatory elements requiring a trial-anderror strategy. All the protocols for host engineering and their applications have been extensively reviewed elsewhere (Luo et al., 2016). Heterologous expression is especially useful to establish generic platforms not only for the functional testing of engineered biosynthetic clusters but also for the analysis of cryptic clusters. As described before, cryptic or silent clusters are groups of genes involved in the biosynthesis of secondary metabolites that are not expressed in the native strain under laboratory conditions (Nguyen et al., 2020). From the point of view of synthetic biology, silent clusters offer a new source of genetic and metabolic diversity that will result in the discovery of new biologically active compounds ( Ji et al., 2019; Uhong Lu et al., 2016). For instance, heterologous expression of whole cryptic biosynthetic clusters was used to produce the grecocycline family of antitumorals in S. albus, by cloning and induced expression of the original biosynthetic silent cluster from Streptomyces sp. Acta 1362 (Bilyk et al., 2016). Other recent examples of this strategy include the heterologous expression of the cluster for the biosynthesis of lasso peptides from Streptomyces leeuwenhoekii in Streptomyces coelicolor (Gomez-Escribano et al., 2019), the use of a synthetic chromosome for the expression of the Streptomyces seoulensis giant type I polyketide synthase gene cluster (asm) in S. lividans (Liu et al., 2019a), and the production of the new polyene macrolactam antibiotic JBIR-156, by the expression of its biosynthetic cluster isolated from Streptomyces rochei in Streptomyces avermitilis (Hashimoto et al., 2020). The increasing amount of genomic information powered the development of expression platforms not only for screening of new bioactive compounds but also for the optimization of the conditions for their biosynthesis (Zhang et al., 2019b). Among these platforms the FAC-MS uses a combined system that integrates fungal artificial chromosomes (FACs) and metabolomic scoring (MS) for the screening of clusters from diverse fungal species and their heterologous expression in Aspergillus nidulans (Clevenger et al., 2017). Following this idea and combining the expression platform with an upstream bioinformatics screening and a downstream metabolomics analysis, Harvey and coworkers developed the HEx system (Harvey et al., 2018). HEx includes a pool of bioinformatic algorithms to identify and prioritize biosynthetic gene clusters in genomic data obtained from fungi and tools to engineer these clusters for expression in the host Saccharomyces cerevisiae. The system has also S. cerevisiae reference strains with improved expression phenotypes and synthetic biology tools to assemble and express synthetic DNA in the heterologous host. The efficiency and yield of the heterologous expression will be analyzed via metabolomics, and the chemical structures of the promising compounds will be solved by liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) (Harvey et al., 2018).

5 Bottom-up strategies: De novo systems In contrast to top-down strategies, the goals of bottom-up approaches are the assembly of new biological systems from scratch using the previously existing information about functional modules and their interactions (DeNies et al., 2020). Despite the increased experimental control, bottom-up strategies are more ambitious due to the intricated relationships between modules, their metabolic outputs, and the spatiotemporal factor that controls the metabolism over time

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(Jia and Schwille, 2019; Laohakunakorn et al., 2020). Bottom-up strategies are more diverse when compared with the top-down approaches and can be applied in many working scenarios. One of the indispensable tools in the biological engineering toolbox is metabolomics, which has a pivotal role for the synthetic biology of secondary metabolism (Nguyen et al., 2012), including its importance for the discovery of new bioactive compounds, the examination of side products of engineered metabolic pathways, and the identification of major bottlenecks for the overproduction of compounds of interest (Forseth et al., 2011). Recently, new methods such as the artificial intelligence algorithms have been applied to the synthetic biology field for the characterization and identification of bioactive compounds from metabolomic data and to predict their biological activities and putative biosynthetic pathways and genes (Toubiana et al., 2019; Bianchini, 2016). Artificial intelligence (AI) is integrated by computer software or algorithms that resemble the behavior of the human brain in analyzing problems and taking decisions. The simplest algorithms are the so-called expert systems where human specialists compile data and information about a problem in a devoted computer software. When interrogated the expert system can take a decision based on the human knowledge by analyzing the data and asking proper questions to the user. For example, these expert systems are used in the antiSmash database of secondary metabolic clusters to interrogate an unknown DNA sequence for the presence of putative biosynthetic gene clusters (Villebro et al., 2019; Blin et al., 2019). More advanced protocols that could be considered as a true AI systems are the machine-learning and the deep-learning approaches. In these systems a computer algorithm can take decisions on a problem based on taxonomic descriptors extracted from a training set of positive and negative events. In machine learning the training process is performed by a controlled set of descriptors that are manually selected by a human curator, whereas in deep learning, all the process including the descriptor selection and the final decision is totally automated (Fig. 4). The deep-learning strategy has been recently applied for the successful discovery of new biological activities in a database of chemical compounds (Liu et al., 2019b). Stokes and coworkers trained a deep neural network to predict molecules with antibacterial activity and performed predictions on multiple open-access chemical libraries to discover a molecule from the Drug Repurposing Hub (Halicin), which displays antibacterial activity against a broad spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae (Stokes et al., 2020). Metabolomic data obtained from secondary metabolite producers, combined with AI and network analysis, could be used as a method to reconstruct the biosynthetic pathways and to infer the involved genes (Toubiana et al., 2019). The increased availability of microbial genome sequences has led to the development of prediction algorithms for secondary biosynthetic gene clusters that analyze the pool of secondary metabolites characterized by techniques such as mass spectrometry (Hannigan et al., 2019; Moore et al., 2019). For families of secondary metabolites such as the nonribosomal peptides and polyketides, specific software was designed to predict the biosynthetic clusters involved in the production of a

FIG. 4 Conceptual approaches for the use of artificial intelligence algorithms in the functional screening of secondary metabolites following the bottom-up approach. A library of secondary metabolites obtained from metabolomic studies can be screening for biological activity using their chemical structures. In a machine-learning approach an artificial intelligence system will be previously trained with a reference dataset where the important chemical features will be manually curated. In a deep-learning approach the artificial intelligence will extract the functional features and classify the database of chemical compounds with no human intervention.

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selected metabolite. The generalized retrobiosynthetic assembly prediction algorithm (GRAPE) is a computational pipeline that includes a global cheminformatics approach to uncover how observed biosynthetic gene clusters are related to known polyketides or peptides, leading to the identification of gene clusters involved in the biosynthesis of new molecules (Dejong et al., 2016). A similar strategy is used by the GNP/iSNAP pipeline that analyze mass spectrometry results to characterize secondary metabolites produced by high molecular weight synthases, linking them to the corresponding biosynthetic clusters ( Johnston et al., 2015). However, with the large number of reactions filling the metabolic space and the biological differences among hosts, identifying the natural biosynthetic pathways that will lead to a biologically active molecule and translate them to an engineered organism to produce a target molecule is a complicated enterprise (Kumar et al., 2018). Applications such as novoPathFinder that integrates retrobiosynthetic information with pathway analysis will help to enumerate novel pathways between two specified molecules without considering hosts, construct heterologous pathways with known or putative reactions for producing target molecule using E. coli or S. cerevisiae as model hosts, and predict new pathways for optimized metabolic design (Ding et al., 2020). Synthetic biology bottom-up strategies based on metabolic design tools as previously described, often require a landmark for testing de novo configurations for biological systems. For this purpose the use of cell-free systems offers a reliable and stable platform that speed up screenings and overcome some of the limitations of cellular hosts (Laohakunakorn et al., 2020). Cell-free extracts that contain all the cytoplasmic and nuclear components required for gene expression can be used for the reconstruction of whole biosynthetic pathways in a test tube (Li et al., 2018). The idea of the cell-free systems is not new as a laboratory tool in basic research (Konomi et al., 1979); however, the application of cell-free systems for the screening or even the production of engineered metabolites constitutes a new avenue to explore in synthetic biology (Morgado et al., 2018) (Table 1).

TABLE 1 Selected examples of the application of synthetic biology for the metabolic engineering of secondary metabolites. Metabolite

Microorganism

Notes

Reference
1

Artemisin

S. cerevisiae

Engineering of S. cerevisiae to produce high titers (up to 100 mg L ) of the antimalarial drug artemisinic acid using an engineered mevalonate pathway, amorphadiene synthase, and a novel cytochrome P450 monooxygenase (CYP71AV1) from the plant Artemisia annua

Ro et al. (2006)

Penicillin

S. cerevisiae

Engineering of the yeast S. cerevisiae to produce and secrete the antibiotic penicillin by whole biosynthetic cluster cloning and heterologous expression from the penicillin producer Penicillium chrysogenum

Awan et al. (2017)

Resveratrol

E. coli S. cerevisiae

Resveratrol production in recombinant S. cerevisiae was compared with that in E. coli. For heterologous production of resveratrol, in both engineered systems, 4-coumarate-coenzyme A ligase from Nicotiana tabacum and stilbene synthase from Vitis vinifera were overexpressed

Beekwilder et al. (2006)

Trypacidin

A. fumigatus

Genome editing by CRISPR-Cas9 single nucleotide insertion in the polyketide synthase of the trypacidin biosynthetic pathway allowed to reconstitute its production in a nonproducing strain

Weber et al. (2017)

Grecocycline

S. albus

Heterologous expression of whole cryptic biosynthetic grecocycline in S. albus, by cloning and induced expression of the original biosynthetic silent cluster from Streptomyces sp. Acta 1362

Bilyk et al. (2016)

Paclitaxel

E. coli S. cerevisiae

Engineered synthetic pathway for the acetylated diol paclitaxel precursor into two modules, expressed in either S. cerevisiae or E. coli. The production of the secondary metabolite was achieved by coculture in the same bioreactor where a metabolic intermediate produced by recombinant E. coli was functionalized by yeast

Zhou et al. (2015)

Noscapine

S. cerevisiae

Engineering of the noscapine gene cluster from Papaver somniferum in S. cerevisiae to produce this antitumoral and its related pathway intermediates

Li and Smolke (2016)

Asperfuranone

A. nidulans

The induced expression of the secondary metabolism regulator scpR gene using the promoter of the alcohol dehydrogenase AlcA led to the transcriptional activation of both the endogenous scpR gene and the asperfuranone biosynthetic cluster, which is a silent cluster under physiological conditions

Bergmann et al. (2010)

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6

Conclusions and further perspectives

Since the first conceptualization of synthetic biology ideas by Szybalski and Skalka (1978), other pioneers as Hopwood described the practical applications of the synthetic biology methods to combine genetic information and generate hybrid metabolites (Hopwood et al., 1985). In the last two decades, isolated visionaries as Craig Venter embraced the project of searching for genetic diversity in nature, pushing the synthetic biology to the limit by creating artificial chromosomes, genomes, and cells (Gibson et al., 2008, 2010; Gibson and Venter, 2014). Nowadays, all these ideas and methods have been complemented by the extensive use of “omics” for the characterization cells considering the point of view of the systems biology (Nguyen et al., 2012; Machado et al., 2017). Genetic and functional diversity found in nature constitutes an almost unlimited source of chemical compounds with potential uses in human health. Among the most interesting biologically active compounds, secondary metabolites from microbes and plants are basic and offer an array of possibilities for the design and production of new pharmaceuticals. Postulates and methods from synthetic biology, complemented with new approaches such as those derived from the artificial intelligence, have been applied to the field of secondary metabolites for design new compounds and optimize their biosynthesis. Future trends in the field will include the expansion of metagenomic studies for the discovery of genetic and biological diversity, which could be related with the production of new metabolites of interest. The generated data will require the design and expansion of current databases for information storage. On the other hand an important improvement of the tools for data retrieval and analysis by using artificial intelligence methods will open a door to exploit secondary metabolites on a more efficient and accurate way enhancing synthetic biology potential. However, the most important question that the synthetic biology needs to answer in the following years is if the scientific community and the pharma industry are prepared for the introduction of these methods in their daily practice.

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Chapter 5

Synthetic biology design tools for metabolic engineering Pablo Carbonell∗ ai2, Polytechnic University of Valencia (Universitat Polite`cnica de Vale`ncia), Valencia, Spain ∗

Corresponding author: E-mail: [email protected]

1 Introduction The bio-based production of chemicals and materials is anticipated to play a major role in the incoming bio-economy boosted by green recovering stimulus by providing an alternative to chemical processes that is both economically viable and ecologically sustainable. Synthetic biology world market has reached 40 billion dollars by 2020 globally. In the new biomanufacturing pipelines, modeling, automation, machine learning, and big data analysis are becoming essential in order to optimize the processes (Carbonell et al., 2019c). The full automation of processes in the Design-Build-Test-Learn synthetic biology pipeline is currently facilitating the engineering of synthetic parts and circuits in a high-throughput fashion generating large amounts of data Carbonell et al., 2016, 2018. Starting from our metabolic knowledge from databases and experimental data, the Learn step informs the Design step through predictive tools on both mechanistic models from first principles or model-free machine learning approaches. Metabolic pathway design tools such as bioretrosynthesis and enzyme selection are then employed in order to proceed from the Design workbench to the actual Build step in the lab. Experimental design tools are used at the Build step to plan the automated assembly of genes in the chassis. Finally, the Test step can be automated by using analytical workflows for experimental quantification such as Galaxy, which is nowadays extended in order to integrate the full Design-Build-Test-Learn pipeline in a common platform to boost automated design (see Fig. 1). There are multiple opportunities and scenarios, where machine learning-based solutions can be integrated as part of that pipeline: machine learning can be applied in order to infer design rules for synthetic biology parts, systems, and devices including prediction of sequence-activity relationships like transcriptional (promoter design) (Segall-Shapiro et al., 2018), translational (ribosome-binding site tuning) (Nielsen et al., 2016) and posttranslational regulation; enzyme design for increased substrate affinity, catalytic efficiency, or to develop novel activities through directed evolution; as well as on the design of other specialized activities such as biosensors or transporters. However, synthetic parts and circuits do not necessarily behave as planned and the engineering process is hampered by failures. The complexity associated with the large combinatorial design space is a major challenge, that is, the number of candidate enzymes and regulatory elements for typical bioproduction pathways has a combinatorial complexity of possible designs that cannot be fully explored even on robotized platforms. The hypothesis is that rationalizing the approach to genetic circuit design by following strategies similar to those in manufacturing engineering design will lead to a more precise and predictable bioengineering. Synthetic biology design tools are an essential part of the Design-Build-Test-Learn pipeline of metabolic engineering by enabling strategies that will impact the ability of the pipeline in order to deliver high-performance microbial cell factories of biomolecules (Carbonell, 2019). In this chapter, modern tools for modeling, experimental design, pathway dynamic regulation, and machine learning are described (see Table 1) and their enabling roles in metabolic engineering as part of powerful biomanufacturing workflows are assessed and highlighted (see Fig. 2).

2 Tools for metabolic modeling 2.1 Computer models of metabolism Microbial cell factory development by means of metabolic engineering seeks to obtain high levels of product through genetic modification. Traditional approaches based on extensive combinatorial trial-and-error experiments are expensive, Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00005-2 © 2021 Elsevier Inc. All rights reserved.

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FIG. 1 Design tools for metabolic engineering. The DBTL cycle.

TABLE 1 Major synthetic biology design tools for metabolic engineering. Task

Main tools

Metabolic modeling

Cobrapy OptKnock OptFlux EMILIO dFBA

Metabolic pathway design

Sympheny GEM-Path RetroPath 2.0 BNICE PathPred Selenzyme EC-Blast antiSMASH 4.0

Metabolic experimental design

JMP OptBioDes Double Dutch RBS Calculator SelProm SensiPath

Metabolic design workflow

Galaxy KNIME Jupyter

time consuming, and undirected. Computer models of metabolism can predict phenotypic consequences of genetic and environmental perturbations affecting cellular metabolism, allowing fast generation of new testable hypotheses, and novel ways of intervention (Llaneras and Pico´, 2008). Kinetic models of metabolism require often unavailable quantitative expressions that link reaction fluxes and metabolite concentrations through detailed enzyme function mechanisms and characterization, hampering their extension to genome scale unless nonmechanistic approximations of the kinetics are assumed (Smallbone et al., 2010) or reduced models are used (Matsuoka and Shimizu, 2015). On the contrary, static constraint-based metabolic (CBM) genome-scale models (GEMs) using basic knowledge of metabolic reaction stoichiometry are available for many organisms, including model organisms like Escherichia coli (O’Brien et al., 2013) or Saccharomyces cerevisiae (Sa´nchez and Nielsen, 2015).

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FIG. 2 A schematic of the synthetic biology design tools for metabolic engineering. Metabolic engineering projects involve conflicting objectives, typically those for production rates for the target compound vs. those for biomass (or growth). Design of experiments (DoE) allows sampling of the large design space (gray area) by selecting a reduced set of representative constructs (D0 to D11 in the figure) involving different combinations of regulatory elements (promoters, copy number, RBS, etc.). Constraint-based metabolic (CBM) analysis of the genome-scale model (GEM) allows predicting modifications in growth, which can be combined with enzyme selection (ES) of multiple variants or directed evolution (DE) and dynamic regulation (DR) in order to explore variations around the nominal construct (filled areas). In that way, the optimal design (D9 in the figure), that is, the one maximizing the objective, can be identified.

Metabolic flux analysis using in silico optimization methods based on CBM GEMs with only stoichiometric information and some basic regulatory information, like flux balance analysis (FBA), have proved very valuable in providing predictions on maximum theoretical yields, optimal flux distribution to maximize flux toward some metabolite reaction bottlenecks, and required gene underexpression or overexpression leading to increase in fluxes toward final products, such as those methods provided by cobrapy (Ebrahim et al., 2013). A large number of new methods have appeared in the last years to tackle the limitations of models using only stoichiometric information (Maia et al., 2015). Methods like OptKnock (Burgard et al., 2003), OptReg (Pharkya and Maranas, 2006), OptStrain (Pharkya, 2004), OptFlux (Rocha et al., 2010), CosMos, k-OptForce, or EMILIO can identify static genetic perturbations (gene additions or/and deletions and upregulation or downregulation of gene expression) leading to increase in fluxes toward final products. Recent efforts have been made to develop a software suite encompassing a comprehensive library of published methods and standard model reaction and metabolite naming (Cardoso et al., 2018).

2.2 Trade-off between growth and production, and consideration of productivity Biomass growth and product yields cannot be simultaneously maximized. At the same substrate uptake rate, a higher growth yield will lead to a higher growth rate at the expense of the product yield. Standard computational design algorithms like those referenced above deal with this trade-off by looking for a single composite objective. To this end, they implement a computationally expensive nested bi-level static optimization (i.e., without temporal varying control of genetic regulation) iterating between maximization of cellular growth and that of production rate of a desired compound. However, productivity and titers are process-level concepts not only associated with yields and biomass growth rates, but also affected by other factors such as fermentation conditions not captured by these algorithms (Zhuang et al., 2013; Wu et al., 2016). It is possible to deal with the trade-off between yield and productivity and predict titer and productivity using dynamic flux balance analysis (dFBA), which incorporates both process dynamics and the metabolic network and relies on dynamic optimization methods (Llaneras et al., 2012; Mahadevan et al., 2002; Jabarivelisdeh and Waldherr, 2018). Dealing with cell resource allocation trade-offs and industry-scale settings. In practice, the metabolic costs of producing enzymes and plasmids during pathway overexpression often lead to metabolic shifts away from optimal FBA predictions. Models that integrate metabolism, biomass composition, and enzyme costs can potentially yield better predictions than those focused on metabolism in isolation (Wu et al., 2016). Dynamic FBA uses only a coarse description of biomass composition. The resource balance analysis and the metabolism and macromolecular expression approaches are FBA

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extensions in this direction (Goelzer et al., 2011; Waldherr et al., 2015). But both approaches are limited to constant specific cell environments. Therefore, they are not suitable to consider microorganisms growing in the heterogeneous timechanging environment of industry-scale bioreactors. To overcome this drawback, dynamic enzyme-cost FBA ( Jabarivelisdeh and Waldherr, 2018; Waldherr et al., 2015) includes dynamic changes of both substrate concentration and a detailed description of biomass composition, and accounts for the enzyme cost. It, therefore, enables the analysis of temporal regulation in the metabolic-genetic network under transient conditions. On the downside, dFBA only considers a single composite objective to be optimized.

2.3 Addressing the interaction between synthetic gene circuits and host One fundamental challenge in synthetic biology is the lack of quantitative tools that accurately describe and predict the behaviors of engineered gene circuits (Liao et al., 2017). One of the fundamental reasons for the disparity between the designed circuit behavior and the actual one is the interdependence between circuit and host. Again, there is an underlying problem of cell resource allocation. Models integrating circuit-host coupling have been recently developed (Liao et al., 2017; Weiße et al., 2015; Qian et al., 2017). Interestingly, they are not essentially different from the ones used in dFBA to link metabolism and enzyme cost. There is a clear opportunity to link synthetic gene regulation, metabolism, biomass composition, and enzyme costs in a common framework.

3

Tools for metabolic pathway design

3.1 Metabolic pathway selection Metabolic pathway search has traditionally been done ad hoc based on a mixture of expert knowledge of the specific biochemical reactions for the choice of pathways and enzymes used for production, and blind random search plus screening for unknown compounds with desired physicochemical properties. In silico retrosynthesis-based pathway design in metabolic engineering is a computational method that works iteratively backwards through biochemical transformations to find the right combination of enzymes to form a pathway that connects source molecules (available substrates and native metabolites in the chassis) to the target molecule (Lin et al., 2019). To date, retrosynthesis-based pathway design has been used in several proofs of concept, but applications truly expanding chemical diversity are still lagging behind. The procedure is highly combinatorial and can lead to multiple pathway solutions. Therefore, performing a manual search to identify all possible solutions is often nonviable. Instead of manual screening, computational pathway design algorithms apply retrosynthesis in an automated fashion to enumerate potential routes linking the target to the source (Wang et al., 2018). The algorithms often take into consideration a multitude of criteria in order to rank the pathways according to the shortest route, minimal number of heterologous reactions, thermodynamic feasibility, and enzyme availability (Carbonell et al., 2011). Several retrosynthesis-based pathway design frameworks are available such as Simpheny (Yim et al., 2011), GEM-Path (Campodonico et al., 2014), RetroPath 2.0 (Del
epine et al., 2018), BNICE (Hadadi and Hatzimanikatis, 2015), PathPred (Moriya et al., 2010), and SimZyme (Pertusi et al., 2014; Cho et al., 2010). Some of them are based on mining available reactions in metabolic databases such as KEGG (Kanehisa et al., 2016) or MetaCyc (Caspi et al., 2019), as well as in the unifying metadatabase MetaNetX (Moretti et al., 2015). Advanced retrosynthetic algorithms such as BNICE and RetroPath 2.0 have the ability of predicting de novo reactions by replacing native enzymatic reactions by generic reaction rules, thus expanding natural chemical diversity. RetroPath 2.0 provides a retrosynthetic solution that is based on the generation of reaction rules at different levels of specificity depending on the number of atoms that are considered around the reaction center. Such variable representation of reactions mimics the natural capabilities of enzymes that can promiscuously accept multiple substrates, allowing fine tuning of the pathway. Currently, there are ongoing efforts toward standardization of the technology through automated extraction of the reaction rules (Plehiers et al., 2018) and the use of machine learning to assist in the retrosynthetic search (Segler et al., 2018).

3.2 Enzyme sequence selection Selecting gene sequence encoding enzymes for specific pathways identified by a retrosynthesis analysis is difficult and time consuming. Plant enzymes are natural candidates. However, plants have huge genomes and, due to gene duplications, many copies and isoforms are present. Selection of enzyme sequences, therefore, requires specialized tools. One such tool for selecting enzymes is the Antibiotics and Secondary Metabolite Analysis SHELL antiSMASH 4.0 software, which specializes in genome mining and the identification as well as annotation of biosynthetic gene clusters (Blin et al., 2017). Tools

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such as CanOE Strategy (Smith et al., 2012) aid the selection of candidate enzymes by finding candidate genes for orphan enzymes. MRE (Kuwahara et al., 2016) or EC-Blast (Rahman et al., 2014) allows sequence selection based on reaction homologies. Selenzyme (Carbonell et al., 2018b) is a free online enzyme selection tool for metabolic pathway design that focuses on reaction rules and is integrated with RetroPath 2.0 as well as workflow systems such as KNIME and Galaxy, discussed in Section 7. The tool provides key information about enzymes based on existing databases and tools such as similarity of sequences and of catalyzed reactions; phylogenetic distance between source organism and intended host species; multiple alignment highlighting conserved regions, predicted catalytic site, and active regions; and relevant properties such as predicted solubility and transmembrane regions.

4 Tools for metabolic pathway experimental design 4.1 Bottom-up synthetic biology design of experiments Previous sections involved a myriad of design decisions concerning the selection of the genetic parts that will be integrated in the implementation of the metabolic engineering project. Now, it is time to plan the experiments in order to devise an optimal combinatorial library that will make the most of both computer-aided design tools and expert selection of genetic parts. Smart experimental sampling has been often addressed in top-down bioprocess engineering through optimal experimental design approaches (Kumar et al., 2013). Bottom-up engineering synthetic biology, in turn, has been less prone to adopt optimal experimental design approaches. Tools for transcriptional design such as SelProm ( Jervis et al., 2019) or translation design like the RBS Calculator (Salis, 2011) are making possible precise bottom-up genetic circuit design. As increasingly more efficient high-throughput capabilities are becoming available in modern biomanufacturing platforms, bottom-up design of experiments is becoming an essential part of the synthetic biology pipeline (Roehner et al., 2016). Adoption of the optimal design of experiments in bottom-up approaches for engineering biology (Kumar et al., 2013) is now a common strategy in metabolic engineering (Bhatia et al., 2017) with tools such as OptBioDes (Carbonell et al., 2019a, b) or JMP (Goos and Jones, 2011). However, its progress is arguably still hampered by the lack of open community standards. Arpino et al. (2013) have listed more than 10 factors or “dials” for bottom-up synthetic biology design of experiments operating at different levels and time/space scales, such as transcriptional, translational, or posttranslational. Some of the considered parameters are shown in Table 2.

TABLE 2 Main factors in bottom-up synthetic biology design of experiments. Class

Factors

Global parameters

Chassis Gene copy number

Transcription-level design

Promoter type Inducer concentration Promoter leakiness Basal expression Promoter strength Gene placement

Translation-level design

Ribosome-binding site strength Codon optimization mRNA decay rate Riboregulators

Transcription, translational, and posttranslational design

Inteins Protein-level design Protein activity

Additional parameters in eukaryotic cells

Posttranslational modifications Translocation Compartments

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There exist a variety of statistical design of experiments approaches, depending on the complexity of the problem: l

l

l l l

Full-factorial approaches are used to screen the full design space in an unbiased way in order to minimize side effects from those factors that are not controlled, for instance, room temperature, etc. Regular fractional factorial design allows reducing the number of experimental runs by using a carefully chosen fractional section of the full experimental design. Strategies exist that allow performing the reduction in ways that can facilitate the experimental protocols, like blocking and stratification. Plackett-Burman designs try to minimize the variance of the estimates of the dependencies between the factors. Definitive screening designs present some improvements on sample size reduction with respect to Plackett-Burman. Optimal designs try to minimize some criteria, for instance, D-optimal design selects the experiments so that they are close to orthogonal designs, which are extreme cases where the experimental data are independent and uncorrelated.

One example is in Bhatia et al. (2017), where RedLibs, an algorithm that allows the rational design of smart combinatorial libraries was introduced. The algorithm is used for pathway optimization through the design of smart ribosome-binding site libraries. Smart libraries are generated with optimal coverage, even distribution, size amenable to screening, and easy assembly (Bhatia et al., 2017). In another example, Stephanopoulos and colleagues introduced a Plackett-Burman design allowing the screening of promoter efficiency in the violacein pathway (Xu et al., 2016). In Berepiki et al. (2020), definitive screening design was used in order to efficiently identify biosensors with enhanced performance. Modular design and the use of insulators can be used to reduce interactions for combinatorial design. A discussion about insulators in synthetic biology and pathway design (Bhatia et al., 2017); and theoretical definitions can be found in Vecchio (2015). For instance, Jones et al. (2016) optimized the production of flavonoids in E. coli in a modular fashion. The resulting design space consisted of more than 200 million combinations that the authors were able to reduce to approximately 3000 through modular design. Design of experiments should be carried out at the learning stage following Design-Build-Test (Petzold et al., 2015). Observations at the end of the iteration of the cycle, including negative data from failures, should inform the next iteration. Active learning approaches from the field of machine learning should guide hypothesis-driven design of experiments by defining the factors and constraints of next-iteration design space (Nielsen et al., 2016) during both the exploration and exploitation phases.

4.2 Optimal design of experiments in metabolic engineering Optimal design of experiments is characterized by designs that are optimal with respect to some statistical criterion (Goos and Jones, 2011). A common criterion is to minimize the variance matrix of the estimator for the parameters of an assumed statistical model between the factors and the response. Data from optimal designs will carry the maximum information that can be extracted from the experiments in order to efficiently fit the assumed statistical model. In that sense, they are conceptually related to maximizing the Fisher information matrix ( Julio and Balsa-Canto, 2008) of the experiment. One advantage of bottom-up optimal designs for metabolic engineering is that they provide improved control for the experimenter on the number of experimental runs. For instance, the number of experimental runs can be set up initially based on the initial experimental and throughput constraints and the optimal design algorithm will suggest a design solution that is guaranteed to make the best effort in order to sample the design space. Design diagnostics can be carried out in order to decide if the size of the design should be increased, allowing a trade-off between the efficiency of the design and the number of experimental runs. The optimal design approach in metabolic engineering has been applied in different contexts and with different objectives: l

l l

Flux analysis: Metabolic flux analysis has been classically an optimization problem, involving both linear (flux balance analysis) or noninteger linear programming problems (see Section 2). Pathway design: The bioretrosynthesis approaches, which boiled down to a multiobjective optimization (see Section 4). Process optimization: From the top-down perspective, processes have been optimized using models and multiobjective optimization (Patel and Padhiyar, 2017).

Through optimal experimental design, we want to achieve the metabolic engineering objective of higher titers through a reduced number of experimental runs. This approach is in contrast to machine learning-based approaches, discussed in Section 6, which requires large amount of data in order to build predictive models. Optimal design will, therefore, follow the strategy of assuming simple models and relationships between the factors rather than trying to fit complex nonlinear relationships. The rationale for such approach is that often the strongest effects have a dominant impact on the response and, therefore, can be easily detected even by assuming linear dependencies. Second, our prior knowledge about most of the

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bottom-up factors of a metabolic engineering project such as gene variants or promoters is generally incomplete and, therefore, those variables are considered as categorical or as ordered factors at the most. For instance, we saw in Section 3 that a tool like Selenzyme can rank gene variants for a given enzyme. However, such predicted ranking will be subject to a high level of uncertainty because of the actual cell and genetic circuit context, which has not been taken into account in the prediction. Because of this, it is generally a good practice to shortlist a set of highly ranked variants from Selenzyme, between 5 and 20 variants, and then to perform the experimental design without any prior assumption about the actual efficiency of each individual enzyme. Such approach will allow an unbiased exploration of the design space through optimal design.

5 Tools for metabolic pathway dynamic regulation 5.1 Biosensors design for metabolic engineering Biosensors are synthetic devices that elicit a biological response in the presence of a chemical signal, typically concentration of some metabolite. Biosensors are widely used in metabolic engineering for screening and pathway regulation ( Johnson et al., 2017; Shi et al., 2018; D’Ambrosio and Jensen, 2017). There exist several classes of biosensors depending on their biological mechanism, including RNA-based riboswitches and aptamers; allosteric regulation effectors of proteins; as well as those based on transcription factors. Libraries of transcription factors-based biosensors have been created by varying regulatory elements such as RBS (De Paepe et al., 2018) or through design of experiments approaches (Berepiki et al., 2020), as described in Section 4. Directed evolution has been applied to biosensors to increase their dynamic range (Snoek et al., 2019), as well as with the use of model-based design for the constraints of dose-response curves (Mannan et al., 2017). There exists a growing list of small biomolecules that can be detected by biosensors (Koch et al., 2018), including polyphenols and amino acids. In order to extend the capabilities of detection of chemicals of interest, the design tool SensiPath (Del
epine et al., 2016) was introduced to develop transcription factor-based biosensors through metabolic pathways, thus expanding the observable chemical space spanned by biosensors.

5.2 Metabolic pathway dynamic regulation Major improvements in yield, titer, and productivity of engineered metabolic pathways can be accomplished by balancing pathway gene expression (Liu et al., 2018). Dynamic regulation of metabolic pathway allows robustness through the application of feedback and feedforward regulation. In order to implement a dynamic regulation strategy, biosensors are needed. Indeed, a dynamic regulation system consists of a sensing component, which can detect the metabolite of interest or physiological state (e.g., growth, stress signals), and a regulator component, which converts the sensor signal into a transcriptional signal, often resulting in the upregulation or downregulation of a key pathway gene (De Paepe et al., 2018; Huyett et al., 2018; Liu and Zhang, 2018). This makes possible to attain higher titers as compared to static regulation (Stevens and Carothers, 2014).

6 Machine learning tools for metabolic pathway design Natural proteins tend to be fit for their jobs, that is, evolution has shaped the protein sequences up to the level of required functionality. However, it is accepted that proteins have latent activities encoded in their sequences and that their promiscuity allows proteins, in particular enzymes, to evolve and become specialized for their required tasks. From the point of view of pathway design, enzyme sequences can be initially selected through algorithms like Selenzyme (Carbonell et al., 2018b), discussed in Section 3. We can go beyond the natural repertoire by evolving the sequence of a gene involved in the pathway, generally an enzyme, in order to obtain higher specificity and efficiency. Directed evolution (Arnold, 2017) has been notably used in the lab as a way of evolving proteins into new variants with desired properties. The main challenge is to define the set of amino acid regions that should be mutated in the sequence in order to improve its functionality. Traditionally, such decision was made by experts but nowadays it can be automated by using technologies such as deep learning (Webb, 2018; Wu et al., 2019). Deep learning algorithms rely on neural networks, which have been traditionally used as one of the most common machine learning algorithms. The main difference is that deep learning algorithms have been designed based on the current large availability of data, allowing the organization of the layers of the neural networks in more complex architectures such as convolutational networks. Several examples exist on using deep learning in metabolic pathway design. For instance, in constraint-based models (Rana et al., 2020) to classify enzymes by EC number based on sequence (Li et al., 2017;

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Ryu et al., 2019) or in enzyme engineering (Mazurenko et al., 2019). A unified deep learning architecture for modeling biological function from sequence has been recently proposed (Wu et al., 2018).

7

Automated design workflows and standardization

7.1 Standardized information representation in synthetic biology Due to the complexity of metabolic engineering projects, reproducibility of the results has been always one of the most sought, although elusive, requirements. Reproducibility allows the benchmarking of results in order to evaluate the ability of engineered strains in terms of their titer, rate, and yields (TRY), as well as in terms of biomanufacturing capabilities. From a synthetic biology viewpoint, it is desirable that the design steps that are necessary in order to provide the blueprints for the producing strains are performed through well-defined protocols. As described in the previous sections, the design toolbox for metabolic engineering consists of multiple small and well-defined tasks with a set of designated input parameters and an associated input/output data flow. In order to rationalize the process and make it amenable to community joint efforts, the data flow has to be represented through standardized data models and the different steps need to be defined through workflows. In order to represent biological information, the Systems Biology Markup Language (SBML) (Hucka et al., 2019) is widely used because of its ability to represent different classes of biological processes, including metabolic, signaling, and regulation networks, along with their mathematical models and environmental conditions. SBML models can, therefore, serve to exchange data between early stages of the design protocols for metabolic engineering, as well as to simulate the cell under different conditions and interventions. They are the most widely used representation for flux balance analysis calculations and strain knock out optimization, as described in Section 2. SBML models, however, are less well fitted for those steps downstream in the metabolic engineering pipeline. As the project moves from in silico simulation (Design) into actual implementation (Build and Test), it is necessary to deal with concepts such as the design space of genetic circuits for some target pathway based on the combination of available genetic parts. To that end, the Synthetic Biology Open Language (SBOL) (Cox et al., 2018) is considered as the preferred representation of the data flow at this stage. Several proposed standard interconversion procedures exist between SBML and SBOL because of their commonalities (Watanabe et al., 2018), although there is no consensus yet for a full interconversion between the languages due to the fact that parts of the information that each language encapsulates cannot be easily represented in the other and vice versa. Therefore, in a typical design pipeline, some design steps such as metabolic modeling and pathway and enzyme selection can be more easily represented through SBML, while other such as DNA design, combinatorial design of experiments, or assembly as more easily represented through SBOL.

7.2 Workflow development for metabolic engineering The data and specifications conveyed by the aforementioned data models are exchanged through the different nodes of the metabolic engineering design workflow. In order to orchestrate the pipeline, there exist multiple workflow engines, several of them open source, encouraging in that way the metabolic engineering community to share design protocols in a more reproducible manner (Table 3). These engines generally support the execution on distributed high-performance computing TABLE 3 Comparison of features between three major workflow systems. Workflow

Architecture

Features

Jupyter notebook

Python on browser Scientific computing Online sharing Online development platforms

Easy reproducibility

KNIME

Java Graphical diagrams

Large collection of tools

Galaxy

Python-based web server Web-based interface Allows Docker containers

Encapsulates common tasks

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(HPC) resources (e.g., grids, clusters, clouds, etc.). Moreover, there is a growing interest into integrating design workflows into experimental protocols of the build and test stages of any metabolic engineering project through worklists for plate readers, liquid handling robots, and other automated lab equipment. By integrating design workflows with lab automation, an improved traceability of samples with rich metadata annotations can be achieved facilitating in that way the development of predictive models and the application of machine learning to inform the design tools discussed in this chapter. Jupyter notebooks (Perkel, 2018) are currently the preferred way to ensure reproducibility of common computational tasks. Notebooks allow sharing any scientific code performing some predefined tasks in a visually and information-rich live document. The dual use of notebooks as a scientific live documents and as workflow of executable tasks have made of them ideal tools in order to achieve the reproducibility goal. Cloud-based notebook development environments such as Google Colaboratory or Code Ocean, a platform sponsored by Springer Nature (Editorial, 2019), are encouraging the collaborative development of community-wide standard workflows. KNIME (Fillbrunn et al., 2017) is a Java-based platform that allows executing workflows using a user-friendly interface. Tools such as RetroPath 2.0 (Del
epine et al., 2018) for metabolic pathway design, reviewed in Section 3, have been developed under KNIME. Galaxy (Afgan et al., 2016) is an open web-based workflow system that has been widely adopted by the bioinformatics and biomedical research communities. Inclusion of additional tools can be easily achieved through XML-based toolconfig files, while community sharing of tools is encouraged on the toolshed repositories. On top of those engines, the common workflow language (CWL) (de la Garza et al., 2016) has been conceived as an engineindependent language for workflow definition allowing improved reproducibility.

7.3 Integration with lab management software One of the major trends of synthetic biology design for metabolic engineering is the integration of their tools with lab management software such as Benchling, Riffyn, or other open-source initiatives (Morrell et al., 2017) within common workflows in order to extend reproducibility from the design to the wet-lab benches. Powerful APIs from workflow and lab management workflow tools and cross-lab standardization initiatives such as those from the Global Biofoundries Alliance (Hillson et al., 2019), closing the loop between workflow and lab management engines, are making possible the transition from metabolic engineering to smart cloud-based biomanufacturing. As discussed in this chapter, the engineering approach of synthetic biology for biological design embraces the role of manufacturing technologies such as supervisory control and data acquisition (SCADA), rapid prototyping and the goal of circular economy as central tenets for the global metabolic engineering innovation agenda.

8 Conclusions and future perspectives Development of synthetic biology design tools for metabolic engineering is a thriving area of research boosted by a cornucopia of available tools to choose from, as discussed in this chapter. Selecting the right application for each problem is not always straightforward. We hope that this chapter has provided a bird’s-eye view on the current state-of-the-art and some discerning recommendation guidelines. As microbial strain engineering becomes the technology of choice to replace industrial chemical production, standardization, and the establishment of one-stop hubs for synbio design apps are the mostdemanded next moves. Ultimately, the suite of automated design applications is poised to become integrated as assets in the biofoundry pipelines to allow seamless transition from design to automated generation of build, test, and learn workflows. In that way, cloud-based platforms and distant wet lab for biomanufacturing will define through the decade the leading market position of metabolic engineering.

Acknowledgments Grant MINECO/AEI, EU DPI2017-82896-C2-1-R. Carbonell acknowledges support from the Universitat Polit
ecnica de Val
encia Talento Programme.

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Chapter 6

Metabolic engineering for microbial cell factories Ali Samy Abdelaala,c and Syed Shams Yazdania,b,∗ a

Microbial Engineering Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India, b DBT-ICGEB Centre for Advanced

Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, India, c Department of Genetics, Faculty of Agriculture, Damietta University, Damietta, Egypt ∗

Corresponding author: E-mail: [email protected]

1 Introduction In 1991, the term “metabolic engineering” was first recognized by James E. Bailey (Bailey, 1991). Since then the number of products that can be produced by metabolic engineering of microbial strains has expanded, partially due to significant developments in other metabolic engineering-related fields, such as DNA sequencing and genetic engineering. With DNA sequencing the majority of metabolic genes and enzymes were identified in several species, and the information obtained are used by means of metabolic engineering to create biochemical pathways or entire organisms with optimized functions (Gibson et al., 2010). Scientists created new genetic techniques and analytical tools in the 1990s that allowed metabolic engineers to control metabolic pathways and track intracellular metabolites to identify new biochemical pathway more precisely. Earlier in the 21st century, metabolic engineers joined other scientists to look for renewable fuels, which are in high demand as oil prices rise and climate change concerns. Recent technological advances in molecular biology and their related tools have led to an increasing interest to redirect metabolic fluxes for industrial and medical purposes by means of metabolic engineering. Metabolic engineering is defined as the directed improvement of cellular activities by modification of specific biochemical reaction(s) or introduction of new one(s) to produce the required amounts of a certain metabolite with the use of recombinant DNA technology (Bailey, 1991). Metabolic engineering is mainly intended for the production of chemicals, fuels, and pharmaceuticals by altering the metabolic pathways. It specifically relies on modeling the biochemical reactions mathematically, calculating the yield of useful products or new products and pinpointing the reaction that restricts the output of these products. Genetic engineering techniques can then be used to modify or introduce reaction to produce the required amount of the substance. The main objective of metabolic engineering is to be able to cost-effectively exploit the organisms to generate the desired substances on an industrial scale. Metabolic engineering is currently used in cell factories for production of amino acids, biofuels, pharmaceuticals, bioplastics, platform chemicals, silk proteins, etc. A comprehensive list of these items along with their commercialization status has been presented in Table 1. Fundamentally, metabolic engineering is based on microbial metabolism. Microbes generate various types of secondary metabolites that can be valuable for humans. Metabolic engineering helps to improve the microbial production of desired substances. Metabolic engineers must take a specific route to achieve this goal. The metabolic engineering process to obtain the final strain to be used as a microbial cell factory can be summarized in Fig. 1.

2 Metabolic engineering approaches Metabolic engineering aims to apply all information about various biological phenomena to optimize a desired compound’s biological synthesis pathway. Organisms could be modified to generate a wide range of compounds either by improving endogenous metabolic pathways or by adding exogenous pathways that are either taken from another organism or designed de novo. While the overexpression of bottleneck enzymes and the elimination of competing pathways remain the main approaches of metabolic engineering, there are other main elements that need to be addressed to effectively develop strains Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00015-5 © 2021 Elsevier Inc. All rights reserved.

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TABLE 1 Status of commercialization of microbial cell factories. Organism

Product

Feed stock

Status

Companies

Location

Opened

Reference

E. coli

Succinic acid

Sorghum and lignocellulose

Commercialized

Myriant Technologies

Louisiana, United States

2013

www. gcinternational. com

Corn sugars

Commercialized

LCY Biosciences

Sarnia, Canada

2018

www.lcygroup. com

1,3-Propanediol (Bio-PDO)

Corn sugars

Commercialized

DuPont Tate & Lyle Bio Products

Tennessee, United States

2013

www. duponttateandlyle. com

1,4-BDO

Sugar

Commercialized

Genomatica and DuPont Tate & Lyle

San Diego, United States

2013

www.genomatica. com

Isobutene

Glucose, sucrose

Demonstration

Global Bioenergies

Leuna, Germany

2017

www.globalbioenergies.com

PHA

Corn sugar and sugarcane

Commercialized

Metabolix, ADM

Clinton, United States

2007

www.metabolix. com

Ethanol

Sugarcane

Commercialized

Many companies

Succinic acid

Corn, wheat, cassava, rice, sugarcane, sugar beets, and forest waste

Commercialized

LCY Biosciences (70%), Mitsui & Co. (30%)

Sarnia, Canada

2018

www.lcygroup. com

Starch, sugars

Commercialized

Reverdia

Cassano, Italy

2012

www.reverdia. com

Isoprene

Sugar, cellulose

Preparing

Amyris, Braskem, Michelin

Brotas, Brazil

www.amyris.com

Squalene

Sugarcane

Commercialized

Amyris

Brotas, Brazil

www.amyris.com

Farnesene

Sugarcane

Commercialized

Amyris

Brotas, Brazil

www.amyris.com

Acetone

Corn

Commercialized

Green Biologics

Minnesota, United States

www. greenbiologics. com

Butanol

Corn

Commercialized

Green Biologics

Minnesota, United States

www. greenbiologics. com

S. cerevisiae

Clostridium acetobutylicum

TABLE 1 Status of commercialization of microbial cell factories—cont’d Organism

Product

Feed stock

Status

Companies

Clostridium autoethanogenum

Ethanol

Steel mill off-gases

Commercial

Lanzatech

Acetone

Syngas

Pilot

Lanzatech

Isopropanol

Syngas

Pilot

Opened

Reference

2018

www.lanzatech. com

Freedom Pines, United States

2019

www.lanzatech. com

Lanzatech

Freedom Pines, United States

2019

www.lanzatech. com

Adipic acid

Plant-based oils

Demonstration

Verdezyne

Carlsbad, United States

2012

www.verdezyne. com

Sebacic acid

Plant-based oils

Demonstration

Verdezyne

Carlsbad, United States

2015

www.verdezyne. com

Dodecanedioic acid (DDDA)

Plant-based oils

Under commercialization

Verdezyne

Carlsbad, United States

2015

www.verdezyne. com

Mannheimia succiniciproducens

Succinic acid

Sucrose, glucose, and glycerol

Commercialized

Succinity

Montmelo´, Spain

2014

www.succinity. com

Not available

Lactic acid

Dextrose and sucrose from cassava, corn starch, sugarcane, or beets

Commercialized

NatureWorks

United States

2003

www. natureworksllc. com

Aspergillus terreus

Itaconic acid

Biochemistry

Commercialized

Qingdao Kehai

Huangdao District, China

Aspergillus niger

Citric acid

Not available

Long-chain dibasic acids (LCDA)

Commercialized

Cathay Industrial Biotech

Shandong Jinxiang, China

Not available

3Hydroxypropionic acid (3HP)

Commercialized

Metabolix

Clinton, United States

Sugar

Demonstration

Novozymes and Cargill

Candida sp.

Location

www.kehai.info/ en

Commercialized Starchy plants

Zymomonas mobilis

Ethanol

Corn sugar

Commercialized

Many companies

Kluyveromyces marxianus

Ethanol

Lignocellulose

Commercialized

Many companies

Yeast

Isobutanol

Sugars

Commercialized

Gevo

2003

www. cathaybiotech. com www.metabolix. com www.novozymes. com

Luverne, United States

2014

www.gevo.com

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FIG. 1 Metabolic engineering process.

for the production of desirable products. Some of these main elements include the selection of host strains that must take into consideration the natural benefit of the host for a specific application, tools for designing and optimizing of metabolic pathways, transcriptional control engineering to fine-tune the rates and timing of pathway expression, enzyme engineering for designing of novel enzymatic activities in particular pathways, developing new technologies to improve the efficiency and expedience of genetic interventions, and advanced strategies to minimize metabolic burden. The commonly used approaches to improve the metabolite flux through the pathway can be classified into the following categories: (a) increasing supply of precursors, (b) enhancing rate-limiting steps in the pathway, (c) eliminating competing pathways, and (d) removing feedback inhibition in the pathway (Fig. 2).

3

Emergence of systems metabolic engineering

Systems metabolic engineering incorporates systems biology, synthetic biology, and evolutionary engineering with conventional metabolic engineering and accelerates the development of high-performance industrial strains. This strategy was first represented by the overproduction of L-valine (Park et al., 2007) and L-threonine (Lee et al., 2007) using Escherichia coli strains.

FIG. 2 Metabolic engineering approaches.

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Although systems metabolic engineering is not yet a widely used term, it covers all the advancements made to improve the field of metabolic engineering. The tools and strategies of systems metabolic engineering are being extended with the help of recent developments in omics, genome-scale metabolic modeling, genetic engineering, and evolutionary engineering, allowing the engineered strains to achieve their high performances (Choi et al., 2019).

4 Systems metabolic engineering strategies 4.1 Design of systems metabolic engineering project In the design of metabolic engineering projects, various technical, economic, and regulatory factors should be considered to effectively move from laboratory to large-scale commercial production for microbial cell factory development. For bioproduction of bulk or specialty chemicals, there are three key performance indicators, that is, product titer, product yield, and productivity, that need to be carefully estimated to assess the competitiveness of a bioprocess with traditional petrochemical processes. Product titer (g/L) refers to the final concentration of the product at the end of fermentation, while product yield (g/g or mol/mol substrate) refers to the amount (gram or mole) of product formed per unit amount (gram or mole) of substrate consumed. Productivity is defined in terms of specific or volumetric productivity. The specific productivity (g/(dcw h) or mol/(dcw h)) refers to the amount (gram or mole) of product produced per unit dry cell weight (dcw) per unit time, while the volumetric productivity (g/(L h) or mol/(L h)) refers to the amount (gram or mole) of product produced per unit volume per unit time. Product titer, product yield, and productivity are closely associated with downstream process cost, substrate cost, and bioreactor scale, respectively. Generally the high-value products may tolerate lower-performance indices, while bulk chemicals require the achievement of the maximum values for all performance indices. At industrial scale, production in fed-batch fermentation mode is most often preferred, mainly due to higher titer, yield, and productivity that can be achieved, flexibility of fermentation operation, and less chance of contamination compared with continuous mode. The use of a defined medium enables the processing of the desired product under controlled conditions, and it simplifies the downstream purification by reducing the quantity of contaminants. Elimination of the formation of by-products during strain development helps in increasing the yield and also necessary in lowering recovery and purification costs (Kim et al., 2015b). For the processing of bulk products, the use of costly inducers such as isopropyl b-D1-thiogalactopyranoside (IPTG) is discouraged; thus constitutive expression of metabolic genes is mostly used in industrial-scale fermentations. Since the best-performing microbial strain is not yet known at the initial stage, many candidate strains could be modeled and compared. Some of the most commonly examined production microorganisms include E. coli, Saccharomyces cerevisiae, Corynebacterium sp., Clostridium sp., and Bacillus sp. It is possible to estimate performance indices through genome-scale metabolic simulation because abundant information on these microbes is available. However, it should be noted that less studied or newly isolated microorganisms can be used if much higher performance is expected; for example, the high C4 flux bacterium Mannheimia succiniciproducens was used for succinic acid production at very high productivity of 38.6 g/(L h) (Lee et al., 2016). A further significant purpose of systems metabolic engineering is the biodegradation of environmental pollutants. While some strains are optimized to efficiently degrade target compounds, other strains may also generate useful compounds through consumption of pollutants (Choi et al., 2014; Joo et al., 2018; Yang et al., 2015). Focus on this interesting research division that aims to detoxify environmental pollutants like micro-/nanoplastics (Urbanek et al., 2018) and spilled oil (Nie et al., 2014) has recently increased. Recently, many metabolic engineering projects have worked on de novo biosynthesis of target bioproducts to fill the gaps that exist due to incomplete or insufficient pathway for the available substrates by inserting single or multiple steps into the metabolic pathway. Some chemical modification is needed after the biological production of precursors for successful production of final products, which is another economic factor that should be considered. Renewable carbon sources have several choices that impact the overall costs of bioprocessing. While the use in the production of target chemicals is most frequent with glucose and sucrose from hydrolyzed starch and raw sugar, the use of lignocellulosic substances in the past few decades has also been studied extensively to avoid conflicts with food uses (d’Espaux et al., 2017). Recently the use of C1 chemicals such as methane (Kalyuzhnaya et al., 2015), methanol (Yu and Liao, 2018), formic acid (Bang and Lee, 2018), and carbon dioxide (Gleizer et al., 2019) is increasing, although the current bioproduct formation through C1 carbon utilization is not as successful as that using traditional carbon sources. These microorganisms need more adaptation to be able to utilize C1 carbon with the same efficiency.

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The last economic factor is the location of the factory. The ideal geographical position of the fermentation region should be considered because the expense of transportation of raw material can be very high in countries where there is no abundance of feedstock. Legal and regulatory considerations should also be included in project design in addition to economic aspects. If the project is to be implemented in an industrial setting, an important step must be taken in the form of patents or trade secrets to secure intellectual property. Any project from an academic laboratory aiming for a commercial product must ensure that patents are filed with appropriate agency prior to the public release of the invention. Finally the usage for end product should eventually be considered in the perspective of international regulations. Genetically modified organisms (GMOs) represent microbial strains used in metabolic engineering, and thus they fall under various jurisdictions and their products fall under various GMO regulations. Such regulations are especially strict when the final goal is to use the products for humans and/or animals. Furthermore, organisms generally recognized as safe (GRAS) are being increasingly used as host strains to produce chemicals for direct human consumptions in response to public concerns.

4.2 Selection of appropriate host platform The metabolic engineering process starts with the selection of the host strain for manipulation. Since it is unlikely that a single wild-type organism will have a phenotype covering all the requirements for the production of diverse products, the choosing of a starting strain is generally made according to the following criteria: a. b. c. d. e.

the metabolic resources (i.e., precursors, pathways, and cofactors) toward the desired product, the bioprocess compatibility, the difficulty of metabolic and genetic engineering and availability of toolsets, the ability to utilize inexpensive feedstocks, the nature and toxicity of the product.

Model microbes such as E. coli and S. cerevisiae remain the most commonly used organisms for metabolic engineering to produce diverse products due to their well-known metabolism and well-developed engineering tools. However, several other strains have been used more efficiently for production of some chemicals, such as Clostridium sp. for acetone and butanol production (Kim et al., 2015a; Qi et al., 2018), Corynebacterium sp. for amino acids production (Park et al., 2014; Becker et al., 2011; Hoffmann et al., 2018), actinomycetes for antibiotics production (Tong et al., 2015), and M. succiniciproducens for succinic acid production (Lee et al., 2016). Therefore the metabolic pathways of these microorganisms can be improved to produce chemicals using the same metabolic pathways with their naturally produced metabolites. Increasingly, with recent advances in inexpensive genome sequencing and new genomic manipulation tools, particularly the use of CRISPR for microbial metabolic engineering, the metabolic engineering of microbes has been greatly facilitated. This made less explored organisms with attractive properties actively used as host strains. In silico genome-scale metabolic modeling and simulation can be a helpful tool to select an effective production organism through an assessment of the metabolic capacities of different organisms. To evaluate the E. coli capacities for biosynthesis of a wide range of chemicals, an extensive study using this approach was performed and it was found that up to 1777 nonnative products could be derived from the E. coli metabolism by introducing heterologous enzymes, and 279 of them have known commercial applications (Zhang et al., 2016). In addition, cyanobacteria and methanotrophic bacteria are primarily used as engineering hosts for using C1 chemicals as carbon sources (Kanno and Atsumi, 2017; Kalyuzhnaya et al., 2015). Also, thermophilic bacteria have been considered as hosts for metabolic engineering as their fermentation processes at high temperatures can reduce the contamination and are more consistent with various industrial chemical processes (Zeldes et al., 2015). Halophiles also can grow in open-field fermentation on the basis of seawater, which mitigate contamination and preserve fresh water (Fu et al., 2014).

4.3 Construction of synthetic metabolic pathways The identity of target molecule determines the overall trajectory of the metabolic engineering project. Chemicals can be generally classified into four categories based on existence in nature (natural vs nonnatural) and inherence of microorganism production pathways (inherent, noninherent, or created) (Lee et al., 2012).

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(i) Natural–inherent chemicals: Endogenous metabolites in naturally isolated microorganisms that can be produced inherently by a native metabolic pathway such as succinate, lactate, L-valine, and 2,3-butanediol. (ii) Natural-noninherent chemicals: Metabolites found in nature but are best produced in heterologous hosts by noninherent pathways from other hosts such as lycopene, glucaric acid, alkanes, isoprene, and isobutanol. (iii) Nonnatural-noninherent chemicals: Chemicals have not yet been found in nature but can be produced in a noninherent host strain by the establishment of heterologous pathways and enzymes, often using a combination of existing genes found in various sources such as styrene, 1,4-butanediol, and adipic acid. (iv) Nonnatural-created chemicals: Chemicals have not yet been found in nature and can only be produced by creating synthetic enzymes and pathways with new functions such as polylactic acid, 5-methyl-1-heptanol, and L-homoalanine. While attempting to get these various categories of molecules, metabolic engineers are considering the efficiency of the metabolic pathway and the most effective ways to construct it. The creation of pathways for desired products with unknown source is one of the biggest challenges faced in metabolic engineering process. New products (enzymes) need to be developed starting from the reactions catalyzing products similar to the desired one. Recent developments in synthetic biology and computational tools actually help to identify such enzymes and design novel and precise metabolic pathways for the desired products. The use of reaction rules developed on the basis of chemical structures of substrates and products in enzymatic reactions along with the usual techniques for the reconstruction of heterologous metabolic pathways by the combination of established metabolic reactions stored in metabolic reaction/pathway databases (Table 2) can help to optimize the design processes (Kanehisa and Goto, 2000; Zhang et al., 2015; Goto et al., 2002; Schomburg et al., 2002; Karp et al., 2019; Keseler et al., 2017; Caspi et al., 2014; Cherry et al., 2012; Li et al., 2010; Winkler et al., 2015; Birkel et al., 2017; Arkin et al., 2018; Morrell et al., 2017). Directed enzyme evolution and de novo design have led to an increase in the

TABLE 2 Key metabolic reaction/pathway databases. Database

Description

Reference

Kyoto Encyclopedia of Genes and Genomes (KEGG)

A knowledge base for gene annotations and pathway identities for genomics data

Kanehisa and Goto (2000)

Central Carbon Metabolic Flux Database (CeCaFDB)

A curated, multipurpose, and open-access database for 13C-flux publications

Zhang et al. (2015)

LIGAND

Chemical compounds and enzyme reactions database

Goto et al. (2002)

BRENDA

A comprehensive database of enzymatic and metabolic information

Schomburg et al. (2002)

BioCYC

A database of microbial genomes and metabolic pathways

Karp et al. (2019)

EcoCYC

A comprehensive database for Escherichia coli K-12 specifically

Keseler et al. (2017)

BsubCYC

A database for the genome and the biochemical machinery of Bacillus subtilis specifically

Caspi et al. (2014)

MetaCYC

Metabolic pathways and enzymes database

Caspi et al. (2014)

Saccharomyces Genome Database (SGD)

A comprehensive database for budding yeast Saccharomyces cerevisiae specifically

Cherry et al. (2012)

BioModels

A central database of mathematical models of biological/biomedical processes

Li et al. (2010)

Learning Assisted Strain EngineeRing (LASER)

A database for metabolic engineering strain designs

Winkler et al. (2015)

JBEI quantitative metabolic modeling library (jQMM)

A library for modeling microbial metabolism

Birkel et al. (2017)

KBase

Knowledgebase for systems biology analysis of different organisms

Arkin et al. (2018)

Experiment Data Depot

A database for biological experiments and metadata

Morrell et al. (2017)

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biological production of natural and nonnatural chemicals. Continued efforts have been provided to develop new enzymes catalyzing new reactions by carefully examining the candidate genes and clusters through mining of omics data using various tools and strategies such as antiSMASH (Blin et al., 2017), PRISM (Skinnider et al., 2017), RODEO (Tietz et al., 2017), and “Genome-Enabled” PrISM (Albright et al., 2014). Improvement in genetic engineering tools and strategies has facilitated the successful implementation of the designed metabolic pathways into producer strains. The novel and effective DNA assembly tools (Table 3) have facilitated the assembly of large-sized gene clusters and consequent expression of plasmid-based metabolic pathway genes (Cohen et al., 1973; Shetty et al., 2008; Katzen, 2007; Gibson et al., 2009; Padgett and Sorge, 1996; Coussement et al., 2014; de Kok et al., 2014; Kouprina and Larionov, 2016). Once heterologous reactions constructed, they need to be optimized for their expression in each host strain according to the fermentation results by several well-known techniques, such as gene expression optimization and codon optimization. Despite the convenience of plasmid-based systems for reconstruction and expression of metabolic pathway gene clusters, these systems have drawbacks in an industrial setting, such as the waste of cellular energy resources, plasmid instability, and copy number fluctuation. Recently, chromosomal integration of gene clusters has been preferred for stable enzyme expression instead of using plasmids. Genomic editing techniques, based on recently developed CRISPR, have facilitated the introduction of metabolic pathways into the host genome (McCarty et al., 2020; Abdelaal et al., 2019; Zhou et al., 2020). The aforementioned tools and strategies have enabled the reconstitution of long and complicated biosynthetic pathways for the development of complex chemicals, such as the integration of complete opioids biosynthetic pathways from different organisms into the chromosome of S. cerevisiae producing thebaine and hydrocodone (Galanie et al., 2015). Similarly the biosynthetic pathway of oxygenated taxanes was allocated into E. coli and S. cerevisiae, and subsequent coculture of the two microbes successfully produced the oxygenated taxanes (Zhou et al., 2015). Also, E. coli was engineered with a reconstructed 50-kb-long biosynthetic gene cluster to produce the antibiotic erythromycin (Fang et al., 2018). Moreover, other diverse bulk chemicals and drugs are produced using such heterologous pathways construction including artemisinic acid (Paddon et al., 2013), 1,5-diaminopentane (Kind et al., 2014), adipic acid (Yu et al., 2014), and 3-hydroxypropionic acid (Borodina et al., 2015). It should also be noted that native enzymes can be evolved or even redesigned to change their substrate specificities, giving rise to more diverse metabolic reactions, including those not present in nature.

TABLE 3 Summary of main DNA assembly methods. Method

Description

Reference

Traditional (MCS)

Traditional vector for integration of a DNA sequence into a multiple cloning site by selecting from different restriction enzymes

Cohen et al. (1973)

BioBrick

Idempotent assembly method using isocaudomers. Simple iterative cloning allows for DNA assembly, but two DNA sequences only can be added together at the same time

Shetty et al. (2008)

Gateway

Circumvent restriction enzyme cloning system, where DNA assembly is constrained by five recombination sites maximum

Katzen (2007)

Gibson

Exonuclease-based one-step method for assembly of multiple overlapping DNA sequences in the correct order

Gibson et al. (2009)

Golden Gate

Effective, multiple DNA sequences assembly method using Type IIS restriction enzyme

Padgett and Sorge (1996)

Single-strand assembly (SSA)

One-step DNA assembly method for combinatorial pathway engineering using multiple overlapping single-stranded DNA sequences

Coussement et al. (2014)

Ligase cycling reaction (LCR)

One-step DNA assembly method using single-stranded bridging oligos complementary to the ends of DNA sequences. Multiple denaturation-annealingligation temperature cycles are used for assembly

de Kok et al. (2014)

Transformation associated recombination (TAR) cloning

A PCR-independent method for assembly and cloning of entire microbe genomes up to several Mb and engineering of large metabolic pathways

Kouprina and Larionov (2016)

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4.4 Optimization of metabolic fluxes Whenever the base strains capable of generating desired products with the reconstructed biosynthetic pathways are developed, the metabolic flux toward the target products needs to be maximized using different tools and strategies. Modern technical advances in omics strategies including high-throughput DNA/RNA sequencing and mass spectrometry allow further data related to cellular physiology and metabolism to be obtained, offering insights for optimization of the production strains. Metabolomics that specifically observe metabolite levels under different conditions can enhance our understanding about enzyme activities to be used for optimizing the strain performance, but the metabolite quantification at low concentrations remains a problem. Fluxomic analysis that provides the strongest overview of the cell metabolism over all omics studies has been typically used to establish industrial strains for different chemicals. Despite the remaining technical issues in precisely evaluating the flux values, the fluxomic analysis is expected to provide schematics to systems metabolic engineering. Different in silico modeling/simulation methods have been successfully applied to enhance various overproducer strains. A recent development in this area is the incorporation of transcriptome, proteome, and fluxome data into genome-scale metabolic models (GEMs) to gain a more detailed description of the cell metabolism such as metabolism and expression (ME) models that incorporate information extracted from the quantitative proteomics data to GEMs (Lloyd et al., 2018). A further modeling method called GECKO integrated with kinetic enzyme parameters offers a mathematical approach to computing the cellular conditions (Sanchez et al., 2017). In parallel, rapid developments in synthetic biology techniques and strategies further accelerate overproducer strain development. Although traditional genome engineering techniques such as recombineering systems are being efficiently used, the recent advent of CRISPR/Cas technologies has changed the engineering mindset. Promoter and ribosome binding site (RBS) libraries have been developed to locate the optimum expression levels of target genes to be precisely regulated for balancing metabolic fluxes toward the target product maximizing the formation of desired products. Moreover, integration of essential genes was employed for tunable and stable control over plasmid copy number, and transcription activator-like effector (TALE) coupled promoters were designed to maintain the expression level independent of gene copy numbers. Gene expression modulating tools including trans-acting element tools such as sRNA, RNAi, and CRISPRi or cis-acting element such as aptamer are used to downregulate the translation of target genes to fine-tune the expression level of target genes and could be easily and efficiently develop strains for enhancing chemical production. These tools coupled with metabolite biosensors are used to identify engineering targets by high-throughput screening (Yang et al., 2018; Rogers and Church, 2016). Moreover, substrate channeling is also an effective strategy to improve metabolic fluxes. The systems metabolic engineering strategies also include strain tolerance and scale-up of fermentation, which would be discussed later in this chapter.

5 The principles and tools for pathway prediction and design Many target chemicals cannot be produced through native metabolic pathways, so creative strategies and tools are required to construct synthetic pathways for efficient production of targeted chemicals. Computational tools based on genetic, genomic, and enzymatic information can help design the most efficient metabolic pathways. The use of effective computational tools can provide more knowledge and faster design to develop novel pathways. Over the last years, a range of computational pathway prediction tools has been generated to (re)design pathways through making knockouts or adding novel enzymes to change existing pathways or identifying possible metabolic pathways. The key tools for the prediction and design of biosynthetic pathways have been listed in Table 4. One user-friendly system for reconstruction of metabolic pathways and comparative analysis is From Metabolite to Metabolite (FMM). This is a freely available web service that finds possible pathways from a metabolite to a product of interest among different species, based mainly on KEGG database (Chou et al., 2009). A more advanced web server framework, Biochemical Network Integrated Computational Explorer (BNICE), identifies all possible pathways from a given set of enzyme reactions and rank them based on discriminative criteria. Also, it predicts novel pathways that are chemically possible. When searching for pathways, it can be adjusted for the length of the pathway and the range of reactions searched over by using enzyme reactions from one or multiple pathways (Hatzimanikatis et al., 2005). Another pathway identification system, DESHARKY, is a Monte Carlo–based metabolic pathway design algorithm based on enzymatic reactions database. The choice of host organism for the pathway is the first step of the pathway prediction after compound design. The algorithm searches for the pathway that connect the metabolic network of the organism most efficiently to generate a target compound (Rodrigo et al., 2008).

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TABLE 4 The key tools for the prediction and design of biosynthetic pathways. Tool

Description

Reference

From Metabolite to Metabolite (FMM)

A web service that finds possible pathways from one metabolite toward a product of interest, based mainly on KEGG database

Chou et al. (2009)

Biochemical Network Integrated Computational Explorer (BNICE)

A tool for identification of all possible pathways from a given set of enzyme reactions, ranking, and prediction of novel pathways

Hatzimanikatis et al. (2005)

DESHARKY

A Monte Carlo-based metabolic pathway design algorithm based on enzymatic reactions database

Rodrigo et al. (2008)

System of Cho et al.

A system for prediction and ranking of pathways based on various features

Cho et al. (2010)

RetroPath

An automated pipeline for retrosynthetic pathway designing, ranking, prediction, and metabolic modeling

Carbonell et al. (2014)

OptStrain

A tool for determination of the minimal pathway modification required to maximize production yield

Pharkya et al. (2004)

Cho et al. have constructed a unified system that is able to predict and rank pathways. The system first predicts a wide range of possible pathways and then prioritizes the pathways based on binding site covalence, chemical similarity, thermodynamic favorability, pathway distance, and organism specificity to synthesize a user-specified product (Cho et al., 2010). A recently published open source, RetroPath2.0, an automated workflow for retrosynthetic pathway designing, has several additional features for ranking, prediction of enzymes activity, prediction of the compatibility with host genes, and metabolic modeling (Delepine et al., 2018). Another approach, OptStrain, aims to determine the minimal pathway modification required to maximize production yield. It also predicts the effect of nonnative reactions that would increase the maximum production capabilities of the host organism by using a stoichiometric model of the host metabolic network (Pharkya et al., 2004).

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The pathways constructed using rational and computational strategies

6.1 De novo pathway design The reconstruction of synthetic pathways for natural-noninherent and nonnatural-noninherent chemicals begins with the design of optimal pathways that lead to their product. Then the best candidate enzymes from different organisms or metagenomes can be introduced to establish a new metabolic pathway. Gap filling with heterologous enzymes between disconnected metabolic reactions to establish a continuous pathway can be used to develop strains for the production of various chemicals. The fatty acid ethyl ester (FAEE) is a good example of a natural-noninherent chemical generated through metabolic engineering of E. coli by combining genes from different species. The wax ester synthase from Acinetobacter baylyi and thioesterases from plants were introduced to produce free fatty acid, while pyruvate decarboxylase and alcohol dehydrogenase from an ethanol producer were introduce to produce ethanol. The integration of these two systems together into E. coli produced FAEE from a renewable resource (Steen et al., 2010). One more example is the production of isoprene by introducing isoprene synthase gene (ispS) from Populus nigra and Pueraria montana into E. coli and Synechocystis sp. PCC6803, respectively (Zhao et al., 2011; Lindberg et al., 2010). Recently, a new example has been reported for butanol production in E. coli, where thioesterase gene (tesBT) from Bacteroides thetaiotaomicron has been expressed for producing butyrate via fatty acid synthesis (FASII) pathway, and carboxylic acid reductase gene (car) from Mycobacterium marinum, phosphopantetheinyl transferase gene (sfp) from Bacillus subtilis and alcohol dehydrogenase gene (adh2) from S. cerevisiae have been expressed for conversion of butyrate to butanol in E. coli ( Jawed et al., 2020). Additionally, different natural alcohols could be generated from E. coli by introducing 2-ketoacid decarboxylase from Lactococcus lactis and alcohol dehydrogenase from S. cerevisiae, which convert 2-ketoacids into alcohols (Atsumi et al., 2008). The biological production of styrene is also a good example for a nonnatural-noninherent chemical production by establishing a synthetic pathway composed of phenylalanine ammonia lyase from Arabidopsis thaliana and cinnamate decarboxylase from S. cerevisiae in E. coli (McKenna and Nielsen, 2011).

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6.2 In silico pathway prediction Various pathway prediction tools have been developed for the accurate prediction of synthetic pathways through examination of all possible routes to a target chemical when obvious gap-filling strategies are not available. It is possible to design multistep synthetic pathways for the biosynthesis of nonnatural chemicals using these tools. Prediction methods can be classified into the following: (a) chemical structure–based methods, which are used to reconstruct multistep metabolic pathways on the basis of changes in chemical structures from the substrate to the product, for example, the design of E. coli for producing 1,4-butanediol (Yim et al., 2011); (b) knowledge-based methods, which are used to predict metabolic pathways on the basis of experimentally identified information about reactions and pathways deposited in several different databases, for example, the reconstruction of a synthetic pathway for 3-hydroxypropanoate production from pyruvate in E. coli using BNICE framework (Henry et al., 2010); and (c) design of optimal metabolic pathways by deleting existing reactions and inserting a minimal number of nonnative reactions using OptStrain, for example, strain construction for production of hydrogen and vanillin (Pharkya et al., 2004).

6.3 Enzyme engineering and creation for synthetic pathways The approach of development of new enzymes with desirable functions is considered when enzymes involved in the pathway leading to a nonnatural chemical are not known. Modifying the substrate specificities of the most feasible enzymes through mutagenesis and directed evolution is a popular approach for developing new enzymes. For example, an E. coli strain has been engineered to produce a nonnatural biodegradable polymer, polylactic acid, using this approach ( Jung et al., 2010). Moreover, various new enzymes catalyzing nonnatural reactions, such as Kemp elimination, retro-aldol reaction, and the Diels-Alder reaction, were synthetically designed using this approach (Rothlisberger et al., 2008; Siegel et al., 2010; Jiang et al., 2008). A design protocol, Rosetta de novo, has been developed that simplifies the design process and effectively overcomes problems with the use of computational design tools to create new catalytic functions, making it a reliable means for the biological production of nonnatural chemicals (Richter et al., 2011).

7 Metabolic flux analysis Metabolic flux analysis (MFA) is an effective method for determining metabolic fluxes in vivo using a stoichiometric model for the key intracellular reactions and mass balancing of intracellular metabolites. Metabolic fluxes are the significant driving force in cell physiology, as they determine the proportion of various pathways in all cellular functions and metabolic processes. Accordingly, accurate quantification of the metabolic fluxes is essential for metabolic engineering, especially metabolite development, where the main objective is to convert the maximum amount of substrate into useful products. Metabolic flux analysis can provide further information about other essential cell physiological features, including (i) identification of branch point control in metabolic pathways (Stephanopoulos and Vallino, 1991), (ii) identification of alternative pathways (Aiba and Matsuoka, 1979), (iii) calculation of nonmeasured extracellular fluxes (Llaneras and Pico, 2008), and (v) calculation of maximum theoretical product yields (Shastri and Morgan, 2004). The systematic analysis of metabolic fluxes takes place in three stages (Stephanopoulos et al., 1998): Stage 1: Developing methods for monitoring metabolic pathways and measure their fluxes. Both radiolabeling and isotopic labeling are the popular methods for illustrating metabolic fluxes. Stage 2: Introducing well-defined perturbations to the bioreaction network and determining the pathway flux. The targeted change of enzymatic activities involved in a metabolic pathway determines the flexibility of metabolic nodes. Stage 3: Analysis of flux perturbation results. The effect of perturbation will determine the biochemical reactions that essentially determine the metabolic flux within the metabolic network. Metabolic flux analysis is an important analytical technique that has many advantages in determining cellular metabolic pathway fluxes; it accurately models cellular metabolism based on the biochemistry only, without detailed information on enzyme kinetic parameters. It can create and analyze genome-scale metabolic models, including all cellular reactions. It can model effects of enzyme deletions and synthetic pathways, and it estimates fluxes through all intracellular reactions based on extracellular measurements. It can also determine fluxes through pathways that are difficult to be measured and identifies targets to channel substrates through desired products.

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8

Enhancing tolerance against products and inhibitors

The industrial strains should be tolerant to target both product accumulation and various inhibitors present in the feedstock. However, inhibitor tolerance is very complex phenotype to engineer as a large number of difficult-to-predict genes are often involved. Host strain tolerance can be rationally improved if the molecular mechanisms of toxicity are known. For example, tolerance could be improved by (a) reducing the entry of toxic substance into the cells, such as regulating ionic membrane gradients of S. cerevisiae for ethanol (Lam et al., 2014); (b) preventing toxic conversion and incorporation into cell biomass, such as alteration of membrane lipid composition by direct incorporation of medium-chain fatty acids into lipids in E. coli (Sherkhanov et al., 2014); and (c) active export of toxic compounds to extracellular spaces, enhancing both productivity and product tolerance, such as the use of efflux pumps for biofuel in E. coli (Fisher et al., 2014). If the toxicity mechanism has yet to be identified, adaptive laboratory evolution (ALE) would be a useful technique for isolating strains resistant to the target compound followed by genome sequencing and systemic analysis of evolved strains, which will expose molecular resistance mechanisms and allow the host strains to be further improved by more rational engineering (Fig. 3). Bioprocess engineering strategies can also overcome the toxicity of the target chemicals by coupling in situ recovery of the chemicals with toxic effects with the fermentation. The selected producer cells from rational engineering or serial subculture do not automatically overproduce the product. This is because the highly enhanced product tolerance is not inherently related to the ability to synthesize product with improved efficiency and yield.

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Scale up and industrial production

During scale-up of processes on a pilot or demonstration scale and ultimately to full industrial-scale production for commercialization, the developed overproducing strain that performs well at the laboratory scale might exhibit less performances. Some of the reasons of this deviation relates to certain changes during fermentation process, such as oxygen transfer profiles in aerobic fermentations and local substrate concentrations due to irregular mixing. These changes can be expected during scale-up and need to be considered at the design of systems metabolic engineering project to develop strains resistant to such changes. Moreover, systems metabolic engineering helps overcome unexpected issues that occur during the scale-up by further iteration of the strain to the systems metabolic engineering cycles to improve the strain performance, which further enhance strain performances based on the feedback obtained from bioprocess operation to ensure the successful establishment of industrial-scale production. While the use of antibiotics is discouraged in fermentation industries, a serious issue of contaminating microorganisms needs much attention. Recent innovative strategy to overcome this problem has been developed through incorporating

FIG. 3 Adaptive laboratory evolution (ALE) strategy.

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xenobiotic nutrient utilization pathways into the production hosts to utilize xenobiotic nutrient compounds (e.g., melamine) as the nitrogen source instead of traditional compounds that are commonly utilized also by contaminating organisms (Lennen, 2016). Another significant concern that must be addressed to develop an engineered industrial production strain is the biocontainment of this strain to secure it and to avoid unwanted environmental spread. Most of these biocontainment systems are investigating the auxotrophic strains, but recent developments in synthetic biology have constructed synthetic biological circuits that enable cell growth only when a specific nutrient combination has been achieved (Chan et al., 2016). These tools and strategies are expected to further extend the existing profile of outstanding overproduction strains and bioprocesses successfully converted into the industry.

10 Conclusion and future perspectives In this chapter, the advancements made in metabolic engineering to achieve industrial-scale microbial cell factories for chemicals, materials, and fuels production were briefly discussed. Metabolic engineering has been accelerated with the help of advanced genetic and computational tools in systems biology and synthetic biology. Various strategies for the successful development of microbial cell factories through systems metabolic engineering are being implemented to enable the engineered strains to achieve high-level production of various metabolites. Computational tools have improved the design of novel biosynthetic pathways for microbial cell factories to produce desired product from renewable resources allowing us to move toward a bio-based economy. Nevertheless, tremendous efforts are still needed in various frontiers of metabolic engineering if the larger goal of replacing chemically synthesized molecules with environmental-friendly biologically synthesized molecules is to be realized. Further innovations are needed in this area to (1) develop microbial strains that can efficiently utilize different carbon sources, specially C1 carbon; (2) reduce the needed time for optimizing strains; (3) perform the fermentation process without the use of antibiotics, for example, by incorporating xenobiotic nutrient utilization pathways into the host or by using marker-free strains; (4) simplify downstream chemical processing; and (5) maximize the production of high value, nonnatural, or new chemicals that have not yet been found in nature.

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Yang, D., Kim, W.J., Yoo, S.M., Choi, J.H., Ha, S.H., Lee, M.H., Lee, S.Y., 2018. Repurposing type III polyketide synthase as a malonyl-CoA biosensor for metabolic engineering in bacteria. Proc. Natl. Acad. Sci. U. S. A. 115, 9835–9844. Yim, H., Haselbeck, R., Niu, W., Pujol-Baxley, C., Burgard, A., Boldt, J., Khandurina, J., Trawick, J.D., Osterhout, R.E., Stephen, R., Estadilla, J., Teisan, S., Schreyer, H.B., Andrae, S., Yang, T.H., Lee, S.Y., Burk, M.J., Van Dien, S., 2011. Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol. Nat. Chem. Biol. 7, 445–452. Yu, H., Liao, J.C., 2018. A modified serine cycle in Escherichia coli coverts methanol and CO2 to two-carbon compounds. Nat. Commun. 9, 3992. Yu, J.L., Xia, X.X., Zhong, J.J., Qian, Z.G., 2014. Direct biosynthesis of adipic acid from a synthetic pathway in recombinant Escherichia coli. Biotechnol. Bioeng. 111, 2580–2586. Zeldes, B.M., Keller, M.W., Loder, A.J., Straub, C.T., Adams, M.W., Kelly, R.M., 2015. Extremely thermophilic microorganisms as metabolic engineering platforms for production of fuels and industrial chemicals. Front. Microbiol. 6, 1209. Zhang, Z., Shen, T., Rui, B., Zhou, W., Zhou, X., Shang, C., Xin, C., Liu, X., Li, G., Jiang, J., Li, C., Li, R., Han, M., You, S., Yu, G., Yi, Y., Wen, H., Liu, Z., Xie, X., 2015. CeCaFDB: a curated database for the documentation, visualization and comparative analysis of central carbon metabolic flux distributions explored by 13C-fluxomics. Nucleic Acids Res. 43, D549–D557. Zhang, X., Tervo, C.J., Reed, J.L., 2016. Metabolic assessment of E. coli as a biofactory for commercial products. Metab. Eng. 35, 64–74. Zhao, Y., Yang, J., Qin, B., Li, Y., Sun, Y., Su, S., Xian, M., 2011. Biosynthesis of isoprene in Escherichia coli via methylerythritol phosphate (MEP) pathway. Appl. Microbiol. Biotechnol. 90, 1915–1922. Zhou, K., Qiao, K., Edgar, S., Stephanopoulos, G., 2015. Distributing a metabolic pathway among a microbial consortium enhances production of natural products. Nat. Biotechnol. 33, 377–383. Zhou, Y., Lin, L., Wang, H., Zhang, Z., Zhou, J., Jiao, N., 2020. Development of a CRISPR/Cas9n-based tool for metabolic engineering of Pseudomonas putida for ferulic acid-to-polyhydroxyalkanoate bioconversion. Commun. Biol. 3, 98.

Chapter 7

CRISPR-based tools for microbial cell factories Rongming Liua,∗, Liya Lianga, Sean Stettnera, Emily F. Freeda, and Carrie A. Eckerta,b a

Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, Boulder, CO, United States b National Renewable Energy

Laboratory (NREL), Golden, CO, United States ∗

Corresponding author: E-mail: [email protected]

1 Introduction Microorganisms have provided a useful platform for the production of various biochemicals and biofuels. To efficiently engineer these organisms, we must have methods to manipulate genes to alter their innate metabolism and improve the production of these compounds. Over the last 20 years, more precise and efficient gene modification tools have been developed for a growing number of organisms (Bassalo et al., 2016b; Dai et al., 2015; Jang et al., 2012). Moreover, systems biology tools (e.g., genomics, transcriptomics, proteomics, metabolomics, and fluxomics) have aided in developing a systems-level understanding of the regulatory machinery of model organisms, which has, in turn, further expanded our ability to engineer microbial cell factories toward target bioproducts or traits (Adamczyk and Reed, 2017; McCloskey et al., 2018). However, deeper reprogramming of cellular metabolism, due to the complexity of the native/introduced metabolic pathway(s) required for biobased production, requires refined expression of pathway genes to balance metabolic flux for cell growth and production, regulation of cofactor and energy metabolism, etc. (Bassalo et al., 2016b; Engstrom and Pfleger, 2017; Liu et al., 2015). Furthermore, engineering complex phenotypes is limited by lack of knowledge of the precise genetic basis of targeted traits such as tolerance to toxic intermediate metabolites or products, and resistance to extreme pH or temperature (Deparis et al., 2017; Long and Antoniewicz, 2018; Nielsen and Keasling, 2016). One way to overcome this limitation is to use random mutagenesis and directed evolution strategies, but these approaches need extended genome-scale sequencing and further analysis for verification of identified mutations. In addition, the genetic space that can be engineered on laboratory timescales is often much smaller than what is needed to fully explore and optimize these complex phenotypes. As such, it is often a time-, budget-, and labor-intensive process to design, build, and test all the elements that may be required to improve strain performance. Therefore strategies are needed to accelerate genetic modification, increase the number of targeted loci in a single experiment, and enhance the efficiency and accuracy of gene editing or regulation. Clustered regularly interspaced short palindromic repeats (CRISPR) and its associated proteins (Cas) are a microbial adaptive immune system discovered from bacteria used for defense against mobile genetic elements (MGEs) (Barrangou et al., 2007; Brouns et al., 2008). The mechanism of CRISPR-Cas-mediated defense can be divided into three stages: adaptation, expression, and interference (Fig. 1). In the adaptation (first) stage, a Cas protein identifies exogenous DNA and incorporates a DNA spacer (20–72 nt that matches the exogenous DNA sequence) into the host CRISPR array. The protospacer adjacent motif (PAM) is a 2- to 5-nt conserved sequence following the DNA sequence targeted by the Cas protein, used for determining targets for the CRISPR-Cas system (Mojica et al., 2009; Paul and Montoya, 2020). Spacers derived from the foreign DNA fragments are integrated into a chromosomal CRISPR locus composed of an array of short direct repeats, and these repeat-spacer-repeat sequences are directly downstream of the AT-rich leader site of a CRISPR locus (Dyda and Hickman, 2015; Swarts et al., 2012). In the expression (second) stage, the CRISPR array is transcribed into a precursor-CRISPR RNA (pre-crRNA), and then the pre-crRNA is processed into smaller units of RNA, called CRISPR RNAs (crRNAs). Each crRNA consists of a single spacer and a repeat sequence. The crRNAs then form a ribonucleoprotein complex with Cas proteins. During the interference (third) stage, the complex binds to invader sequences (protospacers with the correct PAM) that are recognized by base-pairing with complementary crRNA sequences. After binding the

Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00001-5 © 2021 Elsevier Inc. All rights reserved.

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FIG. 1 The CRISPR-Cas system is a microbial adaptive immune system. The three stages of CRISPR-Cas adaptive immunity are adaptation (steps 1–2), expression (steps 3–6), and interference (steps 7–9). In the adaptation stage the Cas protein identifies the injected DNA (step 1) and acquires a spacer into the CRISPR array (step 2). In the expression stage the CRISPR array is transcribed into a pre-crRNA (step 3), and then the pre-crRNA is processed into crRNAs (step 4). Next the crRNAs and Cas protein form a ribonucleoprotein complex (step 5). In the interference stage the complex recognizes (steps 7–8) and cleaves the targeted sequence (step 9).

Cas protein will cleave the targeted sequence upstream of the PAM, resulting in degradation of the foreign DNA. CRISPRCas systems have been divided into two major classes, class 1 and class 2. Class 1 CRISPR-Cas systems use multiple Cas proteins to form a functional complex for binding and degrading the targeted sequence. Class 2 CRISPR-Cas systems use a single, larger Cas protein (with multiple domains) to serve the same functions as class 1. The first demonstration using a CRISPR-Cas system for bacterial genome editing was reported in 2013, and this system was developed based on Streptococcus pyogenes Cas9 (SpCas9; class 2) ( Jiang et al., 2013). Since this initial report the CRISPR-Cas systems have become very popular for gene editing and regulation in many organisms due to its versatility and efficacy (Donohoue et al., 2018; Esvelt et al., 2013; Jiang and Marraffini, 2015; Koonin et al., 2017; Sander and Joung, 2014; Shalem et al., 2015; Shmakov et al., 2017; Zhang et al., 2017a). Moreover, CRISPR-based tools have been widely applied in both model and nonmodel organisms, especially in organisms lacking selectable markers, for single and multiplexed genetic manipulations (DiCarlo et al., 2013b; Dickinson et al., 2013; Kistler et al., 2015; Lee et al., 2015; Liu et al., 2018a, 2019; Sugano et al., 2014). Here, we describe strategies based on the CRISPR-Cas system for engineering microbial cell factories (e.g., Saccharomyces cerevisiae (S. cerevisiae) (baker’s yeast) and Escherichia coli (E. coli)). We will focus on methods for gene editing and regulation at the single gene level, as well as multiplex strategies for genome editing and global transcriptional regulation at the genome level. Finally, we provide a perspective on the challenges and opportunities for the use of advanced CRISPR-based tools to engineer microbial cell factories.

2

CRISPR-Cas editing at the single gene level

The development of CRISPR-Cas genome editing tools has enhanced our ability to engineer microbial cell factories. To engineer a microbe for optimal production of a desired product, the strain often needs to have heterologous enzyme(s) expressed, native competing pathway(s) deleted, redox reactions balanced, and expression of product pathway enzymes be tailored to the correct expression level, which is often accomplished by modification of promoter and/or ribosome binding site sequences or by modifying the gene copy number. CRISPR-Cas systems allow all of these modifications to be made rapidly and relatively easily by enabling gene/pathway knock-ins, gene knockouts, and the introduction of single nucleotide polymorphisms (SNPs). In this section, we focus on CRISPR-Cas editing at the gene or pathway level.

2.1 Gene editing with CRISPR-Cas Gene editing with CRISPR systems is most commonly done using either class 2, type II Cas9 nuclease, or class 2, type V Cas12a (formerly called Cpf1) nuclease (Fig. 2). In both CRISPR systems the nuclease is directed to a specific locus in the genome by the use of a short spacer sequence. The most common Cas9 nuclease, SpCas9, recognizes a 20-nt spacer and has a strong preference for an NGG PAM sequence, although NAG PAMs are also recognized with lower efficiency ( Jiang et al., 2013; Jinek et al., 2012). In order for Cas9 to bind and cleave its genomic target, both a CRISPR RNA (crRNA) and a

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FIG. 2 Cas9- and Cas12a-based genome editing in bacteria. Genome editing with (left) SpCas9 and (right) FnCas12a/Cpf1. With both systems the genome is edited by homology-directed repair using a donor template. The homology-directed repair template also contains a PAM mutation, which prevents edited cells from being targeted by the CRISPR-Cas system. The CRISPR-Cas system is primarily used to select against unedited cells. SpCas9 recognizes an NGG PAM, while FnCas12 recognizes a NTTN PAM. The nuclease is targeted to the correct spot in the genome by a 20-nt (SpCas9) or 24-nt (FnCas12a) spacer that matches a protospacer sequence in the genome. The SpCas9 gRNA contains a unique spacer sequence and constant crRNA and tracrRNA sequences. The FnCas12a gRNA contains a unique spacer sequence and a (different) constant crRNA sequence. SpCas9 makes a blunt cut, while FnCas12a makes a staggered cut. In both cases the induced DNA double-strand break leads to the death of cells with unedited genomes.

transactivating crRNA (tracrRNA) are required. The tracrRNA activates pre-crRNA processing by RNase III and is also required for Cas9 cleavage. The crRNA and tracrRNA can either be expressed separately, as they are in the native system, or can be combined into a single RNA where they are fused by a hairpin sequence ( Jinek et al., 2012). When expressed together, the spacer sequence, crRNA, and tracrRNA are called a single guide RNA or guide RNA (sgRNA or gRNA). The most common Cas12a nuclease for editing microbial genomes is from Francisella novicida (FnCas12a) ( Jiang et al., 2017; Sun et al., 2018; Ungerer and Pakrasi, 2016). FnCas12a recognizes a 23- to 25-nt spacer and an NTTN PAM (Leenay et al., 2016; Zetsche et al., 2015). Unlike Cas9, Cas12a is able to process its own pre-crRNA (Fonfara et al., 2016) and therefore only requires a crRNA, and not a tracrRNA, for binding and cleavage (Zetsche et al., 2015). The spacer sequence combined with the crRNA sequence is referred to as the gRNA. Once a Cas9/gRNA complex or Cas12a/gRNA complex has bound its target DNA sequence, the nuclease will create a DNA double-strand break (DSB). Cas9 creates a blunt cut 3 nt distal to the PAM (Gasiunas et al., 2012), while Cas12a makes a staggered cut 18 nt and 23 nt distal to the PAM (Zetsche et al., 2015). Three components are required to edit a genome using either CRISPR-Cas9 or CRISPR-Cas12a: (1) a nuclease (Cas9 or Cas12a), (2) a gRNA with a spacer sequence targeting the desired locus in the genome, and (3) a homology-directed repair (HDR) donor template containing the desired mutation(s) and a PAM mutation to prevent further cutting by the nuclease. The components can all be expressed separately, in pairs, or in a single plasmid/linear DNA fragment (DiCarlo et al., 2013b; Liu et al., 2019). The genome is edited through homologous recombination and the CRISPR-Cas system functions as a markerless selection against unedited cells (Horwitz et al., 2015; Jiang et al., 2013; Yan et al., 2017). In bacteria the primary role of the CRISPR-Cas system is to select against unedited cells, but CRISPR-Cas9 has been shown to also cause a slight increase in recombination efficiency (Choudhury et al., 2020; Jiang et al., 2013). In S. cerevisiae, on the other hand, the DSB that is created by a Cas protein can increase the rate of homologous recombination by up to 130-fold (DiCarlo et al., 2013b). The HDR donor template can be used for the scarless introduction of SNPs, gene insertions, and/or gene deletions at the desired locus. Using a CRISPR-Cas system, it is even possible to insert an entire heterologous pathway in a specific chromosomal locus in a single reaction, as was done when our group inserted the five-gene (10-kb) isobutanol production pathway into E. coli (Bassalo et al., 2016a) and when EauClaire et. al. assembled and integrated the five-gene (11.8-kb) b-carotene production pathway into S. cerevisiae (EauClaire et al., 2016). CRISPR-Cas systems can also be used to make more significant genome modifications. For example, the REXER method (replicon excision for enhanced genome

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engineering through programmed recombination) uses CRISPR-Cas9 to enable genome insertions of >100 kb and was used to iteratively replace the entire E. coli genome with a recoded synthetic genome containing more than 18,000 targeted mutations (Fredens et al., 2019; Wang et al., 2016). CRISPR-Cas systems combined with HDR generally have high editing efficiencies of 40%–100% in microbial cells (Bao et al., 2018; Bassalo et al., 2016a; DiCarlo et al., 2013b; Jiang et al., 2013; Yan et al., 2017). However, editing efficiency is dependent on a number of factors including gRNA design (Choudhury et al., 2020; Ryan et al., 2014), gRNA expression level (Horwitz et al., 2015), Cas nuclease expression level (Choudhury et al., 2020), position of the targeted locus in the genome (Bassalo et al., 2016a; Choudhury et al., 2020), and length of the homology arms in the HDR donor template (Bassalo et al., 2016a; Li et al., 2018b). While the gRNA expression level, Cas nuclease expression level, and length of the homology arms all generally need to be optimized experimentally for each new microbe, several tools exist to help design highly functional gRNAs (reviewed in Abdelaal and Yazdani, 2020). However, despite these tools, the design rules for generating gRNAs that will result in high editing efficiency are not fully understood (Choudhury et al., 2020). Additionally, some gRNAs have off-target activity and result in cutting of the genome in nontargeted locations (Choudhury et al., 2020; Tsai et al., 2015). It is therefore best to design and test multiple gRNAs, when possible, to achieve the highest editing efficiencies. It is also possible to do more limited genome editing using CRISPR-Cas systems without the need for an HDR donor template. Recently, several groups have developed CRISPR-Cas9-guided base editing systems in bacteria (Li et al., 2019b; Wang et al., 2018; Zheng et al., 2018). In these systems, either a cytidine deaminase or a uracil DNA glycosylase inhibitor (UGI) is fused to a Cas9 nickase to facilitate C to G or T to A substitutions, respectively. A gRNA is used to target the cytidine deaminase or UGI to a specific location in the genome. C or T bases within a
15 to
21 window from the PAM can be edited. This method is used for gene inactivation by the introduction of a premature stop codon and can reach editing efficiencies of 100%. CRISPR-Cas9-guided base editing is a facile method for inactivating genes in industrially relevant bacterial species that have very low rates of HDR and therefore cannot be edited using more traditional CRISPR-Cas genome editing methods.

2.2 CRISPR-Cas editing of single genes in bacteria CRISPR-based genome editing in bacteria was first developed using Cas9 ( Jiang et al., 2013) but later was also developed using Cas12a (Ungerer and Pakrasi, 2016). In some species of bacteria, Cas9 is toxic, but the Cas9 nickase (Cas9n) (Xu et al., 2015) or Cas12a is tolerated ( Jiang et al., 2017; Li et al., 2018b; Ungerer and Pakrasi, 2016). As mentioned earlier, for both Cas9- and Cas12a-based genome editing in bacteria, the desired edit is introduced by providing a homology-directed repair template that also contains a PAM mutation. The CRISPR system is used as a markerless selection against unedited cells. For bacterial species that have naturally low rates of homologous recombination, higher editing efficiencies are achieved if phage-derived recombinases are expressed in the cells either prior to, or simultaneously with, the CRISPRCas system ( Jiang et al., 2013; Walker et al., 2020; Zhou et al., 2020). Due to the highly toxic nature of DNA doublestrand breaks in bacteria (only a limited number of bacterial genera contain the nonhomologous end joining repair pathway; Shuman and Glickman, 2007), editing efficiencies with Cas9 or Cas12a are usually extremely high, generally ranging from 60% to 100% ( Jiang et al., 2013; Yan et al., 2017). Similar editing efficiencies can also be achieved using Cas9n (Li et al., 2018; Xu et al., 2015). The development and optimization of CRISPR-Cas editing systems in a wide variety of bacterial species have enabled rapid and efficient genome modification without the need for selective markers such as antibiotics. Therefore CRISPR-Cas systems have been used for the metabolic engineering of bacterial cells for pharmaceutical compounds, nutraceutical compounds, biofuels, and biopolymers. In a recent example, Gu et al. engineered E. coli to produce 5-methylpyrazine-2carboxylic acid (MPCA), an intermediate required for the production of lipid-lowering and hypoglycemic drugs. They used Cas9 to integrate a four-gene pathway (single plasmid) into the E. coli chromosome (Gu et al., 2020). They further optimized the strain by integrating additional copies of two of the genes in the pathway at different copy numbers. In another recent example, Wang et al. engineered Corynebacterium glutamicum (C. glutamicum) for increased production of glutamate, the essential amino acid and food additive. They used Cas9n fused to an activation-induced cytidine deaminase (base editor) to introduce early stop codons in three genes (Wang et al., 2018). They found that when the pyk and ldhA genes were both inactivated, glutamate production was the highest. Several other recent examples of using CRISPRCas systems to engineer bacterial cell factories are shown in Table 1.

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TABLE 1 Recent examples of bacteria that have been engineered by CRISPR-Cas systems for the production of industrially relevant compounds. Final titer (yield improvement)

Reference

Pathway insertion

5.4 g/L (NA)

Abdelaal et al. (2019)

Cas9

Pathway insertion

15.6 g/L (NA)

Gu et al. (2020)

Escherichia coli

Cas9

Gene deletion

22.3 g/L (1.2-fold)

An et al. (2020)

Nattokinase

Bacillus licheniformis

Cas9n

Heterologous gene insertion; gene deletion

59.7 FU/mL (NA)

Li et al. (2018)

Lactate

Zymomonas mobilis

Cas12a

Heterologous gene insertion

2.1 g/L 2000-fold

Shen et al. (2019)

Glutamate

Corynebacterium glutamicum

Cas9n (D10A)

Base editing

4.2 g/L (3.0-fold)

Wang et al. (2018)

Product

Microorganism

Nuclease

Type of CRISPR editing

n-Butanol

Escherichia coli

Cas9

MPCA

Escherichia coli

4-Hydroxyisoleucine

2.3 CRISPR-Cas editing of single genes in yeast CRISPR-based editing in most yeast species uses Cas9 as the nuclease (DiCarlo et al., 2013b; Jacobs et al., 2014; Min et al., 2016; Sun et al., 2020; Weninger et al., 2016), although the industrially relevant yeast Yarrowia lipolytica (Y. lipolytica) has had both Cas9- and Cas12a-based methods developed (Schwartz et al., 2016; Yang et al., 2020). As with bacterial systems the genome edit is introduced into the cells by an HDR template that also contains a PAM mutation. Unlike in bacteria the CRISPR-Cas system both significantly stimulates HDR and acts as a markerless selection against unedited cells in yeast (DiCarlo et al., 2013b; Horwitz et al., 2015). Many yeast species naturally have high rates of HDR, and therefore high editing efficiencies of 40%–100% can be achieved using CRISPR-Cas systems. If the editing efficiency is low, higher editing efficiencies may be achieved if the native nonhomologous end joining pathway is deleted (Schwartz et al., 2016). Yeast, like bacteria, are also used as cellular factories for the production of pharmaceutical compounds, nutraceutical compounds, biofuels, and biopolymers. CRISPR-Cas9 genome editing has facilitated increased production of these important compounds. In a recent example, Sun et al. engineered the low pH-tolerant yeast strain, Pichia kudriavzevii (P. kudriavzevii), to produce itaconic acid, a monomer that can be copolymerized for many industrial uses including adhesives, plastics, fibers, thickeners, and coatings (Sun et al., 2020). They developed a CRISPR-Cas9 system and then used it to integrate a heterologous gene that encodes the final enzyme required for the production of itaconic acid, as well as to overexpress a native mitochondrial tricarboxylate transporter. Next, they further increased production by using CRISPR-Cas9 to delete both copies of the ICD gene (P. kudriavzevii is diploid) to block flux to a competing pathway. In another recent example, Mertens et al. (2019) used a CRISPR-Cas9 system to introduce SNPs to inactivate genes involved in the production of phenolic off-flavors in lager beer yeasts, which resulted in beer with a more favorable aromatic profile. More recent examples of using CRISPR-Cas9 to engineer yeast cell factories are shown in Table 2.

3 CRISPR-Cas editing at the genome level Genome editing tools have been expanded by the development of CRISPR-Cas systems. Due to the complex nature of engineering a microbial cell factory for enhanced production of a compound, it is often necessary to edit multiple genes. Therefore a group of CRISPR-based multiplex genome editing tools have been developed (Table 3). Furthermore the development of curing strategies for editing (gRNA) plasmids has facilitated an iterative CRISPR editing workflow, which introduces combinatorial mutations in cells at the genome level. In the previous section, we described CRISPR-based systems for editing at the single-gene level. In this section we focus on the CRISPR editing tools for engineering microbial cell factories at the genome level.

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Microbial cell factories engineering for production of biomolecules

TABLE 2 Recent examples of yeast that have been engineered by CRISPR-Cas systems for the production of industrially relevant compounds Final titer (yield improvement)

Reference

Gene deletion

860 mg/L (2.6-fold)a

Schadeweg and Boles (2016)

Cas9

Gene deletion

1.54 mg/L (41-fold)

Jako
ci unas et al. (2015)

Saccharomyces cerevisiae

Cas9

Pathway insertion

0.23 mg/L (NA)

EauClaire et al. (2016)

Muconic acid

Saccharomyces cerevisiae

Cas9

SNP

20.8 g/L (2.5-fold)a

Wang et al. (2020)

Aromatic profile

Saccharomyces cerevisiae S. eubayanus

Cas9

SNP

NA

Mertens et al. (2019)

Isopentanol

Pichia pastoris

Cas9

Gene deletion

191 mg/L (1.9-fold)a

Siripong et al. (2020)

Ethyl acetate (anaerobic)

Kluyveromyces marxianus

Cas9

Gene inactivation by indel formation

5.46 mg/L/OD (2.9-fold)

L€ obs et al. (2017)

Itaconic acid

Pichia kudriavzevii

Cas9

Heterologous gene insertion; G¼ gene deletion

1.2 g/L (3.7-fold)a

Sun et al. (2020)

Product

Microorganism

Nuclease

Type of CRISPR editing

n-Butanol

Saccharomyces cerevisiae

Cas9

Mevalonate

Saccharomyces cerevisiae

b-Carotene

a Strain contains other edits not introduced by CRISPR-Cas technologies. Yield improvement shows the fold improvement resulting from the CRISPR-Cas editing step only.

3.1 CRISPR-optimized MAGE recombineering The Lambda Red recombineering method is widely used in E. coli for making gene deletions, introducing mutations, or integrating genes or larger DNA segments (e.g., exogenous metabolic pathways) (Mosberg et al., 2010; Murphy and Campellone, 2003). Based on this strategy, Wang et al. developed an automated and iterative editing method named multiplex automated genome engineering (MAGE), which targeted many locations in the chromosome of E. coli for the highthroughput modification of metabolic pathways (Wang et al., 2009). Moreover, DiCarlo et al. adapted the MAGE strategy for yeast and named it yeast oligo–mediated genome engineering (YOGE) (DiCarlo et al., 2013a). After the development of multiplex gene editing in E. coli using Lambda Red recombineering combined with a CRISPR-Cas system ( Jiang et al., 2015), Ronda et al. developed CRISPR-optimized MAGE recombineering (CRMAGE) (Ronda et al., 2016). This method enables the iterative, multiplexed, and automated introduction of genetic modifications at a higher frequency than the previous MAGE method. CRMAGE combines the CRISPR-Cas9 system, Lambda Red recombineering, and a self-curing gRNA plasmid for multiplex genome editing. The editing efficiency reached 98% for two simultaneous edits in one round of CRMAGE.

3.2 CRISPR-EnAbled Trackable genome Engineering (CREATE) Our group developed a CRISPR-enabled trackable genome engineering (CREATE) method (Fig. 3) that combines Lambda Red recombineering using a plasmid-based donor template and CRISPR-Cas9 using an arabinose-inducible system to decrease Cas9 toxicity to cells (Garst et al., 2017). The CREATE editing cassette is 200 bp, depending on the specific design, and is amplified from oligo arrays. It consists of a homology arm with targeted mutations as a donor template for recombineering and a partial gRNA sequence (including the spacer) for CRISPR-Cas9 cleavage of targeted sites. Furthermore the editing cassettes can be used as barcodes for tracking the mutation composition in a population of edited cells due to their unique design and short length. With this method, we have demonstrated genome editing at tens to hundreds of thousands of loci in parallel with trackable mapping of mutations, which generates genotype-phenotype relationships at the genome scale.

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TABLE 3 Multiplex genome editing tools based on the CRISPR-Cas system Method

Microorganism

Multiplex editing (numbers of targeted loci)

Efficiency

Reference

Multigene CRISPR editing

Escherichia coli

3

47%

Jiang et al. (2015)

CRMAGE

Escherichia coli

2

70%–98%

Ronda et al. (2016)

CREATE

Escherichia coli

50,000

75%

Garst et al. (2017)

iCREATE

Escherichia coli

162,000

10%–80%

Liu et al. (2018a)

CRISPRm

Saccharomyces cerevisiae

3

19%

Ryan and Cate (2014)

HI-CRISPR

Saccharomyces cerevisiae

3

27%–87%

Bao et al. (2015)

Multigene CRISPR editing

Saccharomyces cerevisiae

6

65%–100%

Mans et al. (2015)

CasEMBLR

Saccharomyces cerevisiae

3

31%

Jakociunas et al. (2015)

Di-CRISPR

Saccharomyces cerevisiae

18

Not reported

Shi et al. (2016)

Automated multiplex genomescale engineering

Saccharomyces cerevisiae

6269 (for overexpression or knockdown)

Not reported

Si et al. (2017)

CHAnGE

Saccharomyces cerevisiae

24,765

61%–88%

Bao et al. (2018)

MINR

Saccharomyces cerevisiae

43,020

65%–98%

Liu et al. (2019)

MAGESTIC

Saccharomyces cerevisiae

35,605

60%

Roy et al. (2018)

High-throughput CRISPR editing

Saccharomyces cerevisiae

622

80%–95%

Guo et al. (2018)

The designed libraries can contain as many as 104–106 individual library members, and the oligo library pools can be synthesized at costs 92% yeast genes. In addition, they used a high-throughput screening system named Illinois Biological Foundry for Advanced Bioengineering (iBioFAB) to test the phenotypes of individual clones in the library, allowing for the automation of experiments that would be very difficult to perform manually. Other methods have been developed for the introduction of point mutations, including CRISPR-Cas9- and HDRassisted genome-scale engineering (CHAnGE) (Bao et al., 2018), and multiplex navigation of global regulatory networks (MINR) (Liu et al., 2019). In addition, Roy et al. developed a CRISPR-Cas9-based method named multiplexed accurate genome editing with short, trackable, integrated cellular barcodes (MAGESTIC) in S. cerevisiae. MAGESTIC uses highthroughput plasmid-based editing and integrated genomic barcodes to enable robust phenotyping and prevent plasmid barcode loss (Roy et al., 2018). Recently, two approaches were developed in S. cerevisiae that combine gene deletion, activation, and repression for application in the metabolic engineering field, demonstrating that different strategies can be performed together (Lian et al., 2017, 2019). In addition to S. cerevisiae, Schwartz et al. constructed a genome-scale sgRNA library in Y. lipolytica and identified high-efficiency sgRNAs that cover 94% genes by a high-throughput quantification methodology (Schwartz et al., 2019). This library was employed in strains with intact NHEJ, resulting in the generation of an indel to inactivate genes or strains with inactive NHEJ that, without a repair template, uncovered gRNA targeting efficiencies. They applied novel mutations discovered from the sgRNA library in NHEJ intact cells for metabolic engineering of lipid production and selected several mutants with increased lipid accumulation using a fluorescent lipid dye and fluorescence-activated cell sorting.

4

Gene regulation tools

CRISPR-Cas systems have been effectively used as targeted genome editing tools in a host of microbial systems. Expanding these genetic tools to enable regulation at the transcriptional level has provided a valuable platform for engineering of these microbial cell factories. Additionally, systematic regulation of transcription at the genome scale can be leveraged to better interpret genotype-phenotype connections under diverse conditions. The CRISPR machinery can be subsequently engineered for transcriptional perturbations relevant to biotechnological phenotypes in a wide range of microbes. Repression and activation of transcription have been achieved by utilizing CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa), respectively. In this section, we will discuss the development, applications, and limitations of these molecular tools in microbial systems.

4.1 CRISPR interference Tools to inhibit gene expression are essential for studies of gene functions. CRISPRi was originally developed in E. coli, where it was shown to efficiently inhibit initiation and elongation of transcription of both individual and multiple concurrent targeted genes (Qi et al., 2013). To achieve this a catalytically dead variant of the Cas9 protein lacking endonuclease activity, termed dCas9, was developed. dCas9 contains two inactivating point mutations (D10A and H841A) in the RUVC1 and HNH nuclease domains ( Jinek et al., 2012). Another catalytically inactive Cas protein, dCas12a (formerly dCpf1), has also been used for CRISPRi in both yeast and bacterial species (Peters et al., 2016; Zhao and Boeke, 2020). Some benefits of dCas12a over dCas9 will be discussed later in this chapter. The system that has primarily been used for CRISPRi contains only the dCas9 protein and a gRNA targeted to the gene of interest. The gRNA target design is different for each organism but generally can range from the
200 to the +300 nucleotide position relative to the transcriptional start site (TSS) (Fig. 5) (Li et al., 2017; Smith et al., 2016). In bacteria the dCas9 and gRNA form a ribonucleoprotein complex (dCas9/gRNA) that will specifically bind to the gene of interest but will not catalyze a DSB. It has instead been shown to effectively bind DNA and block subsequent DNA binding and mRNA elongation by RNA polymerase (RNAP) based on the target site of the gRNA (Fig. 5). This implies a physical inhibition of RNAP by the dCas9/gRNA complex, leading to failed transcription and subsequent repression.

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FIG. 5 Mechanisms of action of CRISPRi and CRISPRa for transcriptional regulation. (left) In CRISPRi, dCas9, which contains the D10A and H841A mutations (white circles), forms the dCas9/gRNA complex. This complex is then targeted to the target sequence (orange square) near the TSS or within the first 300 nucleotides of the gene of interest. Once bound the RNA polymerase (RNAP) can bind in the promoter but will be blocked by the dCas9/gRNA complex, thereby repressing transcription (red arrow). (right) In CRISPRa, another activating domain (teal) is fused to dCas9. Once the dCas9/gRNA complex binds the target region (orange square), the activating domain can recruit RNAP. Once recruited, RNAP can transcribe the gene of interest. This will effectively activate transcription (green arrow).

CRISPRi has also been effectively implemented for transcriptional repression in the yeast S. cerevisiae (Gilbert et al., 2013). The CRISPRi design in yeast is similar to bacteria, but there is one further requirement. In eukaryotes, CRISPRi requires a transcriptional repressor domain fused to dCas9 to effectively repress targeted genes (Farzadfard et al., 2013; Gilbert et al., 2013). The addition of the effector domain is essential as transcriptional regulation in eukaryotes is more complex than in bacteria. This is generally attributed to extensive transcriptional regulator interactions and epigenetic factors. Targeted gene repression with CRISPRi provides a valuable toolkit to probe gene functions and interactions in metabolic engineering. To maximize value-added product yield, CRISPRi can be used to alter carbon/energy flux to promote and to effectively tune the final yield of the product. This system has been applied in multiple microbes for metabolic engineering to drive higher product yield. In E. coli, CRISPRi has been applied to produce diverse products including terpenes (Tao et al., 2018), various alcohols (Kim et al., 2017; Wu et al., 2017), and polyhydroxybutyrate (Elhadi et al., 2016; Zhang et al., 2018). Further examples are included in Table 4. CRISPRi has also proven to be an effective tool in model and nonmodel organisms relevant to biotechnology (Schultenk€amper et al., 2020). These include the biotechnologically relevant Clostridia species to increase product yield of various alcohols (Wen et al., 2017), Pseudomonas putida (P. putida) for mevalonate and pyoverdine production (Kim et al., 2020; Tan et al., 2018), and Kluyveromyces marxianus (K. marxianus) for the production of ethyl acetate (L€ obs et al., 2018) CRISPRi has typically been implemented to target the repression of a few genes simultaneously. However, genomescale CRISPRi allows for the perturbation of all genes in an organism. This can provide context on gene essentiality and/or the effect on cellular physiology under various conditions. Mobile-CRISPRi is a recently developed genome-level system that is designed to be modular for a wide range of microbes (Peters et al., 2019). Mobile-CRISPRi was designed to allow for conjugation between various bacteria and stable genomic integration of the CRISPRi machinery. This system was successfully used to probe the essentiality of genes in P. aeruginosa under various conditions (Qu et al., 2019). Due to its modularity, this approach is appropriate for library-scale screening of biotechnologically relevant microbes under varying

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TABLE 4 Applications of CRISPRi for production of biotechnologically relevant compounds in various microorganisms Microorganism

Metabolic pathway target(s)

Final product

Final titer (yield improvement)

Reference

Escherichia coli

Glyoxylate pathway

Malate

36 g/L (2.3-fold)

Gao et al. (2018)

Escherichia coli

Lyopen pathway

Isoprene

71.4 mg/L (2.6-fold)

Kim et al. (2016)

Escherichia coli

Competing fermentative pathways

n-Butanol

1.06 g/L (3.2-fold)

Kim et al. (2017)

Escherichia coli

Citrate synthase

n-Butanol

0.82 g/L (1.2-fold)

Heo et al. (2017)

Escherichia coli

Cell wall biosynthesis

Polyhydroxybutyrate

93% cell dry weight (3.7-fold)

Zhang et al. (2018)

Clostridia

Hydrogen biosynthesis

Acetone, butanol, ethanol

22.1 g/L (4-fold)

Wen et al. (2017)

Pseudomonas putida

Glycolysis

Mevalonate

237 mg/L (3.3-fold)

Kim et al. (2020)

Synechococcus elongatus PCC7942

Succinate/glycogen pathways

Succinate

0.63 mg/L (12.5-fold)

Huang et al. (2016)

Kluyveromyces marxianus

TCA cycle/ethanol synthesis

Ethyl acetate

100 mg/L (3.8-fold)

L€ obs et al. (2018)

conditions to probe the physiological effects of different pressures. Mobile-CRISPRi would then provide a rapid and efficient screen to aid the engineering design of many relevant microbes. Another genome-scale library CRISPRi screen was done in S. cerevisiae (Smith et al., 2016). Here the authors aimed to determine the gRNA design rules for S. cerevisiae such that future CRISPRi studies would be the most specific and effective. This study posits the need for effective gRNA design for genome-scale CRISPRi, as off-target effects of poorly designed gRNA can be a major limitation. Genome-scale approaches can provide insight for metabolic engineering to understand global effects of repression on each gene in the genome under varying conditions. This is important to grasp how differential gene repression will affect cell growth/viability, product tolerance, and product yield. Transcriptional repression with CRISPRi has been primarily implemented as a binary system where the gene expression output is interpreted as on or off. Though this provides valuable insight into gene essentiality, it does not differentiate intermediate levels of transcription that may be more optimal for product production than complete knockdowns. Some studies have aimed to address this, and it is now poised to continue as an important direction for the CRISPRi field. In one study the authors used a graded approach to dCas9 activity modulation by tiering gRNAs to target different regions near the TSS in S. cerevisiae (Deaner and Alper, 2017). They then assessed the degree of transcriptional repression and found modulatable gene expression levels primarily based on the target site of the gRNA relative to the yeast promoter. Another method traditionally used in metabolic engineering of bacteria and yeast to control gene expression is inducible promoters. These have been effectively used in CRISPRi to control expression of dCas9 and/or the gRNA ( Jang et al., 2018; Kim et al., 2016; Woolston et al., 2018). It is worth noting that inducible promoter systems may not be modular in some microbes where other methods must be utilized. This extends the capability of CRISPRi for determination of the optimal expression levels of genes relevant to industrial product production. There are some limitations to the CRISPRi system. One is inherent to the expression level of dCas9. Organisms exhibit different tolerances to dCas9 expression, and in some cases it is extremely toxic even without expression of a targeting gRNA or with expression of a nontargeting gRNA (Cui et al., 2018). A method to mitigate this is to tune the expression of the dCas9 protein by inducible expression systems. These have been shown to be effective in reducing the toxicity of the dCas9 protein in multiple bacteria and yeast ( Jensen et al., 2017; Vigouroux et al., 2018). As mentioned earlier the dCas9 protein also has limitations in that it cannot process multiple crRNAs and requires endogenous RNAse III for gRNA processing. It has also been implicated that the system requires each gRNA to be expressed under separate promoters to achieve effective gene repression, though gRNA arrays have also been shown to be more effective in some cases (Tian et al., 2019). dCas12a has been shown to overcome this limitation of dCas9 as it can process the crRNAs from an array without the need for endogenous RNAse III (Zetsche et al., 2015). Additionally, the class 1 CRISPR system Cas protein complex, termed

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Cascade, can be utilized as it can also process gRNA arrays transcribed under a single promoter (Tang, 2019). In organisms containing endogenous class 1 CRISPR systems, △ cas3 Cascade (nuclease dead) provides a valuable addition to CRISPRi. For example, our group created a △ cas3 mutant of the native E. coli class 1-E Cascade protein complex. Using a gRNA library against genes from competing metabolic pathways, we showed the system’s efficacy in strain development for the production of 3HP (Tarasava et al., 2018). A further limitation of the CRISPRi system is the competitive activity of the gRNA pool when multiple genes are targeted for repression simultaneously. This occurs when multiple genes are targeted as the gRNAs will competitively share the same pool of the nuclease protein. The efficacy of the system is reduced when the optimum limit of targeted genes is surpassed (Ni et al., 2019). The limits for multiplexing gene targeting are organism specific and will need to be addressed to optimize the ideal range of dCas expression level, gRNA expression level, and number of genes targeted.

4.2 CRISPR activation Though CRISPRi has been shown to be an effective tool to probe microbial physiology and relevant biotechnological phenotypes, CRISPRa presents an equally important tool. CRISPRa aims to accomplish synthetic activation of genes by use of dCas9 (or equivalent Cas protein) fused to a transcriptional activator domain (Bikard et al., 2013; Zalatan et al., 2015). The mechanism is similar to the CRISPRi system, where a gRNA binds to dCas9 forming a complex that then targets the DNA sequence of interest (Fig. 5). The primary difference is that CRISPRa targets the promoter and not the coding sequence to allow for effective recruitment of the RNAP by interaction with the activator protein. CRISPRa has been effectively applied to metabolic engineering in yeast. A CRISPRa system was developed in Y. lipolytica, a yeast capable of high intracellular lipid yield, to activate genes allowing growth on cellobiose (Schwartz et al., 2018). Additionally, a CRISPRa screen was done in S. cerevisiae that identified gene upregulation increasing the organism’s thermotolerance, an important tolerance phenotype (Li et al., 2019a). In 2017 Lian et al developed a trifunctional CRISPR tool in S. cerevisiae incorporating gene deletion, CRISPRi, and CRISPRa (CRISPR-AID) (Lian et al., 2017). Using this system, they achieved a threefold increase in beta-carotene production. This system provides an exciting new avenue in yeast to generate libraries of large combinatorial gene perturbations to identify the most relevant strains. There has been a substantial lack of CRISPRa application in bacteria despite extensive applications in eukaryotic systems (Dominguez et al., 2015). This can be attributed to the general lack of known transcriptional activators in bacteria with high efficacy when fused to dCas9, whereas there are many known transcriptional activators in eukaryotes. Despite this limitation, some bacterial activators for CRISPRa have been described. The first instance of CRISPRa in bacteria was described by fusing the o subunit of RNAP to dCas9 in a o knockout background (Bikard et al., 2013). Other microbial CRISPRa systems in E. coli not requiring a dependency on the background strain include dCas9 fused to SoxS (an activator that interacts with RNAP), AsiA (an antisigma factor), and s54 (Dong et al., 2018; Paget, 2015). In the SoxS system the authors used a CRISPRa approach to promote the expression of a heterologous pdc adhB gene cassette from Zymomonas mobilis in E. coli. They observed a threefold increase in ethanol production relative to control E. coli, supporting this system as a likely candidate for gene activation in metabolic engineering (Dong et al., 2018). As more highly effective and targeted activators are generated in biotechnologically relevant microbes, CRISPRa will become a ubiquitous tool for gene activation. Both CRISPRi and CRISPRa require further optimization to enable precise control and to limit off-target effects. There have been some studies aimed at addressing the tunability of these systems. In one study using CRISPRa, the authors developed an optogenetically controlled system in human cells (Polstein and Gersbach, 2015). Upon activation with blue light, this system effectively targeted and activated multiple genes simultaneously with high spatial and temporal control. With some modification to the specific activator, this system could be extended to microbial cell factories to tune the activation of genes of interest. In another study the authors developed an aptazyme system to regulate gRNA activity in vitro and in mammalian cells in the presence/absence of ligand (Tang et al., 2017). As mentioned previously, inducible systems are also possible in some microbes to control gene expression levels in both CRISPRi and CRISPRa systems. Examples such as these convey the amenability of these systems to allow precise optimization for microbial cell factories.

5 Conclusions In summary, we have described the current development and application of CRISPR-Cas-based tools for genetic manipulation in microbial cell factories. The CRISPR-Cas system is increasingly popular due to its precision, efficiency, and versatility for markerless gene editing and regulation. As CRISPR-Cas systems are developed in more microbes that naturally possess industrially desirable characteristics (e.g., low pH tolerance, oleaginous, and ability to metabolize specific

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carbon sources), it opens up the possibility for the development of new industrial chassis strains for the production of a wide variety of products. We additionally expect that CRISPR-based tools can be further developed for genome-scale modeling, robust directed evolution of proteins and metabolic pathways, and minimization of genomes of targeted strains. Furthermore, new methods for engineering cell factories could be expanded with the adoption of new CRISPR-Cas systems such as Cas12b and Cas13a, CRISPR-based tools for RNA interference (Adli, 2018; Koonin et al., 2017). In addition, many CRISPR-Cas systems have only currently been verified in mammalian cells (Havlicek et al., 2017; Hong et al., 2018; Kampmann, 2018; Kleinstiver et al., 2019; Polstein and Gersbach, 2015; Stepper et al., 2017; Tang et al., 2017) and could potentially be harnessed in microbial systems for improved editing efficiency or new functions. CRISPRi and CRISPRa are rapidly developing CRISPR tools for gene regulation in microbial cell factories. We highlighted recent advances in the application of CRISPRi and CRISPRa in metabolic engineering and their implications. As metabolic control is essential for maximizing microbial value-added product production, gene regulation provides an excellent approach for biotechnological strain development. One benefit of CRISPR tools for gene regulation is that they allow for metabolic perturbations in nonmodel microbes that do not otherwise have developed genetic tools. These tools are also excellent to study essential genes where knockout studies are otherwise not possible. Finally, these tools are excellent for metabolic engineering to redirect resource flux within the cell for beneficial phenotypes. Though this is a relatively new field, CRISPR tools for gene regulation are poised to become prominent methods for novel strain development in biotechnology. In addition to the generation of new CRISPR-based tools, increasing on-target efficiency is also important for further application of CRISPR-Cas systems in different organisms. Recently, there are many studies on engineering of Cas proteins (Bratovi
c et al., 2020; Chen et al., 2017; Kleinstiver et al., 2016; Lee et al., 2018) and gRNA (Cromwell et al., 2018; Yin et al., 2018) to improve the on-target efficiency. We expect that there will be new approaches, such as introducing exogenous protein domains or further modification of Cas proteins and/or gRNA for higher-fidelity CRISPR tools. However, the mechanisms controlling on-target activity are still not fully understood. Therefore there is still further work that needs to be done to understand the mechanisms of action of CRISPR-Cas systems. In particular a greater understanding of what properties of different Cas proteins and what gRNA design principles lead to high editing efficiency across loci/gRNAs will be key for enabling the high-throughput genome editing that is generally necessary to engineer a microbial cell factory. We believe that future CRISPR-based tools may allow a single researcher to design, build, and test hundreds of thousands of edited cells with specific genetic modifications in a single day for a desired phenotype such as maximal production, high resistance to inhibitors, and fast utilization of new substrates in a wider variety of microbial systems.

Acknowledgments Funding is provided by the US Department of Energy grant no. DE-SC0018368 and the Center for Bioenergy Innovation, a US Department of Energy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science.

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Zetsche, B., Gootenberg, J.S., Abudayyeh, O.O., Slaymaker, I.M., Makarova, K.S., Essletzbichler, P., Volz, S.E., Joung, J., van der Oost, J., Regev, A., Koonin, E.V., Zhang, F., 2015. Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR-Cas system. Cell 163, 759–771. Zhang, X., Wang, J., Cheng, Q., Zheng, X., Zhao, G., Wang, J., 2017a. Multiplex gene regulation by CRISPR-ddCpf1. Cell Discov. 3, 17018. Zhang, X.-C., Guo, Y., Liu, X., Chen, X.-G., Wu, Q., Chen, G.-Q., 2018. Engineering cell wall synthesis mechanism for enhanced PHB accumulation in E. coli. Metab. Eng. 45, 32–42. Zhang, M.M., Wong, F.T., Wang, Y., Luo, S., Lim, Y.H., Heng, E., Yeo, W.L., Cobb, R.E., Enghiad, B., Ang, E.L., Zhao, H., 2017. CRISPR-Cas9 strategy for activation of silent Streptomyces biosynthetic gene clusters. Nat. Chem. Biol. https://doi.org/10.1038/nchembio.2341. Zhao, Y., Boeke, J.D., 2020. CRISPR-Cas12a system in fission yeast for multiplex genomic editing and CRISPR interference. Nucleic Acids Res. 48 (10), 5788–5798. Zheng, K., Wang, Y., Li, N., Jiang, F.-F., Wu, C.-X., Liu, F., Chen, H.-C., Liu, Z.-F., 2018. Highly efficient base editing in bacteria using a Cas9-cytidine deaminase fusion. Commun. Biol. 1, 32. Zhou, Y., Lin, L., Wang, H., Zhang, Z., Zhou, J., Jiao, N., 2020. Development of a CRISPR/Cas9n-based tool for metabolic engineering of Pseudomonas putida for ferulic acid-to-polyhydroxyalkanoate bioconversion. Commun. Biol. 3, 98.

Chapter 8

Escherichia coli, the workhorse cell factory for the production of chemicals Antonio Vallea,b and Jorge Bolı´vara,c,∗ a

Department of Biomedicine, Biotechnology and Public Health-Biochemistry and Molecular Biology, Campus Universitario de Puerto Real, University of

Cadiz, Puerto Real, Cadiz, Spain b Institute of Viticulture and Agri-Food Research (IVAGRO) - International Campus of Excellence (ceiA3), University of Cadiz, Puerto Real, Cadiz, Spain c Institute of Biomolecules (INBIO), University of Cadiz, Puerto Real, Cadiz, Spain ∗

Corresponding author: E-mail: [email protected]

1 Introduction: Escherichia coli, a model microorganism for basic and applied research Although the production of chemicals and fuels is still largely based on chemical synthesis using fossil resources, the development of alternative sustainable bio-based processes has sharply risen in the last years. These procedures, also known as biotransformations or biocatalysis, take advantage of the high catalytic activity of the enzymes and their specificity for substrates and catalysis mechanisms. Biotransformations also fit the principles of green chemistry since they are carried out at a mild temperature and pressure and use aqueous solutions and, low amounts, if any, of organic solvents, generating, therefore, fewer wastes than the conventional chemical synthesis. The application of biotechnological procedures to the production of both bulk and fine chemicals has led to biorefinery as a novel concept of a renewable and sustainable industry. Several enzymes have been isolated from wild-type and genetically modified strains for the production of chemicals; however, living microorganisms are also widely used as whole-cell biocatalysts because they facilitate the implementation of enzymatic cascades and supply cofactors often needed in complex biocatalysis (Lin and Tao, 2017). Nevertheless, the specificity of the enzymes for substrates was a primordial bottleneck for their application in the industry before the era of protein engineering. This scenario changed with the optimization of enzymes by directed evolution, a field awarded with the Nobel in Chemistry in 2018 to Arnold (2019). This approach uses molecular biology to generate multiple mutant variants of an enzyme in a short time. The most favorable ones are later screened using selective pressure on the host organism. This powerful approach has boosted the literature and patents based on biocatalysis in the last two decades (Truppo, 2017). The other key element in the application of microorganism to the chemical industry is metabolic engineering. This science has been defined by Nielsen and Keasling as “the rewiring the metabolism of cells to enhance the production of native metabolites or to endow cells with the ability to produce new products” (Nielsen and Keasling, 2016). This is often a difficult task due to the complexity and stability of microbial metabolism. The success in the redirection of metabolic pathways often requires a comprehensive knowledge of intricate biological processes such as the transcription control and enzyme regulation. Systems metabolic engineering is a new science with an overall perspective that integrates kinetic, omics data, and the use of systems biology. The advancements in synthetic biology in the last decades makes now possible the implementation of new biological platforms providing microorganisms with unusual metabolic capacities anticipated by systems metabolic engineering approaches (Wittmann and Lee, 2012). Several microorganisms have been employed to produce chemical compounds, but Escherichia coli stands out among them because it is the best-characterized bacterial species and has been extensively used in both basic molecular biology and biotechnology for the last 60 years. It is considered the “workhorse” of molecular biology due to its fast-growing rate in complex and chemically defined culture media. The extensive research carried out in E. coli has generated a vast knowledge of the genetic, biochemistry, and metabolism of this bacterium, leading to the development of powerful tools for genetic manipulation, including gene deletion (Bloor and Cranenburgh, 2006; Datsenko and Wanner, 2000) and overexpression of autologous and heterologous genes (Rosano and Ceccarelli, 2014). It may be thought that this organism lacks many interesting features for biotechnology, such as growing at extreme temperatures or pH. However, E. coli genome plasticity to incorporate heterologous functional genes is amazing, as was demonstrated by Chen et al. (Chen et al., 2018), who were Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00012-X © 2021 Elsevier Inc. All rights reserved.

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TABLE 1 Some databases with information about Escherichia coli general information as well as omics, metabolic pathways, and in silico analyses or knockout strain collections. Information

Name

URL

General knowledge

PortEco

http://www.porteco.org

EcoliWiki

http://ecoliwiki.net/

Genomics/biochemistry

EcoCyc: Encyclopedia of E. coli Genes and Metabolic Pathways

https://ecocyc.org

Genomics/transcriptomics

EcoGen 3.0

http://ecogene.org/

KEGG

http://www.genome.jp/kegg-bin/show_ organism?org¼eco

Transcriptional regulation

RegulonDB

http://regulondb.ccg.unam.mx

Metabolomics

ECMDB: The E. coli Metabolome Database

http://www.ecmdb.ca

Metabolome/transcriptome/proteome/ flouxome

E. coli Multi-omics Database

http://ecoli.iab.keio.ac.jp/ #transcriptome

In silico metabolome

Metabolic In Silico Network Expansion Databases

https://minedatabase.mcs.anl.gov/#/ home

In silico enzyme selection tool for metabolic pathway design

Selenzyme

http://selenzyme.synbiochem.co.uk

Mutant strain collections

E. coli Genetic Stock Center

http://cgsc.biology.yale.edu/

Keio collection

https://shigen.nig.ac.jp/ecoli/strain/

able to transfer the genes encoding for the entire pathway of chlorophyll biosynthesis in this microorganism. Besides the advancement of genomic, transcriptomic, proteomic, and metabolomic technologies has helped to develop several online resources for the analysis of E. coli genetic, biochemistry, and physiology and the distribution of mutant strains collections. Some examples are shown in Table 1. All these “toolboxes” make E. coli an excellent system to produce chemicals biologically. In this chapter, we will analyze several biotechnological applications reported using E. coli strains as a microbial cell factory in the synthesis of several compounds of industrial interest (Fig. 1). Indeed, E. coli was the first host to produce recombinant proteins, and it is still considered the workhorse in this field since obtaining recombinant gene expression normally takes less than a week due to its short doubling time (Brondyk, 2009). For this reason, approximately a third of the therapeutic proteins, including insulin, are produced nowadays in E. coli. Still the main drawback of using this host in the production of important biopharmaceuticals such as therapeutic antibodies is the natural inability this bacterium has to carry out posttranslational modifications. Indeed, eukaryotic cell culture is currently the system to produce all pharmaceutical antibodies, which is a more complex and expensive platform than E. coli. Nonetheless, some advances allow now to provide E. coli with the capability of producing glycosylated antibodies (Baeshen et al., 2015). Another essential difference in both expression systems is codon usage. However, the remarkable capacity of synthetic biology to produce long synthetic DNA molecules easily overcomes this problem. This chapter will focus on how the versatility of E. coli metabolism; easiness for genetic manipulation and recombinant protein expression has been used in the production of chemicals (Fig. 1). Two types of biocatalysis can be considered: (a) in vitro biocatalysis, which apply free enzymes, either as protein extracts or purified enzymes, and (b) whole-cell biocatalysis, also referred to as biotransformations that use the capacity of the microorganism to transform an external feedstock source into the desired chemical compound. Both approaches have pros and cons. For instance, some in vitro biocatalysis require cofactors that must be added to the reaction and then regenerated and the deactivated enzymes replaced with fresh batches. On the other hand, in whole-cell biocatalysis, microorganisms grow in complex or defined culture media and use these resources for both the production and regeneration of enzymes and cofactors involved in the catalytic conversion. However, some metabolites can interfere with the catalysis, and also the large volumes required in the biotransformation can hamper the downstream process of purifying the compound interest.

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FIG. 1 Escherichia coli roles as a microbial cell factory. (A) Diagram of the uses reported in E. coli as a microbial cell factory. (B) Protein expression has a central role in whole-cell biocatalysis using defined organic compounds (precursor biotransformation) and carbon sources such as glucose, glycerol, or xylose (de novo biotransformation).

Taking into account the feedstock used for whole-cell biocatalysis, two types of biotransformations can be considered (Sheldon and Brady, 2018): (a) “Precursor biotransformation” consists in the cellular modification of an external substrate (s). In this case cell metabolism (re)generates the necessary cofactors and renews any inactive enzyme. (b) In the “de novo biotransformation,” a carbon source is used to produce more microbial cells and metabolites. These metabolites can also precursors of biofuels or other chemicals through metabolic and protein engineering. E. coli has been applied to all of the categories mentioned previously for the production of chemicals. In the following sections the strategies using E. coli, mainly, for the whole-cell-biocatalysis approach will be discussed.

2 Precursor biotransformation Enzymes are highly selective both for substrate(s) and for the chemical transformation they promote molded through millions of years of evolution. This is a necessary feature that enables them to distinguish and catalyze hundreds of different reactions on metabolites that must be transformed in an ordered way within cell metabolism. The plethora of

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E. coli known enzymes is theoretically a huge source of biocatalysts that is still expanding with the finding of new metabolic pathways. For instance, in addition to standard glycolysis, E. coli performs sulfoglycolysis. This new pathway catabolizes 6-deoxy-6-sulfoglucose through four recently discovered reactions and produce DHAP for cell metabolism and 2,3-dihydroxypropane-1-sulfonate as an end product that is exported out of the cell (Denger et al., 2014). Although the low concentration of enzymes in the cells initially hindered their application as catalysts in chemical synthesis, the development of plasmid vectors for protein expression led to the concept of whole-cell biocatalysis through single-step “pathways,” in which the overexpression of a recombinant enzyme via a plasmid vector provides high levels of the catalyzer (Lin and Tao, 2017).

2.1 Whole-cell biocatalysis via single-step “pathways”: Harnessing enzymatic promiscuity A drawback for the use of enzymes in chemical synthesis is the fact that precursors are often synthetic organic compounds, and they are, therefore, nonnatural substrates for them. Nevertheless, enzymes are more promiscuous than first thought, and many are inherently capable of promoting reactions different to those related to their primary function (Kazlauskas, 2005). Promiscuity is crucial to the function of detoxifying enzymes, as they often have to act on nonnatural synthetic compounds. But this feature is not restricted to detoxifying enzymes; on the contrary, it seems to be the rule rather than the exception. Experimental data and metabolic modeling analysis suggest that approximately 40% of the E. coli enzymes are promiscuous and catalyze more than 60% of the metabolic conversions (Nam et al., 2012). For instance, the D-malate dehydrogenase (EcDmlA) operates in malate and leucine metabolism (Vorobieva et al., 2014). This versatility is probably a vestigial feature of primordial cells metabolism that, presumably, had a reduced number of ancient multifunctional enzymes with broad substrate specificities that allowed them to catalyze all chemical conversions needed for life (Rosenberg and Commichau, 2019). In the last decades, enzyme catalytic promiscuity has been harnessed for biocatalysis using E. coli as a host for protein expression. Among the most obvious candidates for this purpose are enzymes involved in the biotransformation of toxic xenobiotic compounds. Xenobiotics are synthetic chemicals that have similar characteristics to those of natural biomolecules. For instance, many nitroaromatic chemicals are poisonous, and several bacteria are able to modify them by NAD(P) H-dependent oxidative or reductive reactions. Enzymes like bacterial nitroreductases are naturally promiscuous enzymes capable of catalyzing the reduction of the nitro groups on several of such compounds (Rolda´n et al., 2008). The synthesis of the benzohydroxamic acid D-DIBOA using E. coli nitroreductase NfsB is a representative case of how biocatalysis can make the most of this natural promiscuity to replace a complex chemical catalysis by just overexpressing an autologous enzyme without further manipulation of its own natural catalytic capabilities. D-DIBOA is analogous to the natural allelopathic herbicide DIBOA that can be chemically synthesized in two reactions. The first step is moderately easy to achieve. However, the second one (Fig. 2A), which involves the reduction of the nitro group, is much more difficult to perform and requires a costly catalyzer, and besides the yield is only 70% (Macı´as et al., 2006). In contrast, E. coli can be used as a whole-cell biocatalyst for this reaction by overexpressing the autologous nitroreductase NfsB with up to 60% yield (Valle et al., 2012). Furthermore, improvements in the bioreaction and the genetic background boost yield up to 100% and concentrations up to 5.1 mM (de la Calle et al., 2019a). The promiscuity of NfsB also allows the synthesis of two chlorinated D-DIBOA derivatives, both in vitro and in vivo (de la Calle et al., 2019b). Such whole-cell biocatalysts for one-step reaction are particularly appropriate for enzymes that do not work in vitro or, as in the case of D-DIBOA synthesis, require expensive cosubstrates or cofactors. The remarkable versatility of E. coli to overexpress proteins has also made this bacterium the most frequent host for the expression of heterologous enzymes. For instance, the E. coli expression system has also shown to be superior to other platforms regarding both expression levels and biocatalytic activity of several eukaryotic enzymes such as human cytochrome P450 NAD(P)H-dependent monooxygenases (Schroer et al., 2010). This family of highly promiscuous enzymes detoxifies xenobiotics through hydroxylation and/or epoxidation of nonactivated carbon-hydrogen bonds of a wide spectrum of substrates. This capability has been used for the synthesis of many chemicals and metabolites such as the anabolic steroid testosterone, the antiinflammatory agent diclofenac, and the analgesic agent phenacetin (Vail et al., 2005).

2.2 Protein engineering: Modification of enzymes by directed evolution Although enzyme promiscuity theoretically allows their application on a multitude of nonnatural substrates, many enzymes are suboptimal as a biocatalyst when they are removed from their natural context. For this reason the development of the concept and methodology of directed evolution marked a milestone that brought about a new era in biocatalysis. A directed evolution experimental design intends to imitate natural evolution, although in a much shorter time (Arnold, 2019). The

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FIG. 2 Precursor biotransformation. (A) Single-step “pathway.” D-DIBOA biocatalytic synthesis by overexpression of the autologous nitroreductase NfsB. The process takes advantage of the cell metabolism only to cofactor synthesis and regeneration. This synthesis is more efficient and less costly than the chemical synthesis (right panel). (B) One-pot multicatalysis. E. coli has been engineered to be host of nonnatural pathways. The expression of the enzymes can be modulate with the use of different expression promoters. (C) Modular multistep biocatalysis. One-pot multicatalysis approach allows the use of enzymes as modular structures that have been combined in several strains to obtain more than one product from the same precursor.

basis to start this process is normally an enzyme displaying a detectable catalytic activity for the target reaction (Fig. 3A). This would be the case of LovD, an acyltransferase found in Aspergillus terreus that has been expressed using E. coli as host with the aim of establishing a whole-cell biocatalytic platform for the synthesis of lovastatin, a compound that reduces endogenous biosynthesis of cholesterol, from monacolin J acid through a-S-methylbutyrate acylation. However, the catalytic activity using this precursor was much lower than that of the natural substrate. The mutant version of LovD obtained through directed evolution and a well-designed screening method increased 11-fold in whole-cell biosynthesis of lovastatin compared with the parental enzyme (Gao et al., 2009). Directed evolution has made it possible to expand the activity of existing enzymes to achieve new catalysis. In a recent work a cytochrome P450 from the rhodobacterium Labrenzia aggregata was modified, with E. coli as a host for protein expression, to achieve an oxidation that had largely eluded efficient catalysis. The evolved enzyme was able to form aldehydes in styrenes by adding oxygen to the less substituted carbon of the carbon-carbon double bond (Hammer et al., 2017).

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FIG. 3 Two ways of harnessing evolution to improve a biocatalyst. (A) Scheme of an experiment for the directed evolution of an enzyme. A parental gene is randomly modified in vitro, cloned in an expression vector, and then transformed into E. coli. High-throughput screenings with selective culture conditions lead to the identification of favorable mutations. Several “generations” are normally needed to achieve the evolved enzyme. (B) Adaptive laboratory evolution (ALE). A whole-cell biocatalyst grown under stressing selective conditions during long periods of time can evolve to improved phenotypes in a nonbiased mode. Genomic sequencing of the enhanced strains allows the identification of beneficial mutations.

In other cases the target activity has been introduced in an already functional site through rational design and directed evolution (Zeymer and Hilvert, 2018). Savile et al. (2010) modified an existing transaminase by combining protein modeling with directed evolution to remove transaminases’ inherent restriction to admit just a methyl radical next to the carbonyl group using E. coli as an expression host. This new enzyme is capable of biocatalyzing the synthesis of asymmetric chiral amines using ketone functional groups as a substrate.

2.3 Cofactors pools and regeneration One of the main advantages of the whole-cell biocatalyst approach is the fact that cofactors are generated and recycled by the host cell. Organic coenzymes such as pyridoxal phosphate (PLP), flavins, NAD(P)H, and coenzyme A are among the most crucial metabolites in the cell and are essential in many biotransformations as well. These complex molecules are remarkably stable in vivo, and their highly complex de novo biosynthesis is carried out in E. coli, only to avoid the dilution caused by cell growth. This means that coenzymes are passed through generations, outliving the enzymes that use them (Hartl et al., 2017). The intracellular concentration of cofactors can be a limiting factor for enzyme overexpression because, in some cases, de novo biosynthesis of coenzymes cannot cope with the high rate of protein production, resulting in a large

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proportion of inactive enzymes (Milker et al., 2017). Nevertheless, the NADP(P)H pool can be increased by genetic engineering of the enzymes involved in their biosynthesis (Heuser et al., 2007) or degradation (Han and Eiteman, 2018) pathways. On the other hand, cofactors are mainly engaged in cellular metabolism, and that could constrain the synthesis of the target product in cofactor-dependent reactions. The use of a regeneration reaction can enhance cofactors’ recovery. Thus, in oxidoreductase NAD(P)H-dependent reactions, cofactor recycling is achieved by coupling the target reaction to another one catalyzed by a dehydrogenase that uses the same cofactor. The most common enzymes exploited to this aim are formate dehydrogenase and glucose dehydrogenase (Kratzer et al., 2015).

2.4 Multistep biosynthesis pathways: One-pot multicatalysis The synthesis of value-added compounds from cheap basic organic molecules such as alkanes, aromatic hydrocarbons, fatty acids, alcohols, or aldehydes often requires more than one step. This kind of biotransformations can be implemented by introducing a biocatalytic cascade resembling the natural metabolic pathways but designed to operate in an unlinked way to cell metabolism, except for regeneration of cofactors (Fig. 2B). This approach, known as onepot multicatalysis biotransformations, benefits from an increased concentration and colocalization of the required enzymes within the cells and low intermediate dispersion (Wu and Li, 2018). Multistep cascades can be as complex as the one reported by Luo and Lee (2017). They designed and constructed an E. coli strain that coexpresses five enzymes to achieve the synthesis of terephthalic acid, a precursor used in the production of films and fibers, from p-xylene. One-pot multicatalysis also enables the construction of modular whole-cell biocatalysts for the biotransformation of a substrate to several end products (Fig. 2C). For instance, Wu et al. (2016) engineered three different biocatalysts in E. coli combining four enzyme modules in three different configurations. These three strains overexpressed from four to eight enzymes to obtain different products ((S)-a-hydroxy acids, (S)-amino alcohols, or (S)-a-amino acids) from the same precursor (styrenes). In addition to enzyme overexpression, the inclusion of outer membrane transporters can improve the catalysis when precursor uptake is a critical factor. For instance, a Nylon 12 monomer was produced from dodecanoic acid methyl ester in E. coli through a three-step pathway. In this case substrate uptake was improved by overexpressing an alkane uptake facilitator (the porin AlkL from Pseudomonas putida) increasing the catalytic activities of the overexpressed enzymes (Ladkau et al., 2016). On the other hand the expression of specific efflux systems to export final products out of cells could prevent the toxicity caused by their intracellular accumulation driving, therefore increasing product titers (Wu and Li, 2018). Although attractive, this approach is challenging since several factors such as optimization of enzyme expression levels or deletion of side reactions mediated by native enzymes have to be taken into account for an efficient bioconversion. The development of synthetic biology tools will help to design multistep pathways. For instance, Selenzyme is a free online tool that guides the user to find the best options for a reaction in a given pathway (Carbonell et al., 2018).

3 De novo biotransformations and metabolic engineering Instead of uncoupling cell metabolism to the production of chemicals, biotransformation processes can make the most of existing metabolic pathways to divert resources toward compounds of industrial interest. However, cell metabolism is a robust tightly regulated network of interconnected pathways challenging to change. Therefore the production of a molecule depends not only on its biosynthetic pathway but also on more complex phenotypes, such as tolerance, competing pathways, or the availability of cofactors and energy. Metabolic engineering deals with the rewiring of metabolism for the production of the target molecule(s) as a property of the cell in its entirety. Such a holistic approach implies that improvements are often possible only through the modification of multiple genes to control endogenous metabolism or to introduce heterologous pathways that overcome cellular regulation (Nielsen and Keasling, 2016). Initially the optimization of a producing strain was achieved in many cases by adaptive laboratory evolution (ALE). In an ALE experiment a microorganism is grown under certain specific conditions during long periods of time, from weeks to years, to select enhanced phenotypes in a nonbiased mode (Fig. 3B). ALE approach benefits from enabling several nonintuitive advantageous mutations to happen in parallel. Several studies illustrate the adaptation of engineered E. coli strains for increasing robustness, yield, or production rates (Portnoy et al., 2011), and we will overview some of them the following sections. ALE has also allowed identifying mutations that improved enzymes’ performance in a way that could not have been predicted a priori. For instance, in an evolved strain adapted to grow on glycerol were identified mutated versions of the glycerol kinase that improved reaction rates up to 130%. This ALE experiment also allowed the identification of advantageous mutations in global regulatory elements (Herring et al., 2006). Nevertheless, in the last decades, the outstanding

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improving knowledge on metabolomics platforms, metabolic flux analysis, computational systems biology, library screening, and synthetic biology has allowed the implementation of in silico designs that could not be afforded otherwise. In the next sections, we will describe the development of several metabolic engineering strategies that have used these tools in E. coli to produce basic chemical commodities such as metabolites, biofuels, and biopolymers. In Table 2 the most significant bioproducts achieved through E. coli cell factories with the yield and genetic manipulation strategies are also summarized.

3.1 Metabolites of industrial interest Metabolites are any C-compound involved in central carbon metabolism (CCM), fermentative pathways, pentose phosphate pathway (PPP), Entner-Doudoroff (ED), amino acid and fatty acid metabolism, and others. Among them, organic acids are important components of the building block chemicals for the production of fine chemicals and food industries (Sauer et al., 2008). Organic compounds are fermentative end compounds (lactic or acetic acids) or intermediate metabolites of CCM (malic, succinic, or pyruvic acids). Another important group with extensive industrial applications are amino acids, which are synthesized from intermediate central metabolites. The metabolic routes of metabolites discussed in the succeeding text are shown in Fig. 4.

3.1.1

D-Lactate

D-Lactate

is a wholesale chemical extensively used in pharmaceutical and food applications, as well as in the synthesis of biodegradable polymers. E. coli naturally produces this metabolite through homofermentative or heterofermentative pathways, although several modified strains are capable of generating this metabolite at considerably higher concentration and yield (Chen et al., 2013). Zhou et al. engineered a high-producing D-lactate strain, starting with an ethanol producer one, in which they deleted genes involved in the synthesis of the competing products formate (pflB), acetate (ackA), ethanol (adhE), and succinate (frdABCD). They later modified this strain to produce optically pure D-lactic at a high rate and 90% yield using 100 g/L of sugars as carbon source (Zhou et al., 2003; Zhou et al., 2005). This strain was further improved by metabolic evolution (Zhou et al., 2006) and deletion of additional competing pathways (aceEF, poxB, and pps together with pflB and frdABCD genes), reaching 138 g/L of lactate in a two-step process, a first step of aerobic cell growth and a second one of anaerobic production (Zhu et al., 2007). Glycerol has also been employed as a carbon source for D-lactate production by overexpressing enzymes responsible for the transformation of glycerol to D-lactate and the deletion of competing pathways, achieving an 85% yield of the theoretical maximum (Mazumdar et al., 2010).

3.1.2 Dicarboxylic acids (succinate and malate) Dicarboxylic acids (C4) such as succinate and malate are listed into the top 12 basic chemicals that can be transformed into other compounds such as 1,4-butanediol, g-butyrolactone, tetrahydrofuran, or various pyrrolidinone derivates (Bozell and Petersen, 2010). E. coli produces succinate and malate in low quantities as intermediates of the tricarboxylic acid cycle (TCA) in aerobic conditions or as an end compound of the reductive branch of TCA in the case of succinate in anaerobic growth. There are three pathways whose implementation can produce these C4 compounds: (a) the PEP-oxaloacetate node of the reductive TCA branch, (b) the oxidative TCA branch, and (c) the glyoxylate shunt. All of them are regulated to maintain the redox balance and to regenerate energy. The overexpression of enzymes of these pathways has been combined with the deletion of competing pathways for the production of C4. In the case of succinate, the first strategy reported was the anaerobic overproduction by overexpression of phosphoenolpyruvate carboxylase (Ppc) with CO2 consumption. Another strategy reported is the inactivation of competing pathways by knocking out the ldhA and pfl genes together with overexpression of the anaplerotic malic enzyme-NADH dependent (maeA). These studies highlighted the relevance of redox balance to enhance the biosynthesis of succinate (Chen et al., 2013), which is supported by in silico metabolic flux analysis (Lee et al., 2002). On the other hand, in silico models show that aerobic production is more favorable, considering that its production is associated with growth. This can be achieved by linearization of the TCA cycle with the deletion of the succinate dehydrogenase (sdhAB) genes and the activation of glyoxylate shunt by deletion of the repressor iclR. This activation is implemented with the removal of acetate production (ack-pta) (Lin et al., 2005; Li et al., 2013). Microbial production of malate has been approached only in the last years. Anaerobic production has been achieved by overexpressing the anaplerotic enzyme Pck from Mannheimia succiniciproducens and blockage of acetate synthesis (pta), obtaining 9.25 g/L of malate (Moon et al., 2008). The aerobic production has also been achieved by linearization of the TCA cycle by deleting the malate dehydrogenase (mdh) and malate quinone oxidoreductase (mqo) genes to obtain a lineal pathway with malate as the end product. Additional deletions of maeA, maeB, and iclR genes together with the overexpression of malate-insensitive

TABLE 2 List of chemicals produced by engineered Escherichia coli strains with the most relevant strategies and yields expressed as mol/mol, except in (*) that is expressed in g/g. Chemicals

Most relevant strategies

Engineered strain

Yield (mol/mol) (g/g)*

Reference

Removal of competing pathways and overexpression of glycerol assimilation enzymes

DpflBDfrdADptaDadhEDdld/pZSglpK-glpD

85%

Zhu et al. (2007)

C-redirection flux of TCA cycle and redox balance and directed adaptive evolution

E2Dsdh-ppc-sucAB

30%

Metabolites D-Lactate

Dicarboxylic acids: Succinate

Moon et al. (2008)

Malate C-redirection flux of TCA cycle and redox balance Pyruvate

Removal of competing pathways and promoter regulation of pyruvate production

Aromatic amino acids: L-Tryptophan

Systems metabolic engineering: redirection of C-flux in central carbon metabolism and deregulation of biosynthetic pathways

L-Phenylalanine

85% LAFCPCPt-accBC-aceE-DackAptaDadhEDcraDpflBDpoxB

DtrpRDtnaADptaDmtr/pSTV28-tktA-ppsA-yddG

55.6%

Akita et al. (2016)

21.9%

Wang et al. (2013)

26%*

Liu et al. (2018)

64%

Fujiwara et al. (2020)

p15A::pheA-Thr326Pro p15A:aroFDpstHDgalDglK DpykADpykFDglKptsG::galP-galK/pSAK-ZYc or pSAKtyrAfbr

L-Tyrosine

Gao et al. (2018)

DadhEDackA-ptaDldhADmaeADmaeBDmdhDiclRDarcA

Biofuels Biohydrogen

Removal of competing pathways

DfrdCDldhADfdnGDppcDnarGDmgsADhycA

115%

Tran et al. (2014)

Bioethanol

Removal of competing pathways and heterologous expression to enhance C-flux

DpflBDadhEDfrdADldhADxylFGHDgatC PpflB::pdcZmadhBZm

87%

Ferna´ndez-Sandoval et al. (2019)

Biohydrogen and bioethanol

Removal of competing pathways and heterologous expression to enhance C-flux

DhycADhyaABDhybBCDldhADfrdABDpgi/pLmZ-GoG

97%–100%

Sundara Sekar et al. (2017)

1-Butanol

Removal of competing pathways and heterologous expression of biosynthetic 1-butanol pathway

DtraD36proAB+ DlacIqZDΜ15DldhADadhEDfrdBCDpta/ PLlacO1:: atoBEC-adhE2CA-crtCA-hbdCA

76%

Ohtake et al. (2017)

1-Propanol

Heterologous expression of biosynthetic 1-propanol pathway

BW38029/pRSF_pduCDEGHOQS

81%

Matsubara et al. (2016) Continued

TABLE 2 List of chemicals produced by engineered Escherichia coli strains with the most relevant strategies and yields expressed as mol/mol, except in (*) that is expressed in g/g—cont’d Yield (mol/mol) (g/g)*

Reference

DlacIDgltA/PLlacO1 thlAatoAD, adc, Cbadh, gltA.LAA poxB acs

54%

Soma et al. (2017)

Removal of competing pathways and heterologous expression of ED pathway

DpgiDgntRDgndDpflBDldhA/pSzp-edd-eda

37%*

Noda et al. (2019)

1,2-PDO

Removal of competing pathways and heterologous expression of ED pathway

DadhEDdldDlldDDfrdADpflBDmgsADaldADarcADldhA:: lldh/pSU18-yahK pJFpduP-pct

42%

Niu et al. (2019)

1,3-PDO

Removal of competing pathways and heterologous expression of ED pathway

△glpK△glpABC△glpD△glpR△eda_edd△pta-ackA/ pKOV-eda-edd

33%–47%

Lee et al. (2018)

1,4-BDO

Implementation a de novo pathway and self-regulated control gene expression

△xylA△yjhH△yagE/LuxI-pLux-xdh-xylX-mdlC

7%

Burgard et al. (2016)

PHA

Reversed fatty acid b-oxidation and heterologous expression of biosynthetic PHA pathway

△ptsG△tesA △pflB △poxB/pBBR1MCS2-prpP-prpE

20%

Zhuang and Qi (2019)

Chemicals

Most relevant strategies

Engineered strain

Isopropanol

Metabolic flux redirection of TCA cycle and removal of competing pathways

Isobutanol Biopolymers

FIG. 4 Metabolites of industrial interest. Schematic pathway of glycolysis, amino acids, fermentation, and TCA cycle. The metabolites of interest are marked in color: C4 (malate and succinate) in brown, pyruvate in orange, D-lactate in blue, and amino acids in Green. Lines with one arrow represent an unidirectional biochemical reaction, with two arrows indicating a bidirectional reaction. The gray dashed lines represent the reaction repressed in anaerobiosis. The acronym’s definition of metabolites and enzymes are as follows: 2KG, 2-a-ketoglutarate; E4P, erythrose-4-phosphate; Gluc 6-P, glucose 6phosphate; IclR, protein repressor of glyoxylate shunt genes; L-Phe, L-phenylalanine; L-Trp, L-tryptophan; L-Tyr, L-tyrosine; OAA, oxaloacetate; PEP, phosphoenolpyruvate; Pdh, pyruvate dehydrogenase enzyme (AceEF + Lpd); and PPP, pentose phosphate pathway. The genes indicated in the metabolic diagram are as follows: aceA, isocitrate lyase; aceB, malate synthase A; aceE, pyruvate dehydrogenase E1 subunit; aceF, pyruvate dehydrogenase E2 subunit; accBC: acetyl-CoA carboxylase complex; ackA, acetate kinase; adhE, acetaldehyde/alcohol dehydrogenase; dld, quinone-dependent D-lactate dehydrogenase; frdABCD, fumarate reductase operon; gltA, citrate synthase; iclR, IclR repressor protein; ldhA, lactate dehydrogenase; lpd, lipoamide dehydrogenase; maeA, NAD+-dependent malic enzyme; maeB, NADP+-dependent malic enzyme; mdh, malate dehydrogenase; mqo, malate:quinone oxidoreductase; noxE: NADH oxidase from Lactococcus lactis; pckA, pyruvate carboxykinase; pflB, pyruvate formate lyase; poxB, pyruvate oxidase; ppc, pyruvate carboxylase; pps, phosphoenolpyruvate synthase; and sdhABCD, succinate dehydrogenase.

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PEP carboxylase (ppc) and an NADH-insensitive citrate synthase (gltA) genes resulted in 0.82 mol/mol yield (Trichez et al., 2018). In another approach, Gao et al. (Li et al., 2018) used in vitro modular pathway optimization combined with in vivo multiplexed combinatorial engineering using CRISPR interference to identify a malate biosynthetic pathway. Using a controlled overexpression of the pyruvate carboxylase (pyc) and citrate synthase (gltA) genes on the mutant DadhEDackAptaDldhADmaeADmaeBDmdhDiclRDarcA produced 0.85 mol malate/mol glucose (Gao et al., 2018). The production of malate from xylose was also recently achieved by introducing a synthetic pathway from xylose to dihydroxyacetone and glyoxylate and the knocking out of the genes encoding the malic enzyme (maeA), malate dehydrogenase (mdh), and fumarate hydratase (fumABC), achieving a yield of 0.80 g malate/g xylose.

3.1.3 Pyruvate Pyruvate is a central metabolite in cells used in the industry as a precursor compound for the synthesis of antioxidants, food additives, weight control supplements, and the medicament precursors N-acetyl-D-neuraminic acid, L-3,4-dihydroxyphenylalanine, and R-phenylacetylcarbitol. Pyruvate synthesis is enhanced with the elimination of genes that reduce its utilization for cell growth. For instance, the deletion of the aceEF and lpd genes (encoding for the pyruvate dehydrogenase complex monomers Pdh and LpD) leads to pyruvate accumulation, although this strain required acetate for production of acetyl-CoA (Zeli
c et al., 2004). Another strategy consists of the deletion of nonessential genes involved in competing pathways such as the synthesis of acetate (ackA), ethanol (adhE), and lactate (ldhA) (Causey et al., 2004). Novel strategies have been focused on avoiding the carbon leakage to fatty acid production by manipulation of the first biochemical reaction of fatty acid synthesis (accBC and aceE genes). The substitution of the natural promoter of these genes by a tetracyclineinducible one and the deletion of the ackA-pta, adhE, cra, ldhA, pflB, and poxB genes lead to 55.6% of theoretical maximum yield (Akita et al., 2016). Blocking acetate synthesis is one of the most usual strategies to increase the production of pyruvate. However, the excess of pyruvate is often diverted toward the TCA, which leads to increased NADH/NAD+ and ATP/ADP ratios and therefore to glycolysis inhibition. To solve this problem, Liu and Cao got controlled oxidation of the NADH pool by expressing the NADH oxidase (noxE) of Lactococcus lactis under a thermoregulated promoter (Liu and Cao, 2018). This system can switch on the noxE expression to promote growth rate and then be switched off to improve pyruvate production.

3.1.4 Amino acids Amino acids are basic precursors of valuable compounds such as antibiotics, pharmaceuticals, animal feed, cosmetics, and food additives. Amino acid metabolism is highly regulated, and for this, strategies for improving amino acid biosynthesis were based on ALE approaches. Nevertheless, such strategies may result in genetic alterations not directly related to the amino acid of interest, causing an unwanted impact on the E. coli physiology and leading to growth delay. The intricate regulatory network of amino acid metabolism requires the integration of metabolic and regulatory information to design systems metabolic engineering strategies and successfully achieve the production of the target metabolite (Chen et al., 2013). Several E. coli strains have been engineered to produce amino acids such as L-valine ( Jin et al., 2007; Oldiges et al., 2014; Weiner et al., 2014), L-alanine (Lee et al., 2004; Zhou et al., 2016), and L-threonine (Dong et al., 2011; Zhao et al., 2020a; Zhao et al., 2020b; Chen et al., 2019; Wang et al., 2019). The aromatic amino acids (AAA) (L-tryptophan, L-phenylalanine, and L-tyrosine) are of special interest because they are precursors of industrial food additives and pharmaceuticals. For instance, L-Phe is an essential component in the production of the sweetener aspartame, and L-Tyr is a precursor in the synthesis of L-Dopa or melanin (Rodriguez et al., 2014). AAA are the end products of the aromatic biosynthetic pathway, which starts with the condensation of phosphoenolpyruvate (PEP) and erythrose 4-phosphate (E4P) to form 3-deoxy-D-arabino-heptulosonate-7-phosphate (DAHP), going through the shikimate pathway to the synthesis of chorismate, the last common precursor of all AAA (Liu et al., 2020). The main strategies used for improving the aromatic amino acids are (a) engineering of the CCM to improve glucose transport and the glycolytic, gluconeogenic, and PPP pathways; (b) identification and relieving of rate-limiting steps in the AAA pathway; (c) implementation of synthetic biology strategies; (d) use of systems biology tools to integrate omics data; and (e) bioprocess engineering to optimize production in bioreactors (Rodriguez et al., 2014). For instance, a nonauxotrophic E. coli strain was engineered to induce at will usually inactive pathways, leading to auxotrophic strains (Liu et al., 2020). Several of these strategies were used in the optimization of the production of L-tryptophan (Zhao et al., 2011; Liu et al., 2012; Wang et al., 2013; Cheng et al., 2013), ´ ez-Viveros et al., 2004; Ba´ez-Viveros et al., 2007; Liu et al., 2013; Liu et al., 2014; Liu et al., 2018), and L-phenylalanine (Ba L-tyrosine ( Juminaga et al., 2012; L€ utke-Eversloh and Stephanopoulos, 2008; Santos and Stephanopoulos, 2008; Cha´vezB
ejar et al., 2012; Xu et al., 2020; Fujiwara et al., 2020).

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3.2 Biofuels Fossil fuels have become an environmental problem, and this has prompted the search for more sustainable energy sources. Among them, biofuels including hydrogen, bioethanol, and advanced fuels such as 1-propanol and 1-butanol have been a major focus of research in the last decades. The metabolic pathways to synthesize these products are summarized in Fig. 5, and the main metabolic engineering strategies for their production in E. coli will be described in the following subsections.

3.2.1 Biohydrogen Hydrogen (H2) has huge potential as an energy source because it is renewable, efficient, and clean, and besides, it is a portable form of energy. All this makes of H2 attractive an alternative energy (Valle et al., 2019). However, H2 is currently obtained from nonclean processes such as water electrolysis or as a by-product of chemical and petroleum industries. E. coli H2 synthesis has been extensively studied by Gonzalez’s and T. K. Wood’s groups, and the main metabolic engineering strategies to increase H2 production have been widely reviewed by different authors (Valle et al., 2019; Rosales-Colunga and De Leo´n Rodrı´guez, 2014; Maeda et al., 2018; Trchounian, 2015). This is an anaerobic process connected to central metabolism through the oxidation of pyruvate to formate. The protein complexes responsible for H2 synthesis and oxidation are located at the cell membrane and include the formate hydrogen lyase system: FHL-1 (composed by Hyd-3 and Fdh-H) and FHL-2 as well as the hydrogenases (Hyd-1 and Hyd-2). These complexes’ operation mode (H2 synthesis or oxidation) strongly depends on pH and the carbon source (Trchounian, 2015; Trchounian et al., 2012). The main strategies to enhance H2 synthesis include the following: (a) removal of competing pathways such as the synthesis of lactate, succinate, and ribulose-5P, by deleting ldhA, frdBC, and gnd genes, respectively (Valle et al., 2015a); (b) elimination of H2 leakage by deletion of the hyaB and hybC genes, which encode for the Hyd-1 and Hyd-2 hydrogenases that drive H2 oxidation; and (c) upregulation of the H2 producer FHL-1 complex by deletion of hycA, the repressor of hyc operon, which encodes for the Hyd-3 (alternatively, FhlA, an activator of the hyc operon [Hyd-3], has also been overexpressed to this aim (Maeda et al., 2008)); (d) increasing formate availability by removing formate-consuming enzymes such as the Fdh-nitratedependent (fdnG) or FdoG-oxygen (fdoG) dehydrogenases or the nitrate reductases encoded by narG or narL genes; and (e) heterologous expression of hydrogenases from Clostridium butyricum (Subudhi and Lal, 2011), Synechocystis (Maeda et al., 2007), and Hydrogenovibrio marinus ( Jo and Cha, 2015). In cases of NADPH-dependent hydrogenases from Clostridium and Bacillus, the glpX and zwf genes were cooverexpressed for the regeneration of NAD(P)H+ pool (Kim et al., 2011). Promising “proofs of concept” such as the DhyaBDhybCDhycADfdoG/pCA24N-FhlA strain that achieved productivity 141-fold with respect to the wild-type strain with glucose as carbon source (Maeda et al., 2008) or the DfrdCDldhADfdnGDppcDnarGDmgsA strain with productivity 4.3-fold higher to that of the wild-type strain on a glycerol-based medium (Tran et al., 2014) are encouraging results to enforce H2 bioproduction in E. coli.

3.2.2 Bioethanol Currently, bioethanol, used as a gasoline blend, is the principal source renewable liquid energy (Wittmann and Lee, 2012). Production of ethanol by Saccharomyces cerevisiae is currently a well-established industry, although it uses starch and sugarcane and therefore competes with food production (Geddes et al., 2011). Alternative carbon sources from raw materials, such as lignocellulosic biomass, are a promising alternative, although natural microorganisms cannot efficiently transform all the sugars contained in lignocellulose into ethanol (Sun et al., 2018). Nevertheless, several modified E. coli strains can metabolize some sugars from pretreated biomass (Sun et al., 2018; Zheng et al., 2012; ParraRamı´rez et al., 2018; Ferna´ndez-Sandoval et al., 2019). Sun et al. (2018) pioneered this field by getting a modified strain that has been broadly used in subsequent studies. This strain, which expresses the pdc (pyruvate decarboxylase) and the adhB (alcohol dehydrogenase) genes from Zymomonas mobilis, uses xylose to produce ethanol at 1.28% (v/v) yield. Another bottleneck is the complex regulatory systems of sugar catabolism that favors glucose utilization in sugar mixtures. Destabilization of this regulation by the elimination of the methylglyoxal synthase (mgsA), an enzyme involved in the tuning of metabolism, leads to cocatabolism of glucose and xylose and enhanced metabolism of pentoses that increased the fermentation rate to ethanol (Yomano et al., 2009). Additional strategies were aimed to prevent glucose utilization and force E. coli to use other monosaccharides present in lignocellulosic hydrolysates. Using this approach, Sun et al. (2018) deleted several genes for glucose uptake (glpK, ptsG, and manZ). This, together with the suppression of competing pathways, the substitution of the frdA locus by a temperature-inducible ptsG expression module, and the expression of the heterologous pdc and adhB genes from Z. mobilis, leads to maximum productivity of 4.06-g ethanol/(L h) using mixed sugars in lignocellulosic hydrolysates as carbon sources (Sun et al., 2018). In another approach the selective consumption of xylose and glucose was achieved under microaerobic conditions in a two-stage continuous culture. Overexpression of

FIG. 5 Biofuels. Schematic pathway of glycolysis, glycerol assimilation, pentose phosphate pathway (PPP), Entner-Doudoroff (ED), TCA cycle (oxidative and reductive branch in anaerobic conditions), fermentation, the heterologous pathways of biofuels (in blue) and native biofuels in brown. Lines with one arrow represent a unidirectional biochemical reaction, with two arrows indicating a bidirectional reaction. The gray dashed lines represent the reaction repressed in anaerobiosis. The acronym’s definition of metabolites and enzymes are as follows: DcuD, putative C4-dicarboxylate transporter; DHA, dihydroxyacetone; FdhF-H, formate dehydrogenase H dependent; FHL-1, formate hydrogen lyase system 1; FHL-2, formate hydrogen lyase system 2; FhlA, formate hydrogenlyase activator factor; h-PEPCK-M, human phosphoenolpyruvate carboxykinase mitochondrial; Hyd-1, hydrogenase 1; Hyd-2, hydrogenase 2; OAA, oxaloacetate; and PEP, phosphoenolpyruvate. The genes indicated in the metabolic diagram are as follows: acs, acetyl-CoA synthase (AMP-forming); adc, acetoacetate decarboxylase; adhB, alcohol dehydrogenase; adhE, acetaldehyde/alcohol dehydrogenase; adhE2, secondary alcohol dehydrogenase-butyraldehyde-butanol dehydrogenase; aldA, aldehyde dehydrogenase A; alsS, acetolactate synthase; atoAD, acetyl-CoA:acetoacetyl-CoA transferase; atoB, acetyl-CoA acetyltransferase; bcd, butyryl-CoA dehydrogenase; cimA, citramalate synthase; crt, crotonase; dhaKLM, dihydroxyacetone kinase subunits K, L, and M; eda, KHG/KPDG aldolase; edd, phosphogluconate dehydratase; etfAB, electrotransfer flavor protein; fdnG, formate dehydrogenase nitrate dependent; fdoG, formate dehydrogenase oxygen dependent; gldA, glycerol dehydrogenase; glf, glucose-facilitated diffusion protein; glpABC, anaerobic glycerol-3-phosphate dehydrogenase subunits A, B, and C; glpK, glycerol kinase; glpX, fructose-1,6-bisphosphatase 1; gnd, gluconate dehydrogenase; hbd, b-hydroxy butyryl-CoA dehydrogenase; hyaB, hydrogenase 1 B subunit; hybC, hydrogenase 2C subunit; hycA, hydrogenase-3 A subunit; ilvA, L-threonine dehydratase biosynthetic; ilvCD, acetohydroxy acid isomeroreductase and dihydroxy-acid dehydratase; kivd, ketoisovalerate decarboxylase; leuB, 3-isopropylmalate dehydrogenase; leuC, isopropylmalate/citramalate isomerase large subunit; leuD, isopropylmalate/citramalate isomerase small subunit; mgsA, methylglyoxal synthase; narG, nitrate reductase; narL, nitrate/nitrite response regulator gene; pdc, pyruvate decarboxylase; pduCDE, propanediol dehydratase C, D, and E subunits; pduQ, propanol dehydrogenase; pfkA, phosphofructokinase I; pgi, phosphoglucose isomerase; ptsG, PTS system glucose-specific EIICB component; tdcE, 2-ketobutyrate formate-lyase/pyruvate formate lyase; thl, acetyl-CoA acetyltransferase; thrA, bifunctional aspartokinase/homoserine dehydrogenase 1; thrABC, fused aspartate kinase/homoserine dehydrogenase subunits 1, 2, and 3; yqhD, NADPH-dependent aldehyde reductase; and zwf, glucose-6-phosphate dehydrogenase. The following genes are described in Fig. 3: aceEF, frdABCD, gltA, ldhA, lpd, mdh, pck, pflB, poxB, ppc, tdcE, and ackA. The species’ acronyms are as follows: Ca, Clostridium acetobutylicum; Cb, C. boidinii; Ec, E. coli; Ll, Lactobacillus lactis; Sc, S. cerevisiae; and Zm, Zymomonas mobilis.

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pdc and adh genes from Z. mobilis in the E. coli mutant DpflBDadhEDfrdADldhADxylFGH using this approach leads to a steady ethanol production of 18 g/L (Ferna´ndez-Sandoval et al., 2019). The redirection of C-flux from glucose through ED and PPP pathway has also been tested by deletion of the phosphoglucose isomerase (pgi) and improving cell growth by adaptive evolution (Huerta-Beristain et al., 2017).

3.2.3 Coproduction of biohydrogen and bioethanol The previous strategies performed using glycerol or glucose can be also used for the coproduction of hydrogen and ethanol in strictly anaerobic fermentations. Glycerol can be also converted into ethanol and hydrogen under anaerobic and acidic conditions. A recombinant E. coli strain overexpressing glycerol dehydrogenase (gldA) and dihydroxyacetone kinase (dhAKLM) together with the deletions of frdABCD and pta allowed ethanol and hydrogen production from raw glycerol at more than 95% of the theoretical maximum yield (Shams Yazdani and Gonzalez, 2008). Through a high-throughput screening in an E. coli mutant collection, novel favorable mutants such as gluconate dehydrogenase (gnd) and tdcE (pyruvate formate lyase) were identified. Deletion of these genes together with the frdA and ldhA genes enhanced hydrogen and ethanol yields using glycerol as the main carbon source (Valle et al., 2015a). Following a similar approach, gene deletion of a putative C4 transporter (DcuD) was described as an advantageous genetic background for improving hydrogen and ethanol yields (Valle et al., 2015b). The overexpression of the human phosphoenolpyruvate carboxykinase (h-PEPCKM) in the dcuD single mutant redirected carbon flux toward both products (Valle et al., 2017). The coproduction of hydrogen and ethanol using glucose was addressed in several mutant strains including ack-pta (acetate production), pfkA or pgi (glycolytic enzymes), and the DhycADhyaABDhybBCDldhADfrdAB strain, which also included deletion of the hydrogenases genes. However, when pfkA or pgi was deleted to increase NAD(P)H pool by redirecting the carbon flux from the glycolytic pathway—or Embden-Meyerhof-Parnas (EMP) pathway—to the pentose phosphate pathway (PPP), the cell growth was compromised and only after adaptive evolution the pfkA mutant was able to grow under anaerobic conditions (Seol et al., 2014). Subsequent works following the same strategies focused on the redirection of EMP and PPP and the regeneration of NAD(P)H were conducted by the same authors (Seol et al., 2016; Sundara Sekar et al., 2017; Sundara Sekar et al., 2016).

3.2.4 1-Butanol, 1-propanol, isopropanol and isobutanol Other advanced biofuels such as n-butanol have similar properties to those of petroleum and can be transported with current pipeline infrastructure and blended with gasoline at any concentrations (Saini et al., 2015). Nevertheless, few microorganisms can generate such chemicals. The exception is some Clostridium species capable of generating 1-butanol, together with acetone, butyrate, and ethanol, although their difficult genetic manipulation and complex metabolism make them hard to scale to the industry (Chen et al., 2013). On the other hand, E. coli does not have the necessary pathways for producing these biofuels, but several strains have been engineered to incorporate nonnative pathways (Dong et al., 2016). For instance, Atsumi et al. (2008) transferred a six-gene pathway for 1-butanol biosynthesis from C. acetobutylicum to E. coli and suppressed competing reactions, although the production obtained was low. To achieve an autoregulated response to fermentative growth, the synthetic pathway’s expression promoter was replaced by the autologous endogenous fermentation regulatory elements (Wen and Shen, 2016). Further improvements will need nonbiased approaches to identify bottlenecks or nonobvious candidates for additional genetic manipulations. Thus a metabolomics study revealed that the pta deletion leads to detrimental pyruvate and butanoate accumulation due to a CoA imbalance. Also the synthesis of butanal from butanoyl-CoA in the alcohol dehydrogenase (AdhE) step was identified as a bottleneck (Ohtake et al., 2017). 1-Propanol is another potential gasoline substitute, but it also has other industrial applications such as propylene synthesis. To enable the production of 1-propanol in E. coli, an engineered strain was constructed carrying both the 1,2-PD and 1-propanol synthetic pathways by controlled expression of the pdu regulon including the diol dehydratase (pduCDE) and the propanol dehydrogenase (pduQ) together with the genes for adenosylcobalamin (AdoCbl) from Klebsiella pneumonia that produce 1-propanol at 0.81 M yield (Matsubara et al., 2016). In a different approach the L-threonine biosynthetic pathway (ThrABC) has also been used for the coproduction of 1-butanol and 1-propanol. In this case L-threonine is transformed into 2-ketobutyrate (2KB) by the threonine deaminase (IlvA) that is used as precursor for both products. The subsequent reactions are catalyzed by the 2-keto acid decarboxylase (KivD) from L. lactis and alcohol dehydrogenase (Adh2) from S. cerevisiae. For 1-butanol synthesis, 2-KB is previously converted into 2-ketovalerate by norvaline biosynthesis pathway, and this precursor is subsequently transformed into 1-butanol by KivD and Adh2 (Chen et al., 2013). 2KB can be also obtained from a heterologous citramalate pathway (CimA, LeuBCD). A combination of the citramalate and L-threonine pathways produced synergistic effects that increase the theoretical maximum yield and productivity. Indeed, for 1-propanol production, an engineered strain called SYN 12 (Dual) expressing both 2-KB synthetic pathways through

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heterologous expression of cimA gene from Methanococcus jannaschii and the kivd gene from L. lactis and overexpression of leuBCD, thrA, ilvA, and yqhD from E. coli reached up to a 50% higher yield of 1-propanol compared with those expressing only one of these 2-KB pathways. Again, in this case, metabolomics helped to identify a new strategy consisting of an increase of PEP/pyruvate ratio and a rise of NAD(P)H for the citramalate pathway to boost threonine and therefore 1-butanol synthesis (Putri et al., 2018). Clostridium can produce isopropanol (IPA) as well, a secondary alcohol with many industrial applications. The first IPA E. coli producing strain was reported by Hanai et al. (2007), who constructed a heterologous fermentative pathway by transferring a module of genes from different organisms—alcohol dehydrogenase (adhE) from C. boidinii, acetoacetate decarboxylase (adc) from C. acetobutylicum, and acetyl-CoA:acetoacetyl-CoA transferase (atoAD) from E. coli—to transform acetyl-CoA to IPA via acetone (Chen et al., 2013). A critical drawback for IPA biological production is the toxicity; IPA inhibits cell growth, which leads to low yield. This problem was addressed by an ALE experiment to improve tolerance by growing E. coli under IPA stress. Genome resequencing analysis of the tolerant strains revealed that mutations in several genes (relA, marC, proQ, yfgO, and rraA) increased IPA tolerance. The transcriptomic analysis indicated that the expression levels of genes related to amino acid biosynthesis, iron ion homeostasis, and energy metabolism had changed in the tolerant strains (Horinouchi et al., 2017). To overcome the imbalance between cell growth and IPA production, Soma et al. (2017) engineered a metabolic switch for a conditional redirection of the metabolic flux from the TCA cycle to isopropanol. However, this strategy caused pyruvate accumulation that was subdued by increasing the transformation of pyruvate to acetyl-CoA using a synthetic bypass (poxB and acs genes), improving titer and yield 4.4-fold. Isobutanol has similar physical properties to those of IPA, although with a higher octane rating. The production of this biofuel in E. coli was achieved via nonfermentative reactions using the valine anabolic pathway as a frame to synthesize the isobutanol precursor 2-ketoisovalerate. To increase 2-ketoisovalerate pool, acetolactate synthase (alsS) from Bacillus subtilis and acetohydroxy acid isomeroreductase and dihydroxy-acid dehydratase (ilvCD) from E. coli were overexpressed together with the deletion of competing enzyme genes of pyruvate and cofactor utilization. These strategies combined with overexpression of L. lactis kivd and adh2 from S. cerevisiae led to microaerobic production of isobutanol at 86% of the theoretical yield (Chen et al., 2013). To build a high-performance strain from the one described earlier, the rebalance redox status was studied by in silico methods such as flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA). These analyses helped to identify the glycolytic conversion of glyceraldehyde 3-phosphate to D-glycerate1,3-bisphosphate, catalyzed by the NAD-dependent GAPDH, as a key biochemical step to improve the redox balance. To modulate this reaction a pathway encoding an NADP-dependent glyceraldehyde-3phosphate dehydrogenase (GAPDN) was constructed and expressed under five different constitutive promoters. These GAPDN strains increased the NADPH/NADP+ ratio and lowered the NADH/NAD+ one and, consequently, also ethanol and lactate production, which led to rising isobutanol titer up to 221% (Liu et al., 2015). An additional strategy consisted of the implementation of the ED pathway because of its complete redox balance. An ED pathway-dependent isobutanol producing strain was obtained by overexpression of ED enzyme genes zwf, pgi, edd, and eda from E. coli and the inactivation of pgi, gntR, gnd ldhA, pflB, and pta together with the overexpression of the isobutanol pathway (alsS, ilvCD, kivD, and adhA). The resulting optimized strain produced 15 g/L of isobutanol with 0.37 g-isobutanol per g glucose yield (Noda et al., 2019).

3.3 Biopolymers The production of biopolymers from renewable biological feedstock is a very attractive option versus conventional polymers derived from petroleum because of their biodegradability and biocompatibility. Several organic acids such as lactic, succinic, fumaric, itaconic, and diols, including 1,2-propanediol (1,2-PDO), 1,3-propanediol (1,3-PDO), and 1,4-butanediol (1,4-BDO), are building blocks for the synthesis of biopolymers. Also, polyhydroxyalkanoates (PHAs) and polylactic acid (PLA) can be obtained from microorganisms and are commonly used for the production of biodegradable polymers (Chen et al., 2013). It is interesting to note that among these chemical building blocks, 1,3-PDO and 1,4-BDO have been successfully produced in E. coli at industrial scale. These ones and other metabolic engineering strategies for biopolymers production are shown in Fig. 6.

3.3.1 Diols: 1,3-PDO, 1,2-PDO, and 1,4-BDO 1,3-PDO is a monomer used in the production of biodegradable and biocompatible synthetic polymers (polyesters, polyurethanes, polyethers), which are used for the manufacturing of many products, from biocides to glues, detergents, and cosmetics, among others. The production of this compound has been traditionally based on chemical synthesis, but many efforts have

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FIG. 6 Biopolymers. Schematic pathway of glycolysis, glycerol assimilation, PPP, ED, fermentation, TCA cycle, and the heterologous pathways of biopolymers (in blue). The genes indicated in the schematic pathway are as follows: 4hbD, 4-hydroxybutyrate dehydrogenase; aceK, isocitrate dehydrogenase kinase/phosphatase; acs, acetyl-CoA synthetase; ald, CoA-dependent aldehyde dehydrogenase; btkB, b-ketothiolase;cat2, CoA-acyltransferase; dhaB1, glycerol dehydratase; dhaBCE, glycerol dehydratase; dhaFG, dehydratase reactivation factor; dhaT, 1,3-PDO oxidoreductase; gabD, succinate semialdehyde dehydrogenase; glcD, glycolate oxidase subunit, FAD-linked; glpR: DNA-binding transcriptional repressor GlpR; lldD, L-lactate dehydrogenase; orfZ, CoA transferase; pct, propionyl-CoA transferase; phaA, b-ketothiolase; phaB, NADPH-dependent acetoacetyl-CoA reductase; phaC, PHA synthase; prpE, propionyl-CoA synthetase; prpP, propionate permease; sucCD, succinyl-CoA synthetase; sucD, CoA-dependent succinate semialdehyde dehydrogenase; tpiA, triose-phosphate isomerase; and ycdW, glyoxylate/hydroxypyruvate reductase. The following genes are described in Figs. 3 or 4: aceA, aceB, aceEF, ackA, adhE, aldA, aldh, dhaKLM, dld, eda, edd, gldA, glpABC, glpD, glpK, gltA, gnd, ldhA, lpd, mdh, mgsA, pckA, pflB, pgi, poxB, ppc, pta, ptsG, tdcE, yqhD, and zwf. The species’ acronyms are as follows: Cb, Clostridium boidinii; Cf, C. freundii; Ck, Clostridium kluyveri; Cp, C. pasteurianum; Kp, Klebsiella pneumonia; Me, Megasphaera elsdenii; Pp, Pseudomonas putida; Re, Ralstonia eutropha; and Zm, Z. mobilis.

been made to develop alternatives by microbiological fermentation, although high yields and concentrations are required to be economically competitive (Przystałowska et al., 2015). Although E. coli wild-type strains are unable to produce 1,3-PDO, several strains have been engineered for the industrial production of 1,3-PDO taking advantage of the ability of Klebsiella pneumonia and Clostridium species to synthesize 1,3-PDO in anaerobic conditions. 1,3-PDO synthesis starts with the conversion of glycerol to 3-hydroxypropionaldehyde (3-HPA) by the glycerol dehydratase (GDHt) (dhaB), which is the reduced to 1,3-PDO by an NADH-dependent 1,3-propanediol oxidoreductase (dhaT). DuPont and Genencor International Inc. constructed an industrial E. coli 1,3-PDO overproducing strain using glucose as carbon source (bio-PDO) by introducing the glycerol and 1,3-PDO pathways from S. cerevisiae and K. pneumonia, respectively. This strain can produce more than 130 g/L, although due to the economic interest of this process, these companies have not revealed many details (Chen et al., 2013). The construction of 1,3-PDO producing strains by expression of dha from K. pneumonia has also been widely reported (Przystałowska et al., 2015; Wang et al., 2007; Lee et al., 2018) although additional strategies have been implemented such as the heterologous expression of glycerol dehydratase (dhaBCE) and the glycerol dehydratase reactivation factor (dhaF and dhaG) from Citrobacter freundii under the control of the T7 lac promoter (Przystałowska et al., 2015). On the other hand, Lee et al. (2018) reported the conversion of glucose into 1,3-PDO by overexpressing the glycerol conversion pathway to

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1,3-PDO and the deletion of: (a) glycerol degradation pathways (glpK, glpABC, and glpD), (b) the glycerol 3-P-regulon repressor (GlpR), (c) the ED pathway genes (edd, eda), and (d) the acetate production pathways (pta and ackA) to increase the carbon redirection to the ED and EMP pathways, respectively. 1,2-PDO is a chiral three-carbon diol with two forms: R-1,2-PDO and S-1,2-PDO. The racemic 1,2-PDO mixture is an important commodity chemical whose large-scale production proceeds mainly from propylene. It is a suitable precursor for the production of polyester, detergents, pharmaceuticals, cosmetics antifreeze, and deicers. However, the biological production of 1,2-PDO remains elusive so far. Despite more than two decades of research, too low titers and yields for industrial applications have been accomplished (Chen et al., 2013). Clomburg and Gonzalez (2011) constructed an E. coli strain capable of producing 5.6 g/L using glycerol as a carbon source. Starting from a previously established fermentative glycerol platform, they deleted competing reactions, overexpressed the mgsA, gldA, and yqhD genes, and replaced the endogenous E. coli PEP-dependent dihydroxyacetone kinase (DhaK) by a heterologous ATP-dependent DhaK from C. freundii. On the other hand the R-1,2-PDO was selectively produced under fermenter-controlled conditions by optimizing the flux by using the deletions of adhE, dld, lldD, frdA, pflB, mgsA, aldA, arcA, and ldhA::lldh genes and the engineered strain with high optical purity at final concentrations of 17.3 g/L (R-) and 9.3 g/L (S-) (Niu et al., 2019). 1,4-BDO is another important bulk chemical, broadly used for plastics and elastic fibers, that was solely manufactured from petroleum, since this compound is not naturally produced by any organism. To implement a new biochemical pathway for its production in a microbial fermentation process, Yim et al. (2011) used E. coli in silico metabolic models and pathway prediction algorithms to infer specific 1,4-BDO pathways with optimal performance. This model was used to implement metabolic engineering strategies for balancing energy and redox requirements, as well as for the removal of toxic byproducts. After these in silico studies, they introduced and optimized two heterologous pathways for 1,4-BDO synthesis in E. coli and rewired the carbon and energy sources into the pathway by deleting the adhE, pflB, ldhA, and mdh genes. Additional work for bioproduction of 1,4-BDO (bio-BDO) was reported using a systems engineering concept that was designed into two parts: 1,4 BD biosynthesis and production control. The first part was fulfilled through a new pathway capable of producing 1,4-BDO from D-xylose by assembling different combinations of enzyme genes to find the optimal version that became the backbone of the whole-cell biocatalyst. The second part was accomplished via synthetic circuits working as gene expression regulators, being a quorum sensing the most appropriate mechanism for dynamic self-control of this complex pathway (Liu and Lu, 2015). Additionally, E. coli was further engineered for the production of bio-BDO using several carbohydrates feedstock. Moreover, downstream recovery and purification processes provide bio-BDO fulfilling industry specifications and performance requirements. This platform is an excellent example of the successful implementation of a biobased process in E. coli validated by the operation of this procedure for over 50 runs at a commercial scale to produce over 4000 tons of bio-BDO. Burgard et al. extensively reviewed these metabolic engineering strategies and downstream processing for commercial bio-BDO production (Burgard et al., 2016).

3.3.2 PHA Polyhydroxyalkanoates (PHAs) are an exceptional group of biodegradable biopolyesters that have gained increasing attention for agrifood and biomedicine applications due to their biocompatibility, as well as in the packaging industry as an alternative to traditional plastics (Insomphun et al., 2016). Several microorganisms can produce and store these polymers, although high production costs hindered industrial production (Chen et al., 2013). Poly(3-hydroxybutyrate), P(3HB), is the most abundant natural PHA, though its fragile structure restricts the commercial use. However, PHAs’ mechanical and thermal properties change with the carbon source and the kind of biosynthetic pathway (Insomphun et al., 2016). Natural PHA synthases (PhaC) are diverse (four classes have been described so far) and can affect monomer structure and molecular weights and therefore polymer properties (Zou et al., 2017). Sustainable production is also a key factor to avoid competition with the food industry, and to this aim, metabolic engineering strategies focused on the biotransformation of agroindustrial waste. Nonetheless, several E. coli strains have been engineered for PHAs biosynthesis (Chen et al., 2013). For instance, a heterologous artificial pathway for dihydroxybutyrate (DHBA) biosynthesis from glycolate (Insomphun et al., 2016) by transferring the propionate CoA transferase gene (pct) from Megasphaera elsdenii, the b-ketothiolase gene (bktB), 3-hydroxybutyryl-CoA dehydrogenase gene (phaB) from Ralstonia eutropha H16, and the thioesterase (tesB) gene from E. coli (Martin et al., 2013) was accomplished. A novel biosynthetic route of quadripolymers was reached by the overexpression of a range homologous and heterologous enzymes from different bacteria: (a) E. coli native glyoxylate bypass pathway (isocitrate lyase, aceA; isocitrate dehydrogenase kinase/phosphatase, aceK; glyoxylate/ hydroxypyruvate reductase, ycdW), (b) the P3HB biosynthesis pathway of R. eutropha (b-ketothiolase, phaA; acetoacetylCoA reductase, phaB and the PHA synthase (phaC) from Pseudomonas), (c) the anaerobic succinate degradation pathway of Clostridium kluyveri (CoA transferase, orfZ; 4-hydroxybutyrate dehydrogenase, 4hbD; succinate semialdehyde

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dehydrogenase, sucD), and (d) the overexpression of propionyl-CoA transferase (pct) from M. elsdenii. This recombinant E. coli strain reached 52.6% wt biopolymer content (Li et al., 2017). In addition to the synthesis of PHBs, the production of even- and odd-chain mcl-PHA monomer with eight or more C was achieved by fatty acid reversal b-oxidation through the integration of two parallel precursor-supplying modules. These modules were performed by deletion of poxB and pflB and the overexpression of PrpP and PrpE from R. eutropha. The engineered strain accumulated around 6% wt mcl-PHA, with odd-chain monomers ranging from 7 to 13 carbon atoms about 20.03% mol (Zhuang and Qi, 2019). An innovative strategy in this field consists of the implementation of color in PHA to improve PHA features for further application industries in packaging, textiles, and medical materials. In this regard an engineered E. coli strain is capable of simultaneously producing indigo and PHB ( Jung et al., 2020).

4 Concluding remarks E. coli has been an essential tool in the development of molecular biology and biotechnology for the last 70 years. This microorganism will not only continue to be in the future to be a preferred platform for the production of enzymes for in vitro biotransformations, but it will probably also play a central role in the development of whole-cell biocatalysts. The easiness of genetic manipulation and protein overexpression will facilitate the development of novel artificially evolved enzymes and adapted genetic backgrounds to open the spectrum of chemical transformations to novel nonnatural pathways for the synthesis of new fine chemicals or building blocks in E. coli. The use of renewable resources for their production not only will boost the valorization of the products but also will help to meet a more sustainable biobased economy.

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Chapter 9

Bacillus subtilis-based microbial cell factories Heykel Trabelsia,∗, Debarun Dhalia, Yazen Yaseena, Val
erie Lecle`reb, Philippe Jacquesa,c, and Franc¸ ois Couttea,b a

Lipofabrik, Polytech-Lille, Cit
e scientifique, Villeneuve d’Ascq, France, b UMR Transfrontalie`re BioEcoAgro N° 1158, Univ. Lille, INRAE, Univ. Lie`ge, UPJV, YNCREA, Univ. Artois, Univ. Littoral C^ ote d’Opale, ICV – Institut Charles Viollette, Lille, France, c Microbial Processes and Interactions (MiPI) TERRA Teaching and Research Centre, BioEcoAgro Joint Research Unit (UMRt 1158), Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium ∗

Corresponding author: E-mail: [email protected]

1 Introduction Bacillus subtilis and related species are ubiquitous microorganisms. They are present in soil and involved in the development of a lot of fermented products mainly in Asia and Africa (Sarkar and Robert Nout, 2015). Their ability to form spores or biofilm allows them to persist in different unfavorable environments. All these elements are the main reasons why these species have been the subject of a very large number of studies and that B. subtilis became the model for Gram-positive bacteria. Indeed, on one hand, to compete with other microorganisms in the soil, it has developed a high set of antimicrobial secondary metabolites including polyketides and nonribosomal peptides (Caulier et al., 2019). Also, it is a member of the so-called plant growth-promoting rhizobacteria (PGPR) (Fierer, 2017) and is thus able to produce bioactive molecules such as plant hormone precursors, which favors plant growth. On the other hand, to develop alkaline fermentation of different vegetables, it has produced an arsenal of hydrolytic enzymes including proteases, pectinases, cellulases, and xylanases and is considered as one of the bacterial champions in secreting proteins. Its excellent fermentation properties, high product yield, and complete lack of toxic by-products led it to be one of the main industrial microorganisms for the production of homologous or heterologous proteins (Liu et al., 2013). It has also been developed as probiotics (Piewngam et al., 2018). The genome of B. subtilis 168, the reference strain, has been sequenced twice (Barbe et al., 2009; Kunst et al., 1997). Its metabolism and its regulation are probably one of the main known in the microbial world (Zhu and St€ ulke, 2018). It was one of the earliest species studied for genome minimization and a promising synthetic biology chassis for the production of heterologous compounds (Liu et al., 2019a). In this chapter, we present an overview of this host microorganism for the production of high-value compounds such as enzymes, biomolecules from primary metabolism, and secondary metabolites. Once the potentialities of this species have been presented, the different genetic and metabolic optimization strategies used in recent years to make these products marketable will be detailed to highlight the privileged position of Bacillus as a cell factory.

2 Bacillus as a workhorse for high-value compound biosynthesis 2.1 Pathways involved in enzymes and primary metabolites biosynthesis Recently, B. subtilis came into view as the main industrial workhorses for recombinant proteins. These organisms are all approved since their development in industrial-level production systems at high growth rate is easy and usually inexpensive. Furthermore, superior protein secretory competencies have much encouraged to develop Bacillus sp. as protein expression cell factories (Soares et al., 2019). To produce recombinant proteins at an industrial scale, stable expression systems are needed. At present, about 60% of commercially available enzymes are produced by Bacillus species, mainly homologous proteins, which are naturally secreted in the growth medium (Sharma et al., 2017). Microorganisms used to produce pharmaceuticals and nutraceuticals must be nontoxic and meet the generally recognized as safe (GRAS) status. Particularly, B. subtilis has been labeled as GRAS strains by the US Food and Drug Administration (Lakowitz et al., 2018). Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00002-7 © 2021 Elsevier Inc. All rights reserved.

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The variety of extracellular enzymes produced by Bacillus sp. includes as an example six different proteases, a-amylase, levansucrase, several b-glucanases, and at least two different lipolytic enzymes (Gurung et al., 2013). The corresponding enzyme genes underlie a complex regulation with DegS-DegU being the major two-component system involved. Additionally, cross-regulations were found with other two-component systems regulating sporulation (KinAKinB/Spo0F-Spo0A) and natural competence (ComP-ComA/ComQ) (Eggert et al., 2003). Furthermore, several regulatory genes including sacV, senN, senS, tenA, tenI, sinR, sinI, and abrB were shown to up- or downregulate the production of extracellular enzymes in B. subtilis (Eggert et al., 2003). Among the various groups of enzymes are those with the largest industrial application, and among these, amylases have received special attention. These enzymes that catalyze the hydrolysis of starch are valued to numerous industrial fields, such as the paper industry, food, detergents, and fermentation. The microbial amylase production level could be significantly improved by the selection of appropriate media and cultural parameters (Trabelsi et al., 2019). At least two genetic elements were involved in a-amylase synthesis in B. subtilis. The first one, amyE, determines the structure of the enzyme, and the other one, amyR, controls specifically the rate of a-amylase synthesis (Matsuzaki et al., 1974). Expression of amyE is positively regulated by an increase in DegU-P levels in the cell, and the residues at the DNA-binding helix-turn-helix (HTH) motif of DegU are necessary for the amyE expression (Gupta and Rao, 2014). Extracellular alpha-amylases were boosted by heterologous PrsA coexpression up to 2.5-fold. The impact of the overexpression of heterologous PrsAs on alpha-amylase secretion is specific to the coexpressed alpha-amylase (Quesada-Ganuza et al., 2019). The proteases are another important group, which represents approximately 30% of the total sales of enzymes worldwide (Singh et al., 2016). Proteases are mainly applied to the food, pharmaceutical, and textile industries (Contesini et al., 2018). Through a markerless genome editing method, B. licheniformis 2709 was genetically modified by disrupting the native lchAC genes related to foaming and the eps cluster encoding the extracellular mucopolysaccharide. The results revealed that genomic expression of the aprE protease was superior to plasmid expression and finally the transcriptional level of aprE dramatically increased by 1.67-fold, while the enzyme activity significantly improved 62.19% compared with the wild-type alkaline protease-producing strain (Zhou et al., 2020). Elsewhere, lipases have gained considerable interest because of their potential and wide utilization in industrial processes particularly as biocatalysts (Suci et al., 2018). These enzymes that catalyze triacylglycerol hydrolysis are broadly used in organic chemistry considering their high specificity (Guncheva and Zhiryakova, 2011). B. subtilis secretes lipases LipA and LipB into the culture medium. Both enzyme genes were differentially expressed depending on the growth conditions (Eggert et al., 2003). Additionally, some platform biochemicals produced by B. subtilis have also been reported, such as acetoin, 2,3-butanediol, riboflavin, lactic acid, shikimate, malate, isobutanol, and ethanol. 2,3-Butanediol is a valuable chemical intermediate in industrial applications, such as the production of spandex and pharmaceutical carriers (Sousa et al., 2019). It is a native secretory metabolite of B. subtilis due to the presence of butanediol dehydrogenases (Zhang et al., 2017a). Wild-type B. subtilis can produce highly purified 2,3-butanediol (>99%) under low-oxygen conditions (Fu et al., 2016). The initial pathway leading from pyruvate to 2,3-butanediol in B. subtilis has been well recognized and results from the transfer of pyruvate to a-acetolactate by the enzyme a-acetolactate synthase, encoded by the alsS gene, followed by transfer of a-acetolactate to acetoin by alsD-encoded a-acetolactate decarboxylase. Adjacent to and divergent from the alsSD operon is located the alsR gene, encoding a positive transcriptional regulator of alsSD (Nicholson, 2008). Media optimization and genetic engineering successfully are applied to boost the yield of 2,3-butanediol production in B. subtilis. Developed medium with sugarcane molasses was tested to produce the 2,3-butanediol in both shake-flask and fermenter scales, which showed peak 2,3-butanediol titers of 50 g/L (Deshmukh et al., 2015). Further, Yang and colleagues tried to enhance NADH and carbon flux to synthesize 2,3-butanediol in B. subtilis. They have disrupted the endogenous NADH oxidase YodC and lactate dehydrogenase LdhA and then introduced a formate dehydrogenase FaD. The resulting strain efficiently converted glucose into 2,3-butanediol with a yield of 56.7 g/L (Yang et al., 2015). Riboflavin and its active forms, the cofactors flavin mononucleotide and flavin adenine dinucleotide, have been extensively used in the food and pharmaceutical industries. After the internalization and conversion of glucose into glucose-6phosphate either by phosphotransferase systems or by nonphosphotransferase systems, and then glucose-6-phosphate dehydrogenase converted glucose-6-phosphate into 6-phosphogluconate. Subsequently, 6-phosphogluconate is oxidized to ribulose-5-phosphate, which represents the precursor of riboflavin biosynthesis (Gu et al., 2018). The riboflavin biosynthetic genes of B. subtilis have been sequenced and appeared to be arranged in a single operon, ribGBAH (Perkins et al., 1999). Recently, B. subtilis, which is not the natural riboflavin overproducer, has been successfully engineered to be an effective cell factory for producing riboflavin by metabolic engineering (Acevedo-Rocha et al., 2019). The use of the optimized medium led to an improvement in riboflavin production by the addition of glycine with 1 g/L producing 144.7 mg/L of riboflavin from B. subtilis ASU8 (Hemida Abd-Alla et al., 2016).

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B. subtilis broad product portfolio of pharmaceutically relevant recombinant proteins include antibody fragments, growth factors, interferons and interleukins, insulin, penicillin G acylase, streptavidin, and different kinases produced in various fermentation systems from the microtiter plates, flask, bioreactor batch, and fed-batch to continuous mode (Lakowitz et al., 2018). More enzymes and biochemical products produced by B. subtilis have been represented in Table 1.

TABLE 1 Overview of enzymes and metabolites produced by Bacillus subtilis. Molecules

Strain

Industrial application

Strategy

Reference

Nattokinase

Bacillus subtilis MX-6

Pharmaceutical as strong fibrinolytic activity

Media optimization using soybean polypeptides

Man et al. (2019)

a-Amylase

Bacillus subtilis 168M

Industrial biocatalysts; use in detergent, paper, and textile industries

Systematic engineering of transport and transcription to boost alkaline aamylase; signal peptide YwbN’ proved to be optimal

Yang et al. (2020a)

Protease

Bacillus subtilis B22

Food, pharmaceutical

Optimization of the production using the cheap substrate as groundnut oil cake. And submerged fermentation under blue light-emitting diodes

Elumalai et al. (2020)

Xylanase

Bacillus subtilis JJBS250

Used in vast industrial processes such as biobleaching of kraft pulp and digestibility of animal feed

Optimization of xylanase production by solid-state fermentation using sugarcane bagasse as the agricultural residue

Alokika (2020)

Chitosanase

Bacillus subtilis PT5

Food, textile, pharmaceutical, and medical applications

Genetic modification using the aprE signal peptide rather than the original csn signal peptide and media optimization using Box–Behnken experimental designs

Su et al. (2017)

Chitinase

Bacillus subtilis NPU 001

Biotechnological applications, especially in the biocontrol of fungal phytopathogens

Utilization of shellfish processing waste

Chang et al. (2010)

Carboxymethyl cellulase

Bacillus subtilis K18

Industrial processes such as in textile, pulp and paper, detergent, and food industries

Optimization using submerged fermentation using potato peel as sole carbon source

Irfan et al. (2017)

Lipase

Bacillus subtilis A. S.1.1655

Food and beverages, detergents, biofuel productions, animal feed

Recombinant lipase from the strain Bacillus subtilis IFFI10210

Ma et al. (2006)

Glutaminase

Bacillus subtilis OHEM11

Food industries as a flavor enhancer and pharmaceutical application

Media optimization with bagasse was the best inducer for the production under solid-state fermentation for newly isolated bacterial strain producing extracellular L-glutaminase

Orabi et al. (2020)

Riboflavin

Bacillus subtilis ATCC 6051

Food, feed, and pharmaceutical industries

Media optimization using a one-factorat-a-time approach to evaluate the effect of different carbon sources

Oraei et al. (2018)

scyllo-Inositol

Bacillus subtilis

Pharmaceutical application potential efficacy in preventing Alzheimer’s disease

Glucose-6-phosphate is converted into myo-inositol-1-phosphate (MI1P) by introducing synthase (MI1PS) from Mycobacterium tuberculosis ino1 into B. subtilis to convert (G6P) into MI1P

Michon et al. (2020)

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TABLE 1 Overview of enzymes and metabolites produced by Bacillus subtilis—cont’d Molecules

Strain

Industrial application

Strategy

Reference

N-Acetylglucosamine

Bacillus subtilis strain BSGN12

Biomedical, food, and chemical industries

An applied strategy that combines pathway enzyme engineering and host engineering to resolve overflow metabolism

Ma et al. (2019)

Poly-g-glutamic

Bacillus sp. FBL-2

Food, medical, water treatment, cosmetic, and agricultural industries

Use low coast agricultural by-products (rice bran and wheat bran) as production media

Song et al. (2019)

Acetoin

Bacillus subtilis TH-49

Food industry and as biofuel

The mutant was obtained by treating B. subtilis N-12 with ultraviolet ray (ultraviolet ray [UV]) or nitrosoguanidine (NTG) or compound mutation and subjecting it to a shakeflask fermentation selection procedure

Xu et al. (2011)

Menaquinone-7

Bacillus subtilis BSMK_11

Dietary supplements or drug treatments in the food, pharmaceutical, and healthcare industries

Overexpressing the genes glpK, glpD, aroGfbr, pyrGfbr, hepS, vgb, and knockout genes mgsA and araM

Yang et al. (2020b)

Dipicolinic acid

Bacillus subtilis 168

Variety of applications, such as antimicrobial agents, antioxidants, chelating reagents, and polymer precursors

Genetic modification by the deletion of acetoin synthesis genes alsSD to increases DPA productivity

Toya et al. (2015)

Adenosine

Bacillus subtilis A509

The pharmaceutical industry as antiarrhythmic agent

Inactivation guanosine 50 monophosphate synthetase gene to increase the metabolic flux from inosine 50 -monophosphate to adenosine

Li et al. (2019a)

Chondroitin

Bacillus subtilis E168C/ pP43-D

Medical and clinical applications

The work provided alternative safer synthetic pathways for metabolic engineering of chondroitin and a useful approach for enhancing production

Jin et al. (2016)

L-Asparaginase

Bacillus subtilis strain hswx88

Anticancer agent

Isolated and identified new extracellular L. asparaginase producer from hotspring

Pradhan et al. (2013)

Isoprene

Bacillus subtilis DSM 402

Pharmaceutical industry

Enhance the production when Kudzu isoprene synthase (kIspS) gene has been heterologously expressed in B. subtilis DSM 402

Gomaa et al. (2017)

D-Ribose

Bacillus subtilis UJS0717

The energy source to improve athletic performance

Production using cheap raw material corn starch hydrolysate was improved by using one-factor-at-a-time experiments

Wei et al. (2015)

Surfactants

Bacillus subtilis

Food, agriculture and pharma

Genetic and media optimization

Ongena and Jacques (2008)

2.2 Gene clusters involved in secondary metabolites biosynthesis Diverse Bacillus species can produce a broad range of active secondary metabolites displaying high diversity in structures and activities. These active compounds represent a versatile source of high added value products owing to their great potential for applications in food (as preservative), agriculture (for plant growth promotion, nutrient acquisition, or antagonistic activities), and pharmaceutical industries (antibiotics). They can be classified according to their activities (toxin,

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siderophore, antibiotic, and surfactant) and their role in ecosystems (competitive weapons, chelating agents, hormones, and plant growth promoters). Another approach is to consider their biosynthetic pathways, allowing the classification in proteins and derivatives so-called RiPPs for ribosomally synthesized and posttranslationally modified peptides (Li and Rebuffat, 2020), compounds as small antibiotics with specific pathways, and products synthesized through pathways including modular megaenzymes as polyketide synthases (PKS) (Shen, 2003) and nonribosomal peptide synthetases (NRPS) (S€ ussmuth and Mainz, 2017). Genome mining for most of the biosynthetic gene clusters (BGCs) can be carried out with specific bioinformatics tools like antiSMASH (Blin et al., 2019) or NeuRiPP (de los Santos, 2019). Increasing knowledge about BGCs and their regulation allow engineering for production and heterologous expression for industrial needs (Lecle`re et al., 2005; Vassaux et al., 2019). B. thuringiensis produces shaped crystal toxins during sporulation (Schnepf et al., 2010). The so-called Cry proteins are toxic for invertebrates and are used in biocontrol for their insecticidal properties. The cry genes, generally located on a plasmid, encode a protoxin that is converted into toxin by proteolytic cleavage. Other RiPPs are bacteriocins and lantibiotics characterized by the presence of unusual residues lanthionine as lichenin, cerein, megacin, coagulin, polyfermenticin, subtilin, sublancin, or subtilolisin (Sansinenea and Ortiz, 2011). Many of these RiPPs have antimicrobial activity, but they differ from antibiotics as they are active against species related to the producing ones. The BGCs generally encompass structural and accessory genes responsible for transport, regulation, processing leading to mature form from the precursor encoded by the biosynthetic gene, and self-immunity. Bacilysin (also known as tetain) and rhizocticin are nonribosomal antimicrobial peptides synthesized by specific enzymes. Bacilysin, among the oldest known natural products from B. subtilis 168, contains L-Ala and L-anticapsin essential for antimicrobial activity. The two residues are linked by an L-amino acid ligase encoded by bacD gene; the other genes of the BGC are involved in the synthesis of the noncanonical anticapsin residue (Ozcengiz and Ogulur, 2015). Rhizocticins are antifungal di- and tripeptides containing the 2-amino-5-phosphono-3-pentanoic acid nonproteinogenic residue. The producer B. subtilis ATCC6633 contains a unique BGC encoding the enzyme machinery for the biosynthesis of the phosphonate (Kaspar et al., 2019). Most of the compounds produced by Bacillus strains through modular PKS, NRPS, or hybrid PKS/NRPS pathways exhibit relevant activities as antibiotics, antifungal, siderophores, or antitumoral agents. Polyketide synthases (PKS) allow decarboxylative condensation of simple precursor units such as acetyl-coenzyme A and malonyl-CoA. This process needs the enzymatic domains b-ketoacyl synthase (KS), acyltransferase (AT), and a phosphopantetheinylated acyl carrier protein (ACP) (Hertweck, 2009). A set of KS-AT-ACP constitutes a module that can also optionally contain modifying enzymatic domains such as ketoreductase (KR), enoyl reductase (ER), or dehydratase (DH). Nonribosomal peptide synthetases (NRPS) are another type of modular megaenzymes in which each module is responsible for the incorporation of one amino acid into the peptide chain. Considering that the building blocks can be proteinogenic and nonproteinogenic units, they are now referred as to monomers. To incorporate a monomer, several basic enzymes are needed represented by an adenylation domain (A), a phosphopantetheinylated peptidyl carrier protein (PCP also named thiolation domain), and a condensation domain catalyzing the peptide bond formation (S€ussmuth and Mainz, 2017). Some NRPS modules also include modifying domains such as epimerization (E) leading to D-monomers or methylation (M). Both PKS and NRPS assembly lines are ending by a thioesterase domain (TE) allowing the release of the neoformed compound, and owing to the presence of a thiol group on the phosphopantetheinyl cofactor transferred onto the PCP or ACP domain, both PKS and NRPS belong to the thiotemplate megaenzymes family. Considering the modular organization of the megaenzyme and the thiotemplate mechanism, it is not surprising that hybrids containing PKS together with NRPS modules can be found. Macrolactins, difficidin, and oxydifficidin are polyketides produced by several Bacillus strains, inhibiting prokaryote protein synthesis with a broad spectrum of antibacterial activity (Hamdache et al., 2011; Harwood et al., 2018; Kaspar et al., 2019; Sansinenea and Ortiz, 2011). Difficidin and macrolactin BGCs are composed of 18 genes, including seven multimodular PKS and nine genes with seven PKS, respectively (Aleti et al., 2015). Bacillaene is a bacteriostatic compound. The BGC is composed of 15 genes spanning over 80 kb in the B. subtilis 168 genome, representing 2% of the total genome length. The core assembly line synthase is composed of five multimodular proteins including two NRPS/PKS hybrids and three PKS. Moreover the gene cluster encompasses 10 genes coding for stand-alone enzymes involved in trans- to the multimodular proteins (Harwood et al., 2018). Amicoumacins belonging to a larger group of bacterial isocoumarin natural products have been identified in B. subtilis and B. pumilus growth medium. They harbor antibacterial activities against clinical-relevant pathogens by ribosome inhibition and antiinflammatory and antiulcer activities. A prodrug synthesized by a hybrid PKS-NRPS BGC is converted into an active compound by a peptidase encoded by a gene belonging to the same BGC (Li et al., 2015b; Park et al., 2016). Many secondary metabolites produced by Bacillus strains are built up by NRPS, like bacillibactin siderophore. Siderophores are low-molecular iron-chelating compounds that bind ferric iron with high affinity and shuttle it into the cell

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through specific receptors, where it can then be solubilized and introduced into different metabolisms. The synthesis of siderophores is iron regulated so that they are only produced and secreted in iron-limited environments. Bacillibactin is a catecholate siderophore containing the Dhb (2,3-dihydroxybenzoate) monomer, necessary for the iron chelation. The structure of the siderophore is (Dhb-Gly-Thr) x3. The NRPS only contains three modules and works with an iterative mechanism. The bacillibactin BGC is an operon named dhbACEBF, encompassing NRPS domains and enzymes for the dhb synthesis from chorismite (Harwood et al., 2018). Bacitracins are bacteriostatic antibiotic polypeptides reported to be associated with strains of B. licheniformis and B. subtilis. They are coproduced as mixtures containing at least 10 distinct forms differing by one or two amino acids, owing to the flexibility of some A-domains of the biosynthetic NRPS complex constituted of three synthetases: BA1, BA2, and BA3. The structure of bacitracin A is complex. It contains a thiazoline as a result of the condensation of isoleucine and cysteine residues in the N-terminal part linked to the C-terminal heptapeptide cyclized by peptide bond between acarboxylic acid group of Asn and amino group of e-carbon of Lys. Due to the presence of E-domains, four amino acids are D-monomers (Harwood et al., 2018; Konz et al., 1997). The emetic toxin cereulide is a cyclic dodecadepsipeptide produced by B. cereus, consisting of three repetitions of the tetramer [D-O-Leu-D-Ala-L-O-Val-L-Val]. The toxin is nonribosomally synthesized through a 24-kb operon, cesTPABCD. CesA and CesB are the NRPS, each containing two modules (Ehling-schulz et al., 2006). Cereulide-producing strains are usually recognized as food-poisoning pathogens although relative isolates are rare in the environment. Bacillus species also produce lipopeptides (LPs) via NRPS. LPs, classified into five families, represent the most abundant group of secondary metabolites, and they have received attention for their antimicrobial, plant defense elicitor, cytotoxic, antitumor, immunosuppressant, and surfactant properties (Ongena and Jacques, 2008; Raaijmakers et al., 2010). They are amphiphilic compounds constituted of a peptide moiety generally cyclized, linked to a b-hydroxy fatty acid, except for iturins where it is a b-amino fatty acid. Surfactins (surfactins, pumilacidins, lichenisins, and esperins) and iturins (iturins, mycosubtilins, bacillomycins, and mojavensins) are heptapeptides, while fengycins (fengycins and plipastatins) are decapeptides. Surfactins are well known for their surfactant properties lowering surface tension from 72 to 27 N/m. Fengycins and mycosubtilins display the best antifungal activity enabling application in biocontrol for their antagonism against phytopathogens (Ongena and Jacques, 2008). LP producing B. subtilis strains can coproduce up to these three families, meaning that the genome harbors the corresponding BGCs, including large NRPS (or hybrid PKS/NRPS for iturins) operons (Harwood et al., 2018). The strain B. subtilis 916 was shown to be able to nonribosomally coproduce a fourth family of lipopeptides named locillomycins. The nonapeptide displaying activity against bacteria and viruses is synthesized by a NRPS containing only six modules, three of them being used twice in an iterative mechanism (Luo et al., 2015). Finally a family of lipoheptapeptides displaying antifungal activities was named kurstakins. The biosynthetic operon containing three genes (krsA, krsB, and krsC) encoding three multidomain NRPS proteins were detected in the genomes of B. thuringiensis and B. cereus (B
echet et al., 2012).

3

Engineered Bacillus subtilis

3.1 Metabolic modeling for strains optimization of B. subtilis Metabolic flux analysis (MFA) using carbon labeling is widely used in several microorganisms to better understand the reaction network and to optimize metabolic flux for the production of biomolecule (Matsuoka and Shimizu, 2010). Carbon labeling (C13) technique was used in B. subtilis, for example, for the production of heterologous cellulase (Toya et al., 2014), for the production of lipase (Song et al., 2013), for the production of riboflavin under glucose limitation (Dauner et al., 2001), or in the function of oxygen availability (Hu et al., 2017). It was used also more recently in B. licheniformis for the production of poly-g-glutamic acid (He et al., 2019). The rapid development of metabolic pathway database online (KEGG, SubtiWiki, etc.), containing extensive regulation information, has favored, in the last decade, the emergence of efficient bioinformatics tools for the modeling of these pathways in many model microorganisms as for B. subtilis (Michna et al., 2014). Gene knockout (KO) is a relevant technique to optimize the production of metabolites of interest in B. subtilis, but the complete genome of this microorganism is composed of more than 4000 genes, and molecular biologists need bioinformatics tools to predict the optimal strategy in gene KO. To solve this problem the model-based prediction has been developed using different computational approaches such as flux balance analysis (FBA) and derivative methods, elementary mode analysis (EMA), or constraint-based (CB) methods. One of the first tools for KO prediction was OptKnock (Burgard et al., 2003), which was initially developed using CB for Escherichia coli, but it was used also for B. subtilis in a combination with Bees Hill Flux Balance Analysis for ethanol production (BHFBA) (Choon et al., 2013, 2014). FBA is very relevant for modeling reaction networks without kinetic information, but it can be also problematic for further

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optimization using kinetic equations. EMA is another approach in pathway modeling; it was used to construct the metabolic network for the production of isobutanol in B. subtilis and to predict gene KO. After the KO of ldh and pdhc genes, the engineering strain overproduced 70% more of this biomolecule (Li et al., 2012). EMA approach was also used for the production of nattokinase (Unrean and Nguyen, 2013). Nevertheless, EMA is limited by the number of metabolic pathways that can be analyzed at the same time. More recently a modeling language for reaction networks with partial kinetic information was formulated using branched-chain amino acid metabolic pathway from B. subtilis as a model (Niehren et al., 2016). This approach uses abstract interpretation and supports unknown kinetic functions and activation or inhibition mechanisms. It was applied to predict single gene KO on the branched-chain amino acid metabolic pathway for the overproduction of surfactin in B. subtilis (Coutte et al., 2015). Despite the efficiency of this method, improvements are needed to increase the safety of predictions of multiple genes KOs. A combination of this approach with FBA or EMA will surely provide solutions.

3.2 Genome editing Through the last decades, thanks to the evolution and the design of new genetic engineering tools, many different methods for editing bacterial genomes have been adapted for B. subtilis. The main idea is to create deletions and insertions or introduce punctual mutations. Here, we summarize the most used procedures.

3.2.1 Homologous recombination-based modification Classical genome modification procedures are based on the use of integrative plasmids harboring homologous regions that will undergo simple or double crossover. The insertion of a selectable marker, mainly antibiotic resistance, into the target strain disrupts the gene or operon by homologous recombination (Fig. 1A). Despite the laborious screening for the expected

A

IntegraƟon casseƩe

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A

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B Integrant

A

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Marker excision

A

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Marker

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(A)

(B)

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B

A

Cas9 protein sgRNA

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NGG DNA cleavage

DSB Repair template

HDR

NHEJ deleƟon

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(C) FIG. 1 Schematic presentation of genome editing techniques used in B. subtilis. (A) Homologous recombination–based deletion using marker gene. (B) Homologous recombination–based deletion using marker gene that undergoes excision for markerless final construct. (C) CRISPR/Cas9-mediated genome editing. DSB, HDR, and NHEJ refer to double-strand breaks, homology-directed repair, and nonhomologous end joining, respectively. (The figure is adapted from Dong, H., Zhang, D., 2014. Current Development in Genetic Engineering Strategies of Bacillus Species; Jacinto, F.V., Link, W., Ferreira, B.I., 2020. CRISPR/Cas9-mediated genome editing: from basic research to translational medicine. J. Cell. Mol. Med. 24, 3766–3778.)

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mutants, this approach was used to create genome-scale deletion libraries (Koo et al., 2017). Nevertheless, the removal of the marker by excision (Fig. 1B) or the use of a new one is mandatory to introduce a new modification. The method is limited to targeting an entire gene and cannot be used for precise point mutations. The experimentalist is very limited and cannot proceed with multiple chromosomal modifications. From a synthetic biology perspective, efficient tools that do not require prior genetic modification and allow modular markerless or scarless genome manipulation are urgently needed. Different marker-free and counter selective methods were developed in the last years. Auxotrophy-based genetic engineering, site-specific recombination-based manipulations, thermosensitive plasmid-based approaches, and toxin genebased genetic engineering strategies were described in detail (Dong and Zhang, 2014). The mazF toxin-antitoxin counterselection marker system is of high interest (Lin et al., 2013; Zhang et al., 2006). The results have shown that mazF can be applied to many Bacillus species without prior genetic background modification of the host. After integration into a target chromosome locus via double-crossover recombination, the mazF cassette is excised by a single crossover event.

3.2.2 CRISPR/Cas9-mediated genome editing The increasing need for clean, efficient, and surgical methods to engineer customized B. subtilis has urged researchers to adopt the CRISPR/Cas9 strategy shown to be very efficient with different other species (Charpentier, 2015; Westbrook et al., 2016). CRISPR that stands for clustered regularly interspaced short palindromic repeats was derived from the immune systems in bacteria and archaea such as Streptococcus pyogenes and Staphylococcus epidermidis (Haft et al., 2005; Marraffini and Sontheimer, 2008). This system functions as an effective immune system, but in the synthetic biology field, it is used for remarkably targeted genome editing. Briefly the Cas9 endonuclease guided by a small sequence called guide RNA (gRNA) generates double-strand breaks (DSB) into a precise sequence. The endogenous DNA repair machinery is then recruited and two possibilities of repair could occur. Nonhomologous end joining (NHEJ) repair mechanism is error prone and could generate indels (insertion-deletion) that result in mutated gene products. This repair pathway has been recently shown to be not effective within B. subtilis (Toymentseva and Altenbuchner, 2019). Meanwhile, homologydirected repair (HDR) is considered the dominant mechanism for precise DSB repair, and it requires higher sequence similarity between the target and the template DNA (Fig. 1C). The first attempts to develop the required tools for efficient use in B. subtilis were successful (Altenbuchner, 2016; Zhang et al., 2016). Multiple insertions were successfully performed to integrate b-galactosidase in different loci of the chromosome (Watzlawick and Altenbuchner, 2019). On the other hand, deletions of a multigene were shown to be efficient. Zhang and colleagues have edited the undomesticated B. subtilis ATCC 6051a by disrupting a set of genes shown to hamper its use in industrial fermentation (Zhang et al., 2016). CRISPR/Cas9 editing approach also enables users to introduce precise mutations (Price et al., 2019), which is quite impossible using traditional genetic engineering tools. More interestingly the use of replicative vectors instead of integrative ones gives the possibility of plasmid curing. For instance, Altenbuchner has used the thermosensitive origin of replication pE194ts. After editing processing, an overnight growth at 42°C is required to remove the plasmid from the edited strain. Furthermore, synthetic biologists are motivated to reduce the time allowed to CRISPR plasmid construction. Some works are nowadays oriented to create technical tricks to make faster genome editing. Price and colleagues have suggested cotransformation of the plasmid harboring gRNAs and linear DNA fragment relative to the repair template (Price et al., 2019). To expand the CRISPR toolbox for B. subtilis, the same team has assessed the efficiency of the MAD7 nuclease instead of Cas9. Their results have shown editing rates reaching 100%. Interestingly the catalytically inactive variant of this nuclease (dMAD7) used for CRISPR interference (CRISPRi) has also demonstrated its ability to reduce target gene expression reduction around 70% (Price et al., 2019). These results give an alternative for Cas9 promoting toxicity when highly expressed. It is also important to keep in mind that most of the recorded successes were performed within domesticated strains of B. subtilis. The task is still challenging with difficulty in transforming strain. To solve the problem of undomesticated strains, an alternative genome integrated method was tested (Mahipant et al., 2019). The authors have successfully used a shuttle Bacillus-E. coli vector to deliver homologous recombinant DNA and propagate itself inside the host cell, increasing the likelihood of genome integration of the recombinant DNA.

3.3 Transcriptional engineering to overproduce biomolecules or proteins of interest by B. subtilis The genetic engineering of B. subtilis into an efficient cell factory requires the control of its transcriptome and its central metabolism, which is widely affected by the environmental conditions through transcriptional factors or global pleiotropic proteins of regulation, such as sigma factors (more than 20 kinds of sigma factors are present in B. subtilis), CodY (global

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transcriptional regulator), CcpA (catabolite control protein), and TnrA (global nitrogen regulator) (Sonenshein, 2007; Zhu and St€ ulke, 2018). To avoid regulation and to unlock or increase the gene expression, many strategies can be used in this host microorganism. One of the most used strategies is the promoter exchange because they are the main targets of regulatory factors and thus keys to control metabolic pathways. Thanks to the comparative analysis of the results from the first strategy, the development of promoter engineering has also experienced a significant boom in recent years. Finally, one of the strategies very developed in other species and that could have meaning in Bacillus is unlocking the transcription of cryptic biosynthesis pathway through the cis-regulatory elements (CRE), which are the target of transcription factors.

3.3.1 Promoter exchange in B. subtilis For over 40 years the transcriptomic regulation via promoters in B. subtilis has been the subject of numerous studies. Studies at the time revealed that there were two types of complex promoter organizations that may influence gene expression. The first one is very efficient for gene expression during the exponential growth phase, which is the case, for example, for ribosomal RNAs. The other one controls the expression of genes during the stationary phase of growth. At the time the promoter P43, which promotes the expression of cytidine/deoxycytidine deaminase, already appeared as one of the most powerful promoters in B. subtilis (Doi, 1984; Goldstein and Doi, 1995). For the past 15 years, this strong promoter was successfully used to replace natural one for the overproduction of many biomolecules or enzymes of interest as, in a nonexhaustive list, N-acetylglucosamine (Liu et al., 2014b), pullulanase (Song et al., 2016a), alkaline polygalacturonate lyase (Zhang et al., 2013), L-asparaginase (Feng et al., 2017), and mannanase (Zhou et al., 2013) or to increase the metabolic flux in acetoin synthetic pathway for the overproduction of 2,3-butanediol (Fu et al., 2016). But P43 is not the only promoter presenting an interesting and strong activity in B. subtilis. Browsing the very extensive scientific literature on this subject, we can notice that the main strong promoters commonly used in B. subtilis are Pveg (veg codes for a protein involved in biofilm formation), PamyE (amyE codes for a-amylase), Pylb (which promotes N-acetyltransferase YlbP), Pxyl (inducible promoter that controls the xyl operon), and Pspac (inducible promoter using isopropyl b-D-1-thiogalactopyranoside (IPTG) as an inducer). In 2012 Phan et al. have compared the P43 and Pylb promoters through the overexpression of GFP. The results reveal that Pylb allowed an overexpression 7.8 times higher than P43. This was then confirmed by the overexpression of the pullulanase (Phan et al., 2012). In a very recent work, the authors have measured the strength of a dozen promoters for optimizing the production of surfactin, thereby lifting regulation via quorum sensing thanks to a fusion with the lacZ gene. This has revealed also that P43 was one of the strongest ahead of Pveg and well ahead of PamyE (Wu et al., 2019a). The same approach has been followed for the production of lichenysin (Qiu et al., 2014). In 2009 Fickers et al. have replaced the promoter of the mycosubtilin operon by the inducible one Pxyl (Fickers et al., 2009). Sun et al. used Pspac to overproduce the surfactin in B. subtilis fmbR (Sun et al., 2009). Promoter exchange can be also carried out with heterologous promoters from other species for the production of lipopeptides such as iturin A (Tsuge et al., 2001), mycosubtilin (Lecle`re et al., 2005), and surfactin (Coutte et al., 2010) using PrepU from S. aureus or PamyQ from B. amyloliquefaciens (a-amylase) for plipastatin production (Ongena et al., 2005). In other studies the production of D-lactic acid was enhanced using the Pldh promoter from B. coagulans (which promotes lactate dehydrogenase) (Awasthi et al., 2018); the lipase A was successfully expressed using the strong constitutive promoter PAE (coming from the A1 sequence of bacteriophage j29) without inducer. In this work, PAE appears better than P43 (Ma et al., 2018). The strong constitutive PHpaII promoter from Sta. aureus was used to express endoglucanase (Liu and Du, 2012). In this study the PHpaII performances were compared with the PsacB ones (sacB codes for levansucrase). Interestingly, these results demonstrated that PsacB might be more efficient than PHpaII (Liu and Du, 2012). As demonstrated by these examples, the promoter exchange is one of the easiest methods of synthetic biology to overproduce biomolecules of interest. The existence of libraries of molecular tools such as those of Bacillus BioBrick Box (Popp et al., 2017) has facilitated this type of study. There are now many studies that have analyzed the properties of promoters of B. subtilis under various conditions. In a recent study the authors characterized the properties of 114 different promoters of B. subtilis by using the gfp gene as a reporter. This made it possible to classify them into four classes based on different culture phases (lag-log phase, exponential phase, intermediate phase, and stationary start and stationary phase). The strength of the latter ranged from 0.03 to 4.53 times that of P43 (Yang et al., 2017). In another similar study, 84 promoter sequences were studied. The authors demonstrated that several native promoters in B. subtilis displayed stronger activities than P43. It is the case of the promoters PtrnQ (trnQ codes for arginine-specific tRNA), PsigX (sigX codes for sX factor), and PgroES (groES code for chaperonin protein), which exhibited 4.53, 3.03, and 1.55 times of P43’s strength in expression, respectively (Song et al., 2016b). In this study the PtrnQ activity was then validated to overexpress a-amylase protein. Interestingly, analysis of the promoter sequences reveals that the strong promoters have a conserved 10 region but the 35 region is subject to important change, as well as the length of the spacer, which varies from 16 to 21. This last work shows us

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that the promoter structure is also an interesting way of optimization. Indeed, even if the change of a native by a strong one brings improvements, the fact remains that the promoter performances are usually limited by the specificity of one sigma factor (Han et al., 2020).

3.3.2 Promoter engineering in B. subtilis In recent years, new approaches have emerged on this theme, particularly in the use of several promoters or the generation of mutated or artificial promoters. For example, a dual promoter system (PHpaII–PamyQ) was used for extracellular expression of b-cyclodextrin glucanotransferase (Zhang et al., 2017b). For the overproduction of pullulanase, the authors first analyzed the activity of several promoters based on transcriptomic data. Then, they have selected the strongest ones and combined them to overproduce this enzyme in B. subtilis. One of their constructs harboring three promoters in a row (PsodA + PfusA + PamyE) showed a maximum activity 21.9 times higher than the strain having only PamyE (Meng et al., 2018). Recently, nattokinase was overexpressed using a combination of triple promoters with P43 and PHpaII in different orders. The best enzyme expression was obtained in 5-L fermenter using the promoter PHpaII-PHpaII-P43 (Liu et al., 2019b). Synthetic promoter library was created by Liu and colleagues from a microarray analysis of the transcriptome data of B. subtilis 168. In this study, 214 promoters with various strengths were evaluated using GFP. From these results, authors have constructed tandem dual promoters displaying an increase of GFP expression from 1.2- to 2.77-fold compared with that of promoter P43, and they were used to overproduce inosine and acetoin (Liu et al., 2018). In another way, promoters can be also newly designed from existing ones, optimizing the main region as upstream sequence (UP), 35 and 10. Cheng et al. present a study where various semiartificial promoters based on the optimization of the promoter of the operon srfA (PsrfA) of the strain B. subtilis 168 were created by shortening its sequence and modifying the nucleotides at the level of the regions preserved 10, 15, and 35. This made it possible to generate a promoter whose strength, evaluated through a fusion with GFP, is 150% greater than that of PsrfA, which is already considered as a strong promoter in the strain 168. This study shows that this type of promoter could be used to develop a self-inducing expression system for the overexpression of gene or heterologous proteins in B. subtilis (Cheng et al., 2016). This same approach had also been developed for the overproduction of aminopeptidase and nattokinase in B. subtilis (Guan et al., 2016). Zhou et al. have optimized the 35, 10 core region and UP of the Pylb promoter, leading to an increase of 195-fold of the superfolded GFP expression (Zhou et al., 2019a). Yang et al. have deleted 27 T or 31A from PHpaII promoter, which enhance by a factor 250 the alkaline aamylase activity in B. subtilis 168 derivative strain (Yang et al., 2020a). In the work developed by Feng et al., P43 promoter variants were created (by replacing the positions 28: A ! G and 13: A ! G), which enhanced the strength of the modified promoter for the production of L-asparaginase (Feng et al., 2017). The same year, Jiao et al. identified and characterized four powerful promoters of B. subtilis using a transcriptomic study: PgroE, P43, PrplK (rplK codes for 50S ribosomal protein L11), and PsspE (sspE codes for small acid-soluble spore protein). At the end of their experiments, the authors created a new artificial and inducible Pg2 promoter from a PgroE-lacO fusion, and then, PsrfA was replaced by this new promoter, allowing the production of surfactin 5.98 g/L. A new modification of the promoter by a point mutation at the level of the 10 and 35 regions made it possible to obtain a Pg3 promoter and an ultimate production of nearly 10 g/L of surfactin ( Jiao et al., 2017). One of the most interesting and complete work is the one proposed by Guiziou and colleagues in 2016. In this comprehensive study the authors used numerous genetic tools including libraries of promoters, ribosome-binding sites (RBS), and protein degradation labels to fine-tune gene expression in B. subtilis. Among these tools, differences in GFP expression ranging from 1 to 14,000 times were measured using different RBS. They also created promoter libraries from three strong promoters (Pveg, PserA, and PymdA) using conserved sequence randomization strategies 10. The expression levels obtained are 100–900 times greater than the control condition (Guiziou et al., 2016). Very recently, another interesting approach was developed by Han et al. Authors have rationally designed hybrid promoters using two or three promoters recognizable by several sigma factors. These natural minimal promoters, which use sA, sH, and sW, present the advantage to be stable during the different growth phases. Then, they also designed 13 synthetic RBSs presenting translation initiation rates from a low to a high level using the RBS Calculator software (Han et al., 2020). All of these works show that the progress in promoter understanding and the construction of promoter libraries are efficient tools to increase B. subtilis’s performances as host microorganism for the production of a marketable biomolecule in different industrial applications. Nevertheless, it most often raises other limitations in the cell, in particular on the stability of mRNAs, the efficiency of the translation, and the RBS efficacy. This will be the subject of the next subchapter.

3.3.3 Expression of cryptic biosynthetic gene clusters Before going deeper into the mechanisms allowing the optimization of translation in Bacillus, it seems interesting to evoke another approach that could be applied to new isolates of Bacillus strains. It is based on the possibility of discovering and/or awakening so-called “sleeping” or “cryptic” biosynthesis pathways. For example, in B. amyloliquefaciens, more than 8.5%

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of the genome codes for the biosynthesis of antibiotics and siderophores by nonribosomal pathways (Chen et al., 2007; Ochi, 2017). To do this an in silico analysis of the genome is necessary with tools such as antiSMASH (Weber et al., 2015). Once all the clusters have been identified by bioinformatics, the question arises of how many are expressed by the cell when it is grown in a laboratory environment. A relatively simple solution would then be to modify the promoter as it was just described previously. However, Rigali and colleagues have proposed a completely different approach, which seems to be extremely relevant and which could be applied to secondary metabolites (Rigali et al., 2018). This approach is based on the cis-regulatory elements (CRE), which are the target of transcription factors. The CRE-transcription factor pair must, therefore, be considered as a key locking system for the transcriptional response (Rigali et al., 2018). A response to an external stimulus, a particular substrate, pheromones, and alormones can then be the key that unlocks or locks the system. To be effective, this rather bottom-up approach can be based on the modeling of metabolic pathways by bioinformatics. If the authors have demonstrated the relevancy of this approach in Streptomyces, some have been interested in these cryptic pathways in Bacillus. For example, the impact of the presence of scandium in the culture medium was evaluated on amylase and bacilysin production in B. subtilis. The results showed that scandium has a significant impact on the production of both biomolecules by activating the two promoters PywfB and PamyE (Inaoka and Ochi, 2011a). In a quite different approach, the same authors have also investigated the impact of a mutation in the rpoB gene (which codes for RNA polymerase b-subunit and confer rifampicin resistance). The introduction of the rpoB mutation S487L into a B. subtilis strain allows awakening of the production of the 3,30 -neotrehalosadiamine (Inaoka et al., 2004; Inaoka and Ochi, 2011b). The same mutation has also led to an enhancement of the biosynthesis of cellulolytic and proteolytic enzymes and a decrease of levansucrase production in B. subtilis natto (Kubo et al., 2013). This finding suggested that both the greater stability and affinity of the RNA polymerase for the promoter region are involved in the overexpression of these genes.

3.4 Engineered Bacillus on the translational level B. subtilis CDS has been divided into three different classes, where the second class represents genes related to transcription and translation machinery, core intermediary metabolism, and stress proteins. Ninety-nine essential genes play a role in protein synthesis, secretion, and quality control (Kobayashi et al., 2003). For the efficient production of heterologous protein (Fig. 2), the gene of interest should be linked to a B. subtilis signal peptide. The signal sequence should be preceded by mRNA stability-enhancing sequences (SES) at the 50 end, followed by a translation initiation sequence or ribosome-binding site (RBS). The terminal end of the gene should end with a 30 SES; the primary function is to protect the mRNA from degradation by 30 exonucleases, thus increasing the half-life of the mRNA, and be translated in a polysomal mode. The presence of the signal peptide in the N-terminus will facilitate the secretion of the final protein (Doi, 1984). Previously the main bottleneck was the production of heterologous proteins due to plasmid instability, proteolysis, and secretion (Bolhuis et al., 1999). This was overcome by Chen and coworkers as they succeed in producing nattokinase and endoglucanase E1 using B. subtilis DB428 (Chen et al., 2010).

3.4.1 Factors affecting translation rate The efficiency of translation is governed by several factors including the rate of translation initiation (regulated by RBS), the stability of mRNA, codon usage, ribonucleases activity, substrate availability, and toxicity of the product. All these factors can be optimized through genetic engineering. Utilizing modular pathway engineering various parameters can be optimized, i.e., selecting most favorable RBS to optimize the rate of translation, degree of interaction of 30S ribosome mRNA, presence of SES on both end of mRNA, presence of tRNAs that can accept codons which are used rarely, effective secretion system for efficient transport of the product into the media (Doi, 1984).

3.4.2 Regulation Stability-enhancing sequences The essential parameters affecting mRNA stability are RNA secondary structures, translation initiation frequency, and codon usage (Steege, 2000). The transcripts of heterologous origin can be stabilized using natural (Agaisse and

FIG. 2 Schematic representation of different modules within the gene circuit. P, promoter; GOI, gene of interest; SES, stability-enhancing sequences; T, terminator.

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Lereclus, 1996) and artificial structures (Smolke and Keasling, 2002) on either end of the transcript. Integrating these sequences can enhance the half-lives by three- to fivefold. A structural modification through the addition of stabilization-enhancing sequences (SES) will provide greater resistance to exonucleases. It was found that the insertion of an inverted repeat in the 50 end of the mRNA before the SD region can form nuclease-resistant structures. However, this modification should be done in such a way so that it does not interfere with the interaction between the ribosome and SD region (Abelson, 1979). The 50 -end stabilizing elements also protect from RNases J1 and Y, which are important enzymes of RNA degradosome (Lehnik-Habrink et al., 2012). The presence of CG-rich inverted repeats followed by several Us provides a transcription-termination signal. This region at the 30 end of the mRNA also provides resistance from exonucleolytic attack during translation. Therefore the addition of such a signal will serve as both termination signal and 30 SES. The effectiveness of this insertion needs to be verified as there might have an impact on the translation (Gilbert, 1976). Combining mRNA stabilizing elements could increase the half-life of the transcript, thus allowing a higher level of recombinant protein production. A library of secondary structures was developed and fused to lacZ reporter, which can enhance the half-life up to 19.8 min (Carrier and Keasling, 1999). The transcript responsible for the production of exoprotease subtilisin has a half-life of 25 min, which is highest in B. subtilis. Its leader region consists of a strong RBS (Hambraeus et al., 2002). Attaching 42 nucleotides from the bacteriophage SP82 sequence to ermC or lacZ enhanced the half-life of mRNA 15-fold (Sharp and Bechhofer, 2005). A class of 50 -mRNA stabilizing element was developed consisting of the transcriptional operator (lacO) of E. coli lac operon and an appropriate RBS with optimum spacer length. This element enhanced the half-life of the b-galactosidase gene more than 60 min in B. subtilis (Phan et al., 2013). B. subtilis mutant strain KJ04 harboring a mutation in the mthA gene is responsible for the production of S-adenosylhomocysteine/methylthioadenosine nucleosidase. This enzyme is involved in the S-adenosylmethionine (SAM) recycling pathways. This mutation increased the level of intracellular SAM concentration by blocking the pathway. The mutant strain showed resistance to 50 mg/mL of streptomycin resistance and produced bacilysin threefold more than the wild type (Tojo et al., 2014). The presence of such stabilizing sequences should be efficient for high protein production. Ribosome-binding site (RBS) The ribosome-binding site (RBS) region includes Shine-Dalgarno sequence (the region upstream of the start codon showing complementation to the 30 -end of 16S rRNA of the small ribosomal unit), the sequence between Shine–Dalgarno sequence and the start codon (spacer sequence), the start codon, and first 5–6 codons of the coding sequence (Chen et al., 1994). The RBS controls the rate of translation. Based on the different RBS sequences, the relative expression of the gene can vary widely. The strength of RBS is calculated based on the translation initiation rate (TIR). The translation initiation rate depends on the following features: Shine-Dalgarno (SD) sequence; spacer region between SD and initiation codon; initiation codon, AUG or GUG; and finally the spatial organization of the initiation domain of the mRNA (Doi, 1984). Enhanced translation rate requires extended SD sequence (Sakai et al., 2001), start codon ATG (Rocha et al., 1999), and a spacer region of 7–8 nucleotide between SD sequence and start codon (Vellanoweth and Rabinowitz, 1992). To predict the comparative level of translation initiation for a particular mRNA, a thermodynamic model was developed. This model uses the nucleotide sequence of the gene and a relative TIR to predict the corresponding RBS sequence that when integrated will provide the required level of gene expression (Salis et al., 2009). The evaluation of RBS revealed that for an efficient translation of heterologous gene there should be high complementary between the 50 end of the mRNA and the SD sequence at the 30 end of B. subtilis 16s rRNA. It was also revealed that the free energy of interaction was on average 17.6 kcal (McLaughlin et al., 1981). But it was later found out by Gold and colleagues that the sequence complementation is not so specific and the free energy of interaction is sometimes lower than 17.6 kcal (Gold et al., 1981). There was an increase in the expression of the interferon gene when the spacing between the SD and the initiation codon (ATG) was rich in As and Ts (Band and Henner, 1984). The nucleotide composition of the spacer sequence was studied in B. subtilis. The expression level of laccase CotA was enhanced in the presence of a polyadenylate-moiety spacer region. The relative expression was better with 7 As in comparison with control. Based on this study a library of spacer variants was constructed to study the expression of laccase CotA and metagenome-derived protease H149. The expression of laccase CotA was increased 11-fold when the sequence of the randomized region is AAGC. While the expression of protease H149 was enhanced 30-fold when the sequence was changed to CTAG in this region (Liebeton et al., 2014). Another study based on RBS library construction was carried out (Guiziou et al., 2016). Preliminary eight ribosome-binding sites were selected from highly and constitutively expressed genes. Only one of them contains consensus SD sequence (GGAGG). These sites were flanked by sequences of different compositions and lengths. These libraries were ligated to the GFP gene to study their impact on the expression level in

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B. subtilis during the exponential phase. All the eight ribosome-binding sites showed an increase from 50-fold to 600-fold in comparison with the background. It was found that the library R0 (GATTAACTAATAAGGAGGACAACXXXATG) has shown the highest expression in comparison with other libraries. High L-asparaginase production was obtained in B. subtilis using optimal promoter and synthetic RBS sequence. Upon evaluation of the translation initiation rate, 300 different synthetic RBS sequences were designed using the RBS calculator (https://www.denovodna.com/software/). The RBS and partial promoter sequence before initiation codon were studied using DNAMAN software (https://www.lynnon.com/), as secondary structures in RBS regions can impede with gene translation and even silence gene expression (Isaacs et al., 2004). The RBS calculator determined that the RBS206 (TTAATTAACCTCCTTCTTCGTT) with maximum translation initiation rate (4,064,405.48 au). But the strain with RBS207 (ATCGTACCTCCTTCTTTGTTTTAATTGCGACGACGTGTAATTGCTTATAGGCG) with translation initiation rate 2,311,665.81 au showed maximum yield. It can be concluded that the development of secondary structures in the RBS region due to mismatch between the RBS and promoter can hamper the enzyme production (Li et al., 2019b). Other factors Besides the previous factors, other factors affect the translation rate directly or indirectly. The translation efficacy was evaluated utilizing datasets of the genome-wide transcriptome and synthetic gfp reporter fusions. It was found out that translation efficacy decreased when the specific growth rate (m) was increased from 0.4 to 1.7 h1. It was observed that the shift causes an increase in both total mRNA and ribosome but a decrease in the free ribosome. It was concluded that the translation initiation regions highly dependent on the growth rate as in the absence of regulators there was differential protein production (Borkowski et al., 2016). A reduced rate of translation could also result from the presence of codons in the heterologous gene, which is infrequently used in B. subtilis. So there is a requirement of increasing the pool of tRNA genes linked to these codons. Constitutive expression of these tRNA genes through linking with the constitutive promoter will enhance the amount of these tRNAs (Green and Vold, 1983). So, it can be concluded that there is a great requirement for the optimization of the translation rate to improve the rate of protein synthesis.

3.5 Engineered Bacillus on transport level One of the promising characteristics of B. subtilis to be used as a microfactory is its ability to secrete proteins/metabolites to the extracellular medium in an efficient manner. This inhibits the formation of inclusion bodies and helps in the downstream processing of the gene product (Guan et al., 2016). It has been highly efficient in the secretion of homologous proteins fused to their respective signal peptides (SPs), but the efficiency of secretion of heterologous proteins is very less, resulting in lower yields of production. In the case of heterologous protein production, the presence of an efficient cellular export system is of great importance as it maintains low intracellular concentration, which could prevent and decrease metabolite toxicity (Dunlop et al., 2011). There are three major types of machinery for the secretion function, namely, the Sec-SRP pathway, Tat pathway, and ATP transporters (Freudl, 2018; Fu et al., 2007). The sec-type signal peptide (transport via the Sec pathway) was found to be the most abundant type (Fu et al., 2007). Signal peptides consist of three distinctive regions: positively charged N domain, hydrophobic core H domain, and hydrophilic signal peptidase (SPase) recognition site or C domain. The hydrophobic property of the signal peptide present in the N-terminus region helps in the transport of the product across the membrane (Blobel and Dobberstein, 1975). After the transport the signal peptide is detached from the mature protein by the signal peptidase (SPase) (Doi and Dol, 2013). So, there is a requirement for the study of several signal peptides for the efficient transport of proteins. B. subtilis secretion systems have been deeply scrutinized. Various signal peptides have been predicted, which was further validated at the genome (Antelmann et al., 2001) and proteome levels (Degering et al., 2010). Based on the SPase recognition sequence, the amino-terminal signal peptides have been classified into four major classes. The utilization of a suitable signal peptide is very much critical for the efficient transport of the proteins/metabolites as it is one of the major bottlenecks for the utilization of these proteins/metabolites for commercial purposes. The promoter and signal peptide from B. amyloliquefaciens a-amylase gene were fused through a Hind III linker to E. coli b-lactamase gene. The construct was transformed into B. subtilis, which resulted in more than 95% secretion of b-lactamase into the growth medium (Palva et al., 1982). A systematic screening approach was done to screen all naturally occurring Sec-type SPs from B. subtilis. Cutinase and esterase were used as reporters to test the efficiency of these SPs for

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heterologous protein secretion. The signal peptidase of Epr protein whereas YncM signal peptidase of unknown protein was found to be efficient for cutinase and esterase secretion, respectively. Although the SPs for esterase secretion was found inefficient for cutinase secretion and vice versa (Brockmeier et al., 2006). A truncated SecA was engineered with deletion of 61 amino acids in C-terminus the expression level of human interferon a enhanced 2.2-fold (Kakeshtia et al., 2010). Construction of an artificial SecB-SecA protein facilitates efficient transport of Sec-B dependent proteins in B. subtilis (Diao et al., 2012). To optimize the secretion system in B. subtilis WB600, 19 different sec-type signal peptides were tested for the efficient transport of aminopeptidase. The best candidate was found to be YncM, which enhanced the secretion 1.2fold in comparison with the native (Guan et al., 2016). Flavin transporter RibM from Streptomyces davawensis was expressed in B. subtilis for efficient excretion of riboflavin into the culture medium (Hemberger et al., 2011). YerP, surfactin transporter based on proton motive force by using liposome and transmembrane transport inhibitors. Overexpression of this transporter in B. subtilis THY-7 enhanced surfactin transport 2.45-fold in comparison with control (Li et al., 2015a). B. subtilis WB800, a protease deletion mutant strain, was used to screen 173 B. subtilis signal peptides. The library was screened for the secretion of E. coli alkaline phosphatase PhoA. The signal peptide YoaW was found to have the maximum potential. In this study the SP was fused with StrepII-SUMO-tag to ease the purification of the secreted protein (Heinrich et al., 2019). However, this signal peptide was found to be the worst candidate for the secretion of aminopeptidase (Guan et al., 2016). So it is difficult to single out the best signal peptide for secretion. In the case of intracellular proteins, even the fusion of signal peptides does not facilitate the secretion. So it might require the development of leaky cells (Kudo et al., 1983).

4

Use of coproducts for the synthesis of high-value chemicals

4.1 Use of original substrates for B. subtilis culture media The great challenge of producing commercial enzymes or biomolecules is the use of costly raw materials and techniques. Thus there is a high demand for finding low-cost substrates to produce stable enzymes under cost-effective conditions. However, it would be more economical if they could be used as a complete growth medium without any supplements and if these inexpensive cost substrates required very few treatments (de Andrade et al., 2016). It is well known that the production of most secondary metabolites is dependent on the composition of the medium (Ravindran et al., 2018). The type and the concentration of carbon, nitrogen sources, phosphate regulation, and other nutrients markedly influence the regulatory mechanisms that device the onset of active molecules biosynthesis (Fonseca et al., 2007; Makkar et al., 2011). Thus the selection of appropriate carbon and nitrogen sources is one of the most crucial steps in the development of an efficient and economical production process. Moreover, modification of the nutrient conditions may change the type and the activity of the produced molecules; the best nutrients production condition appeared to be strain dependent (Fonseca et al., 2007). The ability of Bacillus to degrade various substrates and produce many enzymes gives this organism the flexibility to use unexpectable alternative raw material as a nutrition source and to find a low-cost production medium. Many substrates were tested by the researchers to find the cheap, complete, and ecological friend media starting by all the materials that generate by the agriculture industries ending by using the heavy petroleum waste to reach this goal (Ravindran et al., 2018). The agricultural organic wastes present real environmental problems and their inappropriate discarding is the main reason for pollution. As agroindustrial residues are renewable and in a rich supply (3.5 billion tons/year), they are a potential lowcost raw substrate for microbial enzyme production (Oumer and Abate, 2018). Agroindustrial waste is considered as a rich source for microbial growth due to the high amount of carbohydrates and lipids in its content. Products such as bran, bagasse of sugarcane, straw of wheat, cassava, and beet molasses are various examples of agroindustrial waste (Płaza et al., 2011). Some waste materials are rich in starch like rice water and cereals. Likewise, agroindustrial residues (oil cake) can be used by some Bacillus strain as potential raw material to produce many enzymes. Biotechnology uses of sunflower oil cake, soybean cake, coconut oil cake, mustard oil cake, palm kernel cake, groundnut oil cake, cottonseed cake, olive oil cake, and rapeseed cake, which is highlighted in detail (Banat et al., 2014; Ramachandran et al., 2007).

4.2 Use of renewable resources for the production of secondary metabolites by Bacillus Among the aforementioned substrates, glycerol represents an important renewable carbon source as it is one of the main byproducts of the biodiesel and biofuel production processes worldwide. For example, 1 kg of glycerol is generated from 10 kg of biodiesel when rapeseed oil is used (Kalantari et al., 2017). Raw glycerol that is a bioproduct of biodiesel production with negative value was successfully used to produce lipopeptide molecules such as fengycin from the strain B. subtilis

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LSFM-05 and the strain B. subtilis BBG21 (de Faria et al., 2011; Yaseen et al., 2016) and surfactin by the strains B. amyloliquefaciens OG (Etchegaray et al., 2017) and the strains B. velezensis BS-37 (Zhou et al., 2019b). Moreover the production of the secondary metabolites as lipopeptides has been the subject of numerous studies that use by-products as culture substrate to reduce production costs, but with very relative success (Makkar et al., 2011). For example, effluents from the potato industry were used for the production of surfactin (Thompson et al., 2000) Soybean hulls hydrolysates were used for the production of fatty-acyl-glutamate, another type of biosurfactant produced by B. subtilis (Marti et al., 2015). More recently, Zanotto and colleagues have reviewed a list of nearly 30 sources of coproducts used for the production of surfactin by different strains of Bacillus (Zanotto et al., 2019). Choosing a carbon source and optimizing its concentration cannot be done without studying the nitrogen source associated with it and assessing the right carbon/nitrogen ratio (Willenbacher et al., 2015). Many sources of nitrogen have been also investigated to produce such NRPS and biosurfactant biomolecules as urea (Ghribi and Ellouze-Chaabouni, 2011; Yaseen et al., 2017), polypeptones (Rahman et al., 2007), yeast extracts (Liu et al., 2014a), or corn steep liquor (Gonc¸alves et al., 2015).

4.3 Use of organic waste agroresidue and wastewater for the production of enzyme by Bacillus Food industries of meat treating such as food, leather, and chicken feathers generate high quantities of animal fat and compounds with a high percentage of protein (Taskin and Kurbanoglu, 2011). The discarding of animal waste represents actual environmental challenges. Cellulases are essential industrial enzymes with a broad range of applications, and their costefficient production is a big challenge for which the use of organic waste agroresidue as a sole substrate was explored (Vijayaraghavan et al., 2016). Bacillus is used as a bioconversion of keratinous waste generated during the processing of animal raw materials (Łaba et al., 2017). Also, fish waste is one of the rich sources of proteins that can be used as cheap substrates to produce microbial enzymes. Fish heads, tails, fins, viscera, and chitinous materials make up the wastes from fish industries (Ramkumar et al., 2016). For instance, protease production by B. subtilis has been tested using fish flours from Sardinella aurita. Protease yield was strongly enhanced when Bacillus was grown in media containing only a combined head and viscera preparation. This media increased protease production up to 100% more than commercial peptones (Ellouz et al., 2001). Cuttlefish powder from Sepia officinalis by-products was also investigated for the production of protease by B. subtilis; the use of these flours allows an increase of 75% of the protease production compared with the reference media (Souissi et al., 2008). Extracellular production of a-amylase and b-galactosidase was performed using an optimized mixture of different flours and corn steep liquor or tryptone (Konsoula and Liakopoulou-Kyriakides, 2007). The use of wastewater and sludge has drawn also a significant interest as an alternative substrate source since they are containing nutrients useful in microbial biomolecule production process. Wastewater of various chemical composition, such as formaldehyde wastewater (Panchanathan et al., 2016), methanol wastewater (Cao et al., 2015), dairy wastewater (Kuppamuthu et al., 2017), potato starch wastewater (Banat et al., 2014), and starch treating wastewater (Siddeeg et al., 2020). All these resources have been used as a carbon source to produce various microbial enzymes and secondary benefit molecules such as protease, amylase, or biosurfactant molecules. In another study, citrus juice waste was used for the coproduction of antimicrobial molecules and proteases (Yoo et al., 2011).

4.4 Use of organic waste agroresidue for the production of biomolecule during solid-state fermentation of Bacillus Solid-state fermentation (SSF) is a usual mode of culture to use agrifood residues as substrate. It represents an alternative and cheap mode of culture for the production of biomolecules or enzyme by Bacillus. The scientific literature is full of examples that illustrate the tremendous metabolic adaptation capacity of this microorganism (Table 2). The examples that have been chosen to illustrate this paragraph are only the reflection of a small part of these capacities. Wheat bran was, for example, used in SSF for the production of xylanase by Bacillus sp. AR-009 (Gessesse and Mamo, 1999). Rai et al. have used chicken feather as a substrate for b-keratinase production by B. subtilis strain RM-01 in SSF (Rai et al., 2009). Oil palm empty fruit bunch and rice straw fibers were compared as substrates for the production of a-amylase in SSF. Results indicated that the supplementation of the medium in yeast extract was important, and rice straw fibers was the best substrate to produce this enzyme (Hassan and Karim, 2012). Horn meal was successfully tested for the production of keratinase under optimized conditions in SSF using B. subtilis MTCC9102, which is known for its keratinase production capacity (Kumar et al., 2010). Soybean curd residues (Mizumoto et al., 2006), rapeseed flour/wheat bran (Yao et al., 2012), and degreased soy flour/rice husk/wheat bran (Piedrahı´ta-Aguirre et al., 2014) were used for the production of iturin A in SSF. Das et al. have realized a comparative study between submerged fermentation and SSF for the production of biosurfactants by two different strains of Bacillus using potato peels as a cheap carbon source. The results did not highlight significant differences

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TABLE 2 Examples of various renewable substrates available from different industrial sectors used for the production of microbial active molecules by Bacillus sp. Source industry

Waste/renewable substrate

Beneficial molecules

Reference

Agroindustrial waste

Rice straw

Bioethanol

Khedr et al. (2019)

Agroindustrial waste

Cassava wastewater

Surfactin

de Andrade (2018)

Agroindustrial waste

sugarcane molasses

2,3-Butanediol

Deshmukh et al. (2015)

Agroindustrial waste

Corn steep powder

Fibrinolytic enzyme

Wu et al. (2019b)

Crops

Cassava

Poly-g-glutamic acid

Massaiu et al. (2019)

Crops

Sweet potato

Alpha-amylase

Olanbiwoninu and Fasiku (2015)

Fruit processing industry

Banana husk

Amylase

€ Ozdemir et al. (2009)

Fruit processing industry

Date syrup

Pectinase

Abdul Sattar Qureshi (2012)

Fruit processing industry

Apple pomace

Pectinase

Tepe and Dursun (2014)

Coffee processing residues

Spent of free groundnut

Bioalcohol

Kim et al. (2020)

Food industry

Waste frying oil

Surfactin

Vedaraman and Venkatesh (2011)

Food industry

waste cocking oil

Lipase

Suci et al. (2018)

Oil processing mills

Groundnut oil cake

Protease

Elumalai et al. (2020)

Dairy industry

Whey waste

Lipase

Kuppamuthu et al. (2017)

Animal waste

Cow dung

Carboxymethyl cellulase and protease

Vijayaraghavan et al. (2016)

Animal waste

Fish

Protease

Ellouz et al. (2001)

Petroleum and biodiesel industry

Glycerol

3-Hydroxypropanoic acid

Kalantari et al. (2017)

Petroleum and biodiesel industry

Crude oil

Lipopeptides

Parthipan et al. (2017)

in the production level between the two modes of culture (Das and Mukherjee, 2007). Biosurfactants were also the subject of a study using by-products of olive mill factory for SSF of the strain B. subtilis SPB1. The maximum production yield was 30.67 mg of biosurfactant per gram of solid material (Zouari et al., 2014).

5

Minibacillus

The outstanding progress in systems and synthetic biology tools has opened doors to consider producing “designer cells.” For this purpose, scientists and industrial partners are competing to develop customized and efficient workhorse strains to meet the increasing needs of biosourced chemicals. Interestingly the recorded genome engineering studies have shown that many of the genes are unnecessary for growth in the relatively simple environment within industrial fermenters (Fang et al., 2005). Accordingly, it is becoming largely accepted that reducing the genome size will increase its stability and reduce the production of unwanted by-products (Gil et al., 2004; Koonin, 2003). In this context, targeted deletion of up to 15% of the genome of a common bacterium yielded new and improved strains, including ones that could take up foreign DNA more efficiently (Po´sfai et al., 2006). Lee and colleagues have also shown that the elimination of genes unnecessary for cell growth can increase the productivity of an industrial strain, most likely by reducing the metabolic burden and improving the metabolic efficiency of cells (Lee et al., 2009). Different microorganisms such as Corynebacterium glutamicum, E. coli, Pseudomonas putida, and S. avermitilis have undergone genome minimization with relative success ( Juhas et al., 2014). At the same level, researchers with more focus on B. subtilis are also setting and optimizing methods to create bacillus-based

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cell factories with reduced genome size called Minibacillus. B. subtilis is an intensively studied organism, with extensive genome annotation and excellent knowledge of the major cellular processes. So far the genetic manipulation tools of this organism are well implemented and allowing plenty of possible modifications. All these reasons make Bacillus a very interesting platform to engineer genome-reduced strains that will serve as a chassis for novel applications (Reuß et al., 2016). In a first attempt performed in 2003, Westers and colleagues have succeeded in reducing the B. subtilis genome by 7.7% (Westers et al., 2003). Despite the loss of more than 300 genes the growth and the differentiation processes remained unaffected. In 2007 a new result of the elimination of 25% from B. subtilis genome has been recorded and has shown unstable cell growth (Ara et al., 2007). Recently, Reuß and colleagues have successfully reduced B. subtilis genome by 36%. The constructed strains were viable and have shown similar behavior in complex media compared with wild-type strains (Reuß et al., 2017). Their study about resource allocation and gene dispensability has shed light on the importance of genome reduction to understand gene functions and how to use this knowledge to build smarter cells with even more reduced genomes. To create Minibacillus, the crucial task is to identify the necessary genes. The first minimal genes set required for viable cells in nutritious conditions were published in 2003 (Kobayashi et al., 2003). Among 4100 genes of B. subtilis, only 271 were shown to be essential for growth under the experimental conditions. After 10 years a new assessment of these genes has shown that 31 genes that were thought to be essential are in fact nonessential, whereas 20 novel essential genes have been described (Commichau et al., 2013). According to the Minibacillus project and J€ org St€ulke work, each gene of B. subtilis is classified and assigned as dispensable, essential, reasonably important, paralogue, or functionally paralogue and competence (Reuß et al., 2016). Thus, 642 genes, distributed in six functional categories, are regarded as “necessary” for Minibacillus.

6 Conclusion and future remarks In the global context where fossil resources are running out, health and more sustainable development have become very important challenges. Advances in science, synthetic biology, bioproduction, and biotechnology allow us to glimpse a bright future for biosourced molecules. Microorganisms as producers of many biomolecules have received increasing attention and are now considered to be cellular microfactories. Among all B. subtilis has emerged as an efficient microfactory due to its capability to produce various primary and secondary metabolites. Advances in synthetic biology through the development of genetic and metabolic engineering methods over the past 40 years (deregulation and optimization of transcription, translation, secretion mechanisms, development of genome editing, and minimal genome) have made this host a tool of choice for the bioproduction of many biomolecules of interest. Moreover, it can grow easily on different carbon and nitrogen sources, which participates in a sustainable development approach. In the next two decades, many biomolecules from this species will continue to emerge and find their places in many industrial sectors, like biofuels, biomaterials, biomedical, and biocontrol. Sectors such as not only phytosanitary cosmetics but also detergency are markets where the demand for biologically active molecules is booming driven by increased regulatory and societal pressure. Secondary metabolites produced by Bacillus through their numerous activities, their low toxicity, and their high biodegradability represent prime candidates to replace synthetic molecules and meet the demand of these markets while greatly limiting the impacts on the environment and biodiversity.

Acknowledgments The authors greatly appreciated the support provided from the European INTERREG Va SmartBioControl/BioProd/Bioscreen projects, the ALIBIOTECH program funding administered by the Hauts-de-France Region, the ERA CoBioTECH project BestBioSurf, and the SME Instrument grant agreement no. 849713 under the European Union’s Horizon 2020 research and innovation program.

References Abelson, J., 1979. RNA processing and the intervening sequence problem. Annu. Rev. Biochem. 48, 1035–1069. Acevedo-Rocha, C.G., Gronenberg, L.S., Mack, M., Commichau, F.M., Genee, H.J., 2019. Microbial cell factories for the sustainable manufacturing of B vitamins. Curr. Opin. Biotechnol. Agaisse, H., Lereclus, D., 1996. STAB-SD: a Shine-Dalgarno sequence in the 50 untranslated region is a determinant of mRNA stability. Mol. Microbiol. 20, 633–643. Aleti, G., Sessitsch, A., Brader, G., 2015. Genome mining: prediction of lipopeptides and polyketides from Bacillus and related Firmicutes. Comput. Struct. Biotechnol. J. 13, 192–203.

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Chapter 10

Pseudomonas putida–based cell factories Justyna Mozejko-Ciesielska* Department of Microbiology and Mycology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland *Corresponding author: E-mail: [email protected]

1 Introduction The Pseudomonas genus was first classified as a bacterium belonging to the Monas genus (Migula, 1894). Later on the progress on taxonomic studies has been made resulted in the arrangement of the Pseudomonas genus into 23 genera (Molina et al., 2013). The Pseudomonas species are rod-shaped, Gram-negative bacteria belonging to Gammaproteobacteria. The Pseudomonas genus is one of the most heterogeneous significant groups of known bacteria occurring in various environments from the Antarctica to the Tropics, present in such niches like sediments, clinical samples, water, soil, or plant rhizosphere (Franzetti and Scarpellini, 2007). Pseudomonas strains have shown their ability to thrive under extreme environmental conditions such as low temperature, nutrient limitation, and the presence of toxins. In addition, they adapt to low nutritional environment that is one of the reasons for their wide prevalence. Due to their versatility, degradative potential, and capacity to metabolize a wide range of carbon sources, they are considered to be attractive candidates in a number of biotechnological applications. Especially, Pseudomonas putida is extensively studied as a metabolically versatile bacterium grown in various habitats. Some strains belonging to this species have become efficient cell factories for the production of various products of industrial relevance due to several advantages over other microorganisms (Poblete-Castro et al., 2014a) (Fig. 1). Furthermore, as host-vector system safety level 1 (HV1), certified strains such as P. putida KT2440 enable researchers to conduct their studies in the industrial scale. The publication of complete genomes of P. putida KT2440 (Belda et al., 2016) or P. putida S12 (Kuepper et al., 2015) gave the possibility to model genome-wide pathways (Puchalka et al., 2008) that could be engineered into an efficient cell factory for the potential application in industrial biotechnology. The sequencing data revealed a lack of any recognizable virulence factor or pathogenesis trait, even as an opportunistic pathogen (Martı´nez-Garcı´a and de Lorenzo, 2011). The genome of P. putida contains a single circular chromosome with relatively high guanine-cytosine (GC) content that varies between 43% and 69% (windows of 4 kbp) and has a mean value of 61.6% (Martins dos Santos et al., 2004). Therefore this bacterium could be a potential heterologous host for gene expression from GC-rich bacterial clades like actinobacteria or myxobacteria having the special genetic abilities to biosynthesize secondary metabolites. Furthermore, P. putida metabolism is supportive of various natural product pathways, and its robustness makes this bacterium a target in biofactory design (Loeschcke and Thies, 2015). The metabolic plasticity regarding P. putida metabolism of a broad variety of carbon sources including aromatic compound and fatty acids gives the possibility to redesign the pools of substrates required for the synthesis of precursors essential for the secondary metabolite production (Nikel and de Lorenzo, 2018). Therefore a special attention is put on the central routes of substrate metabolism to redirect the carbon flux to the pathway of products of interests. The central catabolic pathways of P. putida differ in key aspects from other prokaryotes making their usage unique (Poblete-Castro et al., 2012a). Glucose, as a carbon source, does not play the same central role in pseudomonads compared with other industrial bacteria like Escherichia coli or Bacillus subtilis. In the presence of succinate and glucose, the expression of enzymes involved in central pathway for glucose catabolism is suppressed until succinate is consumed (Collier et al., 1996). Furthermore, in P. putida, the uptake of glucose is carried by OprB porin not by a PTS transport system. This bacterium lacks phosphofructokinase, so in the consequence glucose, gluconate and 2-ketogluconate are metabolized via 6-phosphogluconate, which is further oxidized through the EntnerDoudoroff pathway (Fuhrer et al., 2005). Because of adjustment of lipid fluidity to membrane function, an activation of efflux pumps, and increased energy generation, P. putida exhibits a broad tolerance to compounds that are toxic to other bacteria (Ramos et al., 2015). Also, this

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FIG. 1 Overview of Pseudomonas putida traits that make it a versatile host for the production of structurally diverse compounds.

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bacterium is highly resistant to oxidative stress that is of interest for several technical applications in the industrial processes (Kim and Park, 2014). Such properties make P. putida as one of the host for the production of valuable metabolites. Advances in metabolic engineering enable to build microbial cell factories to generate many natural products with diverse biological functions of which P. putida is the well-studied host for efficient and large-scale production. Additionally, this bacterium is amenable to targeted genetic modifications (Hernandez-Arranz et al., 2019). Multiple tools including vector and promoter sets have been developed to enable functional gene expression in this bacterial host creating a new biological agent that can function as biofactories of value-added products (King et al., 2016). This chapter highlights the potential of P. putida strains as a host microorganism for the recombinant synthesis of applicable bioproducts such as polyhydroxyalkanoates, surfactants, terpenoids, and prodigiosin.

2 Pseudomonas putida as a host for the production of natural products 2.1 Polyhydroxyalkanoates Plastic-based products are widely used in everyday life being an essential part of modern society. However, the accumulation of synthetic waste in the natural environment has become a worldwide issue. Therefore there is a need to develop alternative processes to produce materials that will have polymer-like properties. Strains belonging to P. putida species are able to synthesize naturally polymers of industrial interest. Among the various types of biobased polymers, polyhydroxyalkanoates (PHAs) are especially attractive because they have useful properties due to the fact that they are biodegradable, nontoxic, and biocompatible. PHAs are stored intracellularly by microorganisms in the form of insoluble granules that can be used as carbon and energy reserves (Laycock et al., 2014). In the recent years, researchers put great effort into the fermentative PHA production and optimization of the bioprocess toward the high productivity of these biopolymers. PHAs are classified into three groups due to the number of carbon atoms in the monomer units: short chain-length PHA(scl-PHA) includes monomers from 4 to 5 carbons, medium chain-length PHA (mcl-PHA) containing 6–14 carbon atoms, and long chain-length PHA with more than 15 carbon atoms. In particular, mcl-PHAs have gained much attention because of their more favorable properties than scl-PHAs due to their high elasticity, high melting point, low crystallinity, and tensile strength (Philip et al., 2007). Pseudomonas species are especially extensively studied as metabolically versatile bacteria that have become efficient cell factories for the production of medium chain-length PHAs. P. putida generates the precursors for mcl-PHA synthesis from two metabolic pathways. The de novo synthesis fatty acid pathway is used to degrade unrelated substrates like glucose or gluconate. This bacterium is also able to employ b-oxidation pathway to synthesize mcl monomers from aliphatic carbon sources like fatty acids (Madison and Huisman, 1999) (Fig. 2). Generally, in pseudomonads, six proteins have been characterized as being essential for the accumulation and synthesis of polyhydroxyalkanotes: two polymerases (PhaC1 and PhaC2) depolymerase (PhaZ), two phasins (PhaI and PhaF), and two regulatory proteins (PhaD and PhaG) (Sandoval et al., 2007). Although the key proteins involved in the mcl-PHA biosynthesis are known, it is not yet clear how P. putida rearranges the regulation mechanisms that are responsible for this process. Advances in next-generation sequencing technologies provide new opportunities for finding information on the metabolic pathways and mechanisms leading to PHA synthesis. To better understand this bioprocess, many studies concerning P. putida transcriptome during PHA synthesis and accumulation were conducted (Poblete-Castro et al., 2012b; Borrero-de Acun˜a et al., 2014; Beckers et al., 2016; Mozejko-Ciesielska et al., 2017, 2018). Also, to gain insights into the mechanisms of PHA biosynthesis under certain environmental conditions, response of P. putida strains at proteomic level has been studied (Nikodinovic-Runic et al., 2009; Poblete-Castro et al., 2012b; Fu et al., 2015; MozejkoCiesielska and Serafim, 2019; Mozejko-Ciesielska and Mostek, 2019a,b). The efficiency of PHA synthesis is dependent on many factors, including the carbon source-to-nitrogen source ratio, cultivation period, temperature, pH, and the presence of macro- and microelements (Lee et al., 2004). To enhance PHA synthesis to satisfactory amounts and to a low cost making the bioprocess economically reasonable, efforts have been made to use genetically modified P. putida. To improve the efficiency of PHA production with high productivity fermentation processes, attention has been put to engineer the metabolic pathways. Due to the close relationship with mcl-PHA synthesis, the metabolic engineering of the b-oxidation pathway has been commonly adopted to increase this biopolyester accumulation (Table 10.1). The molecular manipulations were successfully applied to increase a monomer fraction with long carbon length in the final PHAs. It was found that in P. putida KT2442 the deletion of fadB gene (encoding 3-hydroxyacyl-CoA dehydrogenase) and fadA gene (encoding 3-ketoacyl-CoA thiolase) elicits a strong intracellular accumulation of 3-hydroxydodecanoate (3HDD) monomer (Ouyang et al., 2007a). The weakened b-oxidation pathway led to the synthesis of up to 41 mol% of 3HDD fraction, which was much higher than that of 15 mol% accumulated by the parent strain. The deletion of another gene encoding 3-hydroxyacyl-CoA dehydrogenase resulted in the poly(3-hydrodecanoate-co-3-hydroxydodecanoate)








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FIG. 2 Metabolic pathways of PHA synthesis by Pseudomonas species.

copolymer [P(3HD-co-3HDD)] synthesis containing 44% 3HD and 56% 3HDD (Ma et al., 2009). Furthermore the genetic engineering was applied to obtain PHA homopolymers at a high level. It was shown that after deletion of the key fatty acid degradation genes fadB, fadA, fadB2x, and fadAx and those coding for 3-hydroxyacyl-CoA dehydrogenase, acyl-CoA dehydrogenase, and 3-hydroxyacyl-CoA-acyl carrier protein transferase (phaG), P. putida KT2442 was able to synthesize homopolymer poly(3-hydroxydecanoate) (P3HD). The data revealed that thermal and mechanical properties of the extracted P(3HD) were better in comparison with an mcl-PHA heteropolymers (Liu et al., 2011). In addition, this mutant was applied as a platform for random and novel diblock copolymer P3HHx-b-P(3HD-co-3HDD) grown on hexanoic and dodecanoic acid as block copolymer precursors. The extracted PHAs consisted of 49 mol% 3HHx, 16 mol% 3HD, and 35 mol% 3HDD (Tripathi et al., 2013). A new recombinant LS46123 strain of P. putida carrying a novel PHA synthase (PhaC116) from a metagenomic library was able to incorporate scl and mcl monomers into PHA polymers from related and nonrelated substrates. The monomeric composition analysis revealed that when the mutant has grown on glucose, the synthesized PHA consists of 32% of 3-hydrobutyrate (3HB), 50% of 3-hydroxyhexanoate (3HHx), and 16% of 3-hydroxyocatnoate (3HO), whereas the supplementation of the culture with nonanoic acid as the substrate resulted in the production of biopolymers with 34 mol% of 3-hydroxyvalerate (3HV), 56 mol% of 3-hydroxyheptanoate (3HHp), and 10 mol% of 3-hydroxynonanoate (3HN) (Sharma et al., 2017). Furthermore, Yuan et al. (2008) proved that an introduction of the genes from other microorganisms is a good strategy to produce mcl-3HA monomers. The recombinant P. putida KT2442 harboring polyhydroxyalkanoate depolymerase gene phaZ from Pseudomonas stutzeri 1317 together with acyl-CoA synthetase (fadD) of E. coli MG1655 and overexpressing putative long-chain fatty acid transport protein FadL was able to produced up to 5.8 g/L of 3HHx and 3HO monomers. Furthermore, Le Meur et al. (2012) working with KT2440 strain harboring E. coli W3110 genes encoding xylose isomerase (XylA) and xylulokinase (XylB) obtained successfully up to 28.7% of cell dry weight (CDW) of mcl-PHA. They proved that sequential feeding strategy using xylose as the growth substrate and octanoic acid as the precursor for biopolymers synthesis seemed to be a beneficial way to achieve more cost-effective mcl-PHA production. Promising results were also reported by Lin et al. (2016) who demonstrated that engineered P. putida A514 strain overexpressing phaJ4 and phaC1 genes (AphaJ4C1) cultivated on vanillic acid under nitrogen limitation was able to accumulate mcl-PHAs in the

TABLE 10.1 An overview of PHA synthesis by engineered Pseudomonas putida strains. Strain

Native host

Expression characteristics

Substrate

PHA content (%)

Monomeric composition (mol%)

KTOY06

P. putida KT2442

DfadAB

Dodecanoate

84.0

3.0 3HHx 22.9 3HO 33.2 3HD 40.9 3HDD

Ouyang et al. (2007a)

KT2047A

P. putida KT2442

DfadAB DfadAx DfadB2x half of DPP2046 and half of DPP2047

Octanoate

17.8

6.7 3HHx 93.3 3HO

Ma et al. (2009)

KTOY08

P. putida KT2442

DfadAB DfadAx DfadB2x

Decanoate

36.7

28.2 3HO 71.8 3HD

Liu et al. (2011)

KTOY08-G

DfadAB DfadAx DfadB2x DphaG

Dodecanoate

46.1

33.8 3HO 40.8 3HD 22.7 3HDD

KTQQ18

DfadAB DfadAx DfadB2x DphaG DPP2047

Tetradecanoate

67.2

2.53 3HDD 97.7 3HTD

KTQQ19

DfadAB DfadAx DfadB2x DphaG DPP2048

Tetradecanoate

84.5

3.7 3HDD 96.3 3HTD

KTQQ20

DfadAB DfadAx DfadB2x DphaG DPP2047 DPP2048

Tetradecanoate

77.5

100.0 3HTD

References

KTOYO6DC

Aeromonas caviae

phaPCJ

Sodium butyrate + sodium hexanoate

61.4

62.6 3HB 37.4 3HHx

Tripathi et al. (2013)

LS46123

P. putida LS461

DphaC1ZC2 + phaC116 from a metagenomic clone

Glucose

9.2

32.0 3HB 50.0 3HHx 16.0 3HO

Sharma et al. (2017)

Nonanoate

10.5

34.0 3HV 56.0 3HHp 10.0 3HN

pYZPst06

P. putida KT2442

phaZ from P. stutzeri, fadD from E. coli, overexpressed fadL

Sodium octanoate

13.0

20.0 3HHx 80.0 3HO

Yuan et al. (2008)

KT2440

Escherichia coli W3110

xylA, xylB

Xylose + octanoic acid

28.7

87% 3HO

Le Meur et al. (2012)

AphaJ4C1

P. putida A514

Overexpressed phaJ4 and phaC1

Lignin

73.5

4.3 3HHx 15.2 3HO 25.7 3HDD 54.8 3HTD

Lin et al. (2016)

KTOY01

P. putida KT2442

DphaC1ZC2 + phaC2 from P. stutzeri and phbA, phbB from Aeromonas caviae

Dodecanoate

38.9

14.3 3HB 8.2 3HHx 46.9 3HO 19.7 3HD 10.9 3HDD

Ouyang et al. (2007b)

Continued

TABLE 10.1 An overview of PHA synthesis by engineered Pseudomonas putida strains—cont’d Strain

Native host

Expression characteristics

Substrate

PHA content (%)

Monomeric composition (mol%)

KT2440 Dgcd

P. putida KT2440

Dgcd

Glucose

38.0

14.0 3HO 71.1 3HD 4.6 3HDD 8.5 3H5DD 1.1 3HTD

KT2440 Dpgl

Dpgl

Glucose

12.9

8.6 3HO 74.2 3HD 4.8 3HDD 8.1 3H5DD 6.5 3HTD

KT2440 Dgcd-pgl

Dgcd-pgl

Glucose

29.5

12.2 3HO 72.4 3HD 5.3 3HDD 9.3 3H5DD 0.8 3HTD

References Poblete-Castro et al. (2013)

KT2440 DphaZ

P. putida KT2440

DphaZ

Glycerol

46.8

16.3 3HO 72.3 3HD 6.9 3HDD 3.4 3H5DD

Poblete-Castro et al. (2014a)

AG2162

P. putida KT2440

DphaZ DfadBA1 DfadBAE2 DaldB + overexpressed phaG, alkK, phaC1, phaC2

p-Coumaric acid

54.2

26.0 3HO 64.0 3HD 8.0 3HDD 2.0 3HTD

Salvachua et al. (2020)

KTHH07

P. putida KT2442

DfadB DfadA DphaC1ZC2 + phaPCJ operon of A. hydrophila 4AK4

Valerate

57.4

1.9 3HB 98.1 3HV

Wang et al. (2011)

DfadB2x DfadAx DfadB DfadA DphaC1ZC2 DphaG + phaPCJ operon of A. hydrophila 4AK4

Valerate

64.9

3HV

KTHH08

3HB, 3-hydroxybutyrate; 3HV, 3-hydroxyvalerate; 3HHx, 3-hydroxyhexanoate; 3HHp, 3-hydroxyheptonoic acid; 3HO, 3-hydroxyoctanoate; 3HD, 3-hydroxydecanoate; 3HDD, 3-hydroxydodecanoate; C12:1, 3-hydroxy-5-cis-dodecanoate; 3HTD, 3-hydroxytetradecanoate.

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concentration of 73.5% per CDW. The authors described a good example of effective approaches to convert lignin into value-added PHAs. Moreover the disruption of genes responsible for PHA synthesis and accumulation was evaluated toward P(3HB-co3HA) copolyester production. Ouyang et al. (2007b) knocked out the PHA synthase pha operon (phaC1-phaZ-phaC2) of P. putida KT2442 by deleting approximately 60% of its sequence. The authors used a vgb gene encoding the Vitreoscilla hemoglobin (VHb) protein as a replacement of the deleted fragment and introduced a plasmid pCJY08 harboring phaC2Ps gene from P. stutzeri. They achieved 38.9% of PHA consisting of 14.3 mol% of 3HB, 8.2 mol% of 3HHx, 46.9 mol% of 3HO, 19.7 mol% of 3HD, and 10.9 mol% of 3HDD. Also, it was shown that P. putida KT2440 recombinant is able to produce significant amounts of mcl-PHAs grown on glucose as the only carbon source. A single deletion of gcd gene encoding glucose dehydrogenase led to the increase of the PHA content in bacterial cells of 100% as compared with the parent KT2440 strain. In addition, the growth rate of the mutant was not affected by the genetic manipulation that is important regarding the total PHA volumetric productivity (Poblete-Castro et al., 2013). Furthermore, Poblete-Castro et al. (2014a) proved that the chromosomal deletion of phaZ gene in P. putida KT2440 enhanced the mcl-PHAs productivity. The DphaZ recombinant produced up to 47% of mcl-PHAs in the cultivation supplemented with glycerol, whereas its parent strain under the same conditions synthesized about 10% less of these biopolymers. However, it was recently proven that not the single deletion of PHA depolymerase but the gene knockouts and gene overexpression integrated into the genome improved mcl-PHAs concentration when the engineered KT2440 strain grown on p-coumaric acid (Salvachu´a et al., 2020). The mutant, with the deletion of phaZ and fadBA1 and fadBA2 involved in b-oxidation combined with the overexpression of phaG, alkK, phaC1, and phaC2 genes, was able to synthesize up to 54.2% of mcl-PHAs. The accumulated biopolyesters contained 3HO and 3HD as the major components and a small amount of 3HDD and 3HTD (Salvachu´a et al., 2020). A mutant constructed from P. putida KT2442 by deleting its mcl-PHA synthase genes phaC1 and phaC2 and harboring PHA synthesis operon phaPCJ from Aeromonas hydrophila 4AK4 accumulated homopolymer poly(3-hydroxyvalerate) (PHV) at the level of 64.9% of CDW when valerate was used as carbon source (Wang et al., 2011). However, the overall costs of polyhydroxyalkanoate production are higher compared with synthetic polymers, mainly due to the lack of efficient PHA producers. The earlier described data demonstrated that the metabolically engineered strains seem to successfully synthesize PHAs at improved final productivity. However, still little is known about regulatory mechanisms that drive PHA synthesis and accumulation. Further research is essential to better understand links between metabolic pathways and PHA content in bacterial cells. It is crucial to enhance the PHA concentration and to receive new, tailor-made biopolyesters for many applications.

2.2 Surfactants Surfactants (synthetic surfactants and biosurfactants) are amphipathic molecules with surface- or interface-related properties (Zhong et al., 2017). The surfactants from microbial origin are considered as promising alternatives to the synthetic counterparts due to their high biodegradability, low toxicity, surface activity, and high stability under extreme conditions (Dobler et al., 2016; Shao et al., 2017). They are divided into several groups including glycolipids (rhamnolipids and sophorolipids), lipopeptide (surfactin), and polymeric compounds (alasan and emulsan) (Chong and Li, 2017). Especially, rhamnolipids are the most intensively studied and the best characterized molecules. Three key proteins are responsible for their biosynthesis, RhlA, RhlB, and RhlC. The acyltransferase RhlA is required for the generation of 3-(3-hydroxyalkanoyloxy)alkanoic acid (HAA). RhlB is a rhamnosyltransferase that catalyzes the reaction toward the formation of monorhamnolipid, whereas the rhamnosyltransferase II (RhlC) is responsible for the synthesis of dirhamnolipids (Fig. 3). The genetically engineered bacteria bearing the rhlA, rhlB, and rhlC genes have the ability to produce rhamnolipids. Most studies toward rhamnolipid production were performed using Pseudomonas aeruginosa. Due to its potential pathogenicity, several studies have been focusing to explore nonpathogenic strains toward high rhamnolipid production. Much attention has been paid to some strains from the P. putida group. There are several data confirming successful processes of rhamnolipid production by the introduction of biosynthesis genes from natural rhamnolipid producers into P. putida resulting in safe production strains with adjustable rhlAB(C) expression characteristics (Tiso et al., 2017) (Table 10.2). However, the usage of this bacterium as a surfactant producer is still challenging due to multilayered interactions between glucose metabolization, cell growth, and product formation. To overcome these difficulties, some strategies have been successfully employed in the past. Ochsner et al. (1995) described for the first time heterologous rhamnolipid production using P. putida as a recombinant production host. They proved that the expression of the rhlA and rhlB genes from P. aeruginosa in the P. putida host could improve its ability for rhamnolipid production. The authors confirmed that under the control of the rhlR and rhlI

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Microbial cell factories engineering for production of biomolecules

FIG. 3 Schematic biosynthesis.

view

of

rhamnolipid

TABLE 10.2 Overview of strategies for rhamnolipid production by engineered Pseudomonas putida strains. Native host

Expression characteristics

Substrate

Maximum yield (g/L)

P. putida KT2442

P. aeruginosa

rhlAB

Glucose

0.60

Ochsner et al. (1995)

P. putida KT2440

P. aeruginosa

DphaC1 + rhlAB

Glucose

1.5

Wittgens et al. (2011)

P. putida KT2440

P. aeruginosa

rhlAB

Glycerol

0.47

Setoodeh et al. (2014)

P. putida KT2440

P. aeruginosa

rhlAB

Glucose

14.9

Beuker et al. (2016b)

P. putida KT2440

P. aeruginosa

rhlAB

Glucose

0.6

Beuker et al. (2016a)

P. putida KT2440

P. aeruginosa

rhlAB

Glucose

2.2

Tiso et al. (2016)

P. putida KCTC 1067

P. aeruginosa

rhlABRI

Acidified soybean oil

7.3

Cha et al. (2008)

P. putida KT2440

P. aeruginosa

rhlABRI

Glycerol

1.68

Cao et al. (2012)

Producer

References

Monorhamnolipids

Mono- and dirhamnolipids P. putida KT2440

P. aeruginosa

rhlABC + alkBGT from P. putida Gpo1

Glucose

1.2

Gehring et al. (2016)

P. putida KT2440

P. aeruginosa

rhlABC

Glucose

0.004

Wittgens et al. (2017)

P. putida GPp104

P. aeruginosa

rhlABC

Glucose

0.11

Schaffer et al. (2012)

P. putida KT2440

B. glumae

rhlABC

Glucose

0.06

Blank et al. (2013)

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rhamnolipid regulatory elements and under the control of the tac promoter, the recombinant P. putida KT2442 produced up to 25 mg/L/h of rhamnolipids reaching the yield of 0.6 g/L during the exponential phase growth. It was revealed that a product formation occurred within 1 h after induction. Wittgens et al. (2011) introduced the rhlAB operon (encoding RhlA and RhlB) from P. aeruginosa into P. putida. In the baffled shake flask experiment using glucose as a carbon source, the recombinant P. putida KT2440 pVLT33_rhlAB produced up to 0.22 g/L of monorhamnolipid. The HPLC-ESI-MS analysis confirmed that the produced rhamnolipids consisted of fatty acids featuring chain lengths between 8-carbon and 12-carbon chains and low amounts with 14-carbon and 16-carbon chains. Furthermore, to improve the rhamnolipid yield, the authors deleted a phaC1 gene responsible for mcl-polyhydroxyalkanoate synthesis. The new knockout produced about seven times more rhamnolipid than the original strain (1.5 g/L). The authors observed that biosurfactant productivity and glucose metabolization rate were constant throughout the fermentation suggesting that they were not dependent on the growth rate. Furthermore the analysis proved that the final product contains not only monorhamnolipids but also up to 20% of the free hydroxy fatty acid suggesting that there is a need to knock out phaC2 gene coding for the second PHA polymerase. It was also proven that medium optimization is required for the efficient rhamnolipid production by P. putida recombinant. The addition of glucose in the amount of 20 g/L to the culture medium led to the increase of rhamnolipid formation from 0.22 to 0.47 g/L. Furthermore, it was observed that the higher IPTG concentration, the higher RL concentration. In addition, glycerol, yeast extract, and peptone influenced positively on heterologous RLs (Setoodeh et al., 2014). So far the highest yield of rhamnolipids was achieved by Beuker et al. (2016b) in the fed-batch bioreactor cultivation using dual-phase feeding profile. After introducing synthetic promoter-controlled expression of rhlAB, P. putida was able to produce up to 14.9 g/L with a yield of 10 mg/g glucose (Yrhamnolipid/substrate). Different rhamnolipid production kinetics could be observed using different media, pH values, and temperature during cultivation integrated foam fractionation process. The maximal specific and the volumetric rhamnolipid productivity values were reached using the ModR medium setup (Beuker et al., 2016a). The rhamnolipid production occurred under glucose limitation corresponding to the onset of the glucose feeding. The usage of the metabolic engineering strategy “driven by demand” enables the pPS05 mutant of P. putida KT2440 to produce 2.2 g/L of a final product at 22 h of the fermentation (Tiso et al., 2016). The native regulation system via coexpression of the cognate autoinducer-dependent transcription factor/autoinducer synthase pair RhlR/RhlI from P. aeruginosa seems to be a good strategy for production of monorhamnolipids. It was reported that heterologous host, P. putida 1067 (pNE2) cultivated in mineral salt medium supplemented with 2% soybean oil as the sole carbon source, was able to synthesize up to 7.3 g/L within 72 h of the fermentation (Cha et al., 2008). The productivity of the process conducted by this recombinant was greater than that of P. aeruginosa EMS1 suggesting that the fatty acid metabolism is regulated differently in P. putida due to the accumulation of 3-hydroxyalkanoates, which may serve as a precursor for rhamnolipid synthases (Cha et al., 2008). Also, rhlABRI cassette from P. aeruginosa BSFD5 including necessary genes for rhamnolipid synthesis was successfully expressed into P. putida KT2440 using the aforementioned strategy. The resulted P. putida KT2440-rhlABRI mutant produced 1.68 g/L of rhamnolipids at 96 h of the bioprocess, after which the concentration decreased reaching about 0.9 g/L at 120 h (Cao et al., 2012). Also, successful results were reported concerning dirhamnolipid production. The heterologous expression of all three rhamnolipid synthesis genes from P. aeruginosa in P. putida KT2440, under the control of the pRha promoter, combined with the introduction of alkBGT genes from P. putida GPo1 yielded up to 1.2 g/L (Gehring et al., 2016). Furthermore, expressing a biosynthetic rhlABC operon in fresh LB medium resulted in a mixture containing 3.0 mmol/L of monorhamnolipids and 3.5 mmol/L of dirhamnolipids (Wittgens et al., 2017). The same authors revealed the possibility of dirhamnolipid synthesis by rhlC gene using extracellular monorhamnolipids as precursors. By mixing cell-free supernatants of P. putida expressing the rhlAB operon with the same volume of fresh LB medium, P. putida bearing rhlC gene was able to produce 2.5 mmol/L of dirhamnolipids during 24 h of cultivation. Furthermore the production of dirhamnolipids in PHAdeficient P. putida GPp104 harboring rhlABC from P. aeruginosa reached up to 113 mg/L/OD600 (Schaffer et al., 2012). Blank et al. (2013) reported that the expression of rhamnolipid production genes, controlled by tac promoter, not only from P. aeruginosa but also from Burkholderia glumae BGR1 resulted in the production both monorhamnolipids (80 mg/L) and dirhamnolipids (50 mg/L). These results confirmed that rhamnolipid production may vary with the origin of the applied biosynthetic genes. Large-scale rhamnolipid production is dependent on the strain engineering strategy, medium composition, and operating approaches. The obtained results strongly confirmed that there is a chance that P. putida will replace the pathogenic P. aeruginosa in rhamnolipid production in the future processes. This bacterium seems to be an appropriate host for potential high-RL yield bioprocesses mainly due to its tolerance to high concentrations of RLs up to 90 g/L (Wittgens et al., 2011).

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Microbial cell factories engineering for production of biomolecules

2.3 Terpenoids Terpenoids constitute a vast family of the most abundant groups of secondary metabolites with diverse biological functions. Albeit structurally different, terpenoids are derived from the same 5-carbon (C5) skeleton of isoprene. They are synthesized from the two precursors, isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP) by either mevalonate (MVA) pathway or the methylerythritol 4-phosphate (MEP) pathway, also known as the DXP or nonmevalonate pathway. MVA pathway is present in most eukaryotes, archaea, and eubacteria, while prokaryotes, plant plastids, and algae harbor the MEP pathway (Rohmer, 1999; Miziorko, 2011). P. putida is able to use the nonmevalonate pathway to produce terpenoids. In this process, D-glyceraldehyde-3-phosphate (G3P) and pyruvate are condensed; then, 2-C-methyl-D-erythritol-4-phosphate is formed after a reductive isomerization reaction. The subsequent coupling with cytidine triphosphate (CTP), a phosphorylation, cyclization, and reductive dehydration lead to the formation of IPP and DMAPP. After their condensation, farnesyl diphosphate (FPP), geranyl diphosphate (GPP), and geranylgeranyl diphosphate (GGPP) are formed as the precursors for terpenoid biosynthesis (Fig. 4). Generally the resulting terpenoids are divided according to the number of carbon atoms: hemiterpenoids (C5), monoterpenoids (C10), sesquiterpenoids (C15), diterpenoids (C20), sesterterpenoids (C25), triterpenoids (C30), tetraterpenoids (C40), and polyterpenoids (>C40) (Schewe et al., 2015). Additionally, products with mixed origin have been described such as meroterpenoids, indole diterpenoids, or prenylated aromatic products (Schmidt-Dannert, 2014). P. putida seems to be a good candidate for the efficient production and accumulation of terpenoids (Table 10.3). First of all, this bacterium naturally exhibits a tolerance to organic solvents. It was revealed that the resistance to toxic compounds like monoterpenes is higher in P. putida than in E. coli or Saccharomyces cerevisiae (Inoue and Horikoshi, 1991; Schempp et al., 2018). This bacterium is able to adjust membrane functions by controlling of lipid fluidity. Furthermore, it is capable of activating a general stress-response system, increasing energy generation and inducing efflux pumps (Ramos et al., 2015). In addition, in P. putida cells, the metabolism of glucose leads to synthesize balanced amounts of the MEP pathway substrates, pyruvate, and GAP. In other microorganisms the limitation of the activity of the MEP pathway was observed resulting in the imbalanced availability of pyruvate and GAP (Liu et al., 2013). Moreover, due to the metabolic plasticity, FIG. 4 Overview of precursor production for terpenoid biosynthesis by mevalonate pathway (MVA) and the methylerythritol 4-phosphate pathway (MEP).

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TABLE 10.3 Overview of approaches for terpenoid production by recombinant Pseudomonas putida strains. Type of terpenoid

Maximum yield

Glycerol

Geranic acid

193 mg/L

Mi et al. (2014)

rhaPBAD, crtEIBYZ + isoprenoid genes of E. coli

Sodium succinate

Zeaxanthin

239.0 mg/L

Beuttler et al. (2011)

Pantoea ananatis

crtEXYIBZ

Yeast extract

Zeaxanthin

0.226 mg/g CDW

Loeschcke et al. (2013)

Pantoea ananatis

crtE, crtB, crtI

Glucose

Lycopene

1.2 ng/mL

HernandezArranz et al. (2019)

Strain

Native host

Expression characteristics

Substrate

DSM 12264

Ocimum basilicum and Myxococcus xanthus

ges from Ocimum basilicum + genes of MVA pathway from Myxococcus xanthus

KT2440

Pantoea ananatis

KT2440 KT2440

References

P. putida is able to metabolize a variety of carbon sources providing an opportunity for the higher pools of substrates for the synthesis of IPP and DMAPP (Nikel and de Lorenzo, 2018). So far, there are the limited studies concerning the synthesis of terpenoids by P. putida; however, all of them confirmed the high potential of this bacterium to become a biofactory for the industrial production of these valuable products. It is known that to enhance the production rate of terpenoids, there is a need to increase the supply of their precursors. The productivity of bacterial terpenoid synthesis is dependent on the flux from intermediates of the central metabolism, primarily acetyl-CoA or pyruvate and glyceraldehyde-3-phosphate toward the terpene synthase substrate geranyl pyrophosphate (GPP), farnesyl pyrophosphate (FPP), or geranylgeranyl pyrophosphate (GGPP) (Chandran et al., 2011). Therefore metabolic engineering of pathways involving in the generation of elevated terpenoid precursor levels is a common strategy in the efficient biotechnological terpenoid processes (Peralta-Yahya et al., 2012). One of a way to increase the isoprene precursor pool was described by Mi et al. (2014) during de novo production of the monoterpenoid. In this study, DSM 12264 strain belongs to P. putida was chosen as a producer of the desired oxidation product of geraniol, geranic acid which can be used as a perfuming agent (Schrader, 2007), antifungal agent (Yang et al., 2011), or skin depigmentation agent (Choi, 2012). The bacterial strain was able to produce significant amounts of geranic acid by the introduction of genes encoding a geraniol synthase from Ocimum basilicum that is responsible for the conversion of the cellular terpenoid biosynthesis intermediate geranyl pyrophosphate (GPP) to geraniol. Furthermore, to improve carbon flux toward monoterpenoid synthesis, genes coding for the MVA pathway of Myxococcus xanthus DSM 16526 leading from acetyl-CoA to IPP and DMAPP were coexpressed with the geraniol synthase gene. The efficiency of the conducted approach was tested under controlled conditions in a fed-batch bioreactor. After 2 days of the fermentation process, the recombinant P. putida produced 193 mg/L of geranic acid corresponding to 9.7 mg/g CDW of the product yield. As the final monoterpenoid acid concentration has not a negative effect on the growth of DSM 12264 strain, high cell density fermentations should be designed to obtain higher biomass and in the consequence terpenoid productivity. It was clearly confirmed that P. putida DSM 12264 could be a potential cell factory for this monoterpenoid acid production due to their resistance to a high geranic acid concentration, a high geraniol oxidation capacity, and its inability to degrade geranic acid. Besides the ability to produce monoterpenes, a recombinant P. putida was reported to accumulate tetraterpenes (carotenoids). Efforts have been made to use genetically modified KT2440 strain for enhanced biosynthesis of zeaxanthin, the xanthophyll carotenoid (Beuttler et al., 2011). This terpenoid could be used as nutraceuticals and pharmaceuticals (Chang et al., 2011). To improve the productivity of zeaxanthin, five carotenoid biosynthesis genes (crtE, crtI, crtB, crtY, and crtZ) from Pantoea ananatis and three genes from the MEP pathway of E. coli (idi, ispA, and dxs) were coexpressed in P. putida KT2440 under control of the rhaPBAD promoter. It was found that media composition had a strong effect on both the terpenoid yield and their purity. It was confirmed that the addition of lecithin improves volumetric yield of zeaxanthin by a factor of 4.7 after 48 h of the fermentation compared with the culture without any additives (51 mg/L vs 239 mg/L). Whereas when the cultivation was supplemented with oleic acid, 2.3 higher yield of this carotenoid could be reached. To biosynthesize zeaxanthin the transfer and expression system named TREX was applied (Loeschcke et al., 2013). The authors also used the crt cluster derived from P. ananatis consisting of genes responsible for the biocatalytic conversion of

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Microbial cell factories engineering for production of biomolecules

the terpenoid precursor farnesyl pyrophosphate (FPP) into the carotenoids b-carotene, zeaxanthin, and zeaxanthin-b-Ddiglucoside. It was shown that TREX system could serve as a valuable tool for establishing the entire biosynthetic pathway for zeaxanthin production. The engineered P. putida showed variable pigmentations with few white to many intensive yellow clones. The results confirmed that TREX-dependent establishment of the carotenoid biosynthesis genes resulted in the synthesis of zeaxanthin in the yield of 226 mg/g CDW. Recently, Hernandez-Arranz et al. (2019) explored different ways of improving the supply of MEP-derived terpenoid precursors (IPP and DMAPP) in P. putida cells using lycopene as a readout. Firstly the authors expressed an exogenous MVA pathway by introducing the vector pMiS1-ges-MVA, containing the genes from M. xanthus that are essential for the conversion of acetyl-CoA into IPP and DMAPP under the control of the rhaPBAD promoter. They achieved about sevenfold higher level of lycopene production and accumulation compared with the strain that did not harbor the MVA pathway. Furthermore, it was demonstrated that upregulation of endogenous DXS could also enhance the precursors supply in P. putida cells. The most successful strategy was the use of nDXS to produce DXP from Ru5P that is an intermediate of the pentose phosphate pathway. The data confirmed the increase of lycopene accumulation up to threefold without a negative effect of P. putida cell growth. The overexpression of DXS together with the DXP reductoisomerase enzyme further increases lycopene accumulation. This approach resulted in 50-fold higher terpenoid productivity compared with the control.

2.4 Prodigiosin Prodigiosin, a red linear tripyrrole pigment, is normally secreted as a secondary metabolite of microbial origin. Its native producer is the opportunistically pathogenic bacterium Serratia marcescens. Many researchers have focused their attention on the prodigiosin synthesis due to its antimicrobial (Lapenda et al., 2015), antimalarial (Papireddy et al., 2011), and anticancer (Li et al., 2018) activity. However, large-scale synthesis of this pigment from the natural hosts generate too high cost attributed to the requirement of the long bacterial incubation period (Elkenawy et al., 2017). Furthermore the cultivations toward its high-level synthesis by S. marcescens are considered not to be safe (Yip et al., 2019). To improve a productivity of this pigment, many studies have been undertaken regarding its biosynthetic pathways. By transferring the related biosynthetic genes from the original producer into an industrial host, cost-effective synthesis and safe bioprocess could be conducted. The heterologous production of prodigiosin is not only highly attractive but also challenging because of the large size of the gene cluster and complex synthesis pathway. Over 30 genes are engaged in the prodigiosin production and regulation in Serratia spp. (Darshan and Manonmani, 2015). To produce prodigiosin, two key intermediates, namely, 2-methyl-3-namylpyrrole (MAP) and 4-methoxy-2,20 -bipyrrole-5-carbaldehyde (MBC) encode by pigA and pigN genes, are essential (Williamson et al., 2006). These enzymes belonging to polyketide synthase and nonribosomal peptide synthase family together with specific enzymatic activation are required for MBC biosynthesis (Garneau-Tsodikova et al., 2006) (Fig. 5). The heterologous prodigiosin production at a pilot scale was optimized by several authors who confirmed that P. putida KT2440 is a promising prodigiosin producer (Table 10.4).

FIG. 5 Schematic metabolic pathway for the prodigiosin biosynthesis.

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TABLE 10.4 Overview of approaches for prodigiosin production by engineered Pseudomonas putida KT2440. Type of substrate

Maximum yield

pigA-pigN DpigD, mutasynthesis approach

Glycerol

17.2 mg/L

Klein et al. (2017)

Serratia marcescens ATCC274

pigA-pigN and cueR

Yeast extract

0.49 mg/g DCW

Loeschcke et al. (2013)

KT2440

Serratia marcescens

pigA-pigN

Glycerol

6.2 mg/g DCW

Domr€ ose et al. (2015)

KT2440

Serratia marcescens

pigA-N

Glycerol

60 mg/L

Domr€ ose et al. (2019)

Strain

Native host

Expression characteristics

KT2440

Serratia marcescens

KT2440

References

To generate prodigiosin derivatives a mutasynthesis approach was applied. This method is based on the feeding of an artificial precursor mutasynthon to a recombinant strain, of which the biosynthetic pathway is usually blocked in an early key step. It was observed that genetic engineering of the prodigiosin pathway together with incorporation of synthetic intermediates resulted in the prodigiosin concentration of 17.2 mg/L (Klein et al., 2017). Furthermore, cyclic 2,3-alkylpyrroles in combination with a mutasynthetic approach led to the production of four new cyclic prodiginines (Klein et al., 2018). Also the TREX expression system using P. putida KT2440 was proposed as a strategy for safe and efficient prodigiosin synthesis (Loeschcke et al., 2013). The transfer of the pig genes from S. marcescens to P. putida and their integration as TREX-pig transposon into the host chromosome based on T7 RNA polymerase-dependent bidirectional transcription of genes resulted in about 0.49 mg/g dry cell weight (Loeschcke et al., 2013). Based on these data, Domr€ose et al. (2015) enhanced this secondary metabolite production by employing unidirectional constitutive pig gene expression from a strong native P. putida promoter targeted by chance as transposon Tn5 inserts the gene cluster at random positions in the bacterial chromosome. The authors did not use T7 RNA polymerase to induce prodigiosin biosynthesis. The application of the plasmid pTREX-pig carrying the whole red pigment gene cluster flanked by the DNA cassettes of the TREX system enabled to produce up to 94 mg/g DCW with the volumetric productivity of 3.5 mg/L/h. It is the highest level of heterologous prodigiosin production determined so far. The results also confirmed that the prodigiosin concentration is correlated with a bacterial growth. Furthermore, it was found that high aeration rate, rich culture medium, and low temperature during the cultivation seem to be beneficial for this red-colored metabolite production. Domr€ose et al. (2019) demonstrated that prodigiosin levels could be influenced by the specific rrn operon copy in which the biosynthetic genes were inserted and further modulated by the distance between the rrn promoter and pig genes.

3 Concluding remarks An excellent P. putida strain is crucial for cost-effective biotechnological applications. The unique metabolic plasticity of P. putida together with its amenability to genetic engineering and its astonishing capacity to produce various natural products makes this bacterium a promising candidate to become a favorite microbial cell factory. This overview of bioproducts that could be produced by heterologous gene expression and strain engineering confirms P. putida potential to be a workhorse for industrial applications being a good provider of the molecular machinery. One of the major limitations of its usage as a marker in genetic engineering is antibiotic resistance that environmental and industrial uses of P. putida KT2440 would benefit from deleting. Therefore there is a need to redesign properties by using plasmids or transposon vectors and to precisely delete some chromosomal segments. This technical challenge will be beneficial in the optimization of P. putida as a workable biotechnological factory (Martı´nez-Garcı´a and de Lorenzo, 2011). The possibility of this bacterium’s engineering for conversion of cheap carbon sources expanded the range of applicable substrate that could influence on the overall production costs making the aforementioned bioprocesses more feasible. Advances in synthetic biology enable to open new avenues for functional genomic studies and to expand the spectrum of P. putida’s bioproduct by employment of novel biosynthetic pipelines (Dos Santos et al., 2004; Loeschcke and Thies, 2015). Not only detailed studies of metabolic pathways but also “omics” approach will provide an essential knowledge for this bacterium metabolic engineering to increase the productivity of naturally derived products. In addition, to maximize a desirable product formation, there is a need to control precisely key pathway enzymes. To develop higher-performance P. putida strains, more efficient tools

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FIG. 6 Schematic view of continuous production of bioproducts.

for metabolic flux control should be applied. One of the ways to the overproduction of valuable metabolites is the use of the regulatory element of promoter. Promoter engineering could be used as an efficient approach to accomplish the desired expression levels of target genes and to optimize metabolite biosynthesis in metabolic engineering and synthetic biology ( Jin et al., 2019). Besides promoters, ribosome binding site (RBS) strategy could serve as a regulator of gene expression. This approach is helpful for optimizing the performance of engineered pathways with multiple heterologous genes, in which the metabolic flux through the subsequent enzyme-catalyzed biosynthetic steps needs to be balanced to function properly in context with the surrounding cellular metabolism (Thiel et al., 2018). Furthermore a greater understanding of genetic and metabolic regulation could lead to the construction of a suitable P. putida platform for production of new natural products in the future. Also, to enhance the productivity of natural products of microbial origin, the optimization processes using continuous cultivations need to be applied. Such fermentation strategy is essential to receive a predictable bioproduct quality providing genetic stability of the bacterial host. The use of this approach enables to provide the bacterial cells with sufficient substrate keeping the active biomass concentration constant as soon as steady-state conditions are reached (Koller and Muhr, 2014) (Fig. 6).

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Further reading Poblete-Castro, I., Rodriguez, A.L., Lam, C.M.C., Kessler, W., 2014b. Improved production of medium-chain-length polyhydroxyalkanoates in glucosebased fed-batch cultivations of metabolically engineered Pseudomonas putida strains. J. Microbiol. Biotechnol. 24, 59–69. Wigneswaran, V., Nielsen, K.F., Sternberg, C., Jensen, P.R., Folkesson, A., Jelsbak, L., 2016. Biofilm as a production platform for heterologous production of rhamnolipids by the non-pathogenic strain Pseudomonas putida KT2440. Microb. Cell Factories 15, 181.

Chapter 11

Streptomyces-based cell factories for production of biomolecules and bioactive metabolites Noura El-Ahmady El-Naggar∗ Department of Bioprocess Development, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria, Egypt ∗

Corresponding author: E-mail: [email protected]

1 Introduction Members of the order actinomycetales contain a high content of guanine-cytosine (GC-content) in their DNA. They are grampositive bacteria with a tendency to form branching filament, which has developed into mycelium in some genera (Gottlieb, 1974) and reproduce by spore formation. On the solid media, filamentous actinomycetes form superficial colonies, which are almost cartilaginous and firmly adhere to the underlying medium (Skinner et al., 1947). The majority of actinomycetes are saprophytic, free living bacteria, which are widely distributed in different habitats, such as plants, water, and soil. Actinomycetes are present also in the extreme environments, especially extreme cold habitats (Raja et al., 2010). Actinomycetes have been described as one of the largest soil populations that may differ according to type of the soil (Kuster, 1968). The actinomycetes had been proven to be efficient producers of antibiotics, and many different industrially valuable secondary metabolites especially antitumor agents, pesticides, herbicides, antiparasitic, enzyme inhibitors, and various enzymes (Takahashi and Omura, 2003; Miyadoh, 1993). Sykes and Skinner (1973) proposed 10 principal families of order actinomycetales, namely, Actinomycetaceae, Thermomonosporaceae, Streptomycetaceae, Dermatophilaceae, Nocardiaceae, Frankiaceae, Micromonosporaceae, Thermoactinomycetaceae, Mycobacteriaceae, and Actinoplanaceae. Streptomyces is the largest genus of actinobacteria classified on the basis of morphology and cell wall chemotype of the order Actinomycetales and family Streptomycetaceae (Hong et al., 2009). Streptomyces species are widespread in aquatic and terrestrial ecosystems. Members of Streptomyces genus are of commercial interest because of their capacity to produce a tremendous number of biomolecules and bioactive secondary metabolites. It produces clinically useful antibiotics such as tetracyclines, aminoglycosides, macrolides, chloramphenicol, ivermectin, rifamycins. In addition to antibiotics, Streptomyces also produce other highly valuable pharmaceutical products including anticancer, immunostimulatory, immunosuppressive, antioxidative agents, insecticides, and antiparasitic drugs which have broad medical and agricultural applications. Streptomyces species produce a range of enzymes that is medically important, including L-asparaginase, uricase, and cholesterol oxidase. Many actinomycetes can produce industrially important enzymes as cellulases, chitinases, chitosanases, a-amylase, proteases, and lipases. Many actinomycetes can produce different pigments that are potentially good alternative of synthetic colors. Streptomyces species have great capacity to produce active surface biomolecules including bioemulsifiers and biosurfactants. Antidiabetic acarbose was produced by strains of Streptomyces via microbial fermentation. Species of Streptomyces have shown the ability to synthesize cholesterol synthesis inhibitors, like pravastatin. Recently, Streptomyces species can be used as environmentally friendly “nano-factories” for nanoparticles synthesis. Some Streptomyces species are a promising for vitamin B12 production.

2 Streptomyces habitats Streptomyces species are widespread in terrestrial and aquatic environments (Pathom-Aree et al., 2006). Streptomyces species are free living, widely distributed and well adapted to saprophytic life in the soils. In dry conditions, Streptomyces Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00011-8 © 2021 Elsevier Inc. All rights reserved.

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spp. are the largest microbial community in the soil. Due to their filamentous shape, they influence the soil’s texture strength and protect it against water and wind erosion (Vetsigian et al., 2011). Streptomyces spp. distribution in soil and water depends on pH, temperature, moisture, food stress, salinity, soil texture, and climate (Locci, 1989). They use various extracellular hydrolytic enzymes to decompose dead or rotting organic materials and breakdown many natural organic compounds like starch, cotton textiles, cellulose, lignocellulose, chitin, paper, rubber, and plastics (Kieser et al., 2000; Rahmansyah et al., 2012; Horn et al., 2012). Streptomycetes are also present in fresh water and marine habitats particularly in sponges (Khan et al., 2011; Qin et al., 2019). Some of them live as symbionts with plant roots (Seipke et al., 2012) and few species of Streptomyces infect plants including Streptomyces scabies, which causes the potato scab disease (Leiminger et al., 2013). Streptomycetes are rare pathogens; However, Streptomyces sudanensis and Streptomyces somaliensis can cause human infections such as mycetoma (Fahal et al., 2015).

3

General characteristics of the genus Streptomyces

Waksman and Henrici (1943) suggested that the name of Streptomyces genus is derived from Latin streptos that means twisted and myces that means fungi reflecting their own morphology resembles close to that of filamentous fungi and their aerial mycelia developed into chains of spores. The actinomycete cell wall preparations consisted of amino acids, amino sugars, and sugars like that of gram-positive bacteria (Cummins and Harris, 1958). The chemical composition of the cell walls allows for a clear differentiation between Streptomyces and other actinomycetes from the fungi (Avery and Blank, 1954). Species of the genus Streptomyces are filamentous, gram positive, and aerobic with DNA GC content between 69% and 78% (Hasani et al., 2014); they produce both prostrate and erect aerial mycelia. Pridham et al. (1958) and Waksman (1959) have indicated that the sporophores of Streptomyces species differ greatly in their morphology. The sporophores branching are usually monopodial. In addition, some species may show verticillate branching. At maturity the aerial hyphae develop into chains of spores consisting of three to many spores. These spores are produced by the formation of cross walls in the aerial hyphae or by the fragmentation of the filaments (Gowdar et al., 2018; Anderson and Wellington, 2001). At maturity, most of the Streptomyces species produce colorful sporulating aerial hyphae that may be white, yellow, green, gray, blue, red, or black. This characteristic is used as a taxonomical feature to differentiate between Streptomyces species into color groups (Pridham et al., 1958) (Fig. 1). Spore chain morphology of Streptomyces species and its surface can be investigated using light and electron microscopy (Williams and Davies, 1967) for cultures grown on plates of starch nitrate agar medium growing for 7–14 days. The mycelia and spores of Streptomyces species are very small in diameter, approximately 1 mm or even smaller (Willemse et al., 2011). The spores are borne in spirales, retinaculiaperti, or rectiflexibiles chains (Fig. 2) (Shirling and Gottlieb, 1966). Tresner et al. (1961) pointed out that the spore’s surfaces are characterized as smooth or rough, the later showing, warty, hairy, or spiny projections. The presence of hairy or spiny spores is characteristic of some species and is therefore a helpful taxonomical feature (Tresner et al., 1961; Dietz and Mathews, 1971) (Fig. 3). These spores germinate after 8–12 h if they are transferred into a fresh medium, giving one or more germ tubes. The length of the germ tubes increases to create a network like filamentous mycelia. On the solid media, Streptomyces species produce in the earliest stages of growth colored, superficial, smooth surfaced circular colonies that are almost cartilaginous and firmly adhere to the media. Later, they develop powdery, granular, floccose, or velvety aerial mycelia (Hasani et al., 2014). The colonies grow slowly and often have a soillike smell due to their volatile geosmin metabolites ( J€uttner and Watson, 2007). The substrate mycelium of many species produces endocellular, water-insoluble pigments that may be also traced in the outer media (Krassilnikov, 1970). The reverse side of colony exhibits, therefore, red, blue, green, olive, yellow, orange, brown, or black pigmentation (Waksman, 1959, 1967). This feature can be used to distinguish Streptomyces species into color groups or series (Gauze et al., 1957; Pridham et al., 1958).

4

Growth requirements of Streptomyces species

Streptomyces species are chemoorganotrophic and aerobic bacteria, and they require inorganic sources of nitrogen, organic carbon supply, and mineral salts. Vitamins or growth factors are not necessary for Streptomyces species growth (Lee et al., 1997). The majority of Streptomyces spp. are mesophilic (Hasani et al., 2014) and grow in temperatures between 30°C and 37°C. Streptomyces spp. grows in pH range from 6.5 to 8.0 (Basilio et al., 2003). They are more drought resistant than other bacteria and need less moisture (Subbarao, 1999). Some studies show that the drained soils like calcareous soils or sandy loam have higher strain rates of Streptomyces than heavy clay soils (Singh et al., 2006).

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FIG. 1 (A) The morphological cultural characteristics of representative Streptomyces species (mixed and pure cultures) cultivated on plates of ISP medium 4, (Continued)

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FIG 1, CONT’D (B) Morphology of individual colonies.

FIG. 2 Spore chain morphology of some species belonging to the genus Streptomyces.

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FIG. 3 Spore surface morphology of some species belonging to the genus Streptomyces. *Hairs are rough.

5 Production of secondary metabolites The metabolites that are generated during a microorganism’s growth phase to perform the metabolic processes and are essential for normal growth and reproduction are called primary metabolites (Martı´n and Demain, 1980). While the secondary metabolites are organic molecules produced by a microorganism and are not necessary for microbial growth, metabolic processes or reproduction of the producing microorganism (Shomura et al., 1979) and their production depends on growth conditions such as type of culture media, which provide the microorganisms with substrates for production of metabolic products (Dahod, 1999). The bioactive secondary metabolites and complex molecules are produced during the lag and stationary growth phases of microorganisms. However, actinomycetes, especially Streptomyces, can produce secondary metabolites during death, stationary, or exponential growth phases. Environmental problems such as the depletion of essential nutrients lead to the production of secondary metabolites (Harir et al., 2018). The function of secondary metabolites to the producer is expected to provide competitive advantage, for example, scavenging nutrients or killing competitor organisms ( Joyce et al., 2011). Secondary metabolites provide protection against competing microorganisms (Demain and Fang, 2000). It was also found that various organisms can generate metabolites with diverse biological abilities, including antibacterial agents, anticancer agents, immunosuppressants, pesticides, immunomodulating agents, receptor antagonists, pigments, toxins, sex hormones, metal transporting agents, and antagonists (Harir et al., 2018). Among actinomycetes, streptomycetes are the main source of secondary bioactive metabolites (Watve et al., 2001). Members of the genus Streptomyces are of commercial importance due to their exceptional ability to produce a tremendous number of bioactive secondary metabolites (Fig. 4) and highly valuable pharmaceutical products including immunosuppressant, anticancer, antiparasitic, antifungal, and antibacterial drugs that have wide applications in agriculture in addition to medicine (El-Naggar and Hamouda, 2016; El-Naggar et al., 2011a, 2013a; Baltz, 2005; Challis and Hopwood, 2003; ElNaggar and Abdelwahed, 2012). It is known that Streptomyces species produce about one-half of the bioactive secondary metabolites already detected, including enzymes, antitumor compounds, and antibiotics (Manivasagan et al., 2014).

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FIG. 4 A wide variety of natural products produced by Streptomyces species.

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Streptomyces species as cell factories for production of antibiotics

6.1 Definition of antibiotics The antibiotics are defined as chemical substances of natural origin derived from microorganisms, which can inhibit or even destroy the growth of bacteria and other microorganisms at low concentrations (Waksman, 1945; Mayer, 1986). Also, Grenni et al. (2018) identified antibiotics as antimicrobial agents that are specifically effective against bacteria or fungi in hosts including both human and animals. In addition, Egorov (1985) defined antibiotics as metabolic products or their modifications with high physiological functionalities that can be used for selectively slowing or inhibiting the microbial growth like fungi, bacteria, protozoa, algae, and viruses or inhibiting the growth of tumors. From medical point of view, antibiotics are defined as chemotherapeutic substances derived from microorganisms or other natural sources, as well as their synthetic analogues and derivatives that have the selective power to inhibit the growth of disease-causing agents in the patient’s body. The vast majority of antibiotics are produced by actinomycetes. It is known that actinomycetes species produce more than two-thirds of the clinically useful antibiotics already recognized (Olano et al., 2009; Kieser et al., 2000). Streptomyces members are superior to other actinomycetes strains in their capacity to produce about 80% of the clinically beneficial antibiotics (Kharat et al., 2009).

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The history of antibiotics derived from Streptomyces sp. began with streptothricin discovery in 1942 (Waksman and Woodruff, 1942) and streptomycin discovery in 1944 by Schatz and Waksman (1944). In 1957 Hazen and Brown extracted the Nystatin from Streptomyces noursei, which is used for fungal disease treatment (Hazen and Brown, 1957). A total of 22,500 secondary bioactive metabolites have been recorded, out of which 10,100 (45%) secondary bioactive metabolites are produced by actinomycetes. Streptomyces species produce approximately 7600 compounds (Berdy, 2005). The genus Streptomyces produces rifamycins, ivermectin, chloramphenicol, macrolides (erythromycin and its relatives), aminoglycosides (streptomycin and its relatives), tetracyclines, and other clinically important non-beta lactam antibiotics (Mohamedin et al., 2015a). Several classes of antibiotics have been discovered, including antibacterial (Table 1) and antifungal antibiotics (Table 2, Fig. 5). Some of these antibiotics have been described as wide-spectrum antibiotics (Table 3), which can suppress the bacterial and fungal growth or rickettsiae and the large viruses. Some antibiotics were defined as narrow spectrum antibiotics that are active only against certain gram-positive bacteria, certain gram-negative bacteria, mycobacteria, or yeast. Also, some of these antibiotics are antiviral, and others are anticancer, immunostimulatory, and immunosuppressive agents (Table 4).

TABLE 1 Collection of certain antibacterial antibiotics that produced by Streptomyces spp. Streptomyces species

Antibiotic

References

Streptomyces pluricolorescens

Pluramycin

Maeda (1956)

Streptomyces sp.

Frenolicin

Van Meter et al. (1961)

Streptomyces ghanaensis

Moenomycin

Ostash and Walker (2010)

Streptomyces minosis

Minomycin

Nishimura (1960)

Streptomyces flavovirens

Pillaromycin

Shibata et al. (1964)

Streptomyces mediterranei

Rifamycin

Margalith and Beretta (1960)

Streptomyces griseofuscus

Bundlins A, B

Sakamoto et al. (1962)

Streptomyces aburaviensis

Aburamycin

Nishimura et al. (1957)

Streptomyces matensis

Matamycin

Margalith et al. (1959)

Streptomyces phaeochromogenus

Ractinomycin

Utahara et al. (1955)

Streptomyces sp.

Chrysomycin

Strelitz et al. (1955)

Streptomyces collinus

Collinomycin

Brockmann and Renneberg (1953)

Streptomyces erythreus

Sarkomycin

Umezawa (1953)

Streptomyces sp. isolate B6921

Himalomycin A and B

Maskey et al. (2003a)

Streptomyces lactamdurans n.1

Cephamycins (b-lactams)

Stapley et al. (1972)

Streptomyces albus

Xanthothricin

Machlowitz et al. (1954)

Streptomyces sp. GW71/2497

Resomycins AC

Maskey et al. (2003b)

Streptomyces fradiae NCIM 2418

Neomycin

Vastrad and Neelagund (2011)

Streptomyces orchidaceus

Cycloserine

Pendela et al. (2008)

Streptomyces cattleya

Fluorometabolites

Barbe et al. (2011)

Streptomyces kanamyceticus

Kanamycin

Umezawa (1957)

Streptomyces rimosus

Oxytetracycline

Rhodes (1984)

Streptomyces lincolnensis

Lincomycin

Mason and Lewis (1964)

Streptomyces lavendulae

Streptothricin

Waksman and Woodruff (1942)

Streptomyces canus

Amphomycin

Heinemann et al. (1953)

Streptomyces griseus

Cycloheximide

Kominek (1972) Continued

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TABLE 1 Collection of certain antibacterial antibiotics that produced by Streptomyces spp—cont’d Streptomyces species

Antibiotic

References

Streptomyces aureofaciens

Tetracycline

Darken et al. (1960)

Streptomyces venezuelae

Chloramphenicol

Shapiro and Vining (1983)

Streptomyces canus strain FIM0916

Aspartocin

Yang et al. (2014)

Streptomyces griseus

Streptomycin

Schatz and Waksman (1944)

Streptomyces virginiae

Staphylomycin

Yanagimoto (1983)

Streptomyces endus

Stendomycin

Thompson and Hughes (1963)

Streptomyces ambofaciens

Spiramycin

Lounes et al. (1996)

Streptomyces kitasoensis

Leucomycin

Hata et al. (1953)

Streptomyces roseosporus

Daptomycin

Mchenney et al. (1998)

Streptomyces niveus

Novobiocin

Kominek (1972)

Streptomyces lydicus

Streptolydigin

Li et al. (2006)

Streptomyces antibioticus

Oleandomycin

Williams (1980)

Streptomyces spp.

Pristinamycin

Blanc et al. (1995)

Streptomyces lindensis

Retamycin

Pamboukian and Facciotti (2004)

Streptomyces sp. B7064

Chalcomycin B

Asolkar et al. (2002)

TABLE 2 Collection of certain antifungal antibiotics produced by Streptomyces spp. Streptomyces species

Bioactive agent(s)

References

Streptomyces filipinensis

Filipin

Barreales et al. (2018)

Streptomyces galbus

Galbonolides

Fauth et al. (1986)

Streptomyces anulatus

Actinomycins

Bister et al. (2004)

Streptomyces nodosus

Amphotericin B

Linke et al. (1974)

Streptomyces cacaoi

Polyoxin B

Isono et al. (1965)

Streptomyces natalensis

Natamycin

Struyk et al. (1958)

Streptomyces spp.

Kitamycin

Hayashi and Nozaki (1999)

Streptomyces prasinus

Prasinons

Box et al. (1973)

Streptomyces venezuelae

Jadomycin

Doull et al. (1993)

Streptomyces kasugaensis

Kasugamycin

Umezawa (1965)

Streptomyces spp.

Carboxamycin

Hohmann et al. (2009)

Streptomyces padanus

Fungichromin

Shih et al. (2003)

Streptomyces hygroscopicus

Herbimycin

Omura et al. (1979)

Streptomyces hygroscopicus

Validamycin

Iwasa et al. (1970)

Streptomyces violaceusniger

Guanidylfungin

Trejo-Estrada et al. (1998)

Streptomyces avermitilis

Ivermectin

˜ mura and Crump (2004) O

Streptomyces griseus

Candicidin

Acker and Lechevalier (1954)

Streptomyces griseochromogenes

Blasticidin

Takeuchi et al. (1958)

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TABLE 2 Collection of certain antifungal antibiotics produced by Streptomyces spp—cont’d Streptomyces species

Bioactive agent(s)

References

Streptomyces tendae

Nikkomycin

Bormann et al. (1985)

Streptomyces humidus

Phenylacetate

Hwang et al. (2001)

Streptomyces diastatochromogenes

Oligomycin

Smith et al. (1954)

Streptomyces canus

Resistomycin and tetracenomycin D

Zhang et al. (2013)

FIG. 5 Plate assay showing antifungal activity of Streptomyces sp.

TABLE 3 Collection of certain wide-spectrum antibiotics produced by Streptomyces spp. Streptomyces species

Bioactive agent(s)

References

Streptomyces tanashiensis

Kalafungin

Johnson and Dietz (1968)

Streptomyces spectabilis

Streptovaricin

Spassova et al. (1991)

Streptomyces aburaviensis

Aburamycin

Nishimura et al. (1957)

Streptomyces longisporus ruber

Prodigiosin like antibiotic

Darshan and Manonmani (2015)

Streptomyces tanashiensis

Luteomycin like antibiotic

Afifi et al. (2012a)

Streptomyces sp. VITLGK012

Gancidin W

Ravi and Kannabiran (2018)

Streptomyces sp. CMB-M0150

Aranciamycins I and J

Khalil et al. (2015)

Streptomyces sp. MST-115088

Wollamides

Khalil et al. (2014)

Streptomyces sp. MST-77755

Blanchaquinone

Clark et al. (2004)

Streptomyces prunicolor T€ u 6384

Aranciamycin anhydride

Nachtigall et al. (2010)

Streptomyces anulatus NEAE-94

Alkenes, alkanes, fatty acid esters, triterpene, unsaturated, and saturated fatty acids

El-Naggar et al. (2017a) El-Naggar et al. (2013b)

Streptomyces violaceusniger, AZ-NIOFD

Sparsomycin

Atta et al. (2009)

Streptomyces lavendulocolor VHB-9

(Z)-3-Aminoacrylic acid and bis(7-methyloctyl) phthalate

Bssn et al. (2017)

Streptomyces sp. FXJ7.328

Diketopiperazine derivatives

Wang et al. (2013)

Streptomyces sp. AGM12-1

Diketopiperazine derivative

Ahmad et al. (2017)

Streptomyces crystallinus AZ151

Hygromycin B

Afifi et al. (2012b)

Streptomyces carpaticus MK-01

Ethyl acetate extract

Subramanian et al. (2017)

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The capacity of Streptomyces species to produce antibiotics isn’t a steady characteristic, but it could be enhanced or entirely lost by using various nutritions and/or cultivation conditions (Waksman, 1961; Molinari, 2009). The physical and nutritional variables needed for microbial growth can be optimized to improve antibiotics production. To achieve good antibiotic production, the majority of antibiotic producers require various complex organic media. The impact of different nitrogen, carbon, phosphorus, and mineral sources should be tested. As well as, growth-promoting substances and vitamins should be optimized. Streptomycetes may produce antibiotics that they never produce on a regular basis, if they are grown under special conditions. Some streptomycetes are unable to produce antibiotics. It is better to say that streptomycetes may or may not produce antibiotics under certain conditions and with specific test organisms. Surely, different strains of streptomycetes have different biological ability, some of which are extremely active and others are of low-active nature. In general, numerous studies have demonstrated that nitrogen assimilation is critical to antibiotic production control. High production of an antibiotic has been found in liquid culture medium that included yeast extract as a single nitrogen source (El-Naggar and Hamouda, 2016). Yeast extract is a complex nitrogen source composed of carbohydrates, vitamins, amino acids, and peptides. The stimulating impact of yeast extract on the production of antibiotics may be attributed to the presence of trace elements. Kawaguchi et al. (1984) stated that the stimulating impact of yeast extract on rifamycin production caused by the B factor contained in the yeast extract. Moreover in media containing nitrogen sources particularly ammonium, the antibiotic production was suppressed (Lebrihi et al., 1992; Bran˜a et al., 1985). On the contrary, synthetic media containing inorganic nitrogen sources have been found to promote the growth and streptomycin production by Streptomyces griseus (Dulaney, 1948). NaCl assists in the release of mycelium-bound antibiotics. NaCl concentration has a significant impact on the microbial antibiotics production due to its influence on the growth medium osmotic pressure (Pelczar et al., 1993). For instance, Streptomyces avermitilis synthesized an antiparasitic compound, avermectin (Burg et al., 1979). On the other hand, Streptomyces sp. synthesized diketopiperazine derivative with antimicrobial and anticancer activities (Ahmad et al., 2017). And also, Streptomyces sp. ASK2 produces an aromatic compound active against multidrug-resistant Klebsiella pneumoniae (Cheepurupalli et al., 2017). Streptomyces sp. CCB-PSK207 synthesized hexane partition capable of protecting Caenorhabditis elegans against infection by Pseudomonas aeruginosa strain PA14 (Fatin et al., 2017). Jiang et al. (2015) have effectively stimulated production of mureidomycin analogues in Streptomyces roseosporus NRRL15998. Lim et al. (2018) obtained a promising auroramycin antibiotic by gene cluster activation strategy from S. roseosporus.

6.2 The antibiotics derived from Streptomyces strains A wide variety of antibiotics are produced by Streptomyces strains including the following:

6.2.1 Aminoglycosides such as streptomycin, kanamycin, and neomycin (Busscher et al., 2005; Vakulenko and Mobashery, 2003; Park et al., 2013) Streptomycin is a strong base antibiotic produced by S. griseus. Waksman discovered this antibiotic in 1944. Streptomycin belongs to the glucosides (aminoglycosides) and is effective against a large number of both gram-positive and gramnegative bacteria, spirochaetes, that cause animal and plant diseases. It is used for the treatment of many bacterial infections including tularemia, plague, Burkholderia infection, brucellosis, endocarditis, Mycobacterium avium complex, tuberculosis, and rat bite fever. Neomycin antibiotic is derived from a strain of streptomycetes and was discovered by Waksman and Lechevalier in 1949 (Waksman and Lechevalier, 1949). It is produced by Streptomyces marinensis (Ellaiah et al., 2004) and Streptomyces fradiae (Dulmage, 1953; Majumdar and Majumdar, 1965). Neomycin is an aminoglycoside (organic complex nitrogenous substance soluble in water that comprises two or more amino sugars joined by glycosidic bonds) and used in various topical drugs such as ointments, creams, and eye drops. It is active against streptomycin-resistant bacteria such as Mycobacterium tuberculosis.

6.2.2 Anthracyclines (Nitiss, 2009; Minotti et al., 2004) 6.2.3 Glycopeptides as teichoplanin and vancomycin (Butler et al., 2014; Van Bambeke, 2006) Vancomycin is glycopeptide antibiotic produced by Streptomyces orientalis (Abdelwahed and El-Naggar, 2011) and belongs to a class of antibiotics called cell wall biosynthesis inhibitors. It is widely used for the treatment of serious grampositive infections, especially those caused by Staphylococcus aureus resistant to methicillin and coagulase-negative staphylococci (Vidal et al., 1992). In many cases the treatment of staphylococcal infections with vancomycin antibiotic has been associated with a weak and ineffective response (Levine et al., 1991). The spread of vancomycin resistance is an acute problem. Therefore it is a priority to discover and create new and efficient antibiotics.

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6.2.4 b-lactams as cephamycins (Paradkar et al., 2001; Liras, 1999; Stapley et al. 1972) Streptomyces clavuligerus is recognized for being capable of producing various b-lactam antibiotics such as cephamycin C, desacetoxycephalosporin C, and isopenicillin N. Also, the clavams (structurally related b-lactam) is formed by S. clavuligerus.

6.2.5 Macrolides as clarithromycin, tylosin, erythromycin, and clarithromycin (Poehlsgaard and Douthwaite, 2003; Gaynor and Mankin, 2003). Nystatin (Brautaset et al., 2011; Fjærvik and Zotchev, 2005). Erythromycin was discovered by Heilman et al. in 1952 (Heilman et al., 1952). It is produced by Streptomyces erythreus and effective for the treatment of serious bacterial infections especially those caused by Niesseria, Hemophilus, and Brucclla group. The polyene macrolide antibiotic (nystatin) produced by Streptomyces fungicidicus ATCC 27432 and Streptomyces noursei (Matsuoka, 1960). Nystatin is effective antifungal antibiotic, used for the human therapy to treat certain kinds of mycoses (Fjærvik and Zotchev, 2005). Nystatin is an important commercial product widely used in various topical drugs of oral and genital candidosis (Fjærvik and Zotchev, 2005). Polyene antibiotics are mainly antifungal antibiotics (active against fungi), but it has no activity against bacteria. This group includes actidione, fradicine, nystatin, and candidin.

6.2.6 Chloramphenicol (Vining and Stuttard, 1995) Chloramphenicol was discovered by John Ehrlich in 1947 (Ehrlich et al., 1947); it was isolated from the metabolites of a soil actinomycete, Streptomyces venezuelae. It has a broad spectrum activity against both gram-positive and gram-negative bacteria, rickettsiae, and the large viruses. Chloramphenicol has been efficient in treating a number of bacterial infections of eyes such as blepharitis and conjunctivitis caused by Escherichia coli, Streptococcus pneumoniae, and Sta. aureus. It is also useful in the treatment of cholera, plague, meningitis, typhoid, and fever.

6.2.7 Nucleosides, ansamycins as geldanamycin and rifamycin (Kang et al., 2012) 6.2.8 Lipopeptides as daptomycin (Mchenney et al., 1998) 6.2.9 Clavulanic acid (b-lactamase inhibitor) Clavulanic acid produced by S. clavuligerus (Reading and Cole, 1977; Saudagar and Singhal, 2007; Ortiz et al., 2007). Clavulanic acid is an effective b-lactamase inhibitor (Saudagar et al., 2008), such as those present in Sta. aureus, K. aerogenes, and E. coli. This antibiotic is used to block and/or weaken certain pathways of bacterial resistance together with other antibiotics such as amoxicillin. Guadinomine (Holmes et al., 2012) is a new antibiotic produced by Streptomyces sp. K01– 0509 that is currently used effectively in the treatment of various infections caused by gram-negative bacteria by blocking the Type III secretion pathway.

6.2.10 Glutarimides (cycloheximide) (Kominek, 1975) 6.2.11 Tetracyclines, polyenes, and polyethers (Okami et al., 1988) Tetracyclines such as chlortetracycline (aureomycin) (Duggar, 1948) and oxytetracycline (terramycin) (Finlay et al., 1950) are a large antibiotic family that have a wide spectrum of activity against large viruses, rickettsiae, and gram-negative and gram-positive bacteria. Chlortetracycline and oxytetracycline are produced by Streptomyces aureofaciens and Streptomyces rimosus, respectively. Tetracyclines are used to treat a number of infections. These include syphilis, malaria, plague, acne, brucellosis, and cholera.

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6.2.12 Streptogramins ( Johnston et al., 2002) 6.2.13 Lantibiotics (mersacidin and actagardine) (Willey and Van Der Donk, 2007) 6.2.14 Amphotericin (Caffrey et al., 2001) 6.2.15 Angucyclines, antitumor agents such as moromycin and landomycin (Kharel et al., 2012)

7 Anticancer, immunostimulatory, immunosuppressive, and antioxidative agents produced by Streptomyces species Besides antimicrobial activities, there are several other simultaneous bioactivities for antibiotics (Table 4), that is, enzyme inhibitors; neurological agents and anticancer, immunostimulatory, immunosuppressive, and antioxidative agents (Berdy, 2005; Sanglier et al., 1996; Berdy, 1995). Streptomyces antioxidans MUSC 164 was found to produce phenolic-related compounds and pyrazines that had antioxidative and neuroprotective activities and able to reduce the free radicals and protect nerves form destruction by hydrogen peroxide (Ser et al., 2016).

TABLE 4 Anticancer, immunostimulatory, and immunosuppressive agents produced by Streptomyces species. Streptomyces species

Bioactive agent(s)

References

Streptomyces costaricanus SCSIO ZS0073

Actinomycin D

Liu et al. (2019)

Streptomyces capoamus

Ciclamycin (complex of the anthracycline class)

Martins and Souto-Maior (2003)

Streptomyces capoamus NRRL B3632

Antitumor antibiotic

Mukhtar et al. (2012)

Streptomyces globisporus 1912

Landomycin E

Zhu et al. (2005)

Streptomyces peucetius

Doxorubicin (adriamycin)

Arcamone et al. (1969)

Streptomyces spp.

Borrelidine

Vino and Lokesh (2008)

Streptomyces peucetius

Daunorubicin (daunomycin)

Takashima et al. (1987)

Streptomyces sp. DO-116

Sapurimycin

Kara et al. (1991)

Streptomyces sp. DO-114

Clecarmycins

Fujii et al. (1995)

Bestatin

Blomgren et al. (1980)

Streptomyces hygroscopicus

Hygromycin A

Uyeda et al. (2001)

Streptomyces filipinensis

Pentalenolactone I

Uyeda et al. (2001)

Streptomyces hygroscopicus

Rapamycin as antifungal, anti-inflammatory, antitumor, and immunosuppressive agent

Kim et al. (2014)

Anticancer agent producers

Immunostimulatory agent producers Streptomyces olivoreticuli Immunosuppressive agent producers

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8 Streptomyces species as cell factories for production of active metabolites applied against causative agents of a number of diseases Streptomyces spp. and their metabolites are used on several crops to control a number of phytopathogenic fungi and bacteria (Table 5). TABLE 5 Some antibiotics produced by Streptomyces spp. used on several crops against a number of plant pathogens. Streptomyces sp.

Antibiotic

Disease

References

Streptomyces griseochromogenes

Blasticidin S

Broad range of plant diseases

Takeuchi et al. (1958)

Streptomyces padanus

Fungichromin

Cabbage damping-off caused by Rhizoctonia solani

Shih et al. (2003)

Streptomyces humidus

Phenylacetic acid and sodium phenylacetate

Phytophthora blight of pepper

Hwang et al. (2001)

Streptomyces kasugaensis

Kasugamycin

Rice blast disease

Umezawa (1965)

Streptomyces hygroscopicus

Validamycin

Rice sheath blight disease

Doumbou et al. (2001)

Streptomyces cacaoi

Polyoxin B and D

Rice sheath blight disease

Isono et al. (1965)

Streptomyces hygroscopicus

Geldanamycin

Pea root rot (caused by Rhizoctonia solani)

Rothrock and Gottlieb (1984)

Streptomyces hygroscopicus

Gopalamycin

Powdery mildew of wheat, downy mildew of grape, rice blast in green house

Nair et al. (1994)

Streptomyces griseus

Faeriefungin

Stem wilt and Asparagus officinalis L. root rot caused by Fusarium oxysporum

Smith et al. (1990)

Streptomyces malaysiensis

Malayamycin

Wheat glume blotch disease

Li et al. (2008)

Streptomyces sp. KNF2047

Neopeptin A and B

Powdery mildew of cucumber plants under glass house conditions

Kim et al. (2007)

Streptomyces violaceusniger YCED9

Neldanamycin, nigericin, and macrocyclic lactone antibiotics complex

Grass seedling disease caused by the Rhizoctonia solani and crown-foliar disease caused by Sclerotinia homoeocarpa

Trejo-Estrada et al. (1998)

Streptomyces melanosporofaciens

Geldanamycin

Common scab on potato tuber

Agbessi et al. (2003)

Streptomyces violaceusniger

Tubercidin

Phytophthora blight of pepper plants

Hwang and Kim (1995)

8.1 Kasugamycin Kasugamycin with fungicidal and bactericidal activities was isolated from Streptomyces kasugaensis metabolites (Umezawa, 1965). Kasugamycin acts as a protein biosynthesis inhibitor in microbes (not in mammalian species). The kasugamycin has been applied on several crops against Pyricularia oryzae cavara causing rice blast disease and Pseudomonas diseases.

8.2 Polyoxin Isono et al. (1965) isolated polyoxins B and D with fungicidal activities, from the metabolites of Streptomyces cacaoi var. asoensis. Mode of action of polyoxins B and D is based on interfering with synthesis of the fungal cell walls by suppressing chitin synthase (Endo and Misato, 1969). Polyoxin D is applied against the causative agent of rice sheath blight disease

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(Rhizoctonia solani) (Doumbou et al., 2001), while polyoxin B is used in fruits, vegetables, and ornamentals to control a number of fungal pathogens.

8.3 Azalomycin Azalomycin F complex is produced by Streptomyces malaysiensis strain MJM1968. Azalomycin F has a strong antifungal activity against phytopathogenic fungi in the agricultural soils like Pestalotia spp. KACC 40501, Alternaria mali KACC, Colletotrichum gloeosporioides KACC 40693 40026, Fusarium chlamydosporum, Cladosporium cladosporioides, R. solani, and Fusarium oxysporum (Cheng et al., 2010).

8.4 Validamycin Validamycin is an antifungal antibiotic produced by Streptomyces hygroscopicus var. limoneus. Validamycin is widely used to treat sheath blight of rice plants caused by R. solani (Iwasa et al., 1970; Horii et al., 1972). Trehalose disaccharide is an essential glucose derivative that plays a key role as an energy source in insects and fungal cells (Elbein et al., 2003). Validamycin A is converted within the fungal cells to validoxylamine A that is a potent competitive inhibitor of the trehalose-degrading enzyme (trehalase) (Kameda et al., 1987). Consequently, inhibition of trehalase has led to inhibition of the metabolism of trehalose and destroys fungi by suppressing their ability to grow or reproduce. Such mode of action provides a desirable biological selectivity of validamycin A because the vertebrates are not dependent for their metabolism on disaccharide trehalose hydrolysis (Elbein et al., 2003).

8.5 Geldanamycin and nigericin Streptomyces violaceusniger YCED9 was documented to produce nigericin, geldanamycin, and macrocyclic lactone antibiotics complex that can inhibit Sclerotinia homoeocarpa that causes crown-foliar disease and R. solani that causes grass seedling disease (Trejo-Estrada et al., 1998). Streptomyces coelicolor HHFA2 was used in vivo (pots and field) for controlling onion bacterial rot (Abdallah et al., 2013).

9 Streptomyces species as cell factories for production of insecticides and antiparasitic agents Streptomyces species produced a variety of macrotetrolides ( Jizba et al., 1991) active against helminths ( Jizba et al., 1997), coccidia (Sakamoto et al., 1979), mites and insects ( Jizba et al., 1991), and blood-sucking parasites (Deepika et al., 2012) and also has immunosuppressive effects (Shichi et al., 1989).

9.1 Tetranactin Tetranactin (Table 6) is a cyclic antibiotic with a molecular composition similar to cyclosporine. It is produced by Streptomyces aureus and used as emulsion on fruits and tea against carmine mites (Ando et al., 1971). TABLE 6 List of some insecticides and antiparasitic agents produced by Streptomyces spp. Streptomyces species

Bioactive agent

References

Streptomyces avermitilis

Avermectin

Burg et al. (1979)

Streptomyces aureus

Tetranactin

Ando et al. (1971)

Streptomyces coelicolor

Prodiginine

Traxler et al. (2013)

Streptomyces bottropensis

Trioxacarcin

Tamaoki et al. (1981)

Streptomyces griseus SDX-4

n-Butanol extract of the fermentation liquor of S. griseus

Yao et al. (2014)

Streptomyces hygroscopicus

Macrolide has insecticidal and antiparasitic activity

Goetz et al. (1983)

Streptomyces sp. AK 409

Pyrocoll, antibiotic, antiparasitic, and antitumor compound

Dieter et al. (2003)

Streptomyces sp. SUK10

Gancidin W

Zin et al. (2017)

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9.2 Ivermectin ˜ mura and Crump, 2004). It is a new, broadIvermectin produced by S. avermitilis is a dehydroderivative of avermectin (O spectrum antiparasitic agent with strong activity against both external and internal arthropods and nematodes.

10 Streptomyces species as cell factories for production of a variety of enzymes Streptomyces species produce a variety of extracellular and endocellular enzymes.

10.1 L-asparaginase L-asparaginase (L-asparagine aminohydrolase, E.C. 3.5.1.1) is the amidase enzyme catalyzing the hydrolysis of L-asparagine giving ammonia and L-aspartic acid (Hosamani, 2012) (Fig. 6). L-asparaginase was used as potential chemotherapeutic protein (El-Naggar et al., 2014a, 2018a) for the treatment of certain human cancer types such as Hodgkin disease, chronic lymphocytic leukemia, acute myelocytic leukemia, and acute lymphocytic leukemia (Narta et al., 2007). Asparaginases are integral components of acute lymphoblastic leukemia (ALL) therapy and are used in all treatment protocols for both children and adults as antileukemia agent. L-asparaginase showed a good antioxidant activity (Maysa et al., 2010). The antitumor effect of L-asparaginase is attributed to the depletion of the circulating blood pools of L-asparagine. Cancer cells cannot synthesize L-asparagine for their needs because they lack L-asparagine synthetase (Keating et al., 1993). Cancer cells are unable to synthesize asparagine-dependent proteins needed for the growth and survival and leading to leukemia cells are selectively killed by depletion of the L-asparagine (Graham, 2003). Therefore the external source of L-asparagine is important for these cells to grow and survive. The normal healthy cells can synthesize L-asparagine, and thus they are defended against L-asparagine deprivation (Duval et al., 2002). Therefore L-asparaginase is commonly used in patients’ therapy by intravenous injection to reduce the L-asparagine concentration in the blood, which is selectively destroying tumor cells (Mitchell et al., 1994). In carbohydrate-containing food, amino acid L-asparagine is the precursor of the Mallard reaction that gives browned food, crust, and roasted flavor of baked or fried foods. Heating above 120°C of reducing sugars and L-asparagine in starchy foods during frying or baking the foods such as cookies, French fries, and potato chips (Mottram et al., 2002) results in acrylamide formation (carcinogenic compound) (Tareke et al., 2002). Addition of L-asparaginase prior to frying or baking the foods reduces the acrylamide formation levels in French fries and fried potato chips where L-asparagine is cleaved into aspartic acid and ammonium ions (Pedreschi et al., 2008). Among the actinobacteria, many species of the genus Streptomyces are recognized as valuable source for L-asparaginase production (Narayana et al., 2008). Several Streptomyces species (Table 7, Fig. 7A) such as Streptomyces parvus NEAE-95 (El-Naggar, 2015a), Streptomyces olivaceus NEAE-119 (El-Naggar et al., 2015), and Streptomyces gulbargensis (Amena et al., 2010) were investigated for L-asparaginase production.

FIG. 6 Schematic description of L-asparaginase mode of action.

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TABLE 7 L-asparaginase-producing Streptomyces spp. Streptomyces pp.

References

Streptomyces parvulus strain sankarensis-A10

Shaik et al. (2017)

Streptomyces fradiae NEAE-82

El-Naggar et al. (2016a), Soliman et al. (2020)

Streptomyces gulbargensis

Amena et al. (2010)

Streptomyces parvus NEAE-95

El-Naggar (2015a)

Streptomyces noursei MTCC 10469

Dharmaraj (2011)

Streptomyces brollosae NEAE-115

El-Naggar et al. (2017b), El-Naggar and Moawad (2015)

Streptomyces ginsengisoli

Deshpande et al. (2014)

Streptomyces olivaceus NEAE-119

El-Naggar et al. (2015)

Streptomyces albidoflavus

Narayana et al. (2008)

Streptomyces lacticiproducens

Arevalo-Tristancho et al. (2019)

Nocardiopsis synnemasporogenes sp. nov., NEAE-85

El-Naggar (2014), El-Naggar et al. (2014a)

Streptomyces labedae VSM-6

Mangamuri et al. (2017)

Streptomyces gulbargensis

Amena et al. (2010)

Streptomyces rochei subsp. chromatogenes NEAE-K

El-Naggar and El-Shweihy (2020a)

Streptomyces tendae

Kavitha and Vijayalakshmi (2010)

FIG. 7 Plate assay showing the ability of some strains belonging to Streptomyces to produce extracellular enzymes (A) L-asparaginase, (B) cholesterol oxidase, and (C) uricase.

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L-asparaginase production is significantly affected by the fermentation media structure and environmental requirements like agitation rate, incubation time, inoculum size, pH, and temperature (El-Naggar et al., 2015). No specified media have been identified for maximum L-asparaginase production by various microorganisms. There are specific requirements for each microorganism for maximum L-asparaginase production (Sharma and Husain, 2015). Production of L-asparaginase by some bacterial strains such as E. coli was nearly completely suppressed by applying glucose at a concentration of 0.5% to the growth medium (Garaev and Golub, 1977). In contrast, another literature reported that glucose was a good source of carbon for maximum L-asparaginase production by Streptomyces ginsengisoli (Deshpande et al., 2014). Also, maximum L-asparaginase production by Streptomyces albidoflavus was obtained by applying maltose as a source of carbon in basal medium, followed by starch and glucose (Narayana et al., 2008). Another literature stated that dextrose and starch have positive effect on L-asparaginase production by S. olivaceus NEAE-119 (El-Naggar et al., 2015). Production of L-asparaginase is induced by applying L-asparagine to the production medium (El-Hadi et al., 2019). It has been proposed that L-asparaginase production is inducible and regulated by L-asparagine concentration. It has been reported that L-asparagine was the best nitrogen source inducing maximum production of L-asparaginase by S. gulbargensis (Amena et al., 2010), Streptomyces karnatakensis, and S. venezuelae (Mostafa, 1979). The optimal L-asparagine concentration was 1% for maximal L-asparaginase production by Streptomyces ABR2 (Sudhir et al., 2012). El-Naggar et al. (2019) stated that adding the organic nitrogen sources to the production medium promotes maximum production of L-asparaginase by Streptomyces brollosae NEAE-115. However, L-asparaginase production has been reduced by inorganic nitrogen supplementation. Furthermore, maximum production of L-asparaginase by S. albidoflavus was documented using yeast extract (Narayana et al., 2008). Yeast extract is necessary for cell proliferation and production of L-asparaginase, but its increased levels inhibit L-asparaginase production (Verma et al., 2007). Yeast extract not only acts as a nitrogen source but also supplies vitamins for enhancement of growth and production of L-asparaginase. Incubation period plays a crucial role in L-asparaginase production. L-asparaginase production decreased at prolonged incubation time, which may be attributable to nutrient resources deficiency, toxic end products accumulation, or due to the inactivation of the enzyme by some proteolytic enzymes (Varalakshmi and Raju, 2013).

10.2 Cholesterol oxidase Cholesterol oxidase (3b-hydroxysterol oxidase, EC 1.1.3.6) is the enzyme that catalyzes the oxidation of cholesterol to cholestenone (cholest-4-en-3-one), with the reduction of oxygen molecule to H2O2 (hydrogen peroxide) (Lario et al., 2003) (Fig. 8). In addition to its clinical applications, microbial cholesterol oxidase has broader applications (Fig. 9) in industries (Navas et al., 2001). Cholesterol oxidase is used for steroid analysis in food samples (Molaei et al., 2014; Khan et al., 2009). Cholesterol oxidase is also employed as biosensor to quantify the concentrations of serum cholesterol, which is necessary for cardiovascular disease diagnosis, atherosclerosis, and other lipid abnormalities. In addition, cholesterol oxidase produced by Streptomyces natalensis was used for pimaricin (natamycin) biosynthesis (Mendes et al., 2007). Pimaricin is an antibiotic that has been widely used for mold growth inhibition in the food industry. Cholesterol oxidase plays a crucial role in leukocyte and macrophage lysis (Antonopoulos, 2002). It has strong insecticidal activity against the larvae of boll weevil “Anthonomus grandis,” which reduces the yield of cotton (Purcell et al., 1993). Cholesterol oxidase has also been reported to be involved in the manifestation of HIV, tuberculosis, and Alzheimer’s diseases (Kumari and Kanwar, 2012). Cholesterol oxidase has also been reported to be used to oxidize diosgenin to 4-ene-3ketosteroids (Greenplate et al., 1995). Cholesterol oxidase has in vitro anticancer activities against rhabdomyosarcoma and breast cancer cell lines. Also, it has in vivo anticancer activities against Ehrlich solid tumor model (El-Naggar et al., 2018b). Different Streptomyces spp. produced cholesterol oxidases (Table 8, Fig. 7B). Cholesterol oxidase production is significantly affected by the medium ingredients and culture conditions like agitation rates, temperature, incubation time, inoculum size, and pH (Hymavathi et al., 2009). The improved cholesterol oxidase production was documented using yeast extract, cholesterol (Yazdi et al., 1999), peptone, potato starch, and malt extract (Varma and Nene, 2003) as substrates. Voelker and Altaba (2001) have found that cholesterol oxidase production is more influenced by organic sources of nitrogen than inorganic. This is because organic nitrogen could contain different types of amino acids and growth factors required for microbial growth and can be metabolized directly by the cells, thereby promoting the production

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FIG. 8 Schematic illustration of mechanism of action of cholesterol oxidase.

Clinical applications Production of steroid hormones precursors Cholesterol oxidase Antimicrobial drug

Pimaricin antifungal antibiotic biosynthesis

Anti-tumor

Insecticidal activity FIG. 9 Broader applications of cholesterol oxidase.

of cholesterol oxidase (Chauhan et al., 2009). El-Naggar et al. (2016b) assessed the impact of environmental conditions and nutritional variables on the production of cholesterol oxidase by Streptomyces cavourensis strain NEAE-42. They found that the most significant factors influenced the production of cholesterol oxidase were concentrations of (NH4)2SO4, cholesterol and initial pH. However, El-Naggar et al. (2018b) found that cholesterol, pH, and incubation time were the most significant factors that influenced the production of cholesterol oxidase by Streptomyces aegyptia strain NEAE-102.

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TABLE 8 Various Streptomyces spp. sources of cholesterol oxidase. Streptomyces spp.

References

Streptomyces sp.

Niwas et al. (2013)

Streptomyces parvus

Praveen et al. (2011)

Streptomyces sp.

Lashgarian et al. (2016)

Streptomyces natalensis

Mendes et al. (2007)

Streptomyces aegyptia NEAE 102

El-Naggar et al. (2017c, 2018b)

Streptomyces lavendulae NCIM 2421

Varma and Nene (2003)

Streptomyces anulatus strain NEAE-94

El-Naggar and El-Shweihy (2020b)

Streptomyces fradiae

Yazdi et al. (2001)

Streptomyces sp.

Lartillot and Kedziora (1990)

Streptomyces lividans

Molna´r et al. (1991)

Streptomyces cinnamomeus

Slotte (1992)

Streptomyces sp.

Lolekha and Jantaveesirirat (1992)

Streptomyces virginiae

Li et al. (2010)

Streptomyces sp.

Pathak et al. (2015)

Streptomyces violascens

Kamei et al. (1978)

Streptomyces cavourensis strain NEAE-42

El-Naggar et al. (2016b)

Streptomyces badius

Moradpour et al. (2013)

Streptomyces lavendulae NCIM 2499

Chauhan et al. (2009)

Streptomyces hygroscopicus

Gadda et al. (1997)

Streptomyces lavendulae YAKB-15

Yamada et al. (2019)

Streptomyces sp. AKHSS

Kavitha and Savithri (2020)

10.3 Uricase (gout treatment enzyme) Uricase (urate oxidase) is an enzyme belonging to the oxidoreductase class that catalyzes uric acid oxidation to hydrogen peroxide, carbon dioxide, and allantoin (more soluble and simply excreted) (Brogard et al., 1972) (Fig. 10). Uricase plays an essential role in the nitrogen metabolism. Uricase is used as effective therapeutic enzyme in the treatment of hyperuricemia and gout, causing a rapid reduction of uric acid in the serum and urine. As well as, uricase is used as a therapeutic enzyme to prevent and treat the hyperuricemia caused by organ transplants and tumoral lysis (Cannella and Mikuls, 2005; Ganson et al., 2005). Administration of uricase has been found effective, more potent, and faster acting urate-lowering drug than allopurinol (Arslan, 2008). It catalyzes uric acid oxidation to poorly toxic and easy cleared allantoin, rapidly excreted by the kidneys (Oldfield and Perry, 2006). Uricase is widely used in clinical analyses as a diagnostic reagent for quantification of uric acid in biological fluids and blood, which consider significant application for uricase (Ada´mek et al., 1990; Arslan, 2008). Gochman and Schmitz (1971) found that uricase was useful for urate determination when combined with peroxidase and 4-aminoantipyrine. It has been used as a protein therapy for the reduction of toxic urate (Colloc’H et al., 1997). It can also be used in hair coloring agent formulations as an additive (Nakagawa et al., 1995). Actinomycetes were found to produce large amounts of uricase when grown on peptone-glucose medium supplemented with uric acid or other purines as reported by Fukumoto et al. (1967) and Watanabe and Fukumoto (1970). They found that the degradation of uric acid requires the presence of K+ ions. Different Streptomyces spp. produced uricase (Table 9, Fig. 7C). Demnerova et al. (1986) demonstrated that Streptomyces is a uricase producer and the enzyme is inducible. Ammar et al. (1987) have indicated that uricase was produced by Streptomyces albogriseolus. Also, El-Naggar (2015b) reported that Streptomyces rochei NEAE-25 is a potential uricase producer.

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FIG. 10 Schematic illustration of mechanism of action of uricase.

TABLE 9 Various Streptomyces spp. sources of uricase. Streptomyces spp.

References

Streptomyces sp.

Fukumoto et al. (1967)

Streptomyces sp.

Watanabe and Fukumoto (1970)

Streptomyces sp.

Watanabe (1971)

Streptomyces cyanogenus

Ohe and Watanabe (1981)

Streptomyces aureofaciens

Demnerova et al. (1986)

Streptomyces albogriseolus

Ammar et al. (1987)

Streptomyces sclerotials, Streptomyces citerus, Streptomyces corchorusii, Streptomyces orientalis

El-Arini (1991)

Streptomyces rochei NEAE-25

El-Naggar (2015b)

Streptomyces exfoliatus

Aly et al. (2013)

Streptomyces graminofaciens, Streptomyces albidoflavus

Azab et al. (2005)

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10.4 Antidiabetic produced by Streptomyces species Diabetes mellitus is a chronic endocrine disorders, with hyperglycemia due to the elevation of blood glucose level either because of insulin secretion deficiencies, insulin resistance, or both. Diabetes mellitus is associated with disturbances of carbohydrates, protein, and fat metabolism (Ramachandran et al., 2010; West, 2000). Uncontrolled diabetes can lead to serious chronic diseases in the peripheral nervous system, kidneys, eyes, and artery disorders and contribute significantly to cardiovascular disease, renal failure, disability, and mortality (Coniff and Krol, 1997). Postprandial hyperglycemia is defined as a sudden increase in blood sugar level after a meal. In healthy persons, pancreas enhances the secretion of insulin to regulate the postprandial blood level of glucose. In patients with type II diabetes, the pancreas fails to secrete sufficient insulin or is insulin resistance, which leads to postprandial hyperglycemia (Stumvoll et al., 2008). In this condition, after food intake, the production of insulin hormone is reduced, and low secretion of glucagon leads to the improper metabolism of glucose in liver and kidney. Hence, there are no sufficient uptake of glucose, and subsequently, its blood sugar levels increase (Meyer et al., 2011). Postprandial hyperglycemia is one of the major risk factors for several diseases such as cardiovascular diseases, stroke, retinopathy, renal failure, and neurologic complications (Lin et al., 2009). An approach to prevent hyperglycemia involves the use of drugs that reduces and slows glucose absorption in the intestines. Thus the treatment strategy for diabetes is to minimize postprandial hyperglycemia (Chakrabarti and Rajagopalan, 2002). This can be accomplished by inhibiting carbohydrate-hydrolyzing enzymes, which act as the main digestive enzymes, such as a-amylase and a-glucosidase (Nair et al., 2013). a-Amylase participates in the digestion of long-chain carbohydrates, and a-glucosidase is responsible for hydrolysis of disaccharides in the small intestine to release absorbable monosaccharides that lead to increase the levels of blood glucose. a-Glucosidase inhibition regulates the release of glucose from the complex dietary carbohydrates (Hillebrand et al., 1979). The synthetic enzyme inhibitors currently used cause gastrointestinal symptoms including abdominal bloating, flatulence, and diarrhea (Bray and Greenway, 1999). The inhibition of a-amylase and glucosidase is the potential future priorities for the management of diabetes (Subramanian et al., 2008). Acarbose (a-glucosidase inhibitor) is a secondary metabolite and belongs to the class of aminoglycoside (Inoue et al., 1997). It is a complex pseudo-oligosaccharide that is potent inhibitor of amylases and cyclodextrin glycosyltransferase. Commercial acarbose is produced by microbial fermentation (Table 10) by Actinoplanes and Streptomyces strains and consists of maltose, deoxyhexose, and unsaturated aminocyclitol (Rockser and Wehmeier, 2009; Wehmeier and Piepersberg, 2004; Lee et al., 1997). It is as a competitive a-glucosidase inhibitor (Wehmeier and Piepersberg, 2004). Studies on acarbose proved that it is useful therapeutic agent for treating carbohydrate-dependent diseases such as postprandial hyperglycemia and obesity (Hanefeld et al., 2008). Therefore, in diabetic patients, acarbose has been clinically used to retard the metabolism of carbohydrates and the formation of high absorbable monosaccharides as glucose and fructose that can occur after a meal (Wendler et al., 2014) and for the treatment of noninsulin-dependent patients with type II diabetes mellitus (Schnell et al., 2007) by inhibiting intestinal a-glucosidase (Zheng et al., 2005; Mahmud et al., 2001). Acarbose regarded as a very safe drug (Lebovitz, 1998) regulates the elevation of high postprandial blood glucose level, thus delaying or preventing high blood pressure and cardiovascular disorders in diabetic patients (Chiasson et al., 2003).

TABLE 10 Antidiabetic and cholesterol synthesis inhibitors enzymes produced by Streptomyces species. Streptomyces species

Bioactive agent(s)

References

Streptomyces lucensis

Amylase inhibitor

Sharova (2015)

Streptomyces coelicoflavus SRBVIT13

a-Glucosidase inhibitor

Kumar and Rao (2018)

Streptomyces M37

a-Glucosidase inhibitor

Ren et al. (2017)

Streptomyces sp. OUCMDZ-3434

a-Glucosidase inhibitor

Chen et al. (2016)

Streptomyces paradoxus VITALK03

Gancidin W

Ravi et al. (2017)

Streptomyces sp.

Pravastatin

Park et al. (2003)

Streptomyces carbophilus

Pravastatin

Hosobuchi et al. (1993)

Antidiabetic

Cholesterol synthesis inhibitors

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10.5 Cholesterol synthesis inhibitors produced by Streptomyces species Statins are drugs currently used for patients to reduce level of cholesterol and the risk of cardiovascular disorders. Statins can inhibit competitively HMG-CoA reductase “3-hydroxy-3-methyl glutaryl CoA reductase,” the key enzyme in cholesterol biosynthesis pathway (Sharpe and Brown, 2013). Statins have proved to be anti-inflammatory, in addition to treating hypercholesterolemia (Montecucco and Mach, 2009; Akasaki et al., 2009). Among the statins, pravastatin was established as a novel drug for hypercholesterolemia treatment, selectively suppressing the biosynthesis of cholesterol in the liver and small intestine (Koga et al., 1990; Del Sol and Nanayakkara, 2008; Barrios-Gonza´lez and Miranda, 2010). Moreover, recent studies have also shown neuroprotective effects of pravastatin on acute ischemic stroke rats (Berger et al., 2008). Moreover, pravastatin has protective effect against cell damage caused by mercury (Harisa et al., 2012). Pravastatin shows anti-inflammatory effects (McGown et al., 2010). Pravastatin is manufactured from compactin by chemical synthesis. In view of the drawbacks of expensive processing and formation of stereoisomers by-products attributed to the chemical synthesis, the synthesis of pravastatin using microbes is preferable (Table 10). Streptomyces carbophilus (Hosobuchi et al., 1993), S. carbophilus SANK-62585 (Matsuoka and Miyakoshi, 1993), Streptomyces roseochromogenus IFO-3411, S. roseochromogenus IFO-3363, and S. roseochromogenus NRRL-1233 demonstrated the ability to transform compactin to pravastatin (Terahara and Tanaka, 1982). Streptomyces sp. Y-110 has recently been reported to be more compactin tolerance, and the biotransformation rate of compactin to pravastatin exceeded 50% (Park et al., 2003).

10.6 Lytic enzymes It has been established that a complex of lytic enzymes produced by Streptomyces recifensis subspecies luticus 2435 stimulated the growth rate and biomass accumulation by microorganisms of different systematic groups (Kilochek and Babenko, 1990). It was shown that biomass growth exceeds control by 30%–80%. A crude preparation of extracellular proteins from Streptomyces sp. ATCC11238 containing endochitinase, exochitinase, chitobiase, and laminarinase (b1,3-glucanase) was active in lysing the cell walls of most of 50 viable filamentous tested ascomycetes (Beyer and Diekmann, 1985). Streptomyces rutgersensis H-46 produced a lytic enzyme (a kind of N-acetylmuramidase), which was active against Streptococcus faecalis (Hayashi et al., 1981). The lytic enzymes (different cell wall hydrolases) from a Streptomyces sp. strain B578 were active under low pH values, low temperatures, and presence of ethanol and sulfite. They were lysing in a synergistic action of gram-negative acetic acid bacteria and lactic acid bacteria (nearly all winerelevant strains) (Bl€attel et al., 2009). Anitha and Rabeeth (2010) reported that lytic enzyme produced by S. griseus degrades fungal cell walls of phytopathogenic fungi. Streptomyces globisporus (Seo et al., 2001) and Streptomyces fulvissimus (Ohbuchi et al., 2001) are also lytic exoenzymes sources. S. cavourensis subsp. cavourensis SY224 has been found to be a high potential biocontrol agent antagonistic to C. gloeosporioides infecting pepper plants (anthracnose in pepper), mainly because of the combined impact of 2-furancarboxaldehyde (nonprotein, heatstable antifungal compound) and the lytic enzymes like protease, chitinase, lipase, and b-1,3-glucanase (Lee et al., 2012).

10.7 Cellulases Cellulases are inducible enzymes produced by a wide range of microorganisms, like fungi and bacteria (Mojsov, 2012a,b). Cellulases are responsible for the hydrolysis of cellulose (Fig. 11) into soluble sugars, and they are composed of a multicomponent enzyme system (Howard et al., 2003) (Fig. 12). The cellulolytic enzymes are consisting of three major components:

FIG. 11 Structure of the repeating unit of cellulose.

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FIG. 12 Cellulolytic enzymes.

FIG. 13 Simplified mechanism of the enzymatic cellulose hydrolysis.

1- endo-b-1,4-glucanases (EC 3.2.1.4) hydrolyze internal b-1,4 glycosidic bonds of cellulosic chains, releasing long chains of cellooligosaccharides and cellobiose; 2- exo-b-1,4-glucanases (EC 3.2.1.91), typically cellobiohydrolases, split off cellobiose from the end of the cellulose chains; 3- b-glucosidase (EC 3.2.1.21) that is primarily responsible for the hydrolysis of cellobiose to glucose (Zhou et al., 2016). Simplified mechanism of the enzymatic cellulose hydrolysis is shown in Fig. 13. Carboxymethyl cellulase (CMCase; endob-1,4-glucanase) is one of the members of cellulase complex. This is a significant enzyme for industry. It is used in the food industry and in bread production to increase loaf volume and maintain freshness. It can be used for extraction and clarification of juices, brewery, and coffee processing (Galante et al., 1998; Omeje and Omeje, 2014). It is commonly used in textile industries; it modifies the cellulosic fiber and textile surface characteristics to ensure the desired surface effect (Kotchoni et al., 2003). CMCase is also used for various purposes such as fermentation of the biomass into biofuels, € the pulp and paper industry, and laundry detergents (Ogel et al., 2001) and in medicine to treat phytobezoars (indigestible food fibers containing cellulose in the human stomach). Many actinomycetes can degrade cellulose and soulibilize the lignin structure as their metabolic activity at high nitrogen level (Eriksson et al., 1990), particularly Streptomyces (Table 11; Fig. 14A).

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TABLE 11 Cellulose degrading Streptomyces spp. Streptomyces spp.

References

Streptomyces flavogriseus

MacKenzie et al. (1984)

Streptomyces albaduncus

Harchand and Singh (1997)

Streptomyces nitrosporeus

Van Zyl (1985)

Streptomyces lividans, Streptomyces flavogriseus

Kluepfel et al. (1986)

Streptomyces nitrosporeus

McCarthy (1987)

Streptomyces viridosporus

Deobald and Crawford (1987)

Streptomyces reticuli

Wachinger et al. (1989)

Streptomyces albogriseolus subsp. cellulolyticus strain NEAE-J

El-Naggar et al. (2014b)

Streptomyces reticuli

Schrempf and Walter (1995)

Streptomyces omiyaensis

Alam et al. (2004)

Streptomyces cellulolyticus

Li (1997)

Streptomyces lividans 66

Theberge et al. (1992)

Streptomyces thermodiastaticus

Crawford and McCoy (1972)

Streptomyces transformant T3-1

Jang and Chen (2003)

Streptomyces malaysiensis

Nascimento et al. (2009)

Streptomyces noboritoensis

Arunachalam et al. (2010)

Streptomyces noboritoensis SPKC1

George et al. (2010)

Streptomyces viridobrunneus

Da Vinha et al. (2011)

Streptomyces drozdowiczii

De Lima et al. (2005)

Streptomyces griseorubens

Prasad et al. (2013)

Streptomyces lividans

Hamed et al. (2017)

Streptomyces thermocoprophilus TC13W

Sinjaroonsak et al. (2019)

Streptomyces aegyptia NEAE-102

El-Naggar et al. (2011b)

Streptomyces viridochromogenes NEAE-26

El-Naggar et al. (2011a)

Streptomyces sp. strain NEAE-D

El-Naggar and Abdelwahed (2012)

Streptomyces macrosporus

Soeka et al. (2019)

Streptomyces ruber

El-Sersy et al. (2010)

10.8 Amylase Amylase is a digestive enzyme that catalyzes a-1,4-glycosidic linkage hydrolysis in polysaccharides to generate D-glucose, maltose, oligosaccharides, and dextrins. Amylases are important enzymes employed widespread during industries of food processing such as maltotetrose syrups, high fructose syrup, production of maltose, oligosaccharide mixtures, preparation of digestive aids, brewing, and baking (Nielsen and Borchert, 2000; Couto and Sanroma´n, 2006). These hydrolytic enzymes are also used to clarify fruit juices or to pretreat animal food to enhance fiber digestibility (Ghorai et al., 2009). Amylase is of great significance in pharmaceutical, detergent, and textile industries (Rosell et al., 2001; Pandey et al., 2000). Numerous Streptomyces species exhibit remarkable capacity for a-amylase production (Fig. 14B). A promising Streptomyces clavifer (Hogue et al., 2006; Yassien and Asfour, 2012), S. gulbargensis (Syed et al., 2009a,b), Streptomyces megasporus strain

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FIG. 14 Plate assay showing the ability of some strains belonging to Streptomyces to produce extracellular enzymes (A) cellulase and (B) amylase. Testtube reaction showing the ability of Streptomyces sp. to produce milk-clotting protease (coagulation of milk) (C).

SD12 (Dey and Agarwal, 1999), Streptomyces erumpens MTCC 7317 (Kar and Ray, 2008; Shaktimay et al., 2010), and Streptomyces praecox NA-273 (Suganuma et al., 1980) had been reported. Microbial a-amylases have been employed in the baking industry for several decades (Mojsov, 2012a,b). Such enzymes can be used in bread dough to break the starch into smaller dextrins in the flour that are fermented by the yeast. Microbial aamylases have been used in the formulations of detergents to increase the detergent stability, to improve cleaning effect, and to degrade the residues of starch from cloth surfaces without damaging the fibers in addition to eco-friendly behavior (Feitkenhauer et al., 2003; Mukherjee et al., 2009). a-Amylases are involved in 90% of liquid detergents (Gupta et al., 2003). In textile industries, amylases are used for desizing processing. During the pulp and paper industries, a-amylase is employed for the degradation of starch to prevent mechanical losses during processing, to increase the product quality and enhance rigidity and elasticity of the paper (Gupta et al., 2003; Van Der Maarel et al., 2002). Biotechnological treatment of starch present in food processing wastewater can purify the effluent and produce valuable products such as microbial biomass protein (Tiwari et al., 2015).

10.9 Proteases and keratinases Proteases (proteolytic enzymes or proteinases) are an enzyme group that catalyzes the peptide bond cleavage in proteins and release short amino acid chains called peptides (Fig. 14C). They are one of the most valuable industrial enzymes, produced commercially and used in bioremediation, effluent treatments, food industry, pharmaceuticals, protein recovery or solubilization, and leather and dairy industries (Anwar and Saleemuddin, 1998 Abdelwahed et al., 2014). Proteases are commonly used in the detergent industry as a safe alternative to substitute dangerous chemicals like caustic soda. Today, many of the top brands in the detergent industry use proteases as a crucial intermediate (Nascimento and Martins, 2006; Gupta et al., 2002). Keratin-rich waste materials (insoluble, fibrous, and highly stable proteins) are naturally produced in enormous quantities; due to the insolubility and nondegradability by common proteolytic enzymes, their practical use was limited. Therefore keratinases are a type of specific proteolytic enzymes capable of hydrolyzing keratin-rich waste materials turning

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them into high-value products (Karthikeyan et al., 2007). Poultry industries generate large amounts of feather waste. Keratinases are widely used in the degradation of insoluble keratinaceous wastes such as wool, hair, and feather that create a solid waste problem in the leather industries and poultry slaughterhouses (Gupta et al., 2013; Korniłłowicz-Kowalska and Bohacz, 2011). Several studies demonstrated that the feather hydrolysates could also be used for manufacturing of nitrogenous fertilizers formulations (Ichida et al., 2001), as animal feed supplements (Bernal et al., 2003), in edible film production (Longshaw et al., 2002), peptides, and amino acids as a raw material (Bressollier et al., 1999). Different studies demonstrated that keratinases are capable of preserving detergent stability, and they can clean more effectively different stains on stained surfaces including sleeve heads, collars, and shirt (Park and Son, 2009; Paul et al., 2014; Jeong et al., 2010; Jaouadi et al., 2015). Keratinases are also used as a supplement for cleaning solutions for lenses. They are widely used for sewage treatment and have also become potential candidates for many other applications including medicine, food industries, and cosmetics as skin lightening additives in beauty products and textile (Gupta and Ramnani, 2006). In the leather industry, keratinolytic proteases are currently used in dehairing process for an eco-friendly leather treatment to replace conventional chemical processes that generate large amounts of sodium sulfide and other chemicals being discharged into water effluents causing water pollution (Verma et al., 2011; Wang and Liao, 2014). Keratinases are also used for the modification of silk and wool fibers. In addition, it is widely known that keratinases have several medical applications as skin medications for the treatment of skin to eliminate acne and psoriasis and removal of human callus. In addition, keratinases are used for dermatophytosis therapy as a vaccine (Gupta and Ramnani, 2006; Verma et al., 2017). Abnormal prion protein folding causes damage to the brain and leads to prion diseases. Prion diseases are fatal neurodegenerative, transmissible illnesses affecting humans and animals and cause enormous loss in the cattle industry. Keratinases can be used potentially to degrade prion proteins in the brain stem of the infected animals (cattle and sheep) (Langeveld et al., 2003). The bioconversion of poultry keratin waste into biogas (bioenergy) is one of the most crucial recent applications of keratinases (Brandelli et al., 2010). Recently, some studies recorded the application of keratinases as a pesticide for nematode management and in the antioxidant production (Yue et al., 2011; Fakhfakh et al., 2013). Keratinases are produced by a number of Streptomyces species (Table 12).

TABLE 12 Streptomyces species producing keratinase. Streptomyces species

References

Streptomyces coelicoflavus

Jadhav and Kulkarni (2014)

Streptomyces graminofaciens

Szabo et al. (2000)

Streptomyces fradiae

Young and Smith (1975), Huang et al. (2006)

Streptomyces minutiscleroticus

Allure and Agsar (2015)

Streptomyces sp7

Tatineni et al. (2007)

Streptomyces albus

Esawy (2007), Nayaka and Babu (2014)

Streptomyces gulbargensis

Syed et al. (2009a,b),

Streptomyces sp.

Tatineni et al. (2008)

Streptomyces thermoviolaceus

Chitte et al. (1999)

Streptomyces aureofaciens K13

Gong et al. (2015)

Streptomyces albidoflavus

Bressollier et al. (1999)

Streptomyces sampsonii GS 1322

Jain et al. (2016)

Streptomyces pactum

Boeckle et al. (1995)

Streptomyces exfoliatus CFS 1068

Jain et al. (2012)

Streptomyces radiopugnans Kr I2

Aly and Tork (2018)

Streptomyces malaysiensis

Pavani et al. (2017)

Streptomyces ornatus S 1220

Poletaev et al. (2013)

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10.10 Chitinolytic enzymes (chitinases) The second most abundant biopolymer found in nature is chitin (after cellulose). Chitin occurs as a structural polymer in the integument of insects and crustacea and in the cell walls of many fungi. Chitin is a linear polysaccharide composed of Nacetylglucosamine residues connected by b-1,4-glucosidic bonds (Fig. 15). Chitin is insoluble in dilute mineral acids, concentrated alkali, organic solvents, or water. Chitin can be solubilized and degraded either enzymatically or by treatment with concentrated mineral acids. Chitinases (EC 3.2.1.14) are described as the enzymes that catalyze the hydrolysis of Nacetyl-D-glucosamine 1,4-b-linkages in chitodextrins and chitin randomly and yield its monomer N-acetyl glucosamine and chitooligosaccharides (Patil et al., 2000; Dahiya et al., 2006). Chitinase plays a vital role in chitin hydrolysis as well as the sustainable use of chitin as a renewable resource.

FIG. 15 Structure of the repeating unit of chitin: homoplymer of N-acetyl-D-glucosamine 1,4-b-linkages.

Dahiya et al. (2006) discussed that chitinases have wide applications in diverse agriculture fields, food technology, management of wastes, and medicine. They are widely applied in control of malaria transmission, treatment of chitinous waste, biocontrol of plant pathogenic fungi, single-cell protein production, and in the production of oligosaccharides, which are biologically active substances of therapeutic value. Chitinases are also used in biocontrol of insects, mosquito; fungi (participating in the lysis of fungal cell wall); and yeast (Bansode and Bajekal, 2006; Wang et al., 2009). Streptomycetes are one of the largest chitinovorous microbial groups in the soil (Metcalfe et al., 2002; Gonzalez-Franco et al., 2003). Several species of Streptomyces are reported for chitinolytic enzyme production (Table 13). Bai et al. (2016) found that almost half of the terrestrial chitinolytic bacteria belong to the Actinobacteria. Streptomyces species is a decomposer of chitin pieces rapidly due to their ability to penetrate these chitin pieces with their hyphae (De Boer et al., 1999; Bai et al., 2016). The chitinase of S. rimosus displayed antifungal characteristics in vitro against Alternaria alternata and Fusarium solani (Brzezinska et al., 2013). Similarly the chitinase of Streptomyces viridificans displayed antifungal characteristics in vitro against Pythium, Curvularia, Sclerotinia, Aspergillus, Colletotrichum, Rhizoctoni, and Fusarium and efficiently lysed the fungal cell walls (Gupta et al., 1995). Prapagdee et al. (2008) found that culture filtrate of S. hygroscopicus containing b-1,3-glucanase and chitinase had an effective antagonistic effect toward Sclerotium rolfsii and C. gloeosporioides. Streptomyces roseolus purified chitinase displayed inhibitory effect on the extensions of fungal hyphal (Xiayun et al., 2012). Taechowisan et al. (2003) reported that S. aureofaciens CMUAc130 produce chitinase that is effective in fungal cell wall lysis. Romaguera et al. (1992) purified five extracellular chitinases from culture filtrate of Streptomyces olivaceoviridis. Streptomyces sp. ANU 6277 chitinase displayed inhibitory antifungal activity against Fusarium udum (Narayana and Vijayalakshmi, 2009). Kim et al. (2003) documented several antifungal activities of Streptomyces sp. M-20 purified chitinase against Botrytis cinerea such as bursting of spore germ tube elongation and spore germination inhibition.

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TABLE 13 Streptomyces species producing chitinase and chitosanase enzymes. Streptomyces species

Enzyme

References

Streptomyces erythraeus

Chitinase

Kamei et al. (1989)

Streptomyces cinereoruber

Chitinase

Okazaki and Tagawa (1991)

Streptomyces lividans

Chitinase

Miyashita et al. (1991)

Streptomyces viridificans

Chitinase

Gupta et al. (1995)

Streptomyces lydicus WYEC108

Chitinase

Mahadevan and Crawford (1997)

Streptomyces olivaceoviridis

Chitinase

Romaguera et al. (1992)

Streptomyces sp. F-3

Chitinase

Sun et al. (2019)

Streptomyces thermocarboxydus

Chitinase

Tran et al. (2019)

Streptomyces plicatus

Chitinase

Abd-Allah (2001)

Streptomyces griseus

Chitinase

Kezuka et al. (2006)

Streptomyces violaceusniger

Chitinase

Nagpure and Gupta (2013)

Streptomyces thermoviolaceus OPC-520

Chitinase

Tsujibo et al. (2000)

Streptomyces antibioticus

Chitinase

Narayana and Vijayalakshmi (2009)

Streptomyces coelicolor A3(2)

Chitinase

Nguyen-Thi and Doucet (2016)

Streptomyces venezuelae P10

Chitinase

Mukherjee and Sen (2006)

Streptomyces chitinivorans

Chitinase

Ray et al. (2016)

Streptomyces aureofaciens

Chitinase

Taechowisan et al. (2003)

Streptomyces venezuelae

Chitinase

Mukherjee and Sen (2004)

Streptomyces halstedii AJ-7

Chitinase

Joo (2005)

Streptomyces albus

Chitosanase

Cheng et al. (2012)

Streptomyces avermitilis

Chitosanase

Heggset et al. (2012)

Streptomyces lividans

Chitosanase

Masson (1993)

Streptomyces roseolus

Chitosanase

Jiang et al. (2012)

Streptomyces griseus

Chitosanase

Ngo et al. (2005)

Streptomyces lividans

Chitosanase

Fink et al. (1991)

Streptomyces hygroscopicus

Chitosanase

Yang et al. (2013)

Streptomyces albolongus ATCC 27414

Chitosanase

Guo et al. (2019)

Streptomyces clavuligerus sp. P_1

Chitosanase

Lei (2012)

10.11 Chitosanase Chitosanase (EC 3.2.1.132) is described as a hydrolytic enzyme responsible for chitosan depolymerization and catalyzes the cleavage of b-1,4-linked glycosidic linkage in the chitosan. There is a growing interest in chitosanases due to its potential applications in medicine, agriculture, and nutraceuticals to hydrolyze chitosan into bioactive forms (lowmolecular weight chitosan or chitosan oligosaccharides). Chitosan are hydrolysis products are used in various fields such as agriculture and medicine. Many Streptomyces species can use chitosan as a single source of carbon (Table 13).

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10.12 Streptomyces species as cell factories for production of lipases Lipases (EC 3.1.1.3, triacyl glycerol acylhydrolases) are the hydrolytic enzymes that play a key role in fat digestion by catalyzing the hydrolysis of insoluble long-chain triglycerides at the interface between the oil and the aqueous layers for the release of free fatty acids, mono- and diacylglycerols, and glycerol (Kempka et al., 2008; Reis et al., 2008; Lutz, 2004). Lipases are widely used as cheap and versatile catalysts through catalyzed reactions in various industries, such as dairy industry, food processing, industries of pharmaceuticals and fine chemicals, agrochemical industry, cosmetics production, paper industry and personal care products, synthesis of surfactants, and polymer synthesis (Sharma et al., 2011). Lipases are the most promising enzymes widely used in detergents to remove oil stains and to improve the washing capacity (Grbavcic et al., 2011; Jurado et al., 2007). Lipases are also used in the production of biodiesel to accelerate the degradation of fatty wastes (Tan et al., 2010; Nie et al., 2006; Bajaj et al., 2010; Jegannathan et al., 2008; Zhao et al., 2015). One of the new biotechnological applications of lipases is the synthesis of important drugs and drug intermediates (Kumar et al., 2017). Lipases are now part of a global food industry (Theil, 1995). It is recommended for the cheese flavors production, and it also speeds up the ripening of cheese and butter. In addition, it is used for lipolysis of fats and oils. By adding lipases, shortchain fatty acids are released, providing a sharp, tangy flavor (Memarpoor-Yazdi et al., 2017). The lipases from different origin can be used to treat oil pollution results from oil spills in processing factories, shore sand, and refineries (Patel et al., 2016). It has also been used to break down wastewater pollutants, including olive oil from the oil factories. Further essential lipase applications have been reported like elimination of biofilm deposits from cooling water systems, purification of waste gasses, and the degradation of polyester waste (Sharma et al., 2011). Microbial lipases are industrially important, because of high stability than lipases derived from animals or plants, and their production could be achieved at low cost with high yields (more convenient and safer) (Sharma et al., 2011). Many species of Streptomyces are found to produce lipases (Table 14).

TABLE 14 Streptomyces species producing lipase. Streptomyces species

References

Streptomyces exfoliates

Aly et al. (2012)

Streptomyces violascens OC125-8

Boran et al. (2019)

Streptomyces rimosus

Abramic et al. (1999)

Streptomyces cellulosae

Boran (2018)

Streptomyces fungicidicus RPBS-A4

Rajanikanth and Damodharam (2016)

Streptomyces griseus

Vishnupriya et al. (2010)

Streptomyces coelicolor A3 (2)

C^ ote and Shareck (2008)

Streptomyces cinnamomeus

Sommer et al. (1997)

11 Streptomyces species as cell factories for production of bioemulsifiers and biosurfactants The production of active surface biomolecules including bioemulsifiers and biosurfactants has also been reported by Streptomyces spp. (Table 15). Streptofactin is an extracellular hydrophobic peptide compound formed by Streptomyces tendae (Gesheva and Negoita, 2012). Glycolipids are produced by Streptomyces coelicoflavus and Streptomyces matensis (Gesheva and Negoita, 2012). Streptomyces luridus has a great ability to produce bioemulsification potential compounds that are capable of emulsifying the oils and hydrocarbons and may be used to bioremediate the oil leak–polluted areas.

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TABLE 15 Bioemulsifiers produced by Streptomyces species. Streptomyces sp.

Chemical structures

References

Streptomyces luridus So3.2

Peptides; fatty acids

Lamilla et al. (2018)

Streptomyces spp.

Not identified

Deepa et al. (2015)

Streptomyces sp.

Glycoprotein

Colin et al. (2013)

Streptomyces sp.

Not identified

Colin et al. (2017)

Streptomyces tendae

Streptofactin

Gesheva and Negoita (2012)

Streptomyces sp. S1

Polysaccharide

Kokare et al. (2007)

Streptomyces matensis, Streptomyces coelicoflavus

glycolipids

Gesheva and Negoita (2012)

Streptomyces sp. S22

Peptidoglycolipid

Maniyar et al. (2011)

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Streptomyces species as cell factories for production of pigments

Many synthetic dyes have dangerous effects on health and ecosystem used as a coloring agent in foods, pharmaceutical, and cosmetic industries (Prakash et al., 2001). Because of the negative impacts of the synthetic pigments, the production of natural pigments is of global interest (Berdy, 2005; Unagul et al., 2005). Actinomycete natural pigments are potentially good alternatives. The pigments of actinomycetes produced either as intracellular or extracellular metabolites dissolve readily in the medium and have bioactive potentials (Soliev et al., 2011). Some of the pigments are soluble in water and others in alkaline or organic solvents. Some of these pigments are pH indicator (Krasil’nikov et al., 1967). Some are controlled by the composition of the medium, whereas others are formed on a variety of different media (Waksman, 1967). Some pigments consist of 1–3 components, while others consist of 7–10 or even 15 components (Krassilnikov, 1970). The pigment-producing capacity of streptomycetes was found dependent on a number of factors, namely, 1. 2. 3. 4. 5.

Medium constitution (Krassilnikov and Egorova, 1960), Type of nitrogen and carbon sources (Lu et al., 2002; Lu et al., 2009), Type of phosphorus source (Bekhtereva et al., 1966), Microelements (Krasil’nikov et al., 1967), Vitamins (Lu et al., 2009).

A large number of Streptomyces species when grown on synthetic and organic media have the capacity to produce various pigments (Fig. 16 and Table 16). They make up three distinct groups: 1. Anthocyanins (red-blue pigments), 2. Carotenoids (yellow, orange, or red pigments), 3. Melanins (black and brown pigments). Streptomyces coelicolor produced blue pigment called actinorhodin that act as antibiotic and can inhibit, at a relatively high concentration, the growth of most gram-positive bacteria like Sta. aureus (Wright and Hopwood, 1976; Bystrykh et al., 1996), but it cannot inhibit the growth of gram-negative bacteria like E. coli. Actinorhodin is a pH indicator, turning blue above pH 8.5 and red below pH 8.5. It is stable for heat and light and used as food additives (Zhang et al., 2006). Streptomyces pilosus strain JAR6 produced red pigment called undecylprodigiosin that is a potent bioactive compound with prominent antimicrobial activity against Enterococcus sp., Shigella sp., Proteus mirabilis, and Salmonella sp.; it was less effective against K. pneumonia, Sta. aureus, and E. coli. The undecylprodigiosin has antitumor activity against HeLa cell lines (Abraham and Chauhan, 2018). The carotenoid yellow pigment produced by Streptomyces griseoaurantiacus JUACT 01. It has significant cytotoxicity against HeLa and Hep G2 cells (Prashanthi et al., 2015). Actinomycetes can also produce dark brown melanoid pigments in the medium of production; these pigments are important and helpful for taxonomic studies (Shirling and Gottlieb, 1966). Because of the chemical composition of melanins, they possess physicochemical characteristics that allow them to function as X-ray and g-ray absorbers, amorphous semiconductors, drug carriers, cation exchangers, and ultraviolet absorbers. Therefore melanins have potential applications (Fig. 17) in medicine, pharmaceuticals, and cosmetics, particularly as a component of photoprotective creams, optical

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FIG. 16 Diffusible pigments produced by different Streptomyces spp.

TABLE 16 Pigments produced by Streptomyces species. Streptomyces species

Bioactive agent

References

Streptomyces coelicolor A3(2)

Methylenomycin

Wright and Hopwood (1976)

Streptomyces glaucescens NEAE-H

Melanin pigment

El-Naggar and El-Ewasy (2017)

Streptomyces coelicolor A3(2)

Actinorhodin

Rudd and Hopwood (1979)

Streptomyces virginiae

Melanin pigment

Amal et al. (2011)

Streptomyces kathirae. FEMS

Melanin pigment

Guo et al. (2014)

Streptomyces sp. MVCS6

Melanin pigment

Sivaperumal et al. (2015)

lenses, bioplastics, paints and varnishes, and other technologies. Melanin pigments have significant biological activities like antitcancer, free radical scavenging ability, antimicrobial, neuroprotection, synthesis of nanoparticles, bioremediation of radioactive waste, liver protection, and antivenin activities. Bacterial melanins have shown good anti-inflammatory activity. In cancer treatment, melanins can prevent the harmful impact of gamma rays on the patients undergoing radiotherapy. The microbial melanin has the ability to chelate metal ions. In view of the potential applications of melanin pigment and increasing demand, the microbial melanin production is necessary by strains that are able to produce a large quantity of melanin pigment. Streptomyces glaucescens NEAE-H can produce a large quantity of extracellular black melanin pigment (El-Naggar and El-Ewasy, 2017) (Fig. 18A). S. glaucescens NEAE-H produced melanin pigment extracellularly, and the granules of the extracted lyophilized purified melanin are shown in Fig. 18B. Its melanin pigment showed considerable antihemolytic activity and good antioxidant and anticancer activities (El-Naggar and El-Ewasy, 2017).

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Anti-inflammatory Neuroprotective

Antioxidant

Antimicrobial

Antivenin

Liver protection

Synthesis of nanoparticles

Anti-virus Antitumor Ultraviolet, X-ray -ray absorbers FIG. 17 Biological activities of melanin.

FIG. 18 (A) Melanin production by Streptomyces glaucescens strain NEAE-H; (B) granules of melanin.

13

Streptomyces species as cell factories for synthesis of nanoparticles

Nanoparticles are novel materials with nanoscale dimensions. Because of their very small size and large surface area-to-the volume ratio, it acquired increasing interest. The chemical and physical characteristics of different nanoparticles are differing from the bulk of the same materials. These differences include melting point; optical absorption; thermal and electrical conductivity; catalytic activity; and mechanical, sterical, and biological properties (Daniel and Astruc, 2004; Zharov and Lapotko, 2005). Nanoparticles have potential applications in different fields including biolabeling, biosensing, food industry, optical receptors, textiles, cosmetics, hyperthermia of tumors, drug delivery, detection of genetic disorders, gene therapy, filters, nanocomposites, catalyst in chemical reactions, and medical imaging (Fig. 19) (Hashim, 2012). They are widely used as antibacterial, antifungal, anti-inflammatory, antiviral, and antiangiogenesis. The most extensively studied nanoparticles today are silver nanoparticles (AgNPs), gold (Au), platinum (Pt) (Baskaran et al., 2016), lead (Pd), titanium oxide nanoparticles (TiO2NPs), CuO (Liu et al., 2018), and carbon-based particles such as carbon nanotubes (CNTs). The nanoparticles can be synthesized using conventional chemical and physical approaches. These conventional approaches have many drawbacks like the aggregation of nanoparticles, expensive, and results in toxic by-products that are environmental hazards (Balasooriya et al., 2017). Recently, many scientists have suggested that microorganisms including algae, yeast, actinomycetes, fungi, and bacteria (Thakkar et al., 2010; Mohanpuria et al., 2008) can be used as environmentally friendly “nanofactories” for nanoparticle biosynthesis (Mandal et al., 2006). The biological agents

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Anti-bacterial Anti-angiogenesis

Anti-inflammatory

Biosensing

Biolabeling

Anti-viral

Anti-fungal

Drug delivery

Gene therapy

Detection of genetic disorders

Optical receptors in electrical batteries

Catalyst in chemical reactions

FIG. 19 Different applications of nanoparticles.

produce a large quantity of enzymes, which can hydrolyze metals and therefore reduce the metal ions (Rai et al., 2009). The green synthesizing process is cost-effective technique, easily scaled up for large-scale production, environment friendly, and nontoxic (Rai and Duran, 2011). The process of green synthesis does not need to use high temperature, energy, pressure, or toxic chemicals (Rafique et al., 2017). Various proteins can occur on the surface of metal nanoparticles as capping and stabilizing agents (Hulkoti and Taranath, 2014; Narayanan and Sakthivel, 2010). Potential Streptomyces spp. fabricating nanoparticles are listed in Table 17. TABLE 17 Examples for Streptomyces species fabricating nanoparticles. Streptomyces species

References

Silver Streptomyces griseorubens

Baygar and Ugur (2016)

Streptomyces exfoliatus ICN25

Iniyan et al. (2017)

Streptomyces intermedius

Dayma et al. (2019)

Streptomyces olivaceus (MSU3)

Sanjivkumar et al. (2019)

Streptomyces olivaceus sp-1392

Subbaiya and Selvam (2014)

Streptomyces albogriseolus

Samundeeswari et al. (2012)

Streptomyces narbonensis SSHH-1E

El-Naggar et al. (2016)

Streptomyces aegyptia NEAE 102

El-Naggar et al. (2014c)

Streptomyces rochei MHM13

Abd-Elnaby et al. (2016)

Streptomyces coelicolor klmp33

Manikprabhu and Lingappa (2014)

Streptomyces parvulus SSNP11

Prakasham et al. (2014)

Streptomyces atrovirens

Subbaiya et al. (2017)

Streptomyces somaliensis

Nejad et al. (2015a)

Streptomyces albidoflavus

Kumar Buddana (2012)

Streptomyces glaucus

Tsibakhashvili et al. (2011)

Streptomyces viridochromogenes

El-Naggar and Abdelwahed (2014)

Streptomyces viridodiastaticus SSHH-1

Mohamedin et al. (2015a,b)

Gold Streptomyces griseus isolate M8

Hamed and Abdelftah (2019) Continued

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TABLE 17 Examples for Streptomyces species fabricating nanoparticles—cont’d Streptomyces species

References

Streptomyces griseus

Khadivi Derakhshan et al. (2012)

Streptomyces ghanaensis

Kumar et al. (2015)

Streptomyces yanglinesis strain NH21

Składanowski et al. (2017)

Streptomyces hygroscopicus

Sadhasivam et al. (2012), Waghmare et al. (2014)

Streptomyces sp. NK52

Prakash et al. (2013)

Streptomyces fulvissimus

Nejad et al. (2015b)

Streptomyces djakartensis

Biglari et al. (2014)

Streptomyces microflavus isolate 5

Nejad et al. (2016)

Streptomyces tuirus DBZ39

Mazhari et al. (2017)

Streptomyces griseoruber

Ranjitha and Rai (2017)

Streptomyces glaucus 71MD

Kalabegishvili et al. (2011)

Streptomyces cyaneus

El-Batal and Al Tamie (2015)

Streptomyces sp.

Składanowski et al. (2017)

Streptomyces viridogens strain HM10

Balagurunathan et al. (2011)

Selenium Streptomyces bikiniensis

Ahmad et al. (2015)

Streptomyces griseobrunneus

Ameri et al. (2016)

Streptomyces sp. ES2-5

Tan et al. (2016)

Zinc and zinc oxide

14

Streptomyces sp. HBUM171191

Waghmare et al. (2011)

Streptomyces sp. (MA30)

Shanmugasundaram and Balagurunathan (2017)

Streptomyces sp.

Balraj et al. (2017)

Production of vitamins

Rickes et al. (1948) reported that a red crystalline compound was produced by S. griseus with growth-promoting activity for Lactobacillus lactis. The crystals were effective as vitamin B12 in the treatment of pernicious anemia. Dulaney and Williams (1953) reported that S. griseus can produce vitamin B12 in a simple synthetic medium and the levels of vitamin B12 were raised if cobalt salts added to the media as precursors. Hall et al. (1953) reported that S. olivaceus is promising for the production of vitamin B12. S. fulvissimus was found to produce significantly high yield of the crystalloid form vitamin B12 when growing on soybean meal medium fortified with cobalt (Atta, 2007). S. rochei produced maximum amount of vitamin B12 with specific fermentation medium (Selvakumar et al., 2012). Musı´lek (1959) reported that production of vitamin B12 was achieved as a subsidiary product in the fermentative production of erythromycin by S. erythreus.

15

Production of odors

A number of actinomycetes, in particular Streptomyces species, are characterized by geosmin production, the volatile organic substance that responsible for the characteristic musty or earthy odor of the soil.

16

Conclusion and future perspective

The genus Streptomyces is recognized as promising cell factories for antibiotics and bioactive secondary metabolites including pharmaceutical, agricultural, and industrial useful compounds. Species of Streptomyces genus produces antibiotics such as rifamycins, ivermectin, chloramphenicol, macrolides, aminoglycosides, and tetracyclines and thus considered

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to be a rich biotechnological resource. Streptomyces spp. are valuable source for production of active metabolites applied on several crops to control a number of phytopathogenic fungi and bacteria. A number of Streptomyces species are capable of producing insecticides, antiparasitic agents, and anticancer, immunostimulatory, immunosuppressive, and antioxidative agents. Numerous Streptomyces species exhibit remarkable capacity for production of industrially important enzymes such as a-amylases, cellulases, alkaline protease, keratinases, and lipases, and these enzymes are of high economic value. Streptomyces spp. were found to produce a variety of enzymes such as uricase, cholesterol oxidase, and L-asparaginase for pharmaceutical industry. The production of active surface biomolecules including bioemulsifiers and biosurfactants has also been reported by Streptomyces spp. A large number of Streptomyces have the capacity to produce various pigments such as melanin that could potentially use as a natural antioxidant in the pharmaceutical industries and cosmetics. Recently, many scientists have suggested that Streptomyces species can be used as environmentally friendly “nanofactories” for nanoparticle biosynthesis. Some Streptomyces spp. demonstrated the ability to produce pravastatin that was established as a novel drug for hypercholesterolemia treatment and has anti-inflammatory effects. Acarbose is useful therapeutic agent for treating postprandial hyperglycemia and obesity produced by microbial fermentation by some Streptomyces strains. Streptomyces spp. can produce vitamin B12. The genus Streptomyces could be promising cell factories for the discovery of antibiotics and bioactive secondary metabolites important for pharmaceutical, agricultural, and industrially useful compounds.

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Med. 17, 171–180. Willemse, J., Borst, J.W., De Waal, E., Bisseling, T., Van Wezel, G.P., 2011. Positive control of cell division: FtsZ is recruited by SsgB during sporulation of Streptomyces. Genes Dev. 25, 89–99. Willey, J.M., Van Der Donk, W.A., 2007. Lantibiotics: peptides of diverse structure and function. Annu. Rev. Microbiol. 61, 477–501.

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Williams, R., 1980. Topics in antibiotic chemistry. J. Med. Microbiol. 13, 613–614. Williams, S., Davies, F., 1967. Use of a scanning electron microscope for the examination of actinomycetes. Microbiology 48, 171–177. Wright, L., Hopwood, D., 1976. Actinorhodin is a chromosomally-determined antibiotic in Streptomyces coelicolor A3 (2). Microbiology 96, 289–297. Xiayun, J., Chen, D., Shenle, H., Wang, W., Chen, S., Zou, S., 2012. Identification, characterization and functional analysis of a GH-18 chitinase from Streptomyces roseolus. Carbohydr. Polym. 87, 2409–2415. Yamada, K., Koroleva, A., Laughlin, M., Oksanen, N., Akhgari, A., Safronova, V., Yakovleva, E., Kolodyaznaya, V., Buldakova, T., Mets€a-Ketel€a, M., 2019. Characterization and overproduction of cell-associated cholesterol oxidase ChoD from Streptomyces lavendulae YAKB-15. Sci. Rep. 9, 1–8. Yanagimoto, M., 1983. Novel actions of inducer in Staphylomycin production by Streptomyces virginiae: physiological studies on Staphylomycin production by Streptomyces virginiae (IV). J. Ferment. Technol. 61, 443–448. Yang, L., Cheng, S., Feng, Z., Zhou, N., Kong, Y., Ming, Y., 2013. Purification chitosanase from Streptomyces hygroscopicus and its catalytic properties. Chin. J. Bioprocess Eng. 3. Yang, H.-J., Huang, X.-Z., Zhang, Z.-L., Wang, C.-X., Zhou, J., Huang, K., Zhou, J.-M., Zheng, W., 2014. Two novel amphomycin analogues from Streptomyces canus strain FIM-0916. Nat. Prod. Res. 28, 861–867. Yao, J.Y., Yin, W.L., Li, X.C., Li, G., Xu, Y., Ai, W.M., Shen, J.Y., 2014. Anti-parasitic activities of specific bacterial extracellular products of Streptomyces griseus SDX-4 against Ichthyophthirius multifiliis. Parasitol. Res. 113, 3111–3117. Yassien, M.A.M., Asfour, H.Z., 2012. Improved production, purification and some properties of a-amylase from Streptomyces clavifer. Afr. J. Biotechnol. 11, 14603–14611. Yazdi, M.T., Kamranpour, N., Zahraei, M., Aghaiepour, K., 1999. Streptomyces fradiae, a potent producer of cholesterol oxidase. Iran. J. Sci., Islamic Republic of Iran 10, 151–157. Yazdi, M.T., Zahraei, M., Aghaepour, K., Kamranpour, N., 2001. Purification and partial characterization of a cholesterol oxidase from Streptomyces fradiae. Enzyme Microb. Technol. 28, 410–414. Young, R., Smith, R., 1975. Degradation of feather keratin by culture filtrates of Streptomyces fradiae. Can. J. Microbiol. 21, 583–586. Yue, X.Y., Zhang, B., Jiang, D.D., Liu, Y.J., Niu, T.G., 2011. Separation and purification of a keratinase as pesticide against root-knot nematodes. World J. Microbiol. Biotechnol. 27, 2147–2153. Zhang, H., Zhan, J., Su, K., Zhang, Y., 2006. A kind of potential food additive produced by Streptomyces coelicolor: characteristics of blue pigment and identification of a novel compound, l-actinorhodin. Food Chem. 95, 186–192. Zhang, Y.L., Li, S., Jiang, D.H., Kong, L.C., Zhang, P.H., Xu, J.D., 2013. Antifungal activities of metabolites produced by a termite-associated Streptomyces canus BYB02. J. Agric. Food Chem. 61, 1521–1524. Zhao, X., Qi, F., Yuan, C., Du, W., Liu, D., 2015. Lipase-catalyzed process for biodiesel production: enzyme immobilization, process simulation and optimization. Renew. Sustain. Energy Rev. 44, 182–197. Zharov, V.P., Lapotko, D.O., 2005. Photothermal imaging of nanoparticles and cells. IEEE J. Sel. Top. Quantum Electron. 11, 733–751. Zheng, Y.G., Shentu, X.P., Shen, Y.C., 2005. Inhibition of porcine small intestinal sucrase by valienamine. J. Enzyme Inhib. Med. Chem. 20, 49–53. Zhou, Y., Wang, X., Wei, W., Xu, J., Wang, W., Xie, Z., Zhang, Z., Jiang, H., Wang, Q., Wei, C., 2016. A novel efficient b-glucanase from a paddy soil microbial metagenome with versatile activities. Biotechnol. Biofuels 9, 36. Zhu, L., Ostash, B., Rix, U., Nur-e-Alam, M., Mayers, A., Luzhetskyy, A., Mendez, C., Salas, J.A., Bechthold, A., Fedorenko, V., 2005. Identification of the function of gene l ndM2 encoding a bifunctional oxygenase-reductase involved in the biosynthesis of the antitumor antibiotic Landomycin E by Streptomyces globisporus 1912 supports the originally assigned structure for Landomycinone. J. Org. Chem. 70, 631–638. Zin, N.M., Baba, M.S., Zainal-Abidin, A.H., Latip, J., Mazlan, N.W., Edrada-Ebel, R., 2017. Gancidin W, a potential low-toxicity antimalarial agent isolated from an endophytic Streptomyces SUK10. Drug Des. Dev. Ther. 11, 351.

Further reading Ara, I., Rizwana, H., Al-Othman, M., Bakir, M., 2012. Studies of actinomycetes for biological control of Colletotrichum musae pathogen during post harvest anthracnose of banana. Afr. J. Microbiol. Res. 6, 3879–3886. Chung, K.J., Lee, J.K., Park, J.W., Seo, D.J., Lee, S.C., 2001. Method for Producing Pravastatin Precursor, ML-236B. Google Patents. Kalakoutskii, L., Kirillova, I.P., Krassilnikov, N., 1967. A new genus of the Actinomycetales—Intrasporangium gen. nov. Microbiology 48, 79–85. Matsuoka, M., Yagishita, K., Umezawa, H., 1953. Studies on the intermediate metabolism of chloramphenicol production. Jpn. J. Med. Sci. Biol. 6, 161– 169. Nascimento, W.C.A.D., Martins, M.L.L., 2004. Production and properties of an extracellular protease from thermophilic Bacillus sp. Braz. J. Microbiol. 35, 91–96. Olfield, V., Perry, C., 2006. Rasburicase. A review of its use in the management of anticancer therapy-induced hyperuricemia. Drugs 66, 529–545. Woodward, R., 1957. Structure and biogenesis of the macrolides. Angew. Chem. 69, 50–58.

Chapter 12

Corynebacterium glutamicum as a robust microbial factory for production of value-added proteins and small molecules: fundamentals and applications Xiu-Xia Liu1, Ye Li1, and Zhong-Hu Bai∗ National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, Wuxi, China ∗

Corresponding author: E-mail: [email protected]

1 Introduction C. glutamicum is a Gram-positive bacterium with its discovery traced back to the post-World War II period in Japan, during which high cost of (L-glutamate production through chemical decomposition simulated the development of a microbial process to produce this amino acid (Udaka, 2008). In 1960 Shigezo Udaka at Kyowa Hakko Kogyo Co. in Japan reported isolation of an glutamate overproducer from
1900 microbial strains by designing a process of forcing L-glutamate production in stationary phase (Kinoshita et al., 1957) and screening with a complementation assay using L-glutamate auxotroph Leuconostoc mesenteroides P-60 (Udaka, 1960). Long-history study of this bacterium has revealed quite a few of its physiological properties expedient for fermentative production of value-added proteins and small molecules: (1) robust sugar consumption under either aerobic or anaerobic conditions regardless of cell growth (Inui et al., 2004a; Okino et al., 2005); (2) no carbon catabolite repression, which enables its cultivation on a variety of single or mixed carbon sources, such as sugars, organic acids, and alcohols (Arndt and Eikmanns, 2008); (3) high stress tolerance (Kitade et al., 2018; Kubota et al., 2016; Liu et al., 2013); and (4) low protease activity and endotoxin-free properties (Lee and Kim, 2018). These characteristics enable its generally recognized as safe (GRAS) status to be used for the production of amino acids and other compounds for health, cosmetic, food, and feed industries, as well as the major host subject to metabolic engineering in seek for further improved performance. In recent years, C. glutamicum has also attracted more attention to be examined of its potential as a useful prokaryotic recombinant protein expression host, due to not only the wide industrial process experience that has been accumulated and to its excellent cultural characteristics but also its own intrinsic advantages as a host such as its own protein secretion system. Therefore, in this chapter, we summarize fundamental knowledge and recent advances of utilizing C. glutamicum as a robust workhorse for production of recombinant proteins and value-added small molecules. In detail, we sum up its protein secretion system, protein expression system, gene editing tools, metabolic engineering for small molecule production by the rational approach, and directed evolution, respectively.

2 Protein secretion system in C. glutamicum There are two major translocation pathways in C. glutamicum, including the Sec-dependent pathway and the Tat-dependent pathway (Date et al., 2006). The Sec pathway is primarily responsible for transporting unfolded proteins, and the Tat pathway is primarily responsible for transporting folded proteins (Kudva et al., 2013).

1

The authors contribute equally to this work.

Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00006-4 © 2021 Elsevier Inc. All rights reserved.

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FIG. 1 Protein secretion across the membrane through the Sec pathway. For cotranslational targeting, when the preprotein is still under translation, the ribosome and the nascent chain of the preprotein are transferred to SecA, aided by SRP. For posttranslational targeting, with the help of SecB, the completely translated preprotein remains unfolded when it is targeted to SecA. Then the protein is able to transport across the membrane through the PCC formed by SecYEG. Type I signal peptidase degrades the signal peptide after translocation of the protein. (Referred from Liu, X., Zhang, W., Zhao, Z., Dai, X., Yang, Y., Bai, Z., 2017a. Protein secretion in Corynebacterium glutamicum. Crit. Rev. Biotechnol. 37, 541–551. https://doi.org/10. 1080/07388551.2016.1206059.)

2.1 The Sec-dependent pathway The Sec transport system is mainly composed of SecYEG, SecA, SecDF, and YajC (see Fig. 1). SecYEG is the core of translocase, while SecYE complex has the capacity of protein transport, which is the functional core of SecYEG. SecG is not necessary but can promote the maximization of transport efficiency (Alami et al., 2007; Bessonneau et al., 2002; Duong and Wickner, 1997). SecDF and YajC interact to help improve transport efficiency and are powered by protonmotive force (PMF) (Schulze et al., 2014). SecA is a peripheral membrane protein, which interacts with all components of the Sec translocase when it functions as a cargo protein receptor and ATP-dependent molecular motor. Moreover, SecA can bind to the signal peptide and mature part of the preprotein (Taufik et al., 2013; Bauer et al., 2014; Rao et al., 2014; Singh et al., 2014). SecB, which is a chaperone protein, can transport the unfolded precursor protein to SecA, and during the process of transportation, it can keep the protein in an unfolded state (Sala et al., 2014; Diao et al., 2012; Driessen, 2001). As shown in Fig. 1, the transport of protein starts from ATP binding to SecA. With the help of SecB, the signal peptide of the unfolded secretory protein N-terminal region inserts into the transmembrane protein-conducting channel (PCC) formed by SecYEG (Rawat et al., 2015). SecA catalyzes ATP hydrolysis to drive protein translocation (de Keyzer et al., 2005; Wowor et al., 2014), accompanied by SecA conformational change and preprotein release (Zimmer et al., 2008), further resulting in polypeptides of secretory protein entering into PCC. Each ATP hydrolysis can transport about 10–13 kDa of protein, and multiple ATP hydrolysis cycles are required to promote transport of large proteins (Bauer et al., 2014; Morita et al., 2012). In the transport process the signal peptide is first inserted into the transporter, and then the N-terminal of the protein and the rest of the protein pass through the transporter. After the protein transport is completed, the type I protease removes the signal peptide and releases the mature protein from the cell membrane (Zalucki and Jennings, 2017; Schallenberger et al., 2012). In C. glutamicum, during the secretion process, after the protein is released from the cell membrane, it also needs to pass through the barrier such as cell wall and S-layer to release to the extracellular (Bayan et al., 2003; Bahl et al., 1997).

2.2 The Tat-dependent pathway The twin-arginine translocation (Tat) pathway transports the folded proteins through the cell membrane (see Fig. 2). The cargo secretory proteins by Tat pathway need to be folded before it can be transported across the membrane (Benoit and

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FIG. 2 Proteins transported across the membrane through the Tat pathway. The signal peptide of the folded preprotein is recognized and associated by TatBC, and the mature part of the secretory protein binds TatA (A); the protein transports across membrane through the active pore formed by TatA (B); the type I signal peptidase cuts off signal peptide, and the mature protein is released from the membrane (C). (Referred from Liu, X., Zhang, W., Zhao, Z., Dai, X., Yang, Y., Bai, Z., 2017a. Protein secretion in Corynebacterium glutamicum. Crit. Rev. Biotechnol. 37, 541–551. https://doi.org/10.1080/07388551. 2016.1206059.)

Maier, 2014). In general the N-terminal signal peptides of these proteins contain a highly conserved twin-arginine (-RR-) motif (Watanabe et al., 2009). But beside this kind of signal peptide with the -RR- motif, a Tat translocation requires that the preprotein must be correctly folded before translocation. Otherwise the preprotein will be degraded, and the translocation will be terminated in advance (Kolkman et al., 2008). The Tat transport system is mainly composed of TatA-like proteins (TatA, TatB, and TatE) and TatC in C. glutamicum. TatA and TatC constitute the smallest functional unit (Goosens et al., 2015). TatE has similar functions with TatA. TatB is not essential for Tat function but beneficial to improve maximal secretory efficiency (Benoit and Maier, 2014). The basic components of the Tat system contain a docking complex including TatB (TatA-like protein) and TatC and a pore complex that is assembled by TatA (Goosens et al., 2014). TatC of the docking complex recognizes the -RR- motif of the Tat signal peptide and aggregates cargo proteins (Rollauer et al., 2012). TatA can form an active pore channel for transporting the cargo protein from the docking complex to the outside of the cell membrane. After the signal peptide is removed by the type I signal peptidase, the mature protein is released from the cell membrane (Tuteja, 2005), and then it crosses the cell wall and S-layer to extracellular (Bayan et al., 2003; Bahl et al., 1997). During the process of translocation, TatC can be regarded as the motor of the Tat translocase, while PMF provides energy source for Tat translocation (Cline, 2015). In C. glutamicum, overexpression of TatC or TatAC resulted in more than threefold increased the secretory expression of a Tat-dependent pro-PG, and overexpression of TatABC further increased secretory expression of pro-PG to 10-fold, indicating that TatB may be a bottleneck for Tat-dependent transport pathway in

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C. glutamicum (Kikuchi et al., 2009). Some proteins in C. glutamicum can only be secreted and expressed in one secretion form, for example, scFv can be secreted by the Sec-dependent signal peptide PorB, but not by the Tat-dependent TorA signal peptide (Yim et al., 2014). The green fluorescent protein (GFP) can be folded correctly and secreted using the Tat-type signal peptide. But when the Tat signal peptide is replaced with Sec signal peptide, only a small amount of GFP can be secreted out of the cell. Some proteins, such as amylase, can be secreted to extracellular under the guidance of either Sec- or Tat-dependent signaling peptides (Watanabe et al., 2009).

3

C. glutamicum protein expression system

C. glutamicum is a “generally recognized as safe” (GRAS) microorganism. It is nonpathogenic and additionally does not produce endotoxins (Song et al., 2012). Moreover, C. glutamicum has the ability to secrete properly folded and functional protein to the culture and the lack of protease activities in the culture supernatant, which can largely simplify the downstream purification process. In view of the advantages of C. glutamicum as a host, it has been widely used in the secretion and expression of foreign proteins, such as industrial enzymes and drug proteins (Table 1). Recently a commercial expression system, Corynebacterium expression system (CORYNEX), has been developed with C. glutamicum as the host of foreign protein expression (Matsuda et al., 2014). However, compared with Escherichia coli, C. glutamicum has some intrinsic disadvantages, such as few available genetic manipulation tools and lower levels of protein expression. Therefore areas requiring further in-depth research to construct better expression system include the following: (1) construction of plasmid vectors for various type of proteins including optimization of promoters, ribosome binding sites, replication origins, and transcriptional terminators for enhanced expression efficiency; (2) genetic engineering of the host strain for better growth and protein synthesis; and (3) process optimization of large-scale cultivation in the bioreactor based on the quality by design (QbD) approach (Nesvera and Patek, 2011). Expression elements and gene editing tools are indispensable for optimization of C. glutamicum expression system. The recent developed expression elements for C. glutamicum are shown in Table 1. Moreover, with the completion of C. glutamicum genome sequencing, it is easier to genetically engineer it.

3.1 Expression plasmid vectors of C. glutamicum Many main expression elements are necessary for the design of recombinant expression system. A complete expression vector, in addition to the inserted gene fragment, should also include at least a replicon, a selective screening marker, a promoter, and a transcription terminator.

3.1.1 Promoters for optimized gene expression in C. glutamicum Promoters play a very important role in the life activities of organisms. The transcription of any gene starts from promoters providing transcription initiation information. In all organisms, especially prokaryotes, transcription controlled by the promoter, as the first step of gene expression, is essential for diverse functions during the process of life activities (Browning and Busby, 2004). Classification of promoters in C. glutamicum expression system In prokaryotes, promoters are mainly divided into two categories: constitutive promoters and inducible promoters. The constitutive promoter can stably control the expression of the target gene throughout the whole growth period and is not affected by environmental factors. For example, the constitutive promoters Ptuf and Paph were used to improve the metabolism of C. glutamicum to increase the production of target products or directly used in the expression of some proteins that have no negative effect on the growth of host cells (Zhang et al., 2017; Ata et al., 2017). However, in some cases, a protein with toxic effects on the growth of bacteria will prolong the growth cycle or even cause the death of the host strain if it is expressed under the control of a constitutive promoter (Yin et al., 2017; Ma et al., 2018; Hwang et al., 2017). In this case an inducible promoter is preferred, which only activates high-level expression of the target gene at a specific time when the strain has proliferated to a certain abundance (van Sluis et al., 1983) and only results in no expression or basal low level of expression at the early stage of the growth (Rytter et al., 2014). So far, many inducible promoters for gene expression have been developed in C. glutamicum. For example, the inducible promoters Ptac, ParaBAD, and PlacUV5 have been widely used in the metabolic engineering of C. glutamicum (Zhang et al., 2018), and a temperature-induced promoter PCJ1OX2 has been used to induce the expression of PyrR protein (Park et al., 2008).

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TABLE 1 Production of heterologous proteins in Corynebacterium glutamicum. Expression vectors

Promoters

Signal peptides

Heterologous proteins

Production

Reference

pWLQN

Ptac

–

Nuclease

20 mg/L

Liebl et al. (1992)

pPGIFN

Ptac

–

g-Interferon

–

Billman-Jacobe et al. (1994)

pEPDP2

PaprE

AprE

Protease BprV

2.5 mg/mL

Billman-Jacobe et al. (1995)

pPSPTG51

PcspB

CspA

Transglutaminase

876 mg/L

Date et al. (2004)

pPSEGF

PcspB

CspA

hEGF

1100 mg/L

Date et al. (2006)

pCGTorA-GFP

Ptac

TorA

GFP

–

Meissner et al. (2007)

PCRD314

Ptac

cgR0949

GFP

1800 mg/L

Teramoto et al. (2011)

PCRC901

Ptac

cgR0949

GFP

2800 mg/L

Watanabe et al. (2013)

pASJ104

PPorB

PorB

Xylanase

615 mg/L

An et al. (2013)

pCES-H36-XynA

PH36

PorB

Xylanase

746 mg/L

Yim et al. (2013)

pH36M2

PH36

PorB

M18 ScFv

68 mg/L

Yim et al. (2014)

pPKStrastFabHL

PcspB

CspA

Fab

57.6 mg/L

Yim et al. (2014)

pNBM4

PilvC

–

Nitrile hydratase

53.4 U/mg DCW

Kang et al. (2014)

pCG-S-XynA

Pcg1514

Cg1514

Xylanase

1067 mg/L

Yim et al. (2016)

pCG-S-AmyA

Pcg1514

Cg1514

Amylase

782.6 mg/L

Yim et al. (2016)

pCG-S-cAb

Pcg1514

Cg1514

VHH

1570 mg/L

Yim et al. (2016)

pSCFV-HP-BEP4

PBEP4

–

ScFv

>100 mg/L

Liu et al. (2017a)

pHCP-S-XynA

Pcg1514

Cg1514

Xylanase

1540 mg/L

Choi et al. (2018)

pEKEX2-t7-RBS50000NHopt

PT7

–

Nitrile hydratase

1432 U/mL

Yang et al. (2019)

P19-X + pEC-X

PcspB2

CspB2

Xylanase

1770 mg/L

Zhang et al. (2019a)

Abbreviations: Fab, fragment of antigen binding; hEGF, human epidermal growth factor; ScFv, single-chain antibody fragment; VHH, variable heavy homodimers.

Sources of engineered promoters in C. glutamicum expression system The current C. glutamicum promoters used often are mainly from the following three sources. First is to randomize the core region sequence with the natural promoter as the skeleton to obtain new promoters with different expression activities. For example, Kim et al. obtained the promoter P4-N14 by modifying the endogenous sigB-dependent promoter and characterized the production of glutathione S-transferase, showing P4-N14 with a strength of 20 times higher than that of the original promoter Pcg3141 (Kim et al., 2016). For another example, on the basis of lac promoter, the promoter library is constructed by randomizing the sequence of the core region 35, 10, and the surrounding sequence. The library contains promoters of different intensities in the range of 284–1665 Miller units (Rytter et al., 2014). A new promoter P4 was developed by mutating the 10 and 35 regions of the aph promoter, which increased a-amylase production by 50% (Zhang et al., 2017). A strong promoter H36 was isolated from a fully synthesized promoter library containing 70-bp random sequence for expressing recombinant human protein scFv in C. glutamicum (Yim et al., 2013). Random mutagenesis of promoter sequence is an effective method to construct promoter library, but this method of random selection is inevitably very laborious. As an alternative, several computational models have been developed to investigate the relationship between promoter sequence and strength, such as the position weight matrix (PWM) model, the artificial neural network (ANN) model, and the biophysical model (Meng and Wang, 2015), which facilitate promoter development more rationally. In addition,

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a statistical method has been established to study the phenotypic impact of a single mutation among different mutants (Jensen et al., 2006). This is an efficient strategy, but there are also problems such as unreliable prediction results. Another way to develop promoters is to introduce heterologous promoters into closed related organisms to play a role according to the fact that this kind of promoter often has high activity between two kinds of organisms (Colussi and Taron, 2005). For example, Ptac, Ptrp, ParaBAD, and Placuv5 promoters from E. coli have been used for metabolic engineering or recombinant protein expression (Zhang et al., 2017; Rytter et al., 2014), showing high expression activity. In addition, the P-R/P-L promoter of phage l was reported to control reporter genes efficiently by lcI repressor in C. glutamicum (Park et al., 2008). It is an efficient way of developing and utilizing promoters. However, when the expression vector is constructed and cloned in E. coli, it is sometimes unable to obtain E. coli clone because the promoter is highly active in E. coli and expression of recombinant protein inhibits the growth of E. coli. The last way of promoter development is to directly explore the natural promoter of the strain itself (Steege and Horabin, 1983). By analyzing the transcription and protein expression level of each gene in the target strain, the promoter with specific expression intensity can be selected and obtained for expressing proteins. For example, a series of efficient promoters from C. glutamicum were identified by analyzing the expression abundance of proteome data of C. glutamicum and applied for improving scFv expression (Zhao et al., 2016; Liu et al., 2017b); a growth-regulated promoter PCP_2836 of aldehydes dehydrogenase coding gene was identified and applied for improving L-valine production in C. glutamicum (Ma et al., 2018).

3.1.2 Sequence optimization of ribosome binding sites (RBS) for gene expression in C. glutamicum It is now clear that the 50 -end structure of each mRNA species plays an important role in the wide range of translation efficiencies of different mRNAs (Schumann and Ferreira, 2004). Therefore 50 -untranslated region (50 -UTR) sequences are one of the important influencing factors in the construction of vectors to improve the expression level of target protein. Especially the ribosome binding site (RBS) in the 50 -UTR sequence allows the ribosome to access the Shine–Dalgarno (SD) sequence near the translation initiation region, and the binding ability of SD to ribosome can directly influence effective protein translation (Martin et al., 2003; Shine and Dalgarno, 1974). In C. glutamicum, changes in the RBS sequences of different constructed vectors lead to difference on the expression level of enhanced green fluorescent protein (eGFP) (Zhang et al., 2017). It is reported that the SD sequence of triosephosphate isomerase in C. glutamicum was used for improving GFP expression, and the results showed GFP production was enhanced by 2.9-fold over that of the original sequence (Teramoto et al., 2011). SD sequence not only affects the expression of the target gene but also indirectly affects the production difference of metabolites related to the target gene. An engineering C. glutamicum RES167aroK was constructed to produce shikimic acid by constructing an RBS library. The shikimic acid production on shaking flask was 4.3 g/L, which was 54 times (80 mg/L) of that of the starting bacterium, and the fed-batch fermentation yield of 5 L fermentor was 11.3 g/L (Zhang et al., 2015a).

3.1.3 Signal peptide applied in C. glutamicum expression vectors The signal peptide is a peptide segment of about 20–40 amino acids, which is fused to a protein for secretion and is an essential element in the process of transporting a protein out of the cell membrane (Zhou et al., 2016). After a protein is translocated across the membrane, signal peptide is cut off by signal peptidase (Schallenberger et al., 2012; Jalal et al., 2011; Rehm et al., 2001). A complete signal peptide consists of three different regions, including N-terminal region, intermediate hydrophobic region, and C-terminal region (von Heijne, 1985). The N-terminal region of the signal peptide is mainly composed of amino acids with positive charges; the a-helix structure formed by the hydrophobic region plays a role in binding cell membrane; the C-terminal region near the C-terminus of the signal peptide contains the cutting site of the signal peptidase (Zalucki and Jennings, 2017) (Fig. 1). There are two main types of signal peptides in C. glutamicum, which are Sec- and Tat-dependent signal peptides, respectively. The two signal peptides both have an N-terminal region, a hydrophobic region, and a C-terminal region. The difference between them is that the N-terminal amino acid sequence of Tattype signal peptide is slightly longer than that of the Sec type and the hydrophobicity of its hydrophobic region is weaker. The most obvious feature is that there is a double arginine residues “-RR-” at the N-terminus of the Tat-type signal peptide, while the Sec type doesn’t have such feature (Fig. 3). The “-RR-” residues are closely related to the Tat transport pathway (Rollauer et al., 2012; Patel et al., 2014). Changes of the amino acid sequence of the signal peptide can affect the stability of mRNA and protein folding process, which further impact the expression and secretion of a protein. The position of amino acids on signal peptide is also very important for its own function, and one amino acid mutation can seriously affect the

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FIG. 3 Structures of the Sec and Tat signal peptides. Both types have an N-terminal region, a hydrophobic region, and a C-terminal region. The Tat-type signal peptide has double arginine residues at its N-terminus, while the Sec type doesn’t have such feature. (Referred from Liu, X., Zhang, W., Zhao, Z., Dai, X., Yang, Y., Bai, Z., 2017a. Protein secretion in Corynebacterium glutamicum. Crit. Rev. Biotechnol. 37, 541–551. https://doi.org/10.1080/07388551.2016. 1206059.)

efficiency of transmembrane transport of a protein (Bensing et al., 2007). Signal peptides having been developed for the C. glutamicum protein expression systems are shown in Table 1.

3.1.4 Other expression elements Replicons At present, most of established expression vectors used in C. glutamicum are shuttle plasmids of C. glutamicum-E. coli, which can replicate in two hosts. These shuttle plasmids are derived from the combination of plasmids in E. coli with native elements (mainly replicons) of C. glutamicum, or resistance genes and E. coli replicates are inserted into the native plasmids of C. glutamicum (Xu et al., 2010; Tauch et al., 2003). Efficient replicon is beneficial to the amplification of the vector in the host and can improve the transcription and expression of the target gene by providing more templates. These endogenous cryptic replicons can result in plasmids of medium copy number (e.g., pBL1, pCG1, pCC1, and pGA1) or of low copy number (e.g., pNG2) (Tauch et al., 2003). Resistance markers A resistance marker gene is an important part of the expression vector skeleton that is essential to select positive clones during plasmid construction and maintain plasmids during cultivation. The common markers used in the construction of C. glutamicum expression vectors are kanamycin (Kan)-, chloramphenicol (Crm)-, and tetracycline (Tet)-resistant markers (Park et al., 2008; Lausberg et al., 2012; Jakoby et al., 1999).

4 Gene editing tools applied in C. glutamicum Gene editing technology, which appeared in the 1990s, is the technology of precisely site-specific modification of endogenous genes in organisms. It can complete site-specific mutation, knockout and knock-in of genes, and has become an important research content in the field of life science. Gene editing technology needs to rely on different DNA recombination systems, such as homologous recombination, specific site recombination, and transposition recombination to achieve functions (Yadav et al., 2018). At present, in C. glutamicum, gene editing tools are divided into the following two categories according to the different use of homologous recombination system:

4.1 pKl8mobsacB and pKl9mobsacB based on homologous recombination technology The suicide plasmids pKl8mobsacB and pKl9mobsacB contain kanamycin resistance gene with the TN5 promoter and the sacB gene from Bacillus subtilis. sacB encodes levansucrase, which can decompose sucrose into fructose and polymerize fructose into fructan. Fructan is toxic to cells and will cause cell death. Based on this principle the sacB gene used as a negative screening marker can be used to screen strains without sacB after two rounds of homologous recombination ( Jager et al., 1992). However, the sacB gene can be easily inactivated owning to a small fragment insertion during the process of homologous recombination and further lose sucrose sensitivity, so it is necessary to frequently test whether sacB has sucrose sensitivity during applications (Eggeling and Bott, 2005).

4.2 Gene knockout system based on Cre/loxP site-specific recombination technology Cre is a recombinase of l integrase superfamily of P1 phage. loxP site is composed of two 13-bp reverse repeat sequences and 8-bp intermediate interval sequences, which can be used for Cre recombinase recognition (Zomer et al., 2016).

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According to the sequence and location of two loxP sites, Cre enzyme can realize sequence inversion or loss between two loxP sites (Suzuki et al., 2005a). The loxP site is introduced into the genome by a suicide plasmid, and gene knockout is achieved by Cre excision of loxP sequence (Suzuki et al., 2005b). It has been reported that the efficiency of Cre/loxP sitespecific recombination in C. glutamicum is higher than that of common homologous double exchange by one order of magnitude (Suzuki et al., 2005c). At the same time a pDTW109 system was developed (Hu et al., 2013), which combines homologous recombination and Cre/loxP technology. The target gene can be knocked out by connecting the upstream and downstream homologous arms of the target gene and the Kan box with loxP site into the bacteria. Finally the Kan fragment in loxP site can be removed by Cre so that the marker-free knockout can be realized. The development of genetic engineering technology also requires us to develop more efficient and convenient gene manipulation tools. At present, CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR-associated proteins) system, which is widely used in eukaryotes and prokaryotes, provides a new idea for the development of new gene editing tools. CRISPR/Cas as an acquired immune system of bacteria and archaea can specifically recognize and cut off foreign genetic materials through RNA mediation to fight against invasion of foreign viruses and plasmids (Barrangou et al., 2007). CRISPR/Cas system can be divided into three types including type I, type II, and type III, according to its distribution in species. At present, type II is the most widely used CRISPR/Cas system, the core of which is a DNA endonuclease named Cas9, so it is also called CRISPR/Cas9 system. Because the simple structure and easy operation of Cas9, CRISPR/Cas9 has been developed as a potential gene editing tool (Ran et al., 2015). In addition to Cas9, type II CRISPR/Cas9 system from Streptococcus pyogenes (S. pyogenes) also requires a mature CRISPR RNA (crRNA), a trans-activating CRISPR RNA (tracrRNA) to perform effective genome editing (Lemay et al., 2017; Doudna and Charpentier, 2014; Larson et al., 2013). The Cas9 protein is an RNA-guided endonuclease that cleaves target DNA and can be guided by a 20-bp complementary region (N20) within the crRNA to its specific target (Hsu et al., 2014; Huang et al., 2016). A specific protospaceradjacent motif (PAM) is at the 30 -end of the N20 sequence (known as the protospacer) (Pohl et al., 2016; Shalem et al., 2014), which locates Cas9 to create a double-strand break (DSB) at the target sequence. Then the DSB are repaired by nonhomologous end joining (NHEJ) or homology-directed repair (HDR) (Paquet et al., 2016; Richardson et al., 2016; Zerbini et al., 2017). A single synthetic guide RNA (sgRNA) can be generated by fusing the crRNA with tracrRNA together, which simplifies genome editing design (Chen et al., 2017a). CRISPR/Cas9 was first used in C. glutamicum to achieve gene silencing (CRISPR interference [CRISPRi]). The cleavage activity of Cas9 protein was removed by mutating the nuclease active site in S. pyogenes Cas9 protein. The cooperation of mutated Cas9 and sgRNA realized 98% downregulation of pgi and pck gene transcription level and 97% downregulation of pyk gene in C. glutamicum, which successfully improved the production of L-lysine and L-glutamic acid (Cleto et al., 2016). The CRISPR-cpf1 system in CRISPR family was used to knock out long genes and insert fragments in C. glutamicum, with the efficiency of 86%– 100% ( Jiang et al., 2017). CRISPR/Cas9 system in combination with RecT recombinase and double-stranded DNA repair fragments was used to improve the efficiency of homologous repair after CRISPR/Cas9 editing and realized the application of CRISPR/Cas9 system in C. glutamicum (Cho et al., 2017). CRISPR/Cas9 system and double-stranded DNA repair template were also used to knock out, insert, and replace genes in C. glutamicum (Peng et al., 2017). The knockout of target gene was also realized by using CRISPR/Cas9 system combined with RecT recombinase and single-strand DNA repair fragment in C. glutamicum (Liu et al., 2017c). The simultaneous inhibition of a single gene and multiple genes in C. glutamicum was realized by CRISPRi technology, which reduced the activity of citrate synthase, thus improving the output of L-lysine (Park et al., 2018). With the development of technology, there will be more reports about CRISPR system in C. glutamicum.

5

C. glutamicum as a major workhorse for production of small molecules

As mentioned before, C. glutamicum shows robust sugar consumption under either aerobic or anaerobic conditions regardless of cell growth, no carbon catabolite repression, and high stress tolerance, which endows this bacterium as a superior host strain for production of value-added small molecules. In this section, we summarize in detail the classic stories of using C. glutamicum to produce the two most important amino acids (L-glutamate and L-lysine), as well as quite a few other small molecules of significant applications in brief. Rational metabolic engineering and directed evolution are the two major strategies used nowadays in the field of metabolic engineering. We address rational engineering in Section 5.1, in which small molecules are categorized according to the native precursors in the central metabolic pathways where they are derived from. In Section 5.2, we review a complementary strategy, directed evolution, in which adaptive laboratory evolution (ALE) and biosensor-based engineering are discussed. Here, we only describe some classic stories and most recent progress of engineering C. glutamicum, which intend to show the versatility of this industrial workhorse for production of various types of small molecules: actually, most of the key intermediates in the central metabolic pathway have been

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FIG. 4 Overview of small molecules produced by recombinant Corynebacterium glutamicum by rational metabolic engineering. Key intermediates in the central metabolic pathway are shown, from which small molecules of important applications have been produced via native or heterologously introduced pathways (indicated as dashed arrows). Abbreviations: AcCoA, acetyl-CoA; ACO, aconitate; DHAP, dihydroxyacetone phosphate; E4P, erythrose-4-phosphate; F6P, fructose-6-phosphate; F1,6dP, fructose-1,6-bisphosphate; G6P, glucose-6-phosphate; GAP, glyceraldehyde 3-phosphate; GO, glyoxylate; MalCoA, malonyl-CoA; OAA, oxaloacetate; 2OG, a-ketoglutarate; PEP, phosphoenolpyruvate; 3PG, 3-phosphoglycerate; PYR, pyruvate; Ru5P, ribulose-5-phosphate; SUC, succinate.

explored as the starting points toward various kinds of small molecules (Fig. 4). Readers can find more specific engineering topics in detail from several other excellent reviews, including utilization of hemicellulosic biomass (Choi et al., 2019; Hirasawa and Shimizu, 2016; Ma et al., 2017; Lee and Wendisch, 2017), nonnatural products (Heider and Wendisch, 2015), aromatic chemicals and natural products (Kogure and Inui, 2018), engineering chassis strains (Heider and Wendisch, 2015; Becker et al., 2018a), adapted laboratory evolution (ALE) (Stella et al., 2019), and transport engineering (P
erez-Garcı´a and Wendisch, 2018).

5.1 Rational metabolic engineering 5.1.1 2OG-derived chemicals Main 2OG-derived chemicals engineered for production in C. glutamicum include L-glutamate, g-aminobutyrate, 1,4diaminobutane, L-ornithine, and L-arginine (Fig. 5). We mainly review the classic fundamental and engineering work of L-glutamate production in detail later, which is the largest amount of the amino acid produced worldwide, reaching 3.1 million tons by fermentation in 2015 (Lee and Wendisch, 2017). L-Glutamate is synthesized from 2OG and ammonia by glutamate dehydrogenase (encoded by the gene gdh) in C. glutamicum, the main pathway for L-glutamate formation when ammonia concentration is sufficiently high in the medium

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FIG. 5 Metabolic pathways for the biosynthesis of 2OG-derived small molecules in recombinant Corynebacterium glutamicum. L-Glutamate, L-ornithine, L-arginine, and g-aminobutyrate are amino acids of high demand in food and healthcare industries, while 1,4-diaminobutane is the precursor for the polymer material. Abbreviations: AdiA, arginine decarboxylase; ArgA, glutamate N-acetyltransferase; ArgB, acetylglutamate kinase; ArgC, Nacetylglutamylphosphate reductase; ArgD, acetylornithine aminotransferase; ArgE, acetylornithinase; ArgF, ornithine carbamoyltransferase; ArgG, argininosuccinate synthase; ArgH, argininesuccinate lyase; ArgI, ornithine carbamoyltransferase; GabB, glutamate decarboxylase; Gdh, glutamate dehydrogenase; PatA, putrescine/cadaverine transaminase; PatD, g-aminobutyraldehyde dehydrogenase; SpeA, arginine decarboxylase; SpeB, agmatine ureo-hydrolase; SpeC, ornithine decarboxylase; SpeF, ornithine decarboxylase. (Referred from Becker, J., Rohles, C.M., Wittmann, C., 2018a. Metabolically engineered Corynebacterium glutamicum for bio-based production of chemicals, fuels, materials, and healthcare products. Metab. Eng. 50, 122–141. https://doi.org/10.1016/j.ymben.2018.07.008.)

(Kholy et al., 1993). Since 2OG is an intermediate in tricarboxylic acid cycle (TCA cycle), distribution of metabolic fluxes in central carbon metabolism (glycolysis, pentose phosphate pathway and anaplerotic pathways, and TCA cycle) determines production of L-glutamate. In history, researchers accidently found that the wild-type C. glutamicum is able to secret L-glutamate with destabilization of membrane through depletion of biotin in culture media, which can also be enhanced through treatment with penicillin G, addition of detergents (Tween 40 or Tween 60) or cerulenin or ethambutol (Hoischen and Kr€amer, 1990; Shimizu and Hirasawa, 2006). GltS sodium-coupled system involving L-glutamate secretion was later identified, which proves membrane transportation is essential to achieve L-glutamate production of high titer (Marin and Kr€amer, 2007). Biotin depletion-induced secretion of L-glutamate is the core technology involved in industrial L-glutamate production processes, although the underlying mechanism was unknown for long time. In recent years the gene product of NCgl1221 was identified (encoding YggB protein) to be a mechanosensitive channel and possible glutamate exporter, which provides a valuable insight into the molecular basis of L-glutamate secretion (Nottebrock et al., 2003; Nakamura et al., 2007). In this report a point mutation in the NCgl1221 gene resulted in L-glutamate secretion without any induction treatments (Nakamura et al., 2007). Overexpression of the NCgl1221 gene increases L-glutamate secretion upon induction, while its disruption substantially abolishes secretion along with increased intracellular glutamate pool under induction. Based on biochemical and genetic characterizations, the possible mechanism was proposed: altered membrane tension is triggered by inhibiting lipid or peptidoglycan synthesis under biotin limitation and penicillin treatment, which results in conformational change of YggB and hence enables exportation of glutamate. The function of YggB as a mechanosensitive channel was further verified in a B. subtilis mutant deficient of two known mechanosensitive channels, since its expression resulted in 8.9-fold higher cell survival rate upon osmotic downshock than the control (Hashimoto et al., 2010). Later, it turned out that N-terminal domain of YggB is responsible for L-glutamate secretion by constructing and testing a series of truncated YggB mutants (Yamashita et al., 2013). On the other hand, another mechanosensitive channel protein, MscCG, is identified and characterized, which is a homolog of YggB and also responsible for L-glutamate efflux (Becker et al., 2013a). Nowadays, many different types of carbon sources are conventionally used for large-scale production process by C. glutamicum for L-glutamate or L-lysine to meet market demand, including cane/beet molasses, starch hydrolysates, and raw sugars (Wittmann and Becker, 2007). Other cheap carbon sources, such as pentoses from lignocellulosic material hydrolysates (Sasaki et al., 2009), or glycerol, a typical byproduct from yeast fermentation for ethanol production (Rittmann et al., 2008), can also be utilized by C. glutamicum strains with engineered pathways. Attentions need to be paid that molasses are naturally rich in biotin, which should be removed before used for glutamate production. Other nutrients, including ammonia (Wittmann and Becker, 2007), minerals (ferrous and potassium ions), (Liebl, 2006) and dissolved oxygen (Kawakita, 2000), all play critical roles for high titer of production L-glutamate. Especially, dissolved oxygen level should be well tuned, since suboptimal oxygen levels result in accumulation of byproducts such as lactate and succinate

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(Dominguez et al., 1993), while excess dissolved oxygen leads to secretion of 2OG (Otto et al., 2011). Typically, industrial C. glutamicum strains are able to produce L-glutamate 100 g/L in 2–3 days with a yield of 60% based on the total amount of sugar (Kawakita, 2000; Kumagai, 2000). The mechanism of enhanced L-glutamate production under induction conditions was further deepened by insights about 2-oxoglutarate dehydrogenase complex (ODHC) activity and identification of a novel 15-kDa regulatory protein OdhI (Niebisch et al., 2006). Unphosphorylated OdhI can bind to OdhA protein (one subunit of ODHC) and hence inhibits ODHC activity, which converts 2OG to succinyl-CoA in the TCA cycle. This inhibition can be prevented by the PknG-catalyzed phosphorylation of OdhI, and dephosphorylation of OdhI by a phosphoserine/threonine protein phosphatase has also been identified (Niebisch et al., 2006). Disruption of the odhI gene abolishes L-glutamate production even under the induction conditions (Schultz et al., 2007), and proteome analyses revealed OdhI protein level significantly increases upon penicillin treatment (Kim et al., 2009, 2010). These results suggest the dual roles of induction treatments such as biotin limitation and penicillin treatment on L-glutamate production, which lead to upregulation of the regulator protein OdhI in its unphosphorylated form and accumulation of 2OG on the one hand and activation of YggB on membrane through altered membrane tension to accelerate L-glutamate on the other hand. Future research includes elucidation of the signaling pathway from induction treatment to upregulation of OdhI and what conditions are required for the phosphorylation and dephosphorylation of OdhI. In a very recent interesting study, an industrial strain was engineered to achieve efficient L-glutamate secretion in biotin-excessive corn stover hydrolysate. The key points of this successful engineering work are C-terminal truncation of L-glutamate secretion channel MscCG (DC110) and attenuation of ODHC activity, which resulted in the highest titer of 65.2 g/L with the overall yield of 0.63 g/g glucose using corn stover without any chemical induction (Wen and Bao, 2019). The concept of metabolic engineering with the design guided by metabolic pathways emerged in the 1990s. This effective strategy has also been applied for L-glutamate production by introduction of genetic modifications to direct more metabolic fluxes toward L-glutamate, but most were proof-of-concept studies in the lab scale. Sato et al. investigated effects of anaplerotic reactions catalyzed by phosphoenolpyruvate carboxylase (PEPC) and a biotin-containing enzyme pyruvate carboxylase (PC) on L-glutamate production (Sato et al., 2008). Overexpression of PEPC led to improved supply of oxaloacetate and drive flux of TCA cycle and hence increased L-glutamate production, while PC exists as nonfunctional enzyme during biotin-limited conditions favoring L-glutamate production, which is further supported by evidence from 13C-labeling metabolic flux analysis and measurement of 13C-enriched L-glutamate by nuclear magnetic resonance spectroscopy (Sato et al., 2008). With mindfulness of enhanced L-glutamate production through enhanced anaplerotic flux by PEPC catalyzed reaction, the same group investigated deletion of pyruvate kinase (encoded by pyk) on L-glutamate production in C. glutamicum under biotin-limited conditions. Deletion of pyk resulted in increased glucose consumption, decreased cell growth, higher L-glutamate production, and L-aspartic acid formation, along with significant increase in phosphoenolpyruvate (PEP) carboxylase activity and a significant decrease in PEP carboxykinase activity. Enhanced overall flux of the anaplerotic pathway from PEP to oxaloacetate may explain both the increased rate of glucose consumption and higher productivity of L-glutamate in the mutant strain (Sawada et al., 2010). L-Glutamate biosynthesis from glucose in C. glutamicum inevitably passes through pyruvate dehydrogenase reaction, which results in loss of one carbon per pyruvate as the form of CO2 with maximum theoretical yield 0.82 g/g glucose. Innovative metabolic design by introduction of a novel phosphoketolase pathway from Bifidobacterium animalis allows pyruvate dehydrogenase step to be bypassed and hence increase to maximum theoretical yield 0.98 g/g glucose (Chinen et al., 2007). Other important 2OG-derived chemicals engineered in C. glutamicum include L-arginine, L-ornithine, g-aminobutyric acid (GABA), and 1,4-diaminobutane (also known as putrescine) (Fig. 5). L-Arginine is a semiessential amino acid with a vasodilatory effect, and L-ornithine is used to treat liver diseases and strengthen the heart ( Jiang et al., 2013). Through pathway engineering, L-arginine production reached titers 92.5 g/L (a yield of 0.4 g/g glucose) and 81.2 g/L (a yield of 0.35 g/g sucrose), respectively (Park et al., 2014). As for L-ornithine, combination of evolutionary engineering with transcriptional profiling resulted in an engineered C. glutamicum strain capable of producing L-ornithine with a titer of 24 g/L ( Jiang et al., 2013), and rational engineering resulted in strains producing L-ornithine in shake flask with titers 38.5 g/L by using glucose and 18.9 g/L by using xylose (Zhang et al., 2019b) and by fed-batch cultivation with the titer 51.5 g/L using glucose (Kim et al., 2015a). Finally, in a very recent study, a quantitative proteomic approach was applied to study effects of Tween 40 (a surfactant used to promote production of L-glutamate and L-arginine) addition to L-ornithine production by C. glutamicum, which identified several genetic modifications resulting in 30% improved L-ornithine production over the parent ( Jiang et al., 2020). GABA is a very common nonprotein amino acid used as a component of pharmaceuticals, foods, and the biodegradable plastic polyamide 4 ( Jorge et al., 2016). Substantial metabolic engineering work has been conducted toward improving its production ( Jorge et al., 2016, 2017a; Shi et al., 2013a; Okai et al., 2014; Wang et al., 2015), with the highest titer up to 63.2 g/L and productivity up to 1.34 g/L/h achieved in fed-batch cultivation ( Jorge et al., 2017a). Finally,

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1,4-diaminobutane is bifunctional molecule that gained interest as a monomer in the production of the synthetic highperformance polyamide nylon 4,6 (Scott et al., 2007). In recent years, comprehensive work has also been conducted to pursue its production in C. glutamicum (Schneider and Wendisch, 2010; Schneider et al., 2012; Nguyen et al., 2015; Li et al., 2018; Meiswinkel et al., 2013a, b). Although these early-stage studies are more proof of concept, they undoubtedly demonstrate rational metabolic engineering to be an effective approach to evolve C. glutamicum for improved production of small molecules.

5.1.2 OAA-derived chemicals Small molecules of another major branch enthusiastically investigated by researchers are derived from OAA, with the biosynthetic pathways depicted in Fig. 6. Among them, L-lysine is a commercially valuable amino acid as a feed additive for swine and poultry, which makes it also the second largest produced amino acid in worldwide annual production after L-glutamate (Lee and Wendisch, 2017). In 2014 the lysine market in 2014 reached $745.2 million (Grand View Research, 2015). In 2015 about 2.4 million tons per year of L-lysine was produced by fermentation worldwide (Lee and Wendisch, 2017), and the market is currently increasing by 7% per year (Eggeling and Bott, 2015). Actually, early production of L-lysine in large scale can be traced back to the 1950s, when the industrial C. glutamicum strains were generated from iterative rounds of random mutagenesis and were almost exclusively used for L-lysine production (Eggeling and Bott, 2015). Production of L-lysine is nowadays also a routine process (Wittmann and Becker, 2007; Leuchtenberger et al., 2005). Large-scale L-lysine in industrial scale typically results in titers up to 160 g/L and yields of 45%–50% of total amount of sugar in
2 days (Pfefferle et al., 2003). L-Lysine biosynthesis uses pyruvate and OAA in the central metabolism as precursors, and aspartate kinase (encoded by lysC) and dihydrodipicolinate synthase (encoded by dapA) are the two key enzymes in the pathway (Wittmann and Becker, 2007) (Fig. 7). A comprehensive analysis of intracellular metabolites, metabolic fluxes, and gene expression of a lysine-producing strain C. glutamicum ATCC13287 by the Wittmann group revealed two distinct phases of growth and L-lysine production (Kr€ omer et al., 2004). During the phase shift from growth to L-lysine production, glucose uptake flux decreases, and the flux from glycolysis is redirected from the TCA cycle toward anaplerotic carboxylation and lysine biosynthesis. Intracellular L-lysine pool increases up to 40 mM prior to its excretion, along with multifaceted changes in the gene expression for the central metabolism. Fluxes are probably adjusted through tuning expression levels of pathway genes, as evidenced by close

FIG. 6 Biosynthetic pathways of small molecules derived from OAA, with applications in healthcare (L-isoleucine and L-methionine), feed industry (L-lysine), and cosmetic industry (ectoine), as the polymer precursors (5-aminovalerate and 1,5-diaminopentane) and as a pharmaceutical precursor (L-pipecolic acid). AecD, cystathionine b-lyase; Asd, aspartate-semialdehyde dehydrogenase; AspB, aspartate aminotransferase; DapA, dihydrodipicolinate synthase; DapB, dihydrodipicolinate reductase; DapC, N-succinyl-aminoketopimelate aminotransferase; DapD, tetrahydrodipicolinate succinylase; DapE, succinyl-diaminopimelate desuccinylase; DapF, diaminopimelate epimerase; DavA, 5-aminovaleramidase; DavB, lysine-monooxygenase; Ddh, diaminopimelate dehydrogenase; EctA, L-diaminobutyrate acetyltransferase; EctB, L-diaminobutyrate transaminase; EctC, ectoine synthase; GabD, glutarate semialdehyde dehydrogenase; GabT, 5-aminovalerate transaminase; Hom, homoserine dehydrogenase; IlvA, L-threonine dehydratase; IlvB(N), bifunctional acetohydroxy acid synthase; IlvC, acetohydroxy acid isomeroreductase; IlvD, dihydroxy-acid dehydratase; IlvE, branched-chain amino transaminase; LdcC, lysine decarboxylase; LysA, diaminopimelate decarboxylase; LysC, aspartokinase; LysDH, lysine 6-dehydrogenase; MetB, cystathionine g-synthase; MetEH, methionine synthases, MetX, homoserine O-acetyltransferase; MetY, O-acetlyhomoserine sulfhydrolase; PatA, putrescine aminotransferase; PatD, g-aminobutyraldehyde dehydrogenase; ProC, pyrroline-5-carboxylate reductase; ThrB, L-homoserine kinase; ThrC, L-threonine synthase. (Referred from Becker, J., Rohles, C.M., Wittmann, C., 2018a. Metabolically engineered Corynebacterium glutamicum for bio-based production of chemicals, fuels, materials, and healthcare products. Metab. Eng. 50, 122–141. https://doi.org/10.1016/j.ymben.2018.07.008.)

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FIG. 7 The biosynthetic pathway of L-lysine from OAA in Corynebacterium glutamicum. Red dashed lines indicate LysC is feedback inhibited by its downstream products L-threonine and L-lysine.

correlations of the glucose uptake flux with expression levels of glucose phosphotransferase genes, as well as central metabolic fluxes with expression levels of glucose-6-phosphate dehydrogenase, transaldolase, and transketolase and most TCA cycle genes. The only exception is increased expression of cytoplasmic malate dehydrogenase despite of reduced TCA cycle flux during lysine-producing phase, possibly due to its function for NADH regeneration. Most genes for L-lysine biosynthesis and glyoxylate cycle are continuously expressed through the two phases, although genes for the later are only active in vivo in lysine production stage. Genes with most significantly altered expression levels mainly encode enzymes at entry nodes into glycolysis, pentose phosphate pathway, TCA cycle, and L-lysine biosynthesis, implying that the phase transition is probably achieved by controlling fluxes at these key pathway points from the transcription level (Kr€ omer et al., 2004). In a following study by the same group, overexpression of fructose 1,6-bisphosphatase (FBPase) resulted in increased L-lysine yield by 40% on glucose and 30% on fructose or sucrose in the feedback-deregulated lysine-producing strain C. glutamicum lysCfbr. 13C metabolic flux analysis revealed overexpression of FBPase redirected flux from glycolysis toward the pentose phosphate pathway and hence led to 23% increased NADPH supply (Becker et al., 2005). Later the same group used systems biology-based rational approach successfully resulted in a hyperproducing C. glutamicum strain LYS-12 (Becker et al., 2011). In this study, metabolic flux profiling and modeling were applied to identify totally 12 genetic modifications (six that enhance flux in the lysine biosynthetic pathway, three that increase flux toward oxaloacetate through anaplerotic carboxylation, two that increase NADPH, and one that reduces flux increases oxaloacetate by reducing TCA flux), which enables LYS-12 to produce lysine at a final titer of 120 g/L and a conversion yield of 55% on glucose after 30-h cultivation at 30°C in 5-L jar fermentor (Becker et al., 2011). Many other substantial efforts have been also made to comprehensively engineer C. glutamicum strains toward improved L-lysine producers, by engineering (1) supply of carbon building blocks (precursors), (2) cofactor regeneration, (3) the biosynthetic pathway, (4) export, and (5) downregulation of competing pathways (Eggeling and Bott, 2015; Chen et al., 2014; Takeno et al., 2016; Hoffmann et al., 2018; Wu et al., 2019; Chen et al., 2011; Schendzielorz et al., 2014; Binder et al., 2012; Binder et al., 2013; Hochheim et al., 2017; Becker et al., 2018b; Buchholz et al., 2013; Becker et al., 2009; van Ooyen et al., 2012; Yanase et al., 2016; Kind et al., 2013; Takeno et al., 2010; Xu et al., 2014; Bommareddy et al., 2014). Meanwhile, several strains have also been developed of utilizing alternative feedstocks to produce L-lysine, including starch (Seibold et al., 2006; Tateno et al., 2007a, b), xylose (Meiswinkel et al., 2013b), arabinose (Schneider et al., 2011), glycerol (Meiswinkel et al., 2013a), cellobiose (Adachi et al., 2013), and mannitol (Hoffmann et al., 2018). Meanwhile, genome-based reverse engineering provides an alternative approach to evolve C. glutamicum for efficient L-lysine production with certain successes (Ohnishi et al., 2002, 2005; Mitsuhashi et al., 2006; Ikeda et al., 2006). Besides L-lysine, other important OAA-derived small molecules produced by C. glutamicum include L-isoleucine, L-methionine, 5-aminovalerate (5AVA), L-pipecolic acid (L-PA), ectoine, and diaminopentane (DAP), all of which are the hot research targets in the research area of metabolic engineering. L-Isoleucine is an essential branched-chain amino acid that has function of increasing endurance and helping heal muscle tissue for human beings and therefore is an important component in infusions and special diets. The biosynthetic pathway of L-isoleucine closely interweaves with that of other amino acids, including L-lysine, L-threonine, L-valine, and L-leucine so that construction of a strain specifically overproducing L-isoleucine without byproduct formation is challenging. Wild-type C. glutamicum strains are almost

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dormant for L-isoleucine production, and producers of C. glutamicum are mainly engineered strains of L-threonine- and L-lysine-producing mutants with feedback-insensitive enzyme variants of aspartokinase (LysC), homoserine dehydrogenase (Hom), and homoserine kinase (ThrB) (Li et al., 2017). Efforts in metabolic engineering to improve L-isoleucine production include attenuating the competing pathways (Dong et al., 2016; Vogt et al., 2015), engineering threonine dehydratase (the only enzyme unique for L-isoleucine biosynthesis in the biosynthetic pathways of aspartate amino acids) (Li et al., 2017; Yin et al., 2012; Guo et al., 2015; Guillouet et al., 2001), deletion of alaT (encoding alanine aminotransferase) to increase intracellular pyruvate, overexpression of the thrABC operon from E. coli (encoding a subunit of aspartate kinase, a subunit of homoserine kinase, and a threonine synthase) (Wang et al., 2013), enhancement of NADPH supply through enhancing the flux through PPP pathway (Ma et al., 2016; Shi et al., 2013b), modified expression of genes involving transport processes (Xie et al., 2012; Yin et al., 2013), proteins with global function (Yin et al., 2013), and ribosome elongation factor G encoded by fusA and ribosome recycling factor encoded by frr (Zhao et al., 2015). Similarly, strategies mainly guided by the fundamental principle of metabolic engineering based on metabolic pathways have also been applied in C. glutamicum for improved production of L-methionine (an important dairy and health supplement) (Li et al., 2017, 2016; Park et al., 2007; Kim et al., 2015b; Hong et al., 2016; Kr€omer et al., 2008, 2006; Bolten et al., 2010; Qin et al., 2015), 5AVA (the precursor to synthesize nylon 5 and a potential C5 platform chemical for synthesis of valerolactam, 5-hydroxyvalerate, glutarate, and 1,5-pentanediol) (Liu et al., 2014; Rohles et al., 2016; Shin et al., 2016; Joo et al., 2017; Jorge et al., 2017b), L-PA (a precursor of immunosuppressants, peptide antibiotics, or piperidine alkaloids) (P
erez-Garcı´a et al., 2016, 2017a), ectoine (used in the cosmetics industry due to its moisturizing effect) (Becker et al., 2013b; P
erez-Garcı´a et al., 2017b; Giesselmann et al., 2019), and DAP (as a polymer building block) (Kind and Wittmann, 2011; Kind et al., 2010a, b, 2011, 2014; Kim et al., 2018; Sgobba et al., 2018; Buschke et al., 2011, 2013; Imao et al., 2017; Lessmeier et al., 2015; Matsuura et al., 2019).

5.1.3 Various more chemicals derived from central metabolism of C. glutamicum Besides production of 2OG- and OAA-derived chemicals as described in previous two sections, C. glutamicum is actually such a versatile microbial factory, so quite a few nodes (intermediates) from its central metabolic pathway have been utilized for synthesis of other small molecules with important applications, although many studies are still at the proofof-concept stage (Fig. 4). Due to the page limit of this book chapter, we only list the key information of these small molecules in Table 2, where readers can find more details from the references if have interest.

5.2 Directed evolution Rational engineering described in the previous section has proved to be a powerful strategy to engineer industrial platform strains for production of small molecules, as well as investigate into the insights of fundamental metabolism. However, till now, we are still not able to precisely predict responses of microorganisms to environmental changes or genetic perturbations, due to the very complex metabolic and regulatory networks, so rational genetic modifications introduced in the lab often don’t meet with desired phenotypes. As an alternative method, directed evolution doesn’t rely on detailed knowledge of the pathways and regulation. Instead, randomly mutations are introduced into the system (as a library), and the desired phenotype is enriched through screen or selection. The following genetic characterization of the isolated mutants will correlate genetic modifications on the mutants with the desired phenotype and hence increase our understanding of fundamental metabolism and microbial physiology. Currently, adaptive laboratory evolution (ALE) and screen/selection using biosensor are the two leading approaches of directed evolution to engineer C. glutamicum.

5.2.1 Adaptive laboratory evolution The strategy of ALE has a long history of applications in improving growth rates and stress tolerance. Procedures of ALE typically involve the following: (1) generation of the genetic diversity, (2) repetitive batch cultivations or continuous cultivations, and (3) selection (Fig. 8). The key point of ALE is that the phenotype can be selected (e.g., faster growth). The best performers (mutants) typically undergone further genetic analysis with data obtained from next-generation sequencing and comprehensive omics, which usually reveals nonintuitive mutations and hence enhances our new understandings of the relationship among the novel gene targets identified, the desired phenotype, and the metabolism and regulation. Besides, advances in automation of laboratory workflows have further accelerated the efficiency of ALE. Recent successful examples of applying ALE to evolve C. glutamicum include the following: (1) growth improvement on glucose (Pfeifer et al., 2017; Wang et al., 2018a), D-xylose (Radek et al., 2017; Br€usseler et al., 2018), cellobiose (Lee et al., 2016), and methanol (Tuyishime et al., 2018); (2) improved thermal tolerance (Oide et al., 2015); (3) improved growth

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TABLE 2 Various small molecules produced in engineered Corynebacterium glutamicum (excluding 2OG- and OAA-derived small molecules). Compound

Precursor

Application

Reference

Biliverdin

a-Ketoglutarate

A prospective recyclable antioxidant and a precursor for optogenetics

Seok et al. (2019)

2,3-Butanediol

Pyruvate

A bulk chemical

Yang et al. (2015) and Radosˇ et al. (2015)

C40 carotenoids (b-carotene, zeaxanthin, and astaxanthin)

Acetyl-CoA

Health-promoting effects

Heider et al. (2014) and Mindt et al. (2019)

C50 carotenoids (C.p.450 and sarcinaxanthin)

Acetyl-CoA

Health-promoting effects

Heider et al. (2014) and Mindt et al. (2019)

7-Chloro-Ltryptophan

Erythrose-4phosphate and phosphoenolpyruvate

The precursor of rebeccamycin

Wei et al. (2019)

Chondroitin

Fructose-6-phosphate and glucose6-phosphate

Clinic

Cheng et al. (2019a)

L-Cysteine

3-Phosphoglycerate

Food, agriculture, and pharmaceutical industries

Wei et al. (2019) and Kondoh and Hirasawa (2019)

Ethanol

Pyruvate

Beverage, fuel

Inui et al. (2004b) and Jojima et al. (2015)

L-Histidine

Ribulose5-phosphate

An essential amino acid for human infants and adults

Schwentner et al. (2019)

Hyaluronic acid (HA)

Fructose-6-phosphate and glucose6-phosphate

Clinical, medical, cosmetic, and food industries

Cheng et al. (2019b)

4-Hydroxybenzoic acid

Erythrose4-phosphate and phosphoenolpyruvate

A precursor for liquid crystal polymers and parabens

Kitade et al. (2018) and Kallscheuer and Marienhagen (2018)

3-Hydroxypropionic acid

DHAP

A building block for biodegradable polyesters

Chen et al. (2017b)

Isobutanol

Pyruvate

Biofuel

Smith et al. (2010) and Yamamoto et al. (2013)

Isopentenol

Acetyl-CoA

A biogasoline candidate

Sasaki et al. (2019)

Itaconic acid

cis-Aconitate

A precursor of polymers, chemicals, and fuels

Otten et al. (2015)

D/L-Lactate

Pyruvate

A promising polymer candidate

Tsuge et al. (2019)

L-Leucine

Pyruvate

Dietary supplement, a flavoring substance and a lubricant for tablet production

Vogt et al. (2014)

Lycopene

Acetyl-CoA

Health-promoting effects

Heider et al. (2012)

Methyl anthranilate

Erythrose4-phosphate and phosphoenolpyruvate

A grape flavor compound, used in the flavoring and cosmetic industry

Luo et al. (2019)

cis, cis-Muconate

Erythrose4-phosphate and phosphoenolpyruvate

A building block for commodity and specialty chemicals in plastic industry

Shin et al. (2018), Kohlstedt et al. (2018), and Becker et al. (2018c) Continued

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TABLE 2 Various small molecules produced in engineered Corynebacterium glutamicum (excluding 2OG- and OAA-derived small molecules)—cont’d Compound

Precursor

Application

Reference

Polyamide cyanophycin

a-Ketoglutarate and oxaloacetate

Clinical diets, as dietary supplements, or in livestock feeds

Wiefel et al. (2019)

g-Polyglutamic acid

a-Ketoglutarate

A biodegradable polymer

Xu et al. (2019)

Protocatechuic acid

Erythrose-4phosphate and phosphoenolpyruvate

A polymer building block and food constituent

Kallscheuer and Marienhagen (2018) and Okai et al. (2017, 2016)

Naringenin

Malonyl-CoA, erythrose-4phosphate, and phosphoenolpyruvate

Health-promoting effects

Kallscheuer et al. (2016, 2017) and Milke et al. (2019a)

Noreugenin

Malonyl-CoA, erythrose-4phosphate, and phosphoenolpyruvate

Health-promoting effects

Milke et al. (2019b)

Resveratrol

Malonyl-CoA, erythrose-4phosphate, and phosphoenolpyruvate

Health-promoting effects

Kallscheuer et al. (2016, 2017) and Milke et al. (2019a)

Roseoflavin

Ribulose 5-phosphate

A promising broad-spectrum antibiotic

Mora-Lugo et al. (2019)

Sarcosine (N-methylglycine)

Glyoxylate

A potential antipsychotic

Mindt et al. (2019)

L-Serine

3-Phosphoglycerate

An essential amino acid, used in pharmaceutical, food, and cosmetic industries

Zhang et al. (2019c)

Succinate

–

A building block of bulk chemicals (1,4-butanediol, tetrahydrofuran, and g-butyrolactone) and commercially important polymers

Okino et al. (2008), Litsanov et al. (2012a, b), Zhou et al. (2015), Zhu et al. (2013), Yamauchi et al. (2014), Lee et al. (2006), and Becker et al. (2013c)

Taurine

3-Phosphoglycerate

A food additive

Joo et al. (2018)

L-Theanine

a-Ketoglutarate

A valuable additive for use in food and beverages

Ma et al. (2020)

L-Valine

Pyruvate

A precursor for many drugs and herbicides

Blombach et al. (2008), Chen et al. (2015), and Hasegawa et al. (2012, 2013)

Violacein

L-Tryptophan

Antiviral and anticancer activity

Sun et al. (2016)

in the raw media containing growth inhibitors (Wang et al., 2018b; Leßmeier and Wendisch, 2015); and (4) alleviation of inhibition byproducts or analogous antimetabolites and hence increase small molecule production ( Jiang et al., 2013; Li et al., 2018; Takeno et al., 2013, 2018).

5.2.2 Biosensors and high-throughput engineering On the other hand, when production of a small molecule is not coupled with growth (which is very common), biosensors can be designed and applied to engineer microbial factories by directed evolution in a high-throughput manner. In recent years the biosensors most commonly investigated and developed are based on transcription factors (TFs), riboswitches, and F€ orster resonance energy transfer (FRET) (Eggeling et al., 2015; Zhang et al., 2015b). In C. glutamicum, several of these

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FIG. 8 Procedures of adaptive laboratory evolution. Genetic diversity is generated via natural mutation occurring during DNA replication or in faster rates with physical, chemical, or genetic manipulations. Mutants with the desired phenotype (i.e., faster growth) are enriched after rounds of repetitive cultivations, which are then isolated and subject to sequencing analysis to identify the genetic mutations causing the resultant phenotype.

biosensors have been developed with the application for improved small molecule production in recent years. Here, we use the LysR-type transcriptional factor ShiR of C. glutamicum as an example to illustrate how this approach works: the metabolite shikimate specifically binds to ShiR (a transcription repressor) so as the activate transcription linearly within the range of 19.5–120.9 fmole at the single-cell level (Fig. 9). Thus ShiR was developed to be a biosensor and applied to screen ribosome binding site (RBS) library of tktA gene (encoding the transketolase that catalyzes the formation of E4P, a key precursor of the shikimate pathway) by FACS, which resulted in 2.4-fold improved shikimate titer over the parent (Liu et al., 2018). Other biosensors developed based on transcription factors include the following: Lrp to detect intracellular L-valine, L-isoleucine, L-leucine, and L-methionine in C. glutamicum strain (Mustafi et al., 2012, 2014; Mahr et al., 2015), the global transcriptional repressor GlxR to detect and improve intracellular cyclic adenosine monophosphate (cAMP) (Schulte et al., 2017), and LysG to sense intracellular L-lysine and L-arginine (Schendzielorz et al., 2014; Binder et al., 2012; Cheng et al., 2019a). In addition to the TF biosensors, a FRET-based sensor toolbox was constructed, which consists of an optimized central L-lysine-/L-arginine-/L-ornithine-binding protein (LAO-BP) flanked by two fluorescent proteins. Variants of LAO-BP with altered affinity and sensitivity were further obtained by circular permutation of the binding protein and introduction of linkers between the fluorescent proteins and the LAO-BP. This toolbox was then applied to monitor the extracellular L-lysine production by C. glutamicum, which demonstrates its high potential for high-throughput screen (Steffen et al., 2016). The natural L-lysine riboswitches of E. coli (ECRS) and B. subtilis (BSRS) were both successfully applied to control the expression of gltA (encoding citrate synthase), which led to diverting of more metabolic flux toward L-lysine synthesis

FIG. 9 Design and application of the transcription factor ShiR as the biosensor to detect intracellular shikimate for high-throughput screen in Corynebacterium glutamicum. (A) The sensor is off without shikimate; (B) the sensor is on in present of shikimate. (Referred from Becker, J., Rohles, C.M., Wittmann, C., 2018a. Metabolically engineered Corynebacterium glutamicum for bio-based production of chemicals, fuels, materials, and healthcare products. Metab. Eng. 50, 122–141. https://doi.org/10.1016/j.ymben.2018.07.008.)

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rather than TCA cycle in C. glutamicum. L-Lysine production was improved by 63% and 38% when using ECRS and BSRS, respectively (Zhou and Zeng, 2015a). Further, work of introduction of a synthetic ON switch to induce translation of lysE (encoding the L-lysine exporter) was tested, which resulted in further increase of L-lysine yield by 89% (Zhou and Zeng, 2015b). Definitely, such biosensors based on riboswitches can be further explored with applications to engineer C. glutamicum by high-throughput screen/selection.

6

Conclusions

In this chapter, we summarize fundamental knowledge and recent advances of utilizing C. glutamicum as a robust workhorse for production of value-added recombinant proteins and small molecules. C. glutamicum was firstly isolated as the natural L-glutamate producer, implying its innate potential for industrial applications. Especially after decades of engineering, the evolved strains show superior performance, as compared with the most often-used model bacterium E. coli. The limited tools for genetic modifications and the resultant less efficient genetic operations were once the major barriers to move forward engineering C. glutamicum, which has been at least partially addressed nowadays (Sections 2–4 in this chapter). We are amazed to discover that C. glutamicum is such a formidable workhorse that not only it has been used to synthesize recombinant proteins of different purposes but also most key nodes in its central metabolic pathway have been explored as the starting points toward synthesis of various kinds of useful small molecules by researchers worldwide (Fig. 4). Although most of the successful stories described in the chapter are only at the proof-of-concept stage, we are expecting further substantial progress will be achieved in near future when the modern disciplines and technologies (synthetic biology, systems biology, CRISPR genomic editing, etc.) are applied in a combinatorial and advanced fashion.

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Chapter 13

Production of high value-added chemicals by engineering methylotrophic cell factories Guihong Yua, Mengying Wanga, Changtai Zhanga, Zengxin Maa, Hui Zhanga, Xuhua Moa, Yuman Suna, Xinhui Xingb,c, and Song Yanga,d,∗ a

School of Life Sciences, Shandong Province Key Laboratory of Applied Mycology, and Qingdao International Center on Microbes Utilizing Biogas

Qingdao Agricultural University, Qingdao, Shandong Province, People’s Republic of China, b MOE Key Lab of Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, People’s Republic of China, c Center for Synthetic and Systems Biology, Tsinghua University, Beijing, People’s Republic of China, d Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin University, Tianjin, People’s Republic of China Corresponding author: E-mail: [email protected]

∗

1 Introduction Currently, microbial cell factories use mainly sugar-based raw materials as the substrates to synthesize products, and there is a disputation of technical and economic sustainability. From this point of view, finding new raw materials for microbial cell factories is of urgent need. Organic one-carbon (C1) compounds, such as methanol, are important chemical feedstocks that are widely sourced and increasingly low in price comparable with that of glucose (Schrader et al., 2009; Olah et al., 2018; Zhu et al., 2020). Methanol can be mainly produced from methane in natural gas and coal (Duan et al., 2018). In recent years, with the breakthrough in methanol synthesis via CO2 hydrogenation, it opens an opportunity for future methanol production from renewable raw materials (Duan et al., 2018). Methanol contains more electron per carbon than glucose, providing an extra advantage to improve the titers and yields of reduced chemicals such as alcohols and carboxylic acids that require more electrons for efficient production. In addition, unlike the C1 gases such as CO2 and methane, methanol can be easily stored and transported and is completely miscible with water, which is an important feature for application in large-scale industrial production. Value-added bioproducts obtained by conversion from C1 compounds (methanol, methylated amines, or methane) are of significant research interest in bioengineering. Methylotrophs are one kind of microorganisms that utilize C1 compounds as the sole carbon and energy source. With the improvement of genome sequencing methods and various omics technologies, the central metabolic network pathway and related functional genes of methylotrophs are rapidly elucidated. More recently the improvement of genetic manipulation or genome editing tools such as CRISPR interference and various gene regulatory elements provide an important foundation for methylotrophic chassis modification and optimization of heterologous expression. Worldwide research has promoted the conversion of methanol into a variety of high value-added chemicals (Duan et al., 2018; Puri et al., 2018; Zhang et al., 2019). According to the difference of the central metabolic network, methylotrophs can be mainly categorized as alpha-proteobacteria and gamma-proteobacteria, and now, among which the application of alpha-proteobacteria has more research reports. Here the recent progress of bioengineering research and metabolic potential of methylotroph-based microbial cell factories (MeCFs), which mainly refer to alphaproteobacteria, was reviewed.

2 New progress in genetic manipulation tools for engineering of MeCFs The development of diverse genetic manipulation tools is a precondition to construct cellular factories for commodity chemicals production. Although methylotrophs, represented mainly by Methylorubrum extorquens (formerly Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00016-7 © 2021 Elsevier Inc. All rights reserved.

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Methylobacterium extorquens), had become important platform organism in C1 bioeconomy, more efforts are still needed to exploit the tools of genetic modification and genome editing for extending future applications. M. extorquens AM1, a pink-pigmented facultative methylotroph isolated and characterized as early as 1961, has been a model strain to study aerobic methylotrophy (Peel and Quayle, 1961). In 2015 Schada von Borzyskowski et al. developed a series of novel brick vectors that provided new constitutive promoters with different strengths, allowing the convenient construction of synthetic operons in M. extorquens AM1 (Schada von Borzyskowski et al., 2015). Based on transcriptomics studies and RNA-Seq data, three new constitutive promoters (PfumC originated from the gene fumC encoding for a fumarase, PcoxB originated from gene coxB encoding for the cytochrome c oxidase subunit II, and Ptuf originated from gene EF-Tu encoding for the translation elongation factor thermal unstable) and the previously reported promoter PmxaF were further characterized. The activity of the four promoters followed the order of PfumC < PcoxB < Ptuf  PmxaF based on analysis of promoter-reporter system including promoter-fluorescent protein and crotonyl-CoA carboxylase/reductase activity. For this system the donor or host brick vectors were developed from the parent plasmids pCM80 and pLM01, respectively, and the construction of the brick vectors is depicted as Fig. 1. If a mini-Tn5 delivery system was inserted in the multiple cloning site, the brick vectors can integrate into the chromosomal DNA of M. extorquens AM1. In 2019 Carrillo et al. developed a new set of genetic tools to exploit the engineering potential of M. extorquens AM1 (Carrillo et al., 2019). First, they offered a series of inducible promoters with wide expression range, among which the strongest strength exceeded the currently available promoter of PmxaF. Second, they discovered a group of repABC regions, including genes repA and repB that encode for partitioning proteins similar to the proverbial ParA and ParB and gene repC that encode the replication initiator protein RepC, and further constructed diverse single-copy mini-chromosomes that could be stably inherited and were compatible with each other and with other high-copy plasmids in M. extorquens. Moreover, they provided repABC regions that can be transient expression without antibiotic selection, which is important to assist with establishing CRISPR/Cas9-based methods in M. extorquens AM1. Recently, Mo et al. developed a clustered regularly interspaced short palindromic repeat interference (CRISPRi) system by balancing the promoter strength of deactivated cas9 (dcas9) derived from Streptococcus pyogenes and sgRNA in M. extorquens AM1 (Mo et al., 2020). When disturbing central metabolism gene glyA by the dcas9 and sgRNA that is promoted by medium PR/tetO and strong PmxaF-g, respectively, the dynamic repression efficacy of cell growth reached 41.9%–96.6% based on the sgRNA targeting sites. They also applied the developed CRISPRi method to significantly repress the expression of exogenous fluorescent protein gene mCherry and phytoene desaturase gene crtI. Moreover, they

FIG. 1 Methylobrick cloning scheme. Brick module A carrying a promoter sequence and brick module B carrying a gene to be expressed in M. extorquens AM1. Module B is combined with module A using isocaudomeric restriction. (This figure was modified from Schada von Borzyskowski, L., RemusEmsermann, M., Weishaupt, R., Vorholt, J.A., Erb, T.J., 2015. A set of versatile brick vectors and promoters for the assembly, expression, and integration of synthetic operons in Methylobacterium extorquens AM1 and other alphaproteobacteria. ACS Synth. Biol. 4 (4), 430–443.)

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FIG. 2 The schematic chart for rapid identification of genes encoding phytoene synthases and desaturases by using CRISPRi combined with a pooled sgRNA. M. extorquens AM1 strains carrying plasmids with different sgRNAs targeting genes were constructed, and then the strains were screened and verified by comparison of the colony colors, transcript levels, and carotenoid concentrations. (This figure was modified from Mo, X., Zhang, H., Wang, T., Zhang, C., Zhang, C., Xing, X., Yang, S., 2020. Establishment of CRISPR interference in Methylorubrum extorquens and application of rapidly mining a new phytoene desaturase involved in carotenoid biosynthesis. Appl. Microbiol. Biotechnol. https://doi.org/10.1007/s00253-00020-10543-w (web archive link).)

rapidly mined the new phytoene desaturase gene META1_3670 by combined application of CRISPRi method and a 26 sgRNAs pool (Fig. 2). In addition, they also enhanced the carotenoid production by disturbing the squalene-hopene cyclase gene shc participated in a competitive pathway. Thus the new CRISPRi system was a proper method in disclosing new functional gene and in fine-tuning gene expression at genome level for raising the titer of high-valued chemicals in M. extorquens AM1. M. extorquens PA1, isolated from the phyllosphere of Arabidopsis thaliana, is closely related with M. extorquens AM1 at phylogenetic level but has several advantages over it, such as simpler genomic structure, faster growth on methanol, and more suitability for transposon mutagenesis, which made it to be another model strain for methylotrophy (Vuilleumier et al., 2009; Knief et al., 2010; Marx et al., 2012; Metzger et al., 2013; Nayak and Marx, 2014). In 2017 Ochsner et al. uncovered 95 crucial methylotrophic genes in M. extorquens PA1 by a method of transposon sequencing, which combined the hypersaturated transposon mutagenesis technology and the high-throughput sequencing technology (Ochsner et al., 2017). They also found that the gene prk (Mext_0980) encoding for phosphoribulokinase, which was functionally related to the transcriptional regulator QscR (a CbbR ortholog) of the serine cycle, was essential during the growth of M. extorquens PA1 on methanol. This study supplied a new strategy to engineer the serine cycle for more efficiently producing value-added products.

3 Advances in engineering of the metabolic pathway in/from methylotrophs The central metabolic network of natural methylotrophs includes three modules: the oxidation module of C1 compound (methanol, methylamine, etc.), the dissimilation and the assimilation modules of C1 intermediates (formaldehyde and formate) (Anthony, 2011; Yang et al., 2017a). Methanol is the main carbon source of methylotrophs, and methanol dehydrogenase (MDH) is responsible for the oxidation of methanol and conversion to formaldehyde. Then, formaldehyde passes through the dissimilation module and is eventually oxidized to CO2. This process can generate reducing equivalents, NADPH and NADH. The assimilation module differs between alpha-proteobacteria and gamma-proteobacteria, among which alpha-proteobacteria methylotrophs, such as M. extorquens AM1, assimilate formate through the serine cycle coupled with the ethylmalonyl-CoA pathway (EMCP), while gamma-proteobacteria, such as Methylomicrobium buryatense 5GB1, assimilate formaldehyde directly through the ribulose monophosphate pathway (RuMP) (Fig. 3) (Chao et al., 2009; Chistoserdova, 2011; Sonntag et al., 2014; Fu et al., 2017).

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FIG. 3 Metabolic modules of alpha-proteobacteria (A) and gamma-proteobacteria (B) methylotrophs. For alpha-proteobacteria methylotrophs, it has three interlocked metabolic cycles, that is, the serine cycle, the ethylmalonyl-CoA (EMC) pathway, and the poly-3-hydroxybutyrate (PHB) cycle for C1 assimilation. The overlapped intermediates have been shown. For gamma-proteobacteria methylotrophs, the intermediate pyruvate generated from RuMP pathway is decarboxylized into acetyl-CoA, which then enters into the TCA cycle.

In recent years, many commodity chemicals have been produced by constructing MeCFs using the intermediates involved in the EMCP as the precursors, such as 1-butanol, mevalonate, and 2-hydroxyisobutyric acid. However, the EMCP is mainly responsible for regenerating glyoxylate from acetyl-CoA to reenter the serine cycle during C1 assimilation, and therefore it is difficult to accumulate the products by simply interrupting this pathway. To solve the problem, Schada von Borzyskowski et al. have tried to introduce a heterologous glyoxylate shunt into the mutant M. extorquens AM1 in which the EMCP was disrupted (Schada von Borzyskowski et al., 2018b), but the growth on methanol could not be restored, although the exogenous isocitrate lyase and apparent malate synthase were confirmed to have the significant activities in the cell-free extracts. It might be because of the strongly decreased activities of methanol dehydrogenase (MDH) and NAD-linked formate dehydrogenase (FDH) and the reduced flux of the TCA cycle. Notably, when cultivating the cells on acetate, the growth was well restored, suggesting a successful replacement of the native glyoxylate regeneration pathway of EMCP under this condition. The growth could also be restored in some degree when overexpressing malate synthase and isocitrate lyase, implying that the native malate synthase-like reactions composed of malyl-CoA lyase and malyl-CoA thioesterase in M. extorquens AM1, likely only partially compensated carbon flux. In addition, as a proof of principle, the engineered MeCF constructed in this study was shown to produce higher titer of crotonic acid than that of the wild-type strain. Thus the result supplied a novel strategy for producing chemicals through the intermediates in the EMCP. Recently the same research group has tried to engineer the methylotrophic bacteria into semiautotrophic bacteria by introducing ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) and phosphoribulokinase (Prk) in M. extorquens AM1, which can construct a Calvin-Benson-Bassham (CBB) cycle to fix CO2 (Schada von Borzyskowski et al., 2018a). They first established a mutant strain that was incapable of growing on methanol by deleting the gene ftfL essential for methanol assimilation through the serine cycle (Delaney et al., 2013). This new mutant could not convert methanol into biomass but still could generate ATP and reducing equivalent from methanol oxidation. Then the mutant strain was equipped with the heterologous CBB cycle to restore the carbon assimilation from CO2, resulting in the semiautotrophic phenotype. Although failed to create a completely autotrophic phenotype, this study demonstrated the plasticity of onecarbon metabolism of M. extorquens AM1 and represented an improvement in building synthetic autotrophic organisms. Yu and Liao referred to the core metabolism of M. extorquens and integrated a modified serine cycle into Escherichia coli to construct a methylotrophic pathway (Yu and Liao, 2018). They improved the process of oxidation from formaldehyde to formate by introducing an NAD-linked formaldehyde dehydrogenase from Pseudomonas putida and further circumvented the hydroxypyruvate reductase with promiscuous activity in E. coli by introducing alanine-glyoxylate transaminase and serine dehydratase from Cupriavidus necator. Finally, this engineered E. coli with the simplified serine cycle could increase the titer of ethanol by 62%. The rate of methanol assimilation reached 0.7 mM/h/OD600, and the molar

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ratio of consuming xylose and methanol was approximately 1:0.7 in Luria-Bertani medium. Although the engineered strain still could not grow on methanol alone, their work achieved the conversion of one-carbon compounds into two-carbon compounds through constructing the modified methylotrophic pathway in E. coli.

4 Improvement of methylotrophic phenotypes via evolution Adaptive laboratory evolution can efficiently improve the phenotypic and physiological characteristics of the bacteria in a short period of time. It provides a way to improve growth efficiency, substrate consumption, and organic solvent tolerance or uncover the potential metabolic capabilities and mechanisms (Cui et al., 2018; Nayak et al., 2016). For example, industrial bioproduction of biofuels with MeCFs usually requires the cells to tolerate high concentrations of alcohols, but the improved phenotypes are controlled by multiple genes and are difficult to achieve through a rational design without well knowing the tolerance mechanisms. In contrast, adaptive laboratory evolution combined mutagenesis is a useful and efficient approach to improve the cell characteristics including organic solvent tolerance and target chemicals production (Cui et al., 2018). In 2009 Lee et al. investigated specialization and trade-offs between the C1 (methanol) and multi-C (succinate) metabolic lifestyles of M. extorquens AM1 by adaptive laboratory evolution (Lee et al., 2009). The results showed that the fitness values on methanol of succinate-evolved mutants were a notable bimodal after 1500 generations, that is, the growing ability on C1 was improved or decreased comparable with the methanol-evolved strains. However, the methanol-evolved strains had no such trade-offs, suggesting that frequent use of C1 resource in nature is important for Methylobacterium to maintain the ability of C1 metabolism. Methylamine dehydrogenase (MaDH) is essential for M. extorquens AM1 grown on methylamine, but the physiological role of N-methylglutamate (NMG) pathway is not well known. In 2016 Nayak et al. performed the experimental evolution on two mutants lacking (or incapable of using) MaDH and uncovered the physiological challenges when M. extorquens AM1 uses methylamine as the carbon and energy source through the NMG pathway (Nayak et al., 2016). Physiological properties of the evolved mutants indicated the upregulation of the NMG pathway and the discovery of new mechanisms to reduce cytoplasmic ammonia accumulation. Highly expressed MaDH can help bacteria to grow rapidly when using methylamine as the main carbon and energy substrate, while the NMG pathway that consumes more energy plays more important role when using methylamine as the sole nitrogen source. This research presents new switch mechanism for facultative methylotrophs dealing with the situation of methylamine as the main carbon and energy source or as the sole nitrogen source. The low tolerance of M. extorquens AM1 to solvent stress such as 1-butanol had impeded its further development as an efficient production platform. To solve this problem, Hu et al. evolved M. extorquens AM1 through the adaptive laboratory evolution (Hu et al., 2016). The adaptive mutants, BHBT3 and BHBT5, showed higher 1-butanol tolerance than the wildtype strain, especially BHBT5, which was capable of tolerating 0.5% (v/v) 1-butanol with half of the normal growth rate. Then whole genome sequencing and allelic exchange experiment indicated that an SNP at gene kefB in the BHBT5 strain occurred, which helped to improve the tolerance of 1-butanol (Table 1). Further the global metabolomics analysis revealed that many key metabolites such as disaccharides, fatty acids, and amino acids were upregulated in the BHBT5 strain under the stress of 1-butanol, while the production of carotenoid was significantly reduced compared with the wild type. In addition, to improve methanol tolerance of M. extorquens AM1, Cui et al. bred a mutant (a strain referred to as CLY2533) with high methanol tolerance using atmospheric and room temperature plasma (ARTP) mutagenesis and adaptive laboratory evolution (Cui et al., 2018). The final cell density of the mutant was 7.1 times of the wild type in high concentration of methanol (5%, v/v). Moreover, to produce mevalonate in evolved strain of CLY 2533, the mvt operon responsible for synthesizing mevalonate was reintroduced into the evolved strain by plasmid pCM110A, and it was found that the volumetric productivity of mevalonate in the CLY-2533-mvt was 1.65-fold of AM1-dcel-mvt in the fed-batch fermentation. In addition, by comparative genomics and overexpression, six mutated genes (i.e., acrB, arsH, aspS, metY, nrd, and suga) and the wild-type form of gntR proved to be relevant with high tolerance to methanol (Table 1). Similarly, Belkhelfa and D€ oring et al. isolated an evolved M. extorquens strains, with stable growth in 10% methanol by adaptive directed evolution (Belkhelfa et al., 2019). The GM3 technology of automated continuous culture (Marlie`re et al., 2011), including turbidostat and conditional medium swap regimes, was used in the parallel evolution of M. extorquens AM1 and of a recently characterized M. extorquens strain TK 0001 (Belkhelfa et al., 2018). This approach enabled the isolation of high methanol tolerance mutants of both strains. The mutants produced more biomass than the wild-type strains at 1% methanol correspondingly. The D-lactate production in G4105_pTE102ldhA, a mutant overexpressing the native D-lactate dehydrogenase, was about 1.4-fold higher than the control cells. Genome sequencing further showed that the gene metY encoding for an O-acetyl-L-homoserine sulfhydrylase was one common mutation site (Table 1). In addition, transcriptomics analysis indicated that the genes of the evolved cells that encode for chaperones and proteases were upregulated compared with

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TABLE 1 Summary of evolution strategies and the represented mutations. Result of evolution

Achieved mutants

Main genes

Adaptive laboratory evolution

High 1-butanol tolerance (0.5%, v/v)

M. extorquens BHBT5

kefB

Encoding for potassium (K+)/proton antiporter coding

SNP

Hu et al. (2016)

ARTP mutagenesis and adaptive laboratory evolution

High methanol tolerance (5%, v/v)

M. extorquens CLY-2533

acrB

Encoding for heavy metal efflux pump

SNP

Cui et al. (2018)

arsH

Encoding for NADPHdependent FMN reductase

SNP

aspS

Encoding for aspartatetRNA synthetase

SNP

metY

Encoding for transsulfuration enzyme

SNP

nrd

Encoding for ribonucleosidediphosphate reductase adenosylcobalamindependent large subunit

SNP

suga

Encoding for capsule polysaccharide export outer membrane protein

SNP

metY

Encoding for an Oacetyl-L-homoserine sulfhydrylase

SNP

Evolution strategies

GM3 technology, turbidostat, conditional medium swap regimes, and adaptive directed evolution

High methanol tolerance (10%, v/v)

M. extorquens G4105 et al.

Gene functions

Mutation type

References

Belkhelfa et al. (2018)

the wild type and the quantity of ribosomal proteins and enzymes associated with energy production from methanol was boosted after a short-term methanol stress as well. These important findings described earlier are helpful for redesign of the strains with better phenotypic and physiological characteristics and beneficial for further development of MeCFs.

5

Producing high value-added chemicals by engineering MeCFs

MeCFs are now becoming the important platform in C1 bioeconomy, which can produce high value-added chemicals, such as a-humulene, mevalonate, butadiene, 2-hydroxyisobutyric acid, and 3-hydroxypropionic acid (Fig. 4). a-Humulene, a natural monocyclic sesquiterpenoid originally produced from Humulus lupulus or Zingiber zerumbet (Katsiotis et al., 1989; Yu et al., 2008), was reported as a potential natural product with antiinflammatory and antitumor activities (Fernandes et al., 2007; Passos et al., 2007). It is also an important precursor of zerumbone that has remarkable antiinflammatory and antitumor activities as well (Kitayama, 2011; Prasannan et al., 2012). Compared with the extraction from plants and chemical synthesis, microbial cell factory is a more proper method for bulk production of a-humulene. Thus Schrader et al. engineered M. extorquens AM1 to convert methanol to a-humulene (Sonntag et al., 2015a). Firstly, they expressed the Z. zerumbet–derived gene zssI, which encodes a-humulene synthase, and the Saccharomyces cerevisiae–derived gene ERG20, which encodes farnesyl pyrophosphate (FPP) synthase in M. extorquens AM1 (Entian and K€ otter, 1998; Yu et al., 2008), resulting in the production of a-humulene about 18 mg/L. Secondly, the production of a-humulene was increased to 58 mg/L by integrating a Myxococcus xanthus–derived mevalonate pathway and by

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FIG. 4 Metabolic engineering of M. extorquens to produce the value-added chemicals. Acetoacetyl-CoA is the precursor of ɑ-humulene and mevalonate. Crotonyl-CoA is the precursor of 1-butanol, butadiene, and crotonic acid. 3-Hydroxypropionic acid, 2-hydroxyisobutyric acid, methylsuccinic acid, and mesaconic acid can be converted from acetyl-CoA, beta-hydroxybutyryl-CoA (BHB-CoA), methylsuccinyl-CoA, and mesaconyl-CoA, respectively.

optimizing the ribosome binding sites of the genes zssI and ERG20. Furthermore the titer of a-humulene was raised to 74 mg/L when reducing the flux of FPP toward carotenoids by using a carotenoid synthesis deficient mutant. Finally, the production of 1.65 g/L was achieved in the optimized fed-batch cultivation, which was higher than that in the engineered E. coli described by Harada et al. (2009). Mevalonate is a precursor metabolite for producing a number of terpenoids served as pharmaceuticals, biofuels, bulk chemicals, fragrances, and flavors, representing a large family of natural chemicals (Bohlmann and Keeling, 2008; Zhang et al., 2011). To increase its production in M. extorquens AM1, Zhu et al. constructed two operons, MVE and MVH, among which MVE was built by mvaS and mvaE genes from Enterococcus faecalis and a MVH was built by hmgcs1 and tchmgr genes, respectively, from Blattella germanica and Trypanosoma cruzi (Zhu et al., 2016). By bringing these two mevalonate pathways in M. extorquens AM1, respectively, they achieved the titers of mevalonate up to 56 mg/L and 66 mg/L. Then the production of mevalonate was increased to 180 mg/L by heterologous expression of the Ralstonia eutropha–derived gene phaA, which encodes for acetoacetyl-CoA thiolase. Furthermore, they optimized the strength of the ribosomal binding site, leading to the increase in the production of mevalonate by 20%. Finally, they achieved the mevalonate titer up to 2.22 g/L by fed-batch fermentation. In addition, Liang et al. from the same research group designed a sensor-assisted transcription regulation engineering (SATRE) strategy to enhance the titer of mevalonate by upregulating the acetyl-CoA flux from methanol in M. extorquens AM1 (Liang et al., 2017). Acetyl-CoA is a significant precursor of many high-value chemicals, but cannot always be efficiently generated. A mevalonate biosensor containing mevalonate synthesis pathway was first constructed and then used for a high-throughput screening of QscR transcriptional regulator library combined with fluorescence-activated cell sorting (FACS), leading to the acquisition of mutated strain (the strain of Q49) that had a 7% increase of acetyl-CoA flux and resulted in the increase of mevalonate production by 60% (Fig. 5). Experimental analysis indicated the global effect of QscR on the carbon flux redistribution. Further study showed that mevalonate was produced up to 2.67 g/L in the strain of Q49 in 5-L bioreactor. This study attempted to engineer M. extorquens AM1 at the transcriptional level and supplied a powerful tool for obtaining other value-added chemicals. Butadiene is an important chemical applied for manufacturing tires, resins, latex, and plastics (White, 2007; Jang et al., 2012), whose direct precursor was crotyl diphosphate. To convert crotonyl-CoA to crotyl diphosphate in M. extorquens AM1, Yang et al. designed a new pathway, recently (Yang et al., 2018). They first screened three functional enzymes, namely, hydroxyethylthiazole kinase (THK) from E. coli, isopentenyl phosphate kinase (IPK) from Methanothermobacter thermautotrophicus, and aldehyde/alcohol dehydrogenase (ADHE2) from Clostridium acetobutylicum by enzyme activity assay, substrate feeding, and product detection, among which one of the rate-limiting enzymes, THK, was optimized by

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FIG. 5 Biosensor-assisted transcriptional regulator engineering (SATRE) approach in M. extorquens AM1. QscR regulates the serine cycle to shift the carbon flux toward acetyl-CoA (Ac-CoA) accumulation. A heterologous mevalonate synthesis pathway responsible for converting acetyl-CoA into mevalonate was introduced into M. extorquens along with a mevalonate biosensor. The high mevalonate production mutants were then screened out by fluorescence-activated cell sorting (FACS). (This figure was modified from Liang, W., Cui, L., Cui, J., Yu, K., Yang, S., Wang, T., Guan, C., Zhang, C., Xing, X., 2017. Biosensor-assisted transcriptional regulator engineering for Methylobacterium extorquens AM1 to improve mevalonate synthesis by increasing the acetyl-CoA supply. Metab. Eng. 39, 159–168.)

random mutagenesis. The variant THKM82V, which was selected by developed high-throughput screening colorimetric assay from about 3000 mutants, exhibited 8.6 times activity of the wild type. Accordingly, 7.6% crotonol was converted to crotyl diphosphate in one-pot in vitro reaction, and the titer of crotyl diphosphate was increased to 0.76 mM. Further the mutant strain with overexpression of genes adhe2, MTH_47, and thiM encoding the native ADHE2, IPK, and THKM82V in M. extorquens AM1 under the strong promoter mxaF did not lead to the conversion from crotonyl-CoA to crotyl diphosphate, but when added crotonol at the midterm of exponential phase, the production of endocellular crotyl diphosphate was increased to 0.60 mg/mL. 2-Hydroxyisobutyric acid (2-HIBA), the precursor of methyl methacrylate, is a crucial building block for further use in manufacturing. Production of 2-HIBA from poly-3-hydroxybutyrate (PHB) overflow metabolism of bacteria needs proper (R)-3-hydroxybutyryl coenzyme A (CoA)–specific coenzyme B12-dependent mutases (RCM). Rohde et al. designed two RCM mutants of M. extorquens AM1 using a predicted mutase from Bacillus massiliosenegalensis JC6 and the closely related thermophilic enzyme from Kyrpidia tusciae DSM 2912 (Rohde et al., 2017). The growth of the engineered strain using the genes of RCM from K. tusciae DSM 2912 was suppressed seriously, while the biomass yield of the strain with genes from B. massiliosenegalensis JC6 was similar to the wild type and successfully achieved the titer of 2-HIBA up to 2.1 g/L in fed-batch bioreactor experiments. 3-Hydroxypropionic acid (3-HP), as a precursor for various chemicals such as 1,3-propanediol and acrylic acid, plays a significant role in industry (Chen and Nielsen, 2013; Kumar et al., 2013). Yang et al. constructed a malonyl-CoA pathway to produce 3-HP from methanol in M. extorquens AM1 by heterologous overexpression of the mcr gene from Chloroflexus aurantiacus, which encoded a bifunctional enzyme with alcohol dehydrogenase and aldehyde dehydrogenase activities, achieving 6.8 mg/L of 3-HP in shake-flask culture (Yang et al., 2017b). Further optimizing the promoter and enhancing the copy number of mcr gene improved titer to 69.8 mg/L. Notably, 3-HP concentration was rapidly reduced when the strains grew to stationary phase. Metabolomics analysis, enzymatic assay in vitro, and b-alanine pathway dependent 13 C-labeling showed that 3-HP was mostly converted to 3-HP-CoA at first and then transformed to acrylyl-CoA and propionyl-CoA during the growth transition from exponential phase to stationary phases. This phenomenon could be partially relieved by knocking out META1_4251 gene, which encoded a putative acrylyl-CoA reductase. This work presents a solid foundation for future engineering MeCFs to bioconversion of methanol into an important economical product, 3-HP. Mesaconic acid and (2S)-methylsuccinic acid, two important dicarboxylic acids, have widely used as solvent ingredients for cosmetics, coatings, and fire retardants (Sonntag et al., 2015b). The precursors of mesaconic acid and (2S)methylsuccinic acid are mesaconyl-CoA and (2S)-methylsuccinyl-CoA in the EMCP, respectively. To de novo synthesis of these two dicarboxylic acids from methanol, Sonntag et al. tested a series of thioesterases toward the EMCP CoA esters, among which the YciA from E. coli, TesB from E. coli and M. extorquens, Bch from Bacillus subtilis, and Acot4 from Mus musculus showed the activities in vitro. YciA had the highest activity (Sonntag et al., 2014). Introducing of yciA into

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M. extorquens AM1 resulted in the titers of 70 mg/L and 60 mg/L of mesaconic and methylsuccinic acid, respectively (Sonntag et al., 2014). In 2015 the same group further optimized the system by different methods (Sonntag et al., 2015b). First, they deleted the phaC encoding polyhydroxyalkanoate synthase to hinder the undesired flux from EMCP to PHB cycle, leading to fivefold increase of the production. However, the suppressors of the strain DphaC often lost the advantage probably because of redirected channeling of acetyl-CoA. Then, they enhanced the production of precursors to 16-fold by adjusting the cobalt concentrations to improve the activities of 2 cobalt-dependent mutases in the EMCP, which further led to 6-fold increase of the production of mesaconic acid and (2S)-methylsuccinic acid, representing a combined titer of 650 mg/L corresponding to the yield of 170 mg/g methanol. This study indicated the possibility of producing dicarboxylic acids from methanol in M. extorquens AM1. Crotonic acid is also an organic acid widely used in industry that can be converted from crotonyl-CoA through the EMCP. As mentioned in preceding part of the text, Schada von Borzyskowski et al. introduced a heterologous glyoxylate shunt into the mutant M. extorquens AM1 (Schada von Borzyskowski et al., 2018b), and then they engineered one of these strains by introducing a CoA-thioesterase into a ccr (encoding crotonyl-CoA carboxylase/reductase) knockout strain. The engineered strain produced crotonic acid in the culture medium, while the control strain only accumulated mesaconic acid and methylsuccinic acid.

6 Metabolic potential of native methylotrophs for synthesizing secondary metabolites Natural products are an important resource of new drugs, and further research of natural products can reveal new metabolic and biosynthetic pathways. However, methylotrophs possess relatively small genomes but have been found to have great metabolic potential. In 2006 Vorholt et al. found that M. extorquens AM1 could produce acyl-homoserine lactones (acyl-HSLs), which were considered to be important regulators in many gram-negative bacteria. One of these was a C14:2-HSL (1), which has not been described before; the others were C14:1-HSL (2), C6-HSL (3), and C8-HSL (4) (Fig. 6) (Penalver et al., 2006). In 2018 to further tap into the secondary metabolic potential of M. extorquens AM1, Piel et al. tried to screen the noncanonical enzyme homologs by an in silico–based mining strategy, leading to the discovery of a unusual minimalistic trans-AT PKS and 10 new polyketides, toblerols A–J (5–14) (Fig. 6) (Ueoka et al., 2018). Methylobacter tundripaludum 21/22, an obligately methanotrophic bacterium isolated from the sediment of Lake Washington, was sequenced and reported in 2015 (Kalyuzhnaya et al., 2015). Then, Puri et al. referred that M. tundripaludum 21/22 could produce N-3-hydroxydecanoyl-L-homoserine lactone (3-OH-C10-HSL) (15) and N-3-hydroxydodecanoyl-L-homoserine lactone (3-OH-C12-HSL) (16) (Fig. 6), where 3-OH-C10-HSL was the signal of quorum sensing system newly characterized in M. tundripaludum 21/22 (Puri et al., 2017). Subsequently, Clardy et al. found that this quorum sensing system could activate the colocated biosynthetic gene cluster discovered by antiSMASH and also regulated the production of a novel metabolite, tundrenone (17) (Fig. 6) (Puri et al., 2018). The structural diversity of metabolites in M. extorquens and M. tundripaludum suggested the metabolic potential of native methylotrophs and promise for more novel natural products and related metabolic pathways, thus providing an important foundation for engineering MeCFs.

7 Conclusions and perspectives Methylotrophic bacteria have very unique one-carbon metabolic pathways and related genes and enzymes. Their study not only expands the understanding of microbial metabolic pathways and affords the discovery of new functional genes but also provides important theoretical and practical value of biocatalytic conversion to high value-added products. At present the different heterologous pathways have been constructed in MeCFs, and methanol has been successfully converted into 1butanol, a-humulene, mevalonic acid, 2-hydroxyisobutyric acid, or 3-hydroxypropionic acid. Further constructions of MeCFs with more efficient biosynthetic pathways, higher industrial conditions tolerance, and improved phenotypes pertaining to bioproduction process efficiency are of significance for enhancing the production of biofuels and biochemicals. Recently a new set of genetic tools has been developed, including new brick vectors, the inducible promoters with wide expression range, CRISPRi system, and transposon sequencing method, making the methylotrophic bacteria easier to be manipulated and exploited for the biotechnological use. Significant progress was also made in improving the phenotypic and physiological characteristics of MeCFs by ARTP mutagenesis and adaptive laboratory evolution, leading to better understanding of the methylotrophic metabolism and improvement of industrial production. In addition, many potential metabolic capabilities and mechanisms in the evolved strains were uncovered at the same time. With the new understanding of metabolic pathways and further development of metabolic engineering tools, researchers will be able to design new

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FIG. 6 Structures of compounds 1–17.

metabolic pathways in native methylotrophs or introduce the methylotrophic pathway into other industrial bacteria, such as E. coli and yeast to achieve the synthetic methylotrophs. At present, synthetic methylotroph has not yet achieved the cell growth and product synthesis by using methanol as the only carbon source and energy source. The partial reason may be that the methylotrophic bacteria and traditional industrial bacteria are quite different in terms of the metabolic enzyme systems and transcriptional regulation. Moreover the heterologous expression of NAD-dependent methanol dehydrogenase in traditional bacteria has a catalytic characteristic of reversible reduction of formaldehyde, which competes for the C1 metabolic flow into the downstream assimilation pathway. Therefore the transformation of the chassis into methanol-utilizing bacteria still has some technical bottlenecks required to be broken. In the future the knowledge learned from engineering native methylotrophs might provide a potential strategy to construct an efficient synthetic methylotroph to improve methanol assimilation and chemical production in nonnative C1 host strain. Overall, current pioneering studies provide new strategies for the industrial high-value biotransformation of methanol using MeCFs. With the development of global shale gas extraction and biogas technology, methanol-based MeCFs can also be further developed for C1-based green biocatalytic transformation and contribute to the bioeconomy extensively.

Acknowledgments This work was supported by the National Key R&D Program of China (grant no. 2018YFA0901500) and the National Natural Science Foundation of China (grant no. 21776149, 41806167).

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Methylobacterium genome sequences: a reference blueprint to investigate microbial metabolism of C1 compounds from natural and industrial sources. PLoS One 4 (5), e5584. White, W.C., 2007. Butadiene production process overview. Chem. Biol. Interact. 166 (1), 10–14. Yang, J., Chen, W., Zhang, M., Yuan, X., Yang, Y., Zhu, L., Yang, S., 2017a. Advances in metabolic network pathways and metabolic engineering of methylotrophic bacteria. Chin. J. Biomed. Eng. 15 (6), 9–16. Yang, Y., Chen, W., Yang, J., Zhou, Y., Hu, B., Zhang, M., Zhu, L., Wang, G., Yang, S., 2017b. Production of 3-hydroxypropionic acid in engineered Methylobacterium extorquens AM1 and its reassimilation through a reductive route. Microb. Cell Fact. 16 (1), 179. Yang, J., Zhang, C., Yuan, X., Zhang, M., Mo, X., Tan, L., Zhu, L., Chen, W., Yao, M., Hu, B., Yang, S., 2018. Metabolic engineering of Methylobacterium extorquens AM1 for the production of butadiene precursor. Microb. Cell Fact. 17 (1), 194. Yu, H., Liao, J.C., 2018. A modified serine cycle in Escherichia coli coverts methanol and CO2 to two-carbon compounds. Nat. Commun. 9, 3992. Yu, F., Okamto, S., Nakasone, K., Adachi, K., Matsuda, S., Harada, H., Misawa, N., Utsumi, R., 2008. Molecular cloning and functional characterization of a-humulene synthase, a possible key enzyme of zerumbone biosynthesis in shampoo ginger (Zingiber zerumbet Smith). Planta 227 (6), 1291–1299. Zhang, F., Rodriguez, S., Keasling, J.D., 2011. Metabolic engineering of microbial pathways for advanced biofuels production. Curr. Opin. Biotechnol. 22 (6), 775–783. Zhang, M., Yuan, X., Zhang, C., Zhu, L., Mo, X., Chen, W., Yang, S., 2019. Bioconversion of methanol into value-added chemicals in native and synthetic methylotrophs. Curr. Issues Mol. Biol. 33, 225–236. Zhu, W., Cui, J., Cui, L., Liang, W., Yang, S., Zhang, C., Xing, X., 2016. Bioconversion of methanol to value-added mevalonate by engineered Methylobacterium extorquens AM1 containing an optimized mevalonate pathway. Appl. Microbiol. Biotechnol. 100 (5), 2171–2182. Zhu, T., Zhao, T., Bankefa, O.E., Li, Y., 2020. Engineering unnatural methylotrophic cell factories for methanol-based biomanufacturing: challenges and opportunities. Biotechnol. Adv. 39, 107467.

Chapter 14

Cyanobacteria-based microbial cell factories for production of industrial products Ragaa A. Hamoudaa,b,∗ and Noura El-Ahmady El-Naggarc a

Department of Biology, Faculty of Sciences and Arts Khulais, University of Jeddah, Jeddah, Kingdom of Saudi Arabia b Microbial biotechnology

department, Genetic Engineering and Biotechnology Research Institute, University of Sadat City, Sadat City, Egypt c Department of Bioprocess Development, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria, Egypt ∗

Corresponding author: E-mail: [email protected]

1 Introduction Cyanobacteria are a division descending from algae. Cyanobacteria are considered the only known oxygen-producing prokaryotes. All cyanobacteria are microscopic, unicellular or multicellular oxygenic photoautotrophs prokaryotes, some are terrestrial species and other species widely spread in the aquatic environment that mostly occur in fresh water and few are marine (Du et al., 2019). Cyanobacteria are photosynthetic prokaryotes that produce at least one of phycobilin pigments and chlorophyll and perform an electron donor through photosynthesis preceding to the development of oxygen. They constitute the biggest group of photosynthetic prokaryotes, as assessed by their prevalence, recurring abundance, and morphological variability (Whitton, 1992). Cyanobacteria are important nitrogen-fixing organisms in the earth that provide a vital source of nitrogen to aquatic and terrestrial environment (Issa et al., 2014). They are prevalent in most environments and areas like lakes, ponds, rivers, springs, wetlands, and streams; they have a key role in oxygen, carbon, and nitrogen metabolisms of many aquatic and terrestrial environments throughout the world (Vincent, 2009). Cyanobacteria consist of 150 genera that contain about 2000 species with a variety of shapes and sizes (Vincent, 2009). They are the first photosynthetic Gram-negative prokaryotes group that develops oxygen, are unique in the microbial world, and develop in different habitat (Lau et al., 2015). Cyanobacteria show the greatest diversity and morphology among all prokaryotes. At a certain time on earth, they would have been the dominant form of life. Cyanobacteria seem to be gelatinous and sometimes in filamentous clusters in colors of brown to black, yellow, blue-green, dark green, and rarely red when they are grown in aquatic and/or moist conditions. Cyanobacteria’s color is largely influenced by chlorophyll A concentrations and the accessory phycobiliprotein pigments such as phycoerythrin (red) and phycocyanin (blue). They can do anoxygenic photosynthesis and store food as cyanophycean starch and glycogen in combination with intracellular preservation substances such as lipids and protein containing polyphosphate bodies and cyanophycean granules (Kulasooriya, 2011). They are between the oldest microorganisms that inhabited the Earth with a record of 3.5 billions of year old fossils (Demoulin et al., 2019). Cyanobacteria morphology appear as unicellular, colonial shape, unbranched filaments, and filaments with false and true branching (Chorus and Bartram, 1999). Since these microorganisms do not exhibit sexual reproduction, their taxonomy was fully morphologically established (Fritsch, 1942; Desikachary, 1959). Cyanobacteria are classified according to morphological structure and subdivided into five orders including Stigonematales (its members with real branches), Nostocales (unbranched filaments with cell distinctions in vegetative cells, heterocysts, and akinete spores), Oscillatoriales (its members with undifferentiated and unbranched filaments), Chaemosiphonales (a small cluster of cyanobacteria that spreads cultivates attached and makes a kind of asexual exospores), and Chroococcales (unicellular and colonial members) (Kulasooriya, 2011). Cyanobacteria are recognized as one of the principal producers of biomass in the world (Paerla and Paul, 2012). In comparison with eukaryotes that use chloroplast organelles to perform photosynthesis, cyanobacteria have no internal Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00007-6 © 2021 Elsevier Inc. All rights reserved.

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membrane-bound organelles and carry out photosynthesis in distinctive folds on the exterior membrane. Cyanobacteria and red algae contain unique light-harvesting antennae of photosystem II called phycobilisomes that are absent in macro- and microgreen algae and higher plants (Dong et al., 2009). As a result of oxygenic photosynthesis on Earth, such prokaryotic algae convert carbon dioxide-rich environment to relatively oxygen-rich environment (Bekker et al., 2004). In the last few years, the variety and physiology of cyanobacteria have acquired a significant interest as they are an abundant source of bioactive and industrial products (Fig. 1) used for biotechnological applications (Lau et al., 2015). Cyanobacteria are recognized to produce many primary metabolites. Cyanobacteria are able to produce around 1100 secondary metabolites (Dittmann et al., 2015; Xiong et al., 2015), which involve several types of pharmaceutical content, toxins, biopesticides, growth factors (Al-Haj et al., 2016), and many types of novel compounds with biological activities ( Jaiswal et al., 2008). As cyanobacteria use solar energy to remediate the greenhouse gas carbon dioxide to precious metabolites, they are used in sustainable biotechnological processes (Lau et al., 2015). However, cyanobacteria have several advantages compared with other algae and higher plants. They are growing quickly with effective capture and conversion of solar energy (Dismukes et al., 2008). Moreover, they can be grown without the necessity of arable land or clean water sources (Nozzi et al., 2013) and can be used to remediate polluted water supplies where they degrade water contaminants such as hydrocarbons (Narro et al., 1992) and xenobiotics (Kuritz and Wolk, 1995). There are many applications of cyanobacteria in agriculture, fertilizers, energy, and pharmaceuticals. A variety of nutrients such as vitamin E, vitamin B, beta-carotene, selenium, manganese, zinc, iron, copper, and essential fatty acids such as g-linolenic acid are found in cyanobacteria (Bishop and Zubeck, 2012). Cyanobacteria produce FIG. 1 Schematic diagram of different products produced by cyanobacteria and the potential applications.

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numerous natural compounds including indoles, toxins, enzymes, amides, macrolides, lipopeptides, fatty acids, polyketides, amino acids, and alkaloids (Lau et al., 2015), which have a high-value commercial property and can prevent the growth of several fungi, yeasts, and G
and G+ bacterial strains (Burja et al., 2001). Polyhydroxyalkanoate, a class of thermoplastics including polyhydroxybutyrate, can be synthesized by cyanobacteria (Quintana et al., 2011). Numerous species of cyanobacteria can produce heterologous bioproducts including polyols, sugars, olefins, fatty alcohols, hydrocarbons, organic acids, fatty acids, and alcohols (Lai and Lan, 2015). In addition, novel polymers and carbohydrates (such as trehalose, glucosylglycerol, and sucrose) are produced under various osmotic stresses by cyanobacteria (Carrieri et al., 2010). A huge number of natural bioactive products such as toxins, bioplastics, coloring dyes, enzymes, and many drugs are produced by cyanobacteria (Mazard et al., 2016). Also, they produce water bloom phenomenon that is common in many regions of the world, which makes considerable threat to plants and animals and danger to the fitness and welfare of human beings, with definite socioeconomic impact. Meanwhile, these blooms are also abundant source of secondary metabolites with unique chemical and molecular compositions (Carmichael, 1992). Scientific researchers worldwide focused on the discovery of hemagglutinating, anticoagulating, antihelminthic, antimitotic, antibacterial, antiviral, antifungal, and toxic metabolites of cyanobacteria (Carmichael, 1992; Jaiswal et al., 2008). Shimizu (2003) reported that Lyngbya majuscula can produce many compounds involving nitrogen-containing complexes such as cyclic peptides, polyketides, and lipopeptides. Rodrı´guez-Meizoso et al. (2008) conveyed that Phormidium sp. has the capability to produce bioactive natural products that prevent the growth of several Gram-positive and Gram-negative bacterial strains, yeasts, and fungi. Cyanobacterial genera as Oscillatoria, Nostoc, Anabaena, and Microcystis are deemed as promising microalgae for the production of a wide range of bioactive natural products (Singh et al., 2017).

2 Bioremediation Bioremediation is an effective and ecofriendly tool for removal of polluted materials (Annamalai and Sundaram, 2020). Cyanobacteria showed great potential in the remedy of wastewater and effluents and in the bioremediation of both aquatic and terrestrial ecosystems (Chu, 2012). Cyanobacteria can produce extracellular polysaccharide substances (EPS). Such EPS may be bound to the cell surface as sheaths and/or capsules or released into the ecosystem (Dixit and Singh, 2014). EPS produced by cyanobacteria are capable of eliminating heavy metals from aqueous solutions (Dixit and Singh, 2014). Dixit and Singh (2014) reported that the EPS formed by cyanobacteria can remove heavy metals from the aqueous solutions. El-Nahhal et al. (2013) reported that cyanobacterial mats can be easily degraded acetochlor. Cyanobacterial mat can degrade low concentrations of the herbicide Diuron (Safi et al., 2014). The maximum growth of Nostoc muscorum ARM 221 and Aulosira fertilissima ARM 68 were obtained where grown in media containing pesticides and in absence of inorganic phosphate because these blue-green algae secreted alkaline phosphatase that helps in break pesticides to take phosphate as a sole source (Ibrahim et al., 2014). Among the three isolates of filamentous cyanobacteria, Nostoc muscorum was tolerated different concentrations of malathion and possessed the highest efficiency in the degradation of the organophosphorus pesticide malathion (Ibrahim et al., 2014). Cyanobacterial strains of Oscillatoria agardhii (nonheterocystis) and Anabaena sphaerica (heterocystis) had the highest efficiency to degrade petroleum hydrocarbons (Gamila et al., 2003). Also, El-Sheekh and Hamouda (2014) reported that Nostoc punctiforme and Spirulina platensis have been able, under heterotrophic conditions, to grow and degrade various crude oil concentrations. Microcystis aeruginosa, Anabaena cylindrica, Aphanizomenon flos-aquae, and Anabaena spiroides are effective in degrading the highest concentrations of fluometuron (El-Bestawy et al., 2007). Synechococcus sp., Cyanothece, Nostoc sp., Oscillatoria sp., and Nodularia sp. are able to degrade industrial effluent contaminants (Dubey et al., 2011). Marine blue-green alga Phormidium valderianum BDU 30501 completely removed and degraded phenol (Safonova et al., 2004). Oscillatoria rubescens, Nostoc linckia, and L. lagerlerimi are effective in the elimination and discoloration of azo dyes such as basic fuchsin, basic cationic, G-Red (FN-3G), orange II, and methyl red (Barathi and Indra, 2015). The consortium of cyanobacteria Phormidium sp., Oscillatoria sp., and green alga Chlorella sp. is able to remove hexavalent chromium (Shukla et al., 2012). Also, An. flos-aquae was able to remediate chromium (Kannan et al., 2012). Cyanothece strain 16S is a very favorable biosorbent for elimination of Cu and Cr from aqueous solutions (Singh et al., 2011). Also, Anjana et al. (2007) declared that immobilized biomass of N. calcicola HH-12 and Chroococcus sp. have effectively biosorption Cr(VI). A nontoxic freshwater cyanobacterium, Gloeothece magna, has capable of adsorbing cadmium and manganese when tested in both fresh and dry biomass (Mohamed, 2001). Anabaena variabilis, Microcystis aeruginosa, and Tolypothrix tenuis have shown highly successful removal of Cd2 from aqueous solutions (De Philippis and Micheletti, 2009). Among 17 cyanobacterial strains obtained in Thailand, T. tenuis was showed high level of Cd tolerance and highest elimination (Inthorn et al., 1996). The heat-dried biomass of Ph. laminosum is capable of removing heavy metals (Ni2+, Fe2+, Cu2+, Zn2+, Pb2+, Cd2+, and Cr3+) (Mohanty and

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Patra, 2011). Cyanospira capsulata and Nostoc PCC7936 eliminated Ni2+ and Zn2+ as single metal and when Cu added to the system (De Philippis et al., 2007). El Bestawy (2019) noticed that A. variabilis and Tolypothrix ceytonica had high efficiency for heavy metal remediation from contaminated industrial effluents (Fe2+, Zn2+, Pb2+, and Cu2+).

3

Biodiesel

With global growth, algae-based biofuel from aquatic microbial oxygenic phototrophs is produced, replacing fossil fuels (Dismukes et al., 2008). Many researches have illustrated the possible use of microalgae as feedstock for renewable biofuel and bioproducts, remarking profits such as prominent growth rate, prominent oil content, and little request of arable land (Mata et al., 2010). The unicellular microalgal species had received more attention due to their prominent lipid productivity and their capability to grow in wastewater (Wu et al., 2014). The harvesting of unicellular microalgae requires an extensive amount of capital and energy because low densities and separation from culture are more difficult especially in large scale (Rawat et al., 2013). Filamentous cyanobacteria can easily be collected and harvested over micro green algae due to its aggregates and hence filtration or flotation (Ho et al., 2015). Owing to their elevated reproduction rate, their elevated photosynthesis capacity, their high capacity for lipid biosynthesis, and their low nutritional requirements, cyanobacteria are recommended for the production of biofuels and biodiesel (Xin et al., 2010; Sharma et al., 2011; Sarsekeyeva et al., 2015; Quintana et al., 2011). Lipids are valuable for hydrocarbonbased biofuel production. Cyanobacteria can be sourced as cell factories for lipid manufacture in large scale that used for biofuel production (Wang et al., 2016). A numerous experiments have been conducted to enhance the production of lipids and secretion in cyanobacteria, such as optimization of the synthesis of lipids, lipid extraction methods, various solvents, and the use of adapted strains with better production and free excretion of fatty acids. Cyanobacterial growth, biomass productivity, and lipid composition can be influenced by nutrient availability, light intense, temperature, and pH (Table 1) (George et al., 2014; Da Ro´s et al., 2012; Ahmad et al., 2011; Mata et al., 2010; Mandal and Mallick, 2009). Nitrogen and phosphorus concentrations of the medium are the significant factors affecting the lipid accumulation and growth of cyanobacteria (Xin et al., 2010; Markou et al., 2014). Many studies found that nitrogen and phosphorus deficiency proliferates lipid accumulation in microalgae (Mayers et al., 2014; Markou and Nerantzis, 2013; Xin et al., 2010; Da Ro´s et al., 2012). Cordeiro et al. (2017) reported that the lipids extracted from M. panniformis and M. novacekii have been contrariwise correlated with nitrogen and phosphorus concentrations. Hence, nutrient-poor culture medium promotes deposit of the lipids, which directly influence cultivation costs through a reduction in nutrient consumption. Synechococcus sp. and Lyngbya sp. were investigated for their growth and biodiesel manufacture in several media such as ASNIII, BG-11, and seawater enrichment medium (Selvan et al., 2013). The crude lipids extracted from Phormidium sp., Cyanobacterium aponinum, and Synechococcus sp., which grow in BG-11 medium under starvation of nitrogen, may be a promising feedstock for the biodiesel manufacture (Karatay and D€ onmez, 2011). The highest level of lipid content produced from Belle Mare Leptolyngbya sp. was 19.09  0.26%, and the yield of the theoretical biodiesel was 170.57  1.74 mL (Beetul et al., 2014). Synechococcus sp. grown in the medium of BG-11 including 0.25 g/L NaNO3 with pH 7 showed the highest production of saturated lipids used for biodiesel manufacture (Karatay and D€ onmez, 2011). The lipid content and quality were increased when Anabaena, Nostoc sp. MIC-BG2, Phormidium sp. MIC-BG1, and Cyanosarcina sp. MIC-BG4 were grown under mixotrophic conditions by adding 2% glucose to BG-11 medium (Ratha et al., 2013). There are many methods used for biomass separation including centrifugation, flocculation, ultrasonic separation, membrane filtration, and foam fractionation ( Johnson et al., 2016). Research investigated seven physical methods for extraction of the lipid content from Synechocystis PCC 6803 including pulsed electric fields (PEF), ultrasound, microwave, freeze drying, French press, autoclaving, and the bead beating (Sheng et al., 2012). FFA-secretion yields significantly increased in genetically engineered Synechocystis sp. PCC 6803 (SD100) in comparison with the wild type (Liu et al., 2011).

4

Biohydrogen

Hydrogen gas is an important of the probable future energy supplies as an alternative to the limited fossil fuel sources. There are several advantages of hydrogen gas as fuel, such as being ecofriendly, powerful, sustainable, and no CO2, and only small amounts of nitrogen oxides are engendered during development and use (Lindblad, 1999). Cyanobacteria have the ability to supply hydrogen (Dutta et al., 2005). About 14 genera of cyanobacteria produce hydrogen under different conditions of cultivation (Reddy et al., 1993). Nonheterocystous, heterocystous, and unicellular forms of cyanobacteria evolve hydrogen (Tiwari and Pandey, 2012; Mitsui et al., 1986). Cyanobacteria generate hydrogen gas because it contains two sets of

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TABLE 1 Optimum conditions for biomass and lipid production by cyanobacteria to possible transesterification to biodiesel. Strains

Conditions

References

Synechococcus sp.

BG-11 medium including 0.25 g/L NaNO3 with pH 7

Karatay and D€ onmez (2011)

Anabaena, Nostoc sp. MIC-BG2, Phormidium sp. MIC-BG11, and Cyanosarcina sp. MIC-BG4

Mixotrophic conditions by adding 2% glucose to BG-11 medium

Ratha et al. (2013)

Synechocystis sp. PCC 6803

Heterophototrophic conditions using 0.4% glucose, intensity of light 200 mmol photons m
2 s
1, and nitrogen elimination from BG-11 medium

Monshupanee and Incharoensakdi (2014)

Synechocystis aquatilis TISTR8612

BG-11 medium with 10% of CO2 concentration

Kaiwan-Arporn et al. (2012)

Anabaena variabilis and Mastigocladus laminosus

BG-11 medium with 2% CO2 concentration

Kotelev et al. (2013)

Synechococcus sp. PCC 7942

BG-11 medium using intensity of light 150 mmol m
2 s
1, 1.2 g/L Na2CO3 at different carbonate concentrations

Silva et al. (2014)

Synechocystis PCC 6803

BG-11 medium with 0.01 mM calliterpenone at pH 7.5 after 10 day

Patel et al. (2014)

Phormidium autumnale

BG-11 medium supplemented with sucrose using C/N ratio of 40

Siqueira et al. (2016)

Anabaena sp.

BG-11 medium supplemented with NH4Cl and NaNO3

Johnson et al. (2017)

Synechocystis sp.

BG-11 medium supplemented with0.6 g/L coir pith 4 mL/L and tannery effluent

Jawaharraj et al. (2016)

Microcystis aeruginosa

ASM-1 medium at temp 25°C and 109 mmol m
2 s
1 light intensity

Da Ro´s et al. (2012)

Arthrospira platensis rsemsu 1/02-P and rsemsu 1/02-T

Zarrouk medium with 2% CO2 and suboptimal temperatures; nitrogen and phosphorus deficiency

Chernova and Kiseleva (2017)

Anabaena sp. PCC 7120

BG-11 medium including NaHCO3 (0.5 g/L) as a carbon source

Johnson et al. (2016)

enzymes; those enzymes are nitrogenase and hydrogenases (Dutta et al., 2005). Nitrogenase(s) catalyzed production of hydrogen (H2) at the same time as nitrogen was reduced to ammonia. Hydrogenase catalyzes the use of hydrogen produced by the nitrogenase (Appel and Schulz, 1998; Bergman et al., 1997). Approximately half of cyanobacteria have the genes needed for metabolizing hydrogen gas (Tamagnini et al., 2006; Vignais and Billoud, 2007; Vignais et al., 2001; Borodin et al., 2000). Cyanobacteria can produce hydrogen either as a nitrogen fixation byproduct by nitrogenase or by reversibly evolving hydrogen by NADPH-dependent NiFe hydrogenase under dark anaerobic conditions (Hwang et al., 2014). Arthrospira maxima filaments have the reversible NiFe hydrogenase encoded by hox HYEFU, enabling this organism to evolve hydrogen gas (Zhang et al., 2005). Anabaena maxima filaments are a suitable candidate for large-scale hydrogen production as a cell factory (Ananyev et al., 2008). Some environmental conditions like gaseous atmosphere, nutrient availability, salinity, light, and temperature affect hydrogen yield (Dutta et al., 2005). Spirulana platensis produces hydrogen in the dark or light under anaerobic conditions, while Synechococcus PCC7942 produces hydrogen in the dark. Nostoc muscorum produces hydrogen in the light than in the dark (Aoyama et al., 1997; Asada and Miyake, 1999; Shah et al., 2003). Temperature affects the production of hydrogen by cyanobacteria, with the maximum hydrogen production by Nostoc muscorum SPU004 and Anabaena variabilis SPU 003 at 40 and 30°C, respectively (Madamwar et al., 2000; Serebryakova et al., 2000; Moezelaar and Stal, 1994). Some heavy metals have enhancement hydrogen production by cyanobacteria because its involvement in the nitrogenase enzyme such

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as ferric ions per liter (5 mg) is more enhancements the hydrogen production by Anabaena cylindrical than 0.5 mg/L (Moezelaar et al., 1996; Jeffries et al., 1978). Inorganic nitrogen sources inversely affect the production of hydrogen by cyanobacteria. Ammonia, nitrate, and nitrite have been reported to hinder nitrogenase in Anabaena cylindrical and Anabaena variabilis SPU003 (Lambert et al., 1979; Madamwar et al., 2000). Also, sulfur affects the hydrogen production by cyanobacteria; meanwhile sulfur deprivation promotes the hydrogen yielding rate in many species of cyanobacteria (Antal and Lindblad, 2005). The production of hydrogen, ethanol, and low molecular organic acids under dark anaerobic conditions has been recorded by S. platensis, and nitrate (NO3) exhaustion enhances hydrogen yielding (Aoyama et al., 1997). The hydrogen production by Gloeocapsa alpicola is increased under sulfur starvation (Antal and Lindblad, 2005). Anabaena cylindrica fabricates hydrogen and oxygen gases simultaneously in light-reduced conditions and argon atmosphere for about 1 month, while N. punctiforme NHM5 generates higher hydrogen when grown under high light over a long period ( Jeffries et al., 1978; Lindberg et al., 2004). A number of plans for enhancing current cyanobacterial strains are available for biotechnological hydrogen yielding (Roeselers et al., 2008). Masukawa et al. (2012) reported that the H2 yielding by the cyanobacterium mutant after 1 week under N2 was 87% compared with that reported from the reference strains under Ar.

5

Bioplastic

Accumulates of nondegradable plastic wastes are growing in last 2 decades; bioplastics have achieved significant attraction because of their potential to replace nondegradable plastic (Lau et al., 2015). The most common type of polyhydroxyalkanoate is probably the poly-3-hydroxybutyrate (P3HB) form of PHB. Polyhydroxyalkanoates (PHAs) are distinguished into short-chain length (SCL-PHAs), mediu- chain length (MCL-PHAs), or long-chain length (LCL-PHAs), each composed of monomers containing of 3–5, 6–14, and >14 carbon atoms, respectively (Fig. 2). Polyhydroxybutyrates (PHBs) are polyesters that have been synthesized as intracellular carbon and energy resources; PHBs are polymer materials and are grouped according to their plastic-like chemical and physical characteristics. They are biodegradable, ecofriendly, and biocompatible and can be used as alternative to petroleum-based plastics to reduce environmental problems (Reis et al., 2003). PHBs can be produced by some microorganisms such as bacteria during fermentative processes, which require a large amount of sugar and continuous oxygen that are considered to be very expensive than nondegradable plastics (Panda et al., 2006). Cyanobacteria capable of collecting glycogen during photosynthesis as a main intracellular carbon and energy storage, glycogen can be transformed to PHBs (De Porcellinis et al., 2017; Osanai et al., 2015). Glycogen can be yield by cyanobacteria, which is a major advantage in comparison with other higher plants or green algae (Nozzi et al., 2013). The glycogen produced by cyanobacteria is more economical than eukaryotic algae and higher plants due to the cell wall of higher plants and algae contain of hard cellulosic that require further treatments to extract glycogen (Bohutskyi and Bouwer, 2013; Mendez et al., 2015). Meanwhile the yielding of PHB is low in comparison with chemotrophic bacteria; most cyanobacteria naturally yield less than 20% of PHB of their cell dry biomass, while chemotrophic bacteria produce 80% of their cell dry biomass (Drosg et al., 2015). One of the main problems facing economic PHB yielding by cyanobacteria that the low level of intracellular PHB storage (Singh et al., 2017). Furthermore, the growth rate of cyanobacteria is too low, so there have been many challenges for further progress of the intracellular PHB yield (Lau et al., 2014; Khetkorn et al., 2016; Carpine et al., 2017). Recent research shows that intracellular PHB and glycogen content in cyanobacteria can be enhanced through optimization of environmental and cultivation conditions including nutrient concentration (N and P), inorganic carbon obtainability, pH, temperature, and light intensity or photoperiod (Khajepour et al., 2015). Synechocystis generates PHB only under nutrient reducing such as nitrogen starvation conditions (Ansari and Fatma, 2016). Nostoc species, S. platensis, Calothrix scytonemicola, and O. okeni TISTR 8549 accumulate PHBs (Sharma and Mallick, 2005; Taepucharoen et al., 2017; Kaewbai-Ngam et al., 2016; Panda et al., 2005). Dutt and Srivastava (2018) observed that nitrogen-starved photosynthetically grown Synechocystis PCC 6803 produced up to 87% of PHB from

FIG. 2 Chemical structure of polyhydroxyalkanoate copolymer produced by microorganisms and used to synthesis bioplastic (Rai et al., 2011).

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intracellular carbon storage rather than from newly fixed CO2. Kamravamanesh et al. (2017, 2018) exhibited that the cyanobacterium Synechocystis sp. PCC 6714 has been achieved by various mutagenesis and can yield up to 37% dry cell weight of PHB with CO2 as the only carbon supplier. Genetic engineering is widely used to enhance PHB production by cyanobacteria compared with wild types (Hondo et al., 2015). Unicellular cyanobacteria can produce succinate and lactate used in bioplastic production (Katayama et al., 2018). The production of succinate and lactate is dependent upon the conditions of growth and incubation (L€ utgens and Gottschalk, 1980). The wild-type cells of Synechocystis 6803 can produce succinate in dark anaerobic conditions; meanwhile, Synechococcus 7002 can produce succinate when grown under nitrogen-replete conditions (Osanai et al., 2015; McNeely et al., 2014). Potassium increases the degradation of glycogen and intracellular succinate and lactate levels, with anaerobic conditions in the dark conditions; also addition of sodium bicarbonate increases succinate with Synechocystis 6803 (Ueda et al., 2016; Hasunuma et al., 2016).

6 Microbial fuel cell Microbial fuel cell (MFC) (Fig. 3) contains an anode and a cathode divided by a cation exchange membrane. Microorganisms oxidize organic compounds in the anode chamber, producing electrons and protons (reaction of anodes): nCH2 O + nH2 O ! nCO2 + 4ne
+ 4nH + The electrons are moved to the cathode across the exterior circuit, while the protons are moved to the cathode across the membrane, oxidizing environment current in the cathode chamber (supplied with oxygen). Electrons and protons are finally spent in the cathode chamber, commonly reducing oxygen to water. Oxygen is commonly operated as the electron acceptor for the cathode reaction in microbial fuel cells (MFCs) (Anton et al., 2014): nO2 + 4ne
+ 4nH + ! 2nH2 O or nO2 + 2ne
+ 2nH + ! nH2 O2 Cyanobacteria have been used as sources of oxygen in the cathode chamber (Anton et al., 2014; He and Angenent, 2006). The benefits of algal cathodes include excluding the requirement of mechanical cathode air supply, which means lower operating costs and lower overall CO2 emissions from the anodic bacterial breathing and also harvesting algal biomass that is directly used as fuel feedstock in the anode (Saba et al., 2017). Algal powders have been used previously as feedstock for MFCs (Velasquez-Orta et al., 2009; Rashid et al., 2013) or pretreated microalgae (Kondaveeti et al., 2014) and macroalgae (Gadhamshetty et al., 2013). Certain studies display to use algae in MFCs rely on the hydrogen molecule production, which is then oxidized at the anode for electron transfer to the MFC circuit. Others have used algae as organic feedstocks to provide the electrons from the oxidation of organic matter for MFCs to the electrochemically active bacteria (Strik et al., 2008). Cyanobacterial isolates such as Anabaena and Nostoc have been operated as biocatalysts in MFCs (Tanaka et al., 1985; Yagishita et al., 1997). The maximum power density generated by photosynthetic MFCs (PMFC) FIG. 3 Schematic diagram of two-chamber MFC (Anton et al., 2014).

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using Spirulina platensis as the biocatalyst reached 6.5 mW/m2 at high open circuit voltage without externally added feedstocks (Fu et al.,2010). The maximum power density generated by using Nostoc sp. ATCC 27893 in the anode of a PMFC reached 250 mA/m2 and 35 mW/m2 (Sekar et al., 2014).

7

Nanoparticle synthesis by cyanobacteria

Silver nanoparticles (AgNPs) have extraordinary antimicrobial properties because of their very large surface area that offers greater touch with microorganisms. While the silver ions and their nanoparticles are extremely toxic and harmful to microorganisms, AgNPs can be used for many other applications. They can be used for spectral absorption of solar energy as selective coatings and in electrical batteries as intercalation material, as optical receptors, in chemical reactions as catalysts, as biolabeling, and as antimicrobials (Al-Katib et al., 2015). Silver nanoparticles are the most attractive nanometal that is extensively used in medical fields, gene therapy, anticancer therapy, medical imaging, diseases diagnosis, drug delivery treatment, health care, and medical device coating (Moghimi et al., 2005). Nanoparticles can be synthesized by chemical, physical, and biological methods; chemical and physical methods are not ecofriendly, take time, and are expensive (Umer et al., 2012; Tan et al., 2007). Chemical and physical techniques are the most common ways for the synthesis of silver nanoparticles. The reducers can reduce the silver ions (Ag+) to form metallic silver (Ag0), which is then agglomerated to oligomeric clusters. Finally, such clusters cause metal colloidal silver particles to form (Raza et al., 2016). The traditional chemical and physical approaches used to create nanoparticles resulted in environmental toxic byproducts. Therefore, due to health concerns, these particles cannot be used in medicine, particularly in clinical areas (Patra and Baek, 2014). These are unreliable, complicated, and expensive methods. The synthesis of environmentally friendly nanoparticles has shown growing interest that during the production process, there are no toxic waste products (Chauhan et al., 2013). This can only be achieved through biologically biosynthesis methods for the production of nanomaterials as an alternative to traditional chemistry and physical methods because they are safe and ecofriendly (Shah et al., 2015). The biosynthesis of nanoparticles is superior than the chemical synthesis because of the low kinetics, which provides good control of crystal growth and reduced production cost (Vijayaraghavan and Nalini, 2010). Microorganisms have been used in a wide range in biosynthesis of AgNPs as fungi (Das et al., 2012), Gram-positive bacteria, Gram-negative bacteria, and algae (Hulkoti and Taranath, 2014). This leads for the emerging concept of green nanobiotechnology or green technology. Many researchers have seriously focused on prokaryotes like cyanobacteria that means of biosynthesis of metallic nanoparticles (Brayner et al., 2007). Because of high rates of growth and high production of the biomass, cyanobacteria and their secondary metabolites could serve as biological models for nanoparticle biosynthesis (Table 2) (Husain et al., 2015) examined AgNP biosynthesis from 30 different cyanobacterial strains. Cyanobacterial culture filtrate, capable of reducing Ag ions to AgNPs (Ahmad et al., 2003). In cyanobacteria, S. platensis and Nostoc linckia (Cepoi et al., 2015), Synechococcus sp. 145-6, Synechocystis sp. 48-3, Limnothrix sp. 37-2-1, Lyngbya sp. 15-2, Cylindrospermopsis sp. USC CRB3, Cylindrospermopsis sp. 121-1, Aphanizomenon sp. 127-1, and Anabaena sp. 66-2 (Patel et al., 2015) have been used in biosynthesis of AgNPs. The aqueous extract of the cyanobacterium A. doliolum efficiently reacts with AgNO3 to form AgNPs (Singh et al., 2014a, b). The biosynthesized silver nanoparticles displayed antimicrobial activity against bacteria and fungi (Ramkumar et al., 2017). Nanoparticles of silver can be effectively used as growth inhibitors for different bacteria as antimicrobial agents against human pathogens (Khati, 2017). The biosynthesized silver nanoparticles are potential anticancer agents that are widely used by many researchers in detecting their cytotoxicity against various types of cancer cells in vitro (Ramkumar et al., 2017). Cyanobacteria such as Plectonema boryanum have been reported for their ability to modify of Au3+ to Au0 and the subsequent creation of gold nanoparticles (GNPs) (Lengke et al., 2006). Biosynthesized AgNPs by Scytonema and Phormidium sp. have antibacterial activities against pathogen bacteria Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa (Al Rashed et al., 2018). Cyanobacterium S. platensis was observed to be very exciting for the synthesis of the nanoparticles of silver (Cicci et al., 2017). Nanoparticles of silver fabricated by Oscillatoria sp. A. doliolum has antibacterial activities against pathogens, E. coli, Klebsiella sp., Salmonella sp., and Pseudomonas sp. determined by disk diffusion method (Singh et al., 2014a, b, 2015). Extract of Scytonema geitleri HKAR-12 has the capability to reduce AgNO3 to Ag0 and could serve as a good applicant for NP biosynthesis (Pathak et al., 2019). N. carneum phycoerythrin was used for producing AgNPs that has efficient, in vitro, and in vivo cytotoxic activities, antihemolytic activities, and antibacterial activities (El-Naggar et al., 2017). Patel et al. (2015) reported that cyanobacteria and its components such as C-phycocyanin and exopolysaccharide have been used to fabricate nanoparticles and also revealed that the shape and size of nanoparticles differ related to the types of organisms that used to synthesis and also according to components.

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TABLE 2 Nanoparticle synthesis by cyanobacteria and its applications. Cyanobacteria

Nanoparticle types

Applications

References

Anabaena doliolum

Ag

Antibacterial and antitumor

Singh et al. (2014a, b)

Westiellopsis sp.

–

Lakshmi et al. (2015)

Oscillatoria willei

–

Mubarak et al. (2011)

Anabaena sp.

Au, Ag, Pd, and Pt

–

Lengke et al. (2007)

Nostoc sp. strain HKAR-2

Ag

Antitumor, antibacterial, and antifungal activities

Sonker et al. (2017)

Anabaena strain L31

ZnO

Conjugation with shinorine (UV-B absorbing compound)

Singh et al. (2014a, b)

Spirulina platensis

Ag

Inhibit growth of the human breast cancer

Rejeeth et al. (2014)

Anabaena cylindrical

Au0

–

R€ osken et al. (2016)

Gloeocapsa

Au0

Antitumor activity

Geetha et al. (2014)

Spirulina platensis

Ag, Au0

–

Govindaraju et al. (2008)

Oscillatoria limnetica

Ag

Antibacterial and anticancer

Hamouda et al. (2019)

Anabaena oryzae, Nostoc muscorum, and Calothrix marchica

Ag

Cytotoxic against EAC in vitro

Khalifa et al. (2016)

8 Exopolysaccharides producing cyanobacteria Many cyanobacteria are bounded by mucilaginous peripheral layers; these layers are polysaccharide in nature. There are three kinds of exopolysaccharide such as sheath, capsule, and soluble polysaccharide (Mouhim et al., 1993; Stewart, 1983). Exopolysaccharide plays defensive role and is essential for their survival in harassed habitats exposed to radiation, desiccation, and elevated temperatures (Kumar et al., 2018). Soluble polysaccharide or released polysaccharide (RPS) and capsular or slime polysaccharide (CPS) are composed of uronic acids, mannose, glucose, galactose, rhamnose, fucose, arabinose, and xylose (Moore and Tischer, 1964). The released polysaccharide (RPS) in numerous cyanobacteria contains protein and is also characterized by the existence of acetate and pyruvate groups. Capsular polysaccharide (CPS) of two cyanobacteria also contains protein, and also, two types of exopolysaccharides in cyanobacteria contain sulfate groups (Sutherland, 1994). The RPS and CPS contain 1–9 different monosaccharides that depend on the cyanobacterial strains (Nicolaus et al., 1999). Polysaccharides from biological sources can be commercially applied in many industries such as for usage as gums, food additives, food thickener, soil stabilizer, and in phytoremediation of contaminated waste (Kumar et al., 2018). Exopolysaccharides are anionic and sticky, which have attract positively charged ions, mostly metal ions, and have hydrophobic nature because of the existence of peptidic moieties, ester-linked acetyl groups, and deoxy sugars like rhamnose and fucose that have emulsifying properties that are essential in industry (De Philippis and Vincenzini, 2003; Shepherd et al., 1995). EPS of Anabaena spiroides were efficient in attaching the heavy metals Mn (II), Cu(II), Pb(II), and Hg(II), and Nostoc strains AfS49 and KaS35 caused clay aggregation and maintenance of soil structure (Freire-Nordi et al., 2005; Falchini et al., 1996). Chemical compositions of EPS fabricated by cyanobacteria are changed related to age and condition of culture such as temperature, intensity of light, and concentration of sulfur, phosphate, potassium, and other metal ions (Kumar et al., 2018). In some cyanobacteria as A. flos-aquae A37, the amounts

286

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of exopolysaccharide differ based on the source of nitrogen (De Philippis and Vincenzini, 1998). Production of EPS by A. cylindrica 10C was slightly adapted when supplied with different sources of nitrogen (Tischer and Davis, 1971; De Philippis and Vincenzini, 1998). Production of EPS by cyanobacteria has been enhanced by nitrogen and phosphate starvation (De Philippis et al., 1993; Otero and Vincenzini, 2003; Huang et al., 2007; Nicolaus et al., 1999; Roux, 1996; De Philippis et al., 1993). Some exclusion are reported as in case of Cyanothece capsulata, the lack of phosphate had no significant result also Anabaena spp. and Phormidium sp., it significantly reduced EPS yield (Nicolaus et al., 1999; De Philippis et al., 1991). Salt stress can be enhanced cyanobacteria to produce larger amounts of EPS with some exceptions in case of C. capsulata when increase in NaCl concentrations no effects in the production exopolysaccharides (Chen et al., 2003; De Philippis and Vincenzini, 1998). EPS yielding by Anabaena sp. PCC 7120 may be regulating by changes in calcium concentration (Singh et al., 2016). Nutrition may be effect on the EPS production; the elevated nutritional conditions can prompt M. aeruginosa to yield more EPS (Wang et al., 2020). Aeration seems important to increase the production of EPS by cyanobacteria because aeration offers for a superior stirring of viscous cultures that can elevate the luminous permeation and thus promote EPS biosynthesis (Moreno et al., 1998; Nicolaus et al., 1999; Su et al., 2007). EPS yielding by Anabaena sp. ATCC 33047 increased with increase in the temperature, while EPS production by Nostoc sp. PCC 7936 was not affected with increase in the temperature. Rise in the temperature causes EPS production decrease by Spirulina sp. (Moreno et al., 1998; Otero and Vincenzini, 2004; Nicolaus et al., 1999). Exopolysaccharide production by cyanobacteria has been affected by different variables like incubation time, carbon sources, temperature, and pH. Chroococcus sp. has the ability to synthesize high EPS amount in a growth medium containing 1% sucrose at pH 7.5 and 25°C for 9 days (Liu et al., 2017). EPS production is improved by continuous light and high light (Fischer et al., 1997). Certain wavelengths affect in EPS fabrication; the gamma irradiation has a stimulating effect on EPS production by Synechococcus mundulus; in the heterocystous N. commune, UV-B irradiation enhancing extracellular yielding of glycan and stimulating photoprotective pigment synthesis (Hussein et al., 2019). High and continuous irradiance amplified significantly EPS production in cyanobacteria as in S. tolypothrichoides (Pereira et al., 2009).

9

Pigments producing cyanobacteria as microbial fuel cell

Many cyanobacteria contain chlorophyll, carotenoids, and several accessory pigments, such as phycoerythrin and phycocyanin, on their membranes. Photosynthesis is accomplished on the thylakoid membranes, which are advanced inside the cells (Castenholz et al., 2001). Cyanobacteria develop by photosynthesis and essentially have chlorophyll and carotenoids, whose major functions are light collecting and photoprotection ( Jordan et al., 2001). The first study of carotenoid yield by cyanobacteria from about 40 species after that added several species (Goodwin, 1980).

10

Carotenoids

Yellow, orange, and red lipid-soluble pigments present in algae, plants, some bacteria, and fungi are carotenoids. Carotenoids are classified into two types, xanthophylls (contain oxygen) and carotenes (hydrocarbons that contain carbon and hydrogen atoms alone and do not contain any oxygen) (Havemen et al., 2010). They have essential roles in algae and plants such as absorbing sunlight energy for photosynthesis and protection of chlorophyll from photodamage (Roach and KriegerLiszkay, 2014). Carotenes are also used as natural coloring materials (Moorhead and Capelli, 1993). Carotenoids have vitamin A activity (beta-carotene is provitamin A converted inside the body into vitamin A), so they have important activity in metabolic functions in animals and human. They also enhance immune system and protect against diseases because they contain unsubstituted beta-ionone rings such as beta-carotene, alpha-carotene, and gamma-carotene (Guerin et al., 2003). Certain essential carotenoids such as zeaxanthin and beta-cryptoxanthin, along with minor recognized carotenoids such as myxoxanthophyll and echinenone, are found in algae and capable of carcinogenesis inhibition (Fazilati et al., 2016). The health benefits of carotenoids for both humans and animals are evidenced by protecting humans against severe illnesses associated with oxidative and inflammatory stress-related disorders including cardiovascular disease, skin degeneration and aging, eye diseases related to age-like cataracts, and cancer. Diets include a large number of carotenoid-rich foods that reduce their risk for different types of cancer (Fiedor and Burda, 2014; Gammone et al., 2015). The carotenoid’s health benefits are the result of free radical scavenging. Spirulina contains b-carotene as a main carotenoid with a dry weight of up to 2000 IU/g that is a potent antioxidant that has anticancerous and radioprotective effects (Fazilati et al., 2016). Different types of carotenoids yield by cyanobacteria/microalga consist of b-carotene, astaxanthin, lutein (with zeaxanthin), lycopene, canthaxanthin, and fucoxanthin (Saini et al., 2018).

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11 3-Phycobiliproteins Phycobiliproteins are the key photosynthetic pigments in cyanobacteria that are not contained in chloroplasts. They do not exist like carotenoids within the membrane. Rather, phycobiliproteins assembled to form clusters bind to the membrane known as phycobilisomes. These are brightly colored water-soluble proteins, containing open-chain tetrapyrroles that are covalently bound. Phycobiliproteins have numerous medicinal uses, including antioxidants, strengthens the immune system and probably reduces risk of heart diseases, prevents cancers, and protects against age-related diseases such as multiple sclerosis and cataract (Scheer and Zhao, 2008; Pandey et al., 2013). Phycobiliproteins are categorized into four major groups, according to color and absorption capacity: allophycocyanin (APC) bluish green, phycoerythrin (PE) red pigment, phycocyanin (PC) blue pigment, and phycoerythrocyanin (PEC) orange pigment (Lee et al., 2016). Phycocyanin and allophycocyanin are present in all cyanobacteria, but phycoerythrin is not present in all cyanobacteria, whereas in filamentous species normally, phycoerythrocyanin is contained (De Marsac, 1977). Depending on cyanobacteria habitats the ratio of these pigments can be changed. The photoproduction of phycobiliproteins is particularly attractive with filamentous cyanobacteria (Boussiba, 2002).

12 Phycocyanin In many cyanobacteria, phycocyanin is the dominant phycobiliprotein (Eriksen, 2008). Phycocyanin has a molecular weight of 70–110 kDa, with an average emission of fluorescence of about 650 nm and a single visible absorption maximum between 615 and 620 nm (Cai et al., 2001). Phycocyanin comprises two subunits, a and b, which are present in equal numbers. But the precise number of pairs of a and b that give the molecular form can differ between the species. Phycocyanin and allophycocyanin are two proteins that are present in all cyanobacteria universally ( Jeffrey, 1980). Phycocyanin is a light harvest phycobiliprotein complex (pigment-protein complex) (Ting et al., 2002). C-phycocyanin activates the immune system and has antiplatelet, hepatoprotective, and neuroprotective activities (Ferna´ndez-Rojas et al., 2014). Phycocyanin can defend against oxidative stress by delay or avoid peroxidation of lipids (Riss et al., 2007). Phycocyanin protects our body against age-related diseases like atherosclerosis, Alzheimer’s, cancer, diabetes mellitus, and rheumatoid arthritis by its antioxidant effect (De Morais et al., 2015).

13 Phycoerythrin Phycoerythrin is a red protein pigment that located in red algae and cryptophytes (Wehrmeyer, 1983). Phycoerythrin consists of (ab) monomers, which organized as usually in a disk-shaped hexamer (ab)6, which is the functional unit of the antenna rods, or trimer (ab)3. There is third type of subunit, the g chain of these typical complexes (Sun et al., 2009). C-phycoerythrin, R-phycoerythrin, and phycoerythrocyanin are found in cyanobacteria. The molecular weight of C-phycoerythrin is 225 kDa (Sidler, 1994). This pigment has a fluorescence emission maximum at 578 nm and a single visible absorption peak between 540 and 570 nm (Bryant, 1982). The presence of phycoerythrin in the phycobilisomes of certain cyanobacteria varies depending on the light conditions, in particular the strength of light (Glazer, 1984). Another pigment protein called phycoerythrocyanin exchanges phycoerythrin in a few cyanobacteria, although its synthesis seems to be regulated by light quantity and not by the strength of light (Federspiel and Grossman, 1990). In laboratory tests and in immunofluorescence microscopy, phycoerythrin can be used for labeling antibodies and other biological elements because of its rapid photobleaching characteristic (Murray et al., 1998).

14 Antiviral, antibacterial, antifungal, and anticancer compounds obtain by cyanobacteria Cyanobacteria have been increasingly screened for antibiotics and other pharmacologically active substances as a promising resource of novel therapies (Tables 3–7) (Browitzka, 1995). Biologically active antibacterial diterpenoids were identified among exometabolites of N. commune (Volk and Mundt, 2007). The sulfated polysaccharide spirulan extracted from S. platensis was shown to be a potent antiviral against human immunodeficiency virus type 1 (HIV-1) and herpes simplex virus type 1 HS (Blinkova et al., 2001). A spirulan-like molecule extracted from A. platensis has antiviral activities against these two viruses, with no cytotoxic effects (Rechter et al., 2006). El-Baz et al. (2013) reported that methanol extract of Spirulina maxima showed antiviral activity against HSV-2 with IC50 0.13 mg/mL and EC50 6.9 mg/mL. Spirulina is a good alternative to treating symptoms of allergic rhinitis (Mao et al., 2005). Mohy El Din et al. (2019) extracted S. platensis by

TABLE 3 Cyanobacteria as cell factory for antibacterial and antifungal agent production. Compounds

Algae

Activity

References

Abietane diterpenes

Microcoleus lacustris

Antibacterial

Gutierrez et al. (2008)

Comnostins A–E

Nostoc commune

Jaki et al. (2000)

C-phycocyanin

Westiellopsis sp.

Sabarinathan and Ganesan (2008)

Diterpenoid

Nostoc commune

Jaki et al. (1999)

Fatty acids (coriolic acid)

Oscillatoria redekei HUB 051

Mundt et al. (2003)

Heterocyclic compound

Oscillatoria agardhii

El-Aty et al. (2014)

Lyngbyazothrins A–D

Lyngbya sp. 36.91

Zainuddin et al. (2009)

Methanol extract

Spirulina platensis

Kaushik and Chauhan (2008)

Muscoride A

Nostoc muscorum

Nagatsu et al. (1995)

Nostocarboline

Nostoc 78-12A

Becher et al. (2005)

Calophycin

Calothrix fusca

Fontonamide and anhydrohapaloxindole A

Hapalosiphon fontinalis

Moore et al. (1987)

Hassallidin A, a glycosylated lipopeptide

Hassallia sp.

Neuhof et al. (2005)

Lobocyclamides A–C, lipopeptides

Lyngbya confervoides

Macmillan et al. (2002)

Macrolide scytophycin

Anabaena sp. HAN21/1, Anabaena cf. cylindrica PH133, Nostoc sp. HAN11/1, and Scytonema sp. HAN3/2

Shishido et al. (2015)

Majusculamide C

Lyngbya majuscula

Moore and Mynderse (1982)

Majusculamide D and deoxymajusculamide D

Lyngbya majuscula

Moore and Entzeroth (1988)

Majusculamides A and B

Lyngbya majuscula

Marner et al. (1977)

Tanikolide

Lyngbya majuscula

Singh et al. (1999)

Tolybyssidins A (1) and B (2)

Tolypothrix byssoidea (EAWAG 195)

Jaki et al. (1999)

Tolytoxin 1

Scytonema mirabile strain BY-8-1, Scytonema burmanicum strain DO-4-1, and Scytonema ocellatum strains DD-8-1, FF-65-1, and FF-66-3

Carmeli et al. (1990)

Toyocamycin

Plectonema radiosum

Stewart et al. (1988)

Tubercidin

Tolypothrix tenuis

Stewart et al. (1988)

Antifungal

Moon et al. (1992)

Continued

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TABLE 3 Cyanobacteria as cell factory for antibacterial and antifungal agent production—cont’d Compounds

Algae

Activity

References

4,4 -Dihydroxybiphenyl

Nostoc insulare 54.79

Antibacterial and antifungal

Volk and Furkert (2006)

Ethanolic extract

Nostoc calcicola

Agrawal (2016)

Hapalindoles

Hapalosiphon fontinalis

Moore et al. (2002)

Norharmane

Nodularia harveyana 44.85

Volk and Furkert (2006)

Pitipeptolides A and B

Lyngbya majuscula

Luesch et al. (2001)

Pitipeptolides C–F

Lyngbya majuscula

Montaser et al. (2011)

Schizotrin A

Schizothrix sp. (TAU strain IL-89-2)

Pergament and Carmeli (1994)

Volatile components consisted of heptadecane (39.70%) and tetradecane (34.61%)

Spirulina platensis

Ozdemir et al. (2004)

Scytophycins

Scytonema pseudohofmanni

0

Antifungal and cytotoxic

Ishibashi et al. (1986)

TABLE 4 Cyanobacteria as cell factory for anticancer agent production. Compounds

Algae

Activity

References

Almiramide D

Oscillatoria nigroviridis

Showing strong toxicity against A549, MDA-MB231, MCF-7, HeLa, and PC3 cell lines

Quintana et al. (2014)

Aurilides B and C

Lyngbya majuscula

Active against NCI-H460 human lung tumor and the neuro2a mouse neuroblastoma cell lines, leukemia, renal, and prostate cancer cell lines

Han et al. (2006)

Biselyngbyaside

Lyngbya sp.

Shows broad-spectrum cytotoxicity in a human tumor cell line panel

Teruya et al. (2009)

Borophycin

Nostoc linckia

Anticancer

Hemscheidt et al. (1994)

Cryptophycins

Nostoc cyanobionts

Potent anticancer agents

Magarvey et al. (2006)

Grassystatins A–C

Lyngbya confervoides

Potent cathepsin E inhibitors

Kwan et al. (2009)

Potential antimetastatic agents targeting invasive breast cancer

Al-Awadhi et al. (2017)

Potential anticancer agent exhibited moderate activity against esophageal and cervical cancer cell lines

DaviesColeman et al. (2003)

Grassystatins D–F Homodolastatin 16

Lyngbya majuscula

Continued

290

Microbial cell factories engineering for production of biomolecules

TABLE 4 Cyanobacteria as cell factory for anticancer agent production—cont’d Compounds

Algae

Activity

References

Largamides A–H

Oscillatoria sp.

Inhibited chymotrypsin

Plaza and Bewley (2006)

Largazole

Symploca sp.

Exhibits potent antiproliferative activity

Taori et al. (2008)

Malyngamide 3 and Cocosamides A and B

Lyngbya majuscula

Weak cytotoxicity against MCF7 breast cancer and HT-29 colon cancer cells

Gunasekera et al. (2011)

Malyngolide Dimer

Lyngbya majuscula

Toxicity against H-460 human lung cell lines and antimalarial activity

Gutierrez et al. (2010)

Obyanamide (1)

Lyngbya confervoides

Cytotoxic against KB cells

Williams et al. (2002)

Pitiprolamide

Lyngbya majuscula

Weak cytotoxic activity against HCT116 colon and MCF7 breast cancer cell lines, as well as weak antibacterial activities against Mycobacterium tuberculosis and Bacillus cereus

Montaser et al. (2010)

Raocyclamides A and B

Oscillatoria raoi

Exhibits a moderate cytotoxicity against sea urchin embryos

Admi et al. (1996)

Symplocamide A

Symploca sp.

Potent cytotoxicity H-460 lung cancer cells and neuro-2a neuroblastoma cells

Linington et al. (2007b)

Tasiamide F

Lyngbya sp.

Potent inhibitor of cathepsins D and E

Al-Awadhi et al. (2016)

Tenuecyclamides A–D

Nostoc spongiaeforme var. tenue

Cytotoxic (inhibit the division of sea urchin embryos)

Banker and Carmeli (1998)

Tolytoxin

Planktothrix paucivesiculata PCC 8926 and Scytonema sp. PCC10023

Against neurodegenerative diseases and cancer

Senol et al. (2019)

Ulongapeptin

Lyngbya sp.

Cytotoxic against KB cells

Williams et al. (2003)

different methods and solvents and reported that both solvents and methods are effective in the efficiency of bioactive compounds; this study concluded that acetone was the best solvent for extracting antibacterial and anticancer agents and antioxidant compounds followed by methanol; also, soaking methods were the best methods for extractions of bioactive compounds over other tested methods. Abd El Sadek et al. (2017) investigated that S. platensis among other tested cyanobacteria (A. oryzae and C. marchica) has the best anticancer activities against Ehrlich ascites carcinoma cell line in vitro. S. platensis and S. platensis with vitamin (B1) or thiamine could be consumed as natural bioactive compounds counteract the CCl4 adverse effects (El-Bialy et al., 2019). Heidari et al. (2012) tested 21 cyanobacterial species against 5 G+, 3 G
bacteria, and 2 fungi; they reported that methanol extracts displayed antimicrobial activity against E. coli, Bacillus subtilis, and B. pumilus and moderate activity against certain fungi. Malathi et al. (2015) investigated that different solvent extracts of Ca. braunii showed varying degrees of inhibitory activity against Aspergillus fumigatus and St. aureus (MTCC-1430).

15

Conclusion and future perspectives

Cyanobacteria can yield a wide range of natural products that can be used in many industries. Research in cyanobacteria still need further study. This future study must be focused on the development of biomass production of cyanobacteria by

Cyanobacteria-based microbial cell factories Chapter

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TABLE 5 Cyanobacteria as cell factory for antimalarial and antiparasite agent production. Compounds

Algae

Activity

References

Aerucyclamides A and B

Microcystis aeruginosa PCC 7806

Antimalarial

Portmann et al. (2008); Pen˜a et al. (2012)

Carmabins A and B

Lyngbya majuscula

Hooper et al. (1998)

Carmabin A (1), dragomabin (2), and dragonamide A (3)

Lyngbya majuscula

McPhail et al. (2007)

Venturamides A and B

Oscillatoria sp.

Linington et al. (2007a, 2007b)

Gallinamide A

Schizothrix sp.

Linington et al. (2008) and Conroy et al. (2011)

Lagunamides A and B

Lyngbya majuscula

Tripathi et al. (2010)

Bastimolide A

Okeania hirsuta

Shao et al. (2015)

C-phycocyanin

Nostoc muscorum

Pankaj et al. (2010)

C-phycocyanin

Spirulina platensis

Wulandari et al. (2018)

Calothrixins A and B

Calothrix sp.

Antimalarial and human cancer cells

Rickards et al. (1999)

Lagunamide C

Lyngbya majuscula

Antimalarial and potent cytotoxic activity against a panel of cancer cell lines, such as P388, A549, PC3, HCT8, and SK-OV3 cell lines

Tripathi et al. (2011)

Dragonamides C and D

Lyngbya polychroa

Active against Leishmania parasite

Gunasekera et al. (2008)

Dragonamide E

Lyngbya majuscula

Active against Leishmania parasite

Balunas et al. (2009)

TABLE 6 Cyanobacteria as cell factory for antivirus agent production. Compounds

Algae

Activity

References

Nostoflan and oseltamivir

Nostoc flagelliforme

Antiinfluenza A virus

Hayashi et al. (2008)

Cyanovirin-N

Nostoc ellipsosporum

Anti-HIV protein

Gustafson et al. (1997)

Nostoflan

Nostoc flagelliforme

Antiherpes simplex virus

Kanekiyo et al. (2007)

Ichthyopeptins A and B

Microcystis ichthyoblabe

Antiviral activity against influenza A virus

Zainuddin et al. (2007)

Scytovirin

Scytonema varium

Anti-HIV protein

Bokesch et al. (2003); Xiong et al. (2006)

Ethanol extract

Spirulina platensis

Against foot and mouth disease virus strains (A, O, SAT2)

Daoud and Soliman (2015)

292

Microbial cell factories engineering for production of biomolecules

TABLE 7 Cyanobacteria as cell factory for other medical agent production. Compounds

Algae

Activity

References

C-phycocyanin

Spirulina platensis

Wound healing

Sevimli-G€ ur et al. (2009)

Viridamides

Oscillatoria nigroviridis

Antitrypanosomal activity

Simmons et al. (2008)

Microviridins D–F

Oscillatoria agardhii (NIES204)

Serine protease inhibitors

Shin et al. (1996)

Cyanobacterin

Scytonema hofmanni

Algicidal

Lee and Gleason (1994)

Scytonemin

Scytonema sp.

Sunscreen

Rastogi et al. (2014, 2015)

affecting of different factors and also genetically modified cyanobacteria that stimulate the biomass. More economic methods will be needed for extractions of bioactive products from cyanobacteria.

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Chapter 15

Integrated omics perspective to understand the production of high-value added biomolecules (HVABs) in microalgal cell factories Chetan Paliwal, Mukul S. Kareya, Rabinder Singh, Asha A. Nesamma, and Pannaga P. Jutur∗ Omics of Algae Group and DBT-ICGEB Centre for Advanced Bioenergy Research, International Centre for Genetic Engineering and Biotechnology, New Delhi, India ∗

Corresponding author: E-mail: [email protected]

1 Introduction Modern civilization is facing environmental menace due to rising global CO2 levels and rampant fossil fuel consumption. These challenges need new strategies to shift toward a resilient and low-carbon economy. One of the plans is to use photosynthetic organisms such as microalgae having higher growth rate cultivated using solar radiation without the requirement of arable land to make the mass production sustainable and with reduced environmental cost (Lardon et al., 2009; Brennan and Owende, 2010; Markou and Nerantzis, 2013; Li et al., 2015). Nevertheless, the reported outdoor productivities have proven unreliable. They often are in a variation under the theoretical limit that may have a deep impact on microalgal growth due to a dynamic and drastic interactive phenomenon (Parlevliet and Moheimani, 2015; Grenier et al., 2019). However, more fundamentally, when grown outside, microalgae are permanently submitted to suboptimal photon flux density and temperature conditions. Microalgae are regarded as attractive green energy feedstocks with a plethora of diverse HVABs (Table 1) such as pigments, polysaccharides, and o-fatty acids (OMEGAs) that can be used in industrial and functional food and aqua feed applications (Nesamma et al., 2015; Paliwal et al., 2017). Several studies report that certain stress conditions like nutrient deficiency could enhance the lipid and secondary metabolites level in microalgae but at the cost of their growth (Hu et al., 2008; Ratha and Prasanna, 2012; Shaikh et al., 2019; Sharma et al., 2012). The improved economic viability of biorefineries also may be accomplished through means of sophisticated genetic engineering techniques, where microalgae can simultaneously generate such high-value metabolites such as carotenoids and biofuels under specific circumstances (Campenni et al., 2013; Paliwal et al., 2015). Nevertheless, there is a need to have a better understanding of their molecular metabolism to achieve overproduction of desirable metabolites (Bajhaiya et al., 2017). Metabolically, Calvin-Benson cycle fixing atmospheric CO2 through ATP, NADPH, and other vital enzymes is regarded as a rate-limiting step in the photosynthetic organisms (Stitt et al., 2010; Raines, 2011). Nevertheless, to face the challenge of improving photosynthetic efficiency, new methodologies are required to allow success. Systems biology is a comprehensive study of understanding biological systems like microalgae utilizing omics (genomics, transcriptomics, proteomics, metabolomics, and lipidomics) underpinning their metabolic capabilities and interactions over time (Clement et al., 2017). Microalgae cells could be referred to as an electronic circuit having repressor and promoters to enable the production/inhibition of some desired metabolite pathways through feed-forward and feedbackward mechanisms (Singh et al., 2016b). Understanding such complex structures is an overwhelming process, but integrated omics studies will contribute for functional gene identification leading to the reconstruction of optimal pathways regulating the cellular metabolism for the production of biofuel precursors and/or HVABs ( Jutur and Nesamma, 2015). Later the genetic/metabolic engineering would improve the strain by manipulation of specific metabolic hubs and enzymes crucial for enhancing the efficiency of the biological system.

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TABLE 1 Various microalgal strains with the potential of producing biofuels and HVABs. Microalgae

Biofuel

HVABs

References

Nannochloropsis gaditana

Biodiesel

Eicosapentaenoic acid (Singh et al., 2016a)

(Camacho-Rodrı´guez et al., 2014; Cancela et al., 2019)

Parachlorella kessleri

Biodiesel

–

(Shaikh et al., 2019)

Asteracys sp.

Biodiesel

Lutein, b-carotene, and atocopherol

(Agarwal et al., 2019)

Chlorella vulgaris

Biodiesel

Canthaxanthin and lutein

(Liang et al., 2009; Mendes et al., 1995)

Haematococcus pluvialis, Chlorella zofingiensis

Biodiesel

Astaxanthin

(MMR et al., 2016; Liu et al., 2014; Zhu et al., 2013)

Dunaliella salina

–

b-Carotene

(Moulton et al., 1987)

Schizochytrium sp.

Biodiesel

Docosahexaenoic acid (Raja et al., 2008)

(Zhu et al., 2013; Yaguchi et al., 1997)

Spirulina platensis

–

Phycocyanin

(Boussiba and Richmond, 1979)

Botryococcus braunii, Porphyridium cruentum

Hydrocarbon fuels

Exopolysaccharides (EPS)

(Banerjee et al., 2002; You and Barnett, 2004)

Phaeodactylum tricornutum

Biodiesel

EPA and DHA

(Hamilton et al., 2014; Branco-Vieira et al., 2018)

Chlamydomonas reinhardtii

Biodiesel

–

(Siaut et al., 2011)

Euglena gracilis

Biogas

a-Tocopherol and paramylon

(Grimm et al., 2015)

Scenedesmus obliquus

Biodiesel

–

(Mandal and Mallick, 2012)

Coelastrella sp.

Biodiesel

b-Carotene and lutein

(Hu et al., 2013; Mansur et al., 2017)

Chlorella sp., Chlamydomonas reinhardtii

Biohydrogen

–

€ (Song et al., 2011; Oncel and K€ ose, 2018)

As mentioned earlier, algae can synthesize diverse biomolecules, and characterization of metabolic pathways for these molecules is an essential step before engineering microalgal strains for industrial applications is possible (Le Chevanton et al., 2013; Biondi et al., 2018). System-level algal metabolism is very complicated, as the amount of genes calling for metabolic enzymes combined with nuanced cell metabolism does not determine metabolic efficiency, and hence the problem level increases in nonannotated organisms (Rizkallah et al., 2020). The phylogenomic analysis could help in identifying the missing links in the pathways for the biosynthesis of industrially relevant bioactive metabolites, and integrating this information with time-course omics analysis would help in identifying transcription hubs that switch on/off in certain conditions altering the metabolite production levels (Kapase et al., 2018; Paliwal et al., 2019). Nevertheless, there is still a need to redesign a genome-scale metabolic models (GEMs) for specified algal strains and biochemical dynamic routing to be analyzed, which is another complex dimension. The present chapter summarizes the different microalgae-based systems biology approaches done so far to improve the production of HVABs in microalgal cell factories. It covers all the omics research carried out on microalgae including phylogenomic, transcriptomic, proteomic, and metabolomic analysis to understand the metabolic processes happening inside the microalgal cells during different abiotic conditions and also discusses how to integrate the omics data to reconstruct the metabolic map for the enhanced HVAB production.

2

Unraveling biosynthetic pathways for the production of HVABs

2.1 Genomic and phylogenomic analysis Microalgae are a versatile feedstock for liquid fuels and HVABs; however, due to the diversification of their origin and evolution, the genomes of many microalgal species remain poorly understood (Wang et al., 2014). Advances in

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whole-genome science, combined with synthetic and systems biology, aid to overcome the technological and economic bottleneck with the help of astute engineering and designing of microalgal cells (Wang et al., 2011). The breakthrough in the full sequence of the three remaining nucleomorph chromosomes of the cryptomonad Guillardia theta enabled the advancement of genomics in algal biology (Qin et al., 2012). As a consequence, several genomes are now being sequenced and/or in progress across microalgae species. Moreover, gene mining and comparative genomics will help us to identify the precursors involved in the production of these HVABs. For example, a collection of nearly 261 functional genes involved lipid biosynthesis in plant models were identified by Sharma and Chauhan (Sharma and Chauhan, 2012). Similarly a total of 398 orthologous genes among various microalgae were computationally identified and characterized, which were a part of the polar and neutral lipid synthesis machinery (Misra et al., 2012). Another study in microalgae also identified approximately 221 putative orthologous genes responsible for OMEGA biosynthesis using Arabidopsis thaliana and Schizochytrium aggregatum as reference datasets (Kapase et al., 2018). The research outlines the phylogenomic evaluation of hypothetical genes and reveals that the domain and motif structure of the microalgal lineages are remarkably conserved. Contrary to the general concept of phylogeny, it has been observed that the phylogenetic signals begin to collapse exponentially with at the deepest divergences resulting in an inaccurate reconstruction of the ancient divergence (Sun et al., 2016). Modeling studies with the help of systems and computational biology coupled with the increasing number of sequenced microalgal genomes present a means to explore the unknown pathways inside the cell (Reijnders et al., 2014). Approximately the number of sequenced microalgal genomes that are publicly available in addition to various omics studies is currently estimated to be around 40–60 (Reijnders et al., 2014; Fu et al., 2019). The platform for the sustainable production of HVABs, including essential fatty acids and carotenoids, seems to be promising in both microalgae and diatoms (Fu et al., 2019; Lauersen et al., 2018). For instance, the diatom Phaeodactylum tricornutum was engineered to overproduce polyunsaturated docosahexaenoic acid (Raja et al., 2008), a high-value long-chain OMEGAs. Similarly in Ostreococcus tauri, a gene, namely, D5-elongase, was engineered in the diatom for heterologous expression of the gene to overexpress the fatty acid biosynthesis pathway (Hamilton et al., 2014). The overexpression of phytoene synthase (psy) and 1-deoxy-d-xylulose-5-phosphate synthase (dxs) genes in Ph. tricornutum accumulated significant levels of carotenoid, fucoxanthin (Eilers et al., 2016; Kadono et al., 2015). Carotenoids are indeed an essential component of photosynthetic processes, including lipid-soluble carotenes and xanthophylls (Barkia et al., 2019). In this regard, the psy gene heterologous expression isolated from Chlorella zofingiensis generated Chlamydomonas reinhardtii transformants with an increased yield (2.0-fold) of violaxanthin and lutein (Cordero et al., 2011). Similarly, the psy gene from Dunaliella salina has also been overexpressed constitutively in Chla. reinhardtii (Couso et al., 2011), leading to an increase of carotenoid content approximately between 125% and 260%. The RNA interference technique has been employed to repress the regulation of gene expression in many microalgal organisms. Phytoene desaturase was identified as a crucial enzyme in the carotenogenic process, but RNAi inhibition of this gene did not influence the carotenoid profile in Chla. reinhardtii (Sun et al., 2008; Vila et al., 2008). Gene coding for the enzyme phytoene desaturase has been metabolically engineered in the green microalga Haematococcus pluvialis using site-directed mutagenesis (Steinbrenner and Sandmann, 2006). The consequence of this genetic manipulation was because the pigment astaxanthin accumulation in the mutants increased in contrast with the wild type. There have been few studies based on the genetic manipulation in the unicellular microalga Chlo. zofingiensis genes such as b-carotenoid ketolase (bkt), b-carotenoid hydroxylase (chyb), lycopene b-cyclase (lcyb), and phytoene desaturase (pds) that are considered to be involved in key carotenogenic reactions (Guerin et al., 2003; Leon et al., 2007; Lorenz and Cysewski, 2000). The change in expression levels of the aforementioned transcripts in these mutants results in increase of lutein and astaxanthin contents. Additionally, the bkt gene engineering in Chla. reinhardtii led to the enhanced accumulation of ketocarotenoids. These ketocarotenoids have a key role in the fish aquaculture as feed supplements and are a rich source of nutraceuticals in human nutrition (Guerin et al., 2003; Leon et al., 2007; Lorenz and Cysewski, 2000). Several microalgae species, including Crypthecodinium cohnii and Schizochytrium limacinum were heterotrophically cultivated in industrial fermenters for the production of OMEGAs i.e. DHA, commonly used as nutraceutical supplements (Barkia et al., 2019; Bailey and Shahidi, 2005; Qu et al., 2013). Despite substantial gaps and bottlenecks related to the available genomic sequences and its understanding of the structure-function relationship of several metabolic pathways, efforts are being made with the help of strategies involving metabolic engineering and systems biology studies (Reijnders et al., 2014; Barkia et al., 2019; Sharma et al., 2018). There has been an ever-growing requirement and demand for these HVABs corresponding to the nutraceutical industry. Furthermore, combining the biofuel production with the concomitant production of these HVABs with the help of computation-driven studies would further enable sustainable production from microalgae (Reijnders et al., 2014; Sharma et al., 2018; Jagadevan et al., 2018).

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2.2 Transcriptomics and proteomics Chla. reinhardtii have been a model system to study the nutrient deprivations and their responses to its circadian clock, photosynthesis, and stress (Yamano and Fukuzawa, 2009). They revealed how carbon-concentrating mechanism (CCM) is regulated and concluded that CCM induction happens specifically at low CO2 and light intensity. Gene expression profiles provide essential information on the environment and help to identify key stress biomarkers and particular changes in the physiology of an organism (Osborn and Hook, 2013). Discovery of novel genes and their functions aids to the understanding of different stress responses, but it needs extra inputs like genomic sequences of unsequenced organisms. The transcriptomic analysis provides a better understanding of genomic level changes happening inside the cells under specific stress condition, and therefore we need to study multiple transcriptomes to analyze and capture more genes actively expressing via analyzing RNA transcripts and their levels. Another transcriptomic analysis examining the effect of nutrient starvation on Chla. reinhardtii deduced that nitrogen limitation diverts the carbon flux toward fatty acid synthesis and downregulated the photosynthesis and protein biosynthesis pathways (Miller et al., 2010). Although preliminary transcriptomic analyses provided fruitful insights on the regulation of critical molecular processes inside the algal cells, the information was strain specific and needed further data from more algal strains. Abiotic stresses enhance the HVAB production in the different microalgae, and several transcriptomic studies have been performed to understand how the metabolic pathways are regulated under these conditions (detailed in Table 2). To summarize, nutrient deprivation strategies mostly decrease the carotenoid levels in the cell but improve the carotenoid/chlorophyll ratio (Sirikhachornkit et al., 2018; Zhang et al., 2018). Low temperature (Shin et al., 2016), red/blue light (Li et al., 2020), high salinity (Li et al., 2019), and heterotrophic growth (Roth et al., 2019) enhance the carotenoid levels in the microalgae by increasing the transcript levels of photosynthesis, carotenoid metabolism, and heterotrophic carbon metabolism, respectively. Proteomics study the protein pool and their functions during the particular condition. The complexity of the proteome of a cell originates from the plethora of posttranslational modifications through which a specific protein may be subjected to by the cellular machinery, in response to the conditions encountered (Shah and Misra, 2011). Various stresses affect the microalgal cells in different ways, and the changes are reflected in their protein complement. Proteins such as enzymes are often referred to as real responses to the stress conditions regulating the metabolite levels along with serving as a transcriptional and translational regulator (Xu et al., 2014). However, the genotypic characteristics of an organism can be predicted through a study of its genome that remains almost the same throughout the stress, and the phenotypic aspects change due to changed protein expression. As the workhorse of the green microalgae group, Chla. reinhardtii is the model organism that has the maximum number of studies reported (Anand et al., 2017), both for organelle and whole-cell proteomics. Several attempts to gain the proteomic insights of different stress conditions in various microalgae have been made in the past decade (Table 3). The proteomic analysis for the HVAB production shows that nitrogen starvation is a poor strategy to enhance the carotenoid levels in the microalgal cell factories decreasing total carotenoid and chlorophyll levels by downregulating the Calvin-Benson cycle, chlorophyll biosynthesis, and other ribosomal proteins (Zhang et al., 2018). In contrast, flux is shifted toward fatty acid synthesis. Light is an essential factor that regulates the synthesis of HVABs, and a relatively higher concentration of astaxanthin, canthaxanthin, and eicosapentaenoic acid (Singh et al., 2016a) was observed in the violaxanthinrich strain of Nannochloropsis oceanica by 10 days grown at a light intensity of 500 mE, while violaxanthin, b-carotene, and chlorophyll levels go down. Proteomic analysis revealed that high light orchestrated the upregulation of essential pathways such as pyruvate and amino acid metabolism, fatty acid biosynthesis, glycolysis and/or phosphogluconate pathway, and light-harvesting complexes, while downregulating glycerophospholipid biosynthesis. The high light upregulated the levels of zeaxanthin epoxidase 2, an active protein in carotenoid biosynthesis, converting violaxanthin to zeaxanthin and antheraxanthin. Exopolysaccharides are another class of HVABs found in microalgae. High salinity conditions increase the production of glycerol and exopolysaccharides in D. salina, while cellular carbohydrate levels get reduced (Wei et al., 2017). The proteome studies show that PS-I complex, electron transport, and ATP synthesis got upregulated in high salinity conditions while carbon fixation and chlorophyll synthesis downregulated. Another HVAB, sulfolipids, are enhanced in Aureococcus anophagefferens CCMP 1984 due to the phosphorus limitation and proteome profile showcase the role of phosphate transporter, alkaline phosphatase, and protein metabolism in the process. The proteomics analysis for overproduction of HVABs in different microalgae is still at infancy and needs more attempts to achieve better understanding.

TABLE 2 Up-/downregulation of genes during different experimental conditions. Algae

Condition

HVABs

Upregulation/downregulation

References

Scenedesmus acutus TISTR8540

Nitrogen depletion

Up: carbohydrate, lipid Down: carotenoids and chlorophyll

Up: glycolysis and starch degradation Down: gluconeogenesis, photosynthesis, triacylglycerol (TAG) degradation, and starch synthesis

(Sirikhachornkit et al., 2018)

Tetraselmis sp. KCTC12432BP

Low temperature: 10°C

Up: lipid, eicosapentaenoic acid (Singh et al., 2016a) Down: carbohydrate

Up: photosynthetic electron transfer chain in the plastid thylakoid membrane Down: electron transfer chain in mitochondria, biosynthesis of phosphatidylethanolamine

(Shin et al., 2016)

Chlorella pyrenoidosa (FACHB-9)

Different CO2

n.d.

Up: carbohydrate metabolism, amino acid metabolism, and carbon metabolism Down: energy metabolism

(Fan et al., 2016)

Dunaliella salina HG-01

Red and blue light

Up: Carotenoid/chlorophyll (blue and red light) b-Carotene (blue and red light)

Up: carotenoid metabolism (red and blue light) photoprotective enzymes (blue light)

(Li et al., 2020)

Parachlorella kessleri NIES-2152

Sulfur deprivation

Up: lipid and carbohydrate

Up: fatty acid metabolism, autophagy, TAG, cysteine, and methanethiol synthesis Down: Calvin-Benson, tricarboxylic acid (Sake et al., 2019), glyoxylate, and C4 dicarboxylic acid

(Ota et al., 2016)

Phaeocystis antarctica

Iron supplementation

n.d.

Up: NADH-cytochrome b5 reductases, vacuolar iron transporter (VIT11 and VIT1) fragments and cytochrome b5, Down: ferric reduction oxidases, mitochondrial iron transporter (mitoferrin), and multicopper oxidases

(Rizkallah et al., 2020)

Dunaliella salina

High salinity

Up: antheraxanthin and zeaxanthin per chlorophyll Down: neoxanthin and violaxanthin per chlorophyll

Up: photosynthesis and glycerol metabolism (high salinity) Down: binding, protein phosphorylation, the protein modification process

(Li et al., 2019)

Chlorella vulgaris

High salinity

n.d.

Up: cytoplasmic calcium signaling pathway Down: photosystem I light-harvesting pathway

(Abdellaoui et al., 2019)

Chromochloris zofingiensis SAG 211-14

Glucose

Up: lutein, violaxanthin, zeaxanthin, and neoxanthin per chlorophyll along with lipid bodies Down: b-carotene

Up: ketocarotenoid biosynthesis and heterotrophic carbon metabolism Down: photosynthetic pathways

(Roth et al., 2019)

Thalassiosira rotula CCMP1647

Silica deprivation

n.d.

Up: cAMP-sialic acid transporter and peroxisomal trans2-enoyl-reductase, flavonoid metabolism, and isoprenoid pathways Down: ribosomal protein s6 kinase beta 2 (S6K2), PGHS-2 (a first enzyme of the prostaglandin pathway)

(Di Dato et al., 2019)

n.d.: not detected.

TABLE 3 Up-/downregulation of proteins during different experimental conditions. Algae

Condition

HVABs

Upregulation/downregulation

References

Scenedesmus acuminatus

Nitrogen deprivation

Up: Car/Chl Down: carotenoid levels by 73.5% Chlorophyll levels by 94% neoxanthin, lutein, and zeaxanthin

Up: central carbon metabolism, fatty acid synthesis and branched-chain amino acid metabolism Down: chlorophyll biosynthesis, the Calvin cycle, and ribosomal proteins

(Zhang et al., 2018)

Dunaliella salina

High salinity

Up: exopolysaccharides, glycerol Down: cellular carbohydrates

Up: light-harvesting, PS-I complex stability, electron transport, and ATP synthesis Down: carbon fixation and chlorophyll synthesis

(Wei et al., 2017)

Thalassiosira pseudonana

Low CO2

n.d.

Up: degradation and signaling Down: Calvin-Benson cycle enzymes, glycolysis, protein, and nitrogen metabolism

(Clement et al., 2017)

Nannochloropsis oceanica IMET1

High light

Up: astaxanthin, canthaxanthin, EPA Down: chlorophyll, bcarotene, violaxanthin

Up: pyruvate metabolism, fatty acid biosynthesis, amino acid metabolism, glycolysis and pentose phosphate pathway, light-harvesting complexes Down: glycerophospholipid

(Wang and Jia, 2020)

Pyropia haitanensis Z-61

High temperature

n.d.

Up: energy metabolism, redox homeostasis metabolism Down: synthesis of lignin, photosynthesis

(Xu et al., 2014)

Aureococcus anophagefferens CCMP 1984

Phosphorus starvation

Up: sulfolipids Down: phospholipids

Up: phosphate transporter and alkaline phosphatase Down: protein degradation

(Wurch et al., 2011)

Cyanidioschyzon merolae 10D

High temperature

n.d.

Up: amino acid synthesis, chlorophyll biosynthesis Down:

(Nikolova et al., 2017)

n.d.: not detected.

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2.3 Metabolomics Metabolites are cellular end products and eventual rejoinders, and a biological system produces to sustain adverse effects of abiotic/genetic stress (Fiehn, 2002; Ma et al., 2018). There are several analytical techniques to measure the metabolite levels of microalgae for a given condition. Still the complexity of the microalgal system makes it impossible to figure out all the metabolites with a single procedure (Liu et al., 2009). Therefore multiple analytical methods like NMR and LC/GC-MS need to be developed with permutation and combination to map the global metabolite map of microalgae for the synthesis of HVABs. The analysis could be targeted with selected metabolites along with quantification or untargeted covering all the detectable metabolites extracted from the biological system differing upon the scale of metabolite identification. Several metabolomic studies have been done to identify the critical regulators for the biosynthesis of different HVABs like carotenoids (Table 4). Mostly researchers study nutrient starvation strategies by depriving the microalgal growth media with the macronutrients like nitrogen / phosphorus to improve the production of carotenoids but they end up with significant damage to the photosynthetic machinery and no significant improvement in carotenoid levels rather an improved carotenoid/chlorophyll ratio (Lv et al., 2017; Shaikh et al., 2019). However, the carotenoid accumulation was observed in D. salina TG in sulfur starvation with higher levels of b-carotene and lutein, where PUFAs like 4,7,10,13,16,19docosahexaenoic acid (Raja et al., 2008) are declined (Lv et al., 2018). The metabolomic analysis also reveals that flux toward fatty acid synthesis is diverted toward sugar biosynthesis, and higher proline levels were observed. It was also found that glycerol levels in sulfur are lower as compared with nitrogen and phosphorus starvation experiments. High light has been proven to enhance the carotenoid production in Asteracys sp. and H. pluvialis by downregulating the sugar metabolism and upregulating the fatty acid synthesis (Lv et al., 2016; Agarwal et al., 2019). Based on the earlier revelations, it can be concluded that metabolomic responses are species specific, and even the algae from a similar environment could show entirely different results.

2.4 Metabolic flux analysis Biological systems have diverse metabolic networks that are extensively intricate. 13C labeling-based flux analysis provides an integrated approach that is essential to understand the dynamics in these metabolic networks. It helps to elucidate the fundamental distribution of molecular fluxes in vivo in the central metabolism and provides a quantitative understanding of the mechanism (Cui et al., 2018; Zamboni et al., 2009). Previous works suggest the effectiveness of analytical technology for understanding various metabolic networks inside the microalgal cell, including the process of lipid biosynthesis (Cui et al., 2018; Xiong et al., 2010). Earlier, NMR technology was required for the effective functioning of metabolic flux analysis (MFA), but perhaps the emergence of mass spectrometry (MS) techniques facilitated the use of labeling intracellular metabolites, thus evaluating the comprehensive analysis of advanced mathematical models (Sake et al., 2019; Klapa et al., 1999). Although 13C-MFA has been developed and continuously been improvised for the last 30 years, it was employed for the study of photoautotrophic metabolism recently (Sake et al., 2019). 13C-MFA has been utilized to understand the pattern profiling of lipids and DHA accumulation in Cr. cohnii. The study systematically demonstrates the mechanisms underlying the biosynthesis of DHA in response to a chemical modulator ethanolamine with the help of 13C-labeling patterns (Cui et al., 2018). Similarly, 13C-MFA was applied to understand the heterotrophic growth profiles in the presence of acetate for the elucidation of its assimilation in Chla. reinhardtii. The study shows that the initial steps of acetate assimilation are allocated in the compartments inside the cell, with the majority of the carbon utilized for maintenance of cell and remaining flux diverting toward lipid production. Not much of the carbon flux is diverted toward the isoprenoid pathway, that is, carotenoid synthesis (Boyle et al., 2017). Compared with other phototrophic organisms, the understanding of Chlorella, which is of high commercial value due to the production of carotenoids, grown on organic substrates remains elusive due to the lack of accurate biochemical and genomic knowledge. As a result a metabolic network was constructed in the presence of different C/N ratio to identify the important metabolic route of the carbon that will be of greater significance for the production of HVABs in Chlorella sp. The study outlines that upon reduction of glycerol concentration in the medium, the uptake rate of glucose is hampered as well as eventually the cell growth rate; however, this event inversely affects the lipid production capability of the cell (Xiong et al., 2010). Similarly a reaction network was reconstructed in Chlo. protothecoides using labeling data of amino acid previously generated in nitrogen-rich and nitrogen-depleted conditions. This would further help in the elucidation of precursor molecules driving the metabolic changes to optimize the biosynthesis of fatty acids inside the cell. The study defines the significance of redox powers and the dependence of cellular machinery on the pentose phosphate (PP) pathway for the generation of these redox equivalents to generate increased concentrations

TABLE 4 Up-/downregulation of metabolites during different experimental conditions. Algae

Condition 1

HVABs

Upregulation/downregulation

References

Asteracys sp.

Glucose 500 mg L ; high light 900-mmol photons m 2 s 2

Up: carotenoids (high light) Down: PUFA (both high light intensity and mixotrophy)

High light Up: a-tocopherol, proline, trehalose, maltose, linolenic acid Down: sucrose and propanoic acid Glucose Up: sucrose, propanoic acid, hexadecanoic acid, and proline Down: maltose, linolenic acid, a-tocopherol, and trehalose

(Agarwal et al., 2019)

Parachlorella kessleri (I)

Nitrogen, phosphorus, and sulfur starvation

Up: PUFAs in phosphorus starvation

Up: a-tocopherol, trehalose (nitrogen and sulfur starvation); proline and mannose (all starvation conditions); citric acid (nitrogen and phosphorus starvation); 2-ketoglutaric acid (nitrogen starvation) Down: glucose, malic acid, myoinositol (all starvation conditions)

(Shaikh et al., 2019)

Haematococcus pluvialis

High light

Up: astaxanthin

Up: fatty acids Down: sucrose, organic, and amino acids

(Lv et al., 2016)

Dunaliella salina TG

Phosphorus starvation

Up: DHA

Up: sucrose, trehalose, amino acids Down: glycerol, serine

(Lv et al., 2017)

Sulfur starvation

Up: b-carotene and lutein Down: DHA

Up: amino acids, proline, sugars, 2-ketoglutaric acid Down: acetic acid, FAs, glycerol, galactose, and myoinositol

(Lv et al., 2018)

Chlamydomonas reinhardtii CC-503

Hyperosmotic stress 0.5-M sorbitol in 85mL culture

n.d.

Up: mono- and disaccharides, amino acids Down: TCA-associated metabolites

(Tietel et al., 2020)

Nannochloropsis salina

Low temperature

Up: EPA

Up: leucine biosynthesis (10°C), glutathione (GSH), g-aminobutyric acid, 2-ketoglutaric acid Down: palmitic acid, oleic acid, and arachidonic acid

(Gill et al., 2018)

n.d.: not detected.

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of lipids continuously (Gopalakrishnan et al., 2015). The integrated study of metabolic flux analysis and computational tools provides a complete snapshot of the metabolic regulations that take place inside the cell. It should also be noted that quantitative measurements of fluxes governing the route and reactions for the production of different metabolites are also feasible using 13C-MFA based on large-scale metabolic framework compartmentalization. Such a technique, as a result, unlocks different avenues to investigate and elucidate the rate-limiting steps in the production of HVABs in microalgae (Xiong et al., 2010; Boyle et al., 2017; Wu et al., 2015).

3 Integrated omics for the redesigning/remapping metabolic pathways for enhanced HVAB production The omics-based datasets are enormous and require multilevel networking to unveil the unique metabolic capabilities of microalgae. Integration of omics information combined with computation approaches would help in developing a metabolic network underpinning the regulators of the biosynthesis of the desired HVAB pathway (Fig. 1). The omics studies on microalgae are mostly isolated ones without any interlinkage, and without any interconnections the omic data fail to provide any competent view. Therefore, nowadays, try to carry out multiomics analysis to retrace the metabolic pathways like transcriptomics, and proteomic analysis together offered critical insight into the cellular metabolism, that is, growth, homeostasis, and photosynthesis helping in unveiling the essential metabolic pathways triggered in response to various physiological conditions. Transcriptomics and proteomics approach provides enough information about how the rapidly changing environment like a drastic change in CO2 levels leads to massive reprogramming of N. oceanica IMET1 (Wei et al., 2019). The shallow levels of CO2 lead to overexpression of carbonic anhydrase and bicarbonate transporters along with C4-like genes, pyruvate orthophosphate dikinase, converting pyruvate into phosphoenolpyruvate. Further, photorespiration was upregulated, and ornithine-citrulline shuttle is downregulated in very low CO2.

3.1 Top-down approach (gene to metabolite) Microalgal systems, like other biological systems, are complex and incomprehensible if studied individually. Therefore we need to develop systems biology-based approaches by equating it with omics dataset to develop advanced metabolic models like the genome-scale metabolic model (GSMM). However, metabolic modeling requires an understanding of metabolic mechanisms at the systems level. In systems biology, there exist two strategies for metabolic pathway reconstruction: First is bottom-up approach that is a predictive approach starting from an already available network of biochemical reactions, and another strategy is the top-down approach that is constructed de novo without any prior knowledge of the biological system. The method tries the reverse engineering of given understanding of genomics, transcriptomics, proteomics, and metabolomics to develop a condition-specific metabolic model (C¸akir and Khatibipour, 2014). The top-down approach in systems biology is based on metabolomics analysis, and regulatory metabolism was drawn through the iterative cycle of metabolic profiles and their pattern analysis (Rosato et al., 2018). The only problem is to get reliable metabolome data on a large scale.

3.2 Genome-scale mEtabolic models (GEMs) GEMs are a type of metabolic reconstruction that is the combination of organism-specific multiomics datasets (Thiele and Palsson, 2010). These are the following steps required for the successful GEMs: 1. 2. 3. 4. 5.

creation of a draft model using annotated genomes, manual curation of the model using available omics data, mathematical modeling of the generated model using constraint-based linear analysis, network evaluation or debugging to check for consistency and eliminate false predictions, simulation and visualization of the predicted model.

GEMs can be a foundation stone for the laboratory and computational studies aiding in the development of new biotechnological answers for the molecular or genetic hurdle for the microalgal metabolic pathways (Reijnders et al., 2014). This looks very prospective, but it is a challenging job to handle the large GEMs if we try to work for the dynamic approach with several time points. Therefore GEMs are the only constraint that controls the flux of the reaction irrespective of metabolic regulation or its concentration (Tomar and De, 2013). Model iRC1080 (Chang et al., 2011) and AlgaGEM (de CGO et al., 2011) are extensively studied GEMs of Chla. reinhardtii accounting for 2191 and 1718 total number of reactions, respectively. Both models differ in the degree of

FIG. 1 Schematic diagram showing how GEMs are reconstructed using integrated omics data.

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compartmentalization of complete responses, that is, 50% of the nontransport reactions happen in cytosol according to model iRC1080, while this figure is about 88% for AlgaGEM. Some other species whose GEMs have been developed are Chlo. vulgaris UTEX 395 model iCZ843 (Zun˜iga et al., 2016) containing 2286 reaction numbers, Ectocarpus siliculosus EctoGEM 1.0 (Prigent et al., 2014) having 1866 reactions, and Phaeodactylum tricornutum metabolic network DiatomCyc (Fabris et al., 2012) consisting of 1719 reactions. One of the genome-scale metabolic networks study was performed on Saccharina japonica based on E. siliculosus reconstruction to shed light on biosynthesis of HVABs like carotenoids depicts an entirely different abscisic acid pathway from what has been defined in land plants. Ultimately, such valuable information would help in redesigning the strategies for the production of HVABs.

4 Microalgal cell factories: An overview Microalgae are known for cell factories with the ability to produce myriads of biomolecules, such as biofuels and HVABs. The production of HVABs is condition specific and time dependent, causing metamorphic shifts in microalgal cells. The microalgal HVABs can be coextracted with lipid and carbohydrate precursors of biofuels to enhance their commercial scope and industrial value (Dewapriya and Kim, 2014; Jutur et al., 2016). The combination of omics and systems biology is a pivotal step toward increasing the production of HVABs and leading to the development of microalgal cell factories. Furthermore, genome editing techniques like CRISPR-Cas9 could be used to efficiently knockdown or knock-in specific key hubs identified by reconstruction of microalgal metabolic map controlling the HVAB production that would develop a genetically robust algal strain.

5 Conclusions and future remarks Microalgal cell factories for the biofuel and/or HVAB production are not feasible until the economic feasibility is achieved. Microalgae and its constituents can be completely valorized to attain a sustainable microalgal biorefinery. There are several hurdles before realizing the concept of the industrial level algal biorefinery in reality. The major bottlenecks are the growthlimiting factors developing during the outdoor cultivation of algae, which not only limits the growth but also decreases the productivities of biofuels and/or HVABs. The different omics analyses provide some indication to the rate-limiting steps for the biosynthesis of HVABs and growth, but the information is not enough if dealt singularly. The construction of substantial metabolic models provides a quite fundamental approach in dealing with the problem, but this needs multiomics analysis and a systems biology approach to the aggregate data and manually curate the obsolete reactions from the algal models. A combinatorial genome-scale metabolic model design and genetic engineering to rule out the regulatory hubs, a hurdle for the growth, and production of biofuels and/or HVABs would be a better strategy to develop green microalgal cell factories.

References Abdellaoui, N., Kim, M.J., Choi, T.J., 2019. Transcriptome analysis of gene expression in Chlorella vulgaris under salt stress. World J. Microbiol. Biotechnol. 35 (9), 141. Agarwal, A., Shaikh, K.M., Gharat, K., Jutur, P.P., Pandit, R.A., Lali, A.M., 2019. Investigating the modulation of metabolites under high light in mixotrophic alga Asteracys sp. using a metabolomic approach. Algal Res. 43, 101646. Anand, V., Singh, P.K., Banerjee, C., Shukla, P., 2017. Proteomic approaches in microalgae: perspectives and applications. 3 Biotech 7 (197), 10. Bailey, A.E., Shahidi, F., 2005. Bailey’s Industrial Oil & Fats Products. John Wiley & Sons. Bajhaiya, A.K., Ziehe Moreira, J., Pittman, J.K., 2017. Transcriptional engineering of microalgae: prospects for high-value chemicals. Trends Biotechnol. 35 (2), 95–99. Banerjee, A., Sharma, R., Chisti, Y., Banerjee, U.C., 2002. Botryococcus braunii: a renewable source of hydrocarbons and other chemicals. Crit. Rev. Biotechnol. 22 (3), 245–279. Barkia, I., Saari, N., Manning, S.R., 2019. Microalgae for high-value products towards human health and nutrition. Mar. Drugs 17 (5), 304. Biondi, N., Cheloni, G., Rodolfi, L., Viti, C., Giovannetti, L., Tredici, M.R., 2018. Tetraselmis suecica F&M-M33 growth is influenced by its associated bacteria. J. Microbial. Biotechnol. 11 (1), 211–223. Boussiba, S., Richmond, A.E., 1979. Isolation and characterization of phycocyanins from the blue-green alga Spirulina platensis. Arch. Microbiol. 120 (2), 155–159. Boyle, N.R., Sengupta, N., Morgan, J.A., 2017. Metabolic flux analysis of heterotrophic growth in Chlamydomonas reinhardtii. PLoS One 12 (5), e0177292. Branco-Vieira, M., San Martin, S., Agurto, C., MAd, S., MAV, F., Mata, T.M., et al., 2018. Potential of Phaeodactylum tricornutum for biodiesel production under natural conditions in Chile. Energies 11 (1), 54. Brennan, L., Owende, P., 2010. Biofuels from microalgae—a review of technologies for production, processing, and extractions of biofuels and co-products. Renew. Sustain. Energy Rev. 14, 557–577.

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Chapter 16

Saccharomyces cerevisiae as a microbial cell factory Ryosuke Mitsui and Ryosuke Yamada∗ Department of Chemical Engineering, Osaka Prefecture University, Sakai, Japan *Corresponding author: E-mail: [email protected]

1 Introduction Yeast (Saccharomyces cerevisiae) is a generally recognized as safe (GRAS) organism that has been useful in industrial food production for a long time (Ostergaard et al., 2000). Since the complete genomic sequence of S. cerevisiae was published in 1996 (Goffeau et al., 1996), studies on yeast in various fields have greatly advanced. S. cerevisiae, when haploid, has 16 chromosomes and is a facultative anaerobic unicellular eukaryote that grows by budding. Yeast has a cell function close to that of higher eukaryotes, and it has been widely studied as a model eukaryote organism (Berlec and Sˇtrukelj, 2013). Yeast has various advantages as a host, such as being safe, requiring inexpensive media, and robustness (Walker and Pretorius, 2018). In particular, high homologous recombination (HR) efficiency in yeast cells is very useful in gene recombination. Generally, gene knock-in is achieved through a HR mechanism. A technique called gap-repair cloning, which utilizes high HR efficiency to construct a plasmid in yeast cells, has also been reported (Matsuo et al., 2010). Moreover, markerless gene manipulations with the advent of CRISPR-Cas have been reported (Mitsui et al., 2019b). To my knowledge the first yeast transformation method was developed in 1978 (Hinnen et al., 1978), and there are several types of plasmid vectors (Baghban et al., 2019). Such ease of genetic recombination is an attractive feature of yeast, and various useful substance-producing yeasts have actually been constructed. In addition, it is well known that yeast has a very high ability to produce ethanol. In heterologous production the high ethanol yield of yeast decreases the yield of the target metabolite, and therefore the native ethanol synthesis pathway is often suppressed by deleting or disrupting the related genes. In addition, unlike many other microorganisms, DeDeken (1966) found that yeast respiration was suppressed even under aerobic conditions when glucose is abundant, and he named the effect the “Crabtree effect” (DeDeken, 1966). Later, it was revealed that glucose is responsible for the repression of the transcriptions of various genes including genes involved in respiration, which was named “glucose repression” (Trumbly, 1992). Due to the repression, glucose inhibits ATP synthesis under aerobic conditions and relies on inefficient ATP production in glycolysis. Although it is impossible to review all the studies that have been carried out on yeast, herein, we introduce the genetic technologies and the current position of yeast as a cell factory. First, we briefly explore yeast transformation methods and the available DNA vectors in yeast. Next, we highlight two typical technologies, namely, the surface display method and various CRISPR-Cas-based technologies, which are considered useful when designing yeast as a cell factory. Further, we introduce several reports of compounds produced using recombinant yeast and the associated strategies.

2 Methods and applications for yeast transformation 2.1 Transformation methods Three methods are generally used to transform yeast: (1) transformation of yeast spheroplasts, (2) transformation of yeast cells by treatment with lithium acetate, and (3) transformation of intact yeast cells by electroporation (Gietz and Woods, 2001). To our knowledge the spheroplasts method was first described in 1978 (Hinnen et al., 1978), the lithium acetate method was developed in 1983 (Ito et al., 1983), and the electroporation method of intact yeast cells was developed by Delorme in 1989 (Delorme, 1989). The spheroplast and the electroporation methods are more efficient than the lithium Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00004-0 © 2021 Elsevier Inc. All rights reserved.

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acetate method, but the operations may be slightly more complicated. The lithium acetate method is widely used because the experimental operation is simple. For S. cerevisiae, there is an advantage that a researcher can select an appropriate transformation method depending on the size of the DNA fragment to be introduced and the required transformation efficiency.

2.2 DNA vectors for expressing genes Vectors used for yeast recombination include yeast episomal plasmid (YEp), yeast replicating plasmid (YRp), yeast centromeric plasmid (YCp), yeast integrative plasmid (YIp), and yeast artificial chromosome (YAC) (Gietz and Woods, 2001). The differences between these vectors are the copy numbers retained in the cell and their genetic stability. S. cerevisiae, including laboratory strains, harbors a plasmid universally. This plasmid is called a 2-mm plasmid because it was observed as a circular DNA chain having a length of about 2 mm when observed with an electron microscope. YEp is developed based on the 2-mm plasmid, which is maintained in yeast cells in 30 copies or more (Baghban et al., 2019). YRp has an autonomously replicating sequence (ARS) on the yeast chromosome and can autonomously propagate as multicopy plasmids in yeast. Like the YEp-type vector, the YRp-type vector is maintained in high copy numbers. However, YEp and YRp may be lost after nonselective long-term cultivation. Instead the YCp-type vector is obtained by inserting a centromere sequence into a YRp-type vector. It is stably distributed to daughter cells during cell division and usually retains one copy of the YCptype vector per cell. YIp-type vector is integrated into the yeast chromosome. To integrate higher copy number of YIp-type vectors, a week promoter in the vector is used for the expression selection marker (Pronk, 2002). YAC has a centromere, telomere, and ARS and behaves like an additional chromosome in yeast cells. YAC allows large DNA fragments (˃100 kbp) to be retained in cells, similar to the native chromosomes of yeast (Naesby et al., 2009). However, the preparation of a DNA fragment using YAC becomes complicated as the number of expressed genes increases. As a host yeast an auxotrophic strain in which genes related to amino acid synthesis or nucleic acid synthesis are deleted or mutated is often used (Pronk, 2002). In that case a gene derived from a wild-type strain that complements the deleted gene is used as a selection marker (Table 1). A transformant can be obtained by selecting a strain whose auxotrophy has been restored. For strains in which auxotrophic markers cannot be used, heterologous genes that confer antibiotic resistance on yeast are used (Table 1).

TABLE 1 Marker gene for yeast recombination. Marker gene

Enzyme 0

Selection nutrient/ drug

References

URA3

Oritidine 5 -decarboxylase

Uracil

Pronk (2002)

HIS3

Imidazoleglycerol-phosphate dehydratase

Histidine

Pronk (2002)

LEU3

b-Isopropylmalate dehydrogenase

Leucine

Pronk (2002)

MET15

O-acetyl homoserine-O-acetyl serine sulfhydrylase

Methionine

Pronk (2002)

TRP1

Indole-3-glycerol-phosphate synthase

Tryptophane

Pronk (2002)

AUR1

Inositol phosphorylceramide synthase

Aureobasidin A

Hashida-Okado et al. (1996)

hphMX

Hygromycin B phosphotransferase

Hygromycin B (hph)

Goldstein and McCusker (1999)

kanMX

Aminoglycoside 30 -phosphotransferase

G418 (Geneticin)

Wach et al. (1994)

natMX

Nourseothricin N-acetyltransferase

Nourseothricin (nat)

Goldstein and McCusker (1999)

patMX

Phosphinothricin N-acetyltransferase

Bialaphos (pat)

Goldstein and McCusker (1999)

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2.3 Promoters Both constitutive and inducible promoters are used to regulate the expression of endogenous and heterologous genes. Wellknown constitutive promoters in yeast include ENO1p, PGK1p, TDH3p, TPI1p, and TEF1p (Ogden et al., 1986; Gatignol et al., 1990; Baghban et al., 2019). Besides, GAL1-10p, MET17p, etc. are often used as inducible promoters (Baghban et al., 2019). By using inducible promoters a culture process in which cell growth and substance production phases are separated can be constructed, and efficient substance production with minimal effects of toxic metabolites can be expected (Paddon et al., 2013; Ma et al., 2019a). S. cerevisiae does not have a strongly inducible promoter in the range of AOX1p for Pichia pastoris (Vogl and Glieder, 2013). This seems to be disadvantageous for the overexpression of heterologous proteins. However, in secretory protein expression, it has been shown that the overexpression of a target protein may result in lower protein yield due to protein aggregation in the endoplasmic reticulum (Baghban et al., 2019). Therefore it is important to express genes with an appropriate promoter with proper consideration to the purpose of gene expression. A gene is always expressed under the constitutive promoter, but the transcription level varies depending on the culture conditions. Transcription levels of 14 constitutive promoters derived from yeast glycolysis were evaluated using GFP as a reporter in a 2-m plasmid vector (Sun et al., 2012). The TEF1p was the strongest, and the PGI1p was the weakest among the 14 promoters under varying glucose and oxygen conditions. TEF1p is commonly used for protein production as a strong constitutive promoter (Gomes et al., 2018; Baghban et al., 2019). GAL1-10p are well-known galactose-inducible promoters. The GAL promoter induces transcription when galactose is the sole carbon source and represses transcription in the presence of glucose (Lohr et al., 1995). Disrupting the GAL80 gene a negative transcriptional regulator of the GAL promoters, such as GAL1p, allows the GAL promoters to function even in the presence of glucose. Apart from GAL promoters a methionine-inducible MET promoter such as MET3p and a copper ioninducible CTR promoter such as CTR3p are also used for gene expression in yeast and are regulated oppositely to GALp by methionine and copper ions in the medium (Knight et al., 1996; Mao et al., 2002). Promoter engineering may play an important role in metabolic engineering applications. (Blazeck and Alper, 2013). Rajkumar et al. (2016) demonstrated the design of low pH-inducible promoters (Rajkumar et al., 2016). They replaced the YGP1 core promoter with the TDH3 core promoter, which was 150-bp upstream of TDH3 containing the transcription start site, and TATA box to increase the basal output of the YGP1p. In addition, they aimed to improve the output of YGP1p at low pH by adding several types of transcription factor binding sites in upstream activation sequence of the native YGP1p. The engineering of YGP1p revealed that transcription factor binding sites, Rlm1 sites and Msn2/4 sites, work together to increase low pH output, while Swi5 sites and Rap1 sites increase overall output. Furthermore, another low pH-inducible promoter based on the CCW14 promoter was constructed by the same strategy as the YGP1p engineering. The best resultant promoter, CCW14v5p, showed that the output at pH 6 was equivalent to that of TDH3p and the output at pH 2.5 was approximately threefold of the output itself at pH 6. Yeast expressing the lactate dehydrogenase gene from Lactobacillus plantarum (ldhL) under the control of CCW14 promoter outperformed the strain with ldhL expressed by the TEF1 promoter, yielding lactate titers of 7.9 g/L versus 0.72 g/L for the TEF1 promoter. The design strategy of the promoter, which involves the modification of the upstream activating sequence, is effective for designing a desired promoter.

3 Engineering of S. cerevisiae 3.1 CRISPR system-mediated engineering The CRISPR system is an applied technique of the adaptive immune system in bacteria and archaea and can cleave the double strand of DNA at any site (Wiedenheft et al., 2012; Luo et al., 2016). In S. cerevisiae the CRISPR system has often been used for high efficiency gene manipulations without using a selection marker because HR efficiency is improved by a double-strand break (DSB) (P^aques and Haber, 1999; Storici et al., 2003; Ronda et al., 2015) (Fig. 1). The CRISPR system is divided into several classes and types according to differences in its components (Makarova et al., 2015; Tarasava et al., 2018); the type II CRISPR-Cas system (from Streptococcus pyogenes is well known) (DiCarlo et al., 2013; Xu et al., 2015; Mitsui et al., 2019a) and the type V CRISPR system, CRISPR-Cpf1, have been used for genetic engineering of S. cerevisiae (Lian et al., 2017; Verwaal et al., 2018). For gene disruptions, single and multiple gene knockouts have been achieved with high efficiency (approximately 100%) in both haploid and diploid yeast using the CRISPR system (Fig. 1). Although gene knock-in of the longer DNA fragments is often less efficient than gene knockout, it allows multiple gene expression cassettes to be inserted on genomic DNA at once, making it a powerful method in yeast metabolic engineering.

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Applications of CRISPR system Disruption (by nonsense mutation induced by NHEJ)

Integration

Transcriptional control by catalytically inactivated Cas9 (dCas9)

dCas9

Disruption (by integrating a stop codon)

Integration+deletion

Regulation by dCas9

Repressor/ Activator dCas9 Deletion

Integration+deletion+assembly

Regulation Regulation by dCas9 and scaffold RNA

FIG. 1 Applications of CRISPR system. Illustration of applications of the CRISPR system used in Saccharomyces cerevisiae. Nonhomologous end joining (NHEJ) is a DNA repair mechanism, which may cause insertion or deletion of several bases. DNA fragments with homologous arms to the target sites are integrated via homologous recombination. (Adapted from Mitsui, R., Yamada, R., Ogino, H., 2019b. CRISPR system in the yeast Saccharomyces cerevisiae and its application in the bioproduction of useful chemicals. World J. Microbiol. Biotechnol., 35, 111.)

Mere gene disruption or overexpression does not always bring the desired results. Sometimes, it is necessary to finely control the level of gene expression. CRISPR-based gene activation (CRISPRa) and inhibition (CRISPRi) have been developed to activate or repress the transcription of target genes (Mitsui et al., 2019b; Zheng et al., 2019) (Fig. 1). These technologies use an inactivated CRISPR protein (dCas9), which lacks endonuclease activity, fused with a transcriptional activation or repression domain. Unlike RNA interference in which RNA neutralizes mRNA to suppress transcription, CRISPRa/CRISPRi regulates transcription by inducing transcriptional activation or repression domains in the promoter regions of target genes. Ni et al. (2019) simultaneously downregulated seven genes by CRISPRi using a multi-gRNA expression plasmid to reduce b-amyrin precursor consumption. The average repression ratio after transcriptional inhibition of the seven genes was 75.5%. Besides, Ni et al. (2019) added methyl-b-cyclodextrin (MbCD) to the medium to transport b-amyrin out of the yeast cells. The engineered strain produced 156.7 mg/L of b-amyrin, which was 44.3% higher than the parent strain without MbCD (Ni et al., 2019). Recently the genome rearrangement of yeast using the CRISPR system has also been reported (Mitsui et al., 2019a; Fleiss et al., 2019). Chromosomal recombination by retrotransposon cleavage has a low lethality and can cause structural mutations such as translocation and duplication while preserving yeast coding sequences and promoter regions. Unlike the introduction of random point mutations on the chromosome, such as UV irradiation, these may alter the expression of multiple genes and yeast traits. Mitsui et al. (2019a) used CRISPR-Cas to cleave the d sequence, which is a retrotransposonrelated sequence present in multiple loci in the yeast genome, to fragment chromosomes. They allowed the yeast to repair the fragmented chromosomes under thermal stress to promote recombination in favor of a thermotolerant yeast. The thermotolerant yeast was also tolerant to ethanol and low pH (Mitsui et al., 2019a). Fleiss et al. cut the long terminal repeats of the retrotransposon Ty3 with CRISPR-Cas and properly examined the repaired yeast chromosomes (Fleiss et al., 2019). They found that the chromosomes were repaired mainly by translocation and duplication.

Saccharomyces cerevisiae as a microbial cell factory Chapter

Appearance on cell wall With α-Agglutinin

With a-Agglutinin (Aga1+Aga2)

With Flo1

Aga2

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FIG. 2 Surface display system in Saccharomyces cerevisiae. A target protein can be displayed on the yeast cell surface by expressing the target protein fused with a protein having two hydrophobic signals, a secretory signal at the N-terminus and a glycosylphosphatidylinositol (GPI) anchor attachment signal at the C-terminus.

S S S S

Cell wall

Aga1

Expressed genes

Promoter Secretory signal sequence

GPI attachment anchor signal sequence

α-Agglutinin, a-Agglutinin, or Flo1 gene

Target protein gene

3.2 Surface-display engineering “Surface display” binds a target protein on the yeast cell surface to utilize not only the inside of the yeast cell but also the outside (surface) of the yeast. The surface display is achieved by expressing the target protein fused with a protein having two hydrophobic signals, a secretory signal at the N-terminus and a glycosylphosphatidylinositol (GPI) anchor at the C-terminus (Kondo and Ueda, 2004). a-/a-Agglutinin and flocculin Flo1 are proteins with two hydrophobic signals and are usually used for fusion display (Kondo and Ueda, 2004; Tanaka et al., 2012) (Fig. 2). Various proteins or peptides can be selected depending on the purpose, and this extends the potential of yeast engineering. For example, stress-tolerant yeasts (Perpin˜a´ et al., 2015; Kuroda and Ueda, 2017); yeasts displaying enzymes such as amylases (Yamada et al., 2010; Yamakawa et al., 2012; Inokuma et al., 2015), cellulases (Yamada et al., 2011; Liu et al., 2016a,b, 2017), and laccase (Bertrand et al., 2016); and yeasts used as adsorbents for heavy metal biosorption (Wei et al., 2016, 2018) have been constructed. Further, anchor proteins, which are one of the important elements of cell surface display, were examined for their properties and differences in N-terminal and C-terminal fusion. (Tanaka et al., 2012).

4 Metabolic engineering of S. cerevisiae 4.1 Cellulose degradation Greenhouse gases are believed to be responsible for global warming and associated climate change, and in recent years, it has become urgent to reduce the emission of those gases into the atmosphere (Wong, 2019). Under such circumstances the Paris Agreement has set the following two long-term goals: “keeping the global average temperature rise well below 2°C above the pre-industrial levels” and “In the late 21st century, balancing greenhouse gas emissions and removals.” For the realization of a sustainable society, it is urgently necessary to develop and commercialize processes with low environmental impact and to break away from monolithic manufacturing based on petroleum fuel. Biorefineries, which produce biofuels and other substances from renewable biomass resources, are promising candidates for building processes with low environmental impact. Bioethanol production by yeast is probably the most advanced research in yeast metabolic engineering. Yeast can produce ethanol from glucose at about 90% of the theoretical yield (0.51 g/g-glucose), and first-generation biofuels based on starch-derived glucose as a substrate are supported by high ethanol production capacity of yeast ( Jansen et al., 2017). Currently, sugarcane or corn is used as a substrate in Brazil and the United States for the production of first-generation biofuels (Sydneya et al., 2019). However, these plants need large land area and energy for their cultivation, which creates competition with other food crops (Pandiyan et al., 2019). Lignocellulosic biomass is an alternative substrate for bioethanol production because it is abundant on the Earth and competes with the food supply. Cellulose is the main component of the lignocellulosic biomass, and for the biodegradation of it into monosaccharides, several enzymes including endoglucanase (EG), cellobiohydrolase (CBH), and b-glucosidase (BGL) are required. It is essential to construct yeast cells that express these enzymes appropriately to produce bioethanol

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directly from lignocellulosic biomass. Yamada et al. expressed three cellulases, Trichoderma reesei EGII, CBHII, and Aspergillus aculeatus BGL1 in yeast, at expression ratios suitable for bioethanol production through the cocktail delta integration method (Yamada et al., 2010, 2011). Furthermore the degradation activity of phosphate-swollen cellulose (PASC) was improved by using diploid yeast, and ethanol was produced from PASC at a yield of 75% of the theoretical yield (Yamada et al., 2011). Another study suggested that the surface-displaying yeast adheres to the cellulose surface via an enzyme, whereby cellulose degradation was more efficient compared with strains that secreted cellulases (Liu et al., 2016b). So far the treated lignocellulose was successfully converted to 89% of the theoretical yield of ethanol by the surface-displaying yeast and commercial cellulases (Matano et al., 2012). The investigation of the interaction between the enzymes and the biomass surface should be valuable because the degradation starts with the approach of the enzymes and the substrate. For example, designing enzymes by protein engineering and optimizing pretreatment methods may improve enzyme-substrate interactions and the degradation efficiency.

4.2 Xylose utilization Lignocellulosic biomass is composed of cellulose, hemicellulose, and lignin. The major component of hemicellulose, D-xylose, typically accounts for 10%–25% of the carbohydrate content of lignocellulosic feedstocks ( Jansen et al., 2017). Therefore, in addition to cellulose degradation, it is important to utilize xylose to produce bioethanol from lignocellulosic biomass. Yeast has 18 genes associated with only hexose transport, and it uses more than 20 genes of sugar transporters to take up monosaccharaides and disaccharides (Wieczorke et al., 1999). However, although S. cerevisiae has genes encoding xylose reductase (XR) (Tr€aff et al., 2002), xylitol dehydrogenase (XDH) (Richard et al., 1999), and xylulokinase (Xks) (Richard et al., 2000) in its genome, it cannot utilize xylose because the expression levels of the genes are too low to allow for xylose utilization. However, yeast can utilize xylose by expressing heterologous XR, XDH, Xks, and xylose isomerase encoding genes (XI) to construct xylose utilization pathways (Matsushika et al., 2009) (Fig. 3). So far the strategies for heterologous overexpression of XR and XDH and for the overexpression of a heterologous XI gene and an endogenous Xks have been reported ( Jansen et al., 2017). In the XR/XDH pathway, the redox imbalance is caused by differences in cofactor requirements of XR and XDH. XR uses both NADH and NADPH, whereas XDH uses only NAD+ (Matsushika et al., 2009; Xia et al., 2017). Due to these coenzyme shifts, xylose fermentation strains with the XR/XDH pathway produce large amounts of D-xylitol as by-product (Runquist et al., 2010). More recent protein engineering modified the preferences of these cofactors and reduced the accumulation of D-xylitol (Runquist et al., 2010). The XI pathway does not require a coenzyme, and it converts xylose directly to xylulose, eliminating the problems associated with the XR/XDH pathway (Matsushika et al., 2009; Hou et al., 2016). Instead, XI activity is important for xylose utilization, and refolding with molecular chaperones improves intracellular XI activity (Hou et al., 2016). The simultaneous saccharification and cofermentation (SSCF) and process are desirable for industrial bioethanol production because of its low costs (Choudhary et al., 2016; Azhar et al., 2017). As described earlier, wild-type S. cerevisiae cannot metabolite pentose like D-xylose. Besides, hydrolyzates of lignocellulosic biomass contain fermentation inhibitors such as acetate and furfural ( Jansen et al., 2017). Thus, to develop the SSCF process using yeast, the yeast should have the FIG. 3 Xylose utilization pathway in Saccharomyces cerevisiae. The xylose utilization pathway can be constructed by expressing genes encoding xylose reductase (XR), xylitol dehydrogenase (XDH), xylose isomerase (XI), and xylulokinase (Xks).

D-Xylose

HXT and GAL2

Cytosol

D-Xylose

XI

XR

Pentose Phosphate Pathway (PPP)

NAD(P)H XDH D-Xylitol

NAD(P)+

Xks D-Xylulose

D-Xylulose-5P

D-Glucose-6P

Glycolysis

D-Glyceraldehyde-3P

Glycolysis

ATP

Xylose utilization pathway

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ability of xylose utilization and high inhibitor tolerance. Chen and Fu (2016) carried out the SSCF by using inhibitor-tolerant yeast after enzymatic prehydration and showed a promising result for commercialization with an ethanol yield of 72.3% (Chen and Fu, 2016). However, the costs of the pretreatment and the hydrolysis of biomass are economic barriers to the use of lignocellulose. Therefore, to develop an economically viable SSCF, it is necessary to develop an efficient pretreatment method and cells that saccharify the biomass without adding expensive enzymes.

4.3 LA production Yeast can survive at pH 1.5 (Abdel-Rahman et al., 2013). Because of the high acid tolerance, yeast has been used for the production of several organic acids such as LA (Yamada et al., 2017) and shikimic acid (Guo et al., 2018). Many studies have been conducted on LA production, and herein, we introduce some of these studies. LA is a substance with high industrial demand in food, chemical, and pharmaceutical industries (Cubas-Cano et al., 2018; Djukic-Vukovic et al., 2019). The global LA market is expected to reach 1960 kt by 2020 (Cubas-Cano et al., 2018). Although LA can be synthesized by both chemical synthesis and microbial fermentation, high optical pure LAs are produced by microbial fermentation (Abdel-Rahman et al., 2013). In S. cerevisiae, JEN1 allows for uptake of D-LA into cell, and CYB2 and DLD1 convert L-LA and D-LA to pyruvate, respectively. Furthermore, it is known that S. cerevisiae can convert methylglyoxal into D-LA by GLO1, GLO2, and GLO4. Although yeast has genes that can metabolize LA, yeast hardly produces LA. Therefore it is necessary to introduce a heterologous stereospecific lactate dehydrogenase (LDH) genes into yeast and newly construct a LA synthesis pathway (Abdel-Rahman et al., 2013). The LDH genes derived from Bos taurus and Pelodiscus sinensis for L-LA production, and those derived from Leuconostoc mesenteroides and Limulus polyphemus (Ishida et al., 2006; Mimitsuka et al., 2015; Baek et al., 2016; Baek et al., 2017; Yamada et al., 2017) for D-LA production are often used in yeast (Ishida et al., 2005; Saitoh et al., 2005; Tokuhiro et al., 2009; Lee et al., 2015; Song et al., 2016). Yeast produces ethanol from pyruvate through acetaldehyde. Similarly, LA is also synthesized from pyruvate through the expression of heterologous lactate dehydrogenase. Therefore it is essential to suppress ethanol synthesis for a high LA yield (Tokuhiro et al., 2009). Pyruvate decarboxylase (PDC) synthesizes acetaldehyde from pyruvate, and alcohol dehydrogenase (ADH) synthesizes ethanol from acetaldehyde. To suppress ethanol production a strategy involving the deletion of one or both of these genes is often used. However, because ADH requires NADH, the deletion of ADH may break the NADH/NAD+ balance in cells. In addition, the deletion of ADH1 leads to the accumulation of the cytotoxic acetaldehyde, which inhibits cell growth (Tokuhiro et al., 2009). Song et al. examined the synergistic effect of the endogenous aldehyde dehydrogenase and acetyl-CoA synthase from Salmonella enterica and acetylating acetaldehyde dehydrogenase (A-ALD) from Escherichia coli on the LA production in Dadh1 mutant (Song et al., 2016). They overexpressed these enzyme genes in the strain expressing the LDH genes form Pe. sinensis japonicas and B. taurus (Song et al., 2016). A-ALD converted acetaldehyde to acetyl-CoA directly, and the three enzymes led to the promotion of acetyl-CoA synthesis and seemed to avoid the toxicity of acetaldehyde in Dadh1 mutant. Their recombinant yeast produced 42 g/L L-LA from glucose at a yield of 0.89 g/g (Song et al., 2016). During LA production the pH of the medium decreases as LA accumulates in the medium, which inhibit cell growth (Eiteman and Ramalingam, 2015). Although S. cerevisiae has relatively higher acid tolerance than LA bacteria, the LA production by yeast requires the addition of a neutralizing agent in the medium to avoid LA stress (Yamada et al., 2017; Baek et al., 2017). LA easily enters the cells in a nondissociated state and reduces intercellular pH (pHi) (Mira et al., 2010). Especially, at a pH below its pKa, LA mainly exists as the undissociated form in medium and inhibits cell growth strongly. Therefore it is necessary to add neutralizing agents such as calcium carbonate and ammonia to the medium, but that increases the cost of LA production. Baek et al. (2017) successfully produced D-LA from recombinant yeast (Baek et al., 2017). They first deleted five genes (ADH1-5) involved in ethanol synthesis, two genes (GPD1, 2) involved in glycerol synthesis, and the gene involved in DLA degradation (DLD1) in yeast expressing the D-LA dehydrogenase gene from Leuconostoc mesenteroides (Lm. ldhA). Next, they obtained the LA-tolerant strain by adaptation, and then the gene involved in acetaldehyde synthesis (PDC1) was deleted, and Lm. ldhA was additionally introduced. The resultant yeast produced 82.6 g/L D-LA from glucose with a yield of 0.83 g/g-glucose in fed-batch fermentation under acidic conditions of pH 3.5 (Baek et al., 2017). Baek et al. (2017) identified five mutated genes in the evolved strain by analysis of the entire genome sequence. Among these genes the deletion of SUR1 encoding the catalytic subunit of mannosyl inositol phosphoryl ceramide synthase increased LA tolerance and production, whereas the deletion of ERF2 encoding a subunit of palmitoyltransferase improved D-LA production without conferring LA tolerance (Baek et al., 2017).

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In low pH stress, cells try to maintain neutral cytoplasmic pH using H+-ATPase (Carmelo et al., 1996). Also the protection and refolding of proteins by molecular chaperones may become active (Sugiyama et al., 2016). However, activation of such stress response mechanisms consumes energy and may reduce the energy required for growth and other essential metabolic functions (Halma et al., 2004). Suzuki et al. (2013) reported that a recombinant yeast that grows on a YPDA liquid medium (pH 2.6) containing 6% LA was successfully constructed by disrupting four genes (Ddse2, Dscw11, Deaf3, and Dsed1) (Suzuki et al., 2013). The disruption of these four genes might promote iron uptake, the maintenance of cell wall integrity, and the synthesis of glucan (Suzuki et al., 2013). In addition, Sugiyama et al. (2016) showed that the overexpression of ESBP6, a gene encoding a protein of unknown function, could grow on a medium containing up to 6.2% L-LA (Sugiyama et al., 2016). In addition to the genes listed here, many other genes that contribute to LA tolerance have been suggested (Kawahata et al., 2006). The strength of weak acid stress is dependent on extracellular pH, and the mechanism of stress response differs depending on the type of acid anion. (Mira et al., 2010). Therefore we hope that unknown genes that contribute to LA resistance, including heterologous genes, may still be found, and we should effort to establish a LA fermentation process without neutralizing agents.

4.4 Production of fine chemicals As the understanding of the metabolic pathways of yeast deepens, research on the synthesis of higher value-added chemicals by introducing heterologous genes has been active. For example, some compounds produced by yeast for biopharmaceuticals are shown in Table 2. While plant secondary metabolites (SMs) play important roles in interspecies competition and defense, many of these natural products have been exploited for use as medicines, fragrances, nutrients, colorants, and so on (Pyne et al., 2019). However, they are produced in small quantities in natural sources, and thus mass production of those by plants that require time and land area for growth is not efficient (Huang et al., 2008; Siddiqui et al., 2012). Therefore alternative producers and efficient bioprocesses for SMs are desired. In response to that, interest in yeast metabolic engineering has also shifted to SM production in recent years. Plant SMs such as phenolics, isoprenoids, alkaloids, and polyketides have excellent chemical properties (Pyne et al., 2019). The most successful work in SM production using recombinant yeast was on the production of artemisinic acid, a precursor of artemisinin (Table 1). Various approaches were performed to construct yeasts that can efficiently produce artemisinic acid. They isolated genes encoding enzymes that are responsible for oxidizing amorphadiene to artemisinic acid in Artemisia annua (Ro et al., 2006). In addition, to suppress the ergosterol synthesis pathway, which competes with the heterogeneous pathway of artemisinic acid synthesis, the native promoter of the ERG9 gene was replaced with a copper ion-inducible promoter CTR3p, and this allowed the control of ERG9 expression with copper ions. In addition, yeast cannot take in sterols, which are components of the cell membrane, from outside the cell under aerobic conditions, and this factor reduces carbon yield under aerobic conditions. To solve this problem, Ro et al. overexpressed upc2-1, a semidominant mutant allele that enhances the activity of the UPC2 gene product, to improve the uptake of sterols under aerobic conditions (Ro et al., 2006). In addition, they overexpressed genes in the mevalonate pathway to enhance the carbon flux toward the heterologous pathway (Paddon et al., 2013). These strategies can be widely applied to heterogeneous production of terpenoids. Actually the artemisinic acid-producing yeast strain was recycled as a farnesene platform, showing 130 g/L of farnesene (Meadows et al., 2016). Several studies have reported heterologous production of patchoulol by recombinant microbial cells (Asadollahi et al., 2008; Albertsen et al., 2011; Henke et al., 2018; Ma et al., 2019a). Ma et al. (2019a) used similar strategies to achieve efficient production of patchoulol (Ma et al., 2019a). They enhanced the rate-limiting step of the mevalonate pathway in yeast and expressed patchoulol synthase (PTS). Also, they overexpressed upc2-1 and suppress the squalene pathway (Ma et al., 2019a). As a result of these efforts, Ma et al. (2019a) achieved the patchoulol production of 466.8 mg/L (20.5 mg/g dry cell weight) in fed-batch fermentation (Ma et al., 2019a).

4.5 Production of recombinant protein Recombinant proteins are used in various industries such as food, detergent, pharmaceutical, and cosmetic, and its market is expected to reach $ 280.5 million by 2022 (Gomes et al., 2018).

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TABLE 2 Compound for biopharmaceutical produced by recombinant yeast. Heterologous enzymes expressed in yeast

Fermentation condition

Production

References

Antiinflammatory

Glycyrrhiza glabra b-amyrin synthase (bAS)

Fed-batch

156.7 mg/L

Ni et al. (2019)

Artemisinic acid (sesquiterpene)

Antimalaria precursor

Artemisia annua amorpha4,11-diene synthase (ADS), cytochrome P450 (CYP71AV1), cytochrome P450 reductase (CPR1), cytochrome b5 (CYB5), alcohol dehydrogenase (ADH1), artemisinic aldehyde dehydrogenase (ALDH1)

Fed-batch

25 g/L

Paddon et al. (2013)

Breviscapine (flavanoid)

Medicine for cardiovascular disease

Salmonella enterica acetylCoA synthase variant (ACSSEL641P), Erigeron breviscapus 4-coumaroylCoA ligase (4CL), cinnamate 4-hydroxylase (C4H), chalcone isomerase (CHI), chalcone synthase (CHS), flavone synthase II (FSII), flavone-6-hydroxylase (F6H), flavonoid-7-Oglucuronosyltransferase (F7GAT), phenylalanine ammonia lyase (PAL)

Fed-batch

293 mg/L

Liu et al. (2018)

Carnosic acid/ pisiferic acid/salviol (diterpene)

Antioxidant/ antimicrobial agent/-

Salvia fruticosa copalyl diphosphate synthase (SfCDS), Salvia pomifera miltiradiene synthase (SpMilS), Cytochrome P450 monooxygenases (CYP76AH24 (F112L) variant, CYP51BE52, and CYP76AK6), cytochrome b5 (SpCytb5), Populus trichocarpa cytochrome P450 reductase (CPR)

Batch

18 mg/L/ 2.65 mg/L/ 15 mg/L

Ignea et al. (2017)

b-Carotene (carotenoid)

Antioxidant

Xanthophyllomyces dendrorhous GGPP synthase (crtE), phytoene synthase/ lycopene cyclase (crtYB), and phytoene desaturase (crtI)

Fed-batch

750 mg/L

Lo´pez et al. (2019)

Cucurbitadienol (triterpene)

Antiinflammatory

Siraitia grosvenorii cucurbitadienol synthase (SgCS)

Fed-batch

63.0 mg/L

Qiao et al. (2019)

Lycopene (carotenoid)

Antioxidant

Salmonella enterica acetaldehyde dehydrogenase (ALD6) and acetyl-CoA synthetase (ACS), Taxus x media GGPP synthase (crtE), Pantoea agglomerans phytoene synthase (crtB), and Blakeslea trispora phytoene desaturase(crtI)

Fed-batch

73.3 mg/L

Ma et al. (2019b)

Compound

Property

b-Amyrin (triterpene)

Continued

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TABLE 2 Compound for biopharmaceutical produced by recombinant yeast—cont’d Heterologous enzymes expressed in yeast

Fermentation condition

Production

References

Anticancer

Rattus norvegicus feedback inhibition-resistant tyrosine hydroxylase (TyrHWR) (R37E, R38E, W166Y), Pseudomonas putida l-dopa decarboxylase (DODC), and Papaver somniferum cytochrome P450 enzyme genes

Batch

2.2 mg/L

Li et al. (2018)

Penicillin G (beta-lactam nonribosomal peptide)

Antibiotic

Penicillium chrysogenum enzymes (pcbAB, npgA, pcbC, pclA, and penDE)

Batch

14.9 ng/mL

Blount et al. (2018)

Patchoulol (aesquiterpene)

Antiinflammatory and anticancer

Pogostemon cablin patchoulol synthase (PTS)

Fed-batch

466.8 mg/L

Ma et al. (2019a)

Resveratrol (Stilbenoid)

Anti-oxidant

Arabidopsis thaliana phenylalanine ammonia lyase (AtPAL2), cinnamic acid hydroxylase (AtC4H) and p-coumaryl-CoA ligase (At4CL2), Vitis vinifera resveratrol synthase (VvVST1)

Fed-batch

800 mg/L

Li et al. (2016)

Taxadiene (diterpenoid)

Anticancer taxol precursor

Taxus brevifolia taxadiene synthase (ts), Taxus baccata x Taxus cuspidata geranylgeranyl diphosphate synthase (GGPPSbc)

Batch

72.8 mg/L

Ding et al. (2014)

Compound

Property

Noscapine (benzylisoquinoline alkaloids)

A number of microbial hosts can be selected for the production of various heterologous proteins, including Aspergillus niger, Bacillus sp., E. coli, Pi. pastoris, S. cerevisiae, and T. reesei (Demain and Vaishnav, 2009). S. cerevisiae has been extensively used for the production of recombinant commercial insulin since the early 1980s (Baeshen et al., 2014). S. cerevisiae has complete intracellular organelles and membrane-bound compartments. Therefore various proteins could be produced and folded correctly in a yeast cell. In a yeast cell the translated proteins are subjected to posttranslational modifications such as proteolytic processing of the signal peptide, disulfide bond formation, subunit assembly, acylation, and glycosylation (Huang et al., 2014). In addition, yeast generally has higher yields of recombinant protein than mammalian cells and can secrete the recombinant protein into extracellular medium, which can significantly reduce downstream purification costs (Huang et al., 2014). Glycosylation may increase resistance, such as the thermostability of an enzyme (Olsen and Thoms, 1991). However, the glycosylation pattern of yeast is composed of many mannose residues (200 mannose residues) and is significantly different from that of humans, and this causes lower activity of therapeutic proteins, produced from yeast, in vivo (Gomes et al., 2018). Also, modification with large amounts of mannose residues may cause protein misfolding and prevent protein escape from the endoplasmic reticulum, which can result in loss of production titer (Gomes et al., 2018). It has been reported that there were appropriate combinations between the target proteins and the promoters (Da Silva and Srikrishnan, 2012). This means that it is difficult to determine a single promoter that can be used for the expression of every protein genes of interest. As one strategy the double promoter expression systems consist of two different promoters that are used simultaneously € urk et al., 2017). In another approach, Sasaki et al. for the production of targeted recombinant proteins in S. cerevisiae (Ozt€

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(2019) applied a promoter shuffling method to the secretory production of endoglucanase II from T. reesei (TrEG) (Sasaki et al., 2019). They integrated multicopies of the TrEG gene into the yeast chromosome and expressed the gene with a promoter library including fifteen promoters from S. cerevisiae, Pi. pastoris, and Hansenula polymorpha. They used a d-integrative plasmid library that contained TrEG and the plasmid library for expressing TrEG and integrated it with the CRISPR system. With this method the CMCase activity of TrEG in yeast reached 559 U/L in culture supernatant. Furthermore a strain expressing TrEG under PGK1p was also constructed by the CRISPR-d integration method, showing 257 U/L CMCase activity in culture supernatant. Real-time PCR revealed that the former strain had 30 copies of the TrEG and the latter strain had 40 copies. Despite the fewer copy numbers of TrEG, the former strain had 2.2-fold higher CMCase activity than the latter strain. These findings indicated that there might be an appropriate interaction between the recombinant protein gene and the promoter. There are many attractive hosts for recombinant protein production that have various properties such as not being glucose suppressed, being able to metabolite lipids or methanol, and being able to highly secrete proteins (Demain and Vaishnav, 2009; Gomes et al., 2018). Yeast has a protein translation mechanism similar to higher eukaryotes, abundant genetic knowledge, and established genetic recombination technology, and by choosing the right promoter and properly controlling the glycosylation pattern, it is a promising choice as a host for recombinant protein production.

5 Conclusion Future studies on biorefinery should develop technologies and processes that can utilize compounds that are currently not readily available, such as monocarbon sources or biodegradable polymers, in addition to lignocellulosic biomass, as substrates. Besides the discovery of new compounds and advances in metabolic engineering will request cells to heterologously produce more complex compounds that require the introduction of multistep heterologous pathways. The high operational flexibility of S. cerevisiae has become a powerful weapon for overcoming these challenges. Besides, there is no universal culture method of yeast, and thus the culture conditions should be finely optimized depending on the target metabolite. These contain enormous considerations and will require a bioinformatics approach using simulations and deep learning. Further, synergistic effects of coculture of different species have been reported, and research on bioprocesses using coculture of S. cerevisiae and other microorganisms with industrially excellent phenotypes may become more active. In this chapter, we have briefly highlighted the necessary information on yeast as a cell factory. Certainly, reports on yeast synthetic biology, metabolic engineering, genetic engineering, etc. are too numerous and cannot be fully introduced here. However, as you can see in this chapter, S. cerevisiae has very useful characteristics as a host and will play a central role in biorefinery in the future.

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Increased ethanol productivity in xylose-utilizing Saccharomyces cerevisiae via a randomly mutagenized xylose reductase. Appl. Environ. Microbiol. 76, 7796–7802. Saitoh, S., Ishida, N., Onishi, T., Tokuhiro, K., Nagamori, E., Kitamoto, K., Takahashi, H., 2005. Genetically engineered wine yeast produces a high concentration of L-lactic acid of extremely high optical purity. Appl. Environ. Microbiol. 71, 2789–2792. Sasaki, Y., Mitsui, R., Yamada, R., Ogino, H., 2019. Secretory overexpression of the endoglucanase by Saccharomyces cerevisiae via CRISPR-dintegration and multiple promoter shuffling. Enzyme Microb. Technol. 121, 17–22. Siddiqui, M.S., Thodey, K., Trenchard, I., Smolke, C.D., 2012. Advancing secondary metabolite biosynthesis in yeast with synthetic biology tools. FEMS Yeast Res. 12, 144–170. Song, J.Y., Park, J.S., Kang, C.D., Cho, H.Y., Yang, D., Lee, S., Cho, K.M., 2016. Introduction of a bacterial acetyl-CoA synthesis pathway improves lactic acid production in Saccharomyces cerevisiae. Metab. Eng. 35, 38–45. Storici, F., Durham, C.L., Gordenin, D.A., Resnick, M.A., 2003. Chromosomal site-specific double-strand breaks are efficiently targeted for repair by oligonucleotides in yeast. Proc. Natl. Acad. Sci. U. S. A. 100, 14994–14999. Sugiyama, M., Akase, S., Nakanishi, R., Kaneko, Y., Harashima, S., 2016. Overexpression of ESBP6 improves lactic acid resistance and production in Saccharomyces cerevisiae. J. Biosci. Bioeng. 122, 415–420. Sun, J., Shao, Z., Zhao, H., Nair, N., Wen, F., Xu, J.H., Zhao, H., 2012. Cloning and characterization of a panel of constitutive promoters for applications in pathway engineering in Saccharomyces cerevisiae. Biotechnol. Bioeng. 109, 2082–2092. Suzuki, T., Sakamoto, T., Sugiyama, M., Ishida, N., Kambe, H., Obata, S., Kaneko, Y., Takahashi, H., Harashima, S., 2013. Disruption of multiple genes whose deletion causes lactic-acid resistance improves lactic-acid resistance and productivity in Saccharomyces cerevisiae. J. Biosci. Bioeng. 115, 467–474.

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Sydneya, E.B., Lettib, L.A.J., Karpb, S.G., Sydneya, A.C.N., Vandenbergheb, L.P.S., Carvalhob, J.C., Woiciechowskib, A.L., Medeirosb, A.B.P., Soccolb, V.T., Soccol, C.R., 2019. Current analysis and future perspective of reduction in worldwide greenhouse gases emissions by using first and second generation bioethanol in the transportation sector. Bioresour. Technol. Rep. 7, 100234. Tanaka, T., Yamada, R., Ogino, C., Kondo, A., 2012. Recent developments in yeast cell surface display toward extended applications in biotechnology. Appl. Microbiol. Biotechnol. 95, 577–591. Tarasava, K., Oh, E.J., Eckert, C.A., Gill, R.T., 2018. CRISPR-enabled tools for engineering microbial genomes and phenotypes. Biotechnol. J. 13, e1700586. Tokuhiro, K., Ishida, N., Nagamori, E., Saitoh, S., Onishi, T., Kondo, A., Takahashi, H., 2009. Double mutation of the PDC1 and ADH1 genes improves lactate production in the yeast Saccharomyces cerevisiae expressing the bovine lactate dehydrogenase gene. Appl. Microbiol. Biotechnol. 82, 883–890. Tr€aff, K.L., J€ onsson, L.J., Hahn-H€agerdal, B., 2002. Putative xylose and arabinose reductases in Saccharomyces cerevisiae. Yeast 19, 1233–1241. Trumbly, R.J., 1992. Glucose repression in the yeast Saccharomyces cerevisiae. Mol. Microbiol. 6, 15–21. Vogl, T., Glieder, A., 2013. Regulation of Pichia pastoris promoters and its consequences for protein production. N. Biotechnol. 30, 385–404. Wach, A., Brachat, A., P€ohlmann, R., Philippsen, P., 1994. New heterologous modules for classical or PCR-based gene disruptions in Saccharomyces cerevisiae. Yeast 10, 1793–1808. Walker, R.S.K., Pretorius, I.S., 2018. Applications of yeast synthetic biology geared towards the production of biopharmaceuticals. Gene 9, 340. Wei, Q., Yan, J., Chen, Y., Zhang, L., Wu, X., Shang, S., Ma, S., Xia, T., Xue, S., Zhang, H., 2018. Cell surface display of MerR on Saccharomyces cerevisiae for biosorption of mercury. Mol. Biotechnol. 60, 12–20. Wei, Q., Zhang, H., Guo, D., Ma, S., 2016. Cell surface display of four types of Solanum nigrum Metallothionein on Saccharomyces cerevisiae for biosorption of cadmium. J. Microbiol. Biotechnol. 26, 846–853. Wieczorke, R., Krampe, S., Weierstall, T., Freidel, K., Hollenberg, C.P., Boles, E., 1999. Concurrent knock-out of at least 20 transporter genes is required to block uptake of hexoses in Saccharomyces cerevisiae. FEBS Lett. 464, 123–128. Wiedenheft, B., Sternberg, S.H., Doudna, J.A., 2012. RNA-guided genetic silencing systems in bacteria and archaea. Nature 482, 331–338. Wong, T.E., 2019. Lasting coastal hazards from past greenhouse gas emissions. Proc. Natl. Acad. Sci. U. S. A. 116, 23373–23375. Xia, P.F., Zhang, G.C., Walker, B., Seo, S.O., Kwak, S., Liu, J.J., Kim, H., Ort, D.R., Wang, S.G., Jin, Y.S., 2017. Recycling carbon dioxide during xylose fermentation by engineered Saccharomyces cerevisiae. ACS Synth. Biol. 6, 276–283. Yamada, R., Tanaka, T., Ogino, C., Fukuda, H., Kondo, A., 2010. Novel strategy for yeast construction using d-integration and cell fusion to efficiently produce ethanol from raw starch. Appl. Microbiol. Biotechnol. 85, 1491–1498. Yamada, R., Taniguchi, N., Tanaka, T., Ogino, C., Fukuda, H., Kondo, A., 2011. Direct ethanol production from cellulosic materials using a diploid strain of Saccharomyces cerevisiae with optimized cellulase expression. Biotechnol. Biofuels 4, 8. Yamada, R., Wakita, K., Mitsui, R., Ogino, H., 2017. Enhanced D-lactic acid production by recombinant Saccharomyces cerevisiae following optimization of the global metabolic pathway. Biotechnol. Bioeng. 114, 2075–2084. Yamakawa, S., Yamada, R., Tanaka, T., Ogino, C., Kondo, A., 2012. Repeated fermentation from raw starch using Saccharomyces cerevisiae displaying both glucoamylase and a-amylase. Enzyme Microb. Technol. 50, 343–347. Zheng, Y., Su, T., Qi, Q., 2019. Microbial CRISPRi and CRISPRa Systems for Metabolic Engineering. Biotechnol. Bioprocess Eng. 24, 579–591.

Chapter 17

Pichia pastoris-based microbial cell factories Ryosuke Yamada∗ Department of Chemical Engineering, Osaka Prefecture University, Sakai, Japan ∗

Corresponding author: E-mail: [email protected]

1 Introduction Pichia pastoris (Komagataella phaffii) is methylotrophic yeast that can grow using methanol as the sole carbon source. P. pastoris is recognized as an important protein-producing host and plays a major role in the production of industrial enzymes and pharmaceutical proteins. In addition, P. pastoris is certified as generally recognized as safe (GRAS) microorganisms, with high safety, easily achieving high-density cultures, and is, therefore, suitable for industrial use (Yang and Zhang, 2018). However, compared with Escherichia coli and yeast Saccharomyces cerevisiae, which are typical hosts, P. pastoris lags in terms of engineering microbial cell factories for the production of useful chemicals. The major drawback of P. pastoris as a cell factory host is the lag in the development of site-specific genetic engineering tools. For this reason, it has been difficult to increase the production efficiency of the target substance by a method such as site-specific gene disruption. However, this issue is being solved by the application of the clustered regularly interspaced short palindromic repeats (CRISPR), which is the latest genome editing technology. If such problems are sufficiently solved, it can be expected that it will become a major cell factory host by taking advantage of the various strengths that P. pastoris originally possesses. This chapter describes the available genetic engineering tools, examples of proteins, and useful chemicals produced in P. pastoris and outlines the strengths and weaknesses of P. pastoris for the construction of microbial cell factories (Fig. 1). Specifically the contents include (1) genetic engineering tools for P. pastoris, (2) protein production by P. pastoris, (3) fermentative chemical production by P. pastoris, and (4) chemical production by P. pastoris whole-cell biocatalyst. Finally, as a conclusion, the contents of this chapter were summarized and future prospects of P. pastoris cell factories were discussed.

2 Genetic engineering tools for P. pastoris In the state-of-the-art construction of a microbial cell factory using metabolic engineering and synthetic biology, the development of efficient genetic engineering tools is a prerequisite for transforming cells into superior cell factories. Here, P. pastoris is no exception, and various genetic engineering tools have been developed. This section describes integrative and episomal gene expression systems important for gene expression. Additionally, this section discusses promoter engineering, which greatly affects gene expression. Furthermore a suitable example of the CRISPR, a genome editing technology that has rapidly developed in recent years, will be described for P. pastoris.

2.1 Integrative expression of genes To construct a useful microbial cell factory, tools are needed to introduce new synthetic pathways into hosts by genetic engineering. In P. pastoris studies, integration of the gene expression cassette, consisting of a gene of interest and a promoter necessary for its expression, into the genome by homologous recombination (HR) is the major method for gene overexpression. However, nonhomologous end joining (NHEJ) often occurs in P. pastoris. This is due to the low HR efficiency of P. pastoris compared with S. cerevisiae (Weninger et al., 2016). In addition, a transformant in which a plurality of gene expression cassettes has been inserted by HR is often obtained, with the transformant selected for the high target gene Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00027-1 © 2021 Elsevier Inc. All rights reserved.

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17.2. Genetic engineering tools

17.3. Protein production • Industrial enzymes • Paratheatrical proteins • Peptides etc.

Integrative expression Episomal expression Promoter engineering Substrate • Alcohols • Organic acids • Lipids etc.

Carbon source • Sugars • Glycerol • Methanol etc.

CRISPR system

Product • Aldehydes • Esters • Fatty acid methyl esters etc.

17.5. Chemical production by whole-cell biocatalyst

Product • Carotenoids • Organic acids etc.

17.4. Fermentative chemical production

FIG. 1 Pichia pastoris-based microbial cell factories. Thick arrows represent the gene or promoter of interest, and thin arrows represent the production or conversion of substances. Each number corresponds to a section number in the chapter.

expression (Weninger et al., 2016). In a conventional experiment, the number of transformants obtained by HR is sufficient. Generally, the transformation of P. pastoris is carried out using electroporation, and standard methods and highly efficient modified methods using lithium acetate have been reported. Generally, with linearized DNA, transformation efficiencies of 103–104 transformants mg 1 DNA are obtained (Pena et al., 2018).

2.2 Episomal expression of genes As mentioned previously, in most P. pastoris studies, gene expression has been carried out by the integration of a gene expression cassette. However, to improve the expression levels and transformation efficiency, episomal gene expression vectors containing the autonomously replicating sequence (ARS) are also used. Reportedly, ARS sequences in P. pastoris include PARS1, PARS2 (Cregg et al., 1985), Cen2 (Nakamura et al., 2018), and mtDNA ARS (Schwarzhans et al., 2017). The transformation efficiency of the P. pastoris ARS-type vector is generally about 105 transformants mg 1 DNA (Cregg et al., 1985), which is marginally higher than that of the integrated-type vector. The episomal expression system in P. pastoris presents characteristics similar to the typical yeast S. cerevisiae. Notably, there are some differences when compared with the S. cerevisiae episomal expression system. For example, if a homologous sequence on the ARS-type vector with a gene on the P. pastoris genome exists, it will be integrated into the genome at relatively high frequency (Cregg et al., 1985). Furthermore, P. pastoris possessing an ARS-type vector demonstrates a faster growth rate on a selective medium than S. cerevisiae with a similar vector (Cregg et al., 1985). Furthermore, in nonselective media culture, the P. pastoris ARS-type vector shows higher stability than S. cerevisiae (Cregg et al., 1985).

2.3 Promoter engineering A promoter is an extremely crucial factor influencing the expression level of a protein. Therefore studies on promoter engineering have been conducted in various microorganisms (Blazeck and Alper, 2013) including P. pastoris, and the expression of proteins by various promoters has been examined (Yamada et al., 2016). In P. pastoris the most frequently used promoter is the methanol-inducible AOX1-derived promoter. AOX1 encodes alcohol oxidase, an important enzyme that catalyzes the initial reaction of the methanol utilization pathway in P. pastoris and has high expression level and methanol inducibility ( Juturu and Wu, 2018). Therefore, if this AOX1-derived promoter is used for protein expression, high expression level and methanol inducibility can be expected. The advantages of using this promoter include the following: (1) expression can be strictly controlled by methanol, (2) an extremely high protein

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expression level can be achieved, and (3) after high-density culture with a carbon source other than methanol, protein expression can be induced by adding methanol ( Juturu and Wu, 2018). In contrast, disadvantages include the flammable and dangerous nature of methanol. Furthermore, methanol is not suitable for food-grade protein production owing to its toxicity in humans. The second most widely used promoter is the GAP-derived promoter, which can be constitutively expressed (Zhang et al., 2009). GAP encodes glyceraldehyde-3-phosphate dehydrogenase, an important glycolytic enzyme with a high expression level. Therefore a high expression level can be expected if the GAP-derived promoter is used in the case of protein expression. The GAP-derived promoter has been used in several microorganisms to achieve constitutive high protein expression (Xu et al., 2019). The GAP promoter has the advantage of methanol-free protein production since constant expression can be expected regardless of the carbon source. In addition, protein expression is possible without using an inducer such as methanol. On the other hand, constitutive expression of proteins that do not require an inducing agent can be disadvantageous for expression of proteins that are toxic to yeast cells ( Juturu and Wu, 2018). The AOX1 and GAP promoters are representative promoters in P. pastoris, but other promoters have been investigated. FLD1 encodes formaldehyde dehydrogenase, and the use of this promoter could achieve protein production similar to the AOX1 promoter (Shen et al., 1998). Reportedly, the use of the PGK, HXT7, or ENO1 promoters achieves higher protein production than the GAP promoter (Yamada et al., 2016). On the other hand, protein expression using high expression promoters such as AOX1 or GAP could reduce the production of mature proteins due to protein misfolding or incomplete posttranslational modification of the protein. Hence, low-strength promoters are often utilized, including PEX8 or YPT1 ( Juturu and Wu, 2018).

2.4 Genome editing by CRISPR In the development of microbial cell factories, it is extremely important to undertake the expression of a foreign gene or knockout an endogenous gene by gene integration to a specific location on the genome or deletion of a specific gene. However, the HR machinery, which is important for performing site-specific gene modifications, remains extremely inefficient in P. pastoris (Weninger et al., 2016). In S. cerevisiae the efficiency of HR is higher than that of NHEJ as a DNA repair mechanism. Thus, using only a homologous sequence of approximately 50 bp enables gene integration into a specific target site on the genome with an efficiency of nearly 100%. Conversely, in P. pastoris, the efficiency of NHEJ, as a DNA repair mechanism, is higher than that of HR. Thus, the target efficiency for the target location is only about 0.1%–30% (Weninger et al., 2016). Reportedly, in various organisms, including P. pastoris, by knocking out KU70, a major protein for NHEJ, the efficiency of NHEJ can be reduced, and the efficiency of HR can be relatively increased (N€a€atsaari et al., 2012). However, NHEJ-deficient microorganisms generally have disadvantages such as a decreased growth rate, which may be problematic for industrial utilization (Carvalho et al., 2010). It is well known that DNA repair induced by double-strand breaks in genomic DNA at a specific location dramatically improves HR efficiency at that location (Pena et al., 2018). Recently, the use of the CRISPR system, a genome editing technique capable of inducing a double-strand break at a designated place on genomic DNA by a simple method with high efficiency, has been widespread in various organisms. By using the CRISPR system, the efficiency of HR has been improved, and genes have been integrated into specific locations on the genome and knocked out of specific genes (Pena et al., 2018). Similarly, for P. pastoris, both the integration of a gene at a specific site and specific gene knockout have been evaluated using the CRISPR system. Using the CRISPR system in P. pastoris, AOX1, DAS1, and DAS2 gene knockout demonstrates more than 90% efficiency (Pena et al., 2018). The knockout efficiency using the CRISPR system largely depends on the location of the gene on the genome, and the knockout efficiency maybe 5% or less depending on the location of the target gene (Pena et al., 2018). Using the CRISPR system in P. pastoris, various studies have been performed to improve the efficiency of site-specific knock-in and knockout technologies (Weninger et al., 2016). It is postulated that the efficiency will continue to improve in the future, becoming an increasingly indispensable technology in the construction of cell factories using P. pastoris.

3 Protein production by P. pastoris 3.1 Useful proteins produced by P. pastoris In the early 1980s, recombinant protein production has emerged for the purpose of cost-effective protein production and overcoming the limitations of extraction from natural sources. They are used in various industries such as food, chemicals, and pharmaceuticals. Notably, the recombinant protein market was worth $1645.0 million in 2017 and is expected to reach

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TABLE 1 Representative proteins produced by Pichia pastoris. Recombinant protein and peptide

Expression promoter

Xylanase

Productivity

Other features

References

AOX1

0.05 mg/mL, 204 U/mL

Secretory expression, from Streptomyces sp.

Fu et al. (2011)

Endoglucanase

AOX1

2.61 mg/mL, 28,721 U/mL

Secretory expression, from Pe. pinophilum

Chen et al. (2012)

Lipase

AOX1

33,900 U/mL

Secretory expression, from R. oryzae

Jiao et al. (2018)

Laccase

AOX1

683.1 U/L

Secretory expression, from Pleurotus ostreatus, for removal of environmental pollutants

Zhuo et al. (2018)

Insulin

AOX1

5 mg/L

Secretory expression

Baeshen et al. (2016)

Formaldehyde dehydrogenase

GAP

NA

Intracellular expression, from Pi. pastoris, for selection of multicopy expression strains

Sunga and Cregg (2004)

Xylose isomerase

GAP

NA

Intracellular expression, from Orpinomyces spp., for xylose utilization

Li et al. (2015)

Nitrile hydratase

AOX1

6.2 U/mL

Intracellular expression, from Rhodococcus rhodochrous, for biotransformation

Pratush et al. (2017)

PIMP-V1/V2

AOX1

53 mg/L

Vaccine for malaria

Spiegel et al. (2015)

DENV-3 E VLP

AOX1

15 mg/L

Vaccine for dengue

Tripathi et al. (2015)

RBD219-N1

AOX1

400 mg/L

Vaccine for severe acute respiratory syndrome (SARS)

Chen et al. (2017)

BoNT Hc

AOX1

NA

Vaccine for botulism

Webb et al. (2017)

NA; Not available.

$2850.5 million by 2022 (Vieira Gomes et al., 2018). Various organisms like mammalian cells, insect cells, yeast, and bacteria are used as hosts for protein production depending on the target protein. Representative yeasts used for recombinant protein production include P. pastoris and S. cerevisiae. Among them, P. pastoris is an excellent protein producer owing to its enhanced ability to produce proteins and the expression of proteins derived from various organisms in a mature state. Furthermore, it reduces the cost of separation and purification because it can be secreted extracellularly (Karbalaei et al., 2020). Table 1 shows representative proteins currently produced by P. pastoris. As secretory expressed proteins, xylanase, and endoglucanase are used in the field of biorefinery, lipase is crucial in the industrial field, and insulin is important in the medical field. Under control of the AOX1 promoter, endoglucanase, produced from Penicillium pinophilum, constitutes 95% of the total secreted proteins and reaches 2.61 mg/mL culture (Chen et al., 2012). Intracellularly expressed proteins include xylose isomerase, formaldehyde dehydrogenase, and nitrile hydratase. Additionally, P. pastoris is an important host in the development of vaccines, such as vaccines against malaria and dengue.

3.2 Strategies for enhancing protein production Protein production by P. pastoris is well investigated, and it is relatively easy to achieve relatively high yield. However, to increase protein production, numerous optimizations are required for each target protein. The general considerations for optimizing cultivation include the copy number of the expressed protein gene in the recombinant strain, the type of

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secretory signal sequence for secretory expression, the concentration of methanol and sorbitol during cultivation, culturing temperature and time, endogenous protease deletion, and coexpression of helper proteins. For example, it has been reported that the activity of Rhizopus oryzae lipase produced by recombinant P. pastoris was increased eightfold by increasing the gene copy number, from one copy to five copies ( Jiao et al., 2018). Besides, the appropriate methanol concentration varies greatly depending on the strain of P. pastoris used for protein production and the phenotype of methanol utilization (Karbalaei et al., 2020). Furthermore, disruption of two endogenous protease genes (PEP4 and YPS1) improved secretary production of human serum albumin and human parathyroid hormone fusion protein by P. pastoris (Wu et al., 2013).

4 Fermentative chemical production by P. pastoris 4.1 Fermentative chemicals produced by P. pastoris As described previously, there are numerous examples of proteins and peptides utilizing the high protein production capacity of P. pastoris as a cell factory. Additionally, by utilizing this high protein production ability, the production of various fermentative chemicals by metabolic engineering has been evaluated. Table 2 presents examples of the useful fermentative chemicals produced by the P. pastoris cell factory. The production of useful fermentative chemicals through the metabolic engineering of yeast has a long history in S. cerevisiae, with an overwhelming number of reports available (Nielsen, 2019). In addition, the production of useful fermentative chemicals by the P. pastoris cell factory has been evaluated for more than 10 years, enabling the simultaneous expression of more than five genes and the production of complex compounds such as polyketide citrinin (Xue et al., 2017). On the other hand, highefficiency gene knockout technologies in P. pastoris using CRISPR are still under development, and studies aimed at improving the product yield by gene knockout in metabolic engineering are scarce and need to be conducted in the near future. A notable point in the production of useful fermentative chemicals by the P. pastoris cell factory is the carbon source used in its production. The typical yeast cell factory host, S. cerevisiae, has a narrow assimilation spectrum as a carbon source, and glucose is generally used as a carbon source (Hara et al., 2017; Yaguchi et al., 2018). On the other hand, P. pastoris can grow using not only glucose but also glycerol and methanol as a sole carbon source. Glycerol is a by-product in the production of biodiesel fuel obtained from renewable resources, contributing to the establishment of a recyclingoriented society, and is required to establish an effective utilization technology. In addition, methanol has been gaining momentum as a petroleum alternative resource that can be inexpensively and stably supplied (Pfeifenschneider et al., 2017). Therefore, in the future, the technology producing various useful chemicals from these carbon resources using P. pastoris will be extremely crucial. Technologies for producing L-lactic acid from glycerol and D-lactic acid from methanol using engineered P. pastoris have already been developed (de Lima et al., 2016; Yamada et al., 2019). Furthermore, using the P. pastoris cell factory, the development of high-efficiency production technologies for various useful chemicals from several carbon resources is anticipated.

TABLE 2 Representative fermentative chemicals produced by Pichia pastoris. Chemicals

Relevant features

References

Lycopene

73.9 mg/L produced from glucose

Bhataya et al. (2009)

Astaxanthin

Produced from glucose

Araya-Garay et al. (2012a)

b-Carotene

339 mg/g cells produced from glucose

Araya-Garay et al. (2012b)

Citrinin

Produced from methanol

Xue et al. (2017)

Riboflavin

175 mg/L produced from glucose and glycerol

Marx et al. (2008)

Glucaric acid

6.61 g/L produced from glucose and myo-inositol

Liu et al. (2016)

L-Lactic

acid

Approximately 28 g/L produced from glycerol

de Lima et al. (2016)

D-Lactic

acid

3.48 g/L produced from methanol

Yamada et al. (2019)

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4.2 Strategies for enhancing fermentative chemical production Using the P. pastoris cell factory, several studies have aimed to enhance the efficiency of fermentative production of useful chemicals. The general considerations are similar to those discussed previously for protein production by P. pastoris. For example, in the production of D-lactic acid from methanol, it has been reported that there exists a correlation between the copy number of the D-lactate dehydrogenase gene in a recombinant strain and its activity and D-lactic acid productivity (Yamada et al., 2019). In addition, the selection of an appropriate carbon source, the use of fed-batch culture mode, and the optimization of operating conditions are key factors for improving the productivity of useful chemicals (Marx et al., 2008; Bhataya et al., 2009; de Lima et al., 2016; Liu et al., 2016). Although examples of fermentative chemicals produced by P. pastoris are minimal, in the coming years, the use of CRISPR and similar technologies is expected to further expand this research area. It is anticipated that the knowledge on culture technology for the production of fermentative chemicals in P. pastoris will amass, allowing a general-purpose P. pastoris culture guideline to be established for the efficient production of fermentative chemicals.

5

Chemical production by P. pastoris whole-cell biocatalyst

Section 4 outlined the production of useful chemicals from fermentable carbon sources by the P. pastoris cell factory. This section provides an overview of biotransformation using whole-cell biocatalyst that expresses one or more enzymes in P. pastoris. There exists extensive research on whole-cell biocatalyst using E. coli or S. cerevisiae (Zajkoska et al., 2013; Khor and Uzir, 2011). Compared with E. coli, P. pastoris is resistant to various stresses and capable of expressing proteins derived from various microorganisms, including eukaryotes. In addition, compared with S. cerevisiae, P. pastoris can be cultured at a higher density and has a higher protein expression level. Owing to these characteristics, biotransformation using P. pastoris as a whole-cell biocatalyst has been widely studied and is a noteworthy field of research (Zhu et al., 2019). A typical example of bioconversion using P. pastoris as a whole-cell biocatalyst catalyst is shown in Table 3. TABLE 3 Representative Pichia pastoris whole-cell biocatalyst. Reaction type Oxidation reaction

Reduction reaction

ATPdependent reaction

Cell surfacedisplayed enzyme reaction

Substrate

Product

Relevant features

References

Benzyl alcohol

Benzaldehyde

Used wild-type strain

Duff and Murray (1989)

L-Lactic

Pyruvic acid

Used recombinant strain expressing glycolate oxidase and catalase

Eisenberg et al. (1997)

D-Phenylamine

Phenylpyruvate

Used recombinant strain expressing D-amino acid oxidase and catalase

Tan et al. (2007)

Acetoin

2,3-Butanediol

Used recombinant strain expressing formaldehyde dehydrogenase and NADHdependent butanediol dehydrogenase

Schroer et al. (2010)

Ethyl 4-chloroacetoacetate

(S)-4-Chloro3-hydroxybutanoate

Used recombinant strain expressing carbonyl reductase glucose dehydrogenase

Engelking et al. (2004)

Ethyl-2-oxo4-phenylbutyrate

Ethyl (R)2-hydroxy4-phenylbutyrate

Used recombinant strain expressing carbonyl reductase

Qian et al. (2014)

Glutamic acid, cysteine, and glycine

Glutathione

Used recombinant strain expressing g-glutamylcysteine synthetase and glutathione synthetase

Fei et al. (2009)

L-Methionine and ATP

S-AdenosylL-methionine

Used recombinant strain expressing S-adenosylmethionine synthase and disrupting cystathionine-b-synthase

He et al. (2006)

Alcohols and acids

12 Short-chain flavor ester

Used recombinant strain displaying lipase on the cell surface

Jin et al. (2012)

Soybean oil and methanol

Fatty acid methyl ester

Used recombinant strains displaying different lipases on the cell surface

Jin et al. (2013)

acid

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5.1 Oxidation reaction Alcohol oxidase, which is intrinsically present in P. pastoris, has low substrate specificity and therefore catalyzes the oxidation reaction of various short-chain alcohols to aldehydes. Accordingly, oxidation of various alcohols using wildtype P. pastoris as a whole-cell biocatalyst has been attempted for a long time (Duff and Murray, 1989). In a typical oxidation reaction, oxygen becomes an electron acceptor, and hydrogen peroxide is produced as a by-product. The excessive hydrogen peroxide accumulation causes inactivation of the enzyme and nonenzymatic side reactions due to hydrogen peroxide (Zhu et al., 2019). Therefore, the enzyme catalase, which eliminates hydrogen peroxide, plays an important role in a highly efficient oxidation reaction. P. pastoris has an intrinsically high catalase activity derived from its intrinsic methanol utilization ability. This is advantageous in performing an oxidation reaction using P. pastoris as a whole-cell biocatalyst. The oxidation of benzyl alcohol to benzaldehyde using a wild-type strain presents a representative example of a reaction using the P. pastoris whole-cell biocatalyst (Duff and Murray, 1989). In the oxidation of L-lactic acid to pyruvic acid and the oxidation of D-phenylalanine to D-phenylpyruvic acid, it was reported that the P. pastoris whole-cell biocatalyst exhibited stable activity even after 10 or more repeated reactions (Eisenberg et al., 1997; Tan et al., 2007).

5.2 Reduction reaction P. pastoris has an excellent ability to regenerate coenzymes, including NADH and NADPH (Zhu et al., 2019). Therefore a reduction reaction using a P. pastoris whole-cell biocatalyst utilizing this feature is being evaluated. In the P. pastoris methanol dissimilation pathway, methanol is oxidized to carbon dioxide by the reaction of the enzymes alcohol oxidase (AOX), formaldehyde dehydrogenase (FLD), and formate dehydrogenase (FDH). The reaction catalyzed by FLD and FDH is NAD+ dependent, and in this process, two molecules of NAD+ are regenerated to NADH; that is, the methanol dissimilation pathway intrinsically possessed by P. pastoris can be employed as a highly efficient NADH regeneration system. For example, using the P. pastoris NADH regeneration system, the efficient reduction of acetoin to 2,3-butanediol has been achieved (Schroer et al., 2010). In the case of the NADPH-dependent reduction reaction, an NADPH regeneration system mainly using the enzyme glucose dehydrogenase (GDH) was employed, utilizing glucose as the cosubstrate (Zhu et al., 2019). In the system, GDH oxidizes glucose to gluconic acid with the formation of NADPH. This GDH may be added to the reaction system externally (Qian et al., 2014) or expressed in P. pastoris (Engelking et al., 2004). In the system where the GDH is externally added, a 77.9% yield and 97.3% enantiomeric excess were achieved in the reduction of ethyl-2-oxo-4-phenylbutyrate to ethyl (R)-2-hydroxy-4-phenylbutyrate (Qian et al., 2014). In addition, in the system where the GDH is expressed in P. pastoris, a 91% yield and 95% enantiomeric excess were achieved in the reduction of ethyl 4-chloroacetoacetate to (S)-4-chloro-3-hydroxybutanoate (Engelking et al., 2004).

5.3 ATP-dependent reaction P. pastoris whole-cell biocatalyst is also used for ATP-dependent reactions. P. pastoris is an energy-efficient microorganism and may be suitable for ATP-dependent reactions that require ATP regeneration (Zhu et al., 2019). For the regeneration of ATP in P. pastoris, sugars such as glucose or methanol are used as the cosubstrate. Glutathione and S-adenosylmethionine (SAM) are examples of chemicals produced by the typical ATP-dependent reaction mediated by the P. pastoris whole-cell biocatalyst. Glutathione is a tripeptide widely used in foods, pharmaceuticals, cosmetics, and the like. It is produced from glutamic acid, cysteine, and glycine as substrates by a two-step reaction employing two enzymes, g-glutamylcysteine synthetase, and glutathione synthetase, consuming one molecule of ATP in each reaction process (Anderson, 1998). Using the P. pastoris whole-cell biocatalyst expressing these two enzymes intracellularly and adding glucose as the cosubstrate, a glutathione yield of 4.15 g/L has been achieved from 15 mM of each amino acid substrate; this was significantly higher than that achieved by the yeast S. cerevisiae (800–2000 mg/L) (Fei et al., 2009). In the reaction employing SAM synthase, SAM is produced from L-methionine and ATP as substrates and is used as a therapeutic agent in several diseases (Chu et al., 2013). A P. pastoris whole-cell biocatalyst that expresses SAM synthase and knocks out cystathionine-b-synthase catalyzing side reactions has been constructed (He et al., 2006). The biocatalyst achieved a yield of 13.5 g/L SAM with methanol as the cosubstrate.

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5.4 Cell surface-displayed enzyme reaction In the biotransformation catalyzed by P. pastoris whole-cell biocatalyst as outlined in Section 5.1–5.3, the enzymes catalyzing the reaction were mainly expressed intracellularly. Hence, it is difficult to improve the reaction rate in a system where the intracellular diffusion rate of the substrate is a rate-limiting step. In addition, when the substrate is a large molecule that cannot enter the cell, intracellular biotransformation is challenging. Displaying the enzyme on the cell surface resolves the problem of substrate diffusion into the cell. Furthermore, when the enzyme is displayed on the cell surface of yeast, it can be used as an immobilized enzyme, which facilitates the recovery and reuse of the enzyme. This technology that displays enzymes on the cell surface by an anchor protein is called cell surface display technology, and biotransformation by P. pastoris cell surface-displayed enzyme has been reported (Tanaka et al., 2012). Lipase, an enzyme that catalyzes the hydrolysis of lipid, also catalyzes transesterification and ester synthesis in the presence of organic solvents and is an extremely important enzyme utilized in industrial production (Hasan et al., 2006). To date, lipases from various microorganisms have been displayed on the cell surface of P. pastoris and have been used as whole-cell biocatalysts (Tanaka et al., 2012). Twelve kinds of esters were synthesized in the presence of heptane by a whole-cell catalyst displaying the industrially useful Candida antarctica lipase B (CALB) on the cell surface of P. pastoris ( Jin et al., 2012). Moreover the whole-cell biocatalyst was easily recovered and reused, with the activity reduced by less than 10% even after repeated use 10 times. In addition, CALB-displayed P. pastoris has been applied to the synthesis of fatty acid methyl esters for biodiesel fuel ( Jin et al., 2013). In the methyl ester reaction between soybean oil and methanol, a yield of 90% or more has been achieved in a 12-h reaction. In addition, a yield of 85% or more was maintained even after repeated use of the catalyst 20 times.

6

Conclusions

In this chapter, gene modification tools, protein production, fermentative chemical production, and production of useful chemical by whole-cell biocatalyst have been outlined for the P. pastoris cell factory. Among the genetic modification tools, the CRISPR system-related technology, rapidly developing in recent years, is of remarkable note. Genetic modification using the CRISPR system will continue to be an area of intense research in P. pastoris. Protein production by P. pastoris has an extensive research history, with several proteins already produced. In the future, the production of biopharmaceuticals, including antibody drugs and vaccines, using the extremely high proteinproducing ability of P. pastoris will gain momentum. In the production of fermentative chemicals by P. pastoris, gene knockout and similar technologies have not been developed when compared with S. cerevisiae, with limited investigations to date. However, in the future, genetic modification using the latest technology, such as the CRISPR system, will enable the production of more diverse compounds with high yield. In particular, the production of various useful chemicals from glycerol, an abundant by-product of biodiesel fuel production, and methanol, a promising alternative carbon source, will gain extensive focus to achieve a sustainable society. In the production of useful chemicals using P. pastoris whole-cell biocatalysts, P. pastoris has numerous strengths lacking in other microorganisms, such as high resistance to hydrogen peroxide afforded by intrinsic catalase activity and high coenzyme regeneration ability derived from the methanol utilization pathway. P. pastoris has several strengths not observed in other microorganisms, including strong protein production capacity and broad assimilation spectrum of carbon sources. In the coming years, it is expected to grow into an important player occupying a major position as a cell factory for producing useful chemicals.

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Chapter 18

Yarrowia lipolytica engineering as a source of microbial cell factories Catherine Madzak∗,† Paris-Saclay University, INRAE†, AgroParisTech, UMR SayFood, Thiverval-Grignon, France ∗

Corresponding author: E-mail: [email protected]

1 Introduction Since several decades the nonconventional yeast Yarrowia lipolytica has attracted attention in the domain of heterologous production due to its effectiveness and versatility as host for gene expression, protein secretion, and surface display. More recently, a better knowledge of the metabolism of this nonconventional yeast, the full sequencing of its genome, and the development of entirely new genetic tools have facilitated complex engineering of various metabolic pathways of this yeast, opening new perspectives for its use as cell factory for various applications. As it becomes more and more difficult for newcomers to the field to acquire a general overview due to the rapid pace of technological development and the ever-increasing number of studies reported in the scientific literature (as illustrated in Fig. 1), this chapter is aimed at providing a beginner’s guide to this yeast and a useful metareview tool for Y. lipolytica aficionados. It will present a historical overview of the use of this oleaginous yeast, outline its potential biotechnological applications, and describe the major tools available for protein expression and metabolic pathway engineering. Besides this the present review will focus mostly on recent technical breakthroughs highlighting how Y. lipolytica can be engineered into a workhorse for biotechnology. Notable applications of this remarkable yeast include the production of single-cell oil and the valorization of industrial wastes into valuable products through whole-cell bioconversion. As it would be impossible to detail within the framework of this chapter all the technical aspects of Y. lipolytica engineering and applications, Table 1 provides a selection of recent reviews on each of these fields, with some including also other microorganisms for a bigger picture. The already high number of reviews dated from 2020, published in only a few months, constitutes the best demonstration of the impressive rate of development of Y. lipolytica technology.

2 Main characteristics of Yarrowia lipolytica Y. lipolytica, a hemiascomycetous oleaginous yeast, has drawn at first the attention of industrialists and scientists by its outstanding proteolytic and lipolytic activities. Accordingly, this nonconventional yeast was generally isolated from protein- and/or lipid-rich substrates and environments, notably from meat and dairy products (especially when fermented, like dry sausages and various types of cheeses), and from sewage or oil-polluted waters. However, Y. lipolytica is also encountered in a larger range of ecosystems: it can be found in marine waters, salt marshes, soils (especially when oil polluted), and mycorrhizae, as well as on a variety of consumable products (including fruits, vegetables, or seafood) and in the excreta of insects or vertebrates that consume them (Groenewald et al., 2014). Most of these wild-type isolates are haploids (MatA or MatB). This is also the case for the laboratory strains derived from them, notably from the French strain W29 (ATCC 20460/CLIB 89) and the German strain H222, at first by crossings and mutagenesis and then by genetic engineering (Barth and Gaillardin, 1996). For a long time, Y. lipolytica was thought to be the only species in its genus, but six new species were added rather recently (Nagy et al., 2014), and a dozen are now listed on the NCBI Taxonomy Browser webpage (https://www.ncbi. nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi), mainly due to the reassessment of a number of species from the asexual † INRAE is France’s new National Research Institute for Agriculture, Food and Environment, created on 01/01/2020 by the merger of INRA, the National Institute for Agricultural Research, and IRSTEA, the National Research Institute of Science and Technology for the Environment and Agriculture.

Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00009-X © 2021 Elsevier Inc. All rights reserved.

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Number of publications per year

140 120 100 80 60 40

total 120

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

20

reviews FIG. 1 Graph of the number of publications per year concerning Y. lipolytica engineering. Scientific peer-reviewed publications were searched on the PubMed website (https://pubmed.ncbi.nlm.nih.gov/) using the following formula: yarrowia AND (engineering OR ((heterologous OR system) AND expression) OR recombinant OR (surface AND display) OR crispr). The search results were manually edited for false positives that occurred for earlier years due to some ambiguity into the use of the terms “engineering” and “recombinant.” Review articles were not only included in the total counts but also highlighted separately as the second columns.

Candida taxon. Unlike most other yeasts of the same class, Y. lipolytica is strictly aerobic and exhibit dimorphic properties (Barth and Gaillardin, 1997): either yeast cells or hyphae (and pseudohyphae) could predominate, depending strongly on cultivation conditions. This ability to grow as two different forms (dimorphic switch) is of practical importance, especially for biotechnological applications, since the monitoring of environmental parameters will be crucial for controlling morphology and optimizing bioprocesses (Timoumi et al., 2018). Since several decades, Y. lipolytica is a model organism among yeasts notably for the study of dimorphism, of protein secretion, and of lipid metabolism and storage. Two volumes of the Microbiology Monographs series have been dedicated to the biology (Barth, 2013a) and the applications (Barth, 2013b) of this yeast. Y. lipolytica can metabolize as carbon source a wide range of substrates, either hydrophilic (glucose, fructose, glycerol, organic acids, and alcohols) or hydrophobic (fatty acids, triglycerides, and alkanes) (Barth and Gaillardin, 1997). In addition, the ability to grow on sucrose has been engineered into some of the most used laboratory strains (Nicaud et al., 1989; Lazar et al., 2013). This versatility constitutes a very valuable asset for the design of biotechnological processes. The remarkable capacity of this yeast for degrading hydrocarbons, and especially alkanes, explains that it was frequently isolated from oil-polluted environments and justifies its use in bioremediation projects (reviews by Bankar et al., 2009; Zinjarde et al., 2014). Vesicular protein secretion has been the subject of extensive studies in Y. lipolytica (reviews by Swennen and Beckerich, 2007; Celi
nska and Nicaud, 2019). In particular the translocation of the nascent polypeptide into the endoplasmic reticulum was found to be mainly cotranslational, as in the mammalian secretion pathway. This peculiarity makes Y. lipolytica very effective for folding and secretion of large and/or complex heterologous proteins, in contrast to Saccharomyces cerevisiae in which the posttranslational secretion pathway predominates. Lipid metabolism and storage constitute a prominent research area for Y. lipolytica aficionados, due to its relevance for single-cell oil (SCO) and biofuel production, and have being the subject of many reviews (Beopoulos et al., 2009a,b; LedesmaAmaro and Nicaud, 2016b; Lazar et al., 2018; Ga´lvez-Lo´pez et al., 2019). Depending on the strain, the carbon source, and growth conditions, wild-type Y. lipolytica is able to accumulate lipids up to around 30%–50% of the cell dry weight (CDW). Such accumulation benefits for the presence of protrusions on the cell surface that facilitate the uptake of hydrophobic substrates from the medium (Fickers et al., 2005) and also of an efficient de novo triacylglycerol (TAG) biosynthesis pathway. The storage lipids that accumulate in a specialized cell compartment, the lipid body, consist mostly of TAG and sterol esters more than free fatty acids (FFA). Genetic engineering can allow to obtain recombinant strains in which lipids account for up to 75% or even 90% of the CDW (obese yeasts; Dulermo and Nicaud, 2011; Blazeck et al., 2014; Qiao et al., 2015). It can also enable to modify the lipid profile for overproduction of fatty acids and derivatives, such as fatty acid methyl esters (FAMEs) for biofuel or polyunsaturated fatty acids (PUFAs) for feed, food, and pharma applications (Beopoulos et al., 2009a,b; Xue et al., 2013; Xie, 2017).

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TABLE 1 Selection of recent reviews relevant to Y. lipolytica engineering and applications. For Y. lipolytica among other yeasts

Main topic reviewed

For Y. lipolytica only

Heterologous expression Engineering tools

Madzak (2015) Larroude et al. (2018b)

Wagner and Alper (2016) Baghban et al. (2019) G€ und€ uz Erg€ un et al. (2019)

Secretion Recombinant proteins

Celin´ ska and Nicaud (2019) Vandermies and Fickers (2019) (bioreactor processes)

Kim et al. (2015) (therapy) Vieira Gomes et al. (2018) Thak et al. (2020)

Metabolic engineering

Abdel-Mawgoud et al. (2018) Madzak (2018)

L€ obs et al. (2017)

Synthetic biology tools (CRISPR technologies, DNA assembly)

Darvishi et al. (2018) Markham and Alper (2018) Shi et al. (2018b) Ganesan et al. (2019)

Raschmanova´ et al. (2018)

New substrates Waste use

Ledesma-Amaro and Nicaud (2016a) Spagnuolo et al. (2018)

Yaguchi et al. (2018) Do et al. (2019)

Lipid metabolism Single-cell oil (SCO)

Ledesma-Amaro and Nicaud (2016b) Lazar et al. (2018) Ga´lvez-Lo´pez et al. (2019)

Carsanba et al. (2018) Dourou et al. (2018) Xue et al. (2018)

Biotechnological Applications (general) Bioprocesses

Liu et al. (2015a) Zhu and Jackson (2015) Timoumi et al. (2018) Miller and Alper (2019) Soong et al. (2019)

Czajka et al. (2017) Yaguchi et al. (2017) Rebello et al. (2018)

Single-cell protein (SCP)

Patsios et al. (2020)

Ritala et al. (2017) Jones et al. (2020)

Biofuel Biodiesel

Xie et al. (2017) Yan et al. (2017)

Adrio (2017), Jin and Cate (2017) Ko and Lee (2018) Spagnuolo et al. (2019)

Food (additives) and pharmaceutical compounds

Xie et al. (2015) (EPA) Braga and Belo (2016) (g-DL) Guo et al. (2016) (a-KG) Cavallo et al. (2017) (CA) Ma et al. (2019), Worland et al. (2020a) (terpenoids) (Muhammad et al., 2020) (terpenoids, polyketides) Bilal et al. (2020) (functional sugars) Fickers et al. (2020) (organic acids, sugar alcohols)

Rzechonek et al. (2018) (erythritol) Hu et al. (2019) (CA) Xu et al. (2019) (xylitol) Chen et al. (2020) (terpenoids) Kothri et al. (2020) (PUFAs)

Major review articles published since 2015, nonexclusively classified by main topic. Abbreviations used: CA, citric acid; EPA, eicosapentaenoic acid (o-3 fatty acid); PUFA, polyunsaturated fatty acid; SCO, single-cell oil; SCP, single-cell protein; a-KG, a-ketoglutarate; g-DL, g-decalactone (peach aroma).

3 A short history of Yarrowia lipolytica use The high potential of this nonconventional yeast for industrial applications has been applied at first, 70 years ago, toward the production of biomass or of metabolites of high commercial value (mainly organic acids) using wild-type isolates or strains improved only by traditional methods (crossings and nongenetically modified mutants) (Bankar et al., 2009; Groenewald et al., 2014; Sibirny et al., 2014). Notably the British Petroleum Company (BP, London, United Kingdom) has applied, in the 1950s, the alkane-degrading properties of Y. lipolytica to the production of single-cell protein (SCP) from crude oil for livestock feeding (Toprina G). Pfizer Inc. (New York City, United States) has also developed, in the 1970s, industrial citric acid production from this yeast. Thanks to such industrial-scale applications, the necessary technical

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know-how for Y. lipolytica cultivation was acquired, and this yeast gained a GRAS (generally recognized as safe) status (biosafety class 1 microorganism; Groenewald et al., 2014). The industrial use of wild-type (or traditionally improved) Y. lipolytica strains remains relevant nowadays, notably for citric acid production by Archer Daniels Midland Company (ADM, Chicago, IL, United States). Other applications in the domain of food industry include erythritol production by Baolingbao Biology Co. (Yucheng, Shandong, China) and prebiotic and probiotic applications by Skotan SA (Chorzo´w, Poland) who also market Y. lipolytica biomass as fodder yeast for various farm and pet animals (reviews by Groenewald et al., 2014 and by Zinjarde, 2014). The high lipid-degrading activity of selected Y. lipolytica strains (Fickers et al., 2003a) has also been applied to bioremediation: a starter for depollution of wastewaters containing freeze-dried yeast cells with their secreted lipases is commercialized by Artechno (Isnes, Belgium). The emergence of molecular biology technologies during the 1980s has driven a renewal of interest for Y. lipolytica as an expression host for the production of heterologous proteins. The main biotechnological developments that ensued are represented as a timeline of major achievements and milestones in Fig. 2. Notably the development of transformation methods and the design of shuttle vectors allowed the construction of nonleaky nonreverting auxotrophic strains, which could be further optimized for heterologous production by deletion of endogenous genes or expression of heterologous ones (Le Dall et al., 1994; Madzak et al., 2000, Madzak, 2003). The identification of strong native promoters (Ogrydziak et al., 1977; M€ uller et al., 1998) followed by the construction of recombinant ones (Madzak et al., 2000) allowed the design of various expression/secretion vectors able to replicate in Y. lipolytica cells or to integrate into their genomes. The commercialization in 2006 by Yeastern Biotech Co. (Taipei, Taiwan) of the YLEX kit allowing heterologous gene expression and protein secretion in Y. lipolytica from a strain and vectors developed at INRA (presently INRAE) (cf. Fig. 2 and Section 4.1; http://www.yeastern.com/) has contributed to expanding the use of this yeast. Thus Y. lipolytica became recognized as an effective and versatile host for the expression of heterologous genes, for the secretion of recombinant proteins, and, more recently, for their surface display (reviews by Madzak and Beckerich, 2013; Madzak, 2015). This yeast is employed as a platform for protein manufacturing by Prot
eus (Seqens Group, Ecully, France) and by Oxyrane UK (Manchester, United Kingdom), both making use of the Y. lipolytica technology developed at INRA. Year after year, continued progress in genetic engineering allowed to envision more and more complex modifications of Y. lipolytica metabolism, including the introduction of complete heterologous metabolic pathways, to use this yeast as cell factory for the production of various compounds of interest or arming yeast for bioconversion processes (reviews by Abdel-Mawgoud et al., 2018; Madzak, 2018). Despite an ever-increasing number of examples of use of genetically modified (GM) strains of Y. lipolytica described in the scientific literature since a few decades, most of these proposed applications remain only at an exploratory stage and do not develop their full potential. Besides economic issues not always fully addressed in such publications, a major hurdle to be overcome is the social acceptance of GM microorganisms especially for food applications. Up to now, commercial and industrial applications of GM Y. lipolytica strains remain limited to only a few, as reviewed previously by Groenewald et al. (2014) and by Sibirny et al. (2014). Only two examples of food/feed additives produced by GM Y. lipolytica cell factories are presently known on the market: carotenoids, a technology developed at first by Microbia (United States) and acquired in 2010 by DSM (Heerlen, Netherlands), and eicosapentaenoic acid (EPA)–rich products, a technology developed by DuPont (Wilmington, DE, United States). The extensive engineering of Y. lipolytica for production of EPA-rich SCO, patented in 2007 by DuPont and fully described a few years later (Xue et al., 2013), has represented an impressive achievement especially considering that only classical genetic engineering technology was available at the time. This work has also established the first commercially viable technology platform using GM Y. lipolytica cells (Xie et al., 2015). DuPont’s EPA-rich SCO has been only briefly (from 2010 to 2013) marketed for use as dietary supplement for human consumption under the name New Harvest. The product was presented as the first alternative to fish-based o-3 oils, of vegetarian source, without any mention of its GM yeast source. Fortunately, their EPA-rich SCO producer strain has been more successful when used as whole cells for feed applications: biomass of an EPA-rich strain of Y. lipolytica is used since 2010 as an o-3 supplement for “harmoniously raised” Verlasso salmon, in a joint venture with AquaChile (Puerto Montt, Chile). GM Y. lipolytica strains have also been applied to therapeutic applications: several recombinant enzymes produced from engineered strains are now marketed or on the edge to marketing stage for enzyme replacement therapies (ERTs). The first example is that of the overexpressed LIP2 homologous lipase (Pigne`de et al., 2000) applied in treating exocrine pancreatic insufficiencies by Mayoly Spindler (Chatou, France) in partnership with AzurRx Biopharma Inc. (Brooklyn, NY, United States/Langlade, France). This ERT project, based on an INRA technology, is presently under phase 2 clinical trial in cystic fibrosis and chronic pancreatitis patients, both prone to steatorrhea (fat malabsorption). The other examples have been, or are presently, developed by Oxyrane (Gent, Belgium), also from an INRA technology. Oxyrane has established a proprietary Y. lipolytica engineering platform allowing to produce various proteins with or without mannose-6-phosphate (M6P) glycan

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FIG. 2 Timeline of important milestones and major achievements in Y. lipolytica engineering. The letters in parentheses after each entry refer to the research centers, universities, or companies listed hereafter. The dashes between letters indicate scientific collaborations, when the slashes separate competing works. The following color code is used, for the country of the principal investigator of each work: blue for the United States, green for France, black for other European countries, red for PR China, and purple for other Asian countries. (a) Pfizer Inc., United States; (b) Institut National de la Recherche Agronomique (INRA), Thiverval-Grignon/Jouy-en-Josas, France; (c) DuPont, United States; (d) Centre National de la Recherche Scientifique (CNRS), Thiverval-Grignon/Jouy-en-Josas, France; (e) Centre Wallon de Biologie Industrielle (CWBI), Belgium; (f) Consortium of French laboratories from the
G
enolevures 2 international project; (g) Yeastern Biotech Co., Taiwan; (h) Ecole Nationale Sup
erieure de Chimie de Paris (ENSCP), Chimie ParisTech, France; (i) Mayoly Spindler SA, France; (j) Universit
e de Toulouse, France; (k) Vlaams Instituut voor Biotechnologie (VIB) at Ghent University (UGhent), Belgium; (l) Oxyrane, Belgium; (m) Korea Research Institute of Bioscience and Biotechnology (KRIBB), South Korea; (n) Ocean University of China (OUC), PR China; (o) University of Texas at Austin (UT Austin), United States; (p) Institut National de Recherche en Informatique et en Automatique (Inria),France; (q) East China University of Science and Technology, PR China; (r) Technische Universit€at (TU) Dresden, Germany; (s) University of Hawaii, United States; (t) Consortium of Shanghai laboratories, PR China; (u) University of California, Riverside (UCR), United States; (v) Clemson University, United States; (w) Consortium of Richland laboratories, United States; (x) Chalmers University of Technology (UT), Sweden; (y) Institute of System and Synthetic Biology (ISSB), France; (z) Poznan University, Poland; (a) University of Maryland, Baltimore County (UMBC), United States; (b)Jinan University, PR China; (g) Toulouse Biotechnology Institute (TBI), Institut National des Sciences Appliqu
ees (INSA), France; (d) Novo Nordisk Foundation Center for Biosustainability at Danmarks Tekniske Universite (DTU), Denmark; (e) University of California, Irvine (UCI), United States; (z) Huazhong University of Science and Technology (UST), PR China. Patents numbers are indicated and references for the highlighted publications are, respectively, the following ones, year by year: Madzak et al. (2000), Fickers et al. (2003b), Dujon et al. (2004), Madzak et al. (2006),

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residues (Tiels et al., 2012) to address a range of lysosomal storage diseases. Indeed the presence of M6P on recombinant enzymes increases their internalization into the patient’s cells and targets them to the lysosomes, the intended site of action of the ERT for lysosomal storage diseases. Oxyrane focused on two enzymes involved in lysosomal storage, wherein deficiency is implicated in severe syndromes: glucocerebrosidase (GCase) for Gaucher disease, a metabolic disorder with multiple manifestations, and for Parkinson’s disease, one of the most prevalent and debilitating neurodegenerative disorders, and acid alpha-glucosidase (GAA) for Pompe disease leading to the accumulation of glycogen in tissues. A recombinant GCase with M6P glycans is in development for neuronopathic Gaucher disease, the high level of glycan phosphorylation allowing efficient uptake of the enzyme by neuronal cells. In addition, an unmodified recombinant GCase is in preclinical testing for potential treatment of Parkinson’s disease. At last a recombinant human GAA produced in Y. lipolytica, OXY2810, is already marketed for use as ERT in Pompe disease. Oxyrane now envision to expand its Y. lipolytica production pipeline to developing ERT for other metabolic diseases. At last, unexpected environmental applications of GM Y. lipolytica strains are currently being developed, based on research works from the Bei Shizhang Advanced Class of Life Science Research (from Huazhong University of Science and Technology, Wuhan, China, and two Beijing universities). This complex engineering project (cf. Section 6.7; Tang et al., 2016) led to the design of Euk.cement, a biological autocementation kit based on live GM Y. lipolytica cells, that has been selected by iGEM (International Genetically Engineered Machine) Foundation (Cambridge, MA, United States). Euk.cement is composed of heavily GM Y. lipolytica cells using surface display and secretion of recombinant peptides/ proteins for immobilization onto silica particles. Once immobilized, cell metabolism promotes carbonate sedimentation, aka autocementation (Tang et al., 2016). In addition, the newly introduced pathways are repressed by light, allowing only in situ activation inside underwater sand layers. Euk.cement is intended for underwater sand stabilization by autocementation for both civil engineering and environmental restoration purposes, as exposed on iGEM website (http://2015.igem. org/Team:HUST-China).

4

Overview of basic tools for Yarrowia lipolytica engineering

A wide range of molecular and genetic tools have been developed since several decades for Y. lipolytica to allow at first heterologous gene expression and then genetic engineering of the metabolic pathways of this industrially relevant yeast. An overview of the potentialities now provided by these tools for building GM Y. lipolytica cells is presented in Fig. 3, together with a resume of the intended applications.

4.1 Expression/secretion vectors and transcription unit components Basically, single or multiple expression cassettes, composed of a promoter, a homologous or heterologous gene (open reading frame (ORF)), and a terminator, can be introduced into competent Y. lipolytica cells using integrative or replicative shuttle vectors (cf. upper left quadrant of Fig. 3). The very recent development of the alternative ylAC system will be described further in this chapter (cf. Section 5.1.2). Heterologous genes (or homologous ones, when overexpression is needed) can be expressed intracellularly to remodel metabolic pathways, the resulting recombinant yeast being used as whole-cell factory for various applications, notably bioconversion (cf. bottom left quadrant of Fig. 3). Expression vectors can also optionally be equipped with signals for secretion and/or targeting to various organelles of the recombinant protein product. As the major elements that could compose TUs have already been abundantly described and compared in previous reviews, notably by Madzak and Beckerich (2013), this chapter will only make a brief resume about them before focusing on recent promoter design and new methods for DNA assembly and genome editing.

4.1.1 Choosing a replicative or an integrative vector Replicative vectors carry an ARS (autonomously replicating sequence)/CEN sequence, in which replicative and centromeric functions are colocalized so that they behave as minichromosomes (Fournier et al., 1993; Vernis et al., 1997). Yue et al. (2008), Blazeck et al. (2011), Loira et al. (2012), Pan and Hua (2012), Verbeke et al. (2013), Kretzschmar et al. (2013), Han et al. (2013), Liu and Alper (2014), Gao et al. (2014), Schwartz et al. (2016), Gao et al. (2016), Bredeweg et al. (2017), Celi
nska et al. (2017), Wong et al. (2017), Rigouin et al. (2017), Schwartz et al. (2017b), Holkenbrink et al. (2018), Wagner et al. (2018), Patterson et al. (2018), Schwartz et al. (2018), Yang et al. (2019b), and Guo et al. (2020). Pictures of YLEX kit (2006) and eGFP-displaying Po1h cells (2008) by the author. Picture of Bodipy-stained obese JMY2900 cells (2008) by courtesy of Dr. R
emi Dulermo, former Postdoc at (b). Composite picture of 3 Po1h cells carrying mCherry-displaying oleosomes (2013) was designed by the author from a picture by courtesy of Prof. Wei Wen Su (s).

Yarrowia lipolytica engineering as a source of microbial cell factories Chapter

 YLEX kit

 replicative vectors (ARS/CEN) • artificial chromosomes (ylAC)  integrative vectors • targeted • linearized vector X • expression cassette • random

shuttle vector

genome editing tools  marker rescue tools (Cre-lox)  TALEN  CRISPR-based tools • NHEJ-reduced strains  in vivo transposition tools

arming yeasts • live vaccines • microbial factories • biosensors

surface display (GPI anchor)

secretory pathway 5’H P ORF T M 3’H  single / multiple copies  docking platforms

intracellular expression pathway engineering  for new substrate use • waste and biomass valorization  microbial factories • organic acids • plant natural products • bioplastics, hybrid materials  high-yield single-cell protein (SCP for feed)

targeting to microbodies or ER (retention)

18

351

secretion (signal sequences)  co-transcriptional pathway  post-translational modifications • N-glycosylation with moderate mannosylation  membrane-associated proteins  protease-deleted strains  glyco-engineered "humanized" strains

heterologous production

 protein engineering  proteins and enzymes for: • depollution • medecine (ERT) • agri-food • industry  peroxysomes (PTS)  oleosomes (oleosin fusion)

fatty acid synthesis pathway lipid accumulation lipid body

arming oleosomes • tunable functional nanoparticles • in vivo assembly of nanofactories

obese yeast  high-yield single-cell oil (SCO) • PUFA-rich yeast for feed • PUFA-rich oil  microbial oil-based biodiesel (biofuel) MatA diploidisation (mating type switching) MatB  combination of engineered pathways

FIG. 3 Overview of tools available in Y. lipolytica for heterologous expression and genetic engineering together with their potential applications. Scheme presenting the various types of tools for engineering Y. lipolytica, in their cellular context, with the corresponding main applications highlighted in italics (color code added for clarity). Technical details and references are given in the text. Briefly, single or multiple TUs can be introduced into Y. lipolytica via the use of replicative or integrative constructs. Alternatively, artificial chromosomes bearing multiple TUs can be assembled in vivo by HR. Numerous CRISPR-based tools are now available for genome editing: CRISPR/Cas9/Cpf1 targeted integration or gene deletion, CRISPRi and CRISPRa for transcriptional regulation. Notably, CRISPRi allows construction of transiently NHEJ-deficient strains for easier HR-based engineering purposes. Besides intracytoplasmic expression the products of heterologous genes can be targeted to several intracellular microbodies (ER, oleosomes, and peroxisomes) to compartmentalize the newly engineered metabolic pathways. Providing addition of a signal peptide, recombinant proteins can enter the secretory pathway to be secreted or membrane associated. The additional addition of a GPI anchor domain allows their display on the cell surface. Y. lipolytica cells can perform posttranslational modification (disulfide bonds and glycosylation) and glycoengineered strains producing mammalian-type core Nglycosylation have been constructed for biopharma applications. Genetically engineered mating-type switching can allow easy diploidization and combination of recombinant pathways. Abbreviations used: 50 H and 30 H, 50 and 30 regions homologous (targeted) or not (random integration) to genome; ARS, autonomously replicating sequence; CEN, centromeric function; Cre-lox, Cre recombinase for Lox sequences; CRISPR, clustered regularly interspaced short palindromic repeats, used with Cas9 or Cpf1 nucleases for knockout/knock-in and with dCas9 for interference (i) or activation (a); ER, endoplasmic reticulum; ERT, enzyme replacement therapy; GPI, glycophosphatidylinositol; HR, homologous recombination; M, selection marker gene; NHEJ, nonhomologous end joining; ORF, open reading frame; P, promoter; PTS, peroxisomal targeting signal; PUFA, polyunsaturated fatty acids; T, terminator; TALEN, transcription activator-like effector nucleases; YLEX, Y. lipolytica–based yeast expression kit (Yeastern Biotech Co., Taiwan). (From Madzak, C., 2018. Engineering Yarrowia lipolytica for use in biotechnological applications: a review of major achievements and recent innovations. Mol. Biotechnol. 60 (8), 621–635. https://doi.org/10.1007/s12033-018-0093-4.)

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This limits the interest of these vectors for heterologous production, since only one (or a few) copy is present per cell and can be lost at high frequency, but makes them however useful for pathway engineering (Wong et al., 2017). Increased copy number (and corresponding expression levels) of replicative vectors has however been obtained through regulating centromere function (Liu et al., 2014). Replicative vectors are also preferred for transient expression, such as needed for marker rescue using the Cre-lox system (Fickers et al., 2003b) for gene editing using CRISPR tools (cf. this section). Integrative vectors, which can be directed by linearization to a locus in the genome (notably rDNA or 30 noncoding regions of selected genes) or to an integrated docking platform (pBR322 and zeta), using homologous recombination (HR), constitute favorite tools for both heterologous expression and metabolic engineering, as reviewed by Madzak (2015, 2018). Due to the predominance of NHEJ (nonhomologous end joining) recombination in Y. lipolytica, linearized vectors need to present large bordering regions with homology to the genome to allow efficient targeting (0.5–1 kb; Barth and Gaillardin, 1996; Fickers et al., 2003b). In the YLEX commercial kit (Yeastern Biotech Co.; cf. Table 3 further in the chapter), the pBR-based vectors can transform the Po1g recipient strain with an efficiency in the range of 105 transformants per mg of plasmid DNA, with near 80% of correct targeting of the integrated pBR322 docking platform (Madzak et al., 2000). Such controlled integration, at a precisely known site of the genome, of a unique copy, is especially useful for using directed mutagenesis to genetically engineer enzymes, the effect of mutations being easily compared by assessing the activity directly in transformant strains (Madzak et al., 2006; Galli et al., 2011). However, for biotechnological applications, the presence of an integrated shuttle vector bacterial backbone (and, especially, of the antibiotic resistance gene) would be a major obstacle to the acceptance of strains engineered that way by regulatory authorities. To palliate this problem the bacterial moiety can be discarded from the so-called autocloning vectors, so only a purified integration cassette is used for transformation, preserving the GRAS status of the recombinant strain (Nicaud et al., 2002; Pigne`de et al., 2000). Zeta-based autocloning vectors have been designed at INRA, which use zeta sequences, namely, Ylt1 retrotransposon long terminal repeats (LTRs; Schmid-Berger et al., 1994), as bordering regions for their integration cassette (Pigne`de et al., 2000). These cassettes can be directed by HR to the numerous copies of Ylt1 naturally present in some Y. lipolytica strains. Alternatively, in Ylt1-devoid strains (such as W29 and its derivatives), they take advantage of the high NHEJ rate to integrate at random into the genome (Pigne`de et al., 2000). However, as random integration presents important drawbacks (lack of control and possible deleterious effects), some W29 derivatives have been fitted with an integrated zeta sequence to serve as docking platform for integrating zeta-based cassettes at a precisely known locus (Bordes et al., 2007; Leplat et al., 2015). As reviewed previously by Madzak and Beckerich (2013), such integrated cassettes are very stable, being retained for more than 100 generations, even in absence of selective pressure. Thus, being usable in a large array of strains, the series of zeta-based autocloning vectors constitute up to now one of the most widely used engineering tools, as examples will be given later.

4.1.2 Vectors carrying multiple transcription units To coexpress several heterologous genes for metabolic pathway engineering purposes, insertion of multiple transcription units (TUs) in a single vector has been performed, as reviewed previously by Madzak (2018). For example, a tandem dual cassette vector was used for coexpression of two fungal desaturases in Y. lipolytica, both carrying the same hp4d promoter (Chuang et al., 2010), and a vector equipped with a triple expression cassette was applied to engineering a strain for optimized growth in glycerol-based media (Celi
nska and Grajek, 2013). In this latter case, despite the use of the same promoter and terminator elements for coexpressing the three heterologous glycerol metabolism genes, the integrated cassettes were stably maintained after more than 40 cultivation steps under selective pressure (Celi
nska and Grajek, 2013). The limitations of this strategy seem to be the overall size of the vector and the possibility of HR between repeated promoter/targeting/ terminator elements from the different cassettes. Concerning the size problem, successful assembly and transformation of very large replicative vectors (carrying five TUs, for a total size of almost 19 kb) have been reported (Wong et al., 2017). Concerning the recombination problem, we will see later that recently developed toolboxes for Y. lipolytica engineering tend to favor the use of varied elements to minimize HR possibilities.

4.1.3 Regulatory components of transcription units During early development of tools for the first Y. lipolytica expression systems, in which INRA played a pioneering role (cf. Fig. 2), the regulatory (promoters and terminators) components of the expression/secretion cassettes were isolated from genes of profusely secreted enzymes, mainly the extracellular alkaline protease (AEP, encoded by XPR2) and also the extracellular LIP2 lipase (Madzak et al., 2004). Although the importance of promoters for transcription efficiency has always been self-evident, the role of terminators has long been underestimated. Examples of their influence on successful heterologous expression in yeast have however been given recently (Curran et al., 2015).

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Promoters The choice of promoters adapted to the specific needs of heterologous protein production (strength, constitutivity, or inducibility by process-friendly inducers) or of genetic engineering (tunability of expression) is of paramount importance in the design of a research strategy. In the early stages of development of expression tools for Y. lipolytica, the inducible promoter from XPR2 gene (encoding the profusely secreted AEP), noted pXPR2, was selected for its strength, but the complexity of its regulation (Ogrydziak et al., 1977) has impaired its industrial use, prompting the search for better adapted promoters. Several other native Y. lipolytica promoters have also been applied to heterologous expression (reviews by Madzak and Beckerich, 2013; Sibirny et al., 2014), notably the strong constitutive pTEF (M€uller et al., 1998) and the inducible pPOX2 ( Juretzek et al., 2000). For this latter promoter (from an acyl-CoA oxidase gene), an incomplete substrate repression (by glucose and glycerol) and the hydrophobic nature of the inducers (fatty acids and alkanes) constitute obstacles to an industrial use. In contrast, pTEF or its improved version obtained by an intron-mediated enhancement strategy, pTEFin (Tai and Stephanopoulos, 2013), remains up to now a preferred choice when a strong constitutive promoter is needed. As an alternative to natural promoters, the option of constructing tailored ones, from selected functional elements, was early envisioned at INRA. A functional dissection of pXPR2 demonstrated that a precise part of its proximal UAS (upstream activating sequence), UAS1B, was only marginally affected by cultivation conditions that regulated the full promoter expression (Blanchin-Roland et al., 2014; Madzak et al., 1999). This UAS1B sequence was used as building block in a series of recombinant promoters (Madzak et al., 2000), among which hp4d (four tandem copies of UAS1B upstream of a core pLEU2) was selected for heterologous expression (cf. scheme of hp4d in Fig. 2). Although fairly independent from the composition and the pH of the culture medium, hp4d is however not considered as constitutive, since its expression varies with the cell growth curve: its expression increases during and following the beginning of the stationary phase (Kopecny´ et al., 2005). This growth phase–dependent expression profile is particularly interesting for the production of heterologous proteins, since it naturally allows to partially dissociate a growth phase and an expression phase, thus maximizing productivity and alleviating possible toxicity problems. Consequently, as reviewed previously by Madzak and Beckerich (2013), hp4d has rapidly become the preferred promoter for such applications. More recently, thanks to the progresses of molecular genetics in the meantime, the building of multi-UAS recombinant promoter has been brought to the next level at the University of Texas at Austin (UT Austin—Blazeck et al., 2011). The concept of “hpNd” has been generalized by inserting from 1 to 32 UAS1B elements (aka UAS1XPR2) upstream of different core promoters, from pLEU2 or pTEF. This large array of recombinant promoters covered a large range of expression levels, including the highest ever reported in Y. lipolytica, as much as eightfold higher than with the preferred natural promoters (Blazeck et al., 2011). This study also demonstrated that transcription factor availability was not a limiting factor and that natural promoters were in fact “enhancer limited,” with room for expression level improvement by multiple UAS addition. Additionally, a new UAS from pTEF (UASTEF) was identified and, used in combination with UAS1XPR2, was used to construct new recombinant promoters, driving expression levels up to sevenfold higher than the native pTEF (Blazeck et al., 2013). Similar studies on pPOX2 allowed to identify UASPOX2, a fatty acid-inducible element, and to use it for constructing a pPOX2-derived promoter with an unparalleled 48-fold induction level (Shabbir-Hussain et al., 2017). This strong inducible recombinant promoter did not only present interesting features for metabolic engineering but also was first and foremost designed for use as fatty acid biosensor. The concept of building multi-UAS recombinant promoters is now generally plebiscited: the newly described promoter from a Y. lipolytica erythrulose kinase gene, pEYK1, has been immediately engineered by addition of tandemly repeated regulating elements, either UAS1XPR2 or its own UAS1EYK1, to obtain new strong inducible recombinant promoters (Trassaert et al., 2017). This new promoter and its recombinant derivatives can be strongly induced by the addition of erythritol or erythrulose, which can even be used as nonmetabolized inducers in a DEYK1 context, providing a nonhydrophobic inducer system that was previously lacking among Y. lipolytica engineering tools. More recently, pEYK1 and pEYD1 (from another erythritol catabolism gene) were further analyzed through phylogenetic footprinting, mutagenesis, and hybrid promoter construction, leading to the design of a series of inducible recombinant promoters with variable strengths for fine-tuning of gene expression levels (Park et al., 2019). However, as far as promoters are concerned, the more is not always the best: a recent INRA study highlighted that the use of stronger promoters can sometimes have a counterproductive effect. A series of vectors equipped with hybrid promoters of variable strengths (carrying two to eight UAS1XPR2) was tested for producing different proteins of industrial interest (Dulermo et al., 2017). If the two strongest promoters (8UAS1-pTEF and hp8d), indeed, generated the highest yields for RedStar2 or a secreted glucoamylase, the weaker 2UAS1-pTEF gave the best yield and the highest activity for a secreted xylanase C (although mRNA levels varied proportionately with UAS1XPR2 copies). This study exemplified how translation and posttranslational traffic can constitute limiting steps for some secreted proteins. Consequently, it allowed the authors to highlight the interest of promoter screening methods: they used Gateway cloning in a series of

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vectors with promoters of variable strengths in combination to activity screening and validated this approach through optimization of the production of YFP and of a secreted a-amylase (Dulermo et al., 2017). A very recent study by Liu et al. (2020) offered an innovative approach to promoter design by using artificial core promoters. These authors developed a high-throughput approach for the screening of a library of 30-bp sequences, used as artificial core (between TATA box and transcriptional start) in recombinant promoters, for the bioconversion of lycopene to b-carotene in Y. lipolytica. Visual observation of changes in the color of colonies was used as a reporter for carotene synthesis. They observed that different base patterns could enhance or weaken the promoter’s strength and, notably, that the presence of T-rich elements and a low GC percentage were favorable for high expression, some features also encountered in strong natural yeast promoters (Liu et al., 2020). This work allowed optimizing the characteristics of recombinant promoters, based on the two strong pEXP1 and pGPD, used for b-carotene production and added the selected artificial core promoters to the global Y. lipolytica toolbox. Terminators The terminator sequences from XPR2 and, to a lesser extent, LIP2 genes constitute historically a favorite choice for designing expression cassettes. Rather recently, however, the importance of the role of terminators in the efficiency of transcription completion and in the half-life of the synthetized mRNAs has been demonstrated in yeasts (Curran et al., 2015). Further exploration of the terminator role in Y. lipolytica remain to be performed, even if a large range of different terminators is now available for the design of vectors (Larroude et al., 2019). More than the need for comparative studies, there is the need for building vectors bearing multiple cassettes that has prompted the essay of new terminator sequences to avoid repeats, prone to HR events (Larroude et al., 2019). Besides these native terminator sequences, short synthetic terminators designed for S. cerevisiae appeared to be fully functional also in Y. lipolytica (Curran et al., 2015). The best of these synthetic elements was able to improve transcription and expression in S. cerevisiae by a factor of 4. Besides their efficiency the much reduced size (35–70 bp) of these synthetic terminators is advantageous for vector design and minimizes the risk of undesired HR between TUs or with the genome (Curran et al., 2015).

4.1.4 Targeting components of transcription units These signals or fused sequences will be added to the heterologous ORF to direct the recombinant product to precise intracellular organelles or to the secretion pathway for ulterior release to the medium or surface display. Cellular organelles targeting and compartmentalization Besides basic intracellular expression, resulting in the release of the protein product into the cell cytoplasm, the recombinant protein can be addressed to specific subcellular compartments through transcriptional fusion of its ORF with specific sequences (cf. middle-bottom right parts of Fig. 3). Thus recombinant enzymes can be directed to oleosomes and lipid body (LB) by fusion with the C-terminal domain of heterologous oleosins (structural proteins from plant oil bodies) or can be retained in the endoplasmic reticulum (ER) when a C-terminal KDEL amino acid sequence is added (Han et al., 2013; Yang et al., 2019b). They can also be targeted to the cell peroxisomes through C-terminal addition of a PTS (peroxisome targeting signal): as determined from studies of peroxisomal protein sequences, the tripeptides AKI and SKL are particularly efficient for targeting Y. lipolytica peroxisomes (Haddouche et al., 2010; Xue et al., 2013). A recent work by Yang et al. (2019b) has fully exploited the potential of compartmentalization for effective biosynthesis of triacylglycerol valuable derivatives in Y. lipolytica: lipase-dependent recombinant pathways were targeted in parallel to LB, to ER, and to peroxisomes to optimize the obtained fatty acid methyl ester (FAME) yield. In addition to its interest for subcellular compartment engineering of metabolic pathways (Yang et al., 2019b), oleosome targeting was developed at first in Y. lipolytica for the purpose of designing tunable functional nanoparticles. Various oleosin-fused recombinant proteins were targeted to oleosomes and anchored on their surface (cf. Y. lipolytica cells with mCherry-displaying oleosomes in Fig. 2), which were purified using floating centrifugation (Han et al., 2013). The size of these armed oleosomes was adjusted using sonication to create stable nanoparticles with a 200–300 nm diameter, for use as scaffold for protein display. The design of engineered oleosomes exhibiting cell-targeting and cell-reporting activities was performed through codisplay of fusion proteins on Y. lipolytica oleosomes: fusions of functional displayed or secreted proteins with either cohesin or dockerin domains, enabling high-affinity interaction, allowed the self-assembly of various catalytic modules (Han et al., 2013). This multifunctional nanooleosome system could be applied to various purposes, such as detection of pathogens, detection or targeting of specific cells from a population (targeted drug delivery), or self-assembly of functionalized nanostructures (nanofactories).

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Signal sequences for secretion Vesicular protein secretion is particularly efficient in Y. lipolytica, which favors the cotranslational secretory pathway like mammalian cells, thus facilitating a better folding of large and/or complex proteins in the presence of ER chaperones (Swennen and Beckerich, 2007). Secretion of heterologous proteins, enabling their easy recovery in the culture medium, can be obtained by using N-terminal secretion signals (pre or prepro regions) from efficiently secreted homologous proteins or, alternatively, their native ones (especially when from plant or fungi origin), as reviewed notably by Madzak and Beckerich (2013). The presence of a secretion signal directs the ribosomal complex carrying the nascent recombinant protein to a translocation pore to the ER, where the cotranslational secretion pathway starts, then proceeds through the Golgi apparatus, and finally ends into secretion vesicles that allow delivery to the culture medium (or insertion into the cell membrane, for transmembrane proteins; cf. upper right quadrant of Fig. 3). The complex yeast posttranslational modification processes, including mannosylation, take place during this transit of the secreted protein in ER and Golgi apparatus. Like seen previously for regulatory elements, the targeting components used at first in Y. lipolytica for the design of TUs were isolated from XPR2 and LIP2 genes, encoding major secreted enzymes. Both XPR2 and LIP2 prepro sequences have been during decades the favorite choices for use as secretion signals (Madzak et al., 2004). Then, XPR2/LIP2 prepro hybrids were successfully used as recombinant signals (Nicaud et al., 2002). Later the pre regions of both XPR2 and LIP2 were shown to be more efficient for secretion, and more reliable for correct maturation of heterologous proteins, than the complete prepro (Swennen et al., 2002; Gasmi et al., 2011). Up to now the XPR2 pre sequence remains the preferred choice for directing secretion of recombinant proteins (Madzak and Beckerich, 2013; Madzak 2018). However, recently, genomic and transcriptomic data mining allowed Celi
nska et al. (2018) to identify five new secretion signals, which were tested, in comparison with previously known and widely used signals, for secretory production of two heterologous proteins. Functional screening of libraries of recombinant strains carrying expression/secretion cassettes built using a Golden Gate (GG) approach enabled them to identify new highly efficient secretion signals and to define a consensus sequence for a potentially robust synthetic signal (Celi
nska et al., 2018). These new GG-compatible elements contribute to expand the molecular toolbox for engineering Y. lipolytica.

Signal sequences for surface display The technology of displaying a recombinant secreted protein on the surface of a yeast cell, known as surface display, has a wide range of biotechnological and biomedical applications. Notably, in the domain of white biotechnologies, it allows to construct recombinant yeasts equipped with various cell surface functionalities, which can be used as whole-cell microbial factories and be easily separated from the desired bioconversion product. Such armed yeasts are generally obtained via transcriptional fusion of the gene of interest to the C-terminal sequence of an abundant cell wall protein, acting as signal for covalent linking to a GPI (glycosylphosphatidylinositol) anchor (reviewed recently by Andreu and Del Olmo, 2018). GPI anchors are posttranslational modifications that link covalently the C-terminal end of secreted proteins to b-1,6glucans from the yeast cell wall. Surface display systems have been developed since a dozen years in Y. lipolytica, which increases further its potential applications as whole-cell microbial factory (cf. upper center of Fig. 3). The first report in this yeast, by Yue et al. (2008), described an autocloning vector making use of the 110C-terminal amino acids of CWP1, a Y. lipolytica cell wall protein, as GPI anchor domain for efficient surface display of different heterologous proteins (cf. surface display of eGFP in Fig. 2). The corresponding zeta-based autocloning pINA1317-CWP110 vector remains up to now the more widely used for surface display in Y. lipolytica. Among the proposed applications of this work was the use of a recombinant Y. lipolytica strain surface-displaying the hemolysin from the bacterium Vibrio harveyi as whole-cell live vaccine for pisciculture (Yue et al., 2008). As reviewed previously by Madzak (2015), other Y. lipolytica GPIanchoring signals, from more recently described cell wall proteins (CWP3, CWP6, and YWP1), were also successfully used for arming this yeast (Yuzbasheva et al., 2011; Moon et al., 2013). Very recently a new GPI-anchoring domain from a function-unknown Y. lipolytica cell wall protein (YALI0F24255p) has allowed efficient display of a soybean seed coat peroxidase, an enzyme that intracellular production would have been toxic to the cells (Wang et al., 2020a). Several alternative strategies have also been developed for GPI-independent surface display, such as the use of flocculation domains from homologous or heterologous (from S. cerevisiae) Flo1 proteins (Yuzbasheva et al., 2011; Yang et al., 2009). Recombinant proteins integrating a protein internal repeat (Pir) domain have been successfully displayed on the surface of Y. lipolytica cells by covalent linkage to both cell wall b-1,3-glucans and structural proteins (Duquesne et al., 2014; Yuzbasheva et al., 2015). At last the use of noncovalent adsorption of a chitin-binding module (CBM) for attaching a recombinant protein to Y. lipolytica cell surface has also been reported (Duquesne et al., 2014).

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4.2 Strains and selection markers The physiology of main Y. lipolytica wild-type isolates and the genealogy of derived laboratory strains have been extensively described long ago (Barth and Gaillardin, 1996, 1997). Recipient strains applied to genetic engineering have also been listed previously (Madzak et al., 2004; Madzak and Beckerich, 2013), so this chapter will only cite the most commonly used ones. The reference strain for the species E150 (MatB, his1, leu2–270, ura3–302, and xpr2–322) was the first to have had its genome fully sequenced and annotated by a group of French laboratories (Dujon et al., 2004). This is however not one of the strains currently used for heterologous production, which have been selected for their high growth rate and secretion level and were further engineered for peculiar applications.

4.2.1 Most commonly used recipient strains The recipient strains that were the most successful for heterologous protein production and that remain nowadays the most commonly used for genetic engineering are all derivatives from the high-secretor W29 (MatA) wild-type strain, isolated from Paris sewer waters. More specifically, these most useful recipient strains, described in Table 2, were all further engineered from Po1d strain (MatA, leu2–270, ura3–302, and xpr2–322), a W29 derivative developed at INRA (Nicaud et al., 1989; Le Dall et al., 1994). This series of Po1 strains were equipped with knockout auxotrophies (Leu and/or Ura) and with the capacity to metabolize sucrose (by addition of a pXPR2:ScSUC2 TU) and have had both their extracellular proteases (AEP and AXP, potent threats for secreted heterologous proteins) deleted (Madzak et al., 2000; Madzak, 2003). Additionally, Po1g Leu strain carries an integrated pBR322 sequence to serve as docking platform for efficient integration of pBR-based expression/secretion vectors (Madzak et al., 2000). Recently a triple auxotrophic strain, Po1j (Leu, Ura, and Trp), has also been designed at UT Austin (Wagner et al., 2018). The possibility to use sucrose as sole carbon source, present in all Po1 strains through heterologous invertase expression, is interesting for developing industrial applications, since it allows them to grow on molasses, a cheap substrate derived from agroindustrial wastes (Nicaud et al., 1989). An optimized version of the invertase expression cassette was more recently engineered into the strain JMY2593 (Lazar et al., 2013) for industrial applications. In contrast, in the JMY2566 strain (cf. Table 2), derived from Po1d and engineered for high-throughput mutant library screening, the pXPR2:ScSUC2 TU has been replaced by an integrated zeta docking platform and a new TU encoding fluorescent RedStar2 protein (Leplat et al., 2015). This zeta docking platform allows controlled integration of a unique copy of zeta-based autocloning vectors, at a known genomic site in JMY2566 genome, thus facilitating the screening for new enzymatic properties (when parent W29 and all Po1 strains are devoid of Ylt1 retrotransposon and native zeta sequences).

TABLE 2 Selection of available high-secretor W29-derived Y. lipolytica recipient strains. Strain name

Auxotrophies/new substrate use Other characteristics 



+

Availability Source: Number

References

Po1f

Leu , Ura /Suc deleted for all extracellular proteases sequenced by Liu and Alper (2014)

CIRM: CLIB 724 ATCC: MYA-2613

Madzak et al. (2000)

Po1g

Leu/Suc+ deleted for all extracellular proteases integrated pBR322 docking platform

CIRM: CLIB 725 Yeastern Biotech Co.: YLEX kit

Madzak et al. (2000)

Po1h

Ura/Suc+ deleted for all extracellular proteases

CIRM: CLIB 882

Madzak (2003)

Po1j

Leu, Ura, Trp/Suc+ deleted for all extracellular proteases

cf. authors

Wagner et al. (2018)

Po1t

None/Suc+ deleted for all extracellular proteases

CIRM: CLIB 883

Madzak (2003)

JMY2566

Ura/none/fluorescent (RedStar2) deleted for AEP extracellular protease integrated zeta docking platform

CIRM: CLIB 1779

Leplat et al. (2015)

Coordinates of libraries and companies: ATCC, Manassas, VA, United States (www.lgcstandards-atcc.org); CIRM, Montpellier, France (www6.inrae.fr/cirm_eng/ Yeasts); YLEX kit (cf. Table 3) is commercialized by Yeastern Biotech Co., Taipei, Taiwan (www.yeastern.com).

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Although designed at first for heterologous protein production, the Po1 series of recipient strains has also become a favorite choice for metabolic pathway engineering: these strains have notably been engineered into obese strains, with enhanced lipid storage (cf. Bodipy staining in Fig. 2), as will be seen hereafter (cf. Section 6.2). The Po1g strain also constitute the basis of Cell Atlas, a set of seven isogenic strains that organelles are tagged with fluorescent fusion proteins, developed for cell biology studies by a group of laboratories from Richland (United States) (Bredeweg et al., 2017). Besides W29 a few other Y. lipolytica wild-type isolates have been distinguished for some peculiar characteristics and were engineered for biotechnological applications, as reviewed by Madzak and Beckerich (2013), especially in the field of organic acid production. Notably the H222 (MatA) strain was at first traditionally improved at the Technische Universit€at Dresden (TUD) to obtain an a-ketoglutarate (a-KG) overproducing derivative that was then metabolically engineered for increasing the production of this compound (Yovkova et al., 2014). Some derivatives from H222 strain were also genetically modified for using sucrose as carbon source or for a higher HR rate (cf. Section 4.2.2), before being applied in the making of industrial processes, such as the production of different organic acids (F€orster et al., 2007; Jost et al., 2015). Similarly, WSH-Z06, a strain naturally overproducing a-ketoglutarate (a-KG), has been genetically modified at Jiangnan University for increased production of this compound (Guo et al., 2014, 2016). Wild-type Y. lipolytica isolates issued from marine waters (strain library of the Ocean University of China, Qingdao) have also been selected for peculiar features (notably a high lipid or protein content) and engineered, through heterologous inulinase expression, for production of citric acid, SCO, or SCP from inulin-containing waste materials (Liu et al., 2010; Zhao et al., 2010; Cui et al., 2011; Shi et al., 2018a). A new Y. lipolytica wild-type isolate, ACA-YC 5029, was recently selected by the universities of Athens and of the Aegean for valorizating crude glycerol through production of several metabolites of pharmaceutical and biotechnological interest (Sarris et al., 2019). Such strategies based on exploiting the natural Y. lipolytica biodiversity by screening libraries of wild-type isolates for interesting performances are currently being more and more developed, thanks to the availability of new potent tools, as will be seen hereafter. Assembled genome sequences are now available for a number of industrially relevant Y. lipolytica strains. The sequence of the wild-type W29 strain was assembled and analyzed (Pomraning and Baker, 2015; Magnan et al., 2016), as those of several of its derivatives: Po1f (Liu and Alper, 2014) and an ionic liquid–resistant laboratory-evolved derivative (YlCW001; Walker et al., 2020) and a ku70 mutant of Po1g (Bredeweg et al., 2017). At last the sequence of the wild-type H222 strain was also made available (Devillers and Neuv
eglise, 2019).

4.2.2 Recipient strains with increased homologous recombination efficiency We have seen that Y. lipolytica, contrary to S. cerevisiae, uses predominantly NHEJ for repairing DNA double-strand breaks (DSB), which results in the need of large flanking homologous regions for obtaining targeted integration of transforming DNA by HR with a genomic locus. To palliate this drawback the knocking-out of Ku70 and/or Ku80 gene(s) has been performed in the French (INRA) Po1d strain and the German (TUD) H222 strain (Verbeke et al., 2013; Kretzschmar et al., 2013). The resulting NHEJ-deficient knocked-out strains all exhibited higher HR rates, despite a few discrepancies between the results from the French and the German research teams (Madzak, 2018). Similarly a set of DKu70 knocked-out strains carrying various auxotrophies have been also derived from Po1g, as part of a Y. lipolytica toolbox, by a group of laboratories from Richland (Bredeweg et al., 2017). However, these DKu70 strains all present the drawback of having a reduced transformation efficiency. Therefore alternative strategies could be preferred, such as those enabled by the CRISPR interference methods (CRISPRi) recently adapted to Y. lipolytica (Schwartz et al., 2017b; cf. Section 5.2): Ku70 and/or Ku80 repression obtained by CRISPRi allow to obtain increased HR, without the drawbacks of a permanent genetic knockout. At last, chemical strategies of cell cycle synchronization, by hydroxyurea (HU) treatment, have been proposed to increase HR in a variety of yeasts, Y. lipolytica included (Tsakraklides et al., 2015). A group of laboratories from South Korea has recently tested and/or combined this chemical method together with genetic knocking-out (Dku70) for increasing HR in Y. lipolytica: the highest efficiency (90%) of gene targeting was observed with HU treatment of wild-type cells ( Jang et al., 2018). Thus this chemical method using HU constitutes a simple and efficient way for increasing HR in Y. lipolytica cells. For complex engineering projects, however, the same authors preferred to use HU-treated DKu70 cells, since this combination enabled reiterated marker rescue by performing several steps of insertion and excision of a URA3 cassette using HR between small bordering homologous regions of only 100 bp (URA3 blaster method; Jang et al., 2018).

4.2.3 Glycoengineered strains and their interest for therapeutic applications The differences observed between the N-glycosylation pathway characteristics in yeasts and in mammalian cells can be a source of problems when recombinant proteins are to be used in therapeutic applications. In particular, glycoproteins produced in yeast cells carry N-glycans of the high mannose type, sometimes with hypermannosylation, able at least to reduce

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the in vivo half-life of the recombinant protein and, at worse, to be immunogenic for mammals, including humans (Kim et al., 2015). This problem entailed the design of glycoengineered strains for several yeast species used for production of recombinant proteins, including Y. lipolytica, to obtain glycoproteins compatible with therapeutic use, aka humanized glycoproteins. Glycoengineering of Y. lipolytica strains was reviewed in more detail previously (Madzak and Beckerich, 2013; Madzak, 2015) and will be only briefly resumed here. A group of laboratories from South Korea has designed a double mutant Y. lipolytica strain devoid of hypermannosylation and of mannosyl phosphorylation activities (both typical of yeast cells), which was then engineered for surfacedisplaying a fungal mannosidase, generating a glycoengineered strain with mannose trimming capacity (Park et al., 2011; Moon et al., 2013). In parallel a group of Belgian laboratories also designed a glycoengineered strain by knocking out both yeast-specific mannosyltransferases and expressing a fungal mannosidase, allowing the production of recombinant proteins carrying homogeneous Man5GlcNAc2 residues (cf. glucan core in Fig. 2; De Pourcq et al., 2012a). Another project from the same authors, in which a mannosyltransferase knocked-out strain was then genetically modified to overexpress a glucosyltransferase and to coexpress fungal mannosidase and glucosidase, led to the production of homogeneous Man3GlcNAc2 residues, which constitute the common core of all mammalian N-glycans (De Pourcq et al., 2012b). Such glycan structures could potentially be remodeled in vitro to generate any desired N-glycan from the mammalian complex type. These new glycoengineered expression platforms are expected to allow the production of humanized recombinant proteins, more compatible with therapeutic applications. The same Belgian research teams, comprising the Oxyrane (Belgium) research group, is also behind the design of the glycoengineered Y. lipolytica strain used for producing recombinant human lysosomal enzymes used in ERTs. The N-glycosylation pathway of this strain was engineered by expression of a bacterial glycosidase that allowed to increase mannose-6-phosphate residues on N-glycans from the recombinant proteins produced (Tiels et al., 2012). These glycan modifications promote a better internalization of the recombinant human lysosomal enzymes by the patient cells, via interaction with specific surface receptors, and an efficient targeting to the lysosomes where accumulated substrates need to be degraded. This glycoengineered expression platform allowed Oxyrane UK to propose ERT for several lysosomal diseases, as was seen in Section 3.

4.2.4 Selection marker genes A wide range of marker genes can be used in Y. lipolytica for strain selection, among which genes enabling auxotrophic complementation predominate (Madzak et al., 2004; Madzak and Beckerich, 2013). LEU2 and URA3 genes are the most commonly used, especially the latter since the fact that it can be counterselected using 5-FOA (5-fluoroorotic acid) medium (Boeke et al., 1987) has made it an irreplaceable tool for marker rescue systems. By allowing to restore an auxotrophy by deletion of the corresponding integrated selection marker TU, marker rescue allows the repeated use of a same marker for multiple sequential engineering steps. For such purposes, several loxP-excisable markers have been designed, making use of the Cre-lox recombination system: the marker TU is bordered by loxP sequences and is excised when heterologous Cre recombinase is transiently expressed in yeast cells (Sauer, 1987; Fickers et al., 2003b). Such excisable markers were frequently included in the newly developed Y. lipolytica toolboxes, as will be seen hereafter (cf. Section 5). An alternative approach of marker rescue involves the use of DKu70 cells, in which enhanced HR rate allows the use of very short (100 bp) repeats for excision of a URA3 blaster cassette ( Jang et al., 2018). Other possible choices of auxotrophic markers are notably LYS5 and TRP1, for which a TRP blaster system has also been described, making use of counterselection with 5-fluoroanthranilic acid (5-FAA; Cheon et al., 2003). With the availability of Y. lipolytica genomic sequences and user-friendly engineering tools, it should now be possible to increase the range of auxotrophic markers available by constructing more easily the corresponding auxotrophic strains. More recently, increased knowledge of Y. lipolytica genome and metabolism has allowed the use of catabolic markers that exert selection on the ability to use a given substrate as sole carbon or nitrogen source. This is the case for the recently described Y. lipolytica EYK1 gene that encodes an erythrulose kinase (Carly et al., 2017) and that can serve as catabolic marker in a DEYK1 context, when erythrulose is used as sole carbon source (Vandermies et al., 2017). Interestingly, this new marker has been shown to improve the efficiency of transformation and to be beneficial to the growth of transformants, in contrast to auxotrophic markers. At last a new counterselectable marker has been very recently provided by a research team from Novogy Inc. (Cambridge, MA, United States) who identified a functional Y. lipolytica AMD1 acetamidase gene (Hamilton et al., 2020). In contrast to the case of other yeasts, unable to grow on acetamide as sole nitrogen source, in which a fungal acetamidase gene could be used as dominant marker, YlAMD1 requires a Damd1 context to exert its selection in Y. lipolytica. Interestingly, this new acetamidase catabolic marker can be applied to marker rescue by using counterselection on media containing fluoroacetamide, a toxic analog of acetamide (Hamilton et al., 2020).

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Defective versions of the URA3 selection marker, with large deletions reducing the size of its promoter, have been used to obtain an amplification of the number of copies of the expression cassettes integrated in the genome (Le Dall et al., 1994). Notably the ura3d4 allele has been extensively used, on multicopy autocloning vectors, for increasing heterologous expression (Madzak et al., 2004; Madzak, 2015). However, as discussed more in detail previously (Madzak and Beckerich, 2013; Madzak, 2018), such multicopy vectors, based on in vivo amplification of the copy number of an integrated cassette, present a number of drawbacks. Notably, their use is time consuming and somewhat unreliable, and, moreover, some instability problems of the randomly integrated multiple copies impair their use for industrial applications. The more recently developed strategies of pushing up expression levels through the design of recombinant promoters with increased UAS numbers and fine-tuned core sequences (cf. “Promoters” section) appear to constitute more simple, elegant, and reliable options. However, a few studies have raised concern about the impact that the use of some auxotrophic markers, especially LEU2, could have on the overall phenotype of a recombinant strain. An unexpected interaction between the biosynthesis of leucine and the accumulation of lipids in Y. lipolytica was demonstrated at UT Austin (Blazeck et al., 2014). Notably, leucine biosynthesis was downregulated under conditions favorable to lipid accumulation, and the supplementation with leucine of an auxotrophic strain was shown to upregulate its lipogenic pathway. This impact of leucine metabolism on lipogenesis has received further confirmation from a multifactorial study from a group of laboratories from Sweden and the United States (Kerkhoven et al., 2017). These elements play in favor of using preferentially dominant markers, as is indeed the case in many of the emerging engineering technologies. Moreover, dominant markers can be used directly into wild-type strains, thus allowing to valorize Y. lipolytica biodiversity by selecting wild-type isolates for interesting characteristics adapted to peculiar applications, a stated objective of a number of research groups (Egermeier et al., 2019; Larroude et al., 2020). Despite its natural resistance to several common antibiotics, Y. lipolytica is sensitive to nourseothricin, to hygromycin B, and to compounds of the bleomycin/phleomycin group. The corresponding resistance genes, HygR (hph gene), NTCR (NAT gene), and PhleoR (ble gene), can thus be used as dominant markers (Gaillardin and Ribet, 1987; Cordero Otero and Gaillardin, 1996; Holkenbrink et al., 2018). More recently the sensitivity of Y. lipolytica to mycophenolic acid, and the use of the guaB resistance gene for selection purpose, has also been reported (Wagner et al., 2018). Notably, HygR gene is a favorite marker in the gene disruption and marker rescue method based on the Cre-lox system that was developed at INRA by Fickers et al. (2003b). This dominant marker, together with NTCR, has also been included in several recently described Y. lipolytica toolboxes, under an excisable form, as will be seen hereafter (cf. Section 5). Another type of dominant markers is represented by heterologous gene TUs that allow Y. lipolytica to metabolize a new substrate. Notably, ScSUC2 expression was intended to be used at first as a dominant marker (Nicaud et al., 1989), but the efficiency of selection was found to be impaired by residual cell growth of untransformed cells when sucrose was use as sole carbon source (Barth and Gaillardin, 1996), so this TU was instead used mainly for recipient strain improvement, as seen for the Po1 strain series (cf. Section 4.2). More recently a DsdA TU, encoding a bacterial D-serine deaminase that allows yeast cells to use D-serine as nitrogen source (Vorachek-Warren and McCusker, 2004), has been used as dominant marker in Y. lipolytica, as part of the EasyCloneYALI toolbox (cf. hereafter; Holkenbrink et al., 2018).

5 A post–2010 era of new engineering technologies As could be seen in Fig. 1, the 2010 years have seen a marked increase of the number of publications concerning Y. lipolytica engineering, which was a consequence not only of the increasing popularity of this yeast but also of the general development of new genetic engineering techniques (such as notably the CRISPR-derived ones) that have greatly impacted the whole domain of molecular biology and rendered many of the previously used methods obsolete. Most of these innovative engineering tools were rapidly adapted to be used in Y. lipolytica as reviewed here and schematized in Fig. 3 (upper left quadrant). The corresponding vectors that have been made publicly available from the Addgene website will be presented in Table 3, shortly hereafter.

5.1 New DNA assembly methods Extensive engineering of Y. lipolytica strains for producing compounds of interest requires the introduction of whole heterologous metabolic pathways into yeast cells and sometimes, in addition, overexpression of selected homologous genes. Each new TU required for the pathway needs itself to be assembled with at least a promoter/ORF/terminator pattern. Such complex genetic engineering projects could be performed using classical sequential integration methods (like was the case for DuPont’s EPA-producing strain; Xue et al., 2013), but this is very laborious and time consuming (at least 1 week per gene). Therefore the design of new rapid DNA assembly methods has represented a great advance for metabolic engineering.

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TABLE 3 Selection of vectors and tools available for Y. lipolytica engineering.

Vector name(s)

Characteristics

Yeast vector type/yeast selection marker/yeast promoter for expression of (element to be added)

Available from

References

Ready-to-clone expression/secretion vectors pYLEX1 (pINA1269) pYLSC1 (pINA1296) pINA1292/1312 pINA1297/1317

YLEX expression/secretion kit, with Po1g strain and transformation kit

Integrative (targeting pBR322 docking platform)/LEU2/ recombinant promoter hp4d: (gene)

Yeastern Biotech Co.

Madzak et al. (2000)

Series of expression, secretion, or surface display autocloning vectors

Integrative (at random or targeting zeta sequences)/URA3 (defective or nondefective allele)/ recombinant promoter hp4d: (gene)

cf. authors

Nicaud et al. (2002) Madzak (2003)

Integrative (targeting 11 selected loci)/URA3, HygR, or NTCR loxPexcisable markers for marker rescue/no promoter (expression cassette to be added by USER)

Addgene: #1000000140

pINA1317CWP110 Kit #1000000140, EasyCloneYALI toolkit of a series of 26 autocloning pCfB expression vectors (also available separately) designed for USER cloning

Yue et al. (2008) Holkenbrink et al. (2018)

Recombinant promoters, with various expression levels (numbers of UASs), for designing new expression vectors pUC-UAS1B1-Leum, etc.: pUC backbone carrying hp4d-type UAS1Bn-Leum promoter cassettes with n ¼ 1–16, 20, or 28

Addgene: #44303/*

Blazeck et al. (2011)

Addgene: #113330/31/ 32/34

Patterson et al. (2018)

Addgene: #70007 #84608 to #84617 #91248 #91249 #107677

Schwartz et al. (2016)

pUC-UAS1B8-TEF, pUC-UAS1B16-TEF, etc.: idem with different fragments of pTEF as minimal (core) promoter Ready-to-use vectors for in vivo transposition of transposon-derived cassettes pJY3919 pJY4092

Vector for Hermes transposition with a markerless version and two negative control vectors

pPS3911, pMT3928

Replicative/TIR-LEU2-TIR/pTEF1: transposase Idem without marker (to be added) Idem without transposase, or without TIRs

Ready-to-use vector systems for CRISPR applications pCRISPRyl

Vector for CRISPR/Cas9 gene editing: markerless gene disruption, targeted integration of HR donor sequence

Replicative/LEU2/pUAS1B8-TEF: Cas9/SCR1’-tRNA hybrid promoter (phyb) for sgRNA (to be added)

pCRISPRyl_AXP, XPR2, A08, D17, or MFE1 disruption vectors

Idem with sgRNA for each corresponding gene locus

pHR_AXP_hrGFP, etc.: GFP HR donor vectors for each locus

Replicative/URA3/pUAS1B8TEF:GFP

pCRISPRi_Mxi1_yl

Vector for CRISPR inhibition

Replic./LEU2/pUAS1B8-TEF: dCas9-Mxi1/phyb:(sgRNA)

Idem + _NHEJ

Vector for CRISPRi of KU70 and KU80

idem with sgRNAs for KU70 and KU80 loci (enhanced HR)

pCRISPRa_VPR_yl

Vector for CRISPR activation

Replic./LEU2/pUAS1B8-TEF: dCas9-VPR/phyb:(sgRNA)

Schwartz et al. (2017a)

Schwartz et al. (2017b)

Schwartz et al. (2018)

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TABLE 3 Selection of vectors and tools available for Y. lipolytica engineering—cont’d

Vector name(s)

Characteristics

Yeast vector type/yeast selection marker/yeast promoter for expression of (element to be added)

pCAS1yl

Vector for CRISPR/Cas9 gene editing

Replicative/LEU2/pTEFin:Cas9/ pTEFin:(sgRNA)

Kit #1000000141, EasyCloneYALI toolkit of a series of 15 pCfB vectors (also available separately) for gene editing pCfB4906 pCfB 6364

pCfB3405

Two vectors for Cas9 endonuclease expression

Integrative (IntB or ku70 loci)/ HygR or DsdA (for D-serine use) loxP-excisable selection markers/pTEF:Cas9

Vector for sgRNA expression (empty)

Replicative/NTCR loxPexcisable marker/pPot1: (sgRNA)

Series of six disruption vectors for six selected loci (sgRNAs)

Replicative/NTCR loxPexcisable marker/pPot1: sgRNAs

Series of six corresponding donor vectors (empty) for targeted integration of (multiple) expression cassette(s)

Integrative (at six selected loci)/ marker-free/no promoter (expression cassette to be added by USER)

Available from

References

id.: #73226

Gao et al. (2016)

Addgene: #1000000141

Holkenbrink et al. (2018)

Ready-to-assemble Golden Gate vector systems for heterologous expression and/or CRISPR applications GoldenMOCS-Yali toolkit: BB1_12_YlpTEF, etc.

Modular toolkit of five biobricks for GG assembly of expression vectors carrying one to four TUs

To be used with one of the six replicative backbones/two promoter bricks: TEF, GPD/GUT1 ORF (or gene)/two terminator bricks

Addgene: #117819 to #117823

pMEG_BB2_YL001

Preassembled GUT1 TU

To be used with one of the six replicative backbones

#117824

pMEG_BB3_YL001

Preassembled replicative vector for homologous expression of glycerol kinase GUT1 gene

#117825

id. _ YL18H_AB, etc.

Six replicative backbones for insertion of one to four TUs using GGcompatible biobricks assembly

#...27 to 32

pMEG_YLCas9

Preassembled replicative vector for expression of Cas9 (sgRNA added by GGA)

#117826

idem _leu2_A/B

Two ready-to-use replicative vectors expressing Cas9 and one of 2 sgRNA for LEU2

cf. authors

Golden Gate toolkit including a series of 64 GGE plasmids (pool of GG bricks)

Modular toolkit of 64 biobricks for one-step GG assembly of autocloning expression vectors carrying 1–3 transcription units

Addgene: #120730 to #120793

GGE029

Destination vector backbone for GGA

GGE114

Preassembled destination vector targeting zeta sequences and carrying URA3 marker

Integrative (at random or targeting 4 selected loci: 5  2 bricks)/6 marker bricks: URA3, LEU2, LYS5, ScSUC2, HygR, NTCR/9 promoters for each of 3 TUs: TEF, GAPHD, PGM, 6 TEF- or EYK1based recombinant prom./5 ORF bricks encoding 3 fluorescent proteins/13 terminator bricks

Egermeier et al. (2019)

Larroude et al. (2019)

Continued

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TABLE 3 Selection of vectors and tools available for Y. lipolytica engineering—cont’d

Vector name(s)

Characteristics

JME4390

Series of five vectors for CRISPR/ Cas9 gene editing of various strains using GG assembly: basal structure of pCRISPRyl vector and pool of GG bricks from Larroude et al. (2019)

JME4393 JME4472 JME4580 JME4599

Yeast vector type/yeast selection marker/yeast promoter for expression of (element to be added) Replicative/five loxP-excisable selection markers: LEU2, LYS5, URA3, HygR, or NTCR/pUAS1B8-TEF:Cas9/phyb: (sgRNA added by GGA)

Available from Addgene: #129656 to #129660

References Larroude et al. (2020)

Coordinates of libraries and companies: Addgene, Watertown, MA, United States (www.addgene.org); Yeastern Biotech Co., Taipei, Taiwan (www.yeastern. com). * Additional Addgene reference numbers for plasmids from Blazeck et al. (2011): #44310 to 25, 27, 64 to 66, 68, 71–72, 74 to 80. Abbreviations used: ARS, autonomously replicating sequence; (d)Cas9, (defective) CRISPR associated protein 9 (endonuclease); CEN, centromeric region; CRISPR, clustered regularly interspaced short palindromic repeats (gene editing method); GFP, green fluorescent protein; GG(A), Golden Gate (Assembly); HR, homologous recombination; HygR, hygromycin resistance; NHEJ, nonhomologous end joining; NTCR, nourseothricin resistance; sgRNA, single guide RNA; ORF, open reading frame; TIR, terminal inverted repeats (transposon’s ends); TU, transcription unit; USER, uracil-specific excision reaction (DNA assembly method); VPR, VP64-p65-Rta (3 transcription factors).

5.1.1 In vivo assembly of metabolic pathways A complete b-carotene synthesis pathway was assembled and integrated in a Y. lipolytica strain, in one step, using in vivo HR, by a group of research teams from Shanghai (Gao et al., 2014). This innovative DNA assembler method proceeded in two step: Firstly, four TUs (a selection marker and overexpression or heterologous expression of three genes) were assembled in vitro by overlap extension PCR, and, secondly, following cotransformation of yeast cells, they were combined in vivo using HR of overlaps between the four DNA cassettes. Despite the low efficiency of HR in Y. lipolytica, the final efficiency of this one-step in vivo assembly was in the range of 20%, even when using small overlaps of only 65 bp. The flanking homologies to the genome that were used for targeting integration of the final construct at the Y. lipolytica rDNA locus needed however to be larger, around 0.6 kb (Gao et al., 2014). The use of this DNA assembly strategy was facilitated by the possibility of a rapid visual screening of successfully engineered colonies: b-carotene synthesis produces an orange/ red color in plated colonies, allowing to select the best producer strains. This in vivo method allowed the assembly of a new 11-kb pathway in only 1 week. The overall control of the integration progress was however not guaranteed, since additional integration of partial pathway elements was observed in some transformants exhibiting the deepest color, probably due to NHEJ events (Gao et al., 2014). The same authors also used this in vivo DNA assembly method for a different 10-kb b-carotene synthesis pathway while showing that integration efficiency was enhanced by a more than 13-fold factor (up to 63%) in a doubly deleted ku70/ku80 strain (Gao et al., 2017). A similar in vivo one-step DNA assembly strategy was also used at Nanjing Tech University for introducing a new arachidonic acid (ARA) metabolic pathway, composed of three genes for a total size of 10 kb, in Y. lipolytica (Liu et al., 2017). In this latter study the effect of the length of the overlapping regions on assembly efficiency was assessed and reported to reach 23% when using overlaps of 1 kb. The selected engineered strain was also shown to exhibit a high growth rate and a satisfactory genetic stability during longterm cultivation (Liu et al., 2017).

5.1.2 Design of artificial chromosomes This very recent innovation constitutes a particular case of in vivo assembly of metabolic pathways but is sufficiently stunning to deserve its own paragraph. A research team from Toulouse Biotechnology Institute (TBI; Universit
e de Toulouse, France) has been able to obtain the in vivo assembly in a Po1d derivative of a 23-kb artificial chromosome composed of two telomeres and an ARS/CEN sequence, bearing two new metabolic pathways, with a more than 90% yield (Guo et al., 2020). The basis of this construct is the ylAC plasmid, in which digestion with different restriction enzymes allows to obtain three fragments corresponding to the telomeric ends and to the middle of the artificial chromosome, each bearing its own selection marker. To select for future self-assembly of the complete chromosome, the left telomeric arm bears an URA3 marker, the middle segment bears an essential HEM1 gene, and the right telomeric arm bears a LEU2 marker together with an ARS/CEN sequence. Complete assembly of all ylAC parts occurs in Y. lipolytica cells through HR between 50 bp bordering homologies from the ylAC restriction fragments and the desired TUs, amplified by PCR. This ylAC system was used

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at first to assemble several versions of an artificial chromosome bearing two reporter genes (for RedStar and an endoglucanase) to demonstrate that both the ARS/CEN and the telomeric ends were needed for assembly of a functional chromosome. This step also served to select promoters and to determine an optimal ylAC/TU ratio for in vivo assembly: a ratio of 1/3 allowed to obtain 95% of correct self-assembly (Guo et al., 2020). It also allowed to compare the stability of either circular or linear assemblies with or without selective pressure and led to the conclusion that the addition of an essential gene was necessary for long-term maintenance of the artificial chromosome: a CRISPR knocked-out 5-aminolevulinate synthase defective Po1dh strain was built to allow the use of YlHEM1 gene for additional selection in all cultivation conditions. The efficiency of the ylAC system was demonstrated at first by engineering Y. lipolytica for cellobiose catabolism and then by simultaneously conferring xylose utilization and cellobiose catabolism to this yeast. This proof of concept of the easy and rapid assembly of large metabolic pathways in Y. lipolytica required less than a week of wet laboratory experiments to assemble in a single step two heterologous pathways, each composed of three genes, in an artificial chromosome of 23 kb with a stability comparable with that of natural chromosomes (Guo et al., 2020). This innovative ylAC system constitutes a powerful new tool with a huge potential for academic investigations in molecular genetics and synthetic biology, as well as for applied research. It constitutes a priceless addition to Y. lipolytica toolbox for the engineering of industrial strains. The ylAC plasmid and other plasmids from the same study will be made available on Addgene website.

5.1.3 In vitro DNA assembly methods Golden Gate assembly (GGA) is a molecular cloning method that makes use of type IIS restriction enzymes (that cut DNA at distance from their recognition site) to generate cassettes with variable nonpalindromic overhangs that can be directionally assembled in one step (Engler et al., 2008). It was applied to Y. lipolytica engineering, at INRA, by the design of a library of donor plasmids carrying variable GG-compatible building blocks, allowing in vitro one-step assembly of TU elements (Celi
nska et al., 2017). The proof of concept of this GGA platform efficiency was evidenced with the same b-carotene metabolic pathway that was also in vivo assembled by Gao et al. (2014). The destination vector used was an autocloning zeta-based vector, in which cassettes could consequently be either integrated at random into the genome of the Ylt1-devoid Po1d strain or targeted at the zeta docking platform of the JMY1212 strain, allowing for comparison of the two strategies. The total efficiency (for both GGA and cell transformation), estimated visually as the percentage of colored colonies, was of 90% for the random integration strategy and of 67% for the platform-targeting one (Celi
nska et al., 2017). However, the random strategy generated transformants with a highly variable production of carotenoid, in contrast to the targeted one. In both cases the global efficiency of obtention of the carotene-producing phenotype was considerably higher than with the strategy of in vivo assembly (67%–90% vs 20%). The same authors used this GGA platform for optimizing expression of these b-carotene synthesis genes by using a promoter shuffling approach that allowed obtaining a sixfold increased yield of this terpenoid (Larroude et al., 2018a). Finally, integration of two copies of this optimized GG-assembled b-carotene pathway into an obese Y. lipolytica strain allowed obtaining the best yield ever reported for this terpenoid using a microbial producer (Larroude et al., 2018a). This INRA modular Golden Gate toolkit was developed further and validated through expression of three different fluorescent reporter proteins and assembly of a three-gene pathway enabling xylose utilization by Y. lipolytica (Larroude et al., 2019). It can be used for one-step GG assembly of three TUs, including a selective marker, and of sequences for genome integration, using a fully combinatorial method. This toolbox comprises an autocloning destination vector backbone and a series of 64 biobricks, including 9 promoters (either constitutive or inducible, for fine-tuned expression) and 6 selection markers (3 auxotrophic ones, 2 for antibiotic resistance, and one metabolic marker). This versatile new tool for Y. lipolytica engineering was made available to the scientific community through Addgene website, as reported in Table 3. Another modular in vitro DNA assembly approach was used at the University of Maryland (Baltimore County) that takes advantage of four different but ligation-compatible restriction sites for enabling assembly of multiple gene pathways on Y. lipolytica replicative vectors. This YaliBricks system, complying with BioBrick standards, was validated through assembly, in 1 week, of a five-gene metabolic pathway of 12 kb for violacein synthesis (Wong et al., 2017). A library of those YaliBricks vectors (including 12 promoters), together with added CRISPR genome editing tools, is in project for deposit at Addgene.

5.2 Genome editing technologies 5.2.1 CRISPR tools for gene editing Since less than a decade, CRISPR tools have been derived from the bacterial CRISPR/Cas9 antiviral defense system ( Jinek et al., 2012) for gene editing purposes in numerous organisms. This genetic engineering technique, which has revolutionized molecular biology, is based on the delivery into a cell of a Cas9 nuclease complexed with a synthetic single guide

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RNA (sgRNA; fusion of the two RNA naturally required) for double-strand breaking of the corresponding genomic locus. This DSB can be repaired by NHEJ, allowing the introduction of insertion/deletion (indel) mutations for gene knockout. Alternatively, in the presence of a donor sequence (DNA cassette bordered by homologies to the genome) serving as a repair template, homology-directed repair by HR results in site-specific integration of the DNA cassette. CRISPR/Cas9 tools can thus be applied to markerless gene disruption and/or integration of a (single or multiple) TU construct at chosen genomic loci. Adaptation of CRISPR tools in a novel organism requires the development and fine-tuning of Cas9 nuclease intracellular expression, a limiting step that was taken in 2016 for Y. lipolytica, by two American universities (California, Riverside and Clemson) and by a group of Shanghai laboratories (Schwartz et al., 2016; Gao et al., 2016). These new tools have already been the subject of a review by Shi et al. (2018b). The American research groups expressed a synthetic Cas9 gene, optimized for Y. lipolytica codon bias, with an 8UAS1pTEF promoter, and used a recombinant promoter, combining a native RNA-PolIII promoter with a tRNA to exploit endogenous tRNA processing, to provide the mature sgRNA (Schwartz et al., 2016). These two DNA constructs were combined on a unique pCRISPRyl replicative vector that was deposited at Addgene (cf. Table 3). When cotransformed with a donor vector, pCRISPRyl promoted markerless HR integration of the donor cassette of this latter with an efficiency of 64% in Po1f strain and 100% in a NHEJ-disrupted DKu70 derivative (Schwartz et al., 2016). A screening of Y. lipolytica genomic loci able to accept exogenous TU integration without any impact on cell growth allowed to select five of them for designing CRISPR tools composed of five pairs of vectors (one for CRISPR/Cas9 and sgRNA expression and one homology donor) targeting each of those selected sites. The selected loci include notably XPR2 and AXP1 genes, in which disruption usefully suppress extracellular protease production. These new tools were used for rapid integration of four genes from a semisynthetic metabolic pathway for lycopene production, at four of the loci (Schwartz et al., 2017a). These new pCRISPRyl vectors, targeting the five selected loci, together with the corresponding homology donor vectors carrying a GFP (green fluorescent protein) TU, for fluorescent cell marking, are all available from Addgene (cf. Table 3). The alternative CRISPR/Cas9 genome editing system that was developed at the same time by the group of Shanghai laboratories makes use of a unique vector to carry Cas9 and the desired sgRNA TUs, with or without a donor DNA cassette, to the targeted loci (Gao et al., 2016). The pCAS1yl vector, expressing Cas9 together with the sgRNA from pTEFin promoters, was shown to allow gene disruption (by NHEJ) with a more than 85% efficiency in Po1f. The similar pCAS2yl vector, which additionally bears a homologous donor DNA cassette, was able to raise disruption efficiency (by HR) to more than 94% in a DKu70DKu80 derivative (Gao et al., 2016). Simultaneous multigene disruptions were also obtained using pCAS1yl vectors carrying tandem sgRNA TUs: a double disruption was realized with a similar efficiency and a triple one with an efficiency reduced by only less than half. In addition, it was possible to reiterate several rounds of genome editing, provided that the replicative CRISPR vector was cured on nonselective medium after each of them (Gao et al., 2016). The pCAS1yl vector was deposited at Addgene (cf. Table 3). More recently a multipurpose EasyCloneYALI toolbox was constructed at Novo Nordisk Foundation Center for Biosustainability (Technical University of Denmark) that encompass different engineering tools, for either marker-mediated integration or markerless (CRISPR/Cas9-based) integration or gene deletion (Holkenbrink et al., 2018). The EasyCloneYALI elements are all available from Addgene and are presented, as two toolkits, in different sections of Table 3. The first toolkit, for expression only, comprises a set of 26 autocloning vectors compatible with USER (uracil-specific excision reaction) cloning, for integration of expression cassettes at 11 chosen intergenic sites that enable high expression levels and unaffected cell growth, with auxotrophic or dominant loxP-excisable markers. The second toolkit, for gene editing and expression, comprises a set of 15 vectors: for Cas9 production, for expression of the sgRNA of choice or of sgRNAs targeting 6 selected loci, and corresponding donor vectors for integration of single or multiple TU(s) at these loci. These EasyCloneYALI CRISPR/Cas9 tools allowed genome editing efficiencies above 80% when nonreplicating DNA constructs were used as donor templates, with no detectable loss of the formerly integrated DNA cassettes even following several (5–11) rounds of successive integrations (Holkenbrink et al., 2018). A new GoldenMOCS-Yali toolkit, oriented to heterologous expression and CRISPR/Cas9-based metabolic engineering in Y. lipolytica wild-type strains, was designed by a group of Austrian laboratories (Egermeier et al., 2019). These new vectors can be added to the previous GoldenMOCS tools, making use of a rapid GG cloning strategy that can be applied to multiple organisms. Some new GoldenMOCS-Yali expression vectors were used to extrachromosomally overexpress the glycerol kinase GUT1 gene of several wild-type Y. lipolytica isolates, allowing enhanced bioconversion of glycerol into citric acid and erythritol (Egermeier et al., 2019). Similarly a CRISPR/Cas9 GoldenMOCS-Yali plasmid allowed single gene knockouts into wild-type strains with efficiencies from 6% to 25%, depending on the genetic background. At last a similar will to valorize the biodiversity of wild-type Y. lipolytica strains is behind the development of a series of CRISPR/Cas9 vectors with different selection markers, including several dominant ones, by an INRAE research team. A proof of concept of wild-type strain genome editing was made by attempting to knock out YlGSY1 gene in nine

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wild-type isolates from various origins. The knocking-out was successful in seven of them, with high efficiencies for four strains and an editing success rate of 100% for two of them (Larroude et al., 2020). These two sets of innovative tools are expected to allow the valorization of Y. lipolytica natural biodiversity through selection of the more interesting wild-type isolates for each peculiar application. They have both been made available from Addgene (cf. Table 3). A thorough analysis of CRISPR/Cas9 mechanism during NHEJ-based knocking-out in Y. lipolytica has been performed, using multiparameter flow cytometry combined to genotypic and phenotypic analyses, by a group of French research teams (Borsenberger et al., 2018). This study demonstrated that the limiting factor in the formation of the RNA/protein complex was not nuclease production but RNA guide structure and expression and provided insight into the whole processing of small RNAs in yeast. In addition, full genome sequencing of a knocked-out strain evidenced no unwanted sequence modification, establishing CRISPR/Cas9 as a safe tool for the edition of Y. lipolytica genomes (Borsenberger et al., 2018). An alternative nuclease has also been used for the design of CRISPR tools for Y. lipolytica: Cpf1 (aka Cas12a), a single RNA-guided endonuclease. In contrast to Cas9, Cpf1 uses naturally a single short CRISPR RNA, recognizes a less frequent T-rich protospacer-adjacent motif (instead of a G-rich one), and generates sticky ends DSB more distal to the recognition site, all features allowing a more reliable, and possibly repeated, on-target editing (Yang et al., 2019a). A CRISPR/Cpf1 system was optimized at the University of Maryland (Baltimore County) and used notably to create, with high efficiency, indel mutations in two genes corresponding to counterselectable markers (93% for arginine permease CAN1 and 96% for URA3; Yang et al., 2019a). In contrast to Cas9 the guide RNA for Cpf1 could be simply transcribed from a type II RNA promoter, and it was demonstrated that its modification with 30 polyUs could improve further genome editing efficiency. Multiplexed genome editing was also achieved, with 75%–83% efficiency for double genomic targets and 41.7% for triple ones (Yang et al., 2019a).

5.2.2 CRISPR tools for repression or activation of transcription The University of California (Riverside) also used their recently developed CRISPR tools to produce a CRISPR interference (CRISPRi) system in Y. lipolytica by using a defective Cas9 version (dCas9) lacking endonuclease activity to sterically repress gene transcription from a sgRNA-targeted locus (Schwartz et al., 2017b). To repress NHEJ in a Y. lipolytica strain, a dCas9 was directed to both Ku70 and Ku80 promoters through the use of multiplex sgRNAs. Additionally, effectiveness of HR was further improved by the use of a dCas9 fused to an Mxi1 repressor (Schwartz et al., 2017b). The corresponding optimized CRISPRi vector (pCRISPRi_Mxi1_yl), with or without the sgRNAs for Ku70 and Ku80, was deposited at Addgene (cf. Table 3). More recently, Tianjin University has developed its own CRISPRi-mediated system for regulation of gene expression in Y. lipolytica using either dCas9 or dCpf1, alone or fused to a KRAB repressor (Zhang et al., 2018). A multiplex sgRNA targeting strategy was used to provoke a simultaneous transcriptional repression on multiple genes or to target several sites on a single gene, thanks to a GoldenBrick one-step assembly strategy. For example, high repression efficiencies were obtained (85% with dCpf1 and 92% with dCas9) by simultaneous use of three different sgRNAs targeting a same integrated gfp gene, avoiding any preliminary step of screening for the best sgRNA (Zhang et al., 2018). At last the CRISPR tools from the University of California (Riverside) have been expanded for CRISPR activation (CRISPRa) purposes, in a collaboration with the University of Texas at Austin. Namely, fusion of a defective Cas9 with a transcriptional activator can be used to activate gene transcription from a sgRNA-targeted locus. After testing several activators the synthetic tripartite activator VPR was selected and a VPR-dCas9 fusion was able to promote transcription of two native b-glucosidase genes, thus allowing GM Y. lipolytica growth on cellobiose as sole carbon source (Schwartz et al., 2018). This work adds a new tool for the engineering of Y. lipolytica strains, demonstrates the versatility of the CRISPR/Cas9 system, and paves the way to investigating the potential of numerous parts of the genome that remain transcriptionally silent. The corresponding vector, pCRISPRa_VPR_yl, is available from Addgene (cf. Table 3).

5.2.3 CRISPR tools for base editing Besides CRISPRi and CRISPRa tools, the concept of using the CRISPR/Cas9 system to recruit some new functionality at a precisely targeted genomic locus has been pushed further by using a fusion of CRISPR/Cas9 with an activation-induced cytidine deaminase (Target-AID) for performing point mutagenesis, at first into plant genomes (Shimatani et al., 2017). This innovative strategy of targeted base editing has been adapted for disruption of multiple genes in Y. lipolytica very recently at Seoul National University (Bae et al., 2020). The principle of this Target-AID base editor is to recruit a cytidine deaminase (CDA) at the target DNA locus, using the CRISPR/Cas9 strategy, to provoke a C to T mutation, with no need of any donor DNA. The sgRNA(s) and a fusion protein, consisting of Cas9 combined to a heterologous CDA1 and to a uracil DNA glycosylase inhibitor, were expressed/produced from a single vector and used to disrupt the target gene(s) by creating

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a stop codon through C to T mutation within the mutational window. A DKu70 context was required to prevent the formation of indels via base excision repair after the cytidine deamination step, thus allowing to increase base editing accuracy (Bae et al., 2020). After optimization of the base editor fusion protein expression level, this Target-AID system was able to operate single gene disruption with 94% efficiency and simultaneous double gene disruption with 31% efficiency.

5.2.4 CRISPR tools for whole genome analysis The relative mechanistic simplicity of targeting CRISPR/Cas9 nuclease to specific targets from the genome via the use of sgRNAs has rapidly inspired a new method for high-throughput analysis of gene function on a whole genome scale through the use of sgRNA libraries. When combined to massive sequencing, CRISPR/Cas9 strategy using a library of sgRNAs enables to apply either positive or negative selection screening on the obtained libraries of knocked-out strains. The development of such genome-wide mutational screening tools is however impaired by the fact that the design of effective sgRNAs is often challenging. Nevertheless, a group of US laboratories has recently designed a library of such effective sgRNAs targeting more than 94% of the genes from Y. lipolytica. This result was obtained thanks to a new methodology enabling to quantify the cutting efficiency of Cas9 promoted by the various sgRNAs on a whole genome scale (Schwartz et al., 2019). Performing high-throughput screening in cells with or without DNA repair functions allowed quantifying individual sgRNA efficiency. The resulting library was used to validate the status of essential Y. lipolytica genes, since false negatives due to inefficient sgRNAs were avoided. This library also provided data about the determinants of the efficiency for CRISPR guides. The genome-wide sgRNA library was also applied to studying lipid accumulation in Y. lipolytica and allowed to identify four new genes in which knockout was beneficial when overproducing lipids (Schwartz et al., 2019). Such genome-wide CRISPR analysis could be used notably for a better understanding of this yeast metabolism or for further improvement of strains applied to biotechnological applications.

5.2.5 Other gene editing and transposomics tools Besides the CRISPR-based methods a few other genome editing strategies have also been applied to Y. lipolytica, like transcription activator-like effector nucleases (TALEN). TALEN are recombinant restriction enzymes obtained by fusion of a nuclease to a TAL effector DNA-binding domain that can be modified to target specific sequences (Boch, 2011). Like for CRISPR methods, TALEN generates genomic DSB subject to NHEJ or HR repair, but, as DNA recognition is built in the enzyme itself (in contrast to using a sgRNA), the use of this system is both more laborious and more costly than that of the CRISPR one. Nevertheless, TALEN-based genome edition has been used at INSA (Toulouse University) to introduce structure-based mutations at specific sites into one of Y. lipolytica key enzyme of lipid synthesis pathways, the giant multifunctional fatty acid synthase (FAS). This TALEN-based protein engineering project has allowed modifying the ketoacyl synthase FAS domain to obtain production of shorter fatty acid chain lengths, with a notable enhancement of the myristic acid yield (Rigouin et al., 2017). Another innovative Y. lipolytica gene editing system, designed at UT Austin, was based on in vivo piggyBac transposition (Wagner et al., 2018). This TTAA-specific transposon and its dedicated hyPBase transposase constitute a transposition system that can be used to mobilize a cargo sequence in the genome. When bordered by inverted terminal repeats (ITRs) from piggyBac, a TU cassette can be mobilized by the heterologous transposase expressed from a replicative vector in Y. lipolytica cells. These authors have expressed in Y. lipolytica a codon-optimized hyperactive piggyBac transposase to develop a platform for scarless genomic editing and construction of genome-wide insertional mutagenesis libraries (Wagner et al., 2018). However, in contrast to a few other transposons that integrate randomly into genomes, piggyBac targets peculiar sequences (TTAA, present only in less than 2/3 of annotated Y. lipolytica ORFs) and also favors integration into transcribed regions. These features limit the interest of piggyBac-generated libraries of mutated yeast cells for whole genome saturation mutagenesis applications, such as Tn-seq. Interestingly, once integrated in the genome, the ITRbordered cargo cassette can be remobilized by an modified piggyBac transposase keeping its excision activity but mutated for integration, which constitutes a new scarless marker rescue system (Wagner et al., 2018). Thus, in addition to genomewide insertional mutagenesis applications, this piggyBac platform can be applied to gene editing purposes such as, notably, efficient random integration of TU cassettes or marker rescue subsequent to CRISPR/Cas9-directed integration of TU cassettes (Wagner et al., 2018). Until recently and despite the availability of genomic and metabolic data, the development of holistic approaches in Y. lipolytica have suffered from a lack of genome-wide tools applicable to functional genomics studies. To palliate this problem the University of California (Irvine) has developed a Tn-seq approach in this yeast. Tn-seq, for transposon insertion sequencing, combines genome-wide transposon-generated insertional mutagenesis with massive sequencing of the transposon insertion junctions to obtain an “inverse image” of the genes contributing to a function of interest (surviving

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insertion mutants allow to identify, by sequencing, the nonessential genes and to attribute them a fitness coefficient for a given growth condition). These authors have used Hermes transposon for genome-wide saturation mutagenesis of Y. lipolytica: a library of more than 500,000 insertion mutants was built using an in vivo transposition system based on heterologous expression of a codon-optimized Hermes transposase. This library was then cultivated for 80 generations on glucose or glycerol as carbon sources, and massive sequencing was performed on the surviving population. Statistical analysis of the sequencing results allowed to classify 22% of Y. lipolytica genes as essential (Patterson et al., 2018). The contributions of nonessential genes to growth on glucose or glycerol were measured and used for evaluation of previously established genome-scale models of Y. lipolytica metabolism (Loira et al., 2012; Kerkhoven et al., 2016), identifying the classes of functions for which the models were not optimal (Patterson et al., 2018). A fluorescence-activated cell sorting (FACS) strategy was used to identify the Bodipy-stained insertion mutants for which lipid accumulation was increased to provide new clues for future metabolic engineering. This functional genomic analysis provided new insights into Y. lipolytica biosynthetic pathways, together with new data on the compartmentalization of enzymes and on the precise functions of some paralogs. The Tn-seq tools for in vivo Hermes transposition that have been designed for this study are available from Addgene (cf. Table 3). They comprise a replicative vector for both Hermes transposase expression and supply of a modified selectable transposon (TIR-bordered LEU2 marker), a markerless version for use of a selection marker of choice, and some negative control vectors. These Hermes transposomics tools can be used in a wide range of Y. lipolytica strains to explore other genetic backgrounds, dissect various biological processes, or contribute to identify strains with robust survival for biotechnological applications.

5.3 Sexual hybridization through mating-type switching Strain mating between either wild-type or laboratory strains from Y. lipolytica and subsequent sporulation are notoriously difficult to obtain (Barth and Gaillardin, 1996). Meanwhile the construction of Y. lipolytica diploid strains would be interesting for both increasing the ploidy for process development and easily merging the properties of two recombinant strains for complex metabolic pathway engineering. The difficulty to obtain efficient mating between Y. lipolytica strains thus represented a problem, which has been recently circumvented by UT Austin and a group of South Korean laboratories, both using similar strategies (Li and Alper, 2020; Han et al., 2020). Both research groups performed mating between strains of the same genetic background after switching the mating type of one of them into the opposite type by site-specific HR at the mating type locus. In the work performed at UT Austin, this strategy was used for combining a strain previously engineered and evolved for xylose utilization with three strains engineered for overproduction of different metabolites (a-linolenic acid, riboflavin, or triacetic acid lactone). The three diploid strains obtained were able to produce these compounds directly from xylose, with yields similar or higher than the parental strains grown on glucose (Li and Alper, 2020). This innovative strategy is expected to ease pathway engineering and to lead promising results in a near future. In the domain of biotechnological processes development, which was the main purpose of the South Korean study, increasing ploidy is expected to ensure a better genetic stability and to enhance both stress resistance and productivity of the strain of interest (Han et al., 2020).

6 Yarrowia lipolytica developing applications and future prospects As exemplified throughout the pages of this review, Y. lipolytica is a yeast with great industrial potential for production of proteins or metabolites, either native or nonnative, thanks to a wide range of available genetic engineering tools. Many interesting features of this yeasts, notably its robust tolerance to a large range of pH variations, to high salt levels, and to organic compounds, are very favorable to bioprocess optimization. Additionally, its GRAS status identifies Y. lipolytica as an attractive host for manufacturing drugs, nutraceuticals, or dietary supplements, as well as for environmental applications. As an exhaustive and technologically detailed review of all its currently developing applications would clearly be outside the scope of this chapter, only a short survey will be made here, and the reader will be addressed to the recent reviews listed in Table 1 for more specific information. As seen at the beginning of this chapter, some wild-type Y. lipolytica strains can accumulate lipids (from both uptake from medium and de novo TAG biosynthesis) up to 50% of CDW, mainly as TAGs and sterol esters, more than FFA. Following genetic engineering, obese yeast cells with modified lipidic profiles can be obtained for overproduction of fatty acids and derivatives. As exemplified hereafter, such lipid-oriented applications dominate the range of possible Y. lipolytica biotechnological utilizations, notably the biosynthesis of fatty acid methyl esters (FAMEs) for biofuel or of polyunsaturated fatty acids (PUFAs) for feed, food, and pharma applications. However, this yeast is also explored as a source of other highvalue lipid-derived compounds like polyhydroxyalkanoates, free hydroxylated fatty acids (chiral HFAs), or polyketides,

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used as building blocks for chemistry, and wax esters for use as industrial lubricants (Sabirova et al., 2011; Markham et al., 2018). Medium chain length PHAs (mcl-PHAs) are biosourced linear polyesters that can be used for producing renewable and biodegradable bioplastics. Production of mcl-PHAs was achieved in Y. lipolytica through heterologous expression of PHA synthase genes and was shown to be increased by redirecting the fatty acid flux toward b-oxidation (by deletion of genes from neutral lipid synthesis pathway), up to more than 7% of CDW (Haddouche et al., 2010, 2011). Production of mcl-PHAs from food waste, a process both using renewable resources and providing solutions to waste management, has been achieved at City University of Hong Kong (Gao et al., 2015). At last, depending on strain genetic backgrounds and culture conditions, tailored PHA homo- or copolymers, with different average molar masses and biophysical/mechanical properties, were produced in INRA/INSA collaborations, increasing the range of possible applications, notably in the biomedical domain (Rigouin et al., 2019).

6.1 High-throughput expression platforms for protein engineering and more Since the first examples of engineering using Y. lipolytica ability for complex protein production (Madzak et al., 2006; Galli et al., 2011), high-throughput platforms were developed in Y. lipolytica, using either JMY1212 (Bordes et al., 2007) or JMY2566 strains (Leplat et al., 2015; cf. Table 2) for expression cloning and mutant library screening. The first platform was notably used at INSA (Toulouse University) for engineering Candida antarctica lipase B (CalB), an enzyme of high importance in industrial biocatalysis, and enabled the selection of variants with enhanced catalytic properties (Emond et al., 2010). The second platform was used for a systematic overexpression approach aimed at identifying putative transcription factors implicated in Y. lipolytica lipid metabolism. This study identified 38 known or new transcription factors impacting the accumulation of lipids under at least one of the cultivation conditions tested and suggested that regulation networks were differing with the carbon source, a critical information for industrial bioprocesses (Leplat et al., 2018). The transcription factor overexpression library created could be useful for various phenotypic screenings.

6.2 Obese Yarrowia lipolytica strains Taking advantage of the remarkable lipid storage capacity of Y. lipolytica, several research groups have engineered this yeast for an obese phenotype. Although all factors implicated in lipid accumulation (mostly of triglycerides) are not fully identified, the availability of glycerol-3-phosphate (G3P) and of fatty acids was shown to be a limiting factor for TAG synthesis, in which homeostasis appears to be ensured through the G3P shuttle and the b-oxidation pathways (Dulermo and Nicaud, 2011). Based on these data the first obese strain was derived from Po1d at INRA by deleting several genes from these two metabolic pathways (GUT2 and POX1 to POX6). This engineered strain accumulates lipids to levels as high as 75% of its DCW (Dulermo and Nicaud, 2011). More recently, UT Austin has adopted an ambitious strategy of combinatorial strain engineering of Y. lipolytica (multiplexing genetic engineering of lipid synthesis targets with phenotypic induction) to construct an obese strain derived from Po1f (Blazeck et al., 2014). Simultaneous perturbation of five targets, from three distinct metabolic pathways, resulted in the obtention of lipid-saturated cells, exhibiting the highest lipid level ever reported (near 90% of CDW) and a very high lipid titer (25 g /L). This rewiring effort also allowed to advance fundamental understanding of lipogenesis, notably by permitting to uncouple lipogenesis from nitrogen starvation (Blazeck et al., 2014). This obese strain has potential for being used as a platform for production of lipids and biofuels. At last the highest carbon to lipid conversion yield to date (85% of theoretical maximal) and the highest lipid yield titer ever (55 g/L) were reported by the Massachusetts Institute of Technology for the obese YL-ad9 strain (Qiao et al., 2015). Very interestingly, this obese strain also exhibited a threefold higher growth rate compared with its parent (LEU2complemented Po1g) strain. YL-ad9 was obtained by simultaneous overexpression of three Y. lipolytica genes (ACC1, DGA1, and SCD), the latter (encoding a d-9 stearoyl-CoA desaturase) having been identified as a rate-limiting step by reverse engineering of mammalian cellular obese phenotypes (Qiao et al., 2015). This innovative work constitutes a significant step toward effective and robust Y. lipolytica processes for producing biodiesel or other oil-derived compounds.

6.3 Whole-cell Yarrowia lipolytica factories for single-cell oil production The production of SCO enriched in essential fatty acids (PUFAs, polyunsaturated fatty acids essential for health but not synthesized by mammals) is of high economical interest (Kothri et al., 2020). Engineering Y. lipolytica for that purpose takes advantage of the fact that its fatty acids have the highest known percentage (more than 50%) of linoleic acid (LA; Beopoulos et al., 2009a,b) among all oleaginous yeasts. In addition, obese Y. lipolytica strains, notably b-oxidation-deficient strains,

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presenting an increased pool of FFA that can be modified for specific purposes, constitute good basis for further engineering of the fatty acid metabolism pathway. The Y. lipolytica o-3/o-6 biosynthetic pathway can be engineered, through heterologous expression of different desaturases, to convert LA into o-3 fatty acids, such as eicosapentaenoic acid (EPA; Xue et al., 2013; Xie et al., 2015), docosahexaenoic acid (DHA; Casas-Godoy et al., 2014), and a-linolenic acid (ALA; Cordova and Alper, 2018; Li and Alper, 2020), or into o-6 fatty acids, like arachidonic acid (AA; Liu et al., 2017, 2019) and g-linolenic acid (GLA; Chuang et al., 2010). These applications target the commercial niche of SCO enriched in essential fatty acids, used as dietary supplements for feed or food, as exemplified by the first commercially viable Y. lipolytica technology platform producing EPA-rich SCO (DuPont, United States—project reviewed by Xie et al., 2015). Strategies for SCO production in Y. lipolytica, together with genetic regulation systems, have been recently reviewed by Ga´lvez-Lo´pez et al. (2019). Besides these o-3/o-6 PUFAs, Y. lipolytica can also be applied to producing other fatty acid–derived compounds: chemical building blocks such as ricinoleic acid, an o-9 fatty acid (Beopoulos et al., 2014), or dicarboxylic acids (Abghari et al., 2016; (Mishra et al., 2018)), odd-chain fatty acids (Park et al., 2018), nutraceuticals like conjugated linoleic acid (Imatoukene et al., 2017), and aroma compounds such g-decalactone for peach flavor (Braga and Belo, 2016), as reviewed notably by Ledesma-Amaro and Nicaud (2016b).

6.4 Use of waste or renewable resources for production of biofuels and more A main key factor for economic viability of processes in the domain of white biotechnology is the possibility of using cheap substrates, especially those issued from agroindustrial waste, representing renewable resources. This is particularly important for biofuel production, which aims to replace at least a part of fossil oil fuels by biomass-derived ones. Y. lipolytica constitutes a particularly interesting possible source of biodiesel, since it can be engineered for utilization of inexpensive carbon sources, especially those issued from agricultural or industrial wastes (Ledesma-Amaro and Nicaud, 2016a; Spagnuolo et al., 2018). On this regard, Y. lipolytica stands up among nonconventional yeasts, as appears from the recent review by Do et al. (2019). Notably, this yeast can grow naturally on glycerol, an industrial by-product of biodiesel or soap production, and on waste cooking oil. This latter property was recently used to develop a platform using traditionally obtained mutants to produce fatty acid methyl esters (FAMEs) that can be used as biodiesel (Katre et al., 2017). Similarly, Y. lipolytica strains engineered, at National University of Singapore, for food waste bioremediation, were grown on waste cooking oil to produce fatty acid ethyl esters (FAEEs) that can be potentially suitable as biodiesel (Ng et al., 2020). As already seen here, several Y. lipolytica laboratory strains were engineered to grow on sucrose as sole carbon source through heterologous invertase expression, which allows them to grow on molasses issued from agroindustrial wastes (Nicaud et al., 1989; Lazar et al., 2013; F€ orster et al., 2007). Alternative substrate use capacities that have been introduced into Y. lipolytica strains also include other biomass-derived sugars, such as galactose (a sugar from notably hemicelluloses and pectins, used as sole carbon source providing overexpression of endogenous genes from Leloir pathway; Lazar et al., 2015), inulin (a fructose polymer for storing energy in some plants; Liu et al., 2010; Zhao et al., 2010; Shi et al., 2018a; Rakicka et al., 2019), and, very recently, lactose (a sugar from acid whey, by-product of cheese and yogurt production; Mano et al., 2020). Interestingly, Wroclaw University of Environmental and Life Sciences and INRA collaborated to construct Y. lipolytica strains displaying both increased lipid accumulation and effective use of several biomass-derived sugars (glucose, sucrose, galactose, fructose, and inulin; Hapeta et al., 2017). One of these strain, YLZ150, a derivative of the wildtype isolate W29, was selected for efficient lipid biosynthesis from this wide range of biomass-derived sugars and constitute an interesting basis for biodiesel or SCO production from nonlipid renewable resources. The use of lignocellulosic materials as substrate would be highly desirable for biofuel production processes, considering their wide abundance, low cost, and high sugar content. However, Y. lipolytica is lacking the pathway of cellulolytic enzymes that would be needed to break down cellulose from vegetal biomass, as well as the ability to metabolize xylose, the major pentose in lignocellulosic hydrolysates. Consequently, several research groups have worked on Y. lipolytica engineering for growth on lignocellulosic biomass by overexpression of endogenous genes and/or expression of fungal enzymes, notably at National Renewable Energy Laboratory (Golden, United States; Wei et al., 2014) and at INRA and INSA Toulouse (Guo et al., 2015b, 2018). Similar strategies were used by INRA and collaborators to engineer Y. lipolytica for utilization of xylose (Ledesma-Amaro et al., 2016; Niehus et al., 2018) to develop a platform for lipids, chemicals, and fuels production from lignocellulosic materials. As demonstrated by Ryu and Trinh (2018), such engineering projects could benefit from an activation of the dormant pentose metabolic pathway of Y. lipolytica that would allow enhancing xylose assimilation through overexpression of the native pentose-specific transporters. The recently developed strategy of sexual hybridization through mating-type switching (cf. this section) is expected to benefit to biofuel production from waste or renewable resources by allowing an easier combination of the properties of two

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GM strains (e.g., xylose utilization with efficient biofuel production), like in the examples demonstrated at UT Austin (Li and Alper, 2020). As seen in Section 5.3, increasing strain ploidy is also expected to be favorable for bioprocess development (Han et al., 2020). The use of Y. lipolytica engineered strains as whole-cell factories for production of FFA-, FAEE- or FAME-based biodiesel has been reviewed recently by Xie (2017) and by Yan et al. (2017), who also propose new engineering pathways using lipase and wax ester synthase for these applications. Interestingly a radically different strategy for microbial biofuel has also been explored at UT Austin: the biosynthesis of pentane from linoleic acid through overexpression in Y. lipolytica of a soybean lipoxygenase. This GM yeast appears to constitute a promising host for the biosynthesis of short-chain nalkane (Blazeck et al., 2013). At last, as reported in the “Cellular organelles” section, a subcellular compartment engineering strategy was recently used, at Huazhong University of Science and Technology, for improving biosynthesis of FAMEs and hydrocarbons in GM Y. lipolytica (Yang et al., 2019b). The targeting of lipase-dependent new metabolic pathways to the lipid body, combined with a compartmentalization of related pathways in other lipid-relevant organelles, namely, the ER and the peroxisome, allowed to obtain a FAME titer of 1.6 g/L, 16-fold higher than with traditional cytosolic pathways. This subcellular compartment engineering approach was also applied to other lipase-dependent pathways for fatty alkenes and alkane synthesis, also resulting in high product yield and titer, with a 14-fold enhancement for this latter (Yang et al., 2019b). The innovative strategy of targeting synthetic pathways to LB-related organelles is also expected to allow optimizing the biosynthesis of other lipid-derived compounds.

6.5 Whole-cell factories producing carotenoids, other terpenoids, and polyketides Engineered Y. lipolytica strains have emerged since a few decades as promising cell factories for biosynthesis of the highly valuable plant natural products that are terpenoids and polyketides. These bioactive compounds have numerous applications in food industry and in the biopharmaceutical and cosmetics areas. (Muhammad et al., 2020) have very recently reviewed the actual or potential uses of GM Y. lipolytica for the production of a wide range of plant natural products. More specifically the biosynthesis of various terpenoids in this yeast was reviewed by Ma et al. (2019) and by Worland et al. (2020a). Terpenoids are a wide class of natural compounds based on isoprene units for which Y. lipolytica constitutes a valuable production host due to its own mevalonate pathway; its high level of acetyl-CoA, a primary terpenoid precursor; and its remarkable lipid production. As already seen in Section 3, Microbia/DSM has developed GM Y. lipolytica as a commercially valuable host for the production of carotenoids, the most popular of tetraterpenoids, for use as color and antioxidant agents that can be labeled as natural, for feed and food. Independently, carotenoid production from Y. lipolytica was also achieved by DuPont, which have made available some information on Y. lipolytica engineering for producing lycopene, an intermediate in carotenoid biosynthesis (Ye et al., 2012). Since then, records of lycopene production by GM Y. lipolytica have been broken by Dresden University using a further engineered INRA obese strain (Matth€aus et al., 2014) and by the University of California (Riverside), which reported the unprecedented lycopene levels for a eukaryotic host of 21 mg/g of DCW (Schwartz et al., 2017c). Recently, very high yields of b-carotene (6.5 g/L and 90 mg/g of DCW) were obtained from a further engineered INRA obese strain using a combinatorial synthetic biology strategy based on GGA to select optimal promoter/gene pairs for each TU of the synthetic pathway, together with process optimization (Larroude et al., 2018a). These results establish Y. lipolytica as the best microbial producer reported up to date for this terpenoid and a promising platform for potential industrial application. A very recent metabolic study from a group of US laboratories used tracing of 13 C-isotope-labeled compounds to follow carbon fluxes during b-carotene biosynthesis in a GM W29-derived strain during coutilization of glucose and lipid-derived substrates as carbon sources (Worland et al., 2020b). The results demonstrated that Y. lipolytica had a segregated metabolic network and was lacking catabolite repression by the carbon source. Coutilization of glucose and lipid feedstock allowed to promote cell growth and to enhance b-carotene yield by a twofold factor compared with glucose alone (Worland et al., 2020b). These effects were attributed notably to the supply of limiting precursor metabolites (like acetyl-CoA, derived from fatty acids) and to an enhanced storage of the carotenoid in the lipid body. This study increased knowledge on carbon fluxes during metabolic coutilization and brought clues for developing more efficient bioprocesses for valorizing lipid-derived feedstocks, including waste oils, through production of high-value terpenoids. Other terpenoids or derivatives produced by engineered Y. lipolytica strains include notably astaxanthin, bionone (rose aroma), linalool, and limonene (Muhammad et al., 2020). Polyketides are bioactive natural products for which biosynthesis in plants or microorganisms implies large multidomain enzymes or multienzyme complexes, the polyketide synthases. The polyketides produced from GM Y. lipolytica are mainly type III polyketides from plants, notably flavonoids (flavones, anthocyanins, eriodictyol, naringenin, and taxifolin) and stilbenoids (stilbene and resveratrol) (Muhammad et al., 2020). Y. lipolytica constitutes a particularly valuable

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production host for flavonoids due to its high levels of malonyl-CoA and acetyl-CoA, both primary flavonoid precursors, and its well-developed lipid body. Notably a consortium of laboratories from China and the United States have recently constructed and optimized Y. lipolytica cell factories for the production of flavonoids and hydroxylated derivatives (Lv et al., 2019). A modular assembly strategy was used to introduce heterologous flavonoid pathways into Y. lipolytica and to optimize them further. A thorough evaluation of the biosynthesis pathways of flavonoid precursors was performed, and their limits were pushed further by overexpressing genes implicated into chorismate and malonyl-CoA biosynthesis. The global performance of GM Y. lipolytica for producing flavonoid derivatives was also improved by optimizing the copy numbers of chalcone synthase and cytochrome P450 reductases, some enzymes identified as bottlenecks for the synthesis of hydroxylated flavonoids (Lv et al., 2019). When combined with process optimization, notably concerning carbon/nitrogen ratio and pH, the newly developed Y. lipolytica cell factories were able to produce, in shake flasks, high yields (in the range of 100–200 mg/L) of several flavonoids such as eriodictyol, naringenin, or taxifolin (Lv et al., 2019). These promising results contribute to establish GM Y. lipolytica as a workhorse for microbial biosynthesis of natural products with health benefits.

6.6 Whole-cell factories for organic acid production As seen previously here, there is a long-established practice of using Y. lipolytica for producing organic acids, especially citric acid. Optimization of citric acid production by GM Y. lipolytica however remains a topical issue (Cavallo et al., 2017; Hu et al., 2019). Notably the use of alternative substrates to glycerol, like waste cooking oil (Liu et al., 2015b), xylose (Ledesma-Amaro et al., 2016), or inulin (Rakicka et al., 2019), was explored. The citric acid yield of 200 g/L obtained from inulin, through expression of a heterologous inulinase, was the highest reported for this yeast, suggesting a potential for industrial application (Rakicka et al., 2019). Among other organic acids with a history of production in Y. lipolytica, aketoglutaric (cf. Section 4.2; Yovkova et al., 2014; Guo et al., 2016) and succinic acid have also benefited of genetic engineering. Notably a GM Y. lipolytica strain derived from H222 was established through process optimization as a remarkable producer of succinic acid, for use as dietary supplement, as food additive, or as building blocks for bioplastics ( Jost et al., 2015). Other carboxylic acids of industrial interest can also be produced in GM Y. lipolytica, such as isocitric acid (Li et al., 2018), itaconic acid (Blazeck et al., 2015; Zhao et al., 2019), and crotonic acid (Wang et al., 2019). Such engineering projects could benefit from the identification of Y. lipolytica keto acid transporters that provides clues to further improving the accumulation of specific organic acids (Guo et al., 2015a). A state of the art of the main uses of Y. lipolytica cell factories for the biosynthesis of organic acids (and also sugar alcohols), with their achievements and their limitations for the industrialization of bioprocesses, has been very recently established by Fickers et al. (2020).

6.7 Bioengineered hybrid materials for environmental applications The robustness and versatility of Y. lipolytica and the increasing availability of genetic engineering tools have prompted since a few years the development of very innovative and unexpected applications of GM strains of this yeast in the environmental domain. Besides more classical bioremediation uses of wild-type or GM strains, as reviewed by Bankar et al. (2009) and Zinjarde et al. (2014), Y. lipolytica was applied to constructing bio-based hybrid materials through surface display of new functionalities. As seen in Section 3, the Bei Shizhang Advanced Class of Life Science Research has developed a GM Y. lipolytica– based autocementation kit, Euk.cement, for both civil engineering (strengthening basal soil of cross-sea bridges) and environmental restoration purposes (for marine ecology damages, such as recession of coral reefs). Euk.cement complex engineering project has employed the INRA JMY1212 strain as a chassis for assembly of three associated recombinant circuits: a light control (darkness induction system) part, which acts as a negative control part, repressing the two other functional parts in presence of blue light, together with a flocculating part and a supporting part (Tang et al., 2016). More precisely, Euk.cement is composed of Y. lipolytica cells surface-displaying different silica-binding peptides, for immobilization onto particles with a silica containing surface (e.g., sand), and secreting a recombinant mussel byssus protein, for consolidating cell binding. The metabolism of immobilized living cells is then promoting carbonate sedimentation for tightly sticking the particles together. In addition, the newly introduced pathways are designed to be repressed by blue light, making the whole process controllable for in situ use, since the GM cells will express the recombinant pathways only when embedded into underwater sand layers (Tang et al., 2016; additional details on constructs on http://2015.igem. org/Team:HUST-China website). More recently a novel bioengineering-based biosilica yeast hybrid material was developed at Ocean University of China by catalytic biosilicification obtained through surface display of a marine sponge silicatein on Y. lipolytica cells

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(Wang et al., 2020b). This heterologous protein was expressed/secreted/displayed using a cassette from the zeta-based autocloning pINA1317-CWP110 vector, randomly integrated in Po1h strain, which required screening of the best strain for biosilicification activity. Efficient aggregation of silicatein-displaying Y. lipolytica cells was obtained, using either orthosilicate or seawater as substrate, generating a sheetlike biosilica  yeast hybrid material with over 70% porosity and an average pore diameter near 10 mm (Wang et al., 2020b). This hybrid material was able to achieve efficient (above 99%) removal of chromium ions or of n-hexadecane in aqueous environments (Wang et al., 2020b). Importantly, this living hybrid material is also renewable: it could be recultivated with new batches of GM yeast cells, with unchanged highly efficient water treatment properties. It provides an effective, low-cost, and scalable bioremediation tool, for detoxification of chromium ions in fresh water or degradation of alkanes, from petroleum spills, in seawater.

6.8 What future prospects? We have seen that, thanks to the addition of genome editing and fast assembly technologies to the global Y. lipolytica toolbox, complex metabolic engineering strategies (including combinatorial assembly for high-throughput performance screening) can now be envisioned for this yeast. The still more recent strategy of in vivo assembly of artificial chromosomes is a game-changing new technology that will undoubtedly have great impact on both academic and applied research on Y. lipolytica (Guo et al., 2020). Thanks to the increased possibilities of addressing recombinant enzymes to various intracellular microbodies, the compartmentalization of new metabolic pathways is expected to allow the design of improved cell factories, as demonstrated recently (Yang et al., 2019b). However, such engineering projects remain dependent on thorough knowledge of the key metabolic processes involved (e.g., for the accumulation of lipids or the production of organic acids) that need to be brought by genomic, transcriptomic, metabolomic, or fluxomic analyses (Morin et al., 2011; Sabra et al., 2017; Lazar et al., 2018; Worland et al., 2020b). If many gene functions have been already characterized in Y. lipolytica, thanks to sequence annotation combined to knockout studies, there is still a vast majority of genes with unknown functions, or a function that remains only putative (http://gryc.inra.fr/). Bioinformatics and applied mathematics have an important role to play in the exploration of Y. lipolytica regulatory networks through design of genome-scale metabolic models (GEMs; Loira et al., 2012; Pan and Hua, 2012; Pomraning et al., 2015; Kerkhoven et al., 2016; Tr
ebulle et al., 2017). Other high-throughput tools, such as recently developed genome-wide profiling Tn-seq or CRISPR-based approaches (Patterson et al., 2018; (Schwartz et al., 2019)), are needed to complete and verify the predictions of these GEMs for a better understanding of cellular processes. The most recent of these GEM studies, by a group of French laboratories, combined network interrogation and validation at the bench to identify context-specific regulatory elements and mechanisms that drive lipid accumulation in Y. lipolytica (Tr
ebulle et al., 2017). These authors reconstructed a gene regulatory network composed of over a hundred transcription factors, over 4000 target genes, and over 17,000 regulatory links (YL-GRN-1). This work notably identified nine new potential regulators of lipid accumulation and validated the effect of six of them by wet laboratory experiments (Tr
ebulle et al., 2017). Thus holistic approaches will be required to better apprehend Y. lipolytica regulatory networks and efficiently remodel its metabolic pathways for the intended biotechnological purposes (Lazar et al., 2018). The generalized use of CRISPR-based genome editing tools, especially in their markerless version or using new dominant markers, now makes affordable a rapid screening of various genetic background to select the more efficient and robust for a desired genetic engineering application. This approach valorizing the biodiversity of Y. lipolytica is already a stated objective in several laboratories (Egermeier et al., 2019; Larroude et al., 2020) and will benefit from existing libraries of wild-type Y. lipolytica isolates, notably at INRAE (CIRM, Montpellier, France) and OUC (Qingdao, PR China). The newly developed strategy of sexual hybridization through mating-type switching (Li and Alper, 2020; Han et al., 2020) is expected to facilitate complex engineering of selected strains by a combinational approach of pathway engineering and to contribute to the robustness of production strains. However, strain selection and metabolic engineering represent only one side of the problem: although not the subject of this chapter, process optimization constitute the other side of any successful application, like exemplified for DuPont’s EPA-rich SCO production by Xie et al. (2017). Microbial cell factories need appropriate cultivation conditions to enable optimized production, or, to quote Vandermies and Fickers (2019) who reviewed bioreactor processes for production of heterologous proteins in Y. lipolytica, “a sports car cannot drive fast on a gravel road.” Indeed, various external factors (pH, temperature, nutrients, and oxygen availability) influence the behavior of yeast cells (or even constitute a stress for them), notably concerning dimorphic transition (Soong et al., 2019; Worland et al., 2020b). As reviewed by Timoumi et al. (2018), these environmental factors have an impact on the morphology of Y. lipolytica and on the production of metabolites and proteins, which needs to be taken into account during process design. In addition to their utility for designing metabolic engineering strategies, GEMs can also help to predict the metabolic responses to environmental conditions, as exemplified

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by Kavsˇcek et al. (2015) for optimization of a bioprocess (lipid production by Y. lipolytica). Some statistical modeling tools, such as response surface methodology, have also proved to be effective for bioprocess optimization (Darvishi et al., 2017). All these technologies and tools will cooperate in the recognition of Y. lipolytica as a workhorse for a growing number of applications in the rapidly developing area of white biotechnology.

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Chapter 19

Engineering of microbial cell factories for production of plant-based natural products Julia Gallego-Jara∗, Gema Lozano Terol, Rosa Alba Sola Martı´nez, Manuel Ca´novas Dı´az, and Teresa de Diego Puente Department of Biochemistry and Molecular Biology (B) and Immunology, Faculty of Chemistry, University of Murcia, Campus de Espinardo, Regional Campus of International Excellence “Campus Mare Nostrum”, Murcia, Spain *Corresponding author: E-mail: [email protected]

1 Introduction Many plant natural products (PNPs) are highly valuable products in industries, but most of these compounds are produced in very low concentrations in plants. Plant metabolites play an essential role in the defense against other organisms and pathogens and thus are important for plant subsistence. PNPs accumulate in specific tissues and organelles, and their production is affected by several factors (Gonc¸alves and Romano, 2018). For thousands of years, humans have used PNPs as natural medicines to treat a variety of diseases, and they are actually employed as flavors, therapeutics, food additives, or fragrances (Che et al., 2016; Yuan et al., 2016). Most PNPs are still extracted from plants, which implies the use of large agricultural extensions and extraction processes that are not environmentally friendly. In addition, plant extraction results in a complex mixture of product compounds and may depend on seasonal variations. Nevertheless, from the development of molecular biology, omics techniques, metabolic engineering, systems biology, and the sequencing data of microorganisms, biotechnology is now able to compete with existing extraction protocols from plants and chemical synthesis of PNPs (Liu et al., 2017). In this chapter, recent progress of microbial cell factory engineering to produce PNPs is summarized (Table 1).

2 Host microorganisms The most used microorganisms for PNP obtention are Escherichia coli (E. coli, a prokaryote) and Saccharomyces cerevisiae (S. cerevisiae, a eukaryote) probably due to the available knowledge of their genomes and metabolomes. However, progress in genomic engineering techniques has allowed us to carry out biotechnological methods in different microorganisms, such as Bacillus subtilis (B. subtilis), Corynebacterium glutamicum (C. glutamicum), Rhodospirillum rubrum (R. rubrum), or Yarrowia lipolytica (Y. lipolytica) (Abdallah et al., 2019; Kallscheuer et al., 2016; Li et al., 2015; Matth€aus et al., 2014; Mohamed N. Baeshen, 2014; Wang et al., 2012). Host selection is an essential step to successfully carry out a biotechnological process. Bacteria have fast growth, wider choice in different culture media, ease to manipulate genetically, high transformation efficiency, and capability to overexpress proteins significantly. However, the metabolic complexity of bacteria is lower than that of plants (Vargas-Maya and Franco, 2017). This fact could be a problem for expressing a foreign gene in a nonnative host due to a number of factors, such as different codon usage, missing chaperones, and posttranslational modifications, which are necessary for the proper functioning of proteins (Rosano and Ceccarelli, 2014). The main advantage of yeast as a host is the similarity between the intracellular structure of plants and yeasts (Cravens et al., 2019).

Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00019-2 © 2021 Elsevier Inc. All rights reserved.

381

TABLE 1 Main natural plant products obtained from microorganisms.

Product

Culture conditions

Main applications

Natural Source

Microorganism Host

Maximum Titer (mg/L)

Supplemented precursor

Metabolic techniques

References

Terpenoids Geraniol

Fed-batch

Flavor and fragrance, insect repellent

Many spp.

Saccharomyces cerevisiae

1680

None

GES and ERG20 enzymatic engineering

Jiang et al. (2017)

Limonene

Fed-batch

pharmaceutical, pesticide, flavor, perfume, jet fuel

Citrus spp.

Saccharomyces cerevisiae

917.7

None

Orthogonal engineering of biosynthetic pathway

Cheng et al. (2019)

Oxygenated taxanes (Taxol precursors)

Fed-batch

Anticancer treatment

Taxus spp.

Escherichia coli

570

None

Cytochrome P450 expression

Walters et al. (2016)

a-Pirene

Fed-batch

Flavor, fragrance, potential jet fuel

Sideritis spp., Salvia spp., Pistacia terebinthus

Escherichia coli

970

None

Novel biosynthetic pathway of a-pinene

Yang et al. (2013)

Menthol

Shaking flask

Pharmaceutical, flavor, perfume, cosmetic

Mentha spp.

Escherichia coli

53

Pulegone

Cell-free one-pot biotransformation system (combining pathway assembly techniques with classical biocatalysis methods)

Toogood et al. (2015)

Valencene

Shaking flask

Fragrance, cosmetic

Citrus spp.

R sphaeroides

352

None

Engineering of biosynthetic pathway

Beekwilder et al. (2014)

b-Carotene

Fed-batch

Pharmaceutical, nutraceutical, cosmetic sand food

Many vegetables and fruits

Escherichia coli

2100

None

Engineering of biosynthetic pathway and central metabolic modules to increase ATP and NADPH

Zhao et al. (2013)

Artemisinic acid (artemisinin precursor)

Fed-batch

Antimalarial treatment

Artemisia annua

Saccharomyces cerevisiae

25,000

None

Engineering of biosynthetic pathway and cytochromes P450 and b5 expression

Paddon et al. (2013)

Lycopene

Fed-batch

Antioxidative, anticancer, andantiinflammatory

Many vegetables and fruits

Escherichia coli

1440

None

Engineering and reconstitution of biosynthetic pathway

Zhu et al. (2015)

Shaking flask

Precursor of alkaloids

Lindera aggregata, Annona squamosa, Ocotea fasciculata

Escherichia coli

160

None

Engineering of biosynthetic pathway with TH enzyme and de novo biosynthetic pathway for BH4

Matsumura et al. (2018)

Alkaloids (S)-reticuline (benzylisoquinoline alkaloids precursor)

Thebaine (benzylisoquinoline alkaloids precursor)

Shaking flask

Precursor of alkaloids

Papaver bracteatum

Escherichia coli

2.1

None

Stepwise culture of four engineered strains

Nakagawa et al. (2016)

Resveratrol

Shaking flask

Antioxidative, antiinflammatory, anticancer, and chemopreventive

Grapes, blueberries, raspberries, mulberries, peanuts

Escherichia coli

23,000

p-Coumaric acid

Engineering of biosynthetic pathway with different gene expression combination

Lim et al. (2011)

Vanillin

Fed-batch

Fragrance, cosmetic

Vanilla spp.

Escherichia coli

2520

Ferulic acid

Engineering of biosynthetic pathway optimizing plasmids copy number and promoters

Barghini et al. (2007)

Naringenin

Fed-batch

Antioxidative, anticancer, and antiinflammatory

Grapefruit and other fruits and herbs.

Escherichia coli

474

p-Coumaric acid

Engineering of naringenin and malonyl-CoA biosynthetic pathways

Xu et al. (2011)

Raspberry ketone

Shaking flask

Flavor

Raspberries, blackberries, grapes, rhubarb

Saccharomyces cerevisiae

7.5

p-Coumaric acid

Engineering of biosynthetic pathway and benzalacetone synthase enzymatic engineering

Lee et al. (2016)

Curcumin

Fed-batch

Antioxidant, anticarcinogenic, and antitumor

Curcuma spp.

Escherichia coli

60

Rice bran pitch

Engineering of artificial biosynthetic pathway

Katsuyama et al. (2008)

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Metabolic engineering strategies in microorganisms for production of PNPs

Modern molecular biology tools are essential for genetic and metabolic engineering of microorganisms and are largely responsible for the success of the biotechnological field. Plasmids are one of the most important tools for genetic engineering. Thus cloning, generation of mutants, overexpression of natural proteins, and expression of exogenous proteins are all based on the use of self-replicating plasmids. In recent years, other more sophisticated strategies, such as optimization of the genetic code or CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein) technology, have become widely used tools (Peng et al., 2017; Salis et al., 2009). The application of all these molecular biology techniques, in addition to advances in omics and bioinformatics, has allowed the development of a large number of methods to produce PNPs in microorganisms (Allen et al., 2019; Lei et al., 2018; Qiu et al., 2018). The main strategies followed by researchers to improve the production of PNPs in a microorganism are increasing the concentrations of intracellular precursors, optimization of the catalytic activities of enzymes, and insertion/deletion of a biosynthetic pathway. Fig. 1 summarizes the general flow and the main metabolic engineering strategies followed for the development of a biotechnological process.

3.1 Increasing precursor availability Production of natural compounds in exogenous organisms is frequently limited by the availability of the precursors of a metabolic pathway. Thus one of the most commonly used strategies to improve high-value compound production is to increase the intracellular concentration of precursors through different methods, such as optimizing enzymatic activities, inhibiting alternative pathways that consume precursors, or supplementing the culture medium with exogenous precursors. For example, increasing the concentration of malonyl-CoA is a common strategy in studies focused on the production of flavonoids or polyphenols, while obtaining a high concentration of L-tyrosine, L-phenylalanine (usually supplementing the culture), or other metabolites derived from the shikimate pathway is essential to develop an alkaloid biotechnological production method (Galanie et al., 2016; Wu et al., 2014a; Xu et al., 2011).

3.2 Biosynthetic pathway engineering Pathway engineering involves gene cluster amplification and the deletion and/or insertion of genes into the host microorganism. Overexpression of a protein using gene cluster amplification in tandem has been employed to improve the production of some interesting molecules, such as the antibiotic validamycin A (Zhou et al., 2014). Gene insertion, usually via expression vectors, is an essential tool to produce an unnatural compound in a microorganism. In addition,

FIG. 1 Main workflow and metabolic engineering strategies followed in a biotechnological process. Main steps necessary to carry out a biotechnological process are summarized: bioinformatic studies (green square), metabolic engineering strategies (orange oval), optimization of the fermentation process and scale-up (yellow square), and isolation and purification of compounds (blue circle).

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although some microorganisms can naturally produce a wide variety of valuable compounds, it is common to introduce a gene or a complete alternative pathway from a natural producer organism that often has greater efficacy. This strategy is very common in terpenoid production (Wang et al., 2016). The design and construction of “customized” biosynthetic routes have substantially advanced in recent years, as these routes have progressed from expressing five to seven enzymes of the same organism to, in some cases, expressing 20–30 enzymes from different organisms (Cravens et al., 2019). Deletion or downregulation of selected genes can also be a very useful strategy to eliminate a route that competes with the route of interest or to reconstruct a complete pathway through gene deletion and insertion or by changing gene promoters (Cheng et al., 2019; Wu et al., 2017).

3.3 Enzymatic engineering When a protein is expressed in a heterologous organism, its activity may not be optimal, possibly due to chemical differences in the environment or to biological differences, such as bad folding or a lack of posttranslational modifications. This inappropriate activity frequently leads to the appearance of “bottlenecks” in the metabolic pathways involving a decrease in the final yield and an accumulation of intermediates, which can have direct consequences at the cellular level. Enzymatic engineering can be applied to improve the activity of these proteins and therefore increase the final production of the product of interest (Lehka et al., 2017; Wu et al., 2017). Other more sophisticated methods have been used, such as the spatial engineering of a protein by fusing an endoplasmic reticulum routing tag to direct the flow toward synthesis (Thodey et al., 2014).

4 Terpenoids Terpenoids, or isoprenoids, are a large family of organic compounds derived from isoprene (C5). Terpenes are synthesized by the condensation of two or more isoprene molecules and are classified according to the number of condensed units (Tholl, 2015). Although many organisms synthetize isoprenoids, plants are, by far, the main producers (Park et al., 2017). In nature, there are two known pathways for synthesizing isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate DMAPP. The first pathway, known as the mevalonate (MEV) pathway, uses acetyl-CoA as a substrate to yield IPP, which can be interconverted into DMAPP by an isomerase (Chang et al., 2014). The second pathway, the nonmevalonate pathway or methyl D-erythritol 4-phosphate (MEP) pathway, uses pyruvate and glyceraldehyde-3-phosphate as substrates to form IPP and DMAPP. Currently, in industry, there is a special interest in the production of terpenoids because of the growing number of applications associated with terpenoid production.

4.1 Lycopene Lycopene is a tetraterpenoid with potent antioxidant activity that is widely used in nutritional supplements. Moreover, recently, new applications for lycopene have been described, such as its use as an anticancer agent and for treating cardiovascular diseases, including atherosclerosis, myocardial infarction, and stroke (Aghajanpour et al., 2017). Lycopene is the natural plant metabolite responsible for the red color in tomatoes, watermelon, grape fruit, and apricots. Fig. 2 shows the biosynthetic pathway of lycopene from IPP and DMAPP. Lycopene has been successfully produced in carotenogenic and noncarotenogenic microorganisms. Thus several methods have been developed to produce lycopene in E. coli, such as the expression of the crtE, crtL, and crtB genes (which encode geranylgeranyl pyrophosphate synthase (GGPPs), phytoene synthase, and lycopene synthase, respectively) from Pantoea agglomerans (Yoon et al., 2007) coupled with nonmevalonate pathway optimization to increase the amount of farnesyl pyrophosphate (FPP) precursors (Gallego Jara et al., 2015) or the expression of Deinococcus wulumuqiensis genes and optimization of plasmid transcription (Xu et al., 2018). In this last study the modified E. coli strain produced 925-mg/L lycopene without any inducer. Yeast has also been used as a host to produce lycopene; thus the the noncarotenogenic yeast Y. lipolytica has been employed to produce lycopene by expressing the genes crtB and crtL (codon optimized) from Pantoea ananatis and the rate-limiting genes from the Y. lipolytica mevalonate pathway. Moreover, to increase lipid body formation and increase lycopene production, POX1 to POX6 genes (b-oxidation first step) and the GUT2 gene (coding for glycerol3-phosphate dehydrogenase) were deleted to reduce the glycerol-3-phosphate flux into gluconeogenesis. A final production of 16 mg/g was reached with this method (to our knowledge the highest in a eukaryotic host) (Matth€aus et al., 2014). More recently, S. cerevisiae was genetically modified through optimization of the natural metabolism of the host and expression of the MVA gene pathway using a constitutive promoter. Finally, they reached a yield of 115.64-mg/L lycopene in a yeast citric acid fed-batch fermenter (Li et al., 2019).

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FIG. 2 Lycopene biosynthetic pathway from IPP and DMAPP. IDI, isopentenyl-diphosphate isomerase; GPPs, geranyl pyrophosphate synthase; GGPPs, geranylgeranyl pyrophosphate synthase. Biosynthetic pathway is detailed in Herna´ndezAlmanza et al. (2016).

4.2 Taxol Paclitaxel (PTX), commercialized under the brand name of Taxol, is an anticancer diterpenoid produced naturally by yew trees (Taxus brevifolia or T. baccata) in very low concentrations (six 100-year-old trees are needed to treat a patient with Taxol) (Wani et al., 1971). For that, it is important to develop a production method based on microbial fermentation to compete with complex chemical synthesis, which is commercially inapplicable (Nicolaou et al., 1994). As shown in Fig. 3, the first intermediate of Taxol biosynthesis is the metabolite taxa-4,11-diene (taxadiene) ( Jennewein et al., 2004). Several studies have successfully produced taxadiene intermediates in microorganisms to develop a semisynthetic production method of Taxol. Thus E. coli, B. subtilis, and S. cerevisiae have been employed as hosts to produce taxadiene intermediates through the expression of different taxadiene synthases and increasing geranyl pyrophosphate (GPP) availability by overexpressing natural or heterologous enzymes (Engels et al., 2008; Huang et al., 2001; Ingy et al., 2019). The main drawback of these semisynthetic methods is that a complex chemical process to reach Taxol at high concentrations after fermentation is necessary. For this reason, great efforts are being carried out to perform oxygenation reactions in microbes. Thus cytochrome P450 and upstream pathway enzymes have been successfully expressed in E. coli, reaching 570  45 mg/L of oxygenated taxanes (including taxol and other oxygenated compounds derived from taxadiene), the highest to date (Walters et al., 2016). This study represents a great advance for metabolic engineering of complex products, not just terpenes.

5

Alkaloids

Alkaloids are organic compounds that contain a nitrogen atom. Alkaloids are present in 20% of plant species, and many of them have pharmacological properties in humans (Kukula-Koch and Widelski, 2017). Thus alkaloids include several common and very used products, such as caffeine, morphine, quinine, or the anticancer agent vinblastine.

5.1 Benzylisoquinoline alkaloids Benzylisoquinolines are alkaloids biosynthesized from L-tyrosine via (S)-reticuline and include important pharmacological compounds, such as the opiates morphine and codeine. Fig. 4 shows the benzylisoquinoline alkaloid biosynthetic pathway from L-tyrosine (Liscombe and Facchini, 2008). For many years, opiate production in microorganisms was only successful when a specific precursor was supplemented (Minami et al., 2008; Siddiqui et al., 2012). However, in recent years, some benzylisoquinoline intermediates have been de novo synthesized in yeast and bacteria (Kumagai, 2011; Trenchard et al., 2015). Thus, recently, two important studies have achieved complete opiate biosynthesis in S. cerevisiae and E. coli (Galanie et al., 2016; Nakagawa et al., 2016). Combining enzyme and metabolic engineering a complete yeast biosynthesis

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FIG. 3 Taxol biosynthetic pathway from IPP and DMAPP. IDI, isopentenyl-diphosphate isomerase; GPPs, geranyl pyrophosphate synthase; GGPPs, geranylgeranyl pyrophosphate synthase. Biosynthetic pathway is detailed in Jennewein et al. (2004).

FIG. 4 Benzylisoquinoline alkaloid biosynthetic pathway from L-tyrosine. Biosynthetic pathway is detailed in He et al. (2018).

of opiates was achieved. The developed strains expressed 21–23 (according to the opiate) enzymes from different organisms, and central metabolic pathways were optimized to drive an improved carbon flux through tyrosine to increase the intracellular pool of this precursor (Galanie et al., 2016). Thebaine (an opiate precursor) and hydrocodone (a semisynthetic opioid) de novo biosynthesis was achieved, although the concentrations were very low. Shortly after, thebaine and hydrocodone were biosynthesized by employing a stepwise culture of four recombinant E. coli strains, each optimized for a stage of the biosynthetic route. With this process the yield was increased in the yeast system by 300-fold (Nakagawa et al., 2016). These studies represent a potential platform for future industrial methods focused on the production of opiates. More recently, (S)-reticuline (an intermediate of major benzylisoquinoline alkaloids) production in E. coli was improved to 160 mg/L. Moreover, human sulfotransferases were expressed in the modified strain to obtain nonnatural benzylisoquinolines (O-sulfated (S)-reticulines) (Matsumura et al., 2018).

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Polyphenols

Polyphenols are organic compounds with two or more aromatic rings and hydroxyl groups (Milke et al., 2018). Flavonoids and stilbenoids, the two main polyphenol families, are the majority antioxidants in the human diet and are very important to prevent or treat certain types of cancer, neurodegenerative and cardiovascular diseases, obesity, or diabetes (Khurana et al., 2013; Landete, 2012; Saura-Calixto et al., 2007). Fig. 5 shows the polyphenol biosynthetic pathway from L-tyrosine.

6.1 Resveratrol Resveratrol and its methylated derivatives are highly studied plant-produced stilbenes. Resveratrol was first isolated in 1940 and today is known to have a high number of applications due to its biological activity against cardiovascular and nervous disorders, although it is also used as an important ingredient in drugs and food supplements (Gambini et al., 2015). Resveratrol is present in different plants, such as Polygonum cuspidatum, Veratrum formosanum, and grandiflora, although in low concentrations (Thapa et al., 2019). One of the first studies that carried out resveratrol production used E. coli and S. cerevisiae. Microorganisms expressed the enzyme 4-coumarate/coenzyme A ligase from Nicotiana tabacum and a stilbene synthase from Vitis vinifera, and cultures were supplemented with p-coumaric acid (Beekwilder et al., 2006; Watts et al., 2006). Resveratrol production methods have also been based on other expensive phenylpropanoic precursors, such as L-tyrosine (Wang et al., 2011, 2015; Wu et al., 2013). From these initial studies, several efforts have been made to develop de novo resveratrol biosynthesis in microorganisms (Li et al., 2015, 2016; Wu et al., 2017). Different strategies have been combined with the expression of an optimized biosynthetic resveratrol pathway to increase total synthesis of resveratrol to 800 mg/L. Metabolic engineering strategies include expressing a recombinant mevalonate assimilation pathway, downregulating the fatty acid biosynthesis pathway, overexpressing the acetyl-CoA carboxylase enzyme posttranslationally modified, or optimizing cytochrome P450 monooxygenase activity (Li et al., 2015; Lim et al., 2011; Wu et al., 2017).

6.2 Naringenin Naringenin is a central metabolite of flavonoid biosynthesis and a precursor for the synthesis of several polyphenols. Moreover, several human health effects are associated with naringenin, such as anticancer, antioxidative, and antiinflammatory activities (Hermenean et al., 2013; Xu et al., 2013). Several naringenin production studies have focused on

FIG. 5 Naringenin and resveratrol biosynthetic pathway from L-tyrosine. TAL, tyrosine ammonia-lyase. Biosynthetic pathway is detailed in Chouhan et al. (2017).

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increasing the availability of malonyl-CoA in different ways. Thus downregulation of fabH and fapB (fatty acid synthesis pathway genes) by posttranscriptional gene silencing increased naringenin production to 391 mg/L (Wu et al., 2014a). CRISPRi technology to downregulate fatty acid biosynthesis improved naringenin yield to 422 mg/L (Cress et al., 2015), while combined genetic modifications increased acetyl-CoA carboxylase activity and acetyl-CoA availability to yield 470 mg/L naringenin from an E. coli culture supplemented with p-coumaric acid (Xu et al., 2011). In recent years, some studies have developed methods for de novo naringenin biosynthesis in microorganisms. Thus, in E. coli, naringenin production of 100.64 mg/L was achieved from glucose through modular pathway engineering to modify the copy numbers of plasmid genes and the promoter strengths (Wu et al., 2014b). C. glutamicum was also used successfully to obtain naringenin by deleting genes related to Corynebacterium aromatic metabolites catabolism and by optimizing the expression of exogenous chalcone synthase and chalcone isomerase genes (Kallscheuer et al., 2016). More recently a new method to produce naringenin in a coculture of E. coli and S. cerevisiae consuming D-xylose as the carbon source has been developed (Zhang et al., 2017).

7 Conclusion and future challenges Plants are a wonderful source of compounds with very diverse applications, ranging from their use as drugs to treat many diseases to their use as interesting compounds for the food or cosmetic sector. However, the difficulties of extracting compounds from natural sources and producing them via chemical synthesis have led to an increase in the price of these compounds. The development of modern molecular biology techniques, together with advances in transcriptomics, metabolomics, proteomics, and bioinformatics, has positioned systems biology and the metabolic engineering of microorganisms as potential methods to obtain products of high industrial value in an economic way. Another future challenge for the metabolic engineering is the obtention of products derived from PNPs. In this way, several novel PNP derivatives with interesting applications have been recently discovered, and heterologous biosynthetic pathways are a very useful tool to synthesize them. Despite the advances made in recent years, there are still very few methods capable of competing with traditional extraction from plants due to the cost of fermentation. The two main strategies to reduce the costs of these processes are to increase the yield of production and reduce the cost of the fermentative processes. To make the fermentation processes economically more favorable, different strategies can be carried out. The use of less controlled culture conditions through employing more robust strains is very promising. On the other hand the design of processes that do not use glucose or other sugars as carbon sources is another goal (Antonovsky et al., 2016). The second and most obvious way to reduce the cost of producing compounds using microorganisms is to increase the yield of the biosynthetic processes. The advances and the price decrease of the techniques available to analyze the transcriptomes and proteomes of plants will help to continue to advance knowledge of the biosynthetic routes of PNPs and their regulation.

Funding This present study was supported by grants from the Ministry of Science, Innovation and Universities (MCIU), the State Research Agency (AEI) and the European Regional Development Fund (FEDER), RTI2018–094393-B-C21-MCIU/AEI/ FEDER, UE, and the Seneca Foundation CARM, 20786/PI/18.

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(Ed.), Escherichia ColiRecent Aavances on Pysiology, Pathogenesis and Biotechnological Applications. IntchOpen, pp. 253–274. Walters, B., Giaw, C., Sagliani, K., Shankar, S., Stephanopoulos, G., 2016. Overcoming heterologous protein interdependency to optimize P450-mediated Taxol precursor synthesis in Escherichia coli. Proc. Natl. Acad. Sci. U. S. A. 113, 3209–3214. Wang, Y., Halls, C., Zhang, J., Matsuno, M., Zhang, Y., Yu, O., 2011. Stepwise increase of resveratrol biosynthesis in yeast Saccharomyces cerevisiae by metabolic engineering. Metab. Eng. 13, 455–463. Wang, G.-S., Grammel, H., Abou-Aisha, K., S€agesser, R., Ghosh, R., 2012. High-level production of the industrial product lycopene by the photosynthetic bacterium Rhodospirillum rubrum. Appl. Environ. Microbiol. 78, 7205–7715. Wang, S., Zhang, S., Xiao, A., Rasmussen, M., Skidmore, C., Zhan, J., 2015. Metabolic engineering of Escherichia coli for the biosynthesis of various phenylpropanoid derivatives. Metab. 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Chapter 20

Engineering of microbial cell factories for omics-guided production of medically important biomolecules Ahmad Bazli Ramzi∗ Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia ∗

Corresponding author: E-mail: [email protected]

1 Introduction The emergence of omics research that focused on studying a comprehensive or global set of biological molecules in parallel with the advent of high-throughput and automated instruments has greatly accelerated biotechnological research especially in pharmaceutical and industrial fields (Hasin et al., 2017). The start and completion of human genome sequencing project had brought considerable interest in the field of genomics, which then served as the catalysts for the emergence of bioinformatics research that is important for analyzing biological datasets (Morozova and Marra, 2008; Stajich, 2002). Since the introduction of next-generation sequencing (NGS) technology, studies and analysis of biological datasets have grown exponentially amid the shift from single dimension molecular biology work to multidimensional omics research (Shendure and Ji, 2008; Thermes, 2014). Throughout the years, the increasingly high-throughput omics technologies have aided in the advancement of systems biology research that aims at the elucidation of the underlying biological and molecular mechanisms from genome, transcriptome, proteome, and metabolome of a particular organism (Westerhoff and Palsson, 2004). From metabolic modeling, disease prognosis to biomarker gene discovery, the use of singular or combined omics platforms have contributed to a better understanding of the genetic resources of the manifested phenotypes using either genome first or phenotype-first workflow (Hasin et al., 2017; Kell, 2006). The phenotype-first approach is highly beneficial for elucidating underlying biological phenomena in complex organisms especially plants that produce numerous secondary metabolites of medical and industrial interests (Rochfort, 2005). This is particularly important for nonmodel plants without sequenced genome given that the identification of bioactive natural products could now be rapidly identified using metabolomics platform where prioritization and targeted profiling are performed for a large number of plant samples (Rochfort, 2005; Wolfender et al., 2019). By combining plant compound identification and bioassays, numerous plant metabolites have been shown to possess medicinal properties including antioxidant, antimicrobial, antiinflammatory, and anticancer activities that provide promising premises for drug discovery and biopharmaceutical development. Driven by the needs of attaining pure and high amount of the industrially important natural products, there is a growing interest on tapping these invaluable resources using metabolic engineering and synthetic biology approaches for implanting and overproducing the plant-derived biomolecules in genetically engineered microorganisms (Ramzi, 2018). Complex biomolecules and natural products have been biosynthesized in engineered model and nonmodel microbial hosts through the introduction of natural and nonnatural biosynthetic pathways for directing the targeted bioproduct formation. Bioproduction of natural products has steadily evolved from the trial and error approach to modular and more systematic plug-and-play biosynthetic concept where the repertoire of the enzymes as biological or genetic parts is rapidly expanding aided by a host of computer-aided design (CAD) and machine learning computations using existing and new genome and transcriptome datasets (Leferink et al., 2016; Li et al., 2018). Despite this, the biosynthesis of the targeted natural products often requires the uncovering of gaps in between final products and intermediates of a specific metabolic pathway. Several challenges besetting commercial production of the medically relevant natural products centered on the lack of comprehensive understanding of the corresponding biosynthetic pathways and enzymes as well as the difficulty in expressing long and complex biosynthetic pathways in the engineered microbial hosts (Choi et al., 2019). This is where multiomics analytic platforms are important by which proteins and Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00024-6 © 2021 Elsevier Inc. All rights reserved.

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expressed genes can be analyzed and identified via a coordinated top-down approach involving metabolome, proteome, and transcriptome studies under normal and regulated conditions ( Jamil et al., 2020). In particular, there is a growing interest in the use of RNA sequencing (RNA seq)-driven transcriptomics as part of next-generation functional genomics strategy for obtaining desired genes and regulatory elements via measurement of gene expression including differently expressed genes (DEGs)-based analysis (Goh et al., 2018; Hagel et al., 2015). Transcriptome analysis is of great interest for gene discovery and protein annotation in medicinal and commodity plants that enables the creation of dedicated computational pipelines and biological datasets guided by computational biology and bioinformatics analysis (Goh, 2018; Xiao et al., 2013). Combination of different omics has been essential in systems biology studies that facilitate the generation of new information and a better understanding of the biological mechanisms at system level. Importantly, the fundamental knowledge obtained would then serve as the foundation for translational work involving metabolic engineering and synthetic biology that focused more on bioproduction and construction of genetic circuits in the engineered hosts. In this chapter, the integration of systems and synthetic biology is discussed as two important complementing advanced biotechnological platforms especially for omics-guided production of known and newly discovered biomolecules as genetic parts in the synthesis of natural products using bioengineered microbes.

1.1 Omics-driven microbial chassis development Microbial platforms serve as the go-to chassis for biological engineering and industrial biotechnology applications where model microbes such as Escherichia coli and Saccharomyces cerevisiae have been widely employed for biocatalysis and bioproduction of natural products through the overexpression of rate-limiting enzymes and modulation of the corresponding metabolic pathways. Conventionally, several strategies were utilized for increasing the product titers including the elimination of competing enzymes through gene silencing and knockout depending on the overall metabolic pathways in the hosts (Ramzi, 2018). Flux balance and stoichiometry analysis were well established in the model microbial hosts but not in newly isolated strains of which there is a critical need for bottom-up strain characterization and genetic toolset development. With the aim of generating as much information as possible, genome-wide characterization and functional testing were performed on the selected microbial chassis that laid the starting platform for downstream biological engineering strategies (Chi et al., 2019). Detailed elaborations on the advantages of using E. coli and S. cerevisiae as microbial cell factories can be found in excellent reviews elsewhere (Trantas et al., 2015; Yang et al., 2020). Recent advancements in genomics and synthetic biology research have seen many nonconventional microbial strains including Corynebacterium glutamicum and Pseudomonas putida being increasingly employed as robust bacterial cell factories and synthetic biology chassis for the synthesis of natural products (Adams, 2016; Unthan et al., 2015). Originally used for amino acid synthesis, C. glutamicum is an interesting reference strain where both systems and synthetic biology approaches have been extensively utilized for modulation and optimization of a rapidly expanding product repertoire including amino acids, biofuels, and natural products (Becker et al., 2018; Kallscheuer et al., 2017; Ramzi et al., 2015). In the efforts to improve the production capacity of this bacterial chassis, the implementation of systems metabolic engineering and omics technologies have facilitated system-wide elucidation of the bacterial primary and secondary metabolite biosynthesis, regulatory mechanisms, and constraints (Kr€omer et al., 2004; Zhang et al., 2018). Omics-guided systems metabolic engineering strategies have been increasingly utilized and succeeded in the programming and optimization for the production of targeted products in C. glutamicum (Ma et al., 2019; Wang, 2019). Intensive use of fluxomics and in silico tools for metabolic pathway simulation and optimization via gene knockouts, knockdowns, and knock-ins rendered strain development using C. glutamicum chassis as extremely beneficial and highly tunable (Hartmann et al., 2017; Ma et al., 2017; Melzer et al., 2009). On the other hand, the needs of having a robust and versatile metabolism have led to the growing interest in the development of P. putida as an alternative synthetic biology chassis. This bacterial chassis is known for its high oxidative stress tolerance and capacity to degrade phenols and xenobiotics in addition to the favorable overproduction of NADPH reducing power when grown on glucose (Nikel et al., 2016, 2015; Nikel and de Lorenzo, 2018). These attributes are particularly important in natural product synthesis involving the formation of growth-limiting intermediates and products such as geranyl pyrophosphate (GPP), geraniol, and geranic acid (Hernandez-Arranz et al., 2019; Mi et al., 2014). System-wide genomic analysis and genome-scale modeling were undertaken for mapping the genes involved in the bacterial metabolism and establishing genetic toolsets essential in metabolic engineering and synthetic biology applications (Belda et al., 2016; Nikel et al., 2016). Using genome engineering tools and standardized plasmid constructs, modification and refactoring of the bacterial genome were demonstrated for enhancing the strain fitness and metabolic capacity (Dvora´k and de Lorenzo, 2018; Lieder et al., 2015). Successful implementation of in silico engineering and latest genome editing techniques

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specifically the clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein (Cas) system has further expanded the computational and genetic tools available for developing P. putida as a major producer of complex biomolecules (Kampers et al., 2019; Occhipinti et al., 2018; Volke et al., 2020; Wirth et al., 2020).

1.2 Design, build, test, and learn of natural product synthesis in engineered microbes With the increasing complexity of genetic engineering principles and genetic toolbox available, microbial hosts have been at the forefront of synthetic biology. Iterative strain design and rapid assembly of genetic parts have aided in streamlining and standardization of biological engineering applications for the synthetic biology community. Toward this, engineering principles have been increasingly adopted in designing and redesigning of genetic parts for bioproduct and biodiagnostic development, thereby promoting a common featurette of synthetic biology specifically the accessibility and scalability of the parts and devices (Cameron et al., 2014; Friedman and Ellington, 2015; Wei and Cheng, 2016). In line with this, synthetic biology-centered biofoundries that aimed at providing comprehensive R&D setups were established to allow rapid design, construction, and testing of bioengineered hosts among the collaborating partners (Hillson et al., 2019). The creation of these biofoundries is expected to expedite the integration of automated high-throughput facilities with CAD tools and workflows based on the iterative design-build-test-learn (DBTL) biological engineering cycle. The DBTL concept is particularly useful for addressing the bottlenecks of pathway engineering and natural product biosynthesis that necessitates pathway reconstruction and identification of new genetic parts to complete the gapped biosynthetic pathways. The Design part is important in selecting genetic parts that are functional and useful in catalyzing the targeted reactions. This selection process is being greatly aided by CAD tools such as Selenzyme and Retropath that are notably useful for the production of flavonoids and terpenoids (Carbonell et al., 2018; Feher et al., 2014). One of the caveats of synthetic biology is the standardization of parts assembly that caters for seamless and reusable parts that include coding sequences for enzymes, reporter proteins, and also regulatory elements such as promoter, terminator, and transcriptional factors (TFs). Such an approach would allow for a modular and flexible design important toward the automation of the Design step (Carbonell et al., 2018; Swainston et al., 2018). Complex and long biosynthetic pathways can now be constructed and validated in a timely fashion via robotic colony-picking instruments hence enabling rapid testing process that was aided by seamless construction and functional expression of the cloned genetic parts. For bioproduction and testing of the desired natural products, quantitative and qualitative analysis often utilize liquid chromatography-mass spectrophotometry (LC-MS) of which when combined with high-throughput cloning and screening methods would then speed up one-step build and test of the functional expression and desired biosynthesis via automated and robotic systems. Considering that the automated DBT process involves a considerable amount of laboratory expenses, it is essential that all the outputs from the cultured microbial strains harboring individual and multiple constructs to be recorded and analyzed especially in terms of product formation and substrate utilization. Through this Learn step, researchers would be able to improve and debottleneck the metabolic pathways through fine-tuning of the genetic parts, plasmid copy numbers, or strength of regulatory elements. By incorporating machine learning (ML) and product data training, the targeted product biosynthesis can be refined based on the established DBTL models of the optimized parts in the tested biochemical pathways. Using this ML-guided approach, a more accurate prediction and testing of selected product outputs could be attained without extensive and costly high throughput screening ( Jervis et al., 2019). By adopting the iterative DBTL cycle, any ML-driven strategies including combinatorial biosynthesis, directed evolution, and rational design of natural and nonnatural biosynthetic pathways and corresponding parts can be improved and streamlined for rapid strain and bioproduct development (Carbonell et al., 2018; Mazurenko et al., 2020).

1.3 Parts discovery for natural and new-to-nature natural products using systems biology platform Bioproduction of natural products is spurred by perennial risks of petroleum overdependency and high demands of sustainable synthesis routes for high-value biochemicals and bioproducts. Concerted R&D efforts by academics and industry have led to a rapidly growing number of commercialized bioproducts that primarily belong to the terpenoid compounds (Schempp et al., 2018). Microbial production of industrially and medically important products has been propagated and sustained through the implementation of metabolic and biological engineering strategies including pathway engineering, increasing precursor supply, alleviating of competing pathways, and overexpression of transcriptional factors (Paramasivan and Mutturi, 2017; Ramzi, 2018). Additional approaches used in improving the performance of the biosynthetic genes as genetic parts also involve codon optimization and enzyme engineering for improving bioactivity and rate of catalysis of the part variants including corresponding enzyme, ribosomal binding sites or TFs

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(Ausl€ander et al., 2017; Skjoedt et al., 2016). The utilization of directed evolution and gene mutagenesis strategies is proven advantageous in diversifying the products formed and increasing the pools of selected enzymes with the highest productivity (Leferink et al., 2016; Pandey et al., 2016). Diversification of plant-derived natural products is also achieved through the expression of cytochrome P450-type enzyme, a special enzyme group that catalyzed the modification and formation of various secondary metabolites including terpenes, flavonoids, and alkaloids (Banerjee and Hamberger, 2018; Chu et al., 2016; Diamond and Desgagne-Penix, 2015). The advancement of omics technologies in natural product research has accelerated the discovery and implantation of plant-derived enzymes, regulatory elements, and biosynthetic pathways in bioengineered microbes. A host of dedicated research partnerships and consortia such as the 1000 plants (oneKP or 1KP) initiative and medicinal plant genomics consortium are employing metabolomics and NGS-based transcriptomics approaches that allow comprehensive metabolite and biosynthetic gene identification in model and nonmodel plants (Go´ngora-Castillo et al., 2012; Leebens-Mack et al., 2019). This omics-driven gene discovery is becoming critical in the construction and reconstruction of plant biosynthetic pathways for de novo natural and nonnatural phytochemical synthesis in engineered microbial chassis (Li et al., 2018; Pyne et al., 2019). In addition to the biosynthetic genes, plant-derived regulatory elements especially TFs are also increasingly employed together in the efforts to improve pathway regulation and biocatalysis in engineered yeast (Naseri et al., 2019, 2017). Taken together, the increasing availability of well-characterized plant TFs and enzymes is expected to provide a breadth of genetic resources for programmable and scalable production of medically important biomolecules and compounds via the integration of systems and synthetic biology platforms.

2 Omics-driven bioproduction of medically important biomolecules and natural products Various microbial engineering strategies have been demonstrated in the production of bioactive compounds with highvalue medicinal properties including antioxidant, antimicrobials, analgesic, and anticancer activities. With the increasing demands for new drugs and biopharmaceuticals, the adoption of automated as well as high-throughput production and screening is becoming increasingly important with special regard to sustainable yet precommercialization-ready biomanufacturing means. As described earlier, the combination of omics technologies and iterative DBTL cycles would represent the way forward in streamlining the bioproduct development processes. Outputs from the plant omics analysis are proven to be highly beneficial for filling the gaps in the complex biosynthetic pathways and providing new and improved biological resources in CAD-based pathway prediction and optimization tools. Further implementation of unbiased ML-driven prediction along with simulation of candidate genes and desired biochemical processes is expected to greatly facilitate in the enhancement of productivity and substrate conversion in the engineered microbial chassis. Following this, this chapter will highlight several plant-derived biomolecules considered essential in the synthesis of medically important compounds in heterologous hosts. A schematic overview of the omics-guided natural product synthesis using systems and synthetic biology platforms is illustrated in Fig. 1 where the integration of these data-driven platforms will serve as the nextgeneration strategies in advanced biomanufacturing and bioproduct development.

SYSTEMS BIOLOGY

DEVICE ASSEMBL Y

D ES IG N

GENE SELECTION

D

IL

BIOLOGICAL PARTS

BU

BIORESOURCES

SYNTHETIC BIOLOGY

OMICS- GUIDED BIOPRODUCT DEVELOPMENT OMICS DATA ANALYSIS DATA & PATHWAY ANALYSIS

LE

AR

N

BIOPRODUC TION

ST TE

FIG. 1 The integration of omics-driven systems biology approach with synthetic biology platform will serve as next-generation strategies to drive bioproduct development aided by advanced biological data analysis, high throughput instruments and automated machinery.

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2.1 Terpene synthase for plug-and-play terpene production Terpenes are one of the largest and most diverse group natural compounds mostly found in plants. Terpenes and derivative terpenoids are categorized based on the number of five-carbon (C5) isoprene skeletons ranging from mono (C10)-, to sesqui- (C15), to tetraterpenes (C40). Bioactive plant-derived terpenes have been expansively studied for the development and commercialization of several medically important bioproducts and biopharmaceuticals including anticancer Taxol and antimalarial artemisinin drugs ( Jansen and Shenvi, 2014). In particular, semisynthesis of artemisinic acid represents an important breakthrough for bioproduct development using microbial engineering approaches (Paddon and Keasling, 2014; Westfall et al., 2012). Biotechnological production of terpenes is primarily focused at implanting the corresponding terpene biosynthesis genes together with mevalonate (MVA) or nonmevalonate 2-C-methyl-d-erythritol-4-phosphate (MEP) pathways that enabled in vivo synthesis of isoprene precursors isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP) from glucose. When combined with the endogenous or exogenous supply of canonical terpene precursors, the expression of recombinant terpene synthase (TPS) conferred the biocatalytic capacity to produce targeted single or multiple terpenes rendering the essentiality of this enzyme in the synthesis and diversification of terpene-based products (Karunanithi and Zerbe, 2019). Given the importance of TPS in the synthesis of diverse terpenes useful in industrial and biopharmaceutical applications, efforts for discovery and characterizing TPS expression profiles in engineered microbes have intensified via rational design, combinatorial synthesis, and protein engineering strategies (Bian et al., 2017; Lauchli et al., 2013; Peralta-Yahya et al., 2011). Advancement of synthetic biology platform including omics-guided automated DBTL pipeline and characterized genetic parts has enabled rapid and powerful terpene biomanufacturing by assembling TPS genes as the go-to terpene-producing parts (Carbonell et al., 2018; Hernandez-Ortega et al., 2018; Leferink et al., 2019). Pathway optimization and product diversification can be further accelerated through the employment of transcriptomic-driven discovery of TPS from medicinal plants. Several TPS genes from medically important plants identified using metabolomics and transcriptomics platforms are listed in Table 1 where the recombinant TPS genes were characterized using E. coli and S. cerevisiae as bioengineered hosts. Depending on the target product, the terpene-synthesizing TPS parts can be tested as part of plug-and-play and combinatorial synthesis strategies for identifying the best-performing parts and pathway iteration. The identification of known and novel plant-derived TPS using individual and multiomics platform is expected to drive the bioproduction of various natural and nonnatural terpenes as biopharmaceutical products and building blocks for advanced biofuels and biomaterials ( Jongedijk et al., 2016; Zhang et al., 2017).

2.2 Strictosidine biosynthetic genes for refactoring of monoterpene indole alkaloid biosynthesis Monoterpene indole alkaloids (MIAs) are a group of structurally diverse alkaloid skeletons produced by plants as specialized secondary metabolites. MIAs are considered as a rich source of medicinal products that include anticancer compounds, camptothecin and vinca alkaloids, vinblastine and vincristine. Several medicinal plants specifically Catharanthus roseus, Camptotheca acuminata, and Rauvolfia serpentina were widely studied in the concerted efforts to fully elucidate the MIA biosynthetic pathways (Go´ngora-Castillo et al., 2012). In vinca alkaloid-producing plants, the biosynthesis of MIAs is known to originate from strictosidine, a common precursor in MIA biosynthetic pathway. This important MIA precursor is generated by stereospecific condensation of monoterpene secologanin and indole tryptamine derived from the nonmevalonate MEP and shikimate pathways, respectively (Kries and O’Connor, 2016; Yu et al., 2015). Among the MIA-accumulating plants, C. roseus represents a well-studied and model medicinal plant in the quest for comprehensive understanding of vinblastine and vincristine biosynthesis via the strictosidine-generating secoiridoid pathway. Using multiomics approach, the complete biosynthetic pathway of both anticancer compounds has been successfully identified through transcriptomic-driven functional studies of C. roseus plant samples (Caputi et al., 2018; Duge De Bernonville et al., 2017; Miettinen et al., 2014). Aided with NGS and bioinformatics platforms, functional genomics studies of C. roseus have allowed the integration of coexpression network analysis and improved gene function annotation that led to the identification of key regulatory genes and enzymes responsible for intermediates and the targeted MIA product formation (Kellner et al., 2015; She et al., 2019; Van Moerkercke et al., 2015). Although relevant functional studies have been performed using C. roseus plant cell cultures, microbial hosts particularly S. cerevisiae remained the preferred system for the construction and expression of complex plant-derived MIA biosynthetic pathways including secologanin and tryptamine, the main precursors for strictosidine and MIA products synthesis (Brown et al., 2015; Sharma et al., 2020). Using metabolic and genome engineering approach, de novo production of strictosidine has been demonstrated in engineered S. cerevisiae where the secoiridoid pathway genes from C. roseus were functionally expressed in the microbial hosts (Brown et al., 2015). Following this, up to 27 genes were assembled to confer the biosynthesis of tabersonine and

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TABLE 1 Microbial expression and production profiles of terpene synthases (TPSs) identified from selected plant transcriptome. The identified TPS parts can be assembled with terpene biosynthesis genes of MVA or DXP route for directing terpene production using glucose in bioengineered microbes. Terpene metabolite Monoterpene

Monoterpene and sesquiterpene

Sesquiterpene

Source

TPS parts

Production profile

References

Persicaria minor (Polygonum minus)

Nerol dehydrogenase (NeDH)



PmNeDH produced neral and geranial

Tan et al. (2018)

Freesiahybrida

Terpene synthase (FhTPS)

 

FhTPS1 and FhTPS4 produced linalool FhTPS2, FhTPS6, and FhTPS7 produced a-terpineol and myrcene as major products

Gao et al. (2018)

Rhodomyrtus tomentosa (Ait.) Hassk

Terpene synthase (RtTPS)



RtTPS1–4 produce mainly (+)-a-pinene and (+)-b-pinene as major products

He et al. (2018)

Eremophila serrulata (A. DC.) Druce (Scrophulariaceae)

Monoterpene synthase (ESO1, ES02)

 

ESO1 produced myrcene ES02 produced Z-(b)-ocimene

Kracht et al. (2017)

Murraya koenigii L

Terpene synthase (MkTPS)

 

MkTPS1 produced ( )-sabinene) MkTPS2 produced a-farnesene

Meena et al. (2017)

Sindora glabra

Sesquiterpene synthase (STPS)

 

SgSTPS1 produced b-caryophyllene SgSTPS2 produced multiple mono- and sesquiterpenes

Yu et al. (2018)

Xanthium strumarium

Terpene synthase (XsTPS)



XsTPS1, XsTPS2 and XsTPS3 produced germacrene D, guaia-4,6-diene and b-elemene, respectively

Li et al. (2016)

P. minor (P. minus)

Sesquiterpene synthase (PmSTPS)



PmSTPS1 and PmSTPS2 produced b-farnesene, a-farnesene, and farnesol as main products

Rusdi et al. (2018)

Hedychium coronarium

Terpene synthase (HcTPS)



HcTPS6 produced b-farnesene

Yue et al. (2015)

catharanthine in yeast using native mevalonyl-CoA as starting compound. Strictosidine synthase (STR), geraniolconverting geraniol synthase (GES) and geraniol hydroxylase (GOH) are considered as key enzymes in the production of the medically important vinca alkaloid-type compounds (Kries and O’Connor, 2016). As shown in Table 2, a variety of MIA intermediates and products was produced in microbial hosts via the functional expression of these key enzymes guided by the transcriptome datasets of MIA-producing plants. Owed to the complex and long pathway comprising of 10– 30 heterologous genes, a modular coculture strain design of upstream and downstream genes of the MIA biosynthetic pathway in lieu to the monoculture system is expected to reduce the metabolic burden and improve the productivity and yields of the natural product biosynthesis (Cravens et al., 2019; Zhang and Wang, 2016). By integrating and fine-tuning of systems and synthetic biology platforms, complete biosynthesis of MIA-based anticancer compounds such as vincristine and vinblastine can be achieved through the assembly of these MIA biosynthetic parts and improved through further pathway refactoring and optimization in the chosen microbial chassis (Carqueijeiro et al., 2020).

2.3 Transcriptome-enabled discovery and microbial expression flavonoid biosynthetic genes Flavonoids are considered as the most common and widely distributed phenolic compounds in plants with protective antioxidant properties by scavenging free radicals in the cells. Initiated at aromatic amino acids specifically L-phenylalanine

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TABLE 2 Identification and microbial expression of key enzymes for monoterpene indole alkaloid (MIA) biosynthesis in MIA-producing plants. MIA metabolite

Host

Key enzymes

Source

References

Strictosidine

S. cerevisiae

  

Strictosidine synthase (CrSTR) Geraniol 8-hydroxylase (CrG8H) Geraniol synthase (CrGS)

C. roseus

Brown et al. (2015)

Cathenamine

E. coli

 

Strictosidine synthase (RsSTR1) & Strictosidine glucosidase (RsSG)

R. serpentine

St€ ockigt et al. (2010)

Vincristine

S. cerevisiae

   

Strictosidine glucosidase (SGD) Dehydrogenase (ADH6) Isomerase (ISO) dehydratase (DH)

C. roseus

Casini et al. (2018)

Akuammicine

E. coli & S. cerevisiae

  

Strictosidine glucosidase (SGD) Alcohol dehydrogenase (GS) Cytochrome P450 (GO, CYP71D1V1)

C. roseus

Tatsis et al. (2017)

Stemmadenine

E. coli & S. cerevisiae

 

Geissoschizine Oxidase (GO) Cinnamyl alcohol dehydrogenase-like (Redox1) Aldo-keto-type reductase (Redox2) Stemmadenine-O-acetyltransferase (SAT)

C. roseus

Qu et al. (2018)

Ocimum basilicum & C. roseus

Campbell et al. (2016)

 

Geraniol synthase (ObGES) Cytochrome P450 monooxygenase (CrG10H) Cytochrome P450 reductase (CrCPR) Iridoid synthase (CrIS)

   

Citronellol, 10-hydroxy citronellol & nepetalactol

S. cerevisiae

Hydroxy-strictosidine

S. cerevisiae



Strictosidine synthase (STR)

Ophiorrhiza pumila

Ehrenworth et al. (2015)

16-methoxy-19acetyltaber sonine

S. cerevisiae

 

Tabersonine 16-hydroxylase 2 (T16H2) 16-hydroxytabersonine 16-O-methyltransferase (16OMT) Tabersonine 19-hydroxylase (T19H) 19-O-acetyltransferase (TAT)

C. roseus

Carqueijeiro et al. (2018)

Geranyl pyrophosphate GPP synthase (AgGPPS) Geraniol synthase (ObGES) Cytochrome P450 geraniol hydroxylase (CrG8H) Geraniol oxidoreductase (CrGOR) Iridoid synthase (CrISY)

Abies grandis, O. basilicum & C. roseus

Yee et al. (2019)

  8-hydroxygeraniol & nepetalactol

S. cerevisiae

    

and L-tyrosine, the production of flavonoids, flavonoid glycosides, and stilbenoids have been demonstrated in bioengineered microbes with the implantation of phenylpropanoid biosynthetic genes primary from plants (Chouhan et al., 2019; Jendresen et al., 2015). Microbial production of industrially important flavonoids including naringenin and quercetin was achieved through the expression of canonical flavonoid biosynthetic genes specifically phenylalanine/tyrosine ammonia lyase (PAL), cinnamate-4-hydroxylase (C4H), 4-coumarate-coA ligase (4CL), and naringenin-forming chalcone synthase (CHS) and chalcone isomerase (CHI) (Shah et al., 2019; Yuan and Alper, 2019). Using naringenin as starting compound, various flavonoid products including flavones, flavonols, and breviscapine have been synthesized in microbial chassis through the introduction of downstream genes such as flavone synthases (FSs), O-methyltransferases (OMTs), and

400

Microbial cell factories engineering for production of biomolecules

UDP-glucosyltransferase (UGT) (Chouhan et al., 2019; Shah et al., 2019). For the production of complex polyphenols such as anthocyanin, E. coli and S. cerevisiae are considered as the preferred chassis where mono- and polyculture strategies have been employed to produce anthocyanin using both strains ( Jones et al., 2017; Levisson et al., 2018). To increase the production levels, several strategies including codon optimization and metabolic pathway interference via CRISPR/Cas platforms have been employed for the biosynthesis of the medically important phenylpropanoids in engineered microbes (Cress et al., 2017; Xiong et al., 2017). Owed to the modularity and versatility of microbial engineering approach, the implementation of omics-guided designing of known and novel genes from plant transcriptome datasets would be extremely useful in the production of natural and nonnatural polyphenols in bioengineered microbes (Ku Bahaudin et al., 2018; Ramzi, 2018). Transcriptome analysis of nonmodel plants has greatly promoted the identification of phenylpropanoid biosynthetic genes and enabled DEG-guided pathway mapping and reconstruction using related plant reference genomes and de novo assemblies. By combining systems and synthetic biology platforms, pathway construction and expression of phenylpropanoid biosynthetic genes from medicinal plants including P. minus, Erigeron breviscapus, Glycyrrhiza uralensis, and Scutellaria baicalensis have been established using E. coli and S. cerevisiae as the main microbial hosts (Table 3). Among these polyphenolic metabolites, the complete construction and biosynthesis of breviscapine in engineered S. cerevisiae exemplified the advanced and highly feasible omics-guided natural product biomanufacturing strategy. This is achieved through the identification and synthesis of known and newly found plant enzymes as functional flavonoid biosynthetic parts for synthetic biology applications. Importantly, this transcriptome-enabled discovery of plant-derived flavonoid biosynthetic genes is anticipated to serve as the next-generation enzyme discovery strategy in microbial chassis design particularly for the bioproduction of polyphenols (Cravens et al., 2019; Liu et al., 2018).

TABLE 3 Transcriptome-guided discovery and expression of plant-derived phenylpropanoid and flavonoid biosynthetic genes in engineered microbial chassis. Polyphenols metabolite

Chassis

Key enzymes

Source

References

Flavanone

S. cerevisiae

      

Phenylalanine ammonia lyase (GuPAL1) Cinnamate-4-hydroxylase (GuC4H1) Cytochrome P450 reductase (ATR1) 4-coumarate-CoA ligase (Gu4CL1) Chalcone synthase (GuCHS1) Chalcone reductase (GuCHR1) UDP-glucosyltransferase (GuUGT1)

G. uralensis & Arabidopsis thaliana

Yin et al. (2020)

S. cerevisiae

     

Phenylalanine ammonia lyase (PAL1) Cinnamate-4-hydroxylase (C4H) Cytochrome p450 reductase (CPR1) 4-coumarate-CoA ligase (4CL3) Chalcone synthase (CHS3) Chalcone isomerase (CHI1)

Arabidopsis thaliana

Koopman et al. (2012)

E. coli

  

4-coumarate-CoA ligase (Oc4CL1) Chalcone synthase (OcCHS2) Chalcone isomerase (OcCHI, MsCHI)

Ornithogalum caudatum & Medicago sativa

Guo et al. (2016)

E. coli & S. cerevisiae



Cinnamic acid-specific coenzyme A ligase (SbCLL-1, SbCLL-5, SbCLL-6, SbCLL-7 & SbCLL-8) Flavone synthase II (SbFNSII-1 & SbFNSII-2)

S. baicalensis

Zhao et al. (2016)

Flavone

 E. coli & S. cerevisiae

 

Cinnamate-CoA ligase (SbCLL-7) Phenylpropanoid & flavonoid O-methyltransferases (SbPFOMTs)

S. baicalensis

Zhao et al. (2019)

S. cerevisiae



Flavone synthase II (CiFNS II)

Chrysanthemum indicum L.

Jiang et al. (2019)

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TABLE 3 Transcriptome-guided discovery and expression of plant-derived phenylpropanoid and flavonoid biosynthetic genes in engineered microbial chassis—cont’d Polyphenols metabolite Breviscapine flavonoid

Chassis

Key enzymes

Source

References

S. cerevisiae

        

E. breviscapus

Liu et al. (2018)



Phenylalanine ammonia lyase (EbPAL) Cinnamate-4-hydroxylase (EbC4H) Cytochrome P450 reductase gene (EbCPR) 4-coumarate-CoA ligase (Eb4CL) Chalcone synthase (EbCHS) Chalcone isomerase (EbCHI) Flavone synthase II (EbFSII) Flavone-6-hydroxylase (EbF6H) Flavonoid-7-O-glucuronosyltransferase (EbF7GAT) UDP-glucose dehydrogenase (EbUDPGDH)

Phenylpropanoid

S. cerevisiae



Cinnamate-4-hydroxylase (C4H)

P. minor (P. minus)

Ramzi et al. (2018)

Flavonol

E. coli



Flavonol synthase (OcFLS1, OcFLS2)

O. caudatum

Sun et al. (2019)

3 Conclusions and perspectives Recent advancement and integration of omics-driven bioproduction of biomolecules and natural products marked an exciting progress in expanding the biopharmaceutical repertoire using industrial and microbial biotechnology. As highlighted in this chapter, the emergence of omics platform especially RNA seq-based transcriptomic analysis has contributed to the rapid identification of newly discovered and characterized plant biosynthetic genes to add to the growing list of natural product biosynthetic parts. The employment of systems biology platforms in parallel with new and readily available computational tools and data analysis suites will serve as an increasingly essential enabling technology in complementing DBTL-driven synthetic biology applications and stimulating bioproduct development using microbial engineering strategies. In sum, omics-guided discovery and bioproduction of biomolecules and natural products will be invaluable in accelerating and translating lab discovery to commercially viable bioproduct manufacturing.

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Chapter 21

Advances and applications of cell-free systems for metabolic production Charles Moritza, Srividhya Sundarama, Christoph Diehla, David Adama, Olivier Borkowskib,c, and Amir Pandia,∗ a

Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany, b Inria Paris, Paris, France

c

Institut Pasteur, Paris, France

∗

Corresponding author: E-mail: [email protected]

1 Introduction Cell-free system is referred to as platforms that enable testing biological systems in an environment beyond cells. Applications of cell-free systems started in the early ages of biochemistry and molecular biology when many biological pathways from metabolism to genetic central dogma were being discovered (Nirenberg and Matthaei, 1961; Krebs and Johnson, 1937). From the discovery of Krebs cycle to decoding the information hidden in DNA, scientists have used cell lysate or in vitro protein translation in numerous foundational discoveries. Nowadays, cell-free systems are used for a variety of applications, diagnosis of environmental and medical samples, bottom-up construction of synthetic cells, production of therapeutic proteins and virus-like particles, and metabolic/enzymatic production (Silverman et al., 2020; Pandi et al., 2020). In this chapter, we address metabolic production of biomolecules using cell-free systems. With the growth of research and applications of life science and technology, scientists use the term “cell free” in a broad range. Some mean in vitro systems where enzymes or proteins are purified to perform experiments in a minimal system away from the complexity of in vivo biological networks. The other group means in vitro protein production from DNA (or mRNA) systems when referring to cell-free systems. Because of this broad range of terminology use, as well as a lack of comprehensive studies, we focus on both views in this chapter. Considering in vivo and cell-free approaches, each has advantages and disadvantages (Claassens et al., 2019). In vivo bioproduction wins the sustainability and cost, whereas the cell-free approach provides high adjustability and simpler design and builds steps through its openness, easier cloning, and pathway maintenance. It should be noted that for high-value products cell-free systems may be more economical as in vivo toxicity and burden limit their yield. The chapter starts by describing in vitro transcription-translation (TX-TL) systems and different types of them based on the means of preparation. The classical TX-TL system is made from prokaryotic or eukaryotic cell lysate mixed with a reaction buffer composed of an energy mix, amino acid mix, and salts. The addition of DNA to the TX-TL system enables the transcription and translation of genes carried by a vector. In vitro TX-TL can also be done using Escherichia coli purified recombinant elements (PURE), a set of the core 36 proteins involved in transcription, translation, and energy regeneration. We then move on biotransformation using purified enzymes with comprehensive coverage on this approach and their applications in a variety of metabolic production examples and photo-/electrobiocatalysis. The last subchapter is about application of TX-TL systems in pathway prototyping, enzyme evolution (that is often thought as not doable in cellfree systems), and bottom-up development of synthetic cells. Altogether, here we introduce cell-free systems (from the purest to complex ones; purified enzymes, PURE system, and lysate based TX-TL systems) and their highlighted applications in metabolic production.

2 In vitro transcription-translation (TX-TL) systems 2.1 E. coli lysate-based TX-TL system A variety of protocols exist for the preparation of cell lysate from a range of prokaryotic and eukaryotic species. Simply, this process of producing these systems involves the growth of cells, lysing of the cell membranes followed by optional further processing and storage. E. coli lysate remains by far the most well developed and widely used for cell-free TX-TL systems; Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00008-8 © 2021 Elsevier Inc. All rights reserved.

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therefore this example will be used to illustrate the steps for the preparation of cell-free lysate for protein synthesis (Fig. 1A). Generally, cells are grown in a rich media and harvested during exponential growth phase when cellular transcription and translation machinery is most active (Zawada, 2012; Liu et al., 2005). Cells are then harvested by centrifugation and lysed by mechanical or biochemical means. Commonly used mechanical methods for cell membrane disruption are French press (Voyvodic et al., 2019), bead beating (Shrestha et al., 2012), and sonication (Kwon and Jewett, 2015). Biochemical methods have been developed utilizing lysozyme or programmed autolysis allowing for lysis by osmotic shock and freeze-thaw (Fujiwara and Doi, 2016; Didovyk et al., 2017). These nonmechanical lysis methods are typically widely accessible as no expensive equipment is required and can also produce a highly active lysate for cell-free TX-TL reactions. After the crude lysate is produced, it may be further processed by runoff reaction and dialysis to free ribosomes for translation and to remove unwanted metabolites. While these processing steps were employed in most early foundational works, many recent protocols now rely on centrifugation alone (Silverman et al., 2019). Once the final lysate is produced, it should be immediately used or aliquoted for storage. A common method of storage is flash freezing and storage at 80°C, after which aliquots should be used within 1 year. Recent methods have also shown that lysate may be freeze-dried and retain activity for at least 60 days at room temperature (Smith et al., 2014). A single 1-L batch of lysate prepared as described earlier will typically yield enough cell-free lysate mixture for over 900  10 mL reactions (assuming 3 mL of lysate produced and 33% reaction volume of crude lysate) (Sun et al., 2013). For use in experiments, frozen samples may be thawed and combined with a reaction buffer containing amino acids, tRNAs, nucleotides, salts, cofactors, and energy molecule regeneration systems (Fig. 1). By adding plasmid or linear DNA to this mix, the cell-free reaction starts, and the coupled transcription and translation produce the protein encoded by the DNA template. Since exonucleases in the lysate digest linear DNA fragments (PCR product), researchers have found ways

FIG. 1 Types of cell-free systems for metabolic production. (A) Preparation of lysate from E. coli for use in TX-TL protein synthesis. (B) Preparation of the purified recombinant element (PURE) system. (C) Traditional in vitro testing of a pathway by isolation of individual enzymes.

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to preserve linear DNA using gamS protein (Sun et al., 2014) or addition of w DNA (Marshall et al., 2017). In the case of using gene expression under the control of a T7 promoter, we need to provide purified T7 polymerase to the mix (Silverman et al., 2019). Recent efforts have also begun to produce E. coli lysate capable of proteins with posttranslational modifications ( Jaroentomeechai et al., 2018). The relative concentrations of these reaction components have been studied extensively to optimize the performance. Recently high-throughput methods using robotic liquid handlers and machine learning were applied by Borkowski et al. to optimize concentration of 11 different components for a requiring 4000 separate reactions and improving yield 20-fold (median) (Borkowski et al., 2020). For a newly produced TX-TL lysate, at least the concentrations of Mg-glutamate and K-glutamate will usually be optimized, requiring the use of only 10–20 of the 10-mL aliquots.

2.2 Other prokaryotic and eukaryotic lysate-based cell-free systems To supplement the widely used E. coli lysate system, in recent years, new methods have been described for cell-free lysate preparation from the bacterial genera Streptomyces, Bacillus, Pseudomonas, and Vibrio (Li et al., 2017; Kelwick et al., 2016; Wang et al., 2018; Failmezger et al., 2018). Yim et al. applied a standardized method of cell lysate preparation from 10 diverse bacteria species and examined the relative expression levels (Yim et al., 2019). While the protein yields are still significantly less than for E. coli lysate at present, there are advantages of using other bacterial species because of characteristics such as increased growth rate or specific biochemical properties (Moore et al., 2018). There has also been growing interest in eukaryotic cell-free lysate systems for protein production because of their ability to produce large proteins with a range of posttranslational modifications. However, there have been challenges with these systems due to difficulties with correct mRNA processing and the presence of different compartments and microenvironments within the eukaryotic cell (Craig et al., 1992). Nonetheless, many improvements have been made, and cell-free TX-TL lysate preparation protocols now exist for rabbit, human, insect, protozoan, and yeast species (Pandi et al., 2020; Hodgman et al., 2013; Johnston et al., 2019; Ezure et al., 2014; Mikami et al., 2008). These systems, most of which are also commercially available, have allowed for production of large proteins with posttranslational modifications including acetylation, core glycosylation, isoprenylation, and disulfide bond formation (Chong, 2014). Currently, protein production levels are still well below those utilizing E. coli lysate; however, when optimization efforts are taken to improve these lysates, they can be dramatically improved (Gagoski et al., 2016). With increasing efforts across the scientific community in this area and a movement toward standardization of preparation processes, we can expect introduction of lysates from more diverse strains and species for uses in bioproduction, screening, and prototyping.

2.3 PURE cell-free system The transcription and translation enabled by lysate-based systems are by means of the presence of cellular proteins and enzymes in the lysate. However, in 2001, Shimizu et al. established an in vitro transcription-translation system reconstituted with purified recombinant elements (PURE) (Shimizu et al., 2001). The PURE system comprises E. coli proteins involved in the genetic central dogma, transcription of DNA (from T7 promoter) to mRNA, and translation of mRNA to proteins. For this minimal in vitro system to encode DNA to proteins, we need ribosomes along with 20 aminoacyl-tRNA synthetases charging tRNAs with their associated amino acids, initiation/elongation/release factors for translation, energy regeneration system, and T7 polymerase for transcription, 36 proteins in overall (Fig. 1B). With a minimal composition of defined components, the PURE system provides higher adjustability than lysate-based systems. Also, there will be no cross reaction/interaction between the cell-free system and the DNA-encoded system as it exists in the lysate. We should note that sometimes enzymes in the lysate bring the advantage of having host metabolism functioning to provide intermediates for the pathways that are implemented in cell free (Dudley et al., 2019). While most cell-free bioproduction works so far have been done in lysate-based systems, the PURE system has been mainly applied for biosensor and synthetic cell development (Laohakunakorn et al., 2020). Of the main limitations of using the PURE system is the high cost of commercial kits and difficulties of homemaking. Whereas the buffer composition of PURE and lysate-based cell-free systems is similar, the PURE system needs around 36 proteins His-tag purified along with ribosome purification. To ease this, Villarreal et al. (Villarreal et al., 2018) and Lavickova et al. (Lavickova and Maerkl, 2019) described the approach of microbial consortia. They prepared the mixed culture of a part of or all PURE proteins overexpressed in separate cells and hence reduced the number of protein purifications to a few or one. Although this approach introduces an alternative to purifying all 36 elements, there is a lack of control over the ratios because of the different growth rate of cells in large consortia and their His-tag binding and purification.

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In vitro systems using purified enzymes

In the past decades, in vivo metabolic engineering has resulted in several designer organisms that can be used for the commercial production of useful biopharmaceuticals, commodity chemicals, and nutraceuticals. However, precise control of metabolic pathways in microbes is still a major limitation due to the complicated regulation in cellular processes like transcription and translation. In vivo bioproduction also suffers from low yield, toxicity, and time-consuming “design-buildtest” cycles to identify the best set of enzymes for biotransformation. In vitro synthetic biology is currently defining a new era where natural or artificial metabolic pathways are constructed based on rational assembly of excised enzymes, in one vessel, for the biosynthesis of natural products and biofuels (Fig. 1C). This approach stands out as an excellent alternative mainly due to the fast turnover rate, higher product yields, unprecedented degree of freedom in pathway design, and modularity and easy purification of target metabolites. The history of cell-free basic research is marked with major milestones. Years after the revolutionary discovery of cellfree fermentation of ethanol by yeast lysate by Eduard Buchner in 1897, several scientists sought out to reconstitute individual enzymes and metabolic pathways in vitro that led to key discoveries in glycolysis, citric acid cycle, and plant CO2 assimilation (You et al., 2018). The first commercial manufacturing by one-enzyme biotransformation for the production of high-fructose corn syrup and semisynthetic antibiotics like cephalosporin came into existence in the 1960s (Demain, 2004). From 1990 onward, advances in genomics and proteomics allowed high-throughput screening of DNA, RNA, and proteins in vitro. The development of technical solutions for controlling and measuring larger systems, like LC-MS, enabled in vitro synthetic biology to evolve to a more complex phenomenon where even tens of enzymes in one pot perform complicated reactions, with the first biomanufacturing example of myo-inositol (inositol) from starch reported in 2017 (You et al., 2017). The design of in vitro synthetic pathways follows a bottom-up approach with three major principles: (1) pathway design and reconstruction, (2) enzyme selection, and (3) coenzyme supply and balance. To build a pathway, identify enzymes, and to eventually speed up the design-build-test process, several databases are currently available with known biochemical reactions including KEGG (Kanehisa et al., 2017), MetaCyc (Caspi et al., 2016), BRENDA ( Jeske et al., 2019), and ModelSEED (Seaver et al., 2020). KEGG (11,000 reactions) and MetaCyc (16,000 reactions) document a very comprehensive set of information on the organisms and their metabolic pathways, while BRENDA is the prime database for enzymes and enzyme-ligand interaction systems that offers detailed kinetic parameters and structural information of enzymes. Besides this, modeling algorithms are available to simulate the biochemical pathways to identify bottleneck enzymes and reactions (Wang et al., 2017). These approaches go hand in hand with possibilities to improve enzyme stability and specificity through rational mutagenesis or directed evolution, aided by their structural information (Bornscheuer et al., 2012; Tufvesson et al., 2011). While these computational tools help in the initial stages of pathway design, advances in mass spectrometry aid in analyzing diverse and new-to-nature metabolites. Some success stories resulting from efficient pathway design are highlighted in the succeeding text.

3.1 Glycolysis Initial attempts to reconstitute the Embden-Meyerhof (EM or glycolysis) pathway in vitro mainly focused on the production of ethanol using individually purified yeast enzymes or thermophilic enzymes (Welch and Scopes, 1985). Multiple studies have been reported on the conversion of glucose to lactate, malate, ethanol, and isobutanol (Zhang, 2015). Evolution has shaped metabolic pathways to naturally regenerate ATP and NAD(P)H cofactors that demand the cell-free approach to balance the cofactors in a viable manner or to go coenzyme free to avoid excess costs. To this end an artificial glycolytic reaction cascade based on the nonphosphorylating Entner-Doudoroff (np-ED) pathway of hyperthermophilic archaea was designed to convert glucose to pyruvate. The cascade comprises only four enzymes—two dehydrogenases, dehydratase and an aldolase, that run in an ATP-independent manner (Guterl et al., 2012). On the other hand the highly regulated EM pathway has also been exploited for forward engineering to feed the limited complex model systems available to date. In a 2016 study the 10-enzyme cascade was run in a continuous stirred-tank reactor, and the dynamic characteristics of the system were tracked using real-time mass spectrometry to monitor dihydroxyacetone phosphate formation (Hold et al., 2016). It is highly conceivable that the large set of data generated from this model can be translated to other complex metabolic studies both in vitro and in vivo.

3.2 Terpene production The glycolytic pathway produces energy in terms of ATP, reducing equivalents, and carbon building blocks like pyruvate, making it an excellent module to make industrially relevant terpenes via the mevalonate pathway. Using the low-cost

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renewable glucose feedstock, the acetyl CoA and ATP generated by glycolysis feed the production of limonene, pinene, and sabinene with high yield and titers of 95% and 15 g L 1 (Korman et al., 2017). The system comprises 27 enzymes and operates continuously for at least 5 days without further addition of substrate or cofactors. The system benefited from the use of a purge valve module that produces NADPH via the NADP+-utilizing dehydrogenase, while excess NADH is purged using an oxidase (Opgenorth et al., 2014). Careful modeling of reaction parameters using CoPASI identified major bottleneck enzymes and the rate-limiting steps.

3.3 Hydrogen production Hydrogen (H2) is a promising transport fuel with enhanced energy conversion efficiency and could contribute to green chemistry. Zhang and coworkers have utilized several low-cost feedstocks such as starch, xylose, and sucrose to demonstrate H2 production to the maximum yield of 12H2 per glucose in comparison with the 4H2 per glucose produced by natural and engineered organisms (You et al., 2018). In 2017, using starch as an energy source, an in vitro synthetic pathway was constructed using 17 thermophilic enzymes grouped into four modules to generate: (1) G6P in an ATP-independent manner, (2) NADPH via the pentose phosphate pathway, (3) hydrogen using a hydrogenase from hyperthermophilic archaea via a biomimetic electron transport chain, and (4) G6P via the nonoxidative pentose phosphate and partial gluconeogenesis pathway. The inorganic phosphate generated in the last module is then used to rephosphorylate G6P. This system resulted in the highest volumetric productivity of 90.2 mmol of H2/L/h (Kim et al., 2017).

3.4 n-Butanol production A liquid biofuel with an energy density comparable with gasoline is the 4-carbon n-butanol. The traditional production of nbutanol relies on the complicated fermentation process of Clostridium acetobutylicum (Ezeji et al., 2007). A synthetic conversion of glucose to n-butanol consisting of 16 thermostable enzymes has been shown to convert one molecule of glucose to one molecule of n-butanol, and two CO2 and H2O. This pathway is completely cofactor (ATP, NADPH, and CoA) balanced and produces n-butanol with a molar yield of 82% (Ezeji et al., 2007). Several examples of the production of commodity chemicals like bioplastics from glucose (Opgenorth et al., 2016) and aromatic alcohols from lignocellulosic biomass (Tramontina et al., 2020) have been reported.

3.5 CO2 fixation As an alternative to the use of regular feedstocks like glucose, underutilized yet abundant feedstocks like CO2 can also be used for in vitro biotransformation. To this end a synthetic crotonyl-coenzyme A (CoA)/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle has been constructed as an optimized reaction cycle that converts five nanomoles of CO2 to glyoxylate per minute per milligram of protein (Schwander et al., 2016), a yield that is nearly five times more efficient than the natural carbon fixation pathways. The cycle comprises 17 enzymes that originate from nine different organisms across all three domains of life and is an excellent representative of enzyme mining, enzyme engineering, and metabolic proofreading. Created in a bottom-up fashion, this study also revealed a set of enoyl-CoA-carboxylases/reductases (ECRs) that could carboxylate several substrates in vitro.

3.6 Photobiocatalysis Photobiocatalysis is currently distinguished in three different ways: (1) photocatalysis that can be coupled to isolated enzymes or cell lysates where the photoexcited electrons are either transferred directly (DET) or indirectly by a mediator, (2) natural whole-cell approaches, and (3) light-dependent enzymes (photoenzymes) including photosystem I (PSI) (Melkozernov et al., 2006), photosystem II (PSII) (Renger and Renger, 2008), and DNA photolyases. As an example for enzymatic photocatalysis, Brown et al. reported the conversion of aldehydes to alcohols by an alcohol dehydrogenase (tbADH) from Thermoanaerobium brockii. The required reducing NADPH equivalents were photochemically regenerated by a biohybrid complex of CdSe quantum dots and a ferredoxin NADP+-reductase from Chlamydomonas reinhardtii with an apparent kcat of 1400 h 1 (Brown et al., 2016). Despite the latest successes, challenges in the area of photobiocatalysis are the incompatibility of enzymes with organic solvents, the small substrate scope, and the catalytic inefficiency (low TON) of enzymes. Another approach was reported where artificial photosensitizers such as xanthene dyes are coupled to hydrogenases for light-induced hydrogen production. The advantages of xanthene dyes are their water solubility, simple chemical modification, and stable triplet state formation for efficient electron transfer (Adam et al., 2017; Rumpel et al., 2014).

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On the other hand, natural photosensitizers such as PSI are huge membrane-bound multicomplex proteins where their high concentrations lead to inefficient light harvesting. To overcome this problem at high concentrations, a direct coupling between PSI and an [FeFe]-hydrogenase from Cl. acetobutylicum for an improved electron transfer was reported (Lubner et al., 2010). Another strategy not to use single photoactive molecules such as xanthene dyes or isolated photosystems is the encapsulation of photosynthetic membranes within cell-sized droplets. These droplets can be triggered by light and supply cofactors like NADPH and ATP. It was shown that this platform can be coupled to the CETCH cycle (synthetic CO2 fixation pathway) (Schwander et al., 2016) and create an artificial photosynthetic system (Miller et al., 2020).

3.7 Enzymatic electrocatalysis Enzymatic electro synthesis (EES) combines a biocatalytic process and electricity (in the form of electrons) to synthesize a desired product. A molybdenum-containing formate dehydrogenase from E. coli was reported as a highly active, reversible electrocatalyst for the interconversion of CO2 and formate (Bassegoda et al., 2014). Apart from direct use of electricity to power a biocatalytic process, the recycling of cofactors like nicotinamide adenine dinucleotide phosphate (NADPH/ NADH) has received increasing attention. The Armstrong group reported an electrochemical regenerating system for the reduction of NADPH by using a ferredoxin-NADPH-oxidoreductase (FNR) in a porous indium tin oxide (ITO) electrode (Megarity et al., 2019). This FNR as NADPH-recycling system was brought together with an L-malate, NADP+ oxidoreductase from E. coli, thereby entrapping both enzymes within ITO nanopores. The coupling of NADPH regeneration and NADPH consumption for conversion of pyruvate to malate bypasses the limitation of different enzyme location and enables a fast and efficient cascade (Morello et al., 2019). Besides the rapid progress and discoveries in this field, huge challenges still exist. While one-step reactions are relatively easy to establish, the multifaceted synthetic pathways necessary to produce complex chemicals are much more difficult. The product/substrate availability of enzymes can also limit the applications; however, this can be addressed by repurposing diverse existing proteins via engineering. Finally, another major challenge will be the development of efficient and sustainable enzymatic electrodes with a focus on enzyme stability, enzyme performance, mass transport of substrate/product, and electron transfer. The insights from studying in vitro cascades contribute to basic research and enable the optimization of metabolic engineering for the production of high value-added compounds. However, there are some limitations that need to be addressed to advance the field of in vitro synthetic biology. First the pathways need to be carefully designed such that all the steps concerning thermodynamics, reaction equilibrium, and cofactor balance are taken into consideration. Simulation of the hypothetical pathways/cycles based on stochastic models should be considered to identify bottleneck reactions and to determine stoichiometric balances and thermodynamic predictions. And yet, experimental reality often differs dramatically from the design due to unexpected reactions, stability of enzymes, etc. Especially the expensive cofactors are often damaged by mishandling. Therefore cheap and effective cofactor regeneration systems are one of the keys toward economically feasible in vitro production pathways. Second, cell-free production and purification of enzymes are costly processes. To make it cost-effective, stable and efficient enzymes are significant to decrease the overall bioproduction costs. Use of thermophilic enzymes, improving the enzyme by directed evolution and enzyme compartmentalization should be considered wherever possible. Considering different chassis for protein production plays an important role here. Third, although carbohydrate feedstocks are cheap and abundant in nature, their use still amounts to a major portion of the production costs. This can be circumvented using plant biomass and other feedstocks, especially for the production of low-cost compounds. Finally, the in vitro scale-up of several compounds is still a major challenge. To this end, translating the information gained from in vitro research can be used to fuel in vivo applications where higher yields might be achieved. Another promising alternative is to implement cell-free protein synthesis to biotransform potential enzymes to value-added products.

4

Applications of TX-TL systems for bioproduction

4.1 Bioproduction and rapid prototyping The earlier examples of metabolic production highlight some of the major advantages of using cell-free systems using isolated enzymes. In the context of bioproduction, TX-TL systems are currently used primarily for the production of proteins. There have been recent advances especially in the production of therapeutic proteins including antibodies, vaccine antigens, virus-like particles, and antimicrobials, which have been reviewed elsewhere (Tinafar et al., 2019). Cell-free TXTL systems have also been used to produce entire MS2, FX174, T7, and, more recently, T4 phages in vitro (Rustad et al.,

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2018). Lysate-based cell-free production of various metabolites has been achieved (Silverman et al., 2020; Pandi et al., 2020) (Table 1). There are also impressive examples of TX-TL systems being applied to produce a range of compounds in the context of the development of synthetic cells, and these will be discussed in a later section. Apart from the examples mentioned earlier that show cell-free systems can be used as production platforms, especially for high value products, these systems also provide tools for prototyping (Fig. 2A). In this regard, in vitro TX-TL systems play an important role between in vitro and in vivo worlds. As one advantage of cell-free systems is the rapid turning of the design cycle for engineered biological systems, in the past few years, these approaches have been applied to the prototyping of metabolic pathways (Pandi et al., 2020; Karim and Jewett, 2018). In vivo engineering of synthetic pathways often suffers from difficult cloning of large combinatorial libraries and toxicity of nonoptimized biological pathways in living cells. Using cell-free systems, different factors taking part in the combinatorial library can be cloned in separate vectors or used as PCR products. Hence, it skips one of the most difficult and time-limiting steps, cloning of multiple genes in one, two, or three vectors. The cloned vectors then can be used at any arbitrary combination and concentration by simply pipetting different amounts of each to the cell-free reaction mix. This gives the ability to test up to thousands of combinations in an experiment, with the limiting factor then being the pipetting and measurement steps. A solution for the pipetting bottleneck is the use of liquid handling robots that synthetic biology research units are increasingly equipping themselves with (Borkowski et al., 2020; Moore et al., 2018). The measurement bottleneck depends on the products. For some products, biosensors can be engineered and used to screen the production of the desired metabolite (Pandi et al., 2019). Other analytical methods also may be used traditionally or in a high-throughput metabolomics approach. In a typical DNA encoded pathway in an in vitro TX-TL system, plasmids are added at desired concentrations and allowed to produce the enzymes, followed by a reaction phase where the other pathway components are introduced. Kelwick et al. used plasmid DNA to encode enzymes for a polyhydroxyalkanoates (PHAs) bioplastic production pathway (Kelwick et al., 2018). As alternatives or control for plasmid-encoded enzymes, doped extracts or purified enzymes can be added to the cell-free mix. In other words, for those enzymatic steps that limit or stop the metabolic flux of the pathway, enzyme-enriched extract or purified enzymes help to identify the bottlenecks. Doped extracts are made from cells in which one gene at a time overexpress an enzyme. Karim et al. applied the enzyme-enriched extract strategy to find the bottleneck of a 5-enzyme pathway, and they could overcome it simply by increasing the concentration of DNA plasmid for the enzyme limiting the pathway (Karim and Jewett, 2016). Dudley et al. used enzyme-enriched extracts to produce limonene by mixing six extracts where one enzyme was overproduced in each (Dudley et al., 2019). In this study, there was no need to add reaction buffer composition for transcription translation because all the enzymes present in the lysate mix and the E. coli native metabolism are also active to produce acetyl-CoA as the starting precursor of the mevalonate pathway. In a recent study, Karim et al. introduced a platform for in vitro prototyping and rapid optimization of biosynthetic enzymes (iPROBE) (Karim et al., 2019). In this work, they added plasmids to express enzymes in the lysate-based cell-free system or

TABLE 1 Few examples of compounds produced in cell-free systems. Product

Cell-free system

Reference

1,4-Butanediol

TXTL (lysate)

Wu et al. (2015)

Violacein

TXTL (lysate)

Nguyen et al. (2015)

(R)-3-hydroxybutyryl-CoA

TXTL (lysate)

Kelwick et al. (2018)

n-Butanol

TXTL (lysate) and enriched lysate

Karim et al. (2019)

Meso-2,3 butanediol

Enriched lysate

Kay and Jewett (2015)

Mevalonate

Enriched lysate

Dudley et al. (2016)

Limonene

Enriched lysate

Dudley et al. (2019)

Ethanol

Purified enzymes

Guterl et al. (2012)

n-Butanol

Purified enzymes

Ezeji et al. (2007)

Terpenes

Purified enzymes

Korman et al. (2017)

Polyhydroxybutyrate

Purified enzymes

Opgenorth et al. (2016)

Raspberry ketone

Purified enzymes

Moore et al. (2017)

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FIG. 2 Applications of cell-free TX-TL systems. (A) Prototyping of DNA-encoded multienzyme pathways. (B) Enzyme evolution using a generated mutant DNA sequence library and in vitro compartmentalization (IVC) for sorting and selection. (C) Development of a synthetic cell through the reconstitution of cellular process in vitro including (1) energy metabolism, (2) production of integral membrane proteins, (3) DNA replication, (4) carbon metabolism and gene regulation, (5) production of membrane components, (6) production of actin filaments.

used enzyme-enriched lysates. Rapid cell-free prototyping of pathways gave the opportunity to design-build-test large combinatorial libraries that would face many bottlenecks in an in vivo approach. Using the cell-free approach, they ranked 3hydroxybutyrate production in vitro and improved its in vivo production in Clostridium by 20-fold. They also identified a new enzymatic pathway for (S)-(+)-1,3-butanediol production. In the end, using a computational approach for the design of the experiment, they optimized n-butanol production and showed a correlation between in vitro and in vivo behavior of the pathway. A critical phase of pathway prototyping for in vivo implementation is how to transfer the chosen candidates into living cells (Karim et al., 2019), because there are factors that vary between in vivo and in vitro environments. One is building

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single gene vectors or linear DNA into multigene plasmids. For this aspect, there are characterizations that synthetic biologists still need to accomplish. For example, one solution is building in vivo monocistronic operons that are easier to characterize and track. Fortunately, synthetic biology has collections of characterized orthogonal biological parts (promoters, RBS, terminators, and insulators) that can be used simultaneously. However, there is a lack of research for some other issues such as the relationship between the concentration of DNA in vitro and the copy number of the plasmid in vivo. Apart from automation, metabolic pathway prototyping can further be speed up using computational and data-driven tools such as the design of experiment (DoE) and machine learning (Borkowski et al., 2020; Moore et al., 2018).

4.2 Engineering and evolution of enzymes Cell-free systems have also been used for engineering and in vitro evolution of enzymes (Fig. 2B). One approach is to generate a mutant library using random or rational mutagenesis, then each mutant colony is lysed, and the behavior of the enzyme is assessed in the lysate (M€ uller et al., 2013; Xiao et al., 2015). Some enzymes cannot be tested in vivo because of the presence of active cellular metabolism and the toxicity of its substrate or product or because the enzyme’s substrate cannot pass through the membrane. Testing in a cell-free lysate may be advantageous in these cases; however, if the lysate also interferes with the enzyme assay, mutant enzymes should be purified and tested in vitro (Schwander et al., 2016). The latter is limited to cases where there are relatively few mutants, for instance, resulting from rational mutagenesis of the active site of the enzyme. In all cases the genotype-phenotype relationship should be trackable, meaning that if a desired mutant has been found, its sequence can be tracked back through the colony that the lysate has been made from. A more dominant application of cell-free systems in directed enzyme evolution is high-throughput and ultrahighthroughput approaches. These methods provide the genotype-phenotype relationship, which was considered to be an advantage of in vivo testing. These approaches make the link between evolved DNA sequence and its phenotype through encapsulation of single DNA molecules after the generation of random sequences. There are different versions of this strategy, initially introduced as in vitro compartmentalization (IVC) (Miller et al., 2006). These techniques can take advantage of various sequence generation types, including error-prone PCR and site-directed mutagenesis. They may have different encapsulation strategies, water/oil emulsion (“selections” in droplets) and water/oil/water emulsion (fluorescence-based “screens”). In several examples, TX-TL systems have been used to produce mutant proteins from DNA libraries generated through the encapsulation and sorting of single DNA fragments. There are also studies in which the authors used encapsulation of cells or cell lysate in a phase of in vitro evolution (Markel et al., 2020; Kintses et al., 2012; Abil et al., 2017). In early studies at the beginning of the 21st century, mutant versions of DNA polymerase were evolved using IVC. In those studies, higher fitness could be selected by gene amplification, such that the evolved enzymes accumulated more copies of their gene (Ghadessy et al., 2001, 2004). Alternatively, there are studies that a fluorescent product of an enzyme is screened using fluorescence-activated cell (here droplets) sorting (FACS) (Mastrobattista et al., 2005). ICV has been used to evolve the activity of DNA polymerases and other DNA-related enzymes, metabolic enzymes, and ligand binding and regulatory activities (Miller et al., 2006).

4.3 Bottom-up development of synthetic cells with cell-free systems Synthetic cells are emerging as new-to-nature cell factories that can be used for a variety of applications, from drug delivery to the production of biomolecules. The construction of synthetic cells may be approached top-down, typically through reduction of existing genomes, or bottom-up. Especially for the latter approach, cell-free TX-TL systems have much to offer the effort (Laohakunakorn et al., 2020). Recent studies have focused increasingly on applications of the PURE system, which has the advantage of being fully defined allowing for piecewise addition of layers of complexity (Caschera and Noireaux, 2014). In living cells the means for compartmentalization is based on a lipid-based bilayer, and for the construction of synthetic cells, this is also an attractive option. Liposomes offer high membrane fluidity, simple and biocompatible building blocks, and high permeability for nutrient exchange. Comparatively, polymersomes typically have a thicker membrane, more stability, lower permeability, and more adjustability owing to the wide choice of block polymers (Rideau et al., 2018). These characteristics can make them attractive for a number of applications including deployment as drug delivery mechanisms and as stable nanoreactors (Lee and Feijen, 2012; Klermund et al., 2017). Methods employed to produce compartments of controlled size and shape include lipid film hydration, emulsion transfer method, electroformation, and microfluidics (Witkowska et al., 2018). Coupled with pico injection methods, it is possible to produce cellular systems in a process akin to microscale assembly lines, inserting additional components into internal space or membrane (Weiss et al., 2018). Mirroring biological membranes, combinations of these methods can produce mosaic membranes containing lipids, proteins, and sugars, allowing for complex and useful characteristics (Otrin et al., 2017).

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Cell-free TX-TL systems compartmentalized as described earlier have been employed extensively for the development of minimal metabolism in synthetic cells (Fig. 2C). This is accomplished through the introduction of DNA-encoded pathways and regulatory networks, which are recently increasing in complexity and functionality (Dubuc et al., 2019). Gramalla et al. displayed a simple and transferable method for construction of a genetic circuit enabled by the PURE system and contained in a liposome (Garamella et al., 2016). Another study displayed more complex cell-free TX-TL metabolism, including a genetic cascade responsive to signals from other synthetic cells (Adamala et al., 2017). Researchers have moved further still toward an autopoetic cell, using TX-TL systems to encode pathways into synthetic cells for the regeneration of their macromolecular building blocks. The PURE system has been used with vesicular systems capable of replenishing both lipid- and protein-based membranes (Bhattacharya et al., 2019; Scott et al., 2016; Vogele et al., 2018). Encapsulated PURE systems have also produced a variety of proteins, including complex enzymes, integral membrane proteins, and cytoskeletal fibers (Fenz et al., 2014). Van Nies et al. achieved the replication of a DNA template within a liposome without loss of information, addressing a long-standing challenge for the synthetic cell community (van Nies et al., 2018). Significant from a bioproduction perspective, teams have used recombinant TX-TL systems to produce photosynthetic synthetic cells (Lee et al., 2018). Berhanu et al. built an encapsulated PURE system to produce ATP using purified ATP synthase and bacteriorhodopsin (Berhanu et al., 2019). This so-called artificial photosynthetic cell could regenerate its ATP for transcription and translation of a target gene. In theory, by continually adding layers of metabolism within dynamic vesicular systems, we will move increasingly toward a living cell-like system (Ganti, 2003). In the pursuit of this goal, there will doubtless be further applied solutions to challenges in the medical and industrial fields and potentially insights into the genesis and functioning of evolved biological systems (Szostak et al., 2001).

5

Conclusions and perspectives

In this chapter, we discussed cell-free systems for the production of biomolecules focused on in vitro transcriptiontranslation systems and biomanufacturing using purified enzymes. In the past few years, the term “cell free” has referred to TX-TL systems and also when using purified enzymes. Since there is an increasing level of complexity from in vitro purified enzymes to the PURE and lysate-based systems, this variety is useful according to the application. Cell-free systems can be directly used to manufacture biomolecules especially for costly products, or they can be applied as prototyping platforms to characterize metabolic pathways for in vivo implementation. We also discussed that these in vitro tools can be used for the bottom-up development of synthetic cells. Although the cost of these systems is higher than in vivo assays, their application for prototyping is convincible with regard to the knowledge they provide. For in vitro production of value-added products, cell-free systems bring advantages such as high yield, non-GMO products, and simpler adaptation to industrial scales. We believe that with the recent progress on data-driven optimization and machine learning, cell-free systems will show more potential through reducing the cost and adjustability for a variety of applications.

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Chapter 22

Microbial biosensors for discovery and engineering of enzymes and metabolism Lennart Schada von Borzyskowskia, Matthieu Da Costab, Charles Moritza, and Amir Pandia,∗ a

Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany, b Center for Synthetic

Biology, Unit for Biocatalysis and Enzyme Engineering, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium ∗

Corresponding author: E-mail: [email protected]

1 Introduction Living systems sense signals from their environment and respond to them to optimally adapt to changing environmental conditions. Biosensors are synthetic genetic constructs inspired by regulatory networks in living cells that control gene expression in response to chemical and physical stimuli (Polizzi and Freemont, 2016; Carpenter et al., 2018; Hicks et al., 2020; Wan et al., 2019a). Since the dawn of molecular biology, many transcription factors (TFs), riboswitches, and other regulatory elements have been discovered (Seshasayee et al., 2011; Thakur and Shankar, 2017). In this chapter, we discuss small molecule-responsive allosteric (SMRA) TFs and riboswitches as the most frequent sensors for detection of small molecules and how they have been applied in metabolic engineering and discovery of metabolic pathways. We first introduce SMRA TFs as the main tools to design and construct biosensors that measure the concentration of cellular metabolites. We also give a brief overview of riboswitches. The following section focuses on the application of biosensors for metabolic and enzyme engineering. These applications consist of the engineering of dynamic regulatory behaviors and the utilization of biosensors for screening and selection of enzymes and pathways. Each of these applications has been reviewed before (Zhang et al., 2015; Cheng et al., 2018; Venayak et al., 2015). Next, this chapter will deal with the question of whether TFs can be utilized as biosensors to identify and subsequently characterize novel enzymes and metabolic pathways. Starting off with general approaches to characterize enzymes with unknown function, several methods toward the goal of utilizing SMRA TFs for this purpose will be summarized. The key requirement for this is the efficient determination of TF binding specificity. It will be highlighted how emerging highthroughput screening systems can be realized, followed by an outlook on the combination and optimization of these methods in the future. The focus of this part will mostly be on bacterial, fungal, and plant model organisms as key targets of metabolic engineering and synthetic biology approaches.

2 Types and construction of microbial biosensors 2.1 Allosteric transcription factor-based biosensors TFs are the main regulatory elements of microorganisms to control gene expression in response to internal and external stimuli (Koch et al., 2019). Microbial SMRA TFs can be repressors that bind to their operator sites and prevent RNA polymerase to transcribe downstream genes. These allosteric TFs upon binding to their specific ligands undergo a conformational change and dissociate from the operator site (Libis et al., 2016a). Hence, RNA polymerase can initiate the transcription of genes under the control of a ligand-inducible promoter. The other type of transcriptional regulation is through the binding of a ligand to an activator TF, which enables binding of this complex to its target sequence and recruits RNA polymerase to transcribe genes under the control of a promoter that does not support gene expression in its default state. The specific binding of TFs to their ligand(s) and promotion of the gene expression from inducible promoters are commonly applied to construct synthetic sensors for a variety of applications (Fig. 1A). To build a biosensor responding to or detecting a metabolite, first, we have to know which organism we are using as our host (chassis). Although the source of TFs can be different organisms, we should know our host regulatory system well enough to engineer a promoter for it. For example, there are well-established ways of engineering a new inducible promoter Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00017-9 © 2021 Elsevier Inc. All rights reserved.

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FIG. 1 Types of biosensors introduced in this study. (A) Typical construction of a biosensor based on a small molecule-responsive allosteric (SMRA) transcription factor (TF) using a repressor. The mechanism shown here is for a repressor that by default represses its cognate promoter. In the presence of a specific metabolite (ligand), the promoter becomes derepressed and triggers the expression of GFP (reporter gene). If there is no TF (or riboswitch) for a metabolite, a biosensor can be built though TF engineering (B) (Tang and Cirino, 2011; de los Santos et al., 2016), engineering of a chimeric TF (C) (Paepe et al., 2019; Jua´rez et al., 2018), or using metabolic enzyme(s) that convert a nondetectable metabolite into a detectable compound (D) (Libis et al., 2016b; Del
epine et al., 2016). The second most used type of biosensors is transcriptional (E) or translational (F) riboswitches (Kent and Dixon, 2019; Seeliger et al., 2012; Caron et al., 2012; Mauger et al., 2013). The examples here are in OFF state in the presence of ligands. However, riboswitches could have activatory function.

in Escherichia coli (Liu et al., 2019; Varman et al., 2018). We also need to know if there is a natural TF binding to our desired metabolite that we are building a biosensor for. There is a comprehensive list of SMRA TFs collected from publications and dedicated databases published by Koch et al. (Koch et al., 2018). In this list, one can also access biosensors that have been engineered, so that for some metabolites there is no need to start biosensor construction from scratch. However, if we need to start from scratch, we need to find the DNA binding site of TFs in the literature or in databases such as RegulonDB (http://regulondb.ccg.unam.mx/). Then, synthetic promoters can be designed by fusing the operator site of a TF to the constitutive promoters of the host or a T7 promoter (Armetta et al., 2019). In case the source of the regulator and the chassis are the same organism or closely related organisms, natural inducible promoters from the genome can also be used (Armetta et al., 2019). The sequence of TFs is available in databases such as Uniprot (https://www.uniprot.org/) and could be codon optimized for improved expression in a foreign chassis.

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The characteristics of biosensors, such as their sensitivity, specificity, and dynamic range, can be improved by promoterribosome binding site (RBS) combinatorial testing and manipulation of the ligand-binding site of the TF (Chen et al., 2018; Rondon et al., 2019; Dabirian et al., 2019; Bernard and Wang, 2017; Pandi et al., 2019a). Since biosensors encode a transcriptional response that could be screened in a high-throughput manner using reporter genes, engineering biosensors with desired behavior or affinity is highly facilitated through the rapid test phase of the design-build-test cycle. Lopreside et al. characterized eight reporter genes (fluorescent, colorimetric, and bioluminescent) in cell-free systems and in vivo to demonstrate a variety of sensitivity, input and output dynamic ranges, response time, and output visibility (Lopreside et al., 2019). Signal amplification is needed to increase the detection ability that might also result in signal digitization and can be achieved using logic gates or tuning the expression level of TFs (Wang et al., 2014; Wang et al., 2015; Wang et al., 2013; Bonnet et al., 2013). Wan et al. applied both gene logic gates and tuning the expression level of small molecule-responsive allosteric TFs to build sensitive biosensors (Wan et al., 2019b). Applying a mathematical model, Hsu et al. optimized a copper biosensor by engineering the promoter and RBS expressing the TF and the reporter gene (Hsu and Chen, 2016). In another study, Ambri et al. described the engineering and optimization of the promoter design of prokaryotic TFs in yeast (Ambri et al., 2018). In a remarkable study led by Christopher Voigt, Meyer et al. developed a directed evolution strategy to simultaneously optimize TF biosensors’ noise, dynamic range, sensitivity, and crossreactivity (Meyer et al., 2019). They engineered 12 highly optimized bacterial sensors, each inducible with a different small molecule in one host, the so-called Marionette strain. They then integrated all 12 sensors into the genome of wild-type E. coli and in strains optimized for cloning and protein expression. This enables up to 12 different genes or operons to be cloned, transformed, and controlled individually to precisely tune the level of expression for multienzyme pathways. However, if there is no SMRA TF or alternative regulatory element for the molecule that should be sensed, there are three strategies to engineer a biosensor (Koch et al., 2019). First, as a well-developed strategy, protein engineering is an option (Fig. 1B). Since the design-build-test cycle of biosensors is quicker because of its rapid test phase, random or rational mutagenesis more likely finds a mutant with the desired function. Tang et al. (Tang and Cirino, 2011) and de los Santos et al. (de los Santos et al., 2016) engineered SMRA TFs that instead of their native ligand were able to bind and respond to mevalonate and vanillin, respectively. Protein engineering computational tools such as Rosetta can be used for rational engineering of the ligand-binding site of TFs (Davis and Baker, 2009; Tinberg et al., 2013; Huang et al., 2011; Jha et al., 2015; Moretti et al., 2016). The second strategy is through engineering a chimeric TF that has ligand-binding and DNA-binding domains from different sources (Fig. 1C). De Paepe et al. engineered a chimeric TF for ligand-specific flavonoid biosensors (Paepe et al., 2019). Jua´rez et al. described a high-throughput method to construct chimeric TFs from a library of ligand-binding and DNA-binding domains ( Jua´rez et al., 2018). The third strategy to expand the detection of small molecules is using enzymatic cascades transforming a nondetectable metabolite into a detectable compound (Libis et al., 2016b) (Fig. 1D). Retropath (Del
epine et al., 2018) and Sensipath (Del
epine et al., 2016) are computational tools to find metabolic pathways that enable enzymatic conversion of the desired molecule to a metabolite that has a transcriptional or translational sensor.

2.2 Riboswitch-based biosensors In addition to protein-based biosensors, there are a number of possibilities for the development of biosensors using singlestrand nucleic acids for the detection of small molecules (Alsaafin and McKeague, 2017). RNA in particular can readily form a wide range of secondary structures, which can be predicted and designed with computational tools (Xu et al., 2016; Gong et al., 2017). Riboswitch biosensors consist of a single RNA aptamer with two parts: One part recognizes the molecule of interest (aptamer domain), and the other modulates an action (expression platform) (Garst et al., 2011). The two segments may be adjacent, apart, or linked by a transducer. A target ligand with specificity for the aptamer domain may bind and cause a conformational shift, which in turn will change activity of the expression platform (ON or OFF) (Kent and Dixon, 2019; Seeliger et al., 2012; Caron et al., 2012; Mauger et al., 2013). This system is used in nature for regulation of transcription and translation in response to cellular and environmental stimuli and has also been leveraged for biotechnology (Fig. 1E and F) (Varani, 2003). Riboswitches are now a popular RNA-based system employed for the development of biosensors for small molecules. A specific riboswitch tool termed the “Spinach” aptamer is of particular interest. This segment of RNA is able to bind and activate the fluorescent molecule 3,5-difluoro-4-hydroxybenzylidene imidazolinone (DFHBI) when in the correct conformation (Paige et al., 2012). This sequence may be combined with a small molecule-specific aptamer domain to create a riboswitch with inducible fluorescence signal output and has been used to build Spinach-based riboswitch biosensors for adenosine, ADP, SAM, guanine, and guanosine 50 -triphosphate (GTP) (Paige et al., 2012). The sensor portion may be based on natural systems, designed rationally, or generated using high-throughput methods such as SELEX (McKeague and Derosa, 2012).

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3 Application of biosensors for the engineering of enzymes and metabolic pathways 3.1 Screening and selection Metabolic engineering and biocatalysis are promising fields for the production of high-value chemicals and are great alternatives to the more prevalent organic chemistry approach, the drawbacks of which are more than well-known (Bornscheuer et al., 2012). While progress in synthetic biology is gaining momentum by constantly providing novel state-of-the-art molecular and computer-aided design tools, those fields suffer from slow innovation in analytical processes. Traditional screening and selection methods heavily rely on techniques such as gas or liquid chromatography (GC/LC) coupled to UV absorbance and/or mass spectrometry (MS) detectors, which are well established and highly accurate devices. However, the use of these analytical systems is costly, including the expensive pieces of equipment, constant maintenance, and dedicated trained personnel (Hicks et al., 2020). Moreover the complexity of biological systems, such as metabolism or protein dynamics, often necessitates directed evolution approaches that generate large mutant libraries of commonly 107–109 variants per week, albeit the tendency is moving toward rational methods leading to “small but smart” libraries (Nobili et al., 2013). Therefore chromatography and mass spectrometry are not suitable for these approaches, since they are limited by library sizes (Petzold et al., 2015) (Table 1). Consequently the fundamental limitation for many engineering projects, such as strain or enzyme engineering, is the identification of mutants with desired features. The use of whole-cell or enzyme biocatalysts in early stage bioprocess development is likely to become more common, under the condition that faster methods with increased detection accuracy and low cost can be developed. To this end, microbial biosensors appear to be a promising and attractive alternative to tackle the overwhelming challenge of high-throughput screening and selection of large libraries (Fig. 2A). Indeed, microbial biosensors exhibit several advantages in relation to the number of colonies screenable, outputs available, and their relatively low cost (Rogers and Church, 2016). Consequently the elaboration of tailor-made biosensors for bioproduction did not take long before coming into sight (Liu et al., 2019). Indeed, in the last few years, we observed a boost in the number of projects linked with the use of microbial biosensors as high-throughput screening tools. For instance, a remarkable area of research consists of developing enzymatic or whole-cell biocatalysts to capitalize lignocellulosic biomass components such as cellulose, hemicellulose, and lignin toward aromatic compounds and monosaccharides (Mizuta and Tokushige, 1975). Many biosensors have already been developed to detect these lignocellulosic derivatives that have wide application in industry and hold the potential to transform the global economy (Alvarez-Gonzalez and Dixon, 2019). As a result, protocatechuic acid (PCA), a central metabolite in the catabolic pathway of lignin-derived molecules, has been the target of responsive TFs for enzyme high-throughput screening in E. coli (Meyer et al., 2019) and Pseudomonas putida ( Jha et al., 2018). Other lignin-derived aromatic compounds such as ferulic acid, p-coumaric acid, and vanillin have been sensed for the identification of lignin-transforming strains. No less than 25 lignocellulosic derivatives with high industrial value have been detected by biosensors to identify biocatalytic pathways and/or novel active enzymes and have been comprehensively listed by Alvarez-Gonzalez and Dixon (2019) and Lee et al. (2019). Sugar-responsive elements also emerged in the past decade. For example, biosensors for several hexose and pentose carbohydrates such as xylose (Teo and Chang, 2015), maltose (Kaper et al., 2008), or L-rhamnose (Kelly et al., 2016) were built and could help in the development of fermentative microorganisms. Moreover, carbohydrates and derivatives that are scarce in nature, also called rare sugars, could be used in a broad range of applications (e.g., pharmaceutical industry, medical industry, chemical synthesis, food and feed as well as pest control industry) and hold a tremendous economic value (Beerens et al., 2012). Despite their low natural availability, efforts have been undertaken to produce them biochemically (Van Overtveldt et al., 2018; Beerens et al., 2017; Franceus et al., 2019). Here also, biosensors might have an important role to overcome the dependence on expensive analytical systems traditionally used for rare sugar detection such as

TABLE 1 The sample throughput capacity of different screening systems. Screening system

Sample throughput (per day)

Chromatography

101–102

Mass spectrometry

102–103

Biosensors

103–104

Adapted from Petzold, C.J., Chan, L.J.G., Nhan, M., Adams, P.D., Analytics for metabolic engineering, Front. Bioeng. Biotechnol. 3 (2015). https://doi.org/10.3389/fbioe.2015.00135.

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FIG. 2 Application of biosensors for engineering of metabolic enzymes and pathways. (A) Screening (using, e.g., a plate reader) or selection (using, e.g., FACS) of metabolic genes through biosensors responding to their products. Libraries are generated by mutagenesis of a target gene or isolation of metagenomic samples (Yeom et al., 2018; Ho et al., 2018; Leavell et al., 2019). (B) Engineering dynamic regulation for metabolic pathways. An intermediate of the metabolic pathway for which a biosensor can be built is used as the regulator of its downstream pathway. The engineered dynamic regulation optimizes the pathway by regulating the expression of enzymes (Venayak et al., 2015; Xu et al., 2014; Tamsir et al., 2011).

the high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD) (Corradini et al., 2012). In that regard, L-sugar-responsive biosensors designed for the detection of L-mannose were created (Kelly et al., 2016), and the rare sugar D-allulose (formerly known as D-psicose) was also successfully employed to screen epimerase libraries (Armetta et al., 2019). Chemical synthesis research also benefited from the recent progress in biosensor technology. Lactams are important compounds to manufacture polymers that find application in many chemical industries (e.g., plastic, nylon, and paint industries). At the moment, many lactam-based molecules like caprolactam or valerolactam are synthesized from petrochemical sources (Zhang et al., 2017). Therefore the identification of natural catalysts for their bioproduction is of great interest. Recently, Yeom et al. developed a biosensor, the so-called caprolactam-detecting genetic enzyme screening system (CL-GESS) that allowed the discovery of a cyclase by screening diverse metagenomic libraries (Yeom et al., 2018). This example is not the only one showing the powerful approach of screening metagenomic libraries with the help of biosensors (Fig. 2A). Recently, Ho et al. (Ho et al., 2018) employed a whole-cell GFP-based biosensor to screen 42,520 colonies from an environmental source and isolated 147 clones that successfully degraded lignin to vanillin and syringaldehyde. Similarly, man-made organic compounds, such as organic acids and derivatives, have shown their value in a broad range of applications as antimicrobial agents, food additives, or biomaterials, among others (Chen and Nielsen, 2016). Many SMRA TF-based biosensors designed for their detection are currently available and have been catalogued by Li et al. (Li et al., 2020). Microbial biosensors could also benefit from the recent progress in microfluidics and flow cytometry. When the detection of a specific metabolite is combined with the expression of a fluorescent protein as output, mutants could be sorted individually in microdroplets and selected on a fluorescent-activated cell sorter (FACS), therefore evaluating a construct in a high-throughput fashion (Armetta et al., 2019; Leavell et al., 2019).

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In line with these examples and to make biosensors industrially relevant and competitive with traditional analytical methods, biosensor sensitivity toward isomers and derivatives of a given molecule needs to be improved or engineered. Consequently, it would be desirable to expand the biosensor toolbox that is available for metabolite detection by synthetic means, eventually outnumbering the natural sensors that are found in microbial genomes.

3.2 Dynamic regulation Natural metabolic pathways have evolved with their regulatory networks that dynamically control the expression of metabolic genes. Natural dynamic regulation of metabolism is genome-scale control of the gene expression of enzymes involved in a pathway. In the microbial world, there are many SMRA TFs that respond to metabolites within enzymatic pathways and tune the gene expression of those enzymes. These are feedback networks and frequently present in microbes. For example, the E. coli genome encodes over 200 TFs regulating metabolism and other pathways (Santos-Zavaleta et al., 2019). Synthetic dynamic control for a metabolic pathway can be implemented on the transcriptional or translational level with ON-OFF or continuous control triggered by metabolite concentration, quorum sensing, or heat shock (Venayak et al., 2015). To engineer artificial transcriptional dynamic regulation, we need to first find an intermediate for which there is a regulator responding to its concentration (Fig. 2B). In wild-type E. coli, this intermediate is typically a metabolite that multiple routes are derived from and that connects the backbone of metabolism to routes yielding the final products, such as amino acids. Important players of the metabolic network, such as malonyl-CoA, pyruvate, or glyoxylate, have TFs responding to their concentration and tuning the expression of genes involved in their metabolism. After finding the metabolite that has a SMRA TF in the synthetic pathway or the pathway we want to optimize, the genes encoding the pathway consuming the metabolite can be cloned under control of the metabolite-TF responding promoter. As a result, these genes are expressed proportionally to the level of the detected intermediate. Additionally, genes encoding the pathway producing the intermediate can be cloned so as to create a negative correlation (Xu et al., 2014). This can be done if there is a promoter enabling that or through engineering a NOT gate using a repressor. A NOT gate is a simple gene circuit comprising a repressor that, in this case, is expressed by the metabolite-TF inducible promoter (Tamsir et al., 2011). Thus the metabolite-producing genes are repressed at high concentrations of the intermediate. Such dynamic engineering of metabolic pathways improves the manufacturing through allocating resources for the production of enzymes, hence reducing the metabolic burden and through Le Chatelier’s principle (Xu et al., 2014). Dynamic regulation can also diminish the toxicity of intermediates in a metabolic pathway (Doong et al., 2018; Ewald et al., 2017; Dahl et al., 2013). Dynamic regulation can also be engineered using quorum sensing (Anesiadis et al., 2008; Williams et al., 2015). Quorum sensing is a bacterial signaling pathway that controls cell density-dependent phenotypes such as virulence, symbiosis, conjugation, motility, sporulation, and biofilm formation (Miller and Bassler, 2001). In a population, bacterial cells produce a constitutive amount of the quorum molecule. By increasing the cell density, the high concentration of the quorum molecule transcriptionally triggers the expression of population-related phenotypes of that strain. In an engineered system the expression of enzyme-encoding genes is controlled by the concentration of the quorum molecule produced by cells. The higher the density of the culture is, the more quorum molecule is produced, in turn increasing the expression of metabolic enzymes of the regulated pathway. This provides a condition similar to the traditional two-stage fermentation strategy in metabolic engineering, where first cells grow to a suitable density, and then expression of a desired pathway is induced. Therefore synthetic quorum sensing can enable precise dynamic regulation by tuning of circuit components such as promoter and RBS. Genetic circuits as one of the main synthetic biology tools enable the engineering of more sophisticated dynamic regulations that take multiple inputs into account to process more complex computation (Brophy and Voigt, 2014; Pandi et al., 2019b; Pandi and Trabelsi, 2020). He et al. developed an autoinduced AND gate responding to both microbial community density and the cell physiological state (He et al., 2017). They constructed a set of quorum sensing circuits through random mutagenesis of the LuxR TF and tuning the strength of the luxRI promoter. As the other input of the AND gate, they implemented the cell stationary phase sensing system. After characterizing the AND gate behavior using a fluorescent output, they placed a metabolic pathway to produce polyhydroxybutyrate (PHB) under control of this circuit. In a remarkable study, Guo et al. engineered E. coli strains using complex recombinase-based state machines (Guo et al., 2020). Using a sophisticated biocomputation tool developed by Roquet et al. (Roquet et al., 2016), Guo et al. engineered E. coli producing high titers of butyrate and poly(lactate-co-3-hydroxybutyrate).

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4 Application of biosensors for the discovery of novel enzymes and metabolic pathways 4.1 The necessity of high-throughput screening approaches for discovery of novel gene and operon functions In the past decades the implementation of both well-known and newly discovered metabolic pathways using in vitro and in vivo approaches has greatly advanced the field of metabolic engineering and resulted in numerous biotechnological applications. However, it stands to reason that many biotechnologically useful enzymes and metabolic pathways still await their discovery. Even in E. coli the most well-studied model bacterium, approximately 35% of genes (the so-called y-ome), still lacks an experimental evidence of function (Ghatak et al., 2019). In comparison, this number is dwarfed by the percentage of genes with unknown functions in less well-studied organisms and by the enormous wealth of sequences without a validated annotation that can be found in metagenomes from diverse environments. Clearly, scientifically interesting and biotechnologically relevant enzymes and pathways still await their discovery in sequence databases. This was also demonstrated by exciting recent studies about new metabolism, for example, unexpected varieties of carbon fixation pathways (Mall et al., 2018; Frolov et al., 2019), the b-hydroxyaspartate cycle for glyoxylate assimilation (Schada von Borzyskowski et al., 2019), or the trifunctional enzyme propionyl-CoA synthase (Bernhardsgr€utter et al., 2018). It is a challenging task for biochemists to fish out the most noteworthy enzymes from the wealth of (meta)genome sequences and correctly assign functions to them. Therefore this aim should ideally be pursued using efficient screening methods.

4.2 Functional screening for novel enzymes with desired activities To identify enzymes with a specific function within a genome or metagenome, functional screening assays with a suitable readout can be performed. This method is well established, but must be developed and validated for each enzyme or metabolic pathway of interest. In short, after creating a (meta)genomic library in a vector of choice, a suitable expression host must be selected to allow for successful protein expression and subsequent functional screening. While suitable gene deletion strains of E. coli have been utilized as host for most studies involving functional screening of (meta)genomic libraries, choosing another bacterial chassis for protein expression can increase the chances for expression of functional proteins and therefore successful screening. This especially holds true if the enzymes of interest should have specific properties, such as thermostability, or the metagenome is derived from an environment that fosters growth of specific bacterial groups, for example, Rhizobia that live in soil. In both cases E. coli would not be ideally suited to express the proteins of interest, and a thermophilic or soil-derived microorganism might yield better results. The former example was realized by functional screening for novel esterases in the host Thermus thermophilus (Leis et al., 2015). The relevance of the latter example was demonstrated by successful screening for previously unknown b-galactosidases from a soil metagenome in the Alphaproteobacterium Sinorhizobium meliloti (Cheng et al., 2017) Other expression hosts that have been used successfully in the past include P. putida, Bacillus subtilis, and several Streptomyces strains (Lewin et al., 2017). Functional screening can be a powerful discovery method and allows efficient screening of large libraries, but it lacks system-level scalability and only permits the investigation of one enzyme function at a time. It can, however, be combined with the utilization of a biosensor that detects a product of the reaction to be identified, as described in more detail earlier. To learn more about the function of several enzymes or a whole pathway, modifying the expression level of the TF that controls expression of the respective operon can be helpful.

4.3 Altering transcription factor expression to study gene and operon function In genetically accessible organisms, the regulatory effects of a TF are commonly investigated by deleting its gene and investigating the resulting phenotype, which often allows to pinpoint the now deregulated gene(s) or pathway(s) by using functional assays, transcriptomics, or proteomics. Reversing this approach, that is, overexpressing a certain TF instead of deleting its gene, is used less commonly. However, analysis of the resulting transgenic organisms and comparison of their gene expression pattern to the wild type also allows to draw conclusions concerning the regulon of the TF and might help to determine the identities of genes currently lacking reliable annotations. This approach is of special interest when genetic tools for gene deletion are not available or if they are less reliable than genetic tools for overexpression. In various plant species the heterologous overexpression of TFs has been used to alter gene expression in phenolic acid metabolism and lignin biosynthesis. Overexpression of two TFs from Antirrhinum majus in tobacco plants resulted in a

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decreased lignin content of vascular tissue, but a detailed biochemical explanation for this phenotype could not be provided (Tamagnone et al., 1998). The flavonol content of tomatoes could be increased by overexpressing two TFs from maize, which was ascribed to the induction of the relevant biosynthetic pathway by the heterologous regulatory proteins (Bovy et al., 2002). Similarly, levels of health-promoting anthocyanins were significantly increased in tomatoes overexpressing two TFs from snapdragon due to upregulation of biosynthetic genes (Butelli et al., 2008). While these studies were rather focused on application than on discovery of previously unknown genes, the ectopic expression of a heterologous TF in tomato plants also served to identify the gene encoding for the enzyme prephenate aminotransferase, a formerly unclear step in the phenylalanine synthesis pathway (Dal Cin et al., 2011). Likewise, it was possible to assign functions to two differentially regulated genes by using metabolomics and transcriptomics to analyze a transgenic variant of Arabidopsis thaliana overexpressing a TF. These genes were shown to act as glucosyltransferases in anthocyanin biosynthesis (Tohge et al., 2005). More systematic application of such approaches could help to elucidate the transcriptional control of complex metabolic pathways in secondary metabolism and to determine the role of (iso-)enzymes and subpathways in biosynthetic routes that are not well understood. However, considerable experimental effort is necessary to determine the effect of TF overexpression on the transcriptome and metabolome of an organism or tissue, and the task of correctly interpreting the resulting data becomes more complex when one TF has regulatory effects on multiple pathways. Such efforts can be facilitated by efficient characterization of all target sequences that a given TF binds to.

4.4 Methods to characterize transcription factor-DNA interactions TFs regulate gene expression by binding to a target sequence, often in concert with a small molecule effector that influences DNA binding of the TF by allosteric interactions. To discover novel enzyme functions based on transcriptional regulation, it is necessary to investigate both the target sequence and the effector molecules of a TF. Frequently a given TF is directly adjacent to the genes that it regulates, but this is not always the case. In particular, regulators that control several operons or exert global control over a cellular trait cannot be in proximity to all their regulatory targets. To determine the binding motif of a TF, it is therefore of interest to investigate its target sequences in a high-throughput manner. In the following, established methods for this purpose will be briefly summarized. Protein binding microarrays (PBM) can be used to efficiently probe the binding of a purified TF to short DNA target sequences of a defined length, for example, all possible sequences containing 8 or 10 base pairs (Berger et al., 2006; Berger and Bulyk, 2009). Bound protein is subsequently identified by adding a fluorophore-labeled antibody, and the binding motif of the probed TF can be determined at high resolution. This technique has been used successfully to elucidate TF-DNA interactions in a variety of organisms, ranging from bacteria (Pompeani et al., 2008) and yeast (Badis et al., 2008) to apicomplexans (Campbell et al., 2010) and mice (Berger et al., 2008; Badis et al., 2009). It requires, however, the expression and purification of tagged TFs and the manufacturing of customized DNA microarrays. The use of bacterial one-hybrid (B1H) systems as method to investigate the binding specificity of TFs is based on the introduction of a TF (the bait) and a library of vectors that contain positive and negative markers and randomized binding sequences (the prey) into a host cell. If the bait binds to the prey sequence, expression of the marker genes is induced, enabling survival of the host cell under selective conditions. DNA from colonies grown under these selective conditions can be isolated and sequenced to determine the binding motif of the bait (Meng et al., 2005; Meng and Wolfe, 2006). This approach has been used successfully to determine binding sites of both prokaryotic (Guo et al., 2009) and eukaryotic TFs (Noyes et al., 2008). Purification of the TF in question is not required in this case; however, some eukaryotic regulators might not express or fold correctly in a bacterial host cell. The bacterial one-hybrid method has been improved to incorporate high-throughput sequencing and selection in liquid media as well (Christensen et al., 2011). Similar protocols relying on binding and selection in yeast have been developed as well (Wilson et al., 1991; Deplancke et al., 2004), but they tend to suffer from the lower transformation efficiency of yeast, compared with bacteria. Genomic SELEX (systematic evolution of ligands by exponential enrichment) (Singer et al., 1997) has been developed from SELEX (Tuerk and Gold, 1990; Ellington and Szostak, 1990) as an alternative method to identify regulatory targets of TFs. In this approach the TF of interest is purified and incubated together with fragments of genomic DNA that were created by sonication and are flanked by fixed sequences. After removing nonbinding DNA by repeated washing, the bound DNA is eluted and can be sequenced. This method has been applied to investigate the transcriptional regulation of E. coli in detail. After first investigating the target sequences of the global regulator Cra (Shimada et al., 2005), the approach was scaled up to determine the regulatory targets of 116 TFs, both in the presence and absence of effector ligands (Ishihama et al., 2016; Shimada et al., 2018a). This successful large-scale study revealed that most TFs in E. coli bind to more than one target sequence. Subsequently the 13

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TFs that only bind to a single target were investigated in detail using gSELEX, and previously unknown functions could be assigned to four of them (Shimada et al., 2018b). Another example is the demonstration that PlaR constitutes a master regulator to enable the assimilation of plant-derived material by E. coli via controlling the expression of operons for ascorbate, galacturonate, fructose, and sorbitol catabolism (Shimada et al., 2019). gSELEX has also been applied to elucidate the transcriptional regulation of other organisms, such as the filamentous fungus Aspergillus nidulans. It was used in combination with RNA-seq to identify previously unknown target sequences of the TF AmyR, a regulator of amylolytic metabolism (Kojima et al., 2016). In a similar way the regulon of the TF XlnR was characterized using gSELEX in the related fungus A. oryzae (Oka et al., 2019). DAP-seq (DNA affinity purification sequencing) was developed as an efficient high-throughput method to characterize the cistrome and epicistrome of an organism (O’Malley et al., 2016; Bartlett et al., 2017). It is based on the in vitro expression of affinity-tagged TFs, which are subsequently incubated with fragmented genomic DNA flanked by sequencing adaptors. Unbound DNA is washed away, while bound DNA can be eluted from the TF, followed by sequencing. In addition to determining the binding motifs of TFs, this approach also serves to analyze the epicistrome by comparing binding of native, methylated gDNA fragments with binding of PCR-amplified, nonmethylated DNA (O’Malley et al., 2016). The usefulness of this method was demonstrated by investigating target sequences for 529 TFs from A. thaliana (O’Malley et al., 2016). DAP-seq was also successfully applied to elucidate cell-to-cell communication (Fischer et al., 2018) and carbon utilization hierarchy (Wu et al., 2020) in the filamentous fungus Neurospora crassa. This novel approach has generated valuable results in the investigation of developmentally relevant TFs in maize (Galli et al., 2018), as well as in the characterization of metal-binding TFs (Garber et al., 2018) and nitrate/nitrite response regulators (Zhang et al., 2020) in bacteria. DAP-seq is more versatile than the well-established method of ChIP-seq (chromatin immunoprecipitation sequencing), which relies on the pull-down of TF-bound DNA after in vivo cross-linking with formaldehyde ( Johnson et al., 2007). This method depends on the availability of a suitable antibody, lacks scalability, and is hard to perform for rare or lowly expressed proteins (Kidder et al., 2011). Just like gSELEX, DAP-seq has the advantage of using native, methylated DNA fragments, but it circumvents the time-consuming in vivo expression and purification of single TFs required by the former method by relying on in vitro expression and affinity bead-facilitated purification of TFs. An integrated approach to identify and characterize TFs in E. coli was undertaken by combining bioinformatics, ChIPseq, RNA sequencing, and mutant studies (Gao et al., 2018). This resulted in the identification of 16 new TFs and the characterization of three TFs among those. Hinting at high-throughput implementations, this study suggested that the construction of transcriptional regulatory networks can be used to predict the function of unknown genes in a regulon, provided that sufficient data about gene expression are available. Taken together, there are several reliable methods to determine the target sequences of TFs in a high-throughput manner. To fully characterize the regulatory network of an organism and to move toward biosensor-facilitated discovery of novel gene functions, it is desirable to also realize approaches that allow the high-throughput characterization of interactions between SMRA TFs and their effectors.

4.5 Toward high-throughput systems to characterize transcription factor-effector interactions To functionally investigate TFs efficiently and to link them to an effector molecule, high-throughput workflows that allow fast characterization of all transcriptional regulators in an organism of interest are highly desirable. Small molecule effectors are often the substrate or product of an enzyme or pathway under the control of the respective SMRA TF. Therefore identification of effector molecules, together with knowledge about the regulon of a SMRA TF, might facilitate the discovery of novel gene functions. The in silico realization of effector molecule discovery was pursued by creating a software that automatically fetches the genomic neighborhood of an input TF and its homologs from the KEGG database (Martı´-Arbona et al., 2014). The rationale behind this is the fact that genes adjacent to a SMRA TF often give hints toward its regulatory function and its effector molecules. Metabolites linked to enzymes encoded by neighboring genes are provided as well, and this information is used to determine and rank the KEGG pathways that are most likely to be part of the regulon of the input TF. The authors validated their software by investigating TFs from Burkholderia xenovorans with both known and unknown functions. For the former class the relevant metabolic pathways were mostly predicted as expected, while the function of a previously unstudied SMRA TF was predicted in silico and subsequently verified experimentally in vitro. The success of this systemic approach to the discovery of TF-effector interactions depends on at least partially correct annotations of the neighboring genes. Therefore its application to the analysis of TFs derived from metagenomes is problematic, since their often rather small contigs might lack information about the relevant genomic neighborhood.

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High-throughput methods to identify protein-metabolite interactions in vivo have emerged very recently. After first investigating the allosteric binding of metabolites to glycolytic enzymes (Link et al., 2013), dynamic transcriptome and metabolome data were used to identify metabolites that control SMRA TFs in E. coli (Lempp et al., 2019). Putative effectors of 71 SMRA TFs were predicted, and five of these interactions were validated in vitro, yielding new insights into the regulation of amino acid metabolism in E. coli. This ambitious study demonstrates that omics methods together with computational approaches enable the discovery of novel TF-effector interactions. Application of this protocol for the investigation of other less well-studied organisms and metabolic pathways promises to yield a wealth of previously unknown interactions. Transcriptional regulation of the uncultured bacterium Candidatus Liberibacter asiaticus was recently studied by implementing a high-throughput screening system in a closely related, culturable model organism (Barnett et al., 2019). In this system a Cand. L. asiaticus TF was expressed under the control of an inducible promoter. Promoters that are putatively regulated by the TF in question were fused to a fluorescent reporter protein to enable for efficient screening. Upon induction the TF was expressed, resulting in a fluorescent signal in case of positive interaction with the probed target sequence. After validating it, this system was used to screen a library of 120,000 compounds to identify inhibitory effectors of Cand. L. asiaticus TFs. Compounds that were tested with positive results in the high-throughput assays underwent further experiments to verify that they specifically target the relevant TF and that the observed decrease in fluorescent signal is not due to general growth inhibition, resulting in several promising candidates for specific inhibition of transcription (Barnett et al., 2019). This study paves the way for future efforts to unravel the transcriptional regulation of uncultured microorganisms and to identify SMRA TF effectors in a high-throughput manner, which in turn could be utilized toward the discovery of novel gene functions.

5

Conclusions and perspectives

In this chapter, we provided a comprehensive overview of microbial biosensors and their applications in engineering and the discovery of metabolic enzymes and pathways. In the engineering section, we gave a brief introduction to common biosensors and their applications in dynamic regulation, screening, and selection. Although application of biosensors in engineering has been studied for a long time, there are further developments to accomplish. For example, we need to build more biosensors responding to metabolites as there are many natural sensors distributed in microbial genomes. Automated design-build-test cycles, protein and promoter engineering, high-throughput and ultrahigh-throughput approaches, and a continuous drop in the cost of chemical DNA synthesis are potential points that can further expand biosensor engineering. While these aspects of biosensors and applications for engineering have been reviewed extensively before, in this chapter, we also focused on the emerging applications in the discovery of natural metabolism that has been hidden in genomes so far. This also facilitates biosensor engineering through discovering the mechanism of action of gene regulators and through identifying SMRA TF binding sites. As outlined earlier a broad variety of methods exist to determine the target sequence of a TF, while more protocols for the efficient investigation of effector molecules are beginning to emerge (Fig. 3). To move toward biosensors for gene function discovery, it will be crucial to leverage and combine the potential of these methods in the future. High-throughput screening systems based on fluorescent readouts could be constructed to determine the effector of a SMRA TF in vivo and subsequently link this effector to the function of adjacent genes or operons. This effort must be supported by both bioinformatic and biochemical approaches. When using a suitable chassis organism for expression, this could enable the rapid characterization of SMRA TFs and regulatory sequences from uncultured organisms, which are heterologously introduced into the new host. Upon positive interaction of effector, TF, and target sequence, the host cell will become fluorescent and could be identified and isolated by fluorescence-assisted cell sorting (FACS) for sequencing and further characterization. The successful screening of effector molecules obviously depends on their transport into the cell. This could pose problems in some chassis organisms, especially in the case of larger molecules, such as polysaccharides or oligopeptides, which are commonly cleaved into their monomers by secreted enzymes outside a microbial cell. In such cases cell-free approaches relying on DAP-seq could be utilized instead. The binding of in vitro expressed TFs to genomic DNA fragments could be investigated in the presence of different added effector molecules to learn more about the regulon of a SMRA TF and its metabolic function. In summary the creation of a toolbox for gene function discovery using biosensors has made great progress in the past few years. The concerted utilization of these tools will certainly yield exciting discoveries of previously unknown metabolism in the future, which in turn can be used again for metabolic engineering approaches. Clearly, discovery and engineering using TFs and biosensors go hand in hand, and researchers will be able to leverage their synergies even more in the coming years.

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FIG. 3 Toward microbial biosensors for gene and operon function discovery. The methods summarized in this chapter are shown in boxes next to their respective objects of study. Protein-binding microarrays (PBM; (Berger et al., 2006; Berger and Bulyk, 2009)), bacterial one-hybrid (B1H; (Meng et al., 2005; Meng and Wolfe, 2006)) genomic SELEX (gSELEX; (Singer et al., 1997)), ChIP-sEq. ( Johnson et al., 2007), or DAP-seq (O’Malley et al., 2016; Bartlett et al., 2017) can be utilized to determine the target sequences of a TF. Genomic neighborhood analysis (Martı´-Arbona et al., 2014) and overexpression or deletion of a TF gene (Tamagnone et al., 1998; Tohge et al., 2005) are different approaches to study the regulons of TFs, while functional screening serves to identify genes encoding for enzymes with desired activities (Leis et al., 2015; Cheng et al., 2017; Lewin et al., 2017). Studies that apply metabolomics and transcriptomics (Lempp et al., 2019) or high-throughput (HTP) fluorescence screening (Barnett et al., 2019) are paving the way toward efficient characterization of SMRA TFs and their effectors.

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Chapter 23

Manipulation of global regulators in Escherichia coli for the synthesis of biotechnologically relevant products M. Julia Pettinari∗ and Diego E. Egoburo Departamento de Quı´mica Biolo´gica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, IQUIBICEN-CONICET, Buenos Aires, Argentina *Corresponding author: E-mail: [email protected]

1 Introduction of Escherichia coli metabolism E. coli is the best known bacterial species. This facultative bacterium is capable of adapting to many different environments, ranging from the intestinal tracts of suitable hosts to fresh water. To grow efficiently, E. coli must optimize its metabolism to use different carbon sources and adjust energy generation mechanisms in response to the levels of oxygen availability. Although oxygen is the most suitable electron acceptor, E. coli is capable of performing anaerobic respiration using alternative electron acceptors such as nitrate, nitrite, and organic molecules (e.g., fumarate) using specific sets of dehydrogenases (Gunsalus, 2006). In the absence of sufficient amounts of electron acceptors, many bacteria carry out fermentation, a process in which an oxidized metabolite such as pyruvate is converted into more reduced metabolites through a series of reactions, several of which involve substrate-level phosphorylation (Becker et al., 1997). In E. coli, mixed acid fermentation leads to the synthesis of a mixture of acids including lactic, acetic, succinic, and formic acids. Furthermore, in E. coli ock and and other members of the family Enterobacteriaceae, formic acid can be further converted to CO2 and H2 (B€ Sawers, 1996). E. coli can use many different carbon sources present in the environment, as it can degrade several carbohydrates, including disaccharides, fatty acids, amino acids, and other organic compounds. The degradation of these substrates involves various pathways that result in different energy and reducing power yields. For example, degradation of glycerol results in the formation of more reducing power than when glucose is used as a substrate, which consequently generates a more reduced intracellular state (Pettinari et al., 2012). Adaptation to different conditions is achieved using strategies that involve changes in all aspects of metabolism, including substrate transport, metabolic pathways, and respiratory chain composition. Regulatory networks switch the expression of all these processes on and off as needed, enabling E. coli and other facultative bacteria to optimize energy generation in response to the availability of carbon sources and electron acceptors. Adaptation to the availability of electron acceptors and the amount of reducing power generated during substrate oxidation is achieved at the metabolic level through changes in pathways that modify reducing power generation and consumption. Other adaptations involve modifications in the respiratory chain, enabling cofactor reoxidation at different final acceptor conditions. For example, when glucose is oxidized completely to CO2 in the presence of high oxygen concentration, E. coli utilizes cytochrome o, and the yield of ATP obtained is maximum. At low oxygen concentration the cytochrome d complex is activated, resulting in a less efficient electron transport chain as it does not actively pump protons (Poole and Cook, 2000). The central carbon metabolism flow is affected by the redox state of the cells. When the intracellular state is reduced, carbon is channeled toward the production of more reduced metabolites to help reoxidize electron transporters. In contrast, when oxygen availability conditions enable the efficient regeneration of electron transporters through the respiratory chain, the substrate is further oxidized, giving rise to more oxidized metabolites. This causes C flux, and consequently the metabolites produced and their relative amounts, to be adjusted in response to growth conditions such as C source and O2 availability (de Almeida et al., 2010). Microbial Cell Factories Engineering for Production of Biomolecules. https://doi.org/10.1016/B978-0-12-821477-0.00018-0 © 2021 Elsevier Inc. All rights reserved.

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Global regulation of metabolism

Microorganisms have an exceptionally complex and fine-tuned regulatory network to orchestrate the activation of genes and respond to environmental changes as fast as possible (Schaechter, 2001; Pollack and Iyer, 2002). This regulation takes place at multiple levels, ranging from the reorganization of genome structure to protein stability. Environmental conditions not only are variable but also can change abruptly. Because of this, microorganisms must be capable of quickly adjusting growth rate, synthesis of macromolecules, and many other functions indispensable for cell viability. Many microorganisms can vary their catabolic routes for the use of a given substrate according to different growth conditions. For example, glucose metabolism reflects a complex equilibrium between ATP generation and the metabolic burden implied in the synthesis of the enzymatic machinery necessary for sugar catabolism. Zymomonas mobilis and some Pseudomonas species utilize the Entner-Doudoroff pathway in place of the most conventional Embden-Meyerhof due to lower energetic requirements (Flamholz et al., 2013). The final metabolic and enzymatic reactions that are used in a given condition are determined by a regulatory network comprising both specific and global regulators (Shimizu, 2015). Global regulators are hierarchical regulators that control, directly or indirectly, several genes and operons simultaneously. These regulators affect all the genes present in an organism (Gottesman, 1984) and are defined by their pleiotropic effects on bacterial metabolism and physiology. The simultaneous and coordinated control of global regulators over genes involved in different metabolic routes enables bacteria to adapt to a wide range of conditions through several levels of metabolic plasticity, optimizing the use of metabolic pathways according to the carbon and electron acceptors available in the medium (Bettenbrock et al., 2014; Shimizu, 2015). In E. coli, it has been reported that only seven global regulators (ArcA, Crp, Fis, FNR, Ihf, Lrp, and NarL) control half of all genes (Martı´nez-Antonio and Collado-Vides, 2003), and extensive evidence suggests that global regulation is to a great extent directly responsible for the cellular response (Keren et al., 2013; Kochanowski et al., 2017). A study on the effect of global regulators under fermentative conditions showed that elimination of global regulators resulted in higher glucose uptake rates, ATP synthesis, organic acid production, and redox balance, demonstrating that global transcription factors have a major role in the regulation of the central carbon metabolism of E. coli (Kargeti and Venkatesh, 2018). One of the environmental factors that has very profound effects on growth and elicits important changes in metabolism is the availability of terminal electron acceptors. In the hierarchical regulation system for the adaptation to these conditions, responses are mainly coordinated by the global regulatory proteins FNR, ArcA, and NarL (Gunsalus, 1992; Unden and Bongaerts, 1997). Fumarate-nitrate reduction (FNR) is activated in anaerobic conditions and is one of the main regulators that control physiological changes to adapt to these conditions (Dibden and Green, 2005). At low oxygen levels the two-component regulator ArcAB (for aerobic respiration control) also controls the expression of many operons, including several involved in fermentative pathways, but also respiratory chain components (Lynch and Lin, 1996). Lastly the dual response regulators NarL-NarP are transphosphorylated by their cognate dual sensor kinases NarQ-NarX in the presence of nitrite and nitrate. Transphosphorylated NarL activates the expression of the nitrate reductase (narGHJI) and formate dehydrogenase-N (fdnGHI) operons while repressing alternate respiratory enzymes (Rabin and Stewart, 1993). Central C metabolism is mainly controlled by global regulators ArcA, Cra, and Crp (Perrenoud and Sauer, 2005; Kochanowski et al., 2017). These regulators coordinate the expression of metabolic genes to optimize growth according to the availability of different C substrates and other growth conditions. DNA microarrays and sequencing and omics techniques have revealed that changes in the culture environment cause enormous changes in gene expression (Costenoble et al., 2011; Tirosh and Barkai, 2011). Kochanowski et al. (2017) attempted to dissect the effect of global regulation over the metabolism of E. coli by analyzing the activity of promoters of more than 90 genes involved in central metabolism across 26 different environmental conditions. Utilizing different carbon sources and modeling the expression pattern by systems biology, they obtained mechanistically simple results. Despite the extended and complex regulatory network of E. coli, the effects exerted by global regulators explained over 70% of the changes in promoter activity (Kochanowski et al., 2017). This effect reached up to 90% when promoters of genes involved in central carbon metabolism were studied, as in this case the activity of Cra and Crp along with the concentration of the signaling metabolites cyclic AMP (cAMP), fructose bisphosphate (FBP), and fructose 1-phosphate (F1P) sufficed to explain most changes in promoter activity. In a similar way, Berthoumieux et al. (2013) found that global regulation involving translation machinery and growth rate was predominant in central metabolism regulation, including the regulation of the global regulators Crp and Fis with only a minor contribution of specific regulation (Berthoumieux et al., 2013). Consistently with this the activity of the global regulators Cra, Crp, and ArcA could explain the abundance of central metabolism enzymes, and particularly those involved in acetate production pathway, when measured by quantitative proteome analysis in E. coli BL21 growing in rich and minimal medium (Li et al., 2014).

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2.1 Signal transduction Global regulators also play a key role in signal transduction, allowing cells to respond to several stimuli simultaneously. External signals can be sensed by two-component systems in which a membrane sensor kinase senses an environmental stimulus and transphosphorylates a cytoplasmic regulator that binds DNA targets activating and repressing genes (Laub and Goulian, 2007; Mitrophanov and Groisman, 2008). ArcAB, CreBC, and NarX/L constitute examples of two-component systems that sense and trigger signaling cascades in response to the levels of oxygen, glucose metabolism intermediaries, and sodium availability, respectively (Chubukov et al., 2014; Shimizu, 2015). Other global regulators are single-component systems like Crp and Cra that sense the intracellular concentration of signal metabolites and trigger a regulatory response (Martı´nez-Antonio et al., 2006; Ulrich et al., 2005). Signal metabolites such as cAMP, phosphoenolpyruvate (PEP), and F1P have been proposed to sense carbon flux in E. coli, and global responses triggered by changes in these metabolites result in the alteration of carbon flux in central metabolism (Kotte et al., 2010; Kochanowski et al., 2015). Along with a few regulatory metabolites, global transcription regulation explains the majority of the changes in gene expression in central metabolism when facing environmental fluctuations (Kochanowski et al., 2017). These results suggest that metabolic regulation in E. coli relies on few regulators that respond to the global intracellular cell state and the growth rate rather than on the activity of individual specific transcription factors. In summary, E. coli metabolism is regulated by the interplay between Crp, Cra, Crs, Fis, PII, NtrBC, CysB, PhoR/B, SoxR/S, MarR, ArcA/B, FNR, NarX/L, RpoS, and (p)ppGpp to provide specific responses to external stimuli. In addition to regulators, key signaling metabolites like F1P, PEP, and acetyl-CoA are important effectors of enzymatic activity, while a-ketoacids like pyruvate and oxaloacetate play key roles in the coordination of carbon uptake and other nutrients modulating the intracellular levels of cAMP by means of Cya activity (Shimizu, 2015).

3 Control of the central carbon metabolism by global regulation 3.1 Global regulation in response to the carbon source: Crp and Cra 3.1.1 Crp In rich environments, when more than one carbon source is available, bacteria are capable of using them sequentially, prioritizing the consumption of the carbon source that is most readily accessible and with the highest energy yield through catabolic repression (Magasanik, 1961). This mechanism implies a complex regulatory network widespread in many kinds of bacteria that can affect up to 10% of all genes (G€orke and St€ulke, 2008). In E. coli, glucose is generally the preferred sugar. It is transported into the cell by the PTS system through a phosphorylation cascade that transfers a phosphate from PEP to the glucose-specific permease EIIAGlc and then to glucose, which enters the cells as glucose-6-phosphate. Unphosphorylated EIIAGlc interacts with proteins that transport many nonpreferred carbon sources inhibiting their uptake. This in turn prevents the induction of the operons needed for the use of these substrates through a process-denominated inducer exclusion (G€ orke and St€ ulke, 2008). Besides this regulatory network, carbon catabolite repression involves global regulation mediated by the cAMP receptor protein or Crp (Kaplan et al., 2008). This global transcription factor controls the transcription of genes involved in the three major pathways for energy generation: glycolysis, the pentose phosphate pathway, and the TCA cycle (Perrenoud and Sauer, 2005; Nanchen et al., 2008) (Fig. 1). Crp activity is controlled by cAMP. When this signaling messenger is available, Crp is activated and binds to specific sites in DNA inducing or repressing the transcription of target genes (Nanchen et al., 2008). cAMP is synthesized by the enzyme adenylate cyclase that is in turn modulated by the phosphorylated EIIA (Shimada et al., 2011b). When glucose levels are low, the PEP to pyruvate ratio is high, so phosphorylated EIIA is the predominant form of this PTS enzyme, adenylate cyclase is activated, and Crp, allosterically modified by cAMP, induces catabolic gene expression (Won et al., 2009). The control of Crp over more than 100 genes involved in transport and catabolism of nonsugar carbon sources is well documented in E. coli. Zheng et al. (2004) implemented microarray analysis to determine the Crp regulon showing that Crp activates 104 operons involved in the catabolism of secondary carbon sources such as nonglucose sugars, amino acid and nucleotide metabolism, ion transport systems, and energy production pathways, including TCA cycle and aerobic respiration genes (Zheng et al., 2004). In addition to this, the transcription factors MelR, RpoH, BlgG, Fis, and PdhR were also shown to be under the control of Crp. More recently, Shimada et al. (2011b) used the Genomic SELEX technique to extend the Crp regulon, predicting near 300 DNA targets, many of which were validated by means of lacZ fusions (Shimada et al., 2011b). Several genes related to glycolysis and pentose phosphate pathway such as fabA, gapA, glK, ppsA, and talA, among others, were found to be regulated by Crp. It also seems to regulate aerobic respiratory pathways, modulating the expression

FIG. 1 Simplified diagram of central C metabolism in Escherichia coli showing the effect of global regulators ArcA, Cra, CreBC, Crp, FNR, and Rob. The regulators are represented as upward and downward pointing boxes to indicate upregulation and downregulation, respectively. Inside the rectangle with a dotted outline are the regulators that control the TCA cycle. In some steps a regulator has been depicted to exert both upregulation and downregulation, as the regulators can bind to different sites on the DNA depending on the conditions. PEP, phosphoenolpyruvate; PPP, pentose phosphate pathway; KDPG, 2-keto-3-deoxy-6-phosphogluconate; DHAP, dihydroxyacetone phosphate.

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of genes encoding NADH-ubiquinone oxidoreductase I, NADH-ubiquinone oxidoreductase II, and cytochrome bo terminal oxidase. Other studies postulated novel targets as part of the Crp regulatory network, including the transcription factors ArgR and RpoS and genes encoding chaperones and proteins involved in organic solvent tolerance (Okochi et al., 2008; Franchini et al., 2015). A recent study has suggested that the sequential use of glycolytic carbon sources and glycerol depends on the metabolic flux that is sensed in part by the signaling cascade mediated by Crp (Okano et al., 2020). Additionally, it has been proposed that Crp also responds to the balance between catabolism and anabolism, since Crp activity is repressed when E. coli faces nitrogen and sulfur starvation (You et al., 2013). Global regulation exerted by Crp not only is constrained to E. coli but also controls several functions related to central metabolism, quorum sensing, virulence, and fimbriae synthesis in other enteric bacteria as Klebsiella pneumoniae and Salmonella enterica (Lee et al., 2005; Wang et al., 2005; Panjaitan et al., 2019).

3.1.2 Cra In many gram-positive bacteria and even among enteric bacteria, carbon catabolite repression takes place without involving the signaling molecule cAMP. This type of regulation on carbon uptake and catabolism is also well established in E. coli in which it is mediated by the catabolite repressor/activator Cra (Saier, 1996). This global regulator was initially denominated FruR because it was shown to repress the fru operon in E. coli (Chin et al., 1987). Salmonella typhimurium fruR mutants exhibited a pleiotropic phenotype and were not able to grow on gluconeogenic substrates (Chin et al., 1987). Later, fruR mutations were shown to alter the expression pattern of genes involved in carbon metabolism and energy production in E. coli significantly (Chin et al., 1989). Further experiments demonstrated that Cra modulates the expression of several operons related to carbon catabolism, including pps, ace, pts, and icd (Ne`gre et al., 1998). Cra is known to repress the expression of genes encoding glycolytic enzymes pfkA, pykA, pykF, acnB, edd, eda, mtlADR, and gapB and to activate genes involved in gluconeogenesis, TCA cycle, and the glyoxylate shunt (Saier, 1996; Sarkar and Shimizu, 2008) (Fig. 1). Initially the activity of Cra was thought to be modulated by intracellular signaling metabolites F1P and FBP. In the absence of these sugars, Cra binds to DNA and activates or represses gene expression (Ramseier et al., 1993). When glucose levels are high, F1P and FBP concentrations increase, and Cra is inhibited, derepressing glycolysis. In contrast, when E. coli is growing on gluconeogenic carbon sources, Cra is active, and gluconeogenesis is induced to synthesize glucose and other C6 sugars. This dependence on FBP concentration has led to consider Cra as the flux sensor in carbon metabolism (Kotte et al., 2010; Kochanowski et al., 2017). This idea could be challenged by some evidence raised recently, suggesting that Cra is not capable to bind FBP but only F1P (Chavarrı´a and de Lorenzo, 2018; Bley Folly et al., 2018). This possible contradiction may be overcome by recent studies that proved that FBP and F1P concentrations correlate and that this ratio is constant through bacterial growth, even when there is no fructose available in the medium (Kochanowski et al., 2017; Singh et al., 2017). Over the last decade the importance of Cra has raised according to the increasing size of the Cra regulon, comprising genes involved in nitrogen metabolism, stress response, DNA repair, and the control of several transcription factors (Shimada et al., 2011a). In addition to this, two facts may highlight the role of Cra in E. coli: (1) In the regulation of the fructose operon, fructose has been considered an important carbon source across bacterial evolution, as it is the only sugar that enters glycolysis without modifications and has a specific PTS for transport in many different bacterial species (Kim et al., 2018), and (2) in the control of Crp, recent studies suggest that Crp transcription is coregulated by Cra and Crp (Zhang et al., 2014) and that in metabolic scenarios where both regulators are active, the gene expression pattern in central metabolism seems to correlate with the regulatory activity of Cra more than with that of Crp (Kim et al., 2018).

3.2 Regulation of intermediary metabolism: CreBC and Rob 3.2.1 CreBC CreBC, for carbon source responsive, is an important two-component system involved in intermediate metabolism when E. coli is growing in minimal medium (Cariss et al., 2008). CreC was initially denominated PhoM because it was shown to control the expression of the pho operon in the absence of its specific regulator, PhoR. CreC is a membrane-bound kinase that phosphorylates CreB when it is active, controlling the creABCD operon. Although the signaling ligand is not well stablished, Cariss et al. (2008) determined that CreC is active under microaerobic conditions when using glycolytic carbon sources or in aerobic conditions when E. coli is growing on low molecular weight fermentative metabolites (Cariss et al., 2008). Under these conditions, CreC phosphorylates CreB, the cognate cytoplasmic regulator that binds to a specific target site in the DNA, activating or repressing the expression of target genes, which is in a similar fashion to Cra and Crp, previously described here (Avison et al., 2001).

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CreBC controls the expression of the following: (1) the ackA/ptaA operon that code for enzymes responsible for the conversion of acetyl coenzyme A into acetate and ATP; (2) talA, belonging to the pentose phosphate pathway; (3) malE, the first gene in the malEFG maltose transporter operon; (4) trgB that encodes an ADP-ribose pyrophosphorylase; and (5) radC, corresponding to a protein involved in DNA recombination and/or repair (Avison et al., 2001; Cariss et al., 2008) (Fig. 1). The genes related to colicin resistance, namely, creD, crbA, and cbrB, are also part of the cre regulon (Avison et al., 2001). Later, DNA microarray studies showed that CreBC affects the expression of cbrC, also involved in colicin resistance; the regulatory peptide E2mokB; and also, genes with no known function such as mppA, ynal, yafU, and yafE (Cariss et al., 2010). Using a flux balance analysis in continuous culture under oxygen and glucose limitation, Nikel et al. (2009) demonstrated that E. coli mutants lacking creB had a lower carbon flux through the pentose phosphate pathway, the Krebs cycle, and the lower part of glycolysis. More interestingly, this analysis suggested that CreB and ArcA jointly modulate the activity of Pfl, PDHc, Ldh, and Ack enzymes (Nikel et al., 2009). Recently a study performed in E. coli K1060 under different levels of oxygen availability using glucose as the sole carbon source revealed that the metabolic profiles of a DcreC mutant had a different pattern from the parental strain, showing a significant increase in several organic acids. Also, this mutant exhibited an important difference in the activity of Ldh and Ack compared with the wild-type strain under O2 limitation (Godoy et al., 2016).

3.2.2 Rob The name Rob stands for right origin binding. It is one of the regulators of the Mar/SoxS/Rob regulon, homologous to MarA and SoxS and member of the AraC family, a subfamily of the stress response factor (Barbosa and Levy, 2000). Microarray studies have revealed changes in more than 150 genes in marA mutants (Barbosa and Levy, 2000), and a set of 40 genes has been proven to be under the direct control of these homologous regulators (Martin and Rosner, 2002). This regulon comprises genes related to the intermediary metabolism, cell wall synthesis, and resistance to antibiotics, acids, and solvents, among other functions (Bennik et al., 2000). The rob gene is localized next to the creABCD operon, which in turn is adjacent to arcAB. Rob is constitutively expressed in E. coli, and its activity is known to increase in the presence of 2,2- or 4,4-dipyridyl (Rosner et al., 2002). Bennik et al. (2000) determined, using lacZ fusions and electrophoretic mobility shift assays, that Rob directly activates the transcription of seven genes: inaA involved in acid stress tolerance, marR, aslB, mdlA, ybaO, yfhD, and ybiS. These last three genes are in turn transcription factors, which could considerably extend the rob regulon. Rob was also proven to repress galT and micF (Bennik et al., 2000). One of the most relevant functions of Rob includes the control of acrAB that encodes the efflux pump AcrAB-TolC, one of the main mechanisms involved in tolerance to organic solvents and antibiotics (White et al., 1997). The marRAB operon also seems to be under the control of Rob (Martin and Rosner, 1997). Interestingly the absence of Rob does not affect the tolerance to toxic compounds significantly, while its overexpression causes an increase on the resistance to a wide range of stressors, including organic solvents, antibiotics, heavy metals, and oxidative compounds (Nakajima et al., 1995; Tanaka et al., 1997). Rob is also an important regulator of the central and intermediary carbon metabolism. This regulator was shown to regulate fpr, fumC, nfo, and zwf ( Jair et al., 1996) (Fig. 1). Zwf catalyzes the first step of the pentose phosphate pathway that produces a great part of the intracellular NADPH and has significant effects over the carbon flux (Nicolas et al., 2007). As previously mentioned, Rob controls galT encoding a key enzyme in the galactose metabolism and implied in the synthesis of glycolipids, glycoproteins, and cell wall (Frey, 1996). More recently, our group has revealed that Drob mutant of E. coli BW25113 exhibited deep changes in the organic acid profile under high O2 availability, producing significantly increased amounts of most fermentative acids (Egoburo et al., 2018).

3.3 Regulation of central carbon metabolism in response to oxygen availability: ArcAB and FNR 3.3.1 ArcAB This two-component system is one of the main mechanisms that controls the metabolism of E. coli in response to the redox state of the environment (Alexeeva, 2003; Shalel-Levanon et al., 2005). ArcB is the transmembrane sensor kinase that is capable of sensing the pool of reduced quinones. When the amount of reduced quinones decreases under microaerobic or anoxic conditions, ArcB undergoes stable phosphorylation and transphosphorylates ArcA, its cognate response regulator (Malpica et al., 2006). ArcA-P binds to the DNA under these conditions, controlling the expression of at least 135 genes involved in many different aspects of cell physiology, with deep effects on metabolism (Iuchi et al., 1989). Among the regulatory targets of ArcA are genes involved in aerobic respiration, like the cyoABCDE operon, which encodes the

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low O2 affinity cytochrome bo oxidase and several TCA cycle genes, including sdh, fumA, pdh, mdh, and ace, that are repressed by ArcA. On the other hand, under low oxygen availability, ArcA activates the high O2 affinity cytochrome d oxidase (cydAB), the pfl operon, and genes involved in fermentation pathway enzymes (Iuchi et al., 1989) (Fig. 1). ArcA acts in a coordinated manner with other global regulators such as CreBC, and both regulators were shown to modulate the carbon flux through the lower glycolysis (Nikel et al., 2009). More recently, statistical analysis of DNA microarrays revealed that more than 1100 genes could be either directly or indirectly regulated by ArcA-P, which represents almost a fourth of all genes in E. coli. Most of the targets found are related to both aerobic and anaerobic catabolism, the glyoxylate shunt, fatty acid degradation, and genes encoding several flavoprotein-like dehydrogenases (Salmon et al., 2005). Although former studies of the ArcAB system have demonstrated that ArcA was mainly active under microaerobic conditions, Nizam et al. (Nizam and Shimizu, 2008; Nizam et al., 2009) applied carbon flux analysis to continuous cultures grown on glucose as the sole carbon source and found that arcA and arcB mutants of E. coli BW25113 exhibited important differences at the TCA cycle, the pentose phosphate pathway, and PDH complex not only under low O2 concentration but also on high oxygen availability, leading to the conclusion that ArcA may be also active under aerobic conditions (Nizam and Shimizu, 2008; Nizam et al., 2009).

3.3.2 FNR The global regulator FNR (for fumarate and nitrate respiration) is one the major effectors in the control of respiration pathways. Along with ArcAB, it coordinates the fine-tuning of catabolism in response to redox state conditions, especially when O2 availability is very low (Green and Paget, 2004). This regulator was discovered in E. coli mutants that could not reduce fumarate or nitrate (Kiley and Beinert, 1998). FNR mediates the transition from aerobic growth to anaerobic metabolism. The structure of FNR is similar to Crp, which served as a model for its study. FNR contains two domains, the carboxy-terminal DNA-binding domain and an amino terminal domain that contains four cysteine residues capable of binding a [4Fe-4S] cluster. This last domain acts as a direct oxygen sensor. In the absence of O2, the cluster is formed, and FNR binds to DNA, modulating the expression or repression of its target genes (Green and Paget, 2004). In vitro experiments using two-dimensional gel electrophoresis have revealed changes in 125 polypeptides in fnr mutants under different anaerobic conditions (Kiley and Beinert, 1998). FNR controls the expression of nearly 30 operons involved in anaerobic respiration. It acts as an activator for the following: (1) genes encoding the terminal oxidoreductases necessary to use alternate electron acceptors instead of oxygen, like narGHIJ (nitrate reductase), nirB (nitrite reductase), and dmsABC (DMSO/TMAO reductase), and proteins involved in the transport of electron acceptors and (2) genes related to central C metabolism, such as pfl, fumB, and fdnGHI (Gunsalus and Park, 1994) (Fig. 1). FNR represses the transcription of genes involved in aerobic respiration as cyoABCDE, cydAB, and ndh, among others (Gunsalus and Park, 1994). In addition to this, Lynch and Lin (1996) have demonstrated that ArcA levels and ArcB activity are also under the control of FNR, revealing the high hierarchy of this regulator (Lynch and Lin, 1996). More recently, it was shown that fnr mutants growing under carbon and nitrogen limitation exhibited an increased transcription of glucose uptake and pentose phosphate genes as well as transcription of some TCA cycle and glyoxylate shunt genes, pointing out the importance of FNR in central metabolism. Deletion of fnr caused changes in the transcription pattern of stress response genes, and the expression of arcA seemed affected in both aerobic and microaerobic conditions (Kumar and Shimizu, 2011; Marzan et al., 2011). The effect of FNR is not limited to metabolic genes. Barbieri et al. (2017) demonstrated that FNR regulates virulence factors in avian pathogenic E. coli (Barbieri et al., 2017). Also, FNR is an important regulator in other enterobacteria. For example, it mediates cell invasion in Shigella flexneri and affects more than 300 genes involved in virulence, anaerobic metabolism, and motility, among other functions, in S. typhimurium (Fink et al., 2007; Marteyn et al., 2010).

4 Manipulation of global regulators: Its effect on the synthesis of biotechnological compounds 4.1 Biofuels Fossil fuels constitute our main source of energy and the starting point for the synthesis of a wide range of chemicals and materials. Recently, their decreasing availability and environmental impact have raised the interest in more environmentally friendly and sustainable energy sources, such as biofuels and chemicals obtained from renewable carbon sources

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through microbial bioprocesses (Ruiz et al., 2012; Choi et al., 2015). However, the extended use of these strategies is limited by their higher cost in comparison with well-established petrochemical industry processes. To reduce the cost of bioprocesses, multiple strategies involving strain and process optimization are needed so that the use of economical feedstocks can achieve high product yields. One of the most extensively used strategies to improve the production of biofuels and other chemicals is genetic and metabolic engineering of producing microbial strains (Majidian et al., 2018). As exposed in the introductory section, genetic engineering of global regulators provides some advantages over traditional systematic step-by-step or random mutation engineering (Pettinari et al., 2008; Santos and Stephanopoulos, 2008). Some examples of overproducing strains obtained through global regulator manipulation are summarized in Table 1.

4.1.1 Ethanol Ethanol is the most commonly produced and extensively commercialized biofuel, used not only as a biofuel but also as feedstock for the production of oxygenated fuels. It is environmentally friendly owing to its clean burning characteristics (Majidian et al., 2018). Although the microorganism of choice for the microbial production of ethanol has traditionally been Saccharomyces cerevisiae, this yeast is unable to produce ethanol from some substrates such as glycerol, an increasingly attractive substrate for microbial bioprocesses, due to abundant availability as a result of its formation as a byproduct during biodiesel production. Glycerol is a reduced substrate that can be readily used by many microorganisms for growth, mainly in aerobic conditions. It was not until 2006 that fermentation of glycerol was described in E. coli (Dharmadi et al., 2006). Studies performed with arcA derivatives of E. coli K1060 showed a remarkable increase in the ethanol yield when grown under microoxic conditions using glycerol as carbon source (Pettinari et al., 2012). These strains presented a considerably increased ethanol to acetate ratio, ethanol concentration, and ethanol productivity using both glucose and glycerol. The increase in ethanol synthesis, accompanied by a general decrease in organic acid formation, led to the conclusion that carbon and reducing power availability were funneled toward ethanol in these mutants (Nikel et al., 2008c). This altered metabolic profile was more marked in a strain that contained an additional mutation affecting the global regulator CreC, which showed a fivefold increase in the ethanol concentration (Nikel et al., 2008b). These results illustrated the great potential of global regulator mutants as a platform for the enhanced production of biotechnologically relevant microbial metabolites. Further manipulation of this strain by expressing the adhE from Leuconostoc mesenteroides and eliminating the native ldhA gene resulted in an ethanol production of 15 g L 1 with a volumetric productivity of 0.34 g L 1 h 1 in a twostage bioreactor culture using glycerol in microaerobic conditions (Nikel et al., 2010). More recently the elimination of CreC resulted in an almost 100% increment in the synthesis of ethanol in E. coli grown in flask cultures under low oxygen availability and glucose as the sole carbon source (Egoburo et al., 2018). Ethanol synthesis in this mutant was further increased by overexpressing the adhE from L. mesenteroides. This mutant strain achieved twofold more ethanol than the wild-type strain in 24 h cultures under O2 limitation (Egoburo et al., 2018).

4.1.2 Butanol and higher alcohols Butanol has several advantages over ethanol as a biofuel due to its lower vapor pressure, lower hydrophilicity, and higher energy density. Remarkably, butanol and higher alcohols have the potential to directly replace fuel oil and can be used in current engines without previous modifications (Majidian et al., 2018). One of the disadvantages of butanol production is the high cost of processes based in microorganisms that produce it naturally, such as some Clostridium species. Genetic engineering of E. coli to obtain butanol-producing strains has been achieved by overexpressing heterologous genes from S. cerevisiae, Bacillus subtilis, Lactococcus lactis, and Clostridium acetobutylicum to take advantage of the highly active amino acid biosynthetic pathway of E. coli, diverting its 2-ketoacid intermediates for alcohol synthesis (Atsumi et al., 2008). In this work, additional gene deletions, including the elimination of FNR and further manipulations, led to the production of higher alcohols including isobutanol, 1-butanol, 2-methyl-1butanol, and 3-methyl-1-butanol in E. coli. This particular approach resulted in highly efficient isobutanol production, reaching 300 mM of this alcohol from glucose under microaerobic conditions (Atsumi et al., 2008). A different strategy developed a reverse b-oxidation cycle in E. coli, achieving carbon elongation and alcohol production. Further manipulation of this bacterial strain included the replacement of crp by a cAMP-independent mutant derivative to diminish catabolite repression and the deletion of arcA to avoid the repression of b-oxidation cycle genes. These modifications increased both productivity and yield compared with native producers, allowing the production of 14 g L 1 of butanol in bioreactor cultures using minimal medium supplemented with glucose (Dellomonaco et al., 2011). Another problem that affects the microbial production of butanol is its high cell toxicity. Considerable efforts have been made to overcome this issue. He et al. (2019) used an error-prone RNA polymerase to develop butanol-resistant E. coli strains.

TABLE 1 Manipulation of global regulators for the overproduction of industrial compounds in Escherichia coli.

Compound

Manipulation on global regulators

Ethanol

DarcA, creC

Dldh, AdhELm overexpression

Two-stage bioreactor culture, glycerol as carbon source and microerobiosis

Ethanol

DcreC

adhELm overexpressionb

Butanol

DarcA, crp*c

Butanol Isobutanol 1-propanol

Additional genetic modifications

Product concentration

Comments

References

15 g/L

High yield, high productivity, fivefold compared with the wild type

Nikel et al. (2008b) and Nikel et al. (2010)

Flask culture, glucose, low aeration

0.7 g/L

100% increase compared with wild type after 24 h

Egoburo et al. (2018)

fadR, atoCc DadhE Dpta DfrdA, DyqhD DeutE

Bioreactor cultures, glucose, microaerobiosis

14 g/L

High butanol yield and productivity. Coproduction of ethanol, fatty acids, and long-chain alcohols

Dellomonaco et al. (2011)

Dfnr

DadhE, DldhA, DfrdAB, Dpta, DpflB, DilvD. Overexpression of ilvIHCD/alsS and adh2Scd

Flask cultures, glucose +2-a-ketoacids, microaerobiosis

22 g/Le

The engineered strain achieved 86% of the theoretical yield. Coproductions of various higher alcohols

Atsumi et al. (2008)

Xylitol

crp*c

DxylB, overexpression of cbXR

Bioreactor cultures, glucose+ xylose, microaerobic

38 g/L

Co-utilization of glucose and xylose

Cirino et al. (2006)

Xylitol

crp*c

DptsG

Bioreactor, corncob hydrolysate

143 g/L

Degradation of complex carbon source, highest production, and productivity to date, tolerant to crude hydrolysate

Yuan et al. (2020)

Lactate

DarcA/DarcB, Dfnr

DpyrD

Bioreactor fermentation, glucose, anaerobic conditions

50–68 g/100 g glucose

5 to 10-fold higher yield on glucose than the control strain

Kim et al. (2013)

Lycopene

crp*c

Bioreactor culture, glucose, aerobiosis

128 mg/L

Engineering of Crp led to high production of both lycopene and bcarotene

Huang et al. (2015)

L-tryptophan

Dcra

DtrpR, DtnaA, DpheA, DtyrA

Flask culture, glucose/ fructose/xylose

1.9 g/L

Increase of 60% compared with the wild-type strain with higher productivity, yield, and carbon source uptake

Liu et al. (2016)

L-threonine

DarcA, Dcra, DiclR

DtdcC

Flask culture, glucose, aerobic conditions

26 g/L

Increase of more than 100% compared with the wild-type strain. Lower acetate synthesis and higher glucose uptake

Ding et al. (2019)

C a

b

Culture conditions

Continued

TABLE 1 Manipulation of global regulators for the overproduction of industrial compounds in Escherichia coli—cont’d

Compound

Manipulation on global regulators

PHB

DarcA, creC

PHB

DarcA

1,3-Propanediol

Additional genetic modifications

Ca

Culture conditions

Product concentration

Comments

References

Bioreactor, glycerol, microaerobiosis

10 g/L

The mutant strain produced 30-fold PHB compared with the control in flask cultures Ethanol was coproduced

Nikel et al. (2006) and Nikel et al. (2008a)

fadB:Cm, DfadJ, atoC512C, fadR601, DompR

Flask cultures, tryptone+fatty acids, aerobiosis

117 mg/L

The mutant strain produced 40-fold PHB than wild type. Increased in several PHAs (64-fold for PHV)

Scheel et al. (2016)

DarcA

Several mutations

Glucose, bioreactor

120 g/L

Industrial production

Cervin et al. (2008)

1,3-Propanediol

DarcA

Overexpression of phaPAFA8f

Bioreactor, glycerol, aerobiosis

24 g/L

High yield and productivity in minimal medium after 46 h

Egoburo et al. (2018)

Succinic acid

DarcA

DptsG, DpoxB, Dpta/pta*, DiclR, DsdhA, Dldhg

Whole-phase bioreactor culture, glucose

85 g/L

High productivity, no acetate formation. Ethanol and lactate cosynthesized

Li et al. (2013)

Succinic acid

DcreC

Overexpression of fdh1Cbh and ppc

Flask cultures, glucose, low aeration

2.6 g/L

Succinic acid yield 40% higher than the wild-type strain

Godoy et al. (2016)

Succinic acid

cra*i

DpflAB DldhA, DptsG

Two-phase bioreactor culture, glucose

22 mM

23% more succinic acid than parental strain

Zhu et al. (2016)

Succinic acid

Dcra

Overexpression of fdh1Cb and pckh

Flask cultures, glucose, no aeration

3 g/L

More than 80% more succinic acid than control strain

Egoburo et al. (2018)

a

Escherichia coli containing a constitutive derivative of creC. adhE from Leuconostoc mesenteroides. Escherichia coli containing a mutant derivative of crp that is independent of cyclic AMP. d adh2 from Saccharomyces cerevisiae. e Concentration of isobutanol. f phaP from Azotobacter FA8 sp. g Escherichia coli strain was engineered by the deletion of pta and the introduction of a mutant derivative. h fdh1 from Candida boidinii. i cra mutant derivative. b c

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This approach resulted in two strains capable of tolerating up to 2% of butanol and other higher alcohols such as isobutanol and 1-pentanol. The mutation sites of the two strains were identified by whole-genome resequencing, revealing rob as one of the key butanol-tolerant genes (He et al., 2019). Crp is another promising target to enhance tolerance to stressful compounds. Several studies have revealed that the manipulation of Crp can lead to phenotypes highly multiresistant to a wide range of compounds, including ethanol, butanol, isobutanol, and toluene (Geng and Jiang, 2015).

4.2 Polyhydroxyalkanoates Polyhydroxybutyrate (PHB) is a short-chain polyhydroxyalkanoate (PHA) that is naturally produced by several microorganisms as a reserve material for carbon and energy. This biopolymer is one of the most promising alternatives to petroleum-based plastics due to its mechanical and thermoplastic properties, comparable with polypropylene and polyethylene. Furthermore, PHB is renewable, biodegradable, and biocompatible (Ruiz et al., 2012). Beyond its industrial applications as an environmentally friendly and more sustainable alternative to traditional plastics, it has been used in tissue engineering, surgical devices, and drug delivery (Yeo et al., 2018). In the last few decades, a remarkable effort has been made to improve PHB production. However, microbial PHA production has been mostly studied in fully aerobic conditions, and current processes for their synthesis require a great amount of energy to maintain these conditions, compromising sustainability (Nikel et al., 2008a). The unregulated respiratory phenotype of arcA derivatives of E. coli was exploited for the production of PHB in low aeration conditions. arcA mutants carrying the PHB biosynthetic genes from Azotobacter FA8 produced 10 times more PHB than the wild-type strain under low oxic conditions in bioreactor cultures with a PHB content of 27%. Another mutant referred to as arcA2 reached 30 times more than PHB, with a content of 35% (Nikel et al., 2006). This mutant strain, which was later found to contain mutations in two global regulators (DarcA creCC), was studied for PHB synthesis in further optimized bioreactor cultures. Using glycerol as carbon source and microaerobic conditions, it achieved more than 10 g L 1 of PHB with a content of 51% in 60 h cultures (Nikel et al., 2008a). Ethanol was coproduced as a valuable byproduct of this process, making it even more interesting in terms of sustainability. More recently, Scheel et al. (2016) studied the production of different PHAs in a DarcA E. coli mutant expressing the PHA synthesis genes from Pseudomonas putida KT2440. The mutation increased polyhydroxyvalerate (PHV) accumulation 64-fold compared with the parent strain using fatty acids as carbon substrate. The mutant derivative also showed an even more marked increase in PHB synthesis and slight increments in medium-chain PHAs (Scheel et al., 2016). Taken together, this evidence suggests that ArcA is an adequate target for enhancement of PHA production in the nonnative producer E. coli using low-cost and renewable carbon sources under different aeration conditions.

4.3 1,3-propanediol 1,3-Propanediol (1,3-PDO) is one of the best known microbial products that can be obtained from glycerol using bacteria such as Lactobacillus brevis, Citrobacter freundii, K. pneumoniae, Enterobacter, and various Clostridium strains. This compound is widely used as monomer for the synthesis of polymers with unique characteristics, such as biodegradability and enhanced light stability and solubility (Yang et al., 2018). 1,3-PDO is used for the synthesis of a new kind of polymer named polypropylene terephthalate, which can be used to produce polyglycol-type lubricant and fibers for textile applications. Besides this, 1,3-PDO is used in cosmetics, food, and medicines (Yang et al., 2018). Although this chemical can be produced through microbial processes in a more sustainable and more environmentally friendly way compared with chemical synthesis, many natural producers are not suitable for industrial applications. For this reason an increased interest has been raised to design engineered E. coli strains for the production of this compound. Availability of NADH is considered one of the main factors limiting the microbial synthesis of 1,3-PDO (Biebl et al., 1999). Manipulation of redox metabolism in E. coli through mutations in redox regulators such as ArcA was used to enhance 1,3-PDO synthesis in an engineered E. coli strain that could directly convert glucose into 1,3-PDO with a volumetric production of 120 g L 1 in 50-h bioreactor cultures (Cervin et al., 2008). These results were patented as a high 1,3-PDO producing bioprocess and are currently commercialized by DuPont. However, it is difficult to pinpoint the effect of ArcA elimination in this strain, as it contains multiple additional genetic modifications. A recent study assessed the specific effect of an arcA deletion in 1,3-PDO synthesis in E. coli. Elimination of ArcA alone increased 1,3-PDO synthesis 2.5-fold compared with the wild-type strain in flask cultures under high aeration (Egoburo et al., 2018). The production of this compound can have toxic effects on producing cells, so several strategies, including the overexpression of chaperones, can increase tolerance. Overexpression of phaP, an PHB granule-associated protein with chaperone-like activity, was observed to enhance both tolerance to 1,3-PDO and the synthesis of this compound from

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glycerol in E. coli (Mezzina et al., 2017). A combination of ArcA elimination and phaP overexpression led to the production of 24 g L 1 of this compound using glycerol as the sole carbon source in bioreactor cultures, with a productivity of 0.5 g L 1 h 1 (Egoburo et al., 2018). The manipulation of ArcA homologues in natural producers, such as K. pneumoniae and C. freundii, also enhanced 1,3-PDO production, biomass formation, and ATP synthesis (Maervoet et al., 2016; Lu et al., 2018), demonstrating the potential of arcA engineering to boost 1,3-PDO synthesis in different bacteria. In a similar fashion, genetic engineering of crp resulted in a higher production of other diols by allowing the simultaneous consumption of different carbon sources, such as glucose and xylose ( Ji et al., 2011).

4.4 Succinic acid The production of succinic acid is one of the most successful examples of bio-based industries (Mazie`re et al., 2017). This compound has a wide range of applications in agriculture, food, and pharmaceutical industries. It can be converted into different products, including green solvents, pharmaceutical products, and biodegradable plastics (McKinlay et al., 2007). It is used as the starting point for the synthesis of 1,4-butanediol, butyrolactone, adipic acid, tetrahydrofuran, esters, and biodegradable and biocompatible polymers, such as polybutylene succinate ( Jiang et al., 2017). Microbial fermentation provides a more sustainable and environmentally friendly alternative to chemical synthesis, as succinate can be obtained from renewable carbon sources such as industrial wastes and CO2 (Zhu and Tang, 2017). In E. coli, three pathways have been subjected to genetic engineering for the optimization of succinate synthesis: the reductive branch of the TCA cycle under anaerobic conditions, the oxidative branch of the TCA cycle under aerobic conditions, and the glyoxylate shunt (Cheng et al., 2012). As mentioned previously, global metabolic engineering provides some advantages over traditional genetic modifications, funneling carbon and reducing power toward the synthesis of reduced compounds (Ruiz et al., 2012). This strategy has also been applied to enhance succinate synthesis. E. coli is capable of producing this acid as a minor fermentation product under anaerobic conditions. An E. coli strain optimized for succinate synthesis under anaerobic growth using glucose as carbon source (strain NZN111) was further engineered to produce succinate in other conditions (Li et al., 2013). Deletion of arcA, aimed to derepress the TCA cycle under microaerobic conditions, resulted in the production of succinate under both aerobic and anaerobic conditions, reaching more than 10 g L 1 of succinate, 80% more than the parent strain. Furthermore, succinate was produced in the late exponential growth phase, and no acetate, ethanol, and lactate were cosynthesized. Finally, fed-batch fermentations and additional genetic modifications led to 85 g L 1 of succinate in a whole-phase growth strategy under aerobic, microaerobic, and anaerobic conditions (Li et al., 2013). More recently, Godoy et al. (2016) studied the effect of the elimination of the global regulator CreC in E. coli under different oxygen concentrations. Interestingly the metabolic profile of this mutant showed a marked increase in succinate synthesis under low aeration and unaerated cultures, producing 13-fold and 50-fold more than the wild type, respectively. This work also revealed that DcreC mutants had enhanced growth and higher NADH/NAD+ ratio, suggesting a more reduced cellular environment. Further optimization of this mutant by overexpression of genes encoding carboxylating enzymes and formate dehydrogenase from Candida boidinii boosted succinate formation, achieving a succinate yield 40% higher than the parent strain in low aeration culture (Godoy et al., 2016). Another novel target for genetic engineering of succinate synthesis is global regulator Cra. Elimination of this regulator has been shown to redirect the carbon flow toward the synthesis of organic acids in E. coli (Yao et al., 2013; Egoburo et al., 2018). Particularly Dcra mutant showed a marked increase of succinate synthesis under both low and nonaerated cultures, producing almost 1 g.L 1 in nonaerated cultures from glucose in 24 h (Egoburo et al., 2018). In this work a similar approach to Godoy et al. (2016) was used, and further modifications in this strain improved succinate formation by almost 100% compared to the wild-type strain with a concentration of 2.9 g L 1 in nonaerated cultures using minimal medium supplemented with glucose. A former study that tested different variants of Cra in a Dpfl Dldh E. coli strain resulted in a significant increase in succinate synthesis using a modified version of Cra that constitutively activates aceBAK (Zhu et al., 2016). The effect on succinate production of both kinds of manipulations supports the notion that cra is a key regulator on the synthesis of this reduced compound.

5

Concluding remarks

In most bacteria, metabolic pathways are versatile so that metabolism can be adjusted to maximize growth in a wide range of conditions. In facultative aerobes such as E. coli, nutrient availability, including C sources and final electron acceptors, determines both the carbon and electron flow, which must be carefully balanced to achieve cellular homeostasis.

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This points out the importance of global regulation, not only by the enormous number of genes and operons controlled by these regulators but also due to the fact that these transcriptional regulators control the carbon and reducing power fluxes simultaneously (Ruiz et al., 2012). Recently, it was demonstrated that elimination of Cra and ArcA has a deep impact on carbon metabolism in E. coli and generates suitable backgrounds for biocompound production (Egoburo et al., 2018). Additionally, a novel systems biology tool developed to identify suitable gene targets aimed to optimize and improve producing strains, interestingly singled out global regulators as key features for genetic engineering for the majority of processes in which they were tested (Koduru et al., 2018). The systematic comprehension of the control over E. coli metabolism exerted by global regulators is key to the development of biotechnological processes generating genetic backgrounds suitable for production of a variety of chemical compounds, as an alternative to traditional step-by-step gene engineering (Pettinari et al., 2008; Ruiz et al., 2012). This powerful approach is expected to become more attractive as metabolic and regulatory models gain robustness, increasing the predictability of the manipulations.

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Index Note: Page numbers followed by f indicate figures and t indicate tables.

A Acarbose, in diabetic patients, 203 Acetamidase catabolic marker, 358 Actinomycetales, 183 Actinomycetes, 183, 212–213 natural pigments, 212 Actinorhodin, 212 Adaptive laboratory evolution (ALE), 121–122, 248–250, 251f Alkaloids, 386–387 Amino acids, 126 Amylase, 206–207 a-Amylase, 203 Anthocyanins (ACNs), 29–30, 427–428 Antibacterial agent production, by cyanobacteria, 288–289t Antibacterial antibiotics, 189, 189–190t Antibiotics, 188–194 antibacterial, 189, 189–190t antifungal, 189, 190–191t, 191f chloramphenicol, 193 definition, 188 erythromycin, 193 guadinomine, 193 history of, 189 neomycin, 192 nystatin, 193 polyene, 193 streptomycin, 192 tetracyclines, 193 used on crops, 195t wide-spectrum collection, 189, 191t yeast extract, 192 Anticancer agent production, by cyanobacteria, 289–290t Antifungal agent production, by cyanobacteria, 288–289t Antifungal antibiotics, Streptomyces sp., 189, 190–191t, 191f Antimalarial agent production, by cyanobacteria, 291t Antiparasitic agent production, by cyanobacteria, 291t Antivirus agent production, by cyanobacteria, 291t AOX1-derived promoter, in P. pastoris, 336–337 ArcAB system, 442–443 Array-based gene synthesis, 24 Array-based oligo synthesis, 23–24 Artemisinic acid production, 326

Artificial intelligence (AI), 58 L-Asparaginase antitumor effect of, 197 mode of action, 197, 197f production fermentation media structure and environmental requirements, 199 incubation period, 199 by Streptomyces spp., 198t ATP hydrolysis, 236 Auroramycin antibiotic, 192 Autonomously replicating sequence (ARS), in P. pastoris, 336 Auxotrophic markers, 359 Azalomycin F complex, 196

B Bacillus genome (BGM), 26 Bacillus subtilis, 3–4, 5t genome editing CRISPR/Cas9, 146 homologous recombination-based modification, 145–146, 145f high-value chemicals synthesis organic waste agroresidue and wastewater, 153 original substrates, 152 renewable resources, 152–153 solid-state fermentation, 153–154, 154t high value compound biosynthesis primary metabolites biosynthesis, 139–141, 141–142t secondary metabolites biosynthesis, 142–144 metabolic modeling, 144–145 minibacillus, 154–155 transcriptional engineering cryptic biosynthetic gene clusters, 148–149 promoter engineering, 148 promoter exchange, 147–148 translational level regulation, 149–151 translation rate efficiency, 149 transport level, 151–152 Bacilysin, 143 Bacitracins, 144 Bacterial one-hybrid (B1H) systems, 428, 431f 1,4-BDO, 132 Benzylisoquinoline alkaloids, 386–387 BioBricks, 25

Biocatalysis, 115 Bioemulsifiers production, by Streptomyces sp., 211 Bioethanol, 127–129 Biofilms, 28 Biofuels, 28–29, 128f bioethanol, 127–129 biohydrogen, 127 1-butanol, 129 coproduction of, 129 isobutanol, 130 isopropanol, 130 production, Y. lipolytica, 369–370 1-propanol, 129–130 Biohydrogen, 127 Biomass lignocellulosic, 323–324 and lipid production conditions, 281t production by cyanobacteria, 277–278 separation methods, 280 Biomaterials, 28–29 Biopharmaceutical compound production, by recombination yeast, 326, 327–328t Bioplastic production, 282–283 Biopolymers, 131f 1,4-BDO, 132 1,2-PDO, 132 1,3-PDO, 130–132 polyhydroxyalkanoates, 132–133 Biorefineries, economic viability of, 303 Bioremediation, by cyanobacteria, 279–280 Biosensing, 27 Biosensors, 250–252, 251f, 421 allosteric transcription factor-based, 421–423 characteristics of, 423 for dynamic regulation, 426 for enzyme engineering, 424–426 functional enzyme screening, 427 gene and operon functions, 431f altering transcription factor expression, 427–428 high-throughput screening, discovery of, 427 lignin-derived aromatic compounds, 424–425 for metabolic pathway engineering, 424–426 riboswitch-based, 423 screening and selection methods, 424–426 for sugar-responsive elements, 424–425 transcription factor-DNA interactions

455

456

Index

Biosensors (Continued) bacterial one-hybrid (B1H) systems, 428, 431f DAP-seq method, 429, 431f gSELEX method, 428–429 high-throughput implementations, 429 protein binding microarrays method, 428, 431f transcription factor-effector interactions, 429–430 Biosilica–yeast hybrid material, 371–372 Biosurfactants, 171 production by Streptomyces sp., 211 Biosynthesis, triterpenoid. See Triterpenoid biosynthesis Biosynthetic gene clusters (BGCs), 143, 148–149 Biosynthetic pathway limitation, 25–26 microfactories, 29–30 Biotechnological process host selection, 381 workflow and metabolic engineering strategies, 384, 384f Biotransformations, 115 b-lactam antibiotics, 193 Blood glucose homeostasis, 27 Bottom-up synthetic biology, 69–70, 69t Butadiene, 271–272 2,3-Butanediol, 140 1-Butanol, 129 Butanol production, 444–447

C cAMP receptor protein (Crp), 439–441 Cancer, 27–28 Candida antarctica lipase B-displayed P. pastoris, 342 Candidatus Liberibacter asiaticus, transcriptional regulation of, 430 Caprolactam-detecting genetic enzyme screening system (CL-GESS), 425 Capsular polysaccharide (CPS), 285–286 Carbohydrate-hydrolyzing enzymes, 203 Carbon-concentrating mechanism (CCM), 306 Carboxymethyl cellulase, 205 Carotenes, 286 Carotenoids, 286 Cas9 protein, 56 Catabolite repressor/activator (Cra), 441 Catharanthus roseus, 397 Cell-free systems, 416 advantage, 413 applications, 407 bioproduction, 413t description, 407 in directed enzyme evolution, 415 DNA-encoded multienzyme pathways, prototyping of, 414f enzyme-enriched extract strategy, 413–414 research history, 410 synthetic cells, bottom-up development of, 415–416

therapeutic protein production, 412–413 types for metabolic production, 408f in vitro synthetic pathways bottom-up approach, 410 CO2 fixation, 411 enzymatic electrocatalysis, 412 glycolysis, 410 hydrogen production, 411 N-butanol production, 411 photobiocatalysis, 411–412 terpene production, 410–411 in vitro transcription-translation (TX-TL) system, 407 E. coli lysate-based, 407–409 prokaryotic and eukaryotic lysate-based, 409 purified recombinant elements (PURE), 409 Cellulases, 204–205 Cellulose degrading Streptomyces sp., 206t Central carbon metabolism, in E.coli, 437–438 ArcAB system, 442–443 control by global regulation, 440f cAMP receptor protein (Crp), 439–441 catabolite repressor/activator (Cra), 441 FNR system, 443 Central metabolism, 248, 249–250t Chitin, 209, 209f Chitinases, 209 Chitinolytic enzymes, 209 Chitosanase, 210 Chlamydomonas reinhardtii, 304–306 AlgaGEM model, 311–313 13 C-metabolic flux analysis, 309 iRC1080 model, 311–313 nutrient starvation effect, 306 Chloramphenicol, 193 Cholesterol oxidase, 199–200, 201t Clavulanic acid, 193 Clustered regularly interspaced short palindromic repeats (CRISPR), 95–96 13 C-metabolic flux analysis, 309 CO2 fixation, 411 Column-based oligo synthesis, 22–23, 23f Computer models, metabolism, 65–67 Constraint-based metabolic (CBM) model, 66 Corynebacterium glutamicum, 3, 4t, 394 gene editing tools, 241–242 generally recognized as safe, 238 protein expression system expression elements, 241 optimized gene expression, 238–240 ribosome binding sites, 240 signal peptide, 240–241 protein secretion system sec-dependent pathway, 236, 236f tat-dependent pathway, 236–238, 237f recombinant protein expression, 235, 240 small molecules production directed evolution, 248–252 rational metabolic engineering, 243–248 Crabtree effect, 319

CreBC system, 441–442 Cre-Lox recombination, 241–242 CRISPR-based genome editing in P. pastoris, 337 Y. lipolytica, 363–365 CRISPR/Cas9 strategy Bacillus subtilis, 146 Y. lipolytica, 365–366 CRISPR-Cas system, 95–96, 96f gene regulation CRISPR activation, 107 CRISPR interference, 104–107, 105f, 106t genome level, 101t CRISPR-enabled trackable genome engineering, 100–101, 102f engineering tools in yeast, 102–104 guide RNAs, 101–102, 103f multiplex automated genome engineering, 100 for P. putida development, 394–395 single gene level in bacteria, 98, 99t gene editing with, 96–98, 97f in yeast, 99, 100t CRISPR-enabled trackable genome engineering (CREATE), 100–101, 102f CRISPR interference (CRISPRi) system in Y. lipolytica, 365 CRISPR system-mediated engineering, of S. cerevisiae, 321–322, 322f Crotonic acid, 273 Cry proteins, 143 Cryptic biosynthesis pathways, 148–149 Cyanobacteria, 8–9, 9t, 278f advantages, 278 for antibacterial and antifungal agent production, 288–289t for anticancer agent production, 289–290t for antimalarial and antiparasite agent production, 291t for antivirus agent production, 291t biodiesel production, 280 biohydrogen production, 280–282 biomass and lipid production conditions, 281t biomass production, 277–278 bioplastic production, 282–283 bioremediation, 279–280 description, 277 diversity, 277 exopolysaccharides, 285–286 medical agent production, 292t morphology, 277 nanoparticle synthesis, 284, 285t phycobiliproteins in, 287 phycocyanin in, 287 phycoerythrin in, 287 pigments, 286 and red algae, 277–278 variety and physiology of, 278 water bloom phenomenon, 279 Cyclization enzymes, 37–43, 39f Cytochrome P450 (CYP), 39, 39f, 44

Index

457

D

E

G

De novo biotransformations, 123–124t adaptive laboratory evolution, 121–122 biofuels, 127–130, 128f bioethanol, 127–129 biohydrogen, 127 1-butanol, 129 coproduction of, 129 isobutanol, 130 isopropanol, 130 1-propanol, 129–130 biopolymers, 131f 1,4-BDO, 132 1,2-PDO, 132 1,3-PDO, 130–132 polyhydroxyalkanoates, 132–133 industrial interest metabolites, 125f amino acids, 126 dicarboxylic acids, 122–126 D-Lactate, 122 pyruvate, 126 De novo pathway design, 88 Deoxyribonucleic acid (DNA) synthesis array-based gene synthesis, 24 biofuels and biomaterials, 28–29 biosensing, 27 biosynthetic pathway building limitation, 25–26 microfactories, 29–30 larger DNA assemblies, 24–25 oligo synthesis array-based oligo synthesis, 23–24 column-based oligo synthesis, 22–23, 23f therapeutics blood glucose homeostasis, 27 cancer, 27–28 disease mechanisms and prevention, 28 novel treatments for bacterial infections, 28 whole-genome synthesis, 25 Design-build-test-learn (DBTL) concept, 65, 66f of natural product synthesis, 395 Designer cells, 154–155 Diabetes mellitus, 203 1,4-Diaminobutane, 245–246 Dicarboxylic acids, 122–126 Diffusible pigments, 213f Dimethoxytrityl (DMT), 22 Directed evolution, small molecules adaptive laboratory evolution, 248–250, 251f biosensors and high-throughput engineering, 250–252, 251f Diterpenoids, 287–290 DNA affinity purification sequencing (DAPseq), 429–430, 431f DNA assembly methods, 29–30, 86t Y. lipolytica artificial chromosomes, design of, 362–363 b-carotene synthesis pathway, 362 Golden Gate assembly (GGA), 363 YaliBricks system, 363 DNA fragments, 56 DNA polymerase, 21 Double-strand breaks (DSBs), 56

EasyCloneYALI CRISPR/Cas9 tools, 364 Enzymatic cellulose hydrolysis, 205f Enzymatic electrocatalysis, 412 Enzymatic electro synthesis (EES), 412 Enzyme replacement therapies (ERTs), 348–350 Erythromycin, 193 Escherichia coli (E. coli), 1–2, 2t de novo biotransformations, 123–124t biofuels, 127–130, 128f biopolymers, 130–133, 131f industrial interest metabolites, 122–126, 125f lysate-based TX-TL system, 407–409 microorganism model, 115–117, 116t, 117f precursor biotransformation cofactors pools and regeneration, 120–121 multistep biosynthesis pathways, 121 protein engineering, 118–120, 120f whole-cell biocatalysis via single-step pathways, 118, 119f transcriptional regulation using Genomic SELEX, 428–429 Escherichia coli metabolism, 437 ArcAB system, 442–443 global regulators cAMP receptor protein (Crp), 439–441 catabolite repressor/activator (Cra), 441 central carbon metabolism, 438–443 description, 438 dual response regulators, 438 fumarate-nitrate reduction (FNR), 438, 443 signal transduction, 439 terminal electron acceptors, 438 intermediary metabolism, regulation of CreBC, 441–442 CreC, 441 right origin binding (ROB), 442 Ethanol production, 444 Ethylmalonyl-CoA pathway (EMCP), 267, 268f Eukaryotic cell-free lysate systems, 409 Euk.cement, 371 Exopolysaccharides, 285–286, 306 Expression plasmid vectors, C. glutamicum expression elements, 241 optimized gene expression, 238–240 ribosome binding sites, 240 signal peptide, 240–241

Galactose-inducible promoters, 321 GAL1-10p promoter, 321 g-aminobutyric acid (GABA), 245–246 GAP-derived promoter, in P. pastoris, 337 Gap-repair cloning, 319 Geldanamycin, 196 Gene assemblies, 56 Gene deletion, 43 Gene expression C. glutamicum engineered promoters, 239–240 promoters classification, 238 Gene knockout, 144–145 Gene knockout system, 241–242 Generalized retrobiosynthetic assembly prediction algorithm (GRAPE), 58–59 Generally recognized as safe (GRAS), 238 Gene regulation CRISPR activation, 107 CRISPR interference, 104–107, 105f, 106t Gene silencing, 43 Gene synthesis array-based gene synthesis, 24 biosensing, 27 therapeutics bacterial infections, novel treatments, 28 blood glucose homeostasis, 27 cancer, 27–28 disease mechanisms and prevention, 28 Genetically modified organisms (GMOs), 84 Genetically modified Y. lipolytica strains, 348, 350 Genetic manipulation methylotrophic cell factories, 265–267, 266–267f Genome editing, 56 Bacillus subtilis CRISPR/Cas9, 146 homologous recombination-based modification, 145–146, 145f Corynebacterium glutamicum, 241–242 CRISPR-Cas system, 96–98, 97f Y. lipolytica CRISPR tools, 363–366 and transposomics, 366–367 Genome-scale metabolic models (GEMs), 66, 84, 311–313, 372 Genomic SELEX method, 428, 431f Geosmin production, 216 Gibson assembly, 56 Global regulators, E. coli metabolism biotechnological compound synthesis biofuels, 443–447 butanol and higher alcohols, 444–447 ethanol, 444 polyhydroxyalkanoates, 447 1,3-propanediol, 447–448 succinate, 448 cAMP receptor protein (Crp), 439–441 catabolite repressor/activator (Cra), 441 central carbon metabolism, 438–443 description, 438 dual response regulators, 438

F Fatty acid ethyl ester (FAEE), 88 Fermentative chemical production, by P. pastoris, 339–340, 339t Flavonoid biosynthetic genes, 398–400, 400–401t Flavonoids, 388 production using Y. lipolytica, 370–371 Flux balance analysis (FBA), 67–68 F€orster resonance energy transfer (FRET), 250–252 Fumarate-nitrate reduction (FNR), 438, 443 Fungal artificial chromosomes (FACs), 57

458

Index

Global regulators, E. coli metabolism (Continued) fumarate-nitrate reduction (FNR), 438, 443 manipulation of, 443–448, 445–446t signal transduction, 439 terminal electron acceptors, 438 Glucose repression, 319 a-Glucosidase, 203 L-Glutamate, 3, 243–246, 244f Glutathione, 341 Glycoengineered Y. lipolytica strain, 357–358 Glycolysis, 410 Golden Gate assembly (GGA), 56, 363 GoldenMOCS-Yali toolkit, 364–365 Gout treatment enzyme. See Uricase (urate oxidase) Gram-positive bacterium, 235 Green fluorescent protein (GFP), 236–238 Guadinomine, 193 Guanine-cytosine (GC) content, 165 Guide RNAs (GRNA), 101–102, 103f, 146

H Hansenula polymorpha, 7, 8t Heterologous expression biosynthetic clusters, 57 genomic-guide, 44 transcriptomic-guide, 44 High-secretor W29-derived Y. lipolytica recipient strains, 356, 356t High-throughput omics technology, 393 High-throughput screening system based on fluorescent readouts, 430 Cand. L. asiaticus trancription factor, 430 protein-metabolite interactions in vivo, 430 High-value added biomolecules (HVABs) production abiotic stresses, 306 in Chlorella sp., 309–311 genomic and phylogenomic analysis, 304–305 integrated omics for genome-scale metabolic models, 311–313 top-down approach, 311 metabolic flux analysis (MFA), 309–311 metabolomics, 309 transcriptomics and proteomics, 306–308 up-/downregulation of genes, 307t Homologous recombination, 145–146, 145f C. glutamicum, 241 a-Humulene, 270–271 Hybrid terpenoids, 52–53 Hydrogen production, 411 3-(3-Hydroxyalkanoyloxy) alkanoic acid (HAA), 171 3-Hydroxydodecanoate (3HDD), 167–168, 171 3-Hydroxyhexanoate (3HHx), 167–168, 171 2-Hydroxyisobutyric acid, 272 5’-Hydroxyl group, 22 3-Hydroxypropionic acid, 272

I Industrial interest metabolites, 125f amino acids, 126 dicarboxylic acids, 122–126

D-Lactate, 122 pyruvate, 126 INRA modular Golden Gate toolkit, 363 Integrated omics for HVAB production genome-scale metabolic models, 311–313 top-down approach, 311 In vitro compartmentalization (IVC) technique, 415 In vitro prototyping and rapid optimization of biosynthetic enzymes (iPROBE), 413–414 In vitro synthetic biology, 410 In vitro synthetic pathways, cell-free systems bottom-up approach, 410 CO2 fixation, 411 enzymatic electrocatalysis, 412 glycolysis, 410 hydrogen production, 411 N-butanol production, 411 photobiocatalysis, 411–412 terpene production, 410–411 In vitro transcription-translation (TX-TL) system, 407 E. coli lysate-based, 407–409 prokaryotic and eukaryotic lysate-based, 409 purified recombinant elements (PURE), 409 In vivo monocistronic operons, 414–415 Isobutanol, 130 Isoprenoids. See Terpenoids Isopropanol, 130 Ivermectin, 197

K Kasugamycin, 195 Keratinases, 207–208 Keratin-rich waste materials, 207–208

L D-Lactate, 122 D-Lactic acid production, 340 Lactococcus lactis, 3, 5t LA production, 325–326 Leucine biosynthesis, 359 Lignocellulosic biomass, 323–324 Lipases cell surface-displayed enzyme reaction, 342 production by Streptomyces sp., 211, 211t Lipopeptides (LPs), 144 Lithium acetate method, for yeast transformation, 319–320 Low pH-inducible promoters, 321 loxP-excisable markers, 358 Lycopene, 385, 386f L-lysine, 3, 246–248, 246–247f Lytic enzymes, 204

M Malate, 122–126 Marionette strain, 423 Marker genes for yeast recombination, 320t for Y. lipolytica, 358–359 MCF. See Microbial cell factories (MCFs)

MeCFs. See Methylotrophic cell factories (MeCFs) Medium chain length PHAs (mcl-PHAs), 282, 367–368 Melanins, 212–213, 214f Mesaconic acid, 272–273 Metabolic engineering, 9–10. See also Synthetic biology of microbial cell factories, 79, 80–81t approaches, 79–82, 82f de novo pathway design, 88 emergence systems, 82–83 enzyme engineering, 89 host platform, 84 metabolic flux analysis, 89 metabolic fluxes optimization, 87 pathway prediction and design, 87–88, 88t process, 79, 82f project design, 83–84 scale up and industrial production, 90–91 in silico pathway prediction, 89 synthetic metabolic pathways, 84–86 tolerance against products and inhibitors, 90 of S. cerevisiae yeast artemisinic acid production, 326 biopharmaceutical compound production, 326, 327–328t cellulose degradation, 323–324 LA production, 325–326 patchoulol production, 326 value-added chemical synthesis, 326 xylose utilization, 324–325 Metabolic flux analysis (MFA), 67, 89, 144, 309–311 Metabolic fluxes, 87 Metabolic pathway, 68 methylotrophs, 267–269, 268f Metabolites, 309 Metabolomic analysis, for HVABs production, 309 Methanol dehydrogenase (MDH), 267 4-Methoxy-2,2’-bipyrrole-5-carbaldehyde (MBC), 176 Methylamine dehydrogenase (MaDH), 269 2-Methyl-(D)-erythritol-4-phosphate (MEP), 1–2, 174f Methyl D-erythritol 4-phosphate (MEP) pathway, 385 2-Methyl-3-namylpyrrole (MAP), 176 Methylorubrum extorquens. See Methylotrophic cell factories (MeCFs) Methylotrophic cell factories (MeCFs) genetic manipulation tools, 265–267, 266–267f high value-added chemicals, 271f butadiene, 271–272 crotonic acid, 273 a-humulene, 270–271 2-hydroxyisobutyric acid, 272 3-hydroxypropionic acid, 272 mesaconic acid, 272–273 (2S)-methylsuccinic acid, 272–273 mevalonate, 271, 272f methylotrophic phenotypes, 269–270, 270t methylotrophs, 267–269, 268f secondary metabolites, 273, 274f

Index

Methylotrophics phenotypes, 269–270, 270t Methylotrophs, 267–269, 268f (2S)-Methylsuccinic acid, 272–273 Mevalonate, 271, 272f Mevalonate pathway, 174, 174f, 385 Microalgal cell factories, 313 Microalgal strains, 304t Microarray oligo synthesis, 23 Microbial a-amylases, 207 Microbial biosensors, types and construction of. See Biosensors Microbial cell factories (MCFs) commercialization of, 79, 80–81t design and optimization, 9–10, 11f metabolic engineering of, 79, 80–81t approaches, 79–82, 82f de novo pathway design, 88 emergence systems, 82–83 enzyme engineering, 89 host platform, 84 metabolic flux analysis, 89 metabolic fluxes optimization, 87 pathway prediction and design, 87–88, 88t process, 79, 82f project design, 83–84 scale up and industrial production, 90–91 in silico pathway prediction, 89 synthetic metabolic pathways, 84–86 tolerance against products and inhibitors, 90 microbial hosts B. subtilis, 3–4, 5t C. glutamicum, 3, 4t cyanobacteria, 8–9, 9t E. coli, 1–2, 2t H. polymorpha, 7, 8t L. lactis, 3, 5t P. pastoris, 6–7, 7t P. putida, 5, 6t S. cerevisiae, 5–6, 6t Y. lipolytica, 8, 9t synthetic biology, 12 Microbial cell factory engineering design-build-test-learn (DBTL) biological engineering cycle, 395 natural products bioproduction, 395–400 omics-driven gene discovery, 396 omics-driven medically important biomolecules production data-driven platforms, integration of, 396, 396f flavonoid biosynthetic genes, 398–400, 400–401t monoterpene indole alkaloids, 397–398, 399t terpene synthases (TPSs), 397, 398t omics-driven microbial chassis development C. glutamicum, 394 P. putida, 394–395 for plant natural products production alkaloids, 386–387 biosynthetic pathway engineering, 384–385 cost of fermentation, 389 enzymatic engineering, 385

funding, 389 future challenges, 389 host microorganisms, 381–383 increasing precursor availability, 384 metabolic engineering strategies, 384–385 polyphenols, 388–389 progress of, 382–383t terpenoids, 385–386 yeast as host, 381 systems biology platform, 395–396 Microbial fuel cell (MFC), 283–284, 283f Microfactory, 29–30 Minibacillus, 154–155 Monoterpene indole alkaloids (MIAs) biosynthesis, 397–398, 399t Multiomics analytics platform, 393–394 Multiplex automated genome engineering (MAGE), 100 Mycoplasma mycoides, 22

N Nanoparticles (NPs) applications, 214, 215f description, 214 green synthesis process, 214–215 Streptomyces sp. based production, 215–216t synthesis by cyanobacteria, 284, 285t Naringenin, 388–389 N-butanol production, 411 Neomycin, 192 Next-generation sequencing (NGS) techniques, 54–55 Nigericin, 196 N-methylglutamate (NMG) pathway, 269 Nonhomologous end joining (NHEJ), 56 Nonmevalonate pathway, 385 Nonribosomal peptide synthetases (NRPS), 29–30, 143–144 Nystatin, 193

O OAA-derived chemicals, 246–248, 246–247f Obese Y. lipolytica strains, 368 Odor production, by Streptomyces sp., 216 2OG-derived chemical engineering, 243–246, 244f Oligo synthesis array-based oligo synthesis, 23–24 column-based oligo synthesis, 22–23, 23f Omics-driven medically important biomolecules production data-driven platforms, integration of, 396, 396f flavonoid biosynthetic genes, 398–400, 400–401t monoterpene indole alkaloids, 397–398, 399t terpene synthases (TPSs), 397, 398t Omics-driven microbial chassis development C. glutamicum, 394 P. putida, 394–395 Omics research, 393 Optimal design, metabolic engineering, 70–71 Organic acid production using Y. lipolytica, 371

459

Organic waste agroresidue enzyme production of, 153 solid-state fermentation, 153–154 Overlapping polymerase chain reaction (O-PCR), 25–26 Oxidosqualene cyclase (OSC), 37, 44–45 2-Oxoglutarate dehydrogenase complex (ODHC)$, 245

P Paclitaxel (PTX), 386 Patchoulol production, 326 1,2-PDO, 132 1,3-PDO, 130–132 Peptidyl synthetases, 52–53 PHAs. See Polyhydroxyalkanoates (PHAs) Phenylpropanoid biosynthetic genes, 400, 400–401t Phosphate-swollen cellulose (PASC) degradation activity, 323–324 Phosphoenolpyruvate (PEP), 245 Phosphoenolpyruvate carboxylase (PEPC), 245 Photobiocatalysis, 411–412 Photosynthetic microbial fuel cells (PMFCs), 283f Photosystem I (PSI), 411–412 Phycobiliproteins, in cyanobacteria, 287 Phycobilisomes, 277–278 Phycocyanin, in cyanobacteria, 287 Phycoerythrin, in cyanobacteria, 287 Phycoerythrocyanin, 287 Pichia pastoris, 6–7, 7t, 336f as cell factory host, drawback of, 335 fermentative chemical production, 339–340, 339t genetic engineering tools for CRISPR system, 337 episomal gene expression, 336 integrative gene expression, 335–336 promoter engineering, 336–337 intracellularly expressed proteins, 338 nonhomologous end joining (NHEJ) in, 335–336 protein production enhancing strategies, 338–339 representative proteins, 338, 338t whole-cell biocatalyst, biotransformation using, 340, 340t ATP-dependent reactions, 341 cell surface-displayed enzyme reaction, 342 oxidation reaction, 341 reduction reaction, 341 Picomoles (pmol), 24 Pigment-producing capacity, of streptomycetes, 212 Pigments cyanobacteria, 286 Streptomyces sp., 212–213, 213t Plant growth-promoting rhizobacteria (PGPR), 139 Plant metabolites, 381

460

Index

Plant natural products (PNPs) extraction process, 381 production of (see Microbial cell factory engineering, for PNP production) Plant secondary metabolites, 326 Plasmids, 384 Plug-and-play synthesis, of terpenes, 397 PNPs. See Plant natural products (PNPs) Poly(3-hydroxydecanoate) (P3HD), 167–168 Polyene, 193 Polyhydroxyalkanoates (PHAs), 132–133, 167–171, 168f, 169–170t, 278–279, 282, 282f bioplastic production pathway, 413–414 Polyhydroxybutyrates (PHBs), 282–283, 447 Polyketides production, 370–371 Polyketide synthases (PKS), 52–53, 143 Polymerase cycling assembly (PCA), 24 Polyoxins, 195–196 Polyphenols, 388–389 Postprandial hyperglycemia, defined, 203 Pravastatin, 204 Precursor biotransformation cofactors pools and regeneration, 120–121 multistep biosynthesis pathways, 121 protein engineering, 118–120, 120f whole-cell biocatalysis, 118, 119f Primary metabolites, B. subtilis, 139–141, 141–142t Prion diseases, 208 Prodigiosin biosynthesis, 176–177, 176f, 177t Prokaryotic cell-free lysate systems, 409 Promoter engineering B. subtilis, 148 in P. pastoris, 336–337 yeast, 321 1,3-Propanediol (1,3-PDO) synthesis, 447–448 1-Propanol, 129–130 Proteases, 207 Protein binding microarrays (PBM), 428, 431f Protein engineering enzymes modification, 118–120, 120f precursor biotransformation, 120f Protein expression, C. glutamicum expression elements, 241 generally recognized as safe, 238 heterologous proteins in, 239t optimized gene expression, 238–240 ribosome binding sites, 240 signal peptide, 240–241 Protein production eukaryotic cell-free lysate systems for, 409 P. pastoris enhancing strategies, 338–339 representative proteins, 338, 338t Protein secretion, C. glutamicum Sec-dependent pathway, 236, 236f Tat-dependent pathway, 236–238, 237f Proteomic analysis, for HVAB production, 306 Protocatechuic acid (PCA), 424–425 Protonmotive force (PMF), 236 Protospacer adjacent motif (PAM), 95–96

Pseudomonas putida, 5, 6t bioproducts, 177–178, 178f development, 394 overview of, 165, 166f polyhydroxyalkanoates, 167–171, 168f, 169–170t prodigiosin, 176–177, 176f, 177t strain engineering, 173, 177–178 surfactants, 171–173, 172t, 172f terpenoids, 174–176, 174f, 175t Purified recombinant elements (PURE) based cell-free systems, 409 Putrescine, 245–246 Pyruvate, 126

Q Quorum sensing, 426

R Rational metabolic engineering central metabolism, 248, 249–250t OAA-derived chemicals, 246–248, 246–247f 2OG-derived chemicals, 243–246, 244f Recipient Y. lipolytica strains high-secretor W29 (MatA) wild-type strain, 356, 356t with increased homologous recombination efficiency, 357 Recombinant protein expression, 235, 240 Recombinant protein production, in S. cerevisiae, 326–329 Released polysaccharide (RPS), 285–286 Replicons, 241 Resistance markers, 241 Resveratrol, 388 Retropath computational tool, 423 Rhamnolipid biosynthesis, 171, 172t, 172f Ribosome binding sites (RBS), 149–151, 177–178, 240, 250–251 Riboswitch-based biosensors, 421, 423 RNA interference technique, 304–305 RNA sequencing (RNA seq)-driven transcriptomics, 393–394, 401 Rob regulator, 442

S Saccharomyces cerevisiae, 5–6, 6t. See also Yeast S-adenosylmethionine (SAM), 341 S-adenosylmethionine (SAM) recycling, 150 Sec-dependent pathway, 236, 236f Secondary metabolites, 51–53, 53f biosynthetic clusters for, 54 bottom-up strategies, 57–59, 58f, 59t B. subtilis gene clusters, 142–144 renewable resources for, 152–153 methylotrophic cell factories, 273, 274f vs. primary metabolites, 52, 53f top-down strategies biological modules, 54–55

customized biosynthetic clusters, 56–57 functional assembly of modules, 55–56 SecYEG, 236 Sensipath computational tool, 423 Sequence-and ligation-independent cloning (SLIC), 25–26 Sexual hybridization through mating-type switching, 367, 369–370, 372 Signal peptides (SPs), 151 C. glutamicum, 240–241 Silver nanoparticles (AgNPs), 284 Single-cell oil production, Y. lipolytica for, 368–369 Site-specific recombination, 241–242 Small molecule-responsive allosteric (SMRA) transcription factor-based biosensors, 421–423 Small molecules, C. glutamicum, 243f directed evolution adaptive laboratory evolution, 248–250, 251f biosensors and high-throughput engineering, 250–252, 251f rational metabolic engineering central metabolism, 248, 249–250t OAA-derived chemicals, 246–248, 246–247f 2OG-derived chemicals, 243–246, 244f Solid-state fermentation (SSF), 153–154, 154t Spinach-based riboswitch biosensors, 423 Spirulina platensis, 283–284, 287–290 Sporophores, of Streptomyces sp., 184 Stability-enhancing sequences (SES), 149–150 Statins, 204 Stilbenoids, 388 Streptofactin, 211 Streptomyces sp. for active metabolites production azalomycin, 196 geldanamycin, 196 kasugamycin, 195 nigericin, 196 polyoxin, 195–196 validamycin, 196 for antibiotics production, 188–194 antibacterial, 189, 189–190t antifungal, 189, 190–191t, 191f chloramphenicol, 193 definition, 188 erythromycin, 193 guadinomine, 193 history of, 189 neomycin, 192 nystatin, 193 polyene, 193 streptomycin, 192 tetracyclines, 193 wide-spectrum collection, 189, 191t yeast extract, 192 anticancer agents, 194, 194t for antidiabetic production, 203, 203t antioxidative agents, 194 for antiparasitic agent production, 196–197, 196t

Index

for bioemulsifiers and biosurfactants production, 211 for cholesterol synthesis inhibitors production, 204 in dry conditions, 183–184 for enzyme production L-asparaginase, 197–199, 198t cholesterol oxidase, 199–200, 201t uricase (urate oxidase), 201–202, 202t filamentous shape, 183–184 growth requirements of, 184–186 habitats, 183–184 immunostimulatory agents, 194, 194t immunosuppressive agents, 194, 194t for industrially important enzymes production amylase, 206–207 cellulases, 204–205 chitinolytic enzymes (chitinases), 209, 210t chitosanase, 210 keratinases, 207–208, 208t lytic enzymes, 204 proteases, 207 for insecticides production, 196–197, 196t for lipases production, 211, 211t milk-clotting protease production, 207f morphological cultural characteristics of, 184, 185–186f for nanoparticles synthesis, 214–215, 215–216t natural products of, 188f for odors production, 216 for pigments production, 212–213, 213t pravastatin production, 204 S. clavuligerus, 193 secondary metabolites production, 187 S. glaucescens NEAE-H, 213 spore chain morphology of, 184, 186f sporophores of, 184 Streptomyces genus characteristics, 184 for vitamins production, 216 Streptomycin, 192 Strictosidine biosynthetic genes, 397–398 Succinate, 122–126 Succinate synthesis, 448 Sulfated polysaccharide spirulan, 287–290 Surface-display engineering, of S. cerevisiae, 323, 323f Surfactants, 171–173, 172t, 172f Synthetic biology, 28 chassis (see Pseudomonas putida, development) metabolic engineering, 65, 66t, 67f biosensors design, 71 bottom-up synthetic biology design, 69–70, 69t computer models, 65–67 design-build-test-learn, 65, 66f dynamic regulation, 71 enzyme sequence selection, 68–69 integration with lab management software, 73 kinetic models, 66 machine learning tools, 71–72

metabolic pathway, 68 optimal design of experiments, 70–71 standardized information, 72 synthetic gene circuits, 68 trade-off between growth, production, 67–68 workflow development, 72–73, 72t secondary metabolites engineering, 51 standardized information representation, 72 Synthetic Biology Open Language (SBOL), 72 Synthetic cells, bottom-up development of, 415–416 Synthetic genes, DNA biosensing, 27 therapeutics bacterial infections, novel treatments, 28 blood glucose homeostasis, 27 cancer, 27–28 disease mechanisms and prevention, 28 Synthetic surfactants, 171 System-level algal metabolism, 304 Systems biology, 303 metabolic pathway reconstruction strategies, 311 Systems Biology Markup Language (SBML), 72 Systems metabolic engineering host platform, 84 metabolic fluxes optimization, 87 project design, 83–84 synthetic metabolic pathways, 84–86

T Tat-dependent pathway, 236–238, 237f Taxol, 386 Terpene production, 410–411 Terpene synthases (TPSs) microbial expression and production profiles of, 398t for plug-and-play terpene production, 397 Terpenoids biosynthesis, 174–176, 174f, 175t lycopene, 385 production using Y. lipolytica, 370 Taxol, 386 Tetracyclines, 193 Tetranactin, 196, 196t Therapeutics, gene synthesis bacterial infections, 28 blood glucose homeostasis, 27 cancer, 27–28 synthetic biology, 28 Tn-seq tools, for in vivo Hermes transposition, 366–367 Top-down approach for high-value added biomolecule production, 311 secondary metabolites biological modules, 54–55 customized biosynthetic clusters, 56–57 functional assembly of modules, 55–56 Transactivating crRNA (tracrRNA), 96–97

461

Transcription activator-like effector nucleases (TALEN), for Y. lipolytica gene editing, 366 Transcriptional engineering biosynthetic gene clusters, 148–149 promoter engineering, 148 promoter exchange, 147–148 Transcription unit (TU) components, Y. lipolytica cellular organelles targeting and compartmentalization, 354 promoters, 353–354 regulatory, 352–354 secretion, signal sequences for, 355 surface display, signal sequences for, 355 terminators, 354 Transcriptomic analysis, for HVAB production, 306 Transformation assisted recombination (TAR), 26 Translation initiation rate (TIR), 150 Trehalose disaccharide, 196 Tricarboxylic acid cycle (TCA cycle), 122–126, 246–247 Triterpenoid biosynthesis bioactivities of, 37, 38t combinatorial approaches, 44–45 cyclization and postmodification, 37, 39f cytochrome P450, 39, 39f enzymatic activity, 43 enzyme discovery, 40–42t, 43 gene deletion, 43 gene silencing, 43 heterologous expression, 44 mutation-based approaches, 43–44 oxidosqualene cyclase, 37 postmodification, 37–43

U Uracil DNA glycosylase inhibitor (UGI), 98 Uracil-specific excision reagent (USER), 25–26 URA3 selection marker, 359 Uricase (urate oxidase), 201–202, 202t

V Validamycin, 196 Vancomycin, 192 Vitamin B12 production, Streptomyces-based cell factories for, 216

W Whole-cell biocatalysis, 118, 119f Whole-cell biocatalyst, P. pastoris, 340, 340t ATP-dependent reactions, 341 cell surface-displayed enzyme reaction, 342 oxidation reaction, 341 reduction reaction, 341 Whole-cell factories, Y. lipolytica for organic acid production, 371 for polyketides production, 370–371 for single-cell oil production, 368–369 for terpenoids production, 370

462

Index

Whole-cell GFP-based biosensor, 425 Whole-genome synthesis, 25

X Xanthene dyes, 411–412 Xanthophylls, 286 Xylose utilization, in S. cerevisiae, 324–325

Y Yarrowia lipolytica, 8, 9t autocementation kit, 371 bioengineered hybrid materials, 371–372 biofuel production, 369–370 bioinformatics and applied mathematics, 372 characteristics of, 345–346 DNA assembly methods artificial chromosomes, design of, 362–363 b-carotene synthesis pathway, 362 Golden Gate assembly (GGA), 363 YaliBricks system, 363 engineering and applications, 347t expression/secretion vectors, 350–355 genetically modified strains of, 348, 350 genome editing technologies CRISPR tools, 363–366 and transposomics, 366–367 glycoengineered strains, 357–358 high-throughput expression platforms, 368 history of, 347–350, 349–350f innovative engineering tools, 359, 360–362t integrative vectors, 352 molecular and genetic tools, 350, 351f obese strains, 368 recipient strains

high-secretor W29 (MatA) wild-type strain, 356, 356t with increased homologous recombination efficiency, 357 replicative vectors, 350–352 scientific peer-reviewed publications, 346f selection marker genes, 358–359 sexual hybridization through mating-type switching, 367 tandem dual cassette vector, 352 transcription unit components cellular organelles targeting and compartmentalization, 354 promoters, 353–354 regulatory, 352–354 secretion, signal sequences for, 355 surface display, signal sequences for, 355 terminators, 354 vectors carrying multiple transcription units, 352 in vivo piggyBac transposition, 366 white biotechnology applications, 372–373 whole-cell factories for organic acid production, 371 for polyketides production, 370–371 for single-cell oil production, 368–369 for terpenoids production, 370 whole genome analysis, 366 zeta-based autocloning vectors, 352 Yeast advantages, 319 biorefinery study, 329 Crabtree effect, 319 CRISPR system-mediated engineering, 321–322, 322f

DNA vectors for gene expression, 320 ethanol production, 319 galactose-inducible promoters, 321 homologous recombination (HR) efficiency, 319 lithium acetate transformation method, 319–320 low pH-inducible promoters, 321 marker gene for yeast recombination, 320t metabolic engineering of artemisinic acid production, 326 biopharmaceutical compound production, 326, 327–328t cellulose degradation, 323–324 LA production, 325–326 patchoulol production, 326 value-added chemical synthesis, 326 xylose utilization, 324–325 promoter engineering, 321 protein translation mechanism, 329 recombinant protein production, 326–329 recombination vectors, 320 surface-display engineering, 323 transformation methods, 319–320 Yeast artificial chromosome (YAC), 320 Yeast centromeric plasmid (YCp), 320 Yeast episomal plasmid (YEp), 320 Yeast integrative plasmid (YIp), 320 Yeast replicating plasmid (YRp), 320

Z Zeaxanthin epoxidase 2, 306 Zeta-based autocloning vectors, 352