PLANT PROTEOMICS : methods and protocols. [3 ed.] 9781071605271, 1071605275

Table of contents :
Preface
Contents
Contributors
Chapter 1: What Is New in (Plant) Proteomics Methods and Protocols: The 2015-2019 Quinquennium
1 Introduction
2 Novelties in the 2015-2019 Period
3 Proteomics Data Validation, and Integration into Other Classic and -Omics Approaches in the Systems Biology Direction
References
Chapter 2: Multiple Biomolecule Isolation Protocol Compatible with Mass Spectrometry and Other High-Throughput Analyses in Mic...
1 Introduction
2 Materials
2.1 Cell Culture Materials
2.2 Sampling and Extraction Materials
2.3 Sampling and Extraction Reagents and Solutions
3 Methods
3.1 Sampling Method
3.2 Metabolite Extraction Method
3.3 Pigment Extraction Method
3.4 Lipid Extraction Method
3.5 Nucleic Acid Purification Method
3.6 Protein Extraction and Purification Methods
4 Notes
References
Chapter 3: Protein Interaction Networks: Functional and Statistical Approaches
1 Introduction
2 Materials
3 Methods
3.1 Selection of Differential Expression Proteins (for Targeted Networks)
Workflow
3.2 Integration Tools
3.2.1 Statistical Integration Networks: Dynamic Protein-Protein Interaction Networks
Workflow
3.2.2 Statistical Interaction Networks between Proteins with Other Omics Datasets
Partial Least Square Regression (PLS) and Variates
Workflow
Data-Driven Integration and Differential Network Analysis (xMWAS)
Workflow
DIABLO
Workflow
3.3 Biological Interaction Network Enrichment
3.3.1 STRING
Workflow
3.3.2 ShinyGO
Workflow
3.4 Merged Functional and Statistical Interaction Networks
Create a STRING Network for Merged Networks
3.4.1 Cytoscape Merged Functional and Statistical Interaction Network
Workflow
3.4.2 Manually Merged Functional and Statistical Interaction Network
Workflow
3.5 Network Visualization Tools
3.5.1 Cytoscape
Workflow
3.5.2 Gephi
Workflow
3.6 Future Perspectives
4 Notes
References
Chapter 4: Specific Protein Database Creation from Transcriptomics Data in Nonmodel Species: Holm Oak (Quercus ilex L.)
1 Introduction
2 Materials
2.1 Nucleotide Sequences
2.1.1 Required Software
2.2 Raw Reads Quality Control
2.3 Preprocessing Raw Data
2.4 Assembling Raw Data
2.5 Removing Redundant Transcripts
2.6 Evaluating the Assembly Structure and Completeness of a Transcriptome
2.7 Annotation of a Transcriptome
2.8 Construction of a Custom Protein Database
3 Methods
3.1 Nucleotide Sequences
3.2 Raw Reads Quality Control (See Note 2)
3.3 Preprocessing Raw Data
3.4 Assembling Raw Data
3.5 Removing Redundant Transcripts
3.6 Evaluating the Assembly Structure and Completeness of a Transcriptome
3.7 Annotation of a Transcriptome
3.8 Construction of a Custom Protein Database
4 Notes
References
Chapter 5: Subcellular Proteomics in Conifers: Purification of Nuclei and Chloroplast Proteomes
1 Introduction
2 Materials
2.1 Nuclei Isolation
2.2 Chloroplast Isolation
2.3 Protein Extraction
3 Methods
3.1 Nuclei Isolation
3.2 Chloroplast Isolation
3.3 Protein Extraction
4 Expected Results
5 Notes
References
Chapter 6: Apoplastic Fluid Preparation from Arabidopsis thaliana Leaves Upon Interaction with a Nonadapted Powdery Mildew Pat...
1 Introduction
2 Materials
2.1 Plant Growth and Pathogen Inoculation
2.2 Apoplastic Fluid Preparation
2.3 Protein Purification by Chloroform-Methanol Precipitation
3 Methods
3.1 Plant Growth and Pathogen Inoculation
3.2 Apoplastic Fluid Preparation
3.3 Protein Purification by Chloroform-Methanol Precipitation
4 Notes
References
Chapter 7: Shotgun Proteomics of Plant Plasma Membrane and Microdomain Proteins Using Nano-LC-MS/MS
1 Introduction
2 Materials
2.1 Plasma Membrane Purification Components
2.2 Detergent-Resistant Membrane Extraction Components
2.3 In-Gel Tryptic Digestion
2.3.1 SDS-Polyacrylamide Gel Components
2.3.2 Tryptic Digestion Components
2.4 In-Solution Tryptic Digestion
2.5 Peptide Purification Components
3 Methods
3.1 Plasma Membrane Purification
3.2 Detergent-Resistant Membrane Extraction
3.3 In-Gel Tryptic Digestion
3.3.1 SDS-Polyacrylamide Gel Electrophoresis
3.3.2 In-Gel Tryptic Digestion for Nano-LC-MS/MS
3.4 In-Solution Tryptic Digestion
3.5 Peptide Purification
3.6 Nano-LC-MS/MS Analysis
4 Notes
References
Chapter 8: A Protocol for the Plasma Membrane Proteome Analysis of Rice Leaves
1 Introduction
2 Materials
2.1 Plant Material
2.2 Reagents, Equipment, and Software
2.3 Buffers
3 Methods
3.1 Extraction of Plasma Membrane Proteins
3.2 In-Solution Trypsin Digestion by Filter-Aided Sample Preparation (FASP)
3.3 Desalting of Peptides
3.4 Mass Spectrometry
3.5 Data Processing Using MaxQuant Software
4 Notes
References
Chapter 9: Isolation, Purity Assessment, and Proteomic Analysis of Endoplasmic Reticulum
1 Introduction
2 Materials
2.1 Isolation of ER Fraction
2.2 Immunoblot Analysis for ER Purity Assessment
2.3 Enzymatic Analysis for ER Purity Assessment
2.4 Concentration Measurement of Proteins
2.5 Proteomic Analysis of ER Proteins
2.6 Search Engine, Software, and Database for Proteomic Analysis
3 Methods
3.1 Isolation of Total ER Fraction
3.2 Isolation of Rough ER Fraction
3.3 Immunoblot Analysis for ER Purity Assessment of ER Fraction
3.4 Enzymatic Analysis for ER Purity Assessment of ER Fraction
3.5 Proteomic Analysis of ER Proteins
3.5.1 Preparation of Peptides for Gel-Free/Label-Free Proteomic Analysis
3.5.2 Mass Spectrometry Analysis
3.5.3 Protein Identification from Acquired Mass Spectrometry Data
3.5.4 Analysis of Relative Protein Abundance Using Acquired Mass Spectrometry Data
3.5.5 Analysis of Absolute Protein Amount Using Acquired Mass Spectrometry Data
3.5.6 Visualization of Protein Abundance
3.5.7 Protein Localization Prediction
4 Notes
References
Chapter 10: Dimethyl Labeling-Based Quantitative Proteomics of Recalcitrant Cocoa Pod Tissue
1 Introduction
2 Materials
2.1 Sample Preparation and Labeling
2.2 Sample Analysis by LC-MS/MS
3 Methods
3.1 Sample Preparation and Labeling
3.1.1 Protein Extraction
3.1.2 Sample Precipitation
3.1.3 Sample Trypsin Digestion
3.1.4 Sample Dimethyl Labeling
3.1.5 Sample Cleaning
3.2 Sample Analysis by LC-MS/MS
3.3 Protein Identification and Quantitation
3.3.1 LC-MS and MS/MS Processing with Progenesis QI for Proteomics
3.3.2 Protein Identification
3.3.3 Protein Identification Refinement with Progenesis QI for Proteomics
3.3.4 Protein Quantitation in Proteolabels
4 Notes
References
Chapter 11: Quantitative Profiling of Protein Abundance and Phosphorylation State in Plant Tissues Using Tandem Mass Tags
1 Introduction
2 Materials
2.1 Protein Extraction
2.2 Filter-Aided Sample Preparation (FASP) and on Filter Digestion
2.3 C18 Desalting
2.4 TMT Labeling
2.5 Phosphopeptide Enrichment
3 Methods
3.1 Protein Extraction
3.2 FASP
3.3 C18 Desalting
3.4 TMT Labeling
3.5 Phosphopeptide Enrichment
4 Notes
References
Chapter 12: Optimizing Shotgun Proteomics Analysis for a Confident Protein Identification and Quantitation in Orphan Plant Spe...
1 Introduction
2 Material
2.1 Plant Material
2.2 Protein Extraction
2.3 SDS Polyacrylamide Gel
2.4 Protein Digestion
2.5 Peptide Desalting
2.6 Solutions for LC-MS/MS
2.7 Equipment and Software
3 Methods
3.1 Protein Extraction by TCA/Acetone/Phenol
3.2 SDS-PAGE Electrophoresis
3.3 Sample Preparation for MS Analysis
3.3.1 Protein Digestion
3.3.2 Peptides Extraction
3.3.3 Peptides Desalting
3.4 nLC-MS/MS
3.5 Protein Identification
3.6 Protein Quantification
4 Anticipated Results
5 Notes
References
Chapter 13: Combining Targeted and Untargeted Data Acquisition to Enhance Quantitative Plant Proteomics Experiments
1 Introduction
2 Materials
2.1 Creation of Inclusion Lists and TDA/DDA LC-MS/MS Methods
3 Methods
3.1 Creation of Inclusion Lists
3.2 Creation of TDA/DDA LC-MS/MS Methods
4 Notes
References
Chapter 14: A Phosphoproteomic Analysis Pipeline for Peels of Tropical Fruits
1 Introduction
1.1 Fractionation Methods
1.2 Enrichment Methods
1.3 Quantification Strategies
1.4 Plant Phosphoproteome Tools and Databases
2 Materials
2.1 Total Protein Extraction
2.2 SDS-Polyacrylamide Gel Electrophoresis
2.3 Reduction, Alkylation, and Digestion
2.4 Direct Phosphopeptide Enrichment
2.5 High pH Reversed-Phase (RP) Fractionation
2.6 SCX-RP Fractionation and Enrichment
2.7 Other Materials
3 Methods
3.1 Tissue Protein Extraction
3.2 Subject the Extract to SDS-Polyacrylamide Gel Electrophoresis (SDS-PAGE) According to Laemmli
3.3 Reduction, Alkylation, and Digestion
3.4 Direct Fe-NTA Enrichment
3.5 Fractionation Prior to Fe-NTA Enrichment
3.5.1 High RP Fractionation and Enrichment
3.5.2 SCX-RP Fractionation and Enrichment
3.6 LC/MS-MS Analysis
4 Notes
References
Chapter 15: Label-Free Quantitative Phosphoproteomics for Algae
1 Introduction
2 Materials
2.1 Cell Culture
2.2 Protein Extraction
2.3 Reduction, Alkylation, and Digestion
2.4 Desalting
2.5 Phosphopeptide Enrichment
2.6 Sample Purification
2.7 LC-MS/MS
2.8 Data Analysis
3 Methods
3.1 Culturing
3.2 Protein Extraction
3.3 Reduction, Alkylation, and Digestion
3.4 Desalting
3.5 Phosphopeptide Enrichment
3.6 Sample Purification
3.7 LC-MS/MS
3.8 Data Analysis
4 Notes
References
Chapter 16: Targeted Quantification of Phosphopeptides by Parallel Reaction Monitoring (PRM)
1 Introduction
2 Materials
3 Methods
3.1 Sample Preparation for DDA and PRM Measurements
3.2 DDA Measurement Using an Orbitrap Mass Spectrometer
3.3 DDA Data Processing with MaxQuant Software
3.4 Target List Construction with Skyline Software Using the MaxQuant Output File
3.5 PRM Data Acquisition
3.6 PRM Data Analysis
3.7 Target List Construction with the Skyline Software by In Silico Digest
4 Notes
References
Chapter 17: Enrichment of N-Linked Glycopeptides and Their Identification by Complementary Fragmentation Techniques
1 Introduction
2 Materials
2.1 Microsomal Preparation
2.2 Digestion and Hydrophilic Interaction Liquid Chromatography (HILIC) Enrichment of N-Glycopeptides
2.3 Identification of N-Glycopeptides by Tandem Mass Spectrometry
2.4 Spectral Data Interrogation and Matching
3 Methods
3.1 Preparation of Microsomal Fraction and Peptide Digestion
3.2 Digestion and N-Glycopeptide Enrichment
3.3 Identification of N-Glycopeptides by Tandem Mass Spectrometry
3.4 Spectral Data Interrogation
4 Notes
References
Chapter 18: High-Resolution Lysine Acetylome Profiling by Offline Fractionation and Immunoprecipitation
1 Introduction
2 Materials
2.1 Protein Extraction
2.2 Filter-Aided Sample Preparation (FASP)
2.3 Desalting and Dimethyl Labeling of Peptides
2.4 ZIC-HILIC Offline Fractionation
2.5 Enrichment of Lysine-Acetylated Peptides
2.6 Desalting of Peptides on SDB-RPS Stop-and-Go-Extraction Tips (Stage Tips)
3 Methods
3.1 Protein Extraction
3.2 Filter-Aided Sample Preparation (FASP)
3.3 Desalting and Dimethyl Labeling of Peptides on C18 Columns
3.4 ZIC-HILIC Offline Fractionation
3.5 Enrichment of Lysine-Acetylated Peptides
3.6 Desalting of Peptides on SDB-RPS Stop-and-Go-Extraction Tips (Stage Tips)
3.7 Guidelines for LC-MS/MS Analysis
4 Notes
References
Chapter 19: A Versatile Workflow for the Identification of Protein-Protein Interactions Using GFP-Trap Beads and Mass Spectrom...
1 Introduction
2 Materials
2.1 Plant Tissue
2.2 Immunoaffinity Pull-Down Procedure
2.3 SDS-Polyacrylamide Gel Electrophoresis
2.4 Western Blot
2.5 Mass Spectrometry
3 Methods
3.1 Pull-Down Procedure
3.1.1 Extraction of Intact Protein Complexes
3.1.2 GFP-Trap a Capture of Protein Complexes
3.2 Validation of the Pull-Down Procedure
3.2.1 SDS-PAGE
3.2.2 Western Blot Analysis
3.3 Sample Preparation for Mass Spectrometry Analysis
3.4 LC-MS/MS Data Acquisition, Data Processing, and Statistical Analysis
4 Notes
References
Chapter 20: In Vivo Cross-Linking to Analyze Transient Protein-Protein Interactions
1 Introduction
2 Materials
2.1 Plant Material
2.2 Formaldehyde Cross-Linking of Cells
2.3 Preparation of Cell Lysates
2.4 Immunoaffinity Purification
2.5 In-Solution Trypsin Digestion
2.6 Peptide Desalting with C18-StageTips
3 Methods
3.1 Formaldehyde Cross-Linking and Preparation of Cell Lysates
3.2 Covalent Coupling of Antibodies to Magnetic Beads
3.3 Immunoprecipitation (Antigen Binding to Ig-Coated Beads)
3.4 In-Solution Digestion
3.5 Peptide Desalting and Purification
4 Notes
References
Chapter 21: Proteome Analysis of 14-3-3 Targets in Tomato Fruit Tissues
1 Introduction
2 Materials
2.1 Plant Material
2.2 Immunoprecipitation of 14-3-3 Complexes from Tomato Fruit Tissue
2.3 SDS-PAGE and SYPRO Ruby Stain
2.4 In-Gel Trypsin Digestion
2.5 LC-MS/MS Analysis
3 Methods
3.1 Immunoprecipitation of 14-3-3 Complex from Tomato Fruit Tissue
3.2 SDS-PAGE and SYPRO Ruby Staining
3.3 In-Gel Trypsin Digestion
3.4 LC-MS/MS Analysis and Protein Identification
4 Notes
References
Chapter 22: The Use of Proteomics in Search of Allele-Specific Proteins in (Allo)polyploid Crops
1 Introduction
2 Methods
2.1 Workflow 1: No Resources Available for mRNA Seq (Fig. 2)
2.2 Workflow 2: Resources Are Available to Generate mRNA Seq Libraries (Fig. 4)
3 Notes
References
Chapter 23: Methods for Optimization of Protein Extraction and Proteogenomic Mapping in Sweet Potato
1 Introduction
2 Materials
2.1 Materials for Phenol Procedure, Method 1 (M1)
2.1.1 Stock Solutions
2.2 Materials for Polyethylene Glycol (PEG) Procedure 4000, Method 2 (M2)
2.2.1 Stock Solutions
3 Methods
3.1 Overview of the Protein Extraction and Optimization Methodology
3.1.1 Tissue Collection
3.1.2 Protein Extraction Using Phenol Procedure (M1) (See Notes 1-4)
3.1.3 Protein Extraction Using Polyethylene Glycol Procedure 4000 (M2)
3.2 LC-MS/MS and Peptide Identification
3.3 Proteogenomic Analysis Workflow
3.4 Proteogenomic Analysis Method (See Note 5)
3.4.1 Data Input Processing for Proteogenomic Analysis
3.4.2 Blast Peptides against Genome and Transcriptome Annotations
3.4.3 Classify Peptides and Generate New Annotations (See Note 6)
3.5 Novel Peptide Analysis and Validation
4 Notes
References
Chapter 24: In Silico Analysis of Class III Peroxidases: Hypothetical Structure, Ligand Binding Sites, Posttranslational Modif...
1 Introduction
2 Materials
2.1 Amino Acid Sequences
2.1.1 PeroxiBase (RedOxiBase)
2.2 Physicochemical Properties
2.2.1 ProtParam v. 1.0
2.3 Topology
2.3.1 TMHMM v. 2.0
2.3.2 HMMTOP v. 2.0
2.4 Signal Peptides and Localization
2.4.1 SignalP v. 4.1
2.4.2 PSORT v. 1.0)
2.5 Posttranslational Modifications
2.5.1 Pyrrolidone Carboxylic Acid Modification (PROSITE v. 20.0)
2.5.2 N-Glycosylation (NetNGlyc v. 1.0)
2.5.3 Palmitoylation (CSS-PALM v. 2.0)
2.5.4 GPI-Anchor (GPI-SOM)
2.6 Tertiary Structure
2.6.1 Modeling of Protein Structure
SwissModel
Phyre2 v. 2.0
2.7 Interactive Visualization of Structures
2.7.1 PyMol v. 2.2
APBS Plugin
2.7.2 UCSF Chimera v. 1.13.1
2.8 Docking Analyses
2.8.1 3DLigandSite v. 1.0
2.8.2 PatchDock Server v. 1.3
2.8.3 SwissDock Server
Target Templates
Substrate Templates (ZINC v. 12)
3 Methods
3.1 Amino Acid Sequences
3.1.1 PeroxiBase
3.2 Physicochemical Properties
3.3 Topology
3.3.1 TMHMM
3.3.2 HMMTOP
3.4 Signal Peptides and Localization
3.4.1 SignalP
3.4.2 PSORT
3.5 Posttranslational Modifications
3.5.1 Pyrrolidone Carboxylic Acid (PCA)
3.5.2 N-Glycosylation
3.5.3 Palmitoylation
3.5.4 GPI-Anchor
3.6 Tertiary Structure
3.6.1 Modeling of Protein Structure
SwissModel
Phyre 2
3.7 Interactive Visualization of Structures
3.7.1 PyMOL
Tertiary Structure
Alignment
APBS Plugin
3.7.2 UCSF Chimera
Tertiary Structure
Active Center
Surface by Properties of Residues
3.8 Docking Analyses
3.8.1 3DLigandSite
3.8.2 Protein-Heme Docking
3.8.3 SwissDock Analysis
Protein-Substrate Docking
3.8.4 Visualization of Docking Results
4 Notes
References
Chapter 25: MALDI Mass Spectrometry Imaging of Peptides in Medicago truncatula Root Nodules
1 Introduction
2 Materials
2.1 Embedding Nodules
2.2 MALDI-MSI Sample Preparation
3 Methods
3.1 Embedding Nodules
3.2 MALDI-MSI Sample Preparation
3.3 MSI Data Acquisition on the MALDI LTQ Orbitrap XL
3.4 Data Processing
4 Notes
References
Chapter 26: Cystatin Activity-Based Protease Profiling to Select Protease Inhibitors Useful in Plant Protection
1 Introduction
2 Materials
2.1 Biotinylated Plant Cystatins
2.2 Insect Midgut Proteins
2.3 Laboratory Tools and Materials
2.4 Media, Buffers and Other Solutions
3 Methods
3.1 Capture of Target Proteases with Biotinylated Cystatins
3.1.1 Heterologous Expression and Purification of the AviTagged Cystatins
3.1.2 Binding of AviTagged Cystatins to NeutrAvidin Agarose Beads
3.1.3 Extraction of Target Proteases
3.1.4 Target Protease Capture on Cystatin-Embedded Agarose Beads
3.2 Mass Spectrometric Analysis of Captured Proteases
3.2.1 Sample Preparation for Mass Spectrometry
3.2.2 LC-MS/MS Analysis
3.2.3 Identification of Captured Proteases
3.2.4 Quantitation of Captured Protease Peptides
3.3 Working Examples
3.3.1 Example 1: The Protease Capture Approach as a Decision Tool to Select Cystatins Useful in Herbivore Pest Control
3.3.2 Example 2: The Protease Capture Approach as an Analytical Tool to Address Basic Questions on the Evolution and Protease ...
4 Notes
References
Chapter 27: A Pipeline for Metabolic Pathway Reconstruction in Plant Orphan Species
1 Introduction
2 Materials
2.1 Datasets
2.1.1 Transcriptomics Datasets
2.1.2 Proteomics Datasets
2.1.3 Metabolomics Datasets
2.2 Integration Tools
3 Methods
3.1 Functional Plant Categorization
3.2 KEGG Metabolic Pathways
3.3 MapMan Metabolic Representation
4 Notes
References
Chapter 28: Detection of Plant Low-Abundance Proteins by Means of Combinatorial Peptide Ligand Library Methods
1 Introduction
2 Plant Proteins: a Minor Component of Plant Extracts with Specific Properties
3 Pretreatments of Plant Extracts to Eliminate Interfering Material
4 The Reduction of Protein Dynamic Range with Low-Abundance Protein Enhancement
5 Materials and Methods
6 Protein Capture with Concomitant Dynamic Range Reduction
6.1 General Capture Method under Physiological Conditions
6.2 Protein Capture in Low-Ionic Strength
6.3 The Capture of Dominantly Acidic Proteins
6.4 The Capture of Dominantly Cationic Proteins
6.5 Focus on Hydrophobic Protein Capture
7 Recovery Protocols of Plant Protein from CPLLs
7.1 Global Protein Harvesting
7.1.1 Global Protein Elution with SDS-Containing Buffers
7.1.2 Global Protein Elution with Guanidine Hydrochloride Solutions
7.2 Fractionated Elution Approaches
7.2.1 Two-Step Elution with Increased Stringency
7.2.2 Three-Step Elution with Increased Stringency (Option 1)
7.2.3 Three-Step Increased Stringency Elution (Option 2)
7.3 Direct on-Bead Protein Digestion
8 Compatibility Between Protein Elution from CPLLs and Analysis
9 Practical Application Examples of CPLL-Treated Plant Extracts
10 Notes
References
Chapter 29: iTRAQ-Based Proteomic Analysis of Rice Grains
1 Introduction
2 Materials
2.1 Plant Material
2.2 Protein Extraction and Quantification
2.3 Protein Digestion and iTRAQ Labeling
2.4 Cation Exchange Liquid Chromatography and Peptide Desalting
2.5 Mass Spectrometry
3 Methods
3.1 Sample Preparation
3.2 Protein Extraction
3.3 Protein Digestion
3.4 iTRAQ Peptide Labeling
3.5 Cation Exchange Chromatography
3.6 C18 Spin Columns and Peptide Desalting
3.7 Mass Spectrometry
4 Notes
References
Index

Citation preview

Methods in Molecular Biology 2139

Jesus V. Jorrin-Novo Luis Valledor Mari Angeles Castillejo Maria-Dolores Rey Editors

Plant Proteomics Methods and Protocols Third Edition

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Plant Proteomics Methods and Protocols Third Edition

Edited by

Jesus V. Jorrin-Novo Agroforestry and Plant Biochemistry, Proteomics and Systems Biology, Department of Biochemistry and Molecular Biology, University of Cordoba, Cordoba, Spain

Luis Valledor Department of Organisms and Systems Biology, Institute of Biotechnology of Asturias, University of Oviedo, Oviedo, Asturias, Spain

Mari Angeles Castillejo Agroforestry and Plant Biochemistry, Proteomics and Systems Biology, Department of Biochemistry and Molecular Biology, University of Cordoba UCO-CeiA3, Cordoba, Cordoba, Spain

Maria-Dolores Rey Agroforestry and Plant Biochemistry, Proteomics and Systems Biology, Department of Biochemistry and Molecular Biology, University of Cordoba, Cordoba, Spain

Editors Jesus V. Jorrin-Novo Agroforestry and Plant Biochemistry Proteomics and Systems Biology Department of Biochemistry and Molecular Biology University of Cordoba Cordoba, Spain Mari Angeles Castillejo Agroforestry and Plant Biochemistry Proteomics and Systems Biology Department of Biochemistry and Molecular Biology University of Cordoba UCO-CeiA3 Cordoba, Cordoba, Spain

Luis Valledor Department of Organisms and Systems Biology Institute of Biotechnology of Asturias University of Oviedo Oviedo, Asturias, Spain Maria-Dolores Rey Agroforestry and Plant Biochemistry Proteomics and Systems Biology Department of Biochemistry and Molecular Biology University of Cordoba Cordoba, Spain

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-0527-1 ISBN 978-1-0716-0528-8 (eBook) https://doi.org/10.1007/978-1-0716-0528-8 © Springer Science+Business Media, LLC, part of Springer Nature 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface You now have in your hands the third edition Plant Proteomics: Methods and Protocols, preceded by the first edition in 2007 (M. Zivy, C. Damerval, and V. Mechin, eds.) and the second one in 2014 (J. V. Jorrin Novo, S. Komatsu, W. Weckwerth, and S. Wienkoop, eds.). The success of the previous editions and the continuous advances and improvements in proteomic techniques, equipment, and bioinformatics tools, and their uses in basic and translational plant biology research that has occurred in the past 5 years encouraged Humana Press to prepare a new updated version. Under the title Advances in Proteomics Techniques, Data Validation, and Integration with Other Classic and -Omics Approaches in the Systems Biology Direction, it contains 29 chapters written by worldwide recognized scientists. The monograph, which starts with an introductory chapter (Chapter 1), is a compilation of protocols commonly employed in plant biology research. They show recent advances at all workflow stages, starting from the laboratory (tissue and cell fractionation, protein extraction, depletion, purification, separation, MS analysis, quantification) and ending on the computer (algorithms for protein identification and quantification, bioinformatics tools for data analysis, databases and repositories). Out of the 29 chapters, 6 are devoted to descriptive proteomics, with a special emphasis on subcellular protein profiling (Chapters 5–10), 6 to PTMs (Chapter 11 and 14–18), 3 to protein interactions (Chapters 19–21), and 2 to specific proteins, peroxidases (Chapter 24) and proteases and proteases inhibitors (Chapter 26). The book reflects the new trajectory in MS-based protein identification and quantification, moving from the classic gel-based approaches to the most recent labeling (Chapters 10, 11, 29), shotgun (Chapters 5, 7, 12, 15), parallel reaction monitoring (Chapter 16), and targeted data acquisition (Chapter 13). MS-imaging (Chapter 25), the only in vivo MS-based proteomics strategy, is far from being fully optimized and exploited in plant biology research. A confident protein identification and quantitation, especially in orphan species, and on low-abundant proteins, is still a challenging topic (Chapters 4, 28). This edition also gives a novel point of view to the proteomics approach with the description of different protocols for proteomics data validation and integration with other classic and -omics approaches in the systems biology direction. Chapter 2 reports on multiple extractions in a single experiment of the different biomolecules, nucleic acids, proteins, and metabolites. Chapter 27 describes how metabolic pathways can be reconstructed from multiple -omics data, and Chapter 3 is on network building. Finally, Chapters 22 and 23 deal with, respectively, the search for allele-specific proteins and proteogenomics. Keeping in mind the history and evolution of proteomics, it is quite probable that the fourth edition will be published in few years, as we are still at the beginning of deciphering the plant proteome to understand the central dogma of the molecular biology in terms of proteins and to exploit the potential of the technique for translational purposes. Cordoba, Spain Oviedo, Spain Cordoba, Spain Cordoba, Spain

Jesus V. Jorrin-Novo Luis Valledor Mari Angeles Castillejo Maria-Dolores Rey

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Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1 What Is New in (Plant) Proteomics Methods and Protocols: The 2015–2019 Quinquennium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Jesus V. Jorrin-Novo 2 Multiple Biomolecule Isolation Protocol Compatible with Mass Spectrometry and Other High-Throughput Analyses in Microalgae . . . . . . . . . . . 11 ´ lvarez, Mo nica Meijon, Francisco Colina, Marı´a Carbo, Ana A ˜ al, and Luis Valledor Marı´a Jesu´s Can 3 Protein Interaction Networks: Functional and Statistical Approaches . . . . . . . . . . 21 Mo nica Escandon, Laura Lamelas, Vı´ctor Roces, Vı´ctor M. Guerrero-Sanchez, Mo nica Meijon, and Luis Valledor 4 Specific Protein Database Creation from Transcriptomics Data in Nonmodel Species: Holm Oak (Quercus ilex L.) . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Vı´ctor M. Guerrero-Sanchez, Ana M. Maldonado-Alconada, Rosa Sa´nchez-Lucas, and Maria-Dolores Rey 5 Subcellular Proteomics in Conifers: Purification of Nuclei and Chloroplast Proteomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 ˜ al, Laura Lamelas, Lara Garcı´a, Marı´a Jesu´s Can and Monica Meijon 6 Apoplastic Fluid Preparation from Arabidopsis thaliana Leaves Upon Interaction with a Nonadapted Powdery Mildew Pathogen . . . . . . . . . . . . 79 Ryohei Thomas Nakano, Nobuaki Ishihama, Yiming Wang, Junpei Takagi, Tomohiro Uemura, Paul Schulze-Lefert, and Hirofumi Nakagami 7 Shotgun Proteomics of Plant Plasma Membrane and Microdomain Proteins Using Nano-LC-MS/MS . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Daisuke Takahashi, Bin Li, Takato Nakayama, Yukio Kawamura, and Matsuo Uemura 8 A Protocol for the Plasma Membrane Proteome Analysis of Rice Leaves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Ravi Gupta, Yu-Jin Kim, and Sun Tae Kim 9 Isolation, Purity Assessment, and Proteomic Analysis of Endoplasmic Reticulum. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Xin Wang and Setsuko Komatsu 10 Dimethyl Labeling-Based Quantitative Proteomics of Recalcitrant Cocoa Pod Tissue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Yoel Esteve-Sa´nchez, Jaime A. Morante-Carriel, Ascensio n Martı´nez-Ma´rquez, Susana Selle´s-Marchart, and Roque Bru-Martinez

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Quantitative Profiling of Protein Abundance and Phosphorylation State in Plant Tissues Using Tandem Mass Tags . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gaoyuan Song, Christian Montes, and Justin W. Walley Optimizing Shotgun Proteomics Analysis for a Confident Protein Identification and Quantitation in Orphan Plant Species: The Case of Holm Oak (Quercus ilex) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Isabel Gomez-Ga´lvez, Rosa Sa´nchez-Lucas, Bonoso San-Eufrasio, Luis Enrique Rodrı´guez de Francisco, Ana M. Maldonado-Alconada, Carlos Fuentes-Almagro, and Mari Angeles Castillejo Combining Targeted and Untargeted Data Acquisition to Enhance Quantitative Plant Proteomics Experiments. . . . . . . . . . . . . . . . . . . . . . . . Gene Hart-Smith A Phosphoproteomic Analysis Pipeline for Peels of Tropical Fruits . . . . . . . . . . . . Janet Juarez-Escobar, Jose´ M. Elizalde-Contreras, Vı´ctor M. Loyola-Vargas, and Eliel Ruiz-May Label-Free Quantitative Phosphoproteomics for Algae . . . . . . . . . . . . . . . . . . . . . . Megan M. Ford, Sheldon R. Lawrence II, Emily G. Werth, Evan W. McConnell, and Leslie M. Hicks Targeted Quantification of Phosphopeptides by Parallel Reaction Monitoring (PRM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sara Christina Stolze and Hirofumi Nakagami Enrichment of N-Linked Glycopeptides and Their Identification by Complementary Fragmentation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eduardo Antonio Ramirez-Rodriguez and Joshua L. Heazlewood High-Resolution Lysine Acetylome Profiling by Offline Fractionation and Immunoprecipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jonas Giese, Ines Lassowskat, and Iris Finkemeier A Versatile Workflow for the Identification of Protein–Protein Interactions Using GFP-Trap Beads and Mass Spectrometry-Based Label-Free Quantification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guillaume Ne´e, Priyadarshini Tilak, and Iris Finkemeier In Vivo Cross-Linking to Analyze Transient Protein–Protein Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heidi Pertl-Obermeyer and Gerhard Obermeyer Proteome Analysis of 14-3-3 Targets in Tomato Fruit Tissues . . . . . . . . . . . . . . . . Yongming Luo, Yu Lu, Junji Yamaguchi, and Takeo Sato The Use of Proteomics in Search of Allele-Specific Proteins in (Allo)polyploid Crops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sebastien Christian Carpentier Methods for Optimization of Protein Extraction and Proteogenomic Mapping in Sweet Potato. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thualfeqar Al-Mohanna, Norbert T. Bokros, Nagib Ahsan, George V. Popescu, and Sorina C. Popescu

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In Silico Analysis of Class III Peroxidases: Hypothetical Structure, Ligand Binding Sites, Posttranslational Modifications, and Interaction with Substrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ¨ thje and Kalaivani Ramanathan Sabine Lu MALDI Mass Spectrometry Imaging of Peptides in Medicago truncatula Root Nodules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Caitlin Keller, Erin Gemperline, and Lingjun Li Cystatin Activity–Based Protease Profiling to Select Protease Inhibitors Useful in Plant Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marie-Claire Goulet, Frank Sainsbury, and Dominique Michaud A Pipeline for Metabolic Pathway Reconstruction in Plant Orphan Species. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cristina Lopez-Hidalgo, Monica Escandon, Luis Valledor, and Jesus V. Jorrin-Novo Detection of Plant Low-Abundance Proteins by Means of Combinatorial Peptide Ligand Library Methods . . . . . . . . . . . . . . . . . . . . . . . . . Egisto Boschetti and Pier Giorgio Righetti iTRAQ-Based Proteomic Analysis of Rice Grains . . . . . . . . . . . . . . . . . . . . . . . . . . . Marouane Baslam, Kentaro Kaneko, and Toshiaki Mitsui

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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381 405 415

Contributors NAGIB AHSAN • Division of Biology and Medicine, COBRE Center for Cancer Research Development, Proteomics Core Facility, Rhode Island, USA Hospital, Providence, Brown University, Providence, RI, USA; Division of Biology and Medicine, Brown University, Providence, RI, USA THUALFEQAR AL-MOHANNA • Department of Biochemistry, Molecular Biology, Entomology, and Plant Pathology, Mississippi State University, Mississippi State, MS, USA ´ LVAREZ • Plant Physiology, Department of Organisms and Systems Biology and ANA A University Institute of Biotechnology (IUBA), University of Oviedo, Oviedo, Spain MAROUANE BASLAM • Department of Biochemistry, Faculty of Agriculture, Niigata University, Niigata, Japan NORBERT T. BOKROS • Department of Biochemistry, Molecular Biology, Entomology, and Plant Pathology, Mississippi State University, Mississippi State, MS, USA EGISTO BOSCHETTI • Scientific Consultant, JAM Conseil, Neuilly-sur-Seine, France ROQUE BRU-MARTINEZ • Plant Proteomics and Functional Genomics Group, Department of Agrochemistry and Biochemistry. Faculty of Sciences, University of Alicante, Alicante, Spain MARI´A JESU´S CAN˜AL • Plant Physiology, Department of Organisms and Systems Biology and University Institute of Biotechnology (IUBA), University of Oviedo, Oviedo, Spain MARI´A CARBO´ • Plant Physiology, Department of Organisms and Systems Biology and University Institute of Biotechnology (IUBA), University of Oviedo, Oviedo, Spain SEBASTIEN CHRISTIAN CARPENTIER • SYBIOMA: Facility for Systems Biology-Based Mass Spectrometry, KULeuven, Leuven, Belgium; Bioversity International, Genetic Resources, Leuven, Belgium MARI ANGELES CASTILLEJO • Agroforestry and Plant Biochemistry, Proteomics and Systems Biology, Department of Biochemistry and Molecular Biology, University of Cordoba, UCOCeiA3, Cordoba, Spain FRANCISCO COLINA • Plant Physiology, Department of Organisms and Systems Biology and University Institute of Biotechnology (IUBA), University of Oviedo, Oviedo, Spain LUIS ENRIQUE RODRI´GUEZ DE FRANCISCO • Laboratorio de Biologı´a, Instituto Tecnologico de Santo Domingo, Santo Domingo, Repu´blica Dominicana JOSE´ M. ELIZALDE-CONTRERAS • Red de Estudios Moleculares Avanzados, Clu´ster Cientı´fico y Tecnologico BioMimic®, Instituto de Ecologı´a A.C. (INECOL), Veracruz, Mexico MO´NICA ESCANDO´N • Agroforestry and Plant Biochemistry, Proteomics and Systems Biology, Department of Biochemistry and Molecular Biology, University of Cordoba, UCO-CeiA3, Cordoba, Spain YOEL ESTEVE-SA´NCHEZ • Plant Proteomics and Functional Genomics Group, Department of Agrochemistry and Biochemistry. Faculty of Sciences, University of Alicante, Alicante, Spain IRIS FINKEMEIER • Plant Physiology, Institute of Plant Biology and Biotechnology, University of Mu¨nster, Mu¨nster, Germany MEGAN M. FORD • Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

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Contributors

CARLOS FUENTES-ALMAGRO • Proteomics Facility, SCAI, University of Cordoba, Cordoba, Spain LARA GARCI´A • Plant Physiology, Department of Organisms and Systems Biology and University Institute of Biotechnology (IUBA), University of Oviedo, Oviedo, Spain ERIN GEMPERLINE • Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA JONAS GIESE • Plant Physiology, Institute of Plant Biology and Biotechnology, University of Mu¨nster, Mu¨nster, Germany ISABEL GO´MEZ-GA´LVEZ • Agroforestry and Plant Biochemistry, Proteomics and Systems Biology, Department of Biochemistry and Molecular Biology, University of Cordoba, UCOCeiA3, Cordoba, Spain MARIE-CLAIRE GOULET • Centre de Recherche et d’Innovation sur les Ve´ge´taux, Universite´ Laval, Que´bec, QC, Canada VI´CTOR M. GUERRERO-SANCHEZ • Agroforestry and Plant Biochemistry, Proteomics and Systems Biology, Department of Biochemistry and Molecular Biology, University of Cordoba, UCO-CeiA3, Cordoba, Spain RAVI GUPTA • Department of Plant Biosciences, Life and Energy Convergence Research Institute, Pusan National University, Miryang, South Korea GENE HART-SMITH • Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia JOSHUA L. HEAZLEWOOD • School of BioSciences, The University of Melbourne, Parkville, VIC, Australia LESLIE M. HICKS • Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA NOBUAKI ISHIHAMA • RIKEN Center for Sustainable Resource Science, Yokohama, Japan JESUS V. JORRIN-NOVO • Agroforestry and Plant Biochemistry, Proteomics and Systems Biology, Department of Biochemistry and Molecular Biology, University of Cordoba, UCOCeiA3, Cordoba, Spain JANET JUAREZ-ESCOBAR • Red de Estudios Moleculares Avanzados, Clu´ster Cientı´fico y Tecnologico BioMimic®, Instituto de Ecologı´a A.C. (INECOL), Veracruz, Mexico KENTARO KANEKO • Graduate School of Science and Technology, Niigata University, Niigata, Japan YUKIO KAWAMURA • United Graduate School of Agricultural Sciences, Iwate University, Morioka, Japan; Department of Plant-bioscience, Faculty of Agriculture, Iwate University, Morioka, Japan CAITLIN KELLER • Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA SUN TAE KIM • Department of Plant Biosciences, Life and Energy Convergence Research Institute, Pusan National University, Miryang, South Korea YU-JIN KIM • Graduate School of Biotechnology and Crop Biotech Institute, Kyung Hee University, Yongin, South Korea SETSUKO KOMATSU • Faculty of Environmental and Information Sciences, Fukui University of Technology, Fukui, Japan LAURA LAMELAS • Plant Physiology, Department of Organisms and Systems Biology and University Institute of Biotechnology (IUBA), University of Oviedo, Oviedo, Spain INES LASSOWSKAT • Plant Physiology, Institute of Plant Biology and Biotechnology, University of Mu¨nster, Mu¨nster, Germany

Contributors

xiii

SHELDON R. LAWRENCE II • Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA BIN LI • United Graduate School of Agricultural Sciences, Iwate University, Morioka, Japan LINGJUN LI • Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA; School of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA CRISTINA LO´PEZ-HIDALGO • Plant Physiology, Department of Organisms and Systems Biology, University Institute of Biotechnology of Asturias (IUBA), University of Oviedo, Oviedo, Asturias, Spain VI´CTOR M. LOYOLA-VARGAS • Unidad de Bioquı´mica y Biologı´a Molecular de Plantas, Centro de Investigacion Cientı´fica de Yucata´n (CICY), Me´rida, Yucata´n, Mexico YONGMING LUO • Faculty of Science and Graduate School of Life Science, Hokkaido University, Sapporo, Japan SABINE LU¨THJE • Oxidative Stress and Plant Proteomics Group, Institute for Plant Science and Microbiology, University of Hamburg, Hamburg, Germany YU LU • Faculty of Science and Graduate School of Life Science, Hokkaido University, Sapporo, Japan; Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan ANA M. MALDONADO-ALCONADA • Agroforestry and Plant Biochemistry, Proteomics and Systems Biology, Department of Biochemistry and Molecular Biology, University of Cordoba, UCO-CeiA3, Cordoba, Spain ASCENSIO´N MARTI´NEZ-MA´RQUEZ • Plant Proteomics and Functional Genomics Group, Department of Agrochemistry and Biochemistry. Faculty of Sciences, University of Alicante, Alicante, Spain EVAN W. MCCONNELL • Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA MO´NICA MEIJO´N • Plant Physiology, Department of Organisms and Systems Biology and University Institute of Biotechnology (IUBA), University of Oviedo, Oviedo, Spain DOMINIQUE MICHAUD • Centre de Recherche et d’Innovation sur les Ve´ge´taux, Universite´ Laval, Que´bec, QC, Canada TOSHIAKI MITSUI • Department of Biochemistry, Faculty of Agriculture, Niigata University, Niigata, Japan; Graduate School of Science and Technology, Niigata University, Niigata, Japan CHRISTIAN MONTES • Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, USA JAIME A. MORANTE-CARRIEL • Biotechnology and Molecular Biology Group, Quevedo State Technical University, Quevedo, Ecuador HIROFUMI NAKAGAMI • Protein Mass Spectrometry Group, Max Planck Institute for Plant Breeding Research, Cologne, Germany RYOHEI THOMAS NAKANO • Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany; Cluster of Excellence on Plant Sciences (CEPLAS), Max Planck Institute for Plant Breeding Research, Cologne, Germany TAKATO NAKAYAMA • Department of Plant-bioscience, Faculty of Agriculture, Iwate University, Morioka, Japan GUILLAUME NE´E • Plant Physiology, Institute of Plant Biology and Biotechnology, University of Mu¨nster, Mu¨nster, Germany GERHARD OBERMEYER • Department of Biosciences, Membrane Biophysics, Paris-LodronUniversity of Salzburg, Salzburg, Austria

xiv

Contributors

HEIDI PERTL-OBERMEYER • Department of Biosciences, Membrane Biophysics, Paris-LodronUniversity of Salzburg, Salzburg, Austria GEORGE V. POPESCU • Institute for Genomics, Biocomputing, and Biotechnology, Mississippi State University, Mississippi State, MS, USA; The National Institute for Laser, Plasma & Radiation Physics, Bucharest, Romania SORINA C. POPESCU • Department of Biochemistry, Molecular Biology, Entomology, and Plant Pathology, Mississippi State University, Mississippi State, MS, USA KALAIVANI RAMANATHAN • Oxidative Stress and Plant Proteomics Group, Institute for Plant Science and Microbiology, University of Hamburg, Hamburg, Germany EDUARDO ANTONIO RAMIREZ-RODRIGUEZ • School of BioSciences, The University of Melbourne, Parkville, VIC, Australia MARIA-DOLORES REY • Agroforestry and Plant Biochemistry, Proteomics and Systems Biology, Department of Biochemistry and Molecular Biology, University of Cordoba, UCO-CeiA3, Cordoba, Spain PIER GIORGIO RIGHETTI • Miles Gloriosus Academy, Milan, Italy VI´CTOR ROCES • Plant Physiology, Department of Organisms and Systems Biology and University Institute of Biotechnology (IUBA), University of Oviedo, Oviedo, Spain ELIEL RUIZ-MAY • Red de Estudios Moleculares Avanzados, Clu´ster Cientı´fico y Tecnologico BioMimic®, Instituto de Ecologı´a A.C. (INECOL), Veracruz, Mexico FRANK SAINSBURY • Centre de Recherche et d’Innovation sur les Ve´ge´taux, Universite´ Laval, Que´bec, QC, Canada; Griffith Institute for Drug Discovery, Griffith University, Brisbane, QLD, Australia ROSA SA´NCHEZ-LUCAS • Agroforestry and Plant Biochemistry, Proteomics and Systems Biology, Department of Biochemistry and Molecular Biology, University of Cordoba, UCOCeiA3, Cordoba, Spain BONOSO SAN-EUFRASIO • Agroforestry and Plant Biochemistry, Proteomics and Systems Biology, Department of Biochemistry and Molecular Biology, University of Cordoba, UCOCeiA3, Cordoba, Spain TAKEO SATO • Faculty of Science and Graduate School of Life Science, Hokkaido University, Sapporo, Japan PAUL SCHULZE-LEFERT • Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany; Cluster of Excellence on Plant Sciences (CEPLAS), Max Planck Institute for Plant Breeding Research, Cologne, Germany SUSANA SELLE´S-MARCHART • Plant Proteomics and Functional Genomics Group, Department of Agrochemistry and Biochemistry. Faculty of Sciences, University of Alicante, Alicante, Spain GAOYUAN SONG • Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, USA SARA CHRISTINA STOLZE • Protein Mass Spectrometry Group, Max Planck Institute for Plant Breeding Research, Cologne, Germany JUNPEI TAKAGI • Faculty of Science and Engineering, Konan University, Kobe, Japan DAISUKE TAKAHASHI • Central Infrastructure Group: Genomics and Transcript Profiling, Max-Planck Institute of Molecular Plant Physiology, Potsdam, Germany; United Graduate School of Agricultural Sciences, Iwate University, Morioka, Japan; Graduate School of Science and Engineering, Saitama University, Saitama, Japan PRIYADARSHINI TILAK • Plant Physiology, Institute of Plant Biology and Biotechnology, University of Mu¨nster, Mu¨nster, Germany

Contributors

xv

MATSUO UEMURA • United Graduate School of Agricultural Sciences, Iwate University, Morioka, Japan; Department of Plant-bioscience, Faculty of Agriculture, Iwate University, Morioka, Japan TOMOHIRO UEMURA • Graduate School of Humanities and Sciences, Ochanomizu University, Tokyo, Japan LUIS VALLEDOR • Department of Organisms and Systems Biology, Institute of Biotechnology of Asturias, University of Oviedo, Oviedo, Asturias, Spain JUSTIN W. WALLEY • Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, USA XIN WANG • College of Agronomy and Biotechnology, China Agricultural University, Beijing, China YIMING WANG • Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany; Department of Plant Pathology, Nanjing Agricultural University, Nanjing, China EMILY G. WERTH • Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA JUNJI YAMAGUCHI • Faculty of Science and Graduate School of Life Science, Hokkaido University, Sapporo, Japan

Chapter 1 What Is New in (Plant) Proteomics Methods and Protocols: The 2015–2019 Quinquennium Jesus V. Jorrin-Novo Abstract The third edition of “Plant Proteomics Methods and Protocols,” with the title “Advances in Proteomics Techniques, Data Validation, and Integration with Other Classic and -Omics Approaches in the Systems Biology Direction,” was conceived as being based on the success of the previous editions, and the continuous advances and improvements in proteomic techniques, equipment, and bioinformatics tools, and their uses in basic and translational plant biology research that has occurred in the past 5 years (in round figures, of around 22,000 publications referenced in WoS, 2000 were devoted to plants). The monograph contains 29 chapters with detailed proteomics protocols commonly employed in plant biology research. They present recent advances at all workflow stages, starting from the laboratory (tissue and cell fractionation, protein extraction, depletion, purification, separation, MS analysis, quantification) and ending on the computer (algorithms for protein identification and quantification, bioinformatics tools for data analysis, databases and repositories). At the end of each chapter there are enough explanatory notes and comments to make the protocols easily applicable to other biological systems and/or studies, discussing limitations, artifacts, or pitfalls. For that reason, as with the previous editions, it would be especially useful for beginners or novices. Out of the 29 chapters, six are devoted to descriptive proteomics, with a special emphasis on subcellular protein profiling (Chapters 5–10), six to PTMs (Chapters 11, and 14–18), three to protein interactions (Chapters 19–21), and two to specific proteins, peroxidases (Chapter 24) and proteases and protease inhibitors (Chapter 26). The book reflects the new trajectory in MS-based protein identification and quantification, moving from the classic gel-based approaches to the most recent labeling (Chapters 10, 11, 29), shotgun (Chapters 5, 7, 12, 15), parallel reaction monitoring (Chapter 16), and targeted data acquisition (Chapter 13). MS imaging (Chapter 25), the only in vivo MS-based proteomics strategy, is far from being fully optimized and exploited in plant biology research. A confident protein identification and quantitation, especially in orphan species, of low-abundance proteins, is still a challenging task (Chapters 4, 28). What is really new is the use of different techniques for proteomics data validation and their integration into other classic and -omics approaches in the systems biology direction. Chapter 2 reports on multiple extractions in a single experiment of the different biomolecules, nucleic acids, proteins, and metabolites. Chapter 27 describes how metabolic pathways can be reconstructed from multiple -omics data, and Chapter 3 network building. Finally, Chapters 22 and 23 deal with, respectively, the search for allelespecific proteins and proteogenomics. Around 200 groups were, almost 1 year ago, invited to take part in this edition. Unfortunately, only 10% of them kindly accepted. My gratitude to those who accepted our invitation but also to those who did not, as all of them have contributed to the plant proteomics field. I will enlist, in this introductory chapter, following my own judgment, some of the relevant papers published in the past 5 years, those that have Jesus V. Jorrin-Novo et al. (eds.), Plant Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2139, https://doi.org/10.1007/978-1-0716-0528-8_1, © Springer Science+Business Media, LLC, part of Springer Nature 2020

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shown us how to enhance and exploit the potential of proteomics in plant biology research, without aiming at giving a too exhaustive list. Key words Omics approaches, Plant proteomics, Protein interactions, PTMs, Proteogenomics, Quantitative proteomics, Shotgun proteomics, Systems biology, Targeted proteomics

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Introduction The success of the previous editions of “Plant Proteomics Methods and Protocols” (Springer Nature Methods in Molecular Biology, vols. 355, 2007, and 1072; 2014; http://www.springer.com/ series/7651) [1, 2] and the continuous advances and improvements in proteomic techniques, equipment, and bioinformatics tools, and their use in basic and translational plant biology research, have encouraged Humana Press to prepare a new updated third version with the title, “Advances in Proteomics Techniques, Data Validation, and Integration with Other Classic and -Omics Approaches in the Systems Biology Direction,” edited by J.V. Jorrı´n Novo, L. Valledor, M.A. Castillejo, and M.D. Rey. Since the last, second, edition, and in a very short period of time, 5 years (2014-May 2019), the number of proteomics papers, in general, and those devoted to plant proteomics studies in particular, has been continuously increasing. There were 22,000 and 2000 hits for a search at WoS with the keywords “proteomics” or “plant + proteomics,” respectively. These figures reflect, on the one hand, that the field of proteomics has been greatly enriched and updated with equipment, techniques, protocols, algorithms, databases, and repositories. Thus, the possibility now exists of having a deeper coverage of the proteome, a more confident protein identification and quantification, and a less speculative and more confident biological interpretation of the data and responses to biological questions based on the protein language. On the other, and on glancing once again at plant proteomics figures, the same conclusion is reached: “the full potential of proteomics is still far from being fully exploited in plant biology research” [3], and there are not many groups carrying out plant proteomics experiments using the latest technological advances and equipment in the field. There are more groups entering proteomics, with new plant experimental systems, proposing new biological studies, but they use classic approaches, keeping proteomics mostly descriptive and speculative. Assuming this situation, this new edition aims to show plant scientists how they can go one step forward by using proteomics as an experimental approach.

2019 Plant Proteomics Methods and Protocols

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Novelties in the 2015–2019 Period The main objective of a proteomics experiment is to identify, characterize, and quantify as many proteoforms or protein species as possible. Its success depends on the experimental system, the protocols for protein extraction and fractionation, the MS strategy, the equipment, and the algorithms and databases employed. Each technique and protocol has to be optimized to the experimental system, the biological process, and the starting hypothesis. Like any analytical technique, MS has to be validated, and its resolution, sensitivity, detection limit, and dynamic range determined (Chapter 12). With respect to the experimental system, a consideration should be made of its biological characteristics such as the level of ploidy, the availability of species-specific protein databases, and its recalcitrance, the latter related to the chemical composition (Chapters 22, 23, 29). In the plant proteomics scenario, orphan and recalcitrant species such as forest trees still remain challenging (Chapters 4 and 12). Up to six consecutive generations of MS proteomics platforms have been developed and employed since its beginning, in the early 1990s, 25 years ago [4]. Human proteome research has moved fast in using the most recent technologies, gel-free/label-free or shotgun (fourth generation) [5], single/multiple reaction monitoring, targeted or mass western (fifth generation) [6], and dataindependent acquisition, DIA, and its sequential windowed dataindependent acquisition of the total high-resolution mass spectra, SWATH (sixth generation) [7] However, plant investigators still cling to the employment of gel-MS, including difference gel electrophoresis, DIGE (first and second generation), isobaric or isotopic labeling, mostly isobaric tags for relative and absolute quantitation, iTRAQ (third generation), and shotgun (fourth generation) [8] (Chapters 10, 11, 13, 15). The optimization of classic protocols for protein extraction [9] and purification [10, 11], together with advances in mass spectrometry techniques [12], the evolution of mass spectrometers, especially the Orbitrap family [13], the feasibility of sequencing and annotating quicker and cheaper complete genomes and transcriptomes for protein database constructions ([14, 15] and Chapters 4 and 12), and the development of algorithms and bioinformatics tools for protein identification, quantification, grouping, and statistical analysis of the data ([16] and Chapter 4, this volume), “[has] taken proteomics to an unimaginable achievement in terms of the number of protein species confidently identified, quantified, and characterized” [4]. We have progressed from identification of hundreds to thousands of gene products in a single experiment. As a result, protein databases and repositories are being created or enriched [17, 18]. Even so, we are only able to visualize a

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small fraction of the whole proteome (1–5%). For a higher coverage, subcellular or protein fractionation has been chosen. In this volume, different chapters deal with the proteome analysis of subcellular fractions, including apoplast, membrane systems, nuclei, and chloroplasts (Chapters 5–9). Chapter 28 describes detecting low-abundance proteins by using the combinatorial peptide ligand library (CPLL) technique. Descriptive and comparative proteomics remain the most represented areas in the current plant proteomics literature, with new plant systems and biological processes continuously being reported. The main interest lies in crops and processes related to productivity and other phenotypes of importance from an agronomic point of view [19, 20]. Stresses associated with climate change and biodiversity are two of the leading topics [21, 22]. It has been claimed that proteomics can lead us to the identification of protein markers [23–26] that are useful in plant breeding programs and in the selection of elite genotypes, but that is still far from reality. One of the difficulties in identifying protein markers is the existence of very similar proteins as members of a multigene family, allelic variants, or individual genes, that give rise to a variable number of proteoforms or protein species as a result of posttranscriptional (alternative splicing) of posttranslational (PTMs) events, without finding out the biological role of each one of them [27, 28]. As bottom-up, peptide-centric, platforms cannot give clear responses to this question, top-down strategies have to be improved [29]. Just as an example, in Chapter 22, by Prof. Carpentier, alternative protocols for allele-specific proteins are proposed. Posttranslational modifications, PTMs, and interactomics remain a challenge, but more and more papers are appearing on these topics [30–32]. As a novelty with respect to the previous two editions, this third edition includes five chapters describing protocols for PTM analysis: Chapters 11, 14, 15, 16 (phospho), Chapter 17 (glyco), and Chapter 18 (acetyl). PTM analysis can be done with gel-based, gel-free, labeling, and targeted parallel reaction monitoring approaches, the topic recently reviewed by Vu et al. [33]. The difficulty of the PTM analysis depends on the type of modification, its stability, stoichiometric levels of protein modification, the existence of multiple sites for specific or different PTMs, and the efficacy of the enrichment protocols for modified proteins and peptides, among other items. Whereas in vitro analysis is quite feasible, changes in the in vivo PTM profiles remain somewhat elusive. Three of the chapters, Chapters 19–21, address the study of protein interactions or interactome, one of the main challenges in the postgenomic era. Interactomics shares with PTMs their methodological strategy and workflow, with a previous MS step directed at purifying or enriching the target and partners complexes. The

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difficulty in characterizing interactions is even greater than PTMs because of the low stability of the interactions and the generation of false positives due to unspecific binding. In order to diminish those false positives, in vivo site-specific chemical cross-linking coupled to MS has appeared as being a powerful technique [34], as it converts unstable complexes into stable ones that can be purified or enriched by using immunoaffinity techniques (Chapter 20 of this book). Both PTMs and interactions studies are favored by computational analysis and in silico predicted PTM motifs and functional association network of genes ([35–37] andChapter 3 of this book). In Chapter 19, Nee et al. report on a mass spectrometry-based labelfree quantification approach to identify protein interaction networks under native conditions. It uses a transgenic plant expressing the protein of interest fused to a GFP-Tag; enrichment of the GFP-tagged protein with its interaction partners is performed by immunoaffinity purification, with the captured purified proteins being analyzed by LC-MS/MS and label-free quantification. FLAG tag-fused is an alternative, as shown in Chapter 21 by Luo et al., who propose a protocol to study 14-3-3 interactors in tomato fruit. Proteomics is being increasingly employed in a directed, targeted, hypothesis-based direction, thus changing the previous view of a holistic approach that did not need a hypothesis. The latter option was a good starting point, but it made proteomics mostly descriptive and speculative, without the possibility of comparing the data with those previously obtained by using other experimental approaches. In the end, experimental data has to be manually validated if it is intended to confidently interpret it from a biological point of view, and if we wish to escape from the tyranny of the blind analysis based on computational tools, and to move from the forest (whole proteomes, subproteomes, functional or structural groups) to the tree (individual proteins). We need to understand when, where, how, and the reasons for the orchestration of thousands of proteins in order to construct the cellular building, to fit it into a developmental program, and to respond to a highly changeable environment. Targeted (Mass Western) proteomics is a bottom-up approach based on the MS analysis of individual proteins, or a selected group of them, through a set of selected peptides, ideally proteotypic ones. These are the basics of a number of recently developed techniques such as single, multiple, or parallel reaction monitoring (SRM, MRM, and PRM), accurate inclusion mass screening (AIMS), and the sequential window acquisition of all theoretical fragments (SWATH) [38]. These approaches offer new possibilities in biomarker discoveries and multiplexing analyses [39]. In Chapter 13, Dr. Hart-Smith addresses the combined use of targeted and untargeted LC-MS/MS data acquisition, a strategy termed TDA/DDA, and its application to a model quantitative

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plant proteomics experiment performed on Arabidopsis. This approach is compatible with different methodologies, including metabolic and chemical labeling and label-free approaches, and can be used to create tailored assay libraries to assist in the interpretation of quantitative proteomics data collected using the Independent Acquisition Data (IDA). MS techniques, in combination with classic protein purification approaches and in silico analyses of gene sequences at the genomic or transcriptomic level, are perfect for the chemical, structural, and functional characterization of proteins, as illustrated in Chapter 24 by Luthje and Ramanathan. They describe a protocol to perform in silico analysis of plant peroxidases, concretely of the secretory pathway family, in order to determine amino acid sequence, PTMs, structure, and ligand sites, among others. Prediction models then have to be validated in wet experiments. In Chapter 26, Goulet et al. introduce an activity-based functional proteomics approach protocol for the selection of protease inhibitors, a group of peptides with a high biotechnologic potential. This protocol is an alternative to the in vitro activity assay with synthetic peptides, with the advantage of additional information on specificity. The procedure involves the capture of target Cys proteases with biotinylated versions of the cystatins, followed by the identification and quantification of captured proteases by mass spectrometry. Genomics, transcriptomics, and proteomics feedback each other. Thus, up to now, protein identification has been based on available protein sequences obtained from annotated genomes and transcriptomes. [However, proteomics could be of great help in improving and correcting genome annotation. With this in mind, the term proteogenomics was coined following a publication by Church’s group in 2004, in which proteomics data were used to annotate the genome of Mycoplasma pneumonia [40]. The field of proteogenomics has expanded and is being applied to a number of living organisms, including plants. Thus, by 2008, Castellana et al. [41], in an MS analysis of Arabidopsis tissues, found that 18,024 peptides did not correspond to annotated genes, discovering 778 new coding genes, and refining, in addition, 695 more gene models. The topic of proteogenomics has recently been reviewed [42]. In Chapter 23 of this book, Al-Mohanna et al. propose a proteogenomic method for the peptide mapping of the haplotypederived sweetpotato genome assembly. Proteogenomics is a very useful tool for genomics studies of species that, like sweet potato, have a complex, hexaploid, genome (2n ¼ 6 ¼ 90).

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3 Proteomics Data Validation, and Integration into Other Classic and -Omics Approaches in the Systems Biology Direction Up to 2010, -omics approaches were developed independently with not much interaction between them. This made proteomics and transcriptomics, as affirmed above, mostly descriptive and speculative. In this decade, papers reporting the integrated employment of the two or three -omics approaches, mostly transcriptomics and proteomics, have started to appear [4]. While defining the contents of the present monograph, it was clear, as pointed out in the invitation letter to contributors, that chapters on protocols for proteomics data validation and integration with other classic and -omics approaches in the systems biology direction would constitute the main novelty in this new edition. As stated in Rey et al. [4], “The logical transition from reductionists to a holistic strategy and integration of multidimensional biological information is currently accepted by the scientific community as the only way to decipher the complexity of living organisms and predict through multiscale networks and models.” The integrated use of the -omics approaches will not only allow us to connect the phenotype and the genotype but also, more importantly, to deepen the knowledge of gene expression mechanisms, including posttranscriptional (RNA splicing, micro-RNAs, small interfering RNA, long noncoding RNAs), and posttranslational (phosphorylation, glycosylation, acetylation, methylation, etc.) events [43]. The new strategy requires novel methodologies, with bioinformatics and computer skills being the real bottleneck. The experimental setup is highly complex considering the heterogeneity of the molecules under study (DNA, RNA, proteins, and metabolites); the levels of analysis; next-generation sequencing for nucleic acids; mass spectrometry for proteins and metabolites, the huge amount of data produced, and the biases generated by each methodology. In the wet lab, one limitation is the independent extraction of each type of biomolecule, making the results not fully comparable. In order to solve this, protocols for sequential extraction of the different types of biomolecules have been developed [44]. Valledor’s group, in Chapter 2, introduces a novel protocol, optimized for microalgae, that allows for the combined extraction of different levels including total metabolites, or their pigments or lipids fractions along with nucleic acids (DNA and RNA) and/or proteins from the same sample, reducing biological and time variations between different levels of data. The workflow, including wet and dry steps, has recently been reviewed [4], including original articles and reviews related to the topic. In order to avoid repetitions, I suggest that the reader go through it.

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Chapter 27, by Lopez-Hidalgo et al., is a good example of how to use the different -omics for gaining biological knowledge. They present a protocol based on a multiomics approach for the metabolic pathway reconstruction in a recalcitrant and orphan plant species, that is, the forest tree Holm oak (Quercus ilex). There are more examples in the very recent current literature, such as the study of substantial equivalence in transgenic crops [45], seed germination in Arabidopsis [46], somatic embryogenesis [47], biotic stress in fruit crops [48], and root development [49]. While I was summarizing the advances in (plant) proteomics methods and protocols in the past 5 years since the second edition of this monograph was published, I began to wonder what the future holds for this discipline and I asked myself two questions: (a) How long will it take before a fourth edition is needed? and (b) Will this third edition become obsolete? The answer to these questions is, in my opinion, are as follows: (a) In a few years’ time and (b) No. Proteomics, and more concretely plant proteomics, is in its infancy, at the descriptive stage, with the proteomes observed being just the tip of the iceberg. We are assembling the pieces of a puzzle that will help us to understand how the cell is built and how it works. We are striving to see light at the end of the very long tunnel that links genotype and phenotype that, however, is still too dark. Every proteomics experiment shows us that life is more complex than we have ever imagined, while research continues to be reductionist and simple. References 1. Thiellement H, Zivy M, Damerval C et al (eds) (2007) Plant proteomics methods and protocols. Methods Mol Biol 355:1–8 2. Jorrin-Novo JV, Komatsu S, Weckwerth W et al (2014) Plant proteomics methods and protocols. In: Methods molecular biology, vol 1072, 2nd edn. Humana Press, Totowa 3. Jorrin Novo JV (2014) Plant proteomics methods and protocols. In: Novo J et al (eds) Chapter 1, plant proteomics methods and protocols, Methods molecular biology, vol 1072, 2nd edn. Humana Press, Totowa, pp 3–13 4. Rey MD, Valledor L, Castillejo MA et al (2019) Recent advances in MS-based plant proteomics: proteomics data validation through integration with other classic –omics approaches. In: Progress in botany. Springer, Berlin, Heidelberg 5. Neilson KA, Ali NA, Muralidharan S et al (2011) Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics 11:535–553

6. Picotti P, Bodenmiller B, Aebersold R (2013) Proteomics meets the scientific method. Nat Methods 10:24–27 7. Gillet LC, Navarro P, Tate S et al (2012) Targeted data extraction of the MS/MS spectra generated by data independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 11: O111.016717 8. Jorrin-Novo JV, Komatsu S, Sanchez-Lucas R et al (2018) Gel electrophoresis-based plant proteomics: past, present, and future. Happy 10th anniversary journal of proteomics. J Proteome 198:1–10 9. Luthria DL, Maria John KM, Marupaka R et al (2018) Recent update on methodologies for extraction and analysis of soybean seed proteins. J Sci Food Agric 98:5572–5580 10. Fesmire JD (2019) A brief review of other notable electrophoretic methods. Methods Mol Biol 1855:495–499 11. Minic Z, Dahms TES, Babu M (2018) Chromatographic separation strategies for precision

2019 Plant Proteomics Methods and Protocols mass spectrometry to study protein-protein interactions and protein phosphorylation. J Chromatogr B Analyt Technol Biomed Life Sci 1102-1103:96–108 12. Ankney JA, Muneer A, Chen X (2018) Relative and absolute quantitation in mass spectrometry-based proteomics. Annu Rev Anal Chem 11:49–77 13. Eliuk S, Makarov A (2015) Evolution of Orbitrap mass spectrometry instrumentation. Annu Rev Anal Chem 8:61–80 14. Jung H, Winefield C, Bombarely A et al (2019) Tools and strategies for long-read sequencing and de novo assembly of plant genomes. Trends Plant Sci 24(8):P700–P724. (in press) 15. Guerrero-Sanchez VM, Maldonado-AlconadaA, Amil-Ruiz et al (2019) Ion torrent and lllumina, two complementary RNA-seq platforms for constructing the holm oak (Quercus ilex) transcriptome. PLoS One 14:e0210356 16. Misra BB (2018) Updates on resources, software tools, and databases for plant proteomics in 2016–2017. Electrophoresis 39:1543–1557 17. Subba P, Narayana Kotimoole C et al (2019) Plant proteome databases and bioinformatic tools: an expert review and comparative insights. OMICS 23:190–206 18. Martens L, Vizcaı´no JA (2017) A golden age for working with public proteomics data. Trends Biochem Sci 42:333–341 19. Duncan O, Trosch J, Fenske R et al (2017) Resource: mapping the Triticum aestivum proteome. Plant J 89:601–616 20. Katam K, Jones KA, Sakata K (2015) Advances in proteomics and bioinformatics in agriculture research and crop improvement. J Proteomics Bioinform 8:3 21. Hu J, Rampitsch C, Bykova NV (2015) Advances in plant proteomics toward improvement of crop productivity and stress resistance. Front Plant Sci 6:209 22. Carrera DA, Oddsson S, Grossmann J et al (2018) Comparative proteomic analysis of plant acclimation to six different long-term environmental changes. Plant Cell Physiol 59:510–526 23. Schneider S, Harant D, Bachmann G et al (2019) Subcellular phenotyping: using proteomics to quantitatively link subcellular leaf protein and organelle distribution analyses of Pisum sativum cultivars. Front Plant Sci 10:638 24. de Lamo FJ, Constantin ME, Fresno DH et al (2018) Xylem sap proteomics reveals distinct differences between R gene- and endophytemediated resistance against Fusarium wilt disease in tomato. Front Microbiol 9:2977

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25. Lankinen A, Abreha KB, Masini L et al (2018) Plant immunity in natural populations and agricultural fields: Low presence of pathogenesis-related proteins in Solanum leaves. PLoS One 13:e0207253 26. Ghatak A, Chaturvedi P, Weckwerth W (2017) Cereal crop proteomics: systemic analysis of crop drought stress responses towards markerassisted selection breeding. Front Plant Sci 8:757 27. Schaffer LV, Millikin RJ, Miller RM et al (2019) Identification and quantification of proteoforms by mass spectrometry. Proteomics 19:SI 1800361 28. Naryzhny S (2019) Inventory of proteoforms as a current challenge of proteomics: some technical aspects. J Proteome 191:22–28 29. Toby TK, Fornelli L, Kelleher NL (2016) Progress in top-down proteomics and the analysis of proteoforms. Annu Rev Anal Chem (Palo Alto, Calif) 9:499–519 30. Hashiguchi A, Komatsu S (2017) Postranslational modifications and plant-environment interaction. Methods Enzymol 586:97–113 31. Wu XL, Gong FP, Cao D et al (2016) Advances in crop proteomics: PTMs of proteins under abiotic stress. Proteomics 16:847–865 32. Friso G, van Wijk KJ (2015) Posttranslational protein modification in plant metabolism. Plant Physiol 3:1469–1487 33. Vu LD, Gevaert K, De Smet I (2018) Protein language: post-translational modifications talking to each other. Trends Plant Sci 12:1068–1080 34. Zhu XL, Yu FC, Yang Z et al (2016) In planta chemical cross-linking and mass spectrometry analysis of protein structure and interaction in Arabidopsis. Proteomics 16:1915–1927 35. Li GXH, Vogel C, Choi H (2018) PTMscape: an open source tool to predict generic posttranslational modifications and map modification crosstalk in protein domains and biological processes. Mol Omics 14:197–209 36. Willems P, Horne A, Van Parys T, et al (2019) The Plant PTM Viewer, a central resource for exploring plant protein modifications. Plant J doi: https://doi.org/10.1111/tpj.14345. [Epub ahead of print] 37. Yao H, Wang X, Chen P et al (2018) Predicted Arabidopsis interactome resource and gene set linkage analysis: a transcriptomic analysis resource. Plant Physiol 177:422–433 38. Ro¨diger A, Baginsky S (2018) Tailored use of targeted proteomics in plant-specific applications. Front Plant Sci 9:1204 39. Chawade A, Alexandersson E, Bengtsson T et al (2016) Targeted proteomics approach

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for precision plant breeding. J Proteome Res 15:638–646 40. Jaffe J, Berg HC, Church GM (2004) Proteogenomic mapping as a complementary method to perform genome annotation. Proteomics 4:59–77 41. Castellana NE, Payne SH, Shen Z (2008) Discovery and revision of Arabidopsis genes by proteogenomics. Proc Natl Acad Sci U S A 105:21034–21038 42. Low TY, Mohtar MA, Ang MY et al (2019) Connecting proteomics to next-generation sequencing: Proteogenomics and its current applications in biology. Proteomics 19: e1800235 43. Hong WJ, Kim YJ, Chandran AKN et al (2019) Infrastructures of systems biology that facilitate functional genomic study in rice. Rice 12:15 44. Xiong J, Yang Q, Kang J et al (2011) Simultaneous isolation of DNA, RNA, and protein from Medicago truncatula L. Electrophoresis 32:321–330

45. Corujo M, Pla M, van Dijk J et al (2019) Use of omics analytical methods in the study of genetically modified maize varieties tested in 90 days feeding trials. Food Chem 292:359–371 46. Ponnaiah M, Gilard F, Gakiere B et al (2019) Regulatory actors and alternative routes for Arabidopsis seed germination are revealed using a pathway-based analysis of transcriptomic datasets. Plant J 99:163–175 47. Pais MS (2019) Somatic embryogenesis induction in woody species: the future after omics data assessment. Front Plant Sci 10:240 48. Li T, Wang YH, Liu JX et al (2019) Advances in genomic, transcriptomic, proteomic, and metabolomic approaches to study biotic stress in fruit crops. Crit Rev Biotechnol 39:680–692 49. Proust H, Hartmann C, Crespi M et al (2018) Root development in Medicago truncatula: lessons from genetics to functional genomics. Methods Mol Biol 1822:205–239

Chapter 2 Multiple Biomolecule Isolation Protocol Compatible with Mass Spectrometry and Other High-Throughput Analyses in Microalgae Francisco Colina, Marı´a Carbo´, Ana A´lvarez, Mo´nica Meijo´n, Marı´a Jesu´s Can˜al, and Luis Valledor Abstract Microalgae are gaining attention in industry for their high value–added biomolecules and biomass production and for studying fundamental processes in biology. The introduction of novel approaches for understanding and modeling molecular networks at different omic levels is paramount for increasing the productivity of these organisms. However, the construction of these networks requires high quality datasets with, if possible, perfectly overlapping datasets. The employ of different materials for different biomolecule isolation protocols, even if they come from the same homogenate, is one of the commonest issues affecting quality. Hence, a new method has been developed, allowing for the combined extraction of different levels including total metabolites, or their pigments or lipid fractions along nucleic acids (DNA and RNA) and/or proteins from the same sample reducing biological and time variation between levels data. Key words Microalgae, Proteomics, Lipids, Metabolite, Pigments, DNA, RNA

1

Introduction Microalgae have gained attention in industry during the last decades. They constitute a sustainable production platform due to their high biomass production together with their generation of high value–added biomolecules such as biodiesel, ß-carotene, astaxanthin, and omega-3. However, research is still necessary to make these microorganisms real economically profitable producers. Moreover, not only is microalgae research industry-focused, but their intermediate plant–animal phylogenetic position makes them a powerful and convenient model to study fundamental processes in biology [1]. Understanding microalgae metabolic networks is complex, but recent advances in omics and systems biology allow for the reliable characterization and (semi)quantitation of hundreds to thousands

Jesus V. Jorrin-Novo et al. (eds.), Plant Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2139, https://doi.org/10.1007/978-1-0716-0528-8_2, © Springer Science+Business Media, LLC, part of Springer Nature 2020

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TUBE NAP

TUBE L

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DNA RNA Proteins

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Storage (-20ºC) until LC/MS analysis Stacking

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Trypsin Resolving

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Fig. 1 Workflow of microalgae metabolite, lipid, or pigment fraction extraction combined with nucleic acid and/or protein extraction from the same sample

of transcripts, proteins, or metabolites and its integration into different functional networks, helping to better understand their functions and relationships. However, this high-throughput capability comes at a cost: the different omic levels require different isolation methods, analytical platforms, and specific data processing pipelines. The biases related to different sample processing could have a major impact over later bioinformatics analyses and metabolic reconstruction. The best strategy to avoid this potential flaw is the development of a multiple extraction protocols, allowing for the fragmentation of a single sample into its different omic layers. These kinds of protocols have been developed for plants, animals, and microorganisms [2–4]. In Chlamydomonas, different strategies have been developed focusing the multiple extraction of metabolites, nucleic acids, and protein fractions [2], but none of these are compatible with the commonly used spectrophotometry- and gravimetry-based physiological indexes as total lipid content or pigment contents. For this reason, we have developed a multiple extraction protocol allowing for either metabolite or total lipid or pigment fraction extraction along with nucleic acid (DNA and RNA) and protein extraction from the same microalga sample (Fig. 1). Moreover, this protocol can be easily coupled to other procedures including the fluorescence-based lipid [5], enzyme-based starch [6], or phenolic compound [7] quantitation and various phenotyping workflows as in vivo quantification of pigments [8] and lipids/carbohydrates [9], photosynthetic performance, and growth [10].

2

Materials

2.1 Cell Culture Materials

1. Chlamydomonas reinhardtii CC-503 cw92 mt+ agg1+ nit1 nit2 (available at the Chlamydomonas Culture Collection, Duke University).

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2. Tris-Acetate-Phosphate Media (TAP) (https://www. chlamycollection.org). For 1 L of media combined the following amounts of stock solutions and autoclave: 10 mL of TAP salts stock, 1 mL of TAP Phosphate Solution, 1 mL of Hutner’s trace elements stock, 2.42 g of Tris base, and 1 mL of glacial acetic acid. Adjust pH to 7.0–7.5. 3. Culture physical environment. Light intensity: 100 μmol/m2 s PAR is a good level for photosynthetically competent cultures on agar. For liquid cultures, light intensities of 200–300 μmol/ m2 s, shaking at 110–150 rpm, and 25
C temperature are recommended. 4. Material needed for the culture: flask, incubator, or culture chamber with temperature, light intensity, photoperiod, and shake control. 2.2 Sampling and Extraction Materials

1. 50 mL conical tubes, 1.5 mL tubes, 2 mL tubes, and 1.5 screwcap tubes. 2. Refrigerated centrifuge. 3. Regimill/Fastprep (beads beating system). 4. Vortex. 5. Vacuum concentrator (speedvac). 6. Heat block. 7. Ultrasound sonicator. 8. Freezers (20 and 80
C).

2.3 Sampling and Extraction Reagents and Solutions

1. Metabolite extraction buffer (MEB): methanol–chloroform– ddH2O (2.5:1:0.5). Store at 4
C; must be cold when added. 2. Phase separation mix (PSM): chloroform–ddH2O (1:1) (see Note 1). 3. Polar metabolites extraction buffer (PMEB): chloroform– ddH2O (1:1). 4. Pigment extraction buffer (PEB): acetone–1 M Tris pH 8– ddH2O (80:5:15). 5. Lipid extraction buffer 1 (LBE1): chloroform–isopropanol (1:1). 6. Lipid extraction buffer 2 (LBE2): hexane. 7. Washing buffer 1 (WB1): 0.75% (v/v) ß-mercaptoethanol in 100% methanol. 8. Washing buffer 2 (WB2): 2 mM Tris pH 7.5, 20 mM NaCl, 0.1 mM EDTA, 90% ethanol. 9. Washing buffer 3 (WB3): 2 mM Tris pH 7.5, 20 mM NaCl, 0.1 mM EDTA, 70% ethanol.

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10. RNase solution: 300 μL of WB2 and 3 μL of 20 mg/mL PureLink RNAse A (Invitrogen). 11. DNase solution: 300 μL of WB2, 3 μL of 10 DNase I Buffer and 3 μL of 2 U/μL DNase I (Ambion). 12. Protein solubilization buffer (PSB): 7 M guanidine hydrochloride, 2% (v/v) TWEEN 20, 4% (v/v) NP-40, 50 mM Tris, pH 7.5, 1% (v/v) ß-mercaptoethanol. 13. Phenol. 14. Protein phase separation mix (PPSM): phenol–ddH2O (0.92:1). 15. Phenol washing buffer: 0.7 M sucrose, 50 mM Tris–HCl pH 7.5, 50 mM EDTA, 0.5% ß-mercaptoethanol, 0.5% (v/v) Plant Protease Inhibitor Cocktail (Sigma-Aldrich). 16. Protein precipitation buffer (PPB): 0.1 M ammonium acetate and 0.5% ß-mercaptoethanol in methanol. 17. Methanol. 18. Protein pellet washing buffer (PPWB): acetone–ddH2O (85:15). 19. Protein pellet solubilization buffer (PPSB): Urea 8 M with 4% SDS.

3

Methods

3.1 Sampling Method

1. Harvest 50 mL of culture and centrifuge at 1900  g for 5 min. Discard the supernatant (see Notes 2 and 3). 2. Resuspend the cell pellet in 700 μL of ddH2O and transfer to a 2 mL tube (tube S) (see Note 4). 3. Centrifuge at 1900  g for 2 min. Discard the supernatant. And spin the tube S on a centrifuge to discard all the supernatant (see Note 5). 4. Weight the tube S and determine the fresh weight (see Note 6).

3.2 Metabolite Extraction Method

Following steps must be done in ice and centrifugations at 4
C unless specified. Metabolites extraction is not compatible with lipid and pigment extractions. 1. Transfer the content of tube S to a new screw-cap tube with glass beads (tube SB). Add 600 μL of MEB and, if needed, resuspend the pellet by pipetting up and down (see Notes 7 and 8). 2. Homogenize pellets homogenization.

by

beads

beating

until

total

Multiple Biomolecule Isolation in Microalgae

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3. Centrifuge at 20,000  g for 6 min and transfer supernatant to tube containing 800 μL of PSM (tube M) (see Note 9). The pellet contains nucleic acids and proteins (tube NAP). 4. Mix well by vortexing and centrifuge tube M, 5 min at 15,000  g. 5. During centrifugation time add 500 μL of WB1 to tube NAP and mix by vortex until the pellet is mostly disaggregated (see Note 10). Keep at 4
C until metabolite extraction is finished. 6. After step 6 is finished, two different phases should be clearly defined with a sharp interphase. Transfer the upper, aqueous layer to a new 2 mL microcentrifuge tube (Tube PM, polar metabolites). Transfer the lower layer, containing nonpolar metabolites, to a new 2 mL tube (Tube NPM) (see Notes 11 and 12). 7. Add 300 μL of PMEB to each PM tube. Mix 1 min at room temperature and centrifuge at 15,000  g for 4 min. 8. Transfer upper layer to a new microcentrifuge tube PM2 (see Note 11). 9. Dry PM2 and NPM tubes in a speedvac or under nitrogen stream. Keep the dried tubes at 20
C or 80
C until analysis. 10. Centrifuge tube NAP at 20,000  g for 10 min. Discard supernatant without disturbing the pellet. 3.3 Pigment Extraction Method

Following steps must be at 4
C unless other conditions are specified. All materials used must be acetone resistant. Pigment extraction is not compatible with metabolite and lipid extractions. 1. Add 500 μL of PEB to tube S for pellet resuspension. Transfer to the glass beads screw cap tubes (tube SB) (see Note 8). 2. Add 500 μL of PEB to tube S and be sure the pellet is completely resuspended. Mix with previous PEB (step 1) in the tube SB. 3. Vortex vigorously for 30 s or Regimill/Fastprep for 30 s. Transfer to a new 1.5 mL tube (tube NAP). 4. Centrifuge for 5 min at 21,100  g. Transfer supernatant to a new tube (tubePi). The pellet containing nucleic acids and proteins (tube NAP) should be whitish-brownish (see Note 13). 5. Read the absorbance of tube Pi (dilute Pi contents if necessary) immediately, since the acetone is highly volatile. Absorbance to be read: 470 nm, 537 nm, 647 nm, 663 nm. Take the background-subtracted mean absorbance of the three replicates (see Note 14).

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6. The concentration of chlorophylls and carotenoids (in μmol mL1) can be obtained with the following equations (see Note 15) according to [11]: Chla ¼ 0, 01373 A 663  0, 000897 A537  0, 003046 A 647 Chlb ¼ 0, 02405 A 647  0, 004305 A537  0, 005507 A 663 Carotenoids ¼ ðA 470  ð17, 1  ðChla þ Chlb ÞÞ=119, 26 7. Air-dry pellets for PEB evaporation at room temperature (tube NAP) (see Note 10). 3.4 Lipid Extraction Method

Lipid extraction is not compatible with pigment and metabolite extractions. 1. Add 200 μL of LBE1 to cell pellet (tube S) and transfer to a glass beads containing screw-cap tube (tube SB). 2. Homogenize using beads beating until total homogenization. Weight a 1.5 mL tube (tube L). 3. Centrifuge at 14,000  g for 5 min at room temperature and transfer supernatant to the tube L. 4. Repeat steps 1 and 2, mixing both fractions in the tube same L. 5. Reextract the pellet with 400 μL of LBE2 and vigorously vortex for 3 min. 6. Centrifuge at 14,000  g for 5 min at room temperature and transfer supernatant to tube L. The pellet contains proteins and nucleic acids (tube NAP) (see Notes 10 and 16). 7. Dry tube L in a speedvac or oven. 8. Determine lipid weight gravimetrically.

3.5 Nucleic Acid Purification Method

The following steps must be carried out at 4
C, unless other conditions are specified. 1. Add 500 μL of WB1 to tube NAP and mix by vortex until the pellet is mostly disaggregated (see Note 17). Centrifuge at 20,000  g for 10 min. Discard supernatant without disturbing the pellet (see Note 18). 2. Resuspend the pellet in 400 μL of PSB and centrifuge at 14,000  g for 3 min. 3. Transfer supernatant to a new silica column (SC1) placed in a nuclease- and protease-free 2 mL tube (see Note 18). Centrifuge at 10,000  g for 1 min. 4. Transfer the flow through to a new tube (tube RP) containing RNA and proteins. Reserve the SC1 containing DNA for later washing steps.

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5. Add 400 μL of acetonitrile to the tube RP and mix first by pipetting and then by vortex. 6. Transfer tube RP sample mix to a new silica column (SC2) placed in a nuclease- and protease-free 2 mL tube (tube P) (see Note 18). 7. Centrifuge SC2 at 12,000  g for 2 min and save the flowthrough containing proteins in tube P. 8. Wash the columns SC1 and SC2 with 600 μL of WB2. Centrifuge at 12,000  g for 2 min and discard the flow through. 9. Add 300 μL of RNase solution to SC1 and incubate 30 min at room temperature. Add 360 μL of DNase solution to SC2 and incubate 30 min at 37
C. 10. Centrifuge SC1 and SC2 at 12,000  g for 1 min. Discard the flow-through. 11. Add 600 μL of WB3 to SC1 and SC2. Centrifuge at 12,000  g for 2 min. Discard the flow-through. 12. Centrifuge SC1 and SC2 1 min at 20,000  g (see Note 19). 13. Place SC1 in a new tube (tube DNA) and SC2 in other one (tube RNA). Add 50 μL of ddH2O to the center of the membrane of SC1 and SC2. Incubate 5 min at room temperature. 14. Centrifuge SC1 and SC2 at 12,000  g for 1 min for eluting both DNA (tube DNA) and RNA (tube RNA). 3.6 Protein Extraction and Purification Methods

Following steps must be at 4
C unless other conditions are specified. 1. Add 100 μL of PSB and 300 μL of phenol to tube P. Mix by vortexing and incubate for 2 min at room temperature (see Note 20). 2. Add 1150 μL of PPSM to tube P and vortex for 1–2 min at room temperature. 3. Centrifuge for 5 min at 10,000  g and room temperature for allowing for phase separation. 4. Transfer the upper phenolic phase containing proteins to a new tube (tube A) and add 600 μL of PWB. Vortex for 1–2 min and then centrifuge 5 min at 10,000  g and room temperature. 5. Transfer the upper phenolic phase to a new tube (tube B), being carefully for not disturbing the interphase (see Note 21). 6. Precipitate the proteins by adding 1.5 mL of PPB to tube B. Incubate over night at 20
C (see Notes 22 and 23). 7. Centrifuge tube B at 10,000  g for 15 min and discard the supernatant carefully using a pipette for not disturbing the pellet.

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8. Fill the tube B with methanol and disaggregate the pellet using an ultrasound sonicator. 9. Centrifuge at 10,000  g for 10 min and discard the supernatant without disturbing the pellet. 10. Wash the pellet with 600 μL of PPWB. Mix until the pellet is completely disaggregated (see Note 24). 11. Centrifuge at 10,000  g for 10 min and discard the supernatant without disturbing the pellet. 12. Air-dry pellets and redissolve in an adequate buffer (see Notes 25 and 26). 13. Resolubilize and quantify proteins (see Note 26). Proceed with protein fractionation, digestion, desalting, and concentration according to [12].

4

Notes 1. Phase separation mix should be prepared in the 1.5 mL tube. 2. Cell concentration should be 5  105–1  106 cells/mL. 3. All the sampling steps must be done quickly. If it is not possible, centrifuge 15 mL of cell culture in 35 mL of cold (80
C) methanol and keep it at 80
C until the extraction will be performed. 4. Weight 2 mL tubes S before transferring the cells. 5. Discarding all supernatant is crucial for the step 4. 6. Maximum fresh weight for extraction should be 50 mg. 7. Tissue should remain frozen during all of the process. 8. Cut the pipette tip for an easier resuspension. 9. If the resultant pellet is green (nonwhitish), proceed to rehomogenize it because it indicates a poor homogenization. Add 200 μL of MEB to the tube containing the pellet. Mix well by vortex until the pellet is completely disaggregated. Centrifuge at 20,000  g for 6 min and transfer supernatant to tube M. 10. If it is needed the nucleic acid extraction, perform immediately the first step of nucleic acid extraction and maintain at 4
C. For performing directly protein extraction, maintain at 4
C or air-dry the pellets (tube NAP) at room temperature and kept overnight at 20
C. For long-term storage keep at 80
C until nucleic acid and protein extractions. This purification is compatible with directly protein extraction and purification without nucleic acid purification. 11. Low-binding tube is preferred.

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12. Sometimes polar phase can be slightly cloudy, becoming transparent if the tube is warmed to room temperature. This indicates a chloroform contamination. In this case, a second wash of the PM tube with WB1 is recommended. Transfer the upper layer to a new PM tube and the lower to the NPM tube. 13. If the pellet remains green colored, repeat steps 3 and 4. 14. The spectrophotometer response is linear with pigment concentration up to an absorbance of 1. When the peak absorbance of the samples exceeds 1, the solutions are diluted further and remeasured. 15. All of these values should be multiplied by the dilution factor of the samples (if sample is diluted). The fresh weight can be used to obtain the moles of each pigment by milligram of fresh weight. 16. If the pellet remains green and hexane still pigmented, repeat steps 4 and 5 but adding 500 μL LBE2 instead of 400 μL. In case the pellet remains green, continue with the extraction because green pellet color may come from the chlorophyll hemo group separation from the dead cells. 17. Pipetting through beads reduces the amount of nonsoluble particles that are taken up. 18. Avoid transferring pellet particles to silica column. 19. This step is for eliminating residual ethanol and completely drying the column for a better elution of nucleic acids. 20. If previous nucleic acid extraction is not performed, disaggregate tube NAP pellet in 400 μL of PSB and transfer the dissolved pellet to a new tube (tube P). Then, follow the protein extraction and purification protocol. 21. For a maximum protein yield, remaining aqueous phase of tube A can be reextracted with 550 μL of phenol, repeating steps 4– 5. 22. If aqueous phase was reextracted, transfer the upper phenolic phase to a 10 mL tube, and precipitate protein with 4 mL of PPB. 23. Pause point: precipitated proteins in acetone are stable for more than 1 week at room temperature, but we recommended keeping them at 20
C until extraction is resumed. 24. Pellets that are not completely dry (but with the acetone completely evaporated) are easier to solubilize. 25. Pellet solubilization should be done in an appropriate buffer depending on the downstream application of proteins. Chlamydomonas best protein pellets buffer solubilizer is urea 8 M, 4% SDS.

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26. Choose protein quantification method depending on the compatibility of protein resuspension buffer used.

Acknowledgments Our research group is generously funded by Spanish Ministry of Science, Innovation and Universities (AGL2016-77633-P and AGL2017-83988-R). M.M., L.V., and F.C. were also supported by Spanish Ministry of Science, Innovation and Universities through Ramo´n y Cajal (RYC-2014-14981, RYC-2015-17871 to M.M. and L.V., respectively) and Programa de Ayudas Predoctorales Severo Ochoa, Autonomous Community of Asturias, Spain (BP14-138) to F.C. programs. References 1. Sasso S, Stibor H, Mittag M et al (2018) From molecular manipulation of domesticated Chlamydomonas reinhardtii to survival in nature. elife 7:e39233 2. Valledor L, Escando´n M, Meijo´n M et al (2014) A universal protocol for the combined isolation of metabolites, DNA, long RNAs, small RNAs, and proteins from plants and microorganisms. Plant J 79:173–180 3. Nakayasu ES, Nicora CD, Sims AC et al (2016) MPLEx: a robust and universal protocol for single-sample integrative proteomic, metabolomic, and lipidomic analyses. MSystems 1: e00043–e00016 4. Salem MA, Ju¨ppner J, Bajdzienko K et al (2016) Protocol: a fast, comprehensive and reproducible one-step extraction method for the rapid preparation of polar and semi-polar metabolites, lipids, proteins, starch and cell wall polymers from a single sample. Plant Methods 12:45 5. Morschett H, Wiechert W, Oldiges M (2016) Automation of a Nile red staining assay enables high throughput quantification of microalgal lipid production. Microb Cell Factories 15:34 6. Smith AM, Zeeman SC (2006) Quantification of starch in plant tissues. Nat Protoc 1:1342 7. Singleton VL, Orthofer R, Lamuela-Ravento´s RM (1999) Analysis of total phenols and other

oxidation substrates and antioxidants by means of Folin-Ciocalteu reagent. In: Methods in enzymology. Academic Press, Cambridge, pp 152–178 8. Gregor J, Marsˇa´lek B (2004) Freshwater phytoplankton quantification by chlorophyll a: a comparative study of in vitro, in vivo and in situ methods. Water Res 38:517–522 9. Chiu L, Ho S-H, Shimada R et al (2017) Rapid in vivo lipid/carbohydrate quantification of single microalgal cell by Raman spectral imaging to reveal salinity-induced starch-to-lipid shift. Biotechnol Biofuels 10:9 10. Strenkert D, Schmollinger S, Gallaher SD et al (2019) Multiomics resolution of molecular events during a day in the life of Chlamydomonas. PNAS 116:2374–2383 11. Sims DA, Gamon JA (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ 81:337–354 12. Valledor L, Weckwerth W (2014) An improved detergent-compatible gel-fractionation LC-LTQ-Orbitrap-MS workflow for plant and microbial proteomics. In: Jorrin-Novo JV, Komatsu S, Weckwerth W, Wienkoop S (eds) Plant proteomics: methods and protocols. Humana Press, Totowa, NJ, pp 347–358

Chapter 3 Protein Interaction Networks: Functional and Statistical Approaches Mo´nica Escando´n, Laura Lamelas, Vı´ctor Roces, Vı´ctor M. Guerrero-Sanchez, Mo´nica Meijo´n, and Luis Valledor Abstract The evolution of next-generation sequencing and high-throughput technologies has created new opportunities and challenges in data science. Currently, a classic proteomics analysis can be complemented by going a step beyond the individual analysis of the proteome by using integrative approaches. These integrations can be focused either on inferring relationships among proteins themselves, with other molecular levels, phenotype, or even environmental data, giving the researcher new tools to extract and determine the most relevant information in biological terms. Furthermore, it is also important the employ of visualization methods that allow a correct and deep interpretation of data. To carry out these analyses, several bioinformatics and biostatistical tools are required. In this chapter, different workflows that enable the creation of interaction networks are proposed. Resulting networks reduce the complexity of original datasets, depicting complex statistical relationships (through PLS analysis and variants), functional networks (STRING, shinyGO), and a combination of both approaches. Recently developed methods for integrating different omics levels, such as coinertial analyses or DIABLO, are also described. Finally, the use of Cytoscape or Gephi was described for the representation and mining of the different networks. This approach constitutes a new way of acquiring a deeper knowledge of the function of proteins, such as the search for specific connections of each group to identify differentially connected modules, which may reflect involved protein complexes and key pathways. Key words Protein networks, String, Omics levels, sPLS, DIABLO, Cytoscape

1

Introduction The classic workflow for proteome analysis, mainly based on the use of univariate statistics and PCAs, is being quietly displaced in favor of new approaches that take advantage of protein interaction knowledge and advanced statistical tools. These novel

Electronic supplementary material: The online version of this chapter (https://doi.org/10.1007/978-1-07160528-8_3) contains supplementary material, which is available to authorized users. Jesus V. Jorrin-Novo et al. (eds.), Plant Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2139, https://doi.org/10.1007/978-1-0716-0528-8_3, © Springer Science+Business Media, LLC, part of Springer Nature 2020

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methodologies allow for the study of the proteome and its interaction with other biomolecules, the environment, and even with itself, providing a holistic perspective. This kind of workflows gives the researcher the possibility of having a deeper understanding of the biological responses behind the observed differences in the experimental systems. Integrative studies heavily rely on computational biology and require the use of specific algorithms, methods, and models to extract and determine the most relevant information in biological terms [1, 2]. Common classification methods (including discriminant analysis; neural networks; decision trees; support vector machine, SVM; and random forest, RF) are suited to single dataset analyses [2], whereas the methods that build predictive models require multiple sources of those which act as predictors and those which are predicted. The most employed methods for the characterization of multiple omics dataset is the combination of unsupervised multivariate statistics, like principal component analysis (PCA), and supervised, like partial least square (PLS) and discriminant analysis (PLS-DA) and its variants [3–5]. PLS methods are suitable to integrate two datasets considering one omic level a predictor of a second omic level, the response. With these methods it is possible to get an overview about the most important variables (proteins, metabolites or transcripts) determining which variables of the predictor explain the maximum variance of the responses [6]. In addition, there are innovative multiple integration tools (for more than two different data inputs) that allow for the construction of these relationship based models, such as DIABLO (Data Integration Analysis for Biomarker discovery using a latent component method for Omics studies) [2], multiple coinertia analysis (MCIA) [7], and xMWAS [8]. Integrative analysis can be pushed beyond sample and variable biplotting or variable filtering, since determined interaction can be depicted as networks, where the variables are the nodes and the relations among them, the edges. As a result, this simple representation collects the complexity of the original data as retrieved by previous analyses. These networks can be topologically evaluated to determine the most connected nodes or hubs within the data as well as subnetworks or clusters with same (or opposite) behavior. Interaction networks described above, and its inferred relationships are based on statistical analyses. However, there is also possible to create or enrich those networks with functional or biologically relevant annotations. This new information layer is obtained from specific tools and databases (STRING, ShinyGO) which gather known functional relations (protein–protein, protein– metabolite associations). In addition, we can create functional networks [9] even for species not included in these databases through BLAST and protein domain analyses.

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In this chapter, different workflows aimed to conduct all of these functional and statistical approaches together with data visualization are described.

2

Materials Next, we describe different approaches aiming to obtain networks that infer proteins connection between themselves and with other omics datasets, specifically with the metabolomic and transcriptomic levels in the example shown. The experiment used as an example consists of a control and two experimental treatments (T1 treatment, T2 treatment) with three biological replicas each. The names given for each replica are C-1; C-2; C-3; T1-1; T1-2; T1-3; T2-1; T2-2; and T2-3. The matrices used—Proteins matrix, Metabolites matrix, and Transcripts matrix—in the different workflows follow the template shown in Fig. 1 (where A: Protein Matrix; B: Metabolite Matrix; and C: Transcript Matrix). Individual matrixes of each dataset have the following arrangement: samples in columns (e.g., control, treatment1, treatment2) and variables in rows (protein1, protein 2, . . .). This arrangement of the matrixes for entry as a dataset in the different programs is crucial to follow to obtain good results in the different workflows. In supplementary, a simplified dataset is provided to carry out the different workflows (Supplementary dataset S1). The networks shown in the chapter have been made with real data from experiments. Protein identification and quantification from raw MS/MS was performed by Thermo Proteome Discoverer™, Metabolites using MZmine 2 [10] and Transcripts from Trinity software [11]. All datasets are normalized following the indications of [12], [13], and [14]. In each workflow, we will specify which matrices we need as starting materials (e.g., Protein matrix with proteins identified, annotated, and quantified for each experimental situation). The different workflows require different tools which are enlisted below: Software: R Program (v.3.6.0), Rstudio, Cytoscape (v. 3.7.1), Cytoscape STRING App (v. 1.4.2), Gephi (v.0.9.2), and spreadsheet. R Libraries: Bioconductor, edgeR, ARTIVA, xMWAS, MixOmixs, igraph, and RColorBrewer.

3

Methods Depending on the approach used, we have developed different workflows, and they are summarized in this protocol index:

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Fig. 1 Example matrices for data entry in R in csv format. (a) Protein matrix, (b) Metabolite matrix, and (c) Transcript matrix (RNA-seq data in this case). Each dataset with samples in columns and variables in rows

1. Selection of differential expression proteins (for targeted networks) (Subheading 3.1). 2. Integration tools for statistical networks.

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(a) Statistical integration networks: Dynamic protein–protein interaction networks (Subheading 3.2.1). (b) With other omics datasets: l

Partial Least Square Regression (PLS) and variates (Subheading 3.2.2.1).

l

Data-driven integration and differential network analysis, xMWAS (Subheading 3.2.2.2).

l

Data Integration Analysis for Biomarker discovery using a Latent component method for Omics studies (DIABLO) (Subheading 3.2.2.3).

3. Biological interaction network enrichment. (a) STRING (Subheading 3.3.1). (b) ShinyGO (Subheading 3.3.2). 4. Merged functional and statistical interaction networks (Subheading 3.4). 5. Network visualization tools. (a) Cytoscape (Subheading 3.5.1). (b) Gephi (Subheading 3.5.2). 6. Future Perspectives. 3.1 Selection of Differential Expression Proteins (for Targeted Networks)

Workflow

For the analysis of protein–protein interactions, besides the global analysis of the proteome, it is possible to analyze in particular the interactions of proteins with differential expression within our experiment. Quantitative analysis of shotgun proteomic data can be performed through statistical tools commonly used to measure the differential expression of genes (proteins in our case) such as EdgeR [15]. This package implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasilikelihood tests. This analysis makes it possible to better group proteins according to their function under certain conditions, reducing network complexity and keeping only the proteins significantly altered for a specific treatment. Then, we will explain the workflow to obtain a selection of differential proteins through which we will obtain the network functionally enriched with programs such as STRING or ShinyGO (Subheading 3.2). 1. Install and load the required packages. These are collections of functions, data, and R code that are stored in a folder according to a well-defined structure, easily accessible for R (see Note 1). In an R console or GUI (we recommend R Studio) type: if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocInstaller::install("edgeR") library(edgeR)

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2. Load your data (Protein matrix, Fig. 1a), indicating the path of the file that contains them and its format. In addition, we must assign a name to the columns of data, indicating the controls and the corresponding treatments. proteins