Guleria, P: Plant Small RNA 012817112X, 9780128171127

Table of contents :
Cover
Plant
Small RNA:
Biogenesis, Regulation
and Application
Copyright
Contributors
Section 1: Basics
1
Introduction to plant small RNAs
Introduction
Discovery history of small RNAs
Diversity of small RNAs
miRNAs
Biogenesis of plant miRNAs
miRNA turnover
Mode of action of miRNAs
miRNA-guided transcript cleavage
miRNA-mediated translation repression
miRNAs in plant development
miRNA-mediated regulation of meristem organization and cell polarity
miRNA-mediated regulation of flower development
miRNA-mediated regulation of root architecture
miRNA-mediated regulation of seed development
siRNAs
Biogenesis of siRNAs
Mode of action of siRNAs
Biological functions of 24-nt transposable element (TE)-derived siRNAs
phasiRNAs
Biogenesis of phasiRNAs
Biological functions of phasiRNAs
Movement of small RNAs
Future perspectives
Acknowledgment
References
Further reading
2
Diversity and types of small RNA
Small regulatory RNAs: Historical milestones
Classifying endogenous small RNA in plants
Hairpin sRNA and microRNA
Natural antisense transcript siRNA
Secondary and trans-acting siRNA
Heterochromatic siRNA
References
3
Biogenesis of small RNA: Molecular pathways and regulatory mechanisms
DNA-dependent RNA polymerase
RNA-dependent RNA polymerase
Dicer
Argonaute proteins in plants
Ago1
Ago10
Ago5
Ago7
AGO2 and AGO3
Ago4
Ago6
AGO8 and AGO9
Determinants for AGO-sRNA sorting and biological function
Small RNA in transgenerational epigenetic inheritance
Interrelationship between sRNA pathways
Analyzing sRNA: Computational challenges from the “dry lab”
References
4
Transcriptome-based identification of small RNA in plants: The need for robust prediction algorithms
Introduction
The need for small RNA Seq in plants
Types of RNA Seq strategies
dUTP-based strand-specific RNA Seq
Bulked segregant analysis (BSA) using RNA Seq
Double-stranded RNA Seq
Differential RNA Seq
Elements of RNA Seq data and analyses
Raw read
Read alignment
Quantification
Transcript identification
Alignment
Differential gene expression analyses
Alternative splicing identification
Identifying gene fusions
Challenges and solutions for annotating small RNAs in plants
Empirical toolkits and databases
Fastx
miRCat
SiloCo
miRBASE
TAPIR [ http://bioinformatics.psb.ugent.be/webtools/tapir/ ]
Emerging algorithms
Validation of expression using time course data
Normalization and log ratio transformation method
Tools and databases available according to the basic steps of RNA Seq data analyses
Quality control
Trimming and adapters removal
Error correction
Bias correction
Other tasks/preprocessing data
Alignment tools
De novo splice aligners
Normalization, quantitative analysis, and differential expression
Open (free) source solutions
Alternative splicing analysis
Differential isoform/transcript usage
Fusion genes/chimeras/translocation finders/structural variations
Single-cell RNA Seq
Integrated packages
Genome-guided assemblers [88, 89]
Co-expression networks
Visualization tools
Functional, network, and pathway analysis tools
Links to databases used for analysis of plant transcriptome data
A case study: Transcriptome analyses from Vigna mungo and identification of miRNAs
Background of the work
Sample preparation and sequencing
Screening and identification of miRNAs from sequenced data
Identification of established and novel miRNA sequences
miRNA target prediction, gene ontology classification, and quantification of target genes
Quantification of miRNAs in different tissues to study their tissue-specific expression
Expression patterns of miRNAs from both mock control (MC) and MYMIV-inoculated (MI) datasets
qPCR validation of miRNA targets in MYMIV-susceptible and -resistant background
References
Further reading
Section 2: Expression and regulation mechanism of small RNA
5
Role of RNA interference in seed germination
Introduction
Mechanism of seed germination
Phases in seed germination
Factors regulating seed germination
The phenomenon of RNA silencing
Mechanism of RNA silencing
miRNAs
Tasi-RNAs
Role of small RNAs in seed germination
miRNA serve as convergence regulatory nodes
Conclusion
Acknowledgment
References
6
Importance of small RNA in plant seed germination
Brief introduction of seed germination
miRNAs related to seed germination in Arabidopsis
miRNAs related to seed germination in crops
siRNAs related to seed germination
References
Further reading
7
Importance of small RNA in plant metabolism
Introduction
Major types of plant sRNA
Biogenesis of small RNA in plants: microRNA and small interference RNA
Diverse functions of sRNA in controlling plant metabolism during stress condition
Role of miRNAs in ABA-mediated stress responses
miRNA-mediated adaptive response to drought and salt stress conditions
Regulation of cold and heat stress tolerance by miRNAs expression
miRNAs expression to hypoxia and oxidative stress
miRNA in response to nutrient homeostasis
Regulating plant metabolism: Role of sRNAs
miRNA-mediated regulation plant phytohormone signaling
miRNA: Transcription factors in regulating plant metabolism
Functional role of miRNA in plant secondary metabolism biosynthesis
Regulatory role of siRNAs in plant stress responses
Conclusion and future prospectus
References
Further reading
8
Small RNA in tolerating various biotic stresses
Small RNA: Discovery, classifications, and biogenesis
Classification
MicroRNAs and isomiRs
Ta-siRNAs
Nat siRNA
Heterochromatic-siRNA
Pathogen-derived sRNAs and miRNA-like molecules
Biogenesis
Methodologies applied for sRNA research
Parameters applied for sRNA prediction
Plant miRNAs and pathogen milRs
Plant ta-siRNAs
Plant isomiRs
Databases available for sRNAs
SRNA-mediated biotic stress responses in plants
SRNA-mediated responses against insects
SRNA-mediated responses against fungi
SRNA-mediated responses against virus
SRNA-mediated responses against bacteria
SRNA-mediated responses against abiotic stress
SRNAs and agricultural improvement
Small RNA as a spray
Conclusion
References
Further reading
9
Role of small RNA in regulating plant viral pathogenesis
Introduction
Illustrations of siRNA-mediated and miRNA-mediated antivirus pathway mechanisms
Role of miRNA in plant antiviral defense
Application of siRNA against plant antiviral defense
Regulation of siRNA for plant viral pathogenesis
siRNA response against bacterial diseases
Role of siRNA to prevent fungal disease
References
10
Salt stress tolerance and small RNA
Introduction
Plant sRNAs: Types and biogenesis
miRNA
siRNA
Trans-acting siRNAs (ta-siRNAs)
Natural antisense siRNAs (nat-siRNAs)
Heterochromatic siRNAs (hec-siRNAs)
Role of sRNAs in salt stress response
Conclusion and future perspective
Acknowledgment
References
Further reading
11
Small RNAs and cold stress tolerance
Introduction
Cold stress sensing and second messengers
Mechanism of cold acclimatization
Small RNAs and cold stress tolerance
Biogenesis of miRNAs and siRNAs
Role of miRNAs in cold stress tolerance
Role of siRNAs in cold stress tolerance
Genes involved in cold stress
Conclusion
References
Further reading
12
Toward elucidating the functions of miRNAs in drought stress tolerance
Introduction
Drought and drought tolerance mechanisms
Drought escape
Drought avoidance
Drought tolerance
Physiological and biochemical mechanisms of drought tolerance
Stomatal aperture regulation
Reactive oxygen species accumulation
Metabolism maintenance
Molecular basis of drought tolerance
Transcription factors
MiRNAs
Discovery of miRNAs
Biogenesis of miRNAs
Functional modes of miRNAs
MiRNA responses to drought stress
Targets of drought-responsive miRNAs
Contribution of miRNAs to drought stress tolerance
Conclusion and future perspectives
References
13
Regulation of photosynthesis and vegetative growth of plants by small RNAs
Introduction
Roles of small RNAs in vegetative growth
Regulation of shoot apical meristem genes
Abaxial-adaxial polarity
Morphology and size of leaves
Guard cell patterning
Leaf senescence
Vegetative phase transition
Regulation of root traits
Roles of small RNAs in photosynthesis
Possible applications of small RNAs in modulating photosynthesis and vegetative growth
Rice
Tobacco
Potato
Maize
Legume
Wheat
Poplar
Tomato
Conclusion
References
14
Heat stress tolerance through small RNA
Introduction
Biogenesis
How do miRNAs regulate stress response?
Heat-responsive miRNAs in plants
MiRNA families in cereal crops and their regulatory pathways
miR156 family
miR159 family
miR160 family
miR164 family
miR166 family
miR167 family
miR168 family
miR169 family
miR172 family
miR319 family
miR393 family
miR395 family
miR397 family
miR398 family
miR408 family
Manipulation of miRNAs for crop improvement
Target mimics
Artificial miRNAs
miRNA manipulation by genome editing technologies
Conclusion
Acknowledgment
References
Further reading
15
Role of small RNA in plant interaction with microbes
Introduction
Structural diversity of sRNAs in plants
miRNAs
siRNAs
miRNAs regulation in response to beneficial microorganism-plant interactions
MicroRNAs-based regulation in plants submitted to beneficial bacteria
MicroRNAs-based regulation in plants submitted to beneficial fungi
sRNAs in response to pathogenic microorganism-plant interactions
Plant defense strategies
Counteracting pathogen strategies
Conclusion and future perspectives
References
16
Plant growth regulation by small RNA-mediated plant-biotic interactions
Introduction
Mode of action of sRNAs in plant defense
Function of small RNA
Small RNAs and pathogenesis
Pathogen-endogenous sRNAs
Pathogen-induced sRNAs
Small RNAs and plant defense
Bacterial infection
Fungal infection
Viral infection
Small RNAs and symbiotic relationships
Summary
References
17
Small RNA and DNA methylation in plants
Brief overview of small RNAs
Transcriptional gene silencing
DNA methylation and demethylation
DNA methylation
Active DNA demethylation
Argonaute proteins and their functions
Argonaute protein family
Argonaute function in Arabidopsis
Mobile small RNAs
Small RNAs and epigenetic reprogramming in plant sexual production
miRNA and DNA methylation
A Dicer-independent pathway for biogenesis of siRNAs that direct DNA methylation in Arabidopsis
sRNA and hybrid vigor
DNA methylation and fleshy fruit ripening
Simpson’s paradox on DNA methylation comparison
Concluding remarks
Acknowledgments
References
Section 3: Application of small RNA
18
Small RNA manipulation in plants: Techniques and recent developments
Introduction
Small RNA silencing pathways
MicroRNA (miRNA) pathway
Small interfering RNA (siRNA) pathway
Other RNA silencing-related pathways
Trans-acting siRNA (tasiRNA) pathway
RdDM pathway in plants
The natsiRNA pathway
Exogenic nucleic acid-based RNA silencing
Recent RNA silencing techniques in plants
Hairpin RNA transgene (HpRNA)-induced RNA silencing
Artificial miRNA (amiRNA) technology
Designing of amiRNAs
Virus-induced gene silencing (VIGS)
Different methods of VIGS induction
Transformation using Agrobacterium tumefaciens
VIGS for potato silencing through Potato Virus X (PVX)
VIGS for Arabidopsis silencing through TRV
TYMV-derived silencing in Arabidopsis
Applications of RNA interference
Crop improvement and nutritional value
RNAi-induced rice and barley
RNAi induced in banana
RNAi induced in jute and cotton
Application of RNAi in biotic stress
RNAi in secondary metabolite biosynthetic pathway
RNAi in seedless fruit production
Application of RNAi developing valuable industrial products
RNAi to induce prolonged shelf life
RNAi to silence toxic compounds and allergens in edible fruits
RNAi for non-hazardous oil production
Limitations of RNAi
Biosafety concerns of RNAi technology
Conclusion
References
19
MicroRNA-mediated regularity functions under salinity stress in plants
Introduction: Role of microRNAs in abiotic stresses in plants
miRNA abundance and dynamics under salinity stress
Regulation of miRNAs and associated target genes under salt stress
Development of microRNA-mediated salinity tolerance in plants
Conclusions and perspectives
References
20
Biotic stress-tolerant plants through small RNA technology
Plant biotic stress
Plant immunity responses against biotic stress factors
Biotic stress response related to small RNAs
Role of small RNAs in virus-plant interactions
Role of small RNAs in bacteria-plant interactions
Role of small RNAs in fungi-plant interactions
Role of small RNAs in nematode-plant interactions
Role of small RNAs in herbivore-plant interactions
Small RNA applications for biotic resistance
Artificial miRNA applications
Site-specific genome editing applications
References
21
Application of miRNA in fruit quality improvement
Introduction
MicroRNA and fruit texture
MicroRNA and fruit coloration
MicroRNA and fruit development
MicroRNA and fruit ripening
Concluding remarks and future prospects
References
22
Physiological modification of plants through small RNA
Foundations of plant physiology
Regulation of the physiological development
Discovery of plant small RNAs
Classification on the basis of RNA source
Classification on the basis of RNA synthesis
General mode of action
Small RNAs in physiological development regulations
Chromatin modification
Target degradation
Lowering translational efficiency
Small RNA-mediated physiological and developmental control
Vegetative growth
Shoot apical meristem development and maintenance
Leaf development
Root development
Phase transition
Floral development
Flower patterning
Flower development
Seed development, maturation, and germination
Seed development and trigger for embryo formation
Seed maturation and storage of products
Seed size control
Seed germination
Conclusions and perspectives
References
23 Small RNA technology for plant abiotic stress tolerance
Introduction
Application of sRNA technology in plants
General mechanism of RNAi
The biology of miRNAs in plants
siRNA-mediated abiotic stress tolerance in plants
MicroRNA-mediated abiotic stress tolerance in plants
Conclusion
References
Section 4: Future scope
24
Challenges of small RNA technology
Small RNAs in plants: An expanding world
RNA interference: Basis of small RNA technology
Small RNA technology: Enormous potential
Disease resistance
Metabolic engineering
Abiotic stress
Male sterility
Small RNA technology: Its challenges and pitfalls
Off-target effects
Stability of transgene
Persistence of double-stranded RNAs (dsRNAs)
Food risk assessment of genetically modified (GM) plants
Biosafety evaluation of genetically modified (GM) plants
Future prospects
Bioinformatics approaches to achieve better gene silencing
Spray-induced gene silencing
CRISPR/Cas9: A platform for editing plant genomes
Conclusions
References
25
Future scope of small RNA technology in crop science
Introduction
Overview of diverse small RNA pathways in plants
Perspectives on small RNA sequencing and trafficking
Developments and challenges of sRNA-mediated gene silencing for crop improvement
Stress memory and forgetfulness in plants
Small RNA-mediated epigenetic engineering and RNA editing in plants
Small RNA trafficking between kingdoms and applications for plant disease control
Conclusion
Acknowledgments
References
Index
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Citation preview

Plant Small RNA Biogenesis, Regulation and Application

­Plant Small RNA Biogenesis, Regulation and Application

Edited by

Praveen Guleria

DAV University, India

Vineet Kumar

Lovely Professional University, India

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom © 2020 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN 978-0-12-817112-7 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Charlotte Cockle Acquisitions Editor: Nancy Maragioglio Editorial Project Manager: Redding Morse Production Project Manager: Sojan P. Pazhayattil Cover Designer: Victoria Pearson Typeset by SPi Global, India

Contributors Muhammad Abdullah School of Life Science; Anhui Provincial Engineering Technology Research Center for Development & Utilization of Regional Characteristic Plants, Anhui Agricultural University, Hefei, People’s Republic of China Uzma Afreen Department of Bio-Engineering, Birla Institute of Technology, Ranchi, India Çimen Atak Faculty of Science and Letters, Department of Molecular Biology and Genetics, Istanbul Kultur University, Istanbul, Turkey Alp Ayan Faculty of Science and Letters, Department of Molecular Biology and Genetics, Istanbul Kultur University, Istanbul, Turkey Ali Mohammad Banaei-Moghaddam Laboratory of Genomics and Epigenomics (LGE), Department of Biochemistry, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran Kailash C. Bansal TERI-Deakin NanoBiotechnology Centre, The Energy and Resources Institute, New Delhi, India Yongping Cai School of Life Science; Anhui Provincial Engineering Technology Research Center for Development & Utilization of Regional Characteristic Plants, Anhui Agricultural University, Hefei, People’s Republic of China Hieu Xuan Cao Institute of Biology/Plant Physiology, Martin-Luther-University of HalleWittenberg, Halle, Germany Özge Çelik Faculty of Science and Letters, Department of Molecular Biology and Genetics, Istanbul Kultur University, Istanbul, Turkey Xi Cheng School of Life Science; Anhui Provincial Engineering Technology Research Center for Development & Utilization of Regional Characteristic Plants, Anhui Agricultural University, Hefei, People’s Republic of China Armin Dadras Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran Summi Dutta Department of Bio-Engineering, Birla Institute of Technology, Ranchi, India

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Contributors

Titash Dutta Department of Biochemistry and Bioinformatics, Institute of Science, GITAM (Deemed to be University), Visakhapatnam, India Muhammad Fahim Centre for Omic Sciences, Islamia College University, Peshawar, Pakistan Paulo Cavalcanti Gomes Ferreira Laboratório de Biologia Molecular de Plantas, Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Cidade Universitária, Rio de Janeiro, Brazil Sayak Ganguli Department of Biotechnology, St. Xavier’s College (Autonomous), Kolkata, India Clicia Grativol Laboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, Brazil Simmi Grewal School of Biotechnology, University of Jammu, Jammu, India Adriana Silva Hemerly Laboratório de Biologia Molecular de Plantas, Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Cidade Universitária, Rio de Janeiro, Brazil Klaus Humbeck Institute of Biology/Plant Physiology, Martin-Luther-University of HalleWittenberg, Halle, Germany Syed Sarfraz Hussain Department of Biological Sciences, Forman Christian College (A Chartered University), Lahore, Pakistan; School of Agriculture, Food and Wine, The University of Adelaide, Urrbrae, SA, Australia Muhammad Irfan Department of Biological Sciences, Forman Christian College (A Chartered University), Lahore, Pakistan Amit Katiyar ICMR-AIIMS Computational Genomics Centre, Division of I.S.R.M., Indian Council of Medical Research, New Delhi, India Lovepreet Kaur Domain of Molecular Biology and Genetic Engineering, School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, India Annangarachari Krishnamachari School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India

Contributors

Abhinav Kumar IILM College of Engineering and Technology, Greater Noida, India Manish Kumar Department of Bio-Engineering, Birla Institute of Technology, Ranchi, India Anita Kumari Plant RNAi Biology Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India Sangram K. Lenka TERI-Deakin NanoBiotechnology Centre, The Energy and Resources Institute, New Delhi, India Binod Kumar Mahto TERI-Deakin NanoBiotechnology Centre, The Energy and Resources Institute; TERI School of Advanced Studies, New Delhi, India Sayed-Amir Marashi Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran Sinan Meriç Faculty of Science and Letters, Department of Molecular Biology and Genetics, Istanbul Kultur University, Istanbul, Turkey Beixin Mo Guangdong Provincial Key Laboratory for Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, People’s Republic of China Xiaowei Mo Guangdong Provincial Key Laboratory for Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, People’s Republic of China Christina Mohr Institute of Biology/Plant Physiology, Martin-Luther-University of HalleWittenberg, Halle, Germany Lionel Morgado Groningen Bioinformatics Centre, University of Groningen, Groningen; Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands Kunal Mukhopadhyay Department of Bio-Engineering, Birla Institute of Technology, Ranchi, India F Nadiya Biotechnology and Bioinformatics Division, Jawaharlal Nehru Tropical Botanic Garden and Research Institute; Inter University Centre for Genomics and Gene Technology, Department of Biotechnology, University of Kerala, Thiruvananthapuram, India

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Contributors

Nageswara Rao Reddy Neelapu Department of Biochemistry and Bioinformatics, Institute of Science, GITAM (Deemed to be University), Visakhapatnam, India Zhongfu Ni State Key Laboratory for Agrobiotechnology and Key Laboratory of Crop Heterosis and Utilization (MOE) and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, People’s Republic of China Leandro de Oliveira Silva Laboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, Brazil Amita Pal Plant Biology, Bose Institute, Kolkata, India Abdul Qayyum Rao National Centre of Excellence in Molecular Biology (NCEMB), University of the Punjab, Lahore, Pakistan Cristian Antonio Rojas Universidade Federal da Integração Latino-Americana, Foz do Iguaçu, Brazil K.K. Sabu Biotechnology and Bioinformatics Division, Jawaharlal Nehru Tropical Botanic Garden and Research Institute, Thiruvananthapuram, India Neeti Sanan-Mishra Plant RNAi Biology Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India Monika Saroha Indian Institute of Wheat and Barley Research, Karnal, India Saad Hussain Shah Centre for Omic Sciences, Islamia College University, Peshawar, Pakistan Murali Sharaff Department of Biological Sciences, P.D. Patel Institute of Applied Sciences, Charotar University of Science and Technology, Changa, India Mohit Sharma Department of Biotechnology, DAV University, Jalandhar, India Pradeep Sharma Indian Institute of Wheat and Barley Research, Karnal, India Shiwani Guleria Sharma Domain of Microbiology, School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, India Bu-Jun Shi School of Agriculture, Food and Wine, The University of Adelaide, Urrbrae, SA, Australia

Contributors

Deepali Singh School of Biotechnology, Gautam Buddha University, Greater Noida, India Pankaj K. Singh Plant Biology, Bose Institute, Kolkata, India Ravi K. Singh Department of Plant Biology, Uppsala BioCenter, Linnean Center for Plant Biology, Swedish University of Agricultural Sciences, Uppsala, Sweden Vartika Sinha Department of Genetics, University of Delhi-South Campus, New Delhi, India Avneet Kour Sudan School of Biotechnology, University of Jammu, Jammu, India Qixin Sun State Key Laboratory for Agrobiotechnology and Key Laboratory of Crop Heterosis and Utilization (MOE) and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, People’s Republic of China Challa Surekha Department of Biochemistry and Bioinformatics, Institute of Science, GITAM (Deemed to be University), Visakhapatnam, India Kai Tang Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN, United States; Shanghai Center for Plant Stress Biology and Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China Sachin Teotia Department of Biotechnology, Sharda University, Greater Noida, India Flávia Thiebaut Laboratório de Biologia Molecular de Plantas, Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Cidade Universitária, Rio de Janeiro, Brazil Jyoti Vakhlu School of Biotechnology, University of Jammu, Jammu, India Shabir H. Wani Mountain Research Centre for Field Crops, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, India Mingming Xin State Key Laboratory for Agrobiotechnology and Key Laboratory of Crop Heterosis and Utilization (MOE) and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, People’s Republic of China Yingyin Yao State Key Laboratory for Agrobiotechnology and Key Laboratory of Crop Heterosis and Utilization (MOE) and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, People’s Republic of China

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Contributors

Yu Yu Guangdong Provincial Key Laboratory for Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, People’s Republic of China Abbu Zaid Plant Physiology and Biochemistry Section, Department of Botany, Aligarh Muslim University, Aligarh, India

CHAPTER

Introduction to plant small RNAs

1

Yu Yu, Xiaowei Mo, Beixin Mo Guangdong Provincial Key Laboratory for Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, People’s Republic of China

­Introduction According to the central dogma of biology, DNAs encode the genetic information required to make proteins, and RNAs are messenger molecules that ensure the genetic information is translated into proteins on ribosomes. Although this classic model has been a major principle of molecular biology for more than half a century, findings about noncoding RNAs in the past few decades have shed light on the essential functions exerted by genomic regions previously thought of as “junk” DNA [1–5]. Small RNAs are a group of 20–30 nucleotide (nt) noncoding RNAs that exist in diverse eukaryotic organisms [1–3]. Despite their tiny size, small RNAs affect numerous biological processes throughout the lives of living organisms by transcriptionally or post-transcriptionally regulating gene expression [6–9]. Due to the widespread impact of small RNAs and their functional potentials as powerful molecular biology tools, tremendous research efforts have focused on the small RNA regulatory machinery, small RNA metabolism, and the underlying mechanisms. Plenty of recent studies have revealed the functions of small RNAs in diseases such as cancer, further underscoring the urgency and importance of deciphering small RNA-related mechanisms [10–12].

­Discovery history of small RNAs Although the essential functions of small RNAs in gene expression regulation and the mechanisms underlying small RNA metabolism were not revealed until two decades ago, the phenomena resulting from regulation mediated by endogenous and transgene-derived small RNAs were already reported in numerous studies in the late 1980s. The first report of an endogenous small RNA was in 1993; the microRNA (miRNA) lin-4 was identified in the nematode Caenorhabditis elegans (C. elegans) through molecular genetic analysis showing that a mutation in the miRNA gene led Plant Small RNA. https://doi.org/10.1016/B978-0-12-817112-7.00001-8 © 2020 Elsevier Inc. All rights reserved.

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CHAPTER 1  Introduction to plant small RNAs

to developmental defects [13]. In the same year, the first small RNA/target module, lin4/lin14, was also identified [14]. A novel mechanism of gene regulation at the posttranscriptional level was established by these two landmark discoveries. However, the broader importance of miRNAs was not fully recognized until 7 years later, when let-7, a 21-nt C. elegans miRNA, was found to regulate the L4-to-adult developmental transition in larva [15]. Shortly thereafter, a large class of small regulatory RNAs exhibiting diverse expression patterns was uncovered in nematodes [16–18]. In plants, the phenomenon of co-suppression was discovered in studies of flavanol and anthocyanin biosynthesis. In the petunia, an overexpression of a genes encoding dihydroflavonol 4-reductase and chalcone synthase led to reduced RNA levels of both the endogenous genes and the transgenes [19–21]. A few years later in 1999, a class of short antisense small interfering RNA (siRNA) was discovered and characterized as the specific determinant of post-transcriptional gene silencing (PTGS) [22]. In 2002, a study using a small RNA cloning strategy, miRNAs in Arabidopsis and rice were found [23], and their corresponding target mRNAs with near-perfect sequence complementarity were predicted [24]. As with the findings in nematodes, the observed silencing effects in plants were similarly mediated by small noncoding RNAs, and these processes were found to be regulated by similar enzymes in most eukaryotes [25–27]. Since the initial discoveries, numerous small RNAs have been identified in diverse eukaryotes through highthroughput sequencing and bioinformatic prediction strategies [28–30].

­Diversity of small RNAs Small RNAs are classified into three major types based on their biogenesis and associated proteins: miRNAs, siRNAs, and Piwi-interacting RNAs (piRNAs), which only present in animals. miRNAs are generated from stem-loop- or hairpin-structured single-stranded RNA (ssRNA) precursors. The processing of precursors into mature miRNAs requires the activity of RNase III-type endonucleases DICER and DROSHA in animals or DICER-LIKE (DCL) in plants [27, 31, 32]. The biogenesis of siRNAs also requires DICERs or DCLs, but siRNAs derive from RNA-DEPENDENT RNA POLYMERASEs (RDRs)-produced long double-stranded RNA (dsRNA) precursors with perfect complementarity or single-stranded transcripts with inverted repeat elements [33]. siRNAs can be further divided into several subgroups: heterochromatic small interfering RNA (hc-siRNA), natural antisense transcript-derived small interfering RNAs (nat-siRNA), and phased small interfering RNAs (phasiRNAs). Despite their different origins, both miRNAs and siRNAs are loaded into ARGONAUTE (AGO) proteins to induce gene silencing. In terms of their mode of action, small RNAs specifically recognize target transcripts through sequence complementarity and mediate transcriptional gene silencing (TGS) or PTGS. In TGS, small RNAs direct DNA methylation and histone modification to inhibit the transcription of transgenes or transposons, thereby contributing to genome stability maintenance [33]. In PTGS, small RNAs

Diversity of small RNAs

­ odulate gene expression via transcript cleavage, decay, or translation inhibim tion of target RNAs that derive from protein-coding genes, transposable elements, transgenes, and viral RNAs [2, 8, 9]. Here we discuss the biogenesis, movements, regulatory functions, and action modes of major small RNA groups involved in PTGS in plants.

­miRNAs In plants, miRNAs and siRNAs are the two major classes of small RNAs [2]. Despite that miRNAs constitute a tiny fraction of small RNAs, they have a considerable effect on diverse biological processes. Defects in miRNA metabolism or activity often lead to developmental abnormalities and compromised stress responses. Over the past two decades, many research efforts have aimed to elucidate the machinery that precisely maintains and dynamically regulates miRNA levels in vivo, as well as the factors that mediate or affect miRNA activities [8, 9]. These studies have yielded tremendous insight into the biological processes of gene silencing.

­Biogenesis of plant miRNAs Biogenesis of miRNA is a multistep process involving transcription, processing, modification, and assembly of the RNA-induced silencing complex (RISC) (Fig. 1). miRNAs have their own MIR genes. In plants, like protein-coding genes, RNA POLYMERASE II (Pol II) transcribes MIR genes and generates long single-stranded primary transcripts (pri-miRNAs) stabilized by a 5′ cap and 3′ polyadenylated (poly A) tail [23, 30, 34]. These pri-miRNAs form imperfectly paired stem-loop or hairpin structures flanked by single-stranded RNA extensions, the miRNA strand and miRNA* strand are on opposite sides of the stem [30]. The RNase III enzyme DCL1 first cleaves pri-miRNAs to produce precursor-miRNAs (pre-miRNAs) harboring the stem-loops. DCL1 further processes these pre-miRNAs into miRNA/miRNA* duplexes with 3′ 2-nt overhangs on each strand [31, 32, 35]. Genetic studies in Arabidopsis thaliana, a model plant, have demonstrated that DCL1 is the predominant enzyme responsible for the dicing of most pri-miRNAs, whereas DCL4 processes several other evolutionarily young miRNAs [36]. To ensure their stability, nearly all plant mature miRNAs undergo 2′-O-methylation catalyzed by a methyltransferase HUA ENHANCER 1 (HEN1) at their 3′ ends [31, 37–40]. AGO1 incorporates methylated miRNAs with the aid of TRANSPORIN 1 (TRN1) in the nucleus, whereas miRNA* strands are removed [41]. Arabidopsis HASTY (HST), the homolog of mammalian exportin-5 gene, is exclusively required for miRNA export from the nucleus to the cytoplasm [42–44], as loss of function in HST leads to decreased abundance of most miRNAs without affecting siRNA accumulation [43]. In addition to the previously mentioned essential core components, many other cofactors help ensure the precise and accurate production of miRNAs. The transcription of MIR genes requires the general transcriptional coactivator Mediator and its interacting transcriptional activators to recruit Pol II to MIR promoters [45]. In the loss-of-function mediator mutant, the transcription of many MIR genes is affected,

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FIG. 1 Overview of miRNA biogenesis, turnover, and modes of action in plants. MIR genes are transcribed by Pol II, which gives rise to single-stranded pri-miRNAs under the facilitation of mediator and CDC5. DCL1 processes pri-miRNAs into mature miRNA duplexes via two steps, together with HYL1, SE, and other cofactors. HEN1 mediates the 3′ end 2-O′-methylation of both strands in the duplexes. miRNA strands are then loaded into AGO1 with the aid of TRN1 and later transported from nucleus to cytoplasm by HST. In the cytoplasm, miRNAs direct PTGS via transcript cleavage and translation repression. miRNA degradation starts with the removal of the methyl group at the 3′ end by SDN1, which is followed by 3′ uridylation through HESO1 and/or URT1. The tailed miRNAs are subsequently degraded by an unknown exonuclease. SDN1 and nucleotidyl transferases (HESO1 and URT1) can act on both AGO-bound miRNAs and free miRNAs in the cytoplasm.

resulting in reduced accumulation of both miRNA precursors and mature miRNAs. A DNA-binding protein CELL DIVISION CYCLE 5 (CDC5) binds to Pol II and MIR promoters, acting as a positive transcription factor by mediating Pol II occupancy at MIR promoters [46, 47]. CDC5 also interacts with PLEIOTROPIC REGULATORY LOCUS 1 (PRL1), a conserved WD-40 protein, to enhance the activity of DCL1

Diversity of small RNAs

[48]. Subunits of Elongator were found to interact with DCL1 and play a role in MIR gene transcription, consistent with a model proposed for co-transcriptional processing during miRNA biogenesis [49]. During MIR gene transcription and pri-miRNA processing, DCL1 is assisted by several essential cofactors that directly interact with DCL1 to form a dicing complex in nuclear dicing bodies (D-bodies) [50–52]. One of the processing cofactors, a forkhead-associated (FHA) domain protein DAWDLE (DDL), stabilizes pri-miRNAs and facilitates their recognition by DCL1 [53, 54]. HYPONASTIC LEAVES 1 (HYL1), a dsRNA-binding protein [55–58], and SERRATE (SE), a C2H2 zinc-finger protein, [38, 39, 59, 60] are required for the accurate and efficient pri-miRNAs processing by DCL1 [50]. Mutants of the HYL1, SE, and DCL1 genes exhibit pleiotropic developmental defects accompanied by decreased abundance of mature miRNAs and increased levels of pri-miRNAs. Two components of the nuclear cap-binding complex (CBC), ABA HYPERSENSITIVE 1 (ABH1)/ Cap-Binding Protein 80 (CBP80) and CBP20, bind pri-miRNAs and facilitate their access to D-bodies for processing [61]. The G-patch domain protein TOUGH (TGH) enhances the dicing activity of DCL1 [62, 63]. Arabidopsis STABILIZED 1 (STA1) regulates DCL1 transcript levels to influence pri-miRNA dicing [64, 65]. MODIFIER of SNC1 2 (MOS2), an RNA-binding protein that binds pri-miRNAs, promotes the processing of pri-miRNA by recruiting pri-miRNAs to the dicing complexes [66, 67]. Some core factors in miRNA biogenesis are also strictly regulated. For example, the active hypo-phosphorylated state of HYL1 is maintained by C-terminal Domain Phosphatase-Like 1 (CPL1), which ensures accurate miRNA processing [68]. In addition, HYL1 is protected from degradation against an unknown endoproteinase by a RING finger-containing E3 ligase CONSTITUTIVELY PHOTOMORPHOGENIC 1 (COP1) [69].

­miRNA turnover Plant miRNAs are protected from degradation by HEN1-catalyzed 2′-O-methylation at the 3′ end. Loss of HEN1 in Arabidopsis results in reduced accumulation of most miRNAs as well as miRNA size heterogeneity [37–40]. A similar result is observed in rice [70]. Further studies on the Arabidopsis hen1 mutant uncovered that 3′-to-5′ truncation and 3′ uridylation are the two major mechanisms of miRNA degradation (Fig. 1). In fact, these mechanisms can also be applied to piRNA degradation in animals, such as Drosophila [71, 72], C. elegans [73], zebra fish [74], and mouse [75]. Therefore, 3′ methylation of small RNAs is a general and essential mechanism for protecting them from degradation. Although several other genes required for small RNA degradation have been identified [62, 63, 76–79], the full scope of small RNA turnover remains unclear. In Arabidopsis, rice, and maize, 3′ uridylation of miRNAs is widely observed in hen1 mutants [37–40, 70, 80]. Two Arabidopsis nucleotidyl transferases HEN1 SUPPRESSOR 1 (HESO1) and UTP:RNA URIDYLYLTRANSFERASE (URT1) uridylate miRNAs with no 3′ methylation and promote their degradation [62, 63, 77–79]. Mutations in HESO1 and URT1 both partially restore the reduced miRNA

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abundance, increased 3′ uridylation, and the developmental defects in hen1 mutant [62, 63, 77–79]. In vitro, HESO1 and URT1 have similar capacity to add U residues to unmethylated RNAs, and the 3′ methyl group of RNAs inhibits their activities [62, 63, 77–79]. Interestingly, they have different preferences on miRNA substrates in  vivo: HESO1 and URT1 prefer U-ending and A-ending miRNAs, respectively [62, 63, 77–79], proposing the possibility that unmethylated miRNAs, especially A-ending miRNAs, first undergo URT1-catalyzed uridylation, which makes them favored for HESO1. The exonucleases responsible for small RNA degradation have also been identified. Reverse genetic screening and in  vitro enzymatic assay identify that SMALL RNA DEGRADING NUCLEASE 1 (SDN1) exhibits 3′-to-5′ exonuclease activity and acts specifically on short ssRNA [76]. SDN1 belongs to a family containing four members. Although knockdown of one or two members does not influence plant growth and development, mutations in three SDN members result in severe morphologic abnormality and increased accumulation of both miRNAs and siRNAs, indicating the redundant function of SDN members in degrading small RNAs [76]. Although the 3′ methyl group of small RNAs partially inhibits the exonuclease activity [76], it has been shown that in both hen1 and wild-type plants, the 3′ truncation of miRNAs is carried out by SDNs [8, 9]. By comparing miRNA profiles, reduced accumulation of 3′ truncated miRNAs is observed in hen1 or wild-type plants with the absence of both SDN1 and SDN2 [8, 9]. Because uridylated miRNAs cannot be degraded by SDN1 in vitro [76], there should be extra exonucleases, such as other SDNs or unidentified exonucleases, which are responsible for degrading uridylated miRNAs. Several exonucleases that prefer uridylated RNAs as substrates have been identified in other eukaryotes. In mammals, uridylated pre-let-7 is degraded by a 3′-to-5′ exonuclease DIS3-like 2 (DIS3L2), whose ortholog in yeast is also able to degrade uridylated RNAs [81, 82]. In Chlamydomonas, the exosome subunit Ribosomal RNA-Processing Protein 6 (RRP6) exhibits 3′-to-5′ exonuclease activity in vitro and acts specifically on uridylated miRNAs. Removal of RRP6 in  vivo leads to elevated abundance of small RNAs [83], implying that RRP6 might be the exonuclease for degradation of uridylated miRNAs. Therefore, studying the orthologs of RRP6 and DIS3L2 would accelerate the discovery of the exonuclease favoring uridylated miRNAs in Arabidopsis. Both HESO1 and URT1, but not SDN1, are evidenced to interact with AGO1 in vivo [78, 84]. In vitro, all three proteins are able to uridylate [77, 78, 84] or truncate [8, 9] AGO1-associated miRNAs and the modified miRNAs remain on AGO1. Because the activities of HESO1 and URT1 are completely inhibited by the 3′ methyl group of miRNAs, which only partially function on SDN1 [76, 77, 79], it is proposed that during the degradation of AGO1-bound miRNAs, SDN1 removes the 3′ methyl group from miRNAs, making unmethylated miRNAs favorable to HESO1 and URT1 for uridylation, and an unidentified exonuclease degrades U-tailed miRNAs. This mechanism may also be applied to free miRNAs, or free miRNAs can be degraded directly by SDN1.

Diversity of small RNAs

­Mode of action of miRNAs Plant miRNAs mainly modulate gene expression in PTGS via two mechanisms: transcript cleavage and translation repression [2, 8, 9] (Fig. 1).

­miRNA-guided transcript cleavage After recognizing the targets through sequence complementarity, miRNAs guide the major miRNA effector AGO1 to cleave target transcript at the phosphodiester bond corresponding to the 10th and 11th nucleotides of miRNA [85]. Besides AGO1, other AGO proteins, including AGO2, AGO4, AGO7, and AGO10, have also been shown to have the cleavage activity, which is exhibited by their RNase H-like PIWI domain [86–90]. Cleavage leaves a 5′ monophosphate on the 3′ fragments, and genome-wide identification of RNAs with a 5′ monophosphate revealed that transcript cleavage happens to almost all miRNA targets [91].

­miRNA-mediated translation repression Due to the widespread presence of miRNA-mediated transcript cleavage, it is rare to observe translation repression guided by miRNAs in plants. Regulation of AP2 and SPL3 by miR172 and miR156/157, respectively, are two early examples. With abnormal accumulation of miR172 and miR156/157, the protein levels of AP2 and SPL3 are changed while their transcript levels remain comparable to those in wild type [92–94]. This leads to the initial proposal for miRNA-mediated translation repression [92–94]. Later on, several other miRNAs were reported to regulate their targets in a similar manner, such as miR159 [95], miR164, miR165/6 [96], miR171, miR395, miR398, and miR834 [97]. Moreover, it has been showed that miRNAs repress translation of their targets by inhibiting protein synthesis rather than promoting protein degradation [96]. Several factors involved in miRNA-guided translation repression have been identified, including KATANIN 1 (KTN1), a microtubule-severing enzyme [97]; VARICOSE (VCS), a component of processing body (P body) [97]; SUO, a GW-repeat protein [98]; and ALTERED MERISTEM PROGRAM 1 (AMP1), an ER membrane protein [96]. Mutations in these genes alter the protein levels of miRNA targets without affecting their transcript levels, indicating that miRNA-mediated translation repression is independent of transcript cleavage. Moreover, most miRNAs are enriched on membrane-bound polysomes [99], implying that miRNA-guided transcript cleavage and translation repression take place on the ER. Nevertheless, transcript cleavage may also occur in a manner independent of polysomes in the cytosol [99–101].

­miRNAs in plant development The broad impacts of miRNA-mediated regulation have been extensively studied. Many miRNA targets in plants are transcription factors that modulate diverse biological processes, including the development of rosette leaves and roots, floral morphogenesis, stem apical dominance, hormone signaling, abiotic stress responses to drought and high salinity, and plant immunity against viruses and bacteria [3, 7].

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Many studies have elucidated the biological influence of miRNAs during plant development, allowing an in-depth understanding of their critical roles (Fig. 2; Table 1).

­miRNA-mediated regulation of meristem organization and cell polarity Plant meristems contain undifferentiated cells in specific zones where growth can take place. Cells of the meristem give rise to plant organs, and the cell differentiation process is strictly regulated at the molecular level. As key regulators of gene expression, miRNAs and their functions are integral components of meristem maintenance. Plant leaves arise from the peripheral regions of the shoot apical meristem and develop a proximal-distal, adaxial-abaxial, and medio-lateral polarized architecture; leaf development progresses as the cells divide, expand, and differentiate. Several miRNAs are known to regulate meristem maintenance and leaf patterning. miR394 specifically accumulates in the protoderm, where the expression of its target LEAF CURLING RESPONSIVENESS (LCR) is restricted; this miRNA-target module helps maintain stem cell identity and is involved in a local WUSCHEL (WUS)–CLAVATA (CLV) feedback loop [102]. Three auxin response factor (ARF) genes, ARF10/16/17, are targeted by miR160 to maintain local auxin efflux in meristem and leaf phyllotaxis [103]. The Arabidopsis miR165/166 family contains eight members, two from miR165 (miR165a and miR165b) and six from miR166 (miR166a-g), differing in only one nucleotide. Expression of miR165/166 in the abaxial side of the leaf primordium restricts the targets PHB and PHAVOLUTA (PHV) to the adaxial leaf region [104, 105]. Meanwhile, adaxially expressed miR390 and TAS3 generate tasiRNAs that restrict ARF2/3/4 expression to the abaxial side [106]. Together, these two pathways establish proper leaf polarity.

­miRNA-mediated regulation of flower development Flower development is an important stage of plant growth, and it includes the ­vegetative-to-reproductive phase transition and floral organization. In Arabidopsis, the miR156/157, miR159, and miR172 miRNA families exhibit regulatory functions during flowering [107]. The miR156/157 family is conserved with 10 members, miR156a-h and miR157a-d, and its involvement in the transition to the reproductive phase is well established. During this process, the abundance of miR156/157 declines, resulting in increased expression of their target genes SQUAMOSA PROMOTER BINDING PROTEIN-LIKE (SPL) [94]. Consistently, miR156/157 overexpression leads to delayed flowering. miR156 in monocot crops appears to be involved in auxiliary meristem initiation. In switchgrass, the miR156-SPL module regulates tillering [108]. In maize, miR156 regulation of TSH4 controls ear development, a process important for grain formation [109]. The miR156-SPL module also involves the control of the age pathway in plants [110]. In tomato, the miR156-modulated age pathway along with the miR319-mediated pathway regulates the transition to flowering in response to gibberellin (GA) signaling, integrating two unrelated miRNA functionalities [111]. miR159 participates in flowering time control under short-day conditions and promotes the floral transition through the regulation of the GAMYB-related genes

miR159 miR172 miR396

Floret development

miR2118

Male sterility

miR172 miR393 miR156 miR157 miR159

Flowering time

miR393 miR319

miR160 miR165 miR166 miR390

Leaf morphogenesis

Stem elongation

miR156 miR393 miR444

Tillering

miR393 miR444 miR395 miR398 miR399

Nutrient homeostasis

miR393 miR444 miR164 miR165 miR166 miR390

Root architecture

miR156 miR172 miR397

Grain size

miR156 miR396 miR397

Antifungal resistance

miR398 miR319

Antiviral resistance

miR444 miR528

Cold tolerance

miR319

Salt-alkali tolerance

Drought tolerance

[

Heavy metal tolerance

Reproductive stage

miR396 miR393 miR393

miR162 miR164 miR528

Ripening stage

FIG. 2 Functions of miRNAs in plant development and stress responses. An overview of the current understanding of miRNA-mediated regulation during development and responses to biotic and abiotic stresses in rice.

Diversity of small RNAs

miR159 miR156

Panicle branching

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Table 1  miRNAs in plant development. miRNA

miRNA targets

Functions of miRNA targets

miRNA156/157

SPL family

miRNA159

MYB33/65/101

miRNA160

ARF10/16/17

miRNA164

CUC/NAC

miRNA165/166

HD-ZIP III family

miRNA167

ARF6/8

miRNA169 miRNA172

HAP2 AP2 family

miRNA319

TCP family

miRNA390/TAS3 miRNA393

ARF2/3/4 TIR1/AFB

miRNA394 miRNA396 miRNA397

LCR GRF LAC

Flowering, tillering, ear development, aging, GA response, grain size, panicle branching Flowering, anther, silique and seed development Auxin response, root cap formation, seed development Meristem boundary identity, auxiliary meristem formation, leaf serration, lateral root formation Meristem maintenance, leaf patterning, root architecture, seed germination Auxin response, seed dispersal and floral organ development, male organ development Nodule cell differentiation Flowering, seed development, floral organ development Cell growth and proliferation, leaf and petal shape, GA response, nitrogen fixation Leaf patterning, lateral root development Lateral root development, nodule development, seed development Meristem maintenance Cell proliferation in leaf, grain size Grain size

MYB33/65/1101, which are GA-specific transcription factors that affect LEAFY activity [112]. miR172 is involved in flower development through translation repression of its target APETALA 2 (AP2). The role of miR172 in flowering control is conserved in a variety of plant species, such as maize, barley, soybean, and rice [92, 93, 113–118].

­miRNA-mediated regulation of root architecture Plant roots allow water and nutrient uptake and are also the site of interaction with the soil environment. Root development is precisely regulated by multiple factors composing a well-organized network. In plants, a few small RNAs and their targets are known to participate in lateral root development. These include miR164 and its target, the plant-specific transcription factor NAC1 DOMAIN CONTAINING PROTEIN 1 (NAC1), which regulate lateral root formation in Arabidopsis and maize [119–121]. Similarly, overexpression of Stu-miR164 under osmotic stress in potato causes reduced expression of StNAC262 and limits the number of lateral roots [122]. The MIR165/166 genes encoding miR165a, miR166a, and miR166b are expressed

Diversity of small RNAs

in the epidermis of the root meristem to restrict PHABULOSA (PHB) expression to the central root cell layers, thereby shaping the root architecture [123, 124]. The specific expression of miR390 at the lateral root initiation site triggers the production of tasiRNAs to inhibit ARF2/3/4 and thus controls the local auxin regulatory network. In turn, miR390 is indirectly repressed by ARF4 and positively regulated by other ARFs [125]. This positive and negative feedback loop between miR390 and ARF2/3/4 defines the proper localization of miR390 and further regulates lateral root development. The miR160 targets ARF10 and ARF16 respond to auxin and exhibit regulatory function during the formation of root cap [126]. In addition, under stress conditions, miR393 is induced and cleaves transcripts of the auxin receptors TIR1 and AFB2, thereby influencing lateral root development [127]. miRNAs are also involved in root symbiosis with the soil environment. In Medicago truncatula, miR169 influences nodule cell differentiation by restricting the expression of HAP2-1, a transcription factor of the CCAAT-binding family, to the nodulation zone [128]. The miR319d-TCP module plays a role in the nitrogen fixation process between common bean and rhizobia [129]. In soybean, miR393 regulates the auxin signaling mediators GmTIR1 and GmAFB3, and this regulation is critical for the determination of nodule development [130]. Interestingly, miR390 overexpression in M. truncatula promotes lateral root growth but inhibits nodule formation, indicating opposite functions of the miR390-ARF module in these developmental processes [131].

­miRNA-mediated regulation of seed development In a subset of land plants, seeds are a resulting product of fertilization. Some of the miRNAs that function during seed production, maturation, and germination include miR159, miR160, miR165/166, miR395, miR402, and miR417 [132]. In Arabidopsis, miR159a/b targeting of MYB33/65 regulates seed size and morphology [133]. The miR172-AP2 module is also involved in the determination of seed size, weight, and oil and protein contents [134]. In Arabidopsis and tomato, the miR167-ARF6/8 pathway appears to regulate seed dispersal and floral organ development [135, 136]. ARF10, the target of miR160, affects seed development, as the silencing of this gene causes twisted seed siliques [137]. Loss of Arabidopsis miR165/166 results in hypersensitivity to ABA during seed germination and early seedling stages [138]. In Brassica napus, increased expression of miR2111, miR399, miR827, and miR408 restricts silique growth due to insufficient inorganic phosphate/copper [139]. In rice, miR156 regulation of SPL genes affects grain size and panicle branching [7]. Specifically, OsSPL13 positively regulates grain length and grain thickness, whereas OsSPL16 negatively controls grain length but positively controls grain width. Grain size is also modulated by the targeting of OsGRF4 and OsLAC by miR396 and miR397, respectively [46, 47, 140]. In terms of grain quality, overexpression of osa-miR5144 leads to reduced levels of its target OsPDIL1;1, a key catalyst in protein-disulfide bond content regulation and in loosely packed starch granule formation in grains [141].

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Overexpression and target mimic investigations of miR393 indicate that it functions in barley seed development by targeting two TIR1/AFB auxin receptors [142]. In addition, many miRNAs are enriched in the embryos of developing and germinating barley seeds, such as miR156, miR168, miR166, miR167, and miR894, as well as barley-specific miR5071, which is potentially involved in defense responses through the targeting of an OsMLA10-like gene [142]. Accordingly, these miRNAs may have important regulatory roles during embryo development.

­siRNAs In plants, around 90% of small RNAs are siRNAs that derive from double-stranded precursors. They can be further categorized by length into the 21-nt, 22-nt, and 24nt classes. siRNAs belonging to the 21-nt and 22-nt classes primarily derive from viral RNAs, transposons, and transgenes, and are bound by AGO1 to guide PTGS. In contrast, 24-nt siRNAs are mainly associated with heterochromatic transposable elements (TEs) and act in TGS through RNA-directed DNA methylation (RdDM).

­Biogenesis of siRNAs As described earlier, 21-nt and 22-nt siRNAs derive from aberrant RNAs expressed from viruses, transposons, transgenes, and some DNA-breaking regions through Pol II-mediated transcription [143–145] (Fig.  3). These aberrant RNAs generally lack a 5′ cap or 3′ polyadenylation tail, making them suitable substrates for RNA DEPENDENT RNA POLYMERASE 6 (RDR6), which converts them into dsRNAs [144]. DCL4 and DCL2 slice these dsRNAs into 21-nt and 22-nt siRNAs, respectively, which are typically incorporated into AGO1 to guide PTGS targeting the original aberrant transcripts for cleavage [87, 89, 146]. Although both DCL2 and DCL4 can process RDR6-dependent dsRNAs, DCL4 outcompetes DCL2, resulting in a high abundance of 21-nt siRNAs and a low level of 22-nt siRNAs [143, 147– 150]. However, 21-nt siRNAs processed by DCL4 has less efficiency in recruiting RDR6 to trigger secondary siRNA, as compared to DCL2-processed 22-nt siRNAs [148–150]. It is also found that defective DCL4 or overexpressing DCL2 leads to a higher level of 22-nt siRNAs than in wild type and enhanced activity of AGO1mediated PTGS on transgenes [148–150]. Consistently, a mutation in DCL2 suppresses the accumulation of DCL2-dependent 22-nt siRNAs and secondary siRNAs in Arabidopsis. Recent evidence further suggests that Arabidopsis DCL2, rather than DCL4, is essential for the graft-transmissible, systemic spreading of PTGS signals through generating 22-nt siRNAs and recruiting RDR6 for secondary siRNA production [148–150]. siRNAs belonging to the 24-nt class are predominately derived from transposons and repetitive elements [33, 151, 152] (Fig.  3). They play essential roles in DNA methylation and chromatin modification via the well-established canonical RdDM pathway requiring Pol IV [33, 153, 154]. The biogenesis of 24-nt siRNAs is initiated by the transcription of ~ 26–45 nt single-stranded siRNA precursors at RdDM loci by the plant-specific RNA polymerase Pol IV [153, 155–158]. RDR2 converts the

Diversity of small RNAs

FIG. 3 Illustrations of siRNA biogenesis and modes of action in plants. Pol IV generates singlestranded siRNA precursors, which are converted into double-stranded RNAs (dsRNAs) by RDR2 and processed into 24-nt siRNA duplexes by DCL3. Methylated siRNAs are loaded into AGO4, and the complexes are recruited to Pol V transcripts, which further recruit DRM2 to catalyze DNA methylation at RNA-directed DNA methylation (RdDM) target loci. Pol II also produces transcripts that are converted into dsRNAs by RDR6. DCL2 and DCL4 process dsRNAs into 21-nt and 22-nt siRNAs, which are either loaded into AGO1 to mediate PTGS through transcript cleavage or guide non-canonical RdDM via associating with AGO2 or AGO6.

precursors into dsRNAs that are subsequently processed by DCL3 to produce 24-nt siRNA duplexes [33, 153, 159]. HEN1 methylates these siRNA duplexes, and the siRNA strands are incorporated into AGO4 [38, 39, 87]. It has been shown that 24-nt siRNA duplexes are processed in the nucleus then translocated to the cytoplasm to incorporate into AGO4 [160]. After the removal of the siRNA* strands, the siRNAAGO4 complexes are imported back to the nucleus to mediate TGS. Besides these DCL-dependent siRNAs, siRNAs independent on DCL have also been discovered in Arabidopsis [161]. It has been shown that some siRNAs are associated with AGO4 and trimmed to a group of heterogeneous siRNAs with the same 5′ end but different 3′ ends by a 3′-to-5′ exonuclease.

­Mode of action of siRNAs As mentioned earlier, siRNAs repress the expression of their targets through both PTGS and TGS (Fig. 3). Pol II- and RDR6-dependent 21-nt and 22-nt siRNAs are preferentially incorporated into AGO1 to guide transcript cleavage of their targets [87, 89]. This ­induces

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the systemic spread of silencing signal in the plant through the amplification of RDR6-dependent secondary siRNAs from target transcripts [162, 163], implying a mechanism of plant defense. In addition to PTGS, some Pol II- and RDR6-dependent siRNAs, usually those derived from transposons, can mediate de novo DNA methylation at target sites through a non-canonical RdDM pathway by associating with AGO2 or AGO6 [164–166]. Moreover, when the abundance of Pol II- and RDR6dependent dsRNAs exceeds the processing capacities of DCL2 and DCL4, the dsRNAs become accessible substrates for DCL3 and give rise to 24-nt siRNAs, which then triggers transposon silencing by the canonical RdDM pathway [167]. Pol IV- and RDR2-dependent 24-nt siRNAs largely direct canonical RdDM. Once the siRNA-AGO4 complex is formed, plant-specific RNA polymerase Pol V generates noncoding transcripts ~ 50 nt in length adjacent to the Pol IV transcription locus, and the siRNA-AGO4 complex is recruited through sequence complementarity [168, 169]. This further recruits DNA REARRANGED METHYLASE 2 (DRM2) to trigger de novo DNA methylation in the CG, CHG, and CHH sequence contexts (H stands for C, T, or A) [33, 151, 168]. The maintenance of CHH methylation requires the 24-nt siRNAs and RdDM, whereas DNA METHYLTRANSFERASE 1 (MET1) and CHROMOMETHYLASE 3 (CMT3) maintain CG and CHG methylation, respectively, in an siRNA-independent manner [33, 151]. Furthermore, 24-nt siRNA-­ directed DNA methylation mainly takes place on euchromatic chromatin arms because one essential protein required for RdDM, DEFECTIVE IN RNA-DIRECTED DNA METHYLATION 1 (DRD1), is not functionally effective at heterochromatic regions [170]. DNA methylation at heterochromatic regions close to the centromere is mediated by CMT2 in an siRNA-independent manner. Nevertheless, CMT2 loci also give rise to 24-nt siRNAs that are Pol IV- and RDR2-dependent, although their functions remain unclear [33]. In most cases, RdDM targets intergenic regions and transposons. However, 24-nt siRNA-dependent RdDM has also been suggested to modulate some protein-­coding genes. It has been reported that a ribonuclease III (RNase III) enzyme RNASE THREE-LIKE 2 (RTL2) processes dsRNAs into > 24-nt siRNA duplexes that are further processed into 24-nt by DCL3 [171]. In the loss-of-function rtl2 mutant, some protein-coding genes show reduced expression levels as compared with wild type, and the plant displays developmental defects that are restored by mutating NRPD1, which encodes the largest subunit of Pol IV.

­Biological functions of 24-nt transposable element (TE)-derived siRNAs One of the major roles of 24-nt TE-derived siRNAs is to guide the dynamic reprogramming of DNA methylation during reproduction in a non-cell-autonomous manner. In the female gametophyte, the central cell undergoes epigenetic depletion of CHH methylation and reactivation of TEs, leading to the generation of 24-nt siRNAs that move into the egg cell and reinforce DNA methylation therein [172–174]. In developing pollen, CHH methylation in microspores is largely lost and subsequently restored in vegetative cells, as diverse TEs are transitionally reactivated in the vegetative nucleus and give rise to 24-nt siRNAs that guide DRM2-dependent

Diversity of small RNAs

CHH ­methylation. However, sperm cells lack the expression of DRM2, and the reduced CHH methylation is recovered only after fertilization, probably through the activity of 24-nt siRNAs that move from the vegetative cells [175–177]. Similarly, DNA methylation levels change dynamically during seed development after fertilization, and 24-nt siRNAs produced in endosperms move into the embryo to reset the DNA methylation in both the CHG and CHH contexts [173, 178, 179]. Collectively, these findings highlight the significant function of hc-siRNAs in plant reproduction.

­phasiRNAs Although miRNA-mediated mRNA cleavage fragments are typically subject to rapid degradation, a small proportion of them are further processed into secondary siRNAs. These siRNAs are known as phasiRNAs and are arranged in a head-to-tail fashion in phase relative to the miRNA cleavage sites. phasiRNA production is a widespread phenomenon in plants, and the biological impacts of some phasiRNAs have been documented [157, 158, 180].

­Biogenesis of phasiRNAs Genome-wide small RNA sequencing and bioinformatics analyses uncovered that phasiRNAs in dicots are generated from protein-coding genes, such as NUCLEOTIDEBINDING LEUCINE-RICH REPEAT (NB-LRR) and PENTATRICOPEPTIDE REPEAT (PPR) genes, whereas phasiRNAs in monocots derive from long noncoding RNAs transcribed from phasiRNA-generating loci (PHAS) [181] (Fig. 4). In addition, trans-acting siRNAs (tasiRNAs) are a special class of 21-nt phasiRNAs that originate from noncoding TAS transcripts in a DCL4-dependent manner [117, 118, 182]. phasiRNA biogenesis can occur in two ways. In the predominant “one-hit” model, phasiRNAs are triggered by a 22-nt miRNA with only one binding site in the target transcript [157, 158, 183]. In Arabidopsis, SUPPRESSOR OF GENE SILENCING 3 (SGS3) stabilizes either the 5′ or 3′ fragment resulting from AGO-mediated slicing and helps to recruit RDR6 [117, 118, 182]. Afterward, RDR6 converts the cleavage fragment into dsRNA, which is then diced into a series of 21- or 24-nt phasiRNAs by DCL proteins [117, 118, 182, 183]. AGO1-guided cleavage is required for this phasing process, because loss of AGO1, which leads to defects in slicing, causes disrupted phasing, although secondary siRNAs can still be produced [184]. In the “two-hit” model, which applies to TAS3, there are two miRNA binding sites present in the target transcript [185]. Besides the specific size of the miRNA trigger, many other factors are also important for phasiRNA production. The complementarity degree between a miRNA and its target and the asymmetry degree of the bulge in the miRNA/miRNA* duplex both influence tasiRNA production [117, 118]. Translation may also play a role in tasiRNA biogenesis. The introduction of premature stop codons to the upstream of miR173 binding site led to reduced tasiRNA abundance [186, 187], implying that the relative positions of short open reading frame and miR173 binding site in TAS1 or TAS2 transcripts are important for tasiRNA generation.

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FIG. 4 Models of phasiRNA biogenesis in plants. PHAS and TAS loci are transcribed into singlestranded RNAs that are targeted by a miRNA-AGO1/7 complex. The 5′ or 3′ cleavage fragment is protected by SGS3 and converted into dsRNA by RDR6. DCL proteins process these dsRNAs into 21-nt or 24-nt phasiRNAs. The 21-nt tasiRNAs produced from TAS loci are primarily loaded into AGO1 and guide transcript cleavage of their targets.

­Biological functions of phasiRNAs The biological functions of phasiRNAs have been extensively studied in plants, particularly the roles of tasiRNAs during plant development. In Arabidopsis, four TAS families, TAS1 to TAS4, are known to produce tasiRNAs. miR173 targets TAS1 and TAS2, and the resulting products can regulate PPR mRNAs, although the biological impact of this regulation remains elusive [188, 189]. tasiRNAs from TAS1 affect plant thermotolerance through the regulation of their targets, the heat stress transcription factor genes HEAT-INDUCED TAS1 TARGET 1 (HTT1) and HTT2 [190]. Production of tasiRNAs from TAS3 is triggered by miR390 [185, 191]. The regulatory relationship between TAS3 tasiRNAs and ARFs established a conserved module in plants important for diverse biological processes, including embryo development, phase transitions, leaf patterning, flower and root architecture establishment, stress

Diversity of small RNAs

responses, and phytohormone crosstalk [125, 192–196]. TAS4 tasiRNAs triggered by miR828 target certain MYB transcription factor genes that regulate anthocyanin biogenesis, including MYB113, PAP1, and PAP2 [36, 197]. A few other TAS loci, TAS5-TAS10, have also been reported, but their biogenesis and functions remain unclear. TAS5 was identified in tomato, but it does not exhibit all of the characteristics of a TAS locus. Surprisingly, it yields a protein-coding transcript, and the resulting siRNAs may function in cis [120, 121]. In moss, TAS6 is localized in close proximity to TAS3 loci, with TAS6 tasiRNA production triggered by miR156 or miR529 [198, 199]. Interestingly, tasiRNA biogenesis from TAS6A and TAS3A may be related because they share the same primary transcript, and their origination loci are adjacent and linked by a short intron [199]. TAS7-TAS10 were independently identified in grapevine and tomato [187, 200], but their biogenesis pathways and functions require further investigation. The largest gene family in dicots producing phasiRNAs is the NB-LRR gene family widely present in many plant species [157, 158]. In Arabidopsis, NB-LRR genes are targets of the 22-nt miR2118 and give rise to 21-nt DCL4-dependent phasiRNAs that potentially mediate feedback regulation of NB-LRR mRNAs at the post-­ transcriptional level [201, 202]. In soybean, many NB-LRR genes predominantly expressed in nodules are targeted by miR482, miR1507, and miR1510, suggesting their possible involvement in nodulation [180]. Another large gene family that generates phasiRNAs is the PPR gene family. Although PPR genes are known to be regulated by TAS1/2 tasiRNAs, some PPR transcripts are directly targeted by miRNAs, including miR7122 and miR161, and are processed into phasiRNAs. The biological impact of this process may be to help silence invasive Phytophthora transcripts during pathogen defense [157, 158, 180, 203]. In monocots, reproductive tissues seem to have involved more phasiRNA events. PHAS loci generate noncoding transcripts that can be recognized and cleaved by miR2118 and give rise to 21-nt phasiRNAs during anther development [157, 158, 180]. In rice, the association between 21-nt phasiRNAs generated from over 700 PHAS loci and MEIOSIS ARRESTED AT LEPTOTENE 1 (MEL1), a germline-­ specific AGO protein, is critical during pre-meiotic germ cell development and ensures meiotic progress [204]. Additionally in maize, a mutant of OUTER CELL LAYER 4 (OCL4) loses 21-nt phasiRNAs, resulting in anther defects and male sterility [157, 158]. Besides 21-nt phasiRNAs, a class of 24-nt meiotic phasiRNAs has also been identified in both male and female reproductive organs in rice and maize, implying a role during germinal development. Its biogenesis involves miR2275 as a trigger and sequential processing by DCL5 [157, 158, 205]. Although the prediction of phasiRNA targets remains limited due to the unique and diverse sequences of phasiRNAs, the broad presence of 21-nt and 24-nt phasiRNAs in diverse grass species has been identified and investigated. The analyses suggest that the similar biological functions of grass phasiRNAs and mammalian piRNAs represent an example of convergent evolution. However, the detailed mechanism of grass phasiRNAs function remains to be revealed [205, 206].

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­Movement of small RNAs Plant small RNAs can move systemically or locally to spread a silencing signal (Fig. 5). Small RNA mobility was first evidenced by the migration of a visually detectable pale green phenotype to distant leaves along the vascular tissue when nitrate reductase (NIA) silencing was induced in a grafting experiment [207]. In another study, a GFP silencing signal transiently expressed in source leaves was found to systemically repress the expression of erGFP mRNAs [163]. In plants, the movement of siRNAs requires plasmodesmata (PD) and phloem for local and systematic translocation, respectively (Fig. 5). Moreover, their mobility is essential for their function against viral or transgenetic adversity [208, 209]. As demonstrated by grafting experiments in Arabidopsis, 24-nt siRNAs can translocate from wild-type shoots to the roots of the triple mutant dcl2/3/4, which is defective in producing 21–24 nt siRNAs. This movement mediates DNA methylation at thousands of loci associated with transgenes or endogenous TEs [210–212]. In contrast to siRNAs, miRNAs and tasiRNAs can establish expression gradients by short-range intercellular trafficking largely through PD to regulate plant development [162, 213]. For instance, miR165/166 represses the expression of PHB in the root vasculature by moving from the endodermis to the vasculature to modulate root protoxylem and metaxylem patterning [124, 214]. miR165/166 also establishes a boundary to restrict the expression of HD-ZIP III genes through its gradient distribution along the abaxial-adaxial axis of leaves [123]. tasiRNA movement is similarly evidenced by the adaxial-to-abaxial gradient of tasiR-ARF tasiRNAs, which restricts the expression of ARF genes [215]. Therefore, these two mobile small RNAs ensure proper leaf patterning. In the meristem, miR394 is specifically expressed in the L1 layer but moves to the underlying L2 and L3 layers to restrict the expression of its target gene LCR, thereby promoting stem cell maintenance during shoot meristem formation [102]. In addition to local movement between cells, some miRNAs can also travel longer distances to the root or shoot. Phosphate, sulfate, and copper deficiency conditions in plants induce significant increases in the levels of miR399, miR395, and miR398, respectively [216–220]. These miRNAs move from the shoot to the root in response to nutrient deficiencies, suggesting that a systemic silencing signal is spread through the movement of these miRNAs to regulate their target genes. In Lotus japonicus nodule organogenesis, miR2111 translocates from the shoot to the root to restrict the expression of the symbiosis suppressor TOO MUCH LOVE (TML), indicating that miRNA movement may be common in plants [209]. Much remains to be elucidated about small RNA movement and the underlying mechanisms. Some of the open questions concern the forms of small RNAs (e.g., single-stranded or double-stranded, precursors or mature forms) that permit movement, the proteins that mediate small RNA mobility, the common characteristics of mobile small RNAs, and the links between small RNA mobility and their mode of action.

Schematic view of small RNA movement in plants. Small RNAs can act as mobile signals and regulate their targets in non-cell-autonomous manner. They move either from cell-to-cell via plasmodesmata or over long distances through phloem. In the destination cells, miRNAs (red) guide the transcript cleavage or translation repression of their target mRNAs, whereas siRNAs (green) mediate RNA-directed DNA methylation or the cleavage of target RNAs.

Movement of small RNAs

FIG. 5

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­Future perspectives Sequence conservation and similar mechanisms of biogenesis and action modes among small RNAs and small RNA/target modules in different species unravel a universal theme for small RNA-mediated gene silencing. Although the large number of studies in the past two decades has paved the foundation for the building of a molecular portrait on small RNA biology, many unknown questions are still remaining to be fully addressed. The concerns include how expressions of small RNAs are specifically regulated differing from other protein coding genes, what cellular components and organelles mediate the directional trafficking of small RNAs, where are the precise subcellular locations for many of the small RNA pathway events to take place, and why different small RNAs species may require specific interacting cofactors, etc. Clearly, a full picture of the small RNA pathway will not only provide in-depth understandings of this evolutionarily conserved biological process but also allow the advancement of science that can take the benefits of its biological consequences. Elaborate efforts need to be taken into immediate consideration as mechanistic and insightful investigations on small RNAs may further provide a new layer for gene expression manipulation, advancing genetic modifications of organisms for better quality and productivity.

­Acknowledgment This work was funded by Guangdong Innovation Team Project (2014ZT05S078) and National Natural Science Foundation of China (31801078, 31571332, 31870287).

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CHAPTER

Diversity and types of small RNA

2 Lionel Morgado

Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands

­Small regulatory RNAs: Historical milestones The disclosure of the DNA double helix in 1953 [1] is an important reference for contemporary genetics. It cemented the “one gene-one enzyme” hypothesis formulated in the 1940s [2] and the idea of DNA as a fundamental molecule for life, igniting the hype felt until modern times with the deciphering of the “genetic code.” Although the “one gene-one enzyme” model evolved into a broader concept that encompasses all proteins as “functional” products originating from DNA encoded genes, non-coding regulatory elements have remained widely underestimated. For a long time, RNA was mostly seen as a template (messenger RNA) and infrastructural molecule (ribosomal RNA and transfer RNA) matching the archetype of the central dogma of molecular biology defined by Crick in 1958 [3], where genetic information flows from DNA to RNA and is translated into a protein. It was only in the early 1990s that gene silencing was accidentally detected in petunia flowers [4]. Transgenic lines were engineered to have a deep purple color as a result of an increased expression of chalcone synthase (a pigment-producing gene), which instead resulted in variegated flowers. The silencing of the transgene was by then linked to antisense RNA, and because both the transgene and the homologous endogenous gene were affected, the phenomenon was termed co-suppression [4, 5]. A similar mechanism reported in the fungus Neurospora crassa was named quelling [6, 7], and a related phenomenon identified in animals was baptized as RNA interference (RNAi). The molecular processes underlying such observations remained unknown for almost a decade. It was only in 1998 that RNAi was described both in plants [8] and C. elegans [9]. This last work by Andrew Fire and Craig Mello was awarded the “Nobel Prize in Physiology or Medicine” in 2006, bolstering the importance of RNAi as an experimental tool and its therapeutic potential. In addition, it was found that post-transcriptional silencing (PTS) can be mediated by another class of RNA molecules with a short length but with a distinct biogenesis: the microRNA (miRNA). The first miRNA was identified in studies with C. elegans where lin-4, a gene without protein-coding capacity, revealed influence in the timing of larval Plant Small RNA. https://doi.org/10.1016/B978-0-12-817112-7.00002-X © 2020 Elsevier Inc. All rights reserved.

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development through the production of tiny regulatory RNA [10]. Only almost a decade later, miRNAs were identified in plants and shown to be involved in various developmental events including flowering [11–13]. In 1994, transcriptional silencing (TS) guided by short-length RNA was discovered, introducing the heterochromatic small interfering RNA (hc-siRNA). By then, it was observed in tobacco plants that viroid RNA can induce methylation of its own complementary DNA, in a mechanism coined RNA-directed DNA methylation (RdDM) [14]. Given the similarities among the regulatory mechanisms previously described and the characteristic short size of the RNA molecules involved, they were included under the general designation of small RNA (sRNA). Research with the model organism Arabidopsis thaliana added two new elements to the list of sRNAs found in plants. In 2004, the plant-specific trans-acting (ta)-siRNA was described as a secondary product of sRNA activity [15, 16]; and the year later, taught us that naturally occurring antisense transcripts independently synthesized in the genome can hybridize and serve as the base material for the production of a new type of siRNA: the natural antisense transcript (nat)siRNA [17]. In the meantime, key components for sRNA activity, such as Dicer [18] and Argonaute [19], were identified. With the advent of Next-Generation Sequencing (NGS) at the turn of the millennium, it has become feasible to survey entire sRNA populations composed of millions of different sequences. This diversity can be observed from distinct plant species, but also a significant variation in sRNA production is present within species and even inside the same organism. Diversity can be observed inclusively by cell type, for distinct developmental time-points, or conditioned by environmental factors. Contemporary experimental methodologies such as small RNA sequencing (sRNA-seq) have the capacity to capture a large number of these molecules, but there is still room for improvement as the currently available technology often neglects important molecules. In essence, sRNA-seq behaves as a random sampling process, introducing variation in the pool of molecules that are indeed captured during an experiment. On one hand, this issue mostly affects less abundant instances, but on the other hand, it remains an adversity for accurate data analysis and interpretation. The identification and classification of sRNA from such high-throughput data is a non-trivial task because plants can produce millions of sRNAs encompassing diverse pathways, which are collectively captured in a single sequencing experiment. Dealing with sRNA is challenging not only at the experimental level but also downstream for existing computational methods. Sorting of sRNA by classes implies taking into consideration multiple facets, such as the sRNA precursors, structural properties of the mature molecules, and information about the targets. A number of computational tools have been developed to detect known sRNA in newly synthesized sequencing libraries and also to help in the identification of novel contenders, but a unifying framework with the capacity to recognize all known sRNA categories has yet to be developed [20]. Given the diversity of sRNAs, the exact number remains unknown, but the latest studies suggest that innumerous sequences remain to be characterized [21], many of which may be part of intricate regulatory networks [22] that have been largely overlooked to date. In such a scenario, the detailed description of a relatively large number of sRNA features constitutes a central pillar for sRNA categorization.

­Classifying endogenous small RNA in plants

­Classifying endogenous small RNA in plants Although intensive research has been undertaken in the past 20 years on the topic of plant sRNA, we are still far from fully understanding this facet of biology. Nevertheless, innumerous molecular entities have been observed interacting with sRNA and used to define distinct pathways. Current classification schemes emphasize sRNA biogenesis and their mode of action, and explore some properties of the sRNA sequence, such as the length of the mature form [23]. Almost all eukaryotic organisms investigated to date produce sRNA or at least have the cellular machinery necessary for their biogenesis, and plants are not an exception. In addition to being of internal origin, sRNAs are seen as mobile-signaling molecules that can have an exogenous source, such as a virus [24], and have demonstrated the capacity to undertake regulatory activity in host organisms. Still, the majority of the sRNA sequences in plants is of internal origin and displays several well-studied features. In short, plant endogenous sRNAs are mostly 21–24 nucleotides (nt) in length and result from cleavage of double-stranded RNA substrates by dicer-like (DCL) enzymes. The RNA substrates themselves can originate either from a single-stranded RNA precursor with a stem-loop conformation or from a double-helix. If the sRNA originates from a hairpin structure, they are referred to as hairpin-derived sRNA (hpsRNA), and if they originate from a double helix, they are referred to as small interfering RNA (siRNA). The hpsRNA class can be further considered microRNA (miRNA) if the hairpin is processed in such a way that it produces only one or very few functional units. siRNA comprises all other classes of known sRNA: secondary siRNA such as transacting (ta)-siRNA, natural antisense transcript (nat)-siRNA, and heterochromatic (hc)-siRNA. In the case of secondary siRNA, two non-mutually exclusive groups can be defined: the phased siRNA, which originates from a precursor processed in a precise and sequential manner, and the ta-siRNA, which is a plant-specific sRNA type with targets originated in trans. In the case of nat-siRNA, the precursor double helix is derived from overlapping RNA segments produced independently of each other, whereas secondary siRNA and hc-siRNA precursors are preceded by the action of an RNA-dependent RNA polymerase (RDR) over single-stranded RNA. To become active in plants, sRNAs must load into AGO proteins, which guide silencing complexes to their targets according to sequence pairing principles. When associated with an AGO, an sRNA can regulate genomes at the transcriptional (TS) or post-transcriptional (PTS) level depending on the specific AGO to which the sRNA binds. Both modes of action have been intensively studied, but PTS mechanisms such as mRNA cleavage and translation inhibition are better understood. PTS is typically observed for miRNA, secondary siRNA, and nat-siRNA, whereas TS is more often associated with the action of hc-siRNA. Functional siRNA characterization is the key to identifying hc-siRNA as no clear structural features to discriminate between hc-siRNA and other siRNA have been defined to date. Because sRNA biology is complex, in the followings sections the reader is given the possibility to dive deeper into each category of endogenous sRNAs known to exist in plants and to further explore the similarities and differences among different types.

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­Hairpin sRNA and microRNA A hairpin sRNA (hpsRNA) is defined as an sRNA derived from a precursor that has the capacity to fold into a hairpin-like shape. This precursor can then be processed by any DCL to produce sRNA mostly in the 20–24 nt range. Despite miRNA being an extensively studied subclass under the umbrella of hpsRNA, this parent category is not as well characterized; hpsRNA are more heterogeneous and therefore more difficult to describe [23]. In plants, a primary transcript (pri-miRNA) is produced in the nucleus by RNA polymerase Pol II and further processed into the hairpin-structure precursor (premiRNA) by DCL1. A pre-miRNA can be composed of thousands of nucleotides and incorporates inverted repeats that allow the molecule to acquire the hairpin or stemloop conformation. After folding into a double-stranded structure, DCL1 produces the mature miRNA duplexes. Typically, only one of the strands (the “guide”) gives rise to an active miRNA, whereas the opposite strand (the “passenger”) is degraded. Nonetheless, cases where both strands become active have been identified [25, 26]. Although pre-miRNAs typically range from 60 to 80 nt in animals [27], in plants they can be several hundreds of nucleotides in length. Perhaps as a result, the stemloop structure in plant miRNA is more variable in size (usually larger) and can contain big bulges. Moreover, in plants the pairing between the arms of the stem-loop conformation shows an overall higher degree of complementarity in the miRNA region [28–30]. Unlike in animals, about 80% of mature plant miRNAs contain a uracil at the 5′ ends, which seems to be essential for proper binding and activation of the RNA-induced silencing complex (RISC). The role of miRNAs in the directed regulation of gene expression is well-known. Individual mature miRNAs, with a characteristic length of 21–22 nt, can target different transcripts, but also miRNAs with distinct sequences can target the same transcript [31, 32]. Interestingly, recent evidence indicates that miRNAs can also regulate the concentration of non-coding RNA, including sRNAs [33]. The targeting of noncoding transcripts has been suggested as a mechanism for negative regulation of miRNA concentration in a process called “target mimicry” [34]. miRNAs are thought to be subject to strong evolutionary constraints and tend to be highly conserved across species [13]. Lineage-specific miRNAs differ significantly from more conserved sequences, lacking targets or using unknown non-canonic target criteria, having low abundance, heterogeneous processing from the precursor, and normally encoded by single genes instead of multiple paralogs [23].

­Natural antisense transcript siRNA The main difference between nat-siRNAs and other sRNAs is the fact that nat-­ siRNAs originate from an RNA duplex formed after the hybridization of a pair of natural antisense transcripts (NATs). NATs are RNA transcripts that may or may not have protein-coding capacity and share complementarity to other RNA transcripts independently produced within the cell [35–37].

­Classifying endogenous small RNA in plants

Considering the physical distance between NAT producing loci, two main categories emerge: cis-NAT and trans-NAT. cis-NATs are transcribed from the same genomic locus but typically from opposite DNA strands and thus form perfect pairs, whereas trans-NATs are transcribed from distant genomic locations. Cis-NAT overlapping regions do not have a characteristic length and can occur in five orientations [38]: 1. Head-to-head—consists of the interception in the 5′ ends of both transcripts; 2. Tail-to-tail—comprises the interception in the 3′ ends of both transcripts; 3. Completely overlapping—a transcript in one strand of the genome is overlapped by the entire length of the other transcript in the opposite strand; 4. Nearby head-to-head—nearby transcripts in a head-to-head manner where the 5′ end of a transcript is near the 5′ end of another transcript in the genome; 5. Nearby tail-to-tail—nearby transcripts in a tail-to-tail manner where the 3′ end of a transcript is near the 3′ end of another transcript in the genome. Research in multiple species indicates that NATs can assume a variety of significant regulatory roles, such as in alternative splicing [39, 40], RNA editing [41, 42], DNA methylation [43], genomic imprinting [44–49], and animal X-chromosome inactivation [50], but regulation by NATs is not well understood [51]. In general, cisNATs have very specific targets operating mostly locally and in a one-to-one fashion [52], but trans-NATs can share complementarity with multiple transcripts and form complex regulatory networks in processes such as plant response to stress [17]. NAT sequences shared among species such as rice and Arabidopsis have been identified [53], with cis-NATs showing high positional conservation between different species [53, 54]. Although the molecular requirements for nat-siRNA biogenesis remain elusive, it is clear that the mere existence of a NAT pair is, by itself, insufficient for siRNA production [51]. Accumulation of nat-siRNAs was observed under diverse stressors, positioning this category of sRNA in the adaptation to biotic and abiotic stress [51]. Once annealed, NATs can be fragmented into 21-nt segments by DCL1, enabling PTS, but there are also reports of 24-nt segments produced by DCL3 that may guide DNA methylation [51] (Fig. 1).

­Secondary and trans-acting siRNA Secondary siRNAs seem to be part of a mechanism to amplify silencing signals intended to affect pathways with a large number of genes, such as in disease resistance and development [55–58]. Two steps describe the core of secondary siRNA biogenesis: the assembly of a double-stranded precursor prompted by cleavage of a single-stranded RNA segment that afterward is targeted by RDR to synthesize its complementary strand, and the subsequent processing into siRNA by DCL enzymes. The initial cleavage event is facilitated by an sRNA, after which multiple molecular complexes are recruited for downstream processing. Secondary siRNAs often come from RNA precursors processed by DCL enzymes in a sequential manner from its

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FIG. 1 Main endogenous sRNA biosynthesis pathways operating in plants. (A) miRNA, (B) nat-siRNA, (C) secondary siRNA, (D) hc-siRNA.

beginning [59], a phenomenon called phasing. Although not mandatory, this pattern is a strong indicator of siRNA production and has thus been used for secondary siRNA loci detection. Multiple-hit target transcripts are more prone to generate secondary siRNA, especially if susceptible to sRNAs loaded to AGO1 and AGO7 [23]. When targeting involves transcripts produced at distant loci, a secondary siRNA can further be classified as a ta-siRNA. Involvement of ta-siRNA in developmental timing and patterning has been shown [60]. They were first identified in Arabidopsis [15, 16] as a new kind of non-coding RNA that shared similarities with miRNA but that had key differences. Unlike miRNA, a ta-siRNA locus (known as a TAS gene) produces a non-protein-coding transcript that evolves into a double-stranded RNA segment assisted by RDR6. This form is then processed by DCL4 or DCL2, after an initial cleavage event typically facilitated by miRNA, resulting respectively in 21-ntor 22-nt-long RNA segments [61] that can subsequently be incorporated into RISC and direct the cleavage of target mRNA [33, 62]. The ta-siRNA pathway appears to be specific to plants. TAS genes were detected in many plants such as maize [63] and rice [64] and, as secondary siRNA in general, ta-siRNA show high conservation among species [59].

­Heterochromatic siRNA Heterochromatic siRNAs (hc-siRNAs) are typically 24-nt long and mostly derived from transposons, repeats, and heterochromatic regions. Their biogenesis is primarily connected to the PolIV-RDR2-DCL3 pathway [65, 66], some of which are also dependent on PolV to produce RNA scaffolds used to recruit the DNA

­References

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[69] E.J. Finnegan, R.K. Genger, W.J. Peacock, et al., DNA methylation in plants, Annu. Rev. Plant. Physiol. Plant. Mol. Biol. 49 (1998) 223–247. [70] V.A. Guseinov, B.F. Vanyushin, Content and localisation of 5-methylcytosine in DNA of healthy and wilt-infected cotton plants, Biochim. Biophys. Acta 395 (3) (1975) 229–238. [71] V. Pavet, C. Quintero, N.M. Cecchini, et al., Arabidopsis displays centromeric DNA hypomethylation and cytological alterations of heterochromatin upon attack by Pseudomonas syringae, Mol. Plant Microbe Interact. 19 (6) (2006) 577–587. [72] B.  Huettel, T.  Kanno, L.  Daxinger, et  al., RNA-directed DNA methylation mediated by DRD1 and Pol IVb: a versatile pathway for transcriptional gene silencing in plants, Biochim. Biophys. Acta Gene Struct. Expr. 1769 (5–6) (2007) 358–374. [73] F. Vaistij, L. Jones, D. Baulcombe, Spreading of RNA targeting and DNA methylation in RNA silencing requires transcription of the target gene and a putative RNA-dependent RNA polymerase, Plant Cell 14 (4) (2013) 857–867. [74] S. Katiyar-Agarwal, H. Jin, Role of small RNAs in host-microbe interactions, Annu. Rev. Phytopathol. 48 (1) (2010) 225–246. [75] A. Yu, G. Lepere, F. Jay, et al., Dynamics and biological relevance of DNA demethylation in Arabidopsis antibacterial defense, Proc. Natl. Acad. Sci. U. S. A. 110 (6) (2013) 2389–2394. [76] R.H. Dowen, M. Pelizzola, R.J. Schmitz, et al., Widespread dynamic DNA methylation in response to biotic stress, Proc. Natl. Acad. Sci. U. S. A. 109 (32) (2012) E2183–E2191. [77] N. Pumplin, O. Voinnet, RNA silencing suppression by plant pathogens: defence, counterdefence and counter-counter-defence, Nat. Rev. Microbiol. 11 (11) (2013) 745–760.

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Biogenesis of small RNA: Molecular pathways and regulatory mechanisms

3 Lionel Morgado

Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands; Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands

The origin and faith of sRNA is dependent on a relatively small number of protein families, including DNA-directed RNA polymerase (Pol), RNA-directed RNA polymerase (RDR), dicer-like proteins (DCL), and Argonaute (AGO). The pathway of a particular sRNA category demands specific combinations of molecules from each of these families, but overall the known mechanisms in plants comprise a comparable chain of processing events. In a first phase, Pol acts over DNA to produce a singlestranded RNA sequence that will, at some point, generate a double-stranded RNA precursor. The single-stranded RNA precursor can form a double strand without further processing, as happens with hairpin sRNA such as microRNA (miRNA) or, in the case of natural antisense, small interfering RNA (nat-siRNA); or become doublestranded by action of RDR, as seen in heterochromatic (hc)-siRNA and secondary siRNA such as trans-acting (ta)-siRNA. Afterward, DCL cuts the double-stranded polymer into short segments that will further achieve a mature single-stranded sRNA form. For mature sRNA sequences to become active, a variety of proteins can be combined with AGO in large protein complexes such as RNA-induced silencing complex (RISC) and RNA-induced transcriptional silencing (RITS). These core molecules for small RNA biogenesis and function are further described in the sections that follow.

­DNA-dependent RNA polymerase The RNA polymerase (Pol), which is officially named DNA-dependent RNA polymerase, comprises a set of molecules responsible for the transcription of cellular RNA. In plants, a total of five DNA-dependent RNA polymerases (Pol I-V) can be found, two of which (Pol IV and Pol V) have been strongly linked to RNAdirected DNA methylation [1]. In particular, Pol IV is involved in the production of 24-nucleotide (nt) sRNA that load to AGO4 and guide the de novo methylation machinery to loci that produce transcripts by action of Pol V [2]. DNA-dependent RNA polymerases are composed of multiple subunits. Each of these subunits follows a naming convention where the name of the subunit adopts Plant Small RNA. https://doi.org/10.1016/B978-0-12-817112-7.00003-1 © 2020 Elsevier Inc. All rights reserved.

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the radical NRPxn; where x = {A, B, C, D, E} is used to designate Pol I to Pol V, ­respectively; and where n = 1–12 is used to indicate the subunit from the largest to smallest [3]. Pol II, IV, and V share many subunits, which may explain why they can be found under similar pathways, but at the same time they are also composed of distinct subunits that confer functional specificity [4–6]. Evolutionary studies suggest that the largest, second, fourth, fifth, and seventh subunits of Pol IV and Pol V originated from the corresponding Pol II subunits [7–9].

­RNA-dependent RNA polymerase A RNA-dependent RNA polymerase (RDR) is an enzyme that produces doublestranded RNA from single-stranded polymers, undertaking an important process to initiate or amplify the production of certain sRNA categories, such as secondary small interfering RNA (siRNA) and heterochromatic-siRNA (hc-siRNA). This amplification can be dependent or independent of primers. In the first case, a primary siRNA is necessary to guide the RDR to the target template. RDR genes belong to a small family that shares a DLDGD catalytic domain. The exact number of RDRs in plants varies from species to species. For example, Arabidopsis thaliana and potato (Solanum tuberosum) have been reported among the plant species with the highest number of RDR variants, having 6 and 7, respectively, whereas studies in rice and Nicotiana benthamiana (a close relative of tobacco) reported 5 and 3, respectively [10–13].

­Dicer Dicer-like enzymes (DCL) are needed to cleave double-stranded substrates or singlestranded stem-loop RNA, after which small regulatory RNA gains 5′ phosphate and 3′ hydroxyl (–OH) ends [14]. These chemical groups have been used to isolate functional sRNA candidates prior to sequencing from other short-length RNA fragments present in the cell like RNase degradation products that carry 5′ hydroxyl groups and 2′ or 3′ phosphates instead [14, 15]. In plants, it is possible to find at least four kinds of DCL proteins, each participating in specific silencing mechanisms with some redundancy among them. Still, the number of biochemical studies performed to understand DCL action is limited; therefore, the mechanistic details remain obscure. It is known that DCL1 has affinity for bulges and terminal structures; DCL4 prefers long double-stranded transcripts and interacts with dsRNA-binding protein DRB4; and DCL3 prefers short double-stranded precursors and cooperates with Pol IV and RDR2 in the transcriptional-silencing pathway.

­Argonaute proteins in plants Apart from their biogenesis, the mode of action of a given sRNA is tightly related with the Argonaute protein to which it can associate [16]. Argonautes are i­ ndispensable in all sRNA-guided gene-silencing pathways identified so far, forming the core of the

­Argonaute proteins in plants

RNA-induced silencing complex (RISC) and RNA-induced transcriptional silencing (RITS). In their mature form, RISC and RITS encompass a single-stranded small RNA bound to an Argonaute protein. As endogenous small RNAs are generated as duplexes, only one strand (the guide strand) is retained and the other (passenger) strand is discarded. Once loaded, an sRNA guides the mature RISC/RITS to their targets using complementary base-pairing principles. Argonautes are highly conserved proteins with family members in most eukaryotes. Although eukaryotes have two kinds of Argonautes, that is, AGO and PIWI, only AGO proteins can be found in plants. Also in plants, AGOs can be grouped into three phylogenetic clades with a highly variable number of elements from species to species. Arabidopsis has 10 members with specialized or redundant functions among them: AGO1, AGO5, and AGO10 in the first clade; AGO2, AGO3, and AGO7 form the second clade; and the third clade is composed of AGO4, AGO6, AGO8, and AGO9 [16]. Each clade is predominantly associated with a given kind of sRNA; the first is mostly related with the miRNA pathway, proteins in the second clade are involved in nat-siRNA and ta-siRNA pathways, and the third clade is recurrently mentioned in RNA-directed DNA methylation (RdDM), a hc-siRNA pathway [16]. These sequence-based clades concur with the three functional groups established. Thus, members of the AGO1/5/10 clade are RNA slicers, AGO2/3/7 bind sRNAs (AGO7 also cuts), and AGO4/6/8/9 are chromatin modifiers [17–19]. Within each clade, there is a main player with ubiquitous and high expression level (AGO1, AGO7, and AGO4), whereas other members are mostly flower/embryo-specific players (AGO5/10, AGO2/3, and AGO8/9) (Fig. 1) [20–23].

FIG. 1 Phylogenetic tree for AGO proteins in Arabidopsis thaliana.

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­AGO1 AGO1 was the first element of the AGO family to be discovered through the identification of mutants in Arabidopsis that exhibit pleiotropic developmental defects [24]. Mutations in the AGO1 gene can induce severe changes such as dwarfism and plant sterility, or less-accentuated modifications related with organ polarity [16]. AGO1 is a key component in PTS pathways, involving miRNA or siRNA with a characteristic length of 21–22-nt. In genetic studies with AGO1 null mutants, a decrease in the number and abundance of miRNAs was observed accompanied by an increased expression of mRNAs they target [25]. Transgene siRNAs, virus siRNAs, and ta-siRNAs also associate with AGO1 complexes [17, 26–28]. Interestingly, AGO1 can repress its own mRNA guided by miR168 in a homeostatic regulatory loop [29].

­AGO10 AGO10 is a close paralog of AGO1, with which it shares redundant functions. Embryos with AGO1-AGO10 double mutants are not viable, demonstrating their importance in plant development. Moreover, AGO1 and AGO10 single mutations behave dominantly in AGO10 and AGO1 single homozygous mutants, respectively, indicating that AGO1 and AGO10 act redundantly during postembryonic development [16]. It is important for the formation of primary and axillary shoot apical meristems and in leaf adaxial definition. Curiously, it acts as a negative regulator of AGO1, but it is not a target of small RNA-mediated repression itself [30].

­AGO5 AGO5 is the second-closest paralog of AGO1. AGO5 mutations do not exhibit obvious developmental defects [21], and therefore the biological relevance of AGO5 remains enigmatic. AGO5-associated miRNAs seem to have an important function in petunia and antirrhinum flowers [26] and in medicago nodules [31]. Also, rice contains six putative paralogs of Arabidopsis AGO5, where a mutation in one of them is enough to produce sterile plants. In Arabidopsis, AGO5 seems to be predominantly expressed in ovules [32, 33] and to promote the initiation of megagametogenesis in the functional megaspore [34].

­AGO7 The function of AGO7 is related with the plant-specific ta-siRNAs [35]. The production of ta-siRNAs depends on specific miRNAs generated by AGO1 that guide the cleavage of ta-siRNA precursor RNAs (TAS) [36]. As with most miRNAs, ta-siRNAs can bind to AGO1 and guide cleavage of target mRNAs that exhibit near-­ perfect complementarity. Among several ta-siRNA precursors that have been identified, TAS3 seems to be in the origin of a special kind that can only bind to AGO7 [37]. In cooperation with ta-siRNAs, AGO7 can target mRNA-encoding proteins ­involved in hormone response important for normal plant growth [38–40].

­Argonaute proteins in plants

­AGO2 and AGO3 AGO2 and AGO3 are thought to be a product of a recent duplication because they share a high degree of similarity and are close by genomic locations, both falling within a direct tandem with only 3 kilobases (kb) separating the stop codon of AGO2 and the initiating codon of AGO3 [16]. AGO2 and AGO3 do not seem to be abundantly expressed in plants. In both AGO2 and AGO3 mutants identified by reverse genetics, no obvious developmental defects are observed [21]. There are however reports of AGO2 expression in ovules [33], as happens with AGO5. AGO2 plays a role in the natural cis-antisense siRNA pathway [41] and antiviral defense [42]. Recently, AGO2 was also implicated in DNA double-strand break repair by homologous recombination [43, 44].

­AGO4 Forward genetic studies in Arabidopsis mutants impaired in transcriptional gene silencing identified AGO4 [45]. Analysis of small RNA accumulation in various RNA interference mutants grouped AGO4 with other molecular entities in a pathway dedicated to the maintenance of epigenetically silent states at repetitive loci, transposons, and heterochromatin through the action of endogenous 24-nt siRNA [46]. Cajal bodies [46] and AB bodies [47] are among the two kinds of AGO4-containing complexes identified in the cell nucleus that intervene in epigenetic processes, namely RNAdirected DNA methylation. This AGO is also important in the response to bacterial pathogens such as Pseudomonas syringae [48].

­AGO6 The discovery of an AGO6 mutant was made during studies comprising the reactivation of a transcriptionally silent transgene [49]. The activity of AGO6 overlaps partially with that of AGO4 in TS pathways, and AGO6 mutants have less stringent effects in general than AGO4 mutations. Very recently, AGO6 was described in a process that bridges post-transcriptional degradation of TE mRNA into 21–22-nt siRNA and the establishment of transcriptional silencing through DNA methylation. Loading AGO6 with 21–22-nt sRNA seems to activate a RdDM pathway that targets long centromeric high-copy transcriptionally active TEs in reproductive precursor cells prior to gametogenesis [50].

­AGO8 and AGO9 In the third Arabidopsis AGO clade, two additional members can be found: AGO8 and AGO9. These proteins are close homologues separated by 50 kb, which suggests that, like AGO2 and AGO3, they arose from a recent duplication. Given the high sequence similarity, it would be reasonable to believe that they have similar activity and

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that they act redundantly, but AGO8 messenger RNA is expressed at low levels in all Arabidopsis tissues tested, and it is most probably a pseudogene [21]. The AGO8 and AGO9 mutants identified by reverse genetics do not exhibit obvious developmental defects, but AGO9 seems to be involved in heterochromatic silencing of TEs in the pericentromeric regions of all five chromosomes in the ovule [51]. AGO9 and 24-nt siRNAs prevent that subepidermal cells can adopt a megaspore-like identity, being crucial to specify cell fate in the ovule [32]. Thus, AGO9 restricts reproductive potential to the functional megaspore antagonizing AGO5 that promotes the initiation of megagametogenesis in this cell [34]. Also, AGO9 and other genes related with RdDM are expressed in pollen grains [52].

­Determinants for AGO-sRNA sorting and biological function Understanding the mechanisms that guide a particular sRNA to load into a specific AGO impacts our ability to predict the biological function of a sRNA and to effectively use it as an experimental tool. Obviously, the structure of an AGO protein is decisive in the sRNA sorting process. There are three domains transversal to all plant AGOs studied so far: PAZ, Mid, and PIWI. They are not exactly the same in every plant but can rather have different lengths and a variable location in each sequence [53]. It is through diverse contact points in these domains that sRNA is bound to AGO. The 3′ end of a sRNA binds to the PAZ domain [54, 55], whereas the Mid domain forms a pocket-like structure that interacts with the 5′ phosphate of the terminal nucleotide typically present in the sRNA sequences [56–59]. These interfaces can favor specific sRNA sequence base compositions like special 5′ nucleotides and select for loading duplexes with unstable 5′ ends [60, 61]. The third domain called PIWI is structurally similar to ribonuclease H (RNase H) and can act as an endonuclease (slicer) to cleave target RNAs through a DDX catalytic triad (X can be H or D, with D being amino acid aspartate and H amino acid histidine) [57, 62]. For example, AGO1 is known to have the capacity to cleave miRNA-targeted mRNAs through a DDH motif [63]. AGO2 and AGO3 are the only Arabidopsis AGOs that lack a conventional DDH motif within the PIWI domain [26] and instead have a degenerate DDD motif similar to that in bacterial RNase H enzymes [31]. High-throughput sequencing in combination with immunoprecipitation (IP) techniques have made it possible to determine the sequences of sRNA bound to different AGO families. So far, the analyses endorsed small inter- or intra-clade AGO groups, and revealed general description guidelines leaving space for more detailed studies. One of the initial observations was that sequences involved in a specific pathway have recurrently been a particular length. Small RNAs involved in PTS are mostly of size 21–22-nt, whereas sequences promoting TS have a typical length of 24 bases. The length of sRNAs is strictly related to the activity of dicer proteins proposed to localize in different subcellular compartments. In plants, dicers DCL1 and DCL4

­Determinants for AGO-sRNA sorting and biological function

produce 21-nt RNAs, DCL2 22-nt RNAs, and DCL3 24-nt RNAs. The distance between the PAZ and Mid domains in a AGO protein is pointed at as one reason for the conditional loading of certain sRNA lengths, as the 3′ and 5′ ends of a sequence need to interact with them, respectively, which constrains the sRNA accommodation in the AGO protein. However, this is not a sufficient parameter to describe AGO-sRNA sorting as many exceptions are known and AGO-associated sRNA libraries further expose this situation. Contrary to what happens in animals and flies, in plants the 5′ nucleotide is a strong indicator of AGO-sRNA sorting, and therefore sRNA sequences involved in specific pathways can be partially distinguished base on this mark. Enrichment for sequences starting with pyrimidines occur in AGOs from the first clade (AGO1/10: U and AGO5: C), adenosine dominates the third clade (AGO4/6/9), and AGO2 from the second clade. For the remaining AGOs, there is no IP library available (AGO3/8) or no preference for a specific terminal nucleotide is observed (AGO7). It was in fact shown via mutational analysis that changing the 5′ nucleotide of an sRNA can redirect its destination. For example, the substitution of the 5′ terminal U of miR391 and miR393b with A and the 5′ terminal A of the corresponding passenger strands with U took to relocation of miRNAs from AGO1 to AGO2 [64]. But again, this is not an absolute rule. For example, changing the 5′ nt in miR390 (a miRNA that binds to AGO7) does not change its affinity for this protein [17], which suggests that other important features can be encoded by the primary structure [55, 57]. Another case involves most MIR165/166 family members that have a 5′ U, which should assign them to AGO1 but specifically associate with AGO10 instead [30]. Further, miR390 has a 5′ A and binds to AGO7 instead of AGO2 as would be expected [17]. Moreover, both AGO2 and AGO4 associate mostly to small RNAs with 5′ A, but only a small fraction of sRNAs detected in IP experiments appear to be shared [65]. It is currently not completely clear what defines the preference for a specific AGO inside the same clade [23]. A partial answer can be given by the spatial expression of AGO proteins. In lines engineered to coexpress AGO proteins under the same promoter, intra-clade AGOs with naturally different sRNA profiles revealed to load more comparable sequences after being modified [18]. This helps explain the stronger activity of specific AGOs in certain tissues or developmental stages despite the sequences loaded having kinship with other intra-clade proteins or even with AGOs from different clades. The subcellular localization of the AGO proteins is likely another factor that determines their access to different sRNA categories [46]. This can explain why 24-nt repeat-associated sRNA, which are produced and have a function in the nucleus, are mostly attached to AGO4, which is found in the nucleus as well. In a recent study with epigenetic recombinant inbred lines [66], it was observed that, when unmethylated, a TE can be a source of 21–22-nt sRNAs sliced by DCL4 or DCL2 that load to AGO1/2, and that after some generations, the concentration of TE transcripts can accumulate to a point where DCL2/4 get saturated and 24-nt sRNA start being produced by DCL3. These 24-nt sRNA can now load to AGO4 and induce local transcriptional silencing. These observations indicate that s­ubpopulations of

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TS and PTS sRNAs can have very similar structures, differing only in few nucleotides. Under these production shift events, it is not surprising that sRNA without the necessary properties to load to AGO is produced, resulting in sequences without an obvious biological role. In general, the sequence of PTS-sRNAs is better studied than in the cases devoted to TS, but not much is known about the structural properties of the second kind. In fact, heterochromatic sRNAs are mostly treated like black-boxes for which the only properties typically inspected are the sequence length and the genomic annotations where they map. Studying AGO-bound sRNAs is a starting point to fill this gap and to improve our understanding about the function and structure of sRNAs in plants.

­Small RNA in transgenerational epigenetic inheritance The traditional view in genetics assumes that all heritable variations between individuals of a population are encoded in DNA. However, many examples have been reported where DNA alone cannot explain the observed phenotypic differences. Epigenetics, which refers to the study of mitotically and/or meiotically heritable changes in gene function that cannot be explained by changes in DNA sequence [67, 68], has brought new insights into molecular mechanisms that may underlie this missing heritability. DNA methylation and several histone chemical modifications are widely accepted as epigenetic mechanisms. Some authors further include sRNA in this list as experimental evidence emerges for the transgenerational preservation of meaningful patterns in sRNA populations [69]. However, a stringent definition of epigenetics dictates that those marks must be passed not only through cell divisions but for at least two generations [70]. Such extended criterion aims at discerning epigenetics from what is known as “parental effects,” in which the environment of the parents can influence their offspring. For example, it is known that, in some plants, the mother has a strong effect on the seeds it produces and that at the time of dispersal, the offspring is still surrounded by the maternal tissues of the seed coat that play a crucial role, for example, in the hormonal regulation of seed germination. Because sRNA can travel long distances inside a plant organism [71] and even be transmitted from the maternal tissues of a plant to the offspring [72], it is considered by many specialists as not being a true epigenetic mark. Either way, the close relationship with other epigenetic footprints like DNA methylation [73] gives sRNA a solid role as mediator of transgenerational epigenetic inheritance. Compared to animals, in plants the inheritance of epigenetic marks seems more stable. The existence of sRNA-mediated molecular pathways for de novo DNA methylation and its maintenance explains such steady transmission. DNA methyltransferases are active during gametogenesis and embryogenesis, allowing plants to evade the reset that happens in animals. Epigenetic inheritance in plants is frequently associated with sRNA originated in transposable elements and that regulates nearby genes [74, 75], which can culminate in inheritable epialleles with long-lasting phenotypic effects. As sessile organisms, plants can benefit by passing environmental cues to their progeny

­Interrelationship between sRNA pathways

because seeds disperse mostly locally, which increases the chance for new plants to experience environments similar to their ancestors. As for its relevance for biology and agricultural sciences, transgenerational epigenetic inheritance in plants has attracted much attention, but the vast majority of studies have been focused on DNA methylation alone, leaving many unanswered questions about the effect of sRNA in such phenomenon. Only recently has the scientific community been able to direct efforts to elucidate this facet of sRNA biology and endorse studies that cover complete plant genomes in transgenerational studies [76, 77].

­Interrelationship between sRNA pathways If miRNA structure and regulation is quite well studied, the same cannot be said for other sRNA categories. The lack of clear physical features is a significant issue for sRNA recognition, further aggravated by the fact that many genomic loci can be involved in the biogenesis of sRNA from distinct pathways. It has been noted that some transposon families like Athila can switch the production of siRNA from 24-nt to 21–22-nt when methylation is lost [78, 79]. This transition starts with the synthesis of transcripts by Pol II that are afterward degraded into 21–22-nt siRNA. Although most of these siRNAs mediate post-transcriptional silencing, some can enter a non-canonical RdDM pathway dependent on Pol II, RDR6, and DCL2/4 [79]. This mechanism has the capacity to reestablish transposable element methylation and correct lost silencing marks that can be further reinforced by other pathways for DNA methylation. Remarkably, transitions from post-transcriptional silencing to transcriptional silencing have also been observed and studied in detail in Évadé (EVD) [66]. Loss of methylation in this retrotransposon, triggers the production of 21–22-nt siRNA via the action of DCL2 and DCL4. Nevertheless, very high levels of EVD transcripts can saturate the available DCL2 and DCL4, redirecting the siRNA precursors to vacant DCL3 proteins and thus shifting siRNA production from 21 to 22-nt siRNA that act mostly post-transcriptionally to 24-nt heterochromatic siRNA. Heterochromatic siRNA can further attenuate the production of PTS-sRNA units via DNA methylation. The numerous cases of epigenetically activated sRNAs that have been recently recorded demonstrate clearly the antagonist and complex TS-PTS relationship [80]. It is further known that loss of methylation in transposable elements can result in epigenetic activation and consequent transcription, whereby transposon mRNA becomes preferentially targeted by miRNA. To further complicate the equation, the genes for these miRNAs can be controlled via DNA methylation. It is known that plant organisms have large and very intricate sRNA networks connecting genomic loci that may be involved in multiple pathways both as sRNA producers and also as targets. For a clear understanding of sRNA regulation, it is therefore fundamental to accurately identify which sequences are involved in which pathways. Only by doing so can we can crack the complex networks formed by these regulatory elements.

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­ nalyzing sRNA: Computational challenges from A the “dry lab” A considerable number of experimental methods for sRNA detection have been developed over the years. Traditional approaches include northern blotting, reverse transcription polymerase chain reaction (RT-PCR), microarrays, and more recently NGS protocols like sRNA-seq [81]. Each method has its own limitations, but the use of sRNA-seq has gained momentum in recent years due to its capacity to record millions of sequences at a genome-wide scale in a single run for a relatively low cost. The existence of such large number of sequences makes impractical the application of other experimental assays to determine additional properties for each of the sequences captured under the current technology. For example, gene-­ specific experimental validation of PTS targets using well-established methods like real-time reverse transcription polymerase chain reaction (qRT-PCR), luciferase reporter assays, and western blot are labor intensive and not easily scalable to genome-wide studies [82, 83]. In the case of transcriptional silencing, direct experimental target validation has only been proposed recently [84], and therefore it is still in its infancy. This is where computational methods come into play, aiding the extraction of additional information from sRNA primary structure and prioritizing candidates for experimental validation. Nevertheless, the computational analysis of sRNA-seq data is far from a trivial task. Libraries are composed of mixtures of sRNA sequences with diversified origins, structural properties, and involved in distinct modes of silencing. In addition, the observation of a sequence by itself is not a guarantee of sRNA regulation as cells can produce inactive short-length RNA. Despite the great interest in separating those units that can guide silencing from other sequences, computational methods for general high-throughput function detection are almost inexistent [85]. Using the existing software tools, function detection can almost exclusively be inferred partially for PTS target prediction, implying that a large share of sRNA that often guides transcriptional silencing remains ignored. Once functional sRNAs are separated from sequences for which no activity is expected, determining the mode of silencing for each unit is the next step. Doing so gives important clues about the duration/transmission of silent states and the type of biological pathways that induce/maintain these states. Regulation at a post-­ transcriptional stage is interpreted as a faster response than transcriptional silencing but with a less-lasting effect. However, the lack of experimental and computational methods to determine sRNA-mediated transcriptional silencing is a relevant constraint for research in this area. In addition, it is well known that tools for PTS target prediction are characterized by high false positive rates, which complicates laboratorial validation. Software to distinguish transcriptional from post-transcriptional sRNA can, in principle, improve post-transcriptional silencing inference. Although the central role of plant AGO proteins in defining the mode of silencing is widely accepted and frequently mentioned in sRNA research, the number of computational methods that have been devised to explore the potential of a given sRNA sequence

­References

to associate with a specific AGO remains anecdotal [86]. Determining such capacity has been strictly confined to experimental approaches such as sRNA sequencing after AGO immunoprecipitation (AGO-IP). Another limitation for sRNA studies is the high variability found within and across sRNA species as well their dynamic nature, which impacts our ability to capture them in the lab. Indeed, the detection of sRNA can be experimentally difficult as their expression can be low or dependent on specific developmental stages, cell types, or stimulation. In silico analysis of artificial sRNA (artsRNA) can give preliminary answers to theoretical models and motivate experimental validation. The exploration of artsRNA is not a new concept, as conservation principles have been used in the past to detect miRNA [87]. This philosophy can be revised to develop programs for general sRNA detection in an era where new genomes are published on a daily basis. As seen so far, mining sRNA-seq data involves innumerous analytical steps and can even go beyond the inspection of the mature sRNA sequences, precursors, and targets. Studies regarding variation in sRNA abundance among samples and many other downstream analyses (e.g., gene ontology, network analysis, etc.) are common practice. Currently, there are many individual programs for very specific sRNA-­related applications dispersed over the Internet, but only a small number of integrative frameworks exist. These often emphasize miRNAs and ignore other important sRNA categories. This is especially evident in plants where only a handful of (frankly incomplete) software platforms can be found. Biologists are often not acquainted with programming and lack the necessary computational skills to build the pipelines they need, which culminate in a tremendous time investment to achieve only modest solutions. There is a clear and urgent demand for novel computational tools to empower sRNA researchers, without which it will be almost impossible to decipher the molecular pathways and regulatory mechanisms underlying sRNA biogenesis in complex organisms such as plants.

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[62] G. Meister, Argonaute proteins: functional insights and emerging roles, Nat. Rev. Genet. 14 (7) (2013) 447–459. [63] Y.  Qi, A.M.  Denli, G.J.  Hannon, Biochemical specialization within Arabidopsis RNA silencing pathways, Mol. Cell 19 (3) (2005) 421–428. [64] S. Mi, T. Cai, Y. Hu, et al., Sorting of small RNAs into Arabidopsis Argonaute complexes is directed by the 5′ terminal nucleotide, Cell 133 (2008) 116–127. [65] V.N. Kim, Sorting out small RNAs, Cell 133 (1) (2008) 25–26, https://doi.org/10.1016/j. cell.2008.03.015. [66] A. Marí-Ordóñez, A. Marchais, M. Etcheverry, et al., Reconstructing de novo silencing of an active plant retrotransposon, Nat. Genet. 45 (2013) 1029–1039. [67] A.D. Riggs, R.A. Martienssen, V.E. Russo, Introduction, in: Epigenetic Mechanisms of Gene Regulation, vol. 32, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 1996, pp. 0–4. [68] P. Cubas, C. Vincent, E. Coen, An epigenetic mutation responsible for natural variation in floral symmetry, Nature 401 (6749) (1999) 157–161. [69] A. Bilichak, Y. Ilnytskyy, R. Woycicki, et al., The elucidation of stress memory inheritance in Brassica rapa plants, Front. Plant Sci. 6 (2015) 5. [70] A. Bird, Perceptions of epigenetics, Nature 447 (7143) (2007) 396–398. [71] C.A.  Brosnan, O.  Voinnet, Cell-to-cell and long-distance siRNA movement in plants: mechanisms and biological implications, Curr. Opin. Plant Biol. 14 (5) (2011) 580–587. [72] R.K. Slotkin, M. Vaughn, F. Borges, et al., Epigenetic reprogramming and small RNA silencing of transposable elements in pollen, Cell 136 (3) (2009) 461–472. [73] J. Law, S.E. Jacobsen, Establishing, maintaining and modifying DNA methylation patterns in plants and animals, Nat. Rev. Genet. 11 (3) (2010) 204–220. [74] R.  Martienssen, A.  Barkan, W.C.  Taylor, et  al., Somatically heritable switches in the DNA modification of Mu transposable elements monitored with a suppressible mutant in maize, Genes Dev. 4 (3) (1990) 331–343. [75] B. McClintock, The control of gene action in maize, Brookhaven Symp. Biol. 18 (1965) 162–184. [76] T.J. Hardcastle, S.Y. Muller, D.C. Baulcombe, Towards annotating the plant epigenome: the Arabidopsis thaliana small RNA locus map, Sci. Rep. 8 (2018) 6338. [77] L. Morgado, V. Preite, C. Oplaat, et al., Small RNAs reflect grandparental environments in apomictic dandelion, Mol. Biol. Evol. 34 (8) (2017) 2035–2040. [78] A.D. McCue, S. Nuthikattu, S.H. Reeder, et al., Gene expression and stress response mediated by the epigenetic regulation of a transposable element small RNA, PLoS Genet. 8 (2) (2012) e1002474. [79] S. Nuthikattu, A.D. McCue, K. Panda, et al., The initiation of epigenetic silencing of active transposable elements is triggered by RDR6 and 21-22 nucleotide small interfering RNAs, Plant Physiol. 162 (1) (2013) 116–131. [80] K.M. Creasey, J. Zhai, F. Borges, et al., miRNAs trigger widespread epigenetically activated siRNAs from transposons in Arabidopsis, Nature 508 (7496) (2014) 411–415. [81] T. Tian, J. Wang, X. Zhou, A review: microRNA detection methods, Org. Biomol. Chem. 13 (8) (2015) 2226–2238. [82] J.  Hausser, M.  Zavolan, Identification and consequences of miRNA–target interactions—beyond repression of gene expression, Nat. Rev. Genet. 15 (9) (2014) 599–612. [83] D.W. Thomson, C.P. Bracken, G.J. Goodall, Experimental strategies for microRNA target identification, Nucleic Acids Res. 39 (16) (2011) 6845–6853.

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CHAPTER

Transcriptome-based identification of small RNA in plants: The need for robust prediction algorithms

4

Sayak Gangulia, Pankaj K. Singhb, Amita Palb, a

Department of Biotechnology, St. Xavier’s College (Autonomous), Kolkata, India, b Plant Biology, Bose Institute, Kolkata, India

­Introduction Over the past few decades, scientists from all over the world have studied the common variations in gene expression levels in organisms under different conditions. Numerous polymerase chain reaction (PCR)-based methods were randomly used with moderate success rates. Despite these efforts, the dream of catching the entire plethora of genes and their expression levels at a particular developmental stage or in a specific organ was only made possible with the advent of RNA sequencing (RNA Seq) using Next Generation Sequence (NGS) techniques. With the increase in gradual complexity of organism systems, the choice of methods of RNA Seq, as well as the importance of understanding which analyses pipeline to choose from, an entire universe of available options emerged as greater challenges. The key focus of any RNA Seq experiment is the elucidation of the spatiotemporal profile of expressed genes and their corresponding abundances across and within samples. The choice of the biological sample is design-dependent, and minuscule quantities of RNA isolated from such tissues provide enough information that serve as starting points for such complex analyses [1]. Both organisms with or without genome information can be used for RNA Seq-based transcriptome analyses as de novo transcriptomics has established itself as one of the leading fields of expression studies. Annotated sequences of related species are generally used for the alignment and making of the reads. Computational methods even allow for identification of microsatellite markers from available sequence information [2]. Challenges that still remain in this emergent field are the identification of novel transcript isoforms from annotated genes, alternatively spliced transcripts and their locations, and annotation of novel transcripts, microRNAs [3], and their genes from plants with no reference genomes, or in response to specific treatments. The most important question for an RNA Seq study is to find the answer Plant Small RNA. https://doi.org/10.1016/B978-0-12-817112-7.00004-3 © 2020 Elsevier Inc. All rights reserved.

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to the biological ­question being asked. This can only be achieved if the experimental design is robust with apt counter-measures for dealing with sudden alterations in data. The most important aspects of the design process is to focus on the type of library that is being prepared, the depth of sequencing, and the suitable number of biological and experimental replicates needed to ascertain the goals of the experiment. Along with this, the sequencing needs to be performed properly to ensure that the generated data remains unbiased. This can only be achieved if proper algorithms and pipelines are chosen and executed at the different levels of data acquisition and analyses.

­The need for small RNA Seq in plants As the years have gone by, small RNAs and their involvement in almost all the regulatory processes in plants have gradually been revealed. Due to their specific growth conditions and sustainability criteria, plants present a very interesting ecological niche as a group. They are the essential colonizers that enable the transition of vegetation from one region to another, as well as serving as food and fodder to numerous members of different food chains. In the plant kingdom, miRNAs, the major class of small RNAs ubiquitously reported from all plants under study throughout the world, have been identified in 53 dicotyledonous and 12 monocotyledonous plants, which include Arabidopsis thaliana (427 mature), Gossypium hirsutum (80 mature), and Solanum tuberosum (343 mature) among the dicots, and Brachypodium distachyon (525 mature), Oryza sativa (713 mature), and Zea mays (321 mature) forming the monocot group. Several specific roles of these RNAs have been identified such as development [4, 5], signal transduction in response to plant growth regulators [6, 7], biosynthesis of simple and complex lipids [8], production of secondary products [9] and abiotic and biotic stress responses [10, 11], and innate immunity [12]. Zhang et al. [13] suggested a very interesting hypothesis (referred to as the xenomiR hypothesis) in which microRNAs of plant origin escaped breakdown by the human digestive system and was absorbed into the bloodstream, subsequently acting as endomiRNAs and inducing different phenotypes. The reports of Zhou et al. [14] and Du et al. [15] emphasize the therapeutic value of such plant-derived microRNAs in targeting influenza A viruses and pulmonary fibrosis, respectively. Thus, information is needed for the understanding of the repertoire of functions of these small RNAs, which can only be achieved if more and more RNA Seq experiments are carried out. With the advent of single-cell sequencing, tissue-specific expressions have become easier to identify and detect, which allows easier reconstruction of regulatory pathways.

­Types of RNA Seq strategies ­dUTP-based strand-specific RNA Seq Regulatory non-coding RNA (ncRNA) transcripts are important landmarks toward the process of transcript mapping because they have the ability to regulate expression at the mRNA or chromatin levels. Standard RNA Seq methods fail to differentiate the

­Types of RNA Seq strategies

strands of DNA from which the actual RNA is encoded. The relative expression of the two strands enables quick identification of ncRNAs. Briefly, dUTP is incorporated into the second strand simultaneously as the first strand is being synthesized. Following this end repair, A-tailing and adaptor ligation events take place after which the strand with the dUTP is digested and the remaining strand is applied using PCR, which imparts strand specificity (Fig. 1).

­Bulked segregant analysis (BSA) using RNA Seq In this method, transcriptome profiling is generated from a pool of two samples, which are resultant of an admixture of a bulk of mutant and wild-type (WT) plants. Initially, the allelic frequencies are quantified, which reveals the map position of the mutant gene and establishes a linkage disequilibrium. Further in-depth mapping of the mutated gene is performed using standard RNA Seq data. This is done because the expression of the gene may be lower than the WT pool. The single nucleotide polymorphisms (SNPs) linked with the mutated gene can be further employed for chromosome walking (Fig. 2) [16].

­Double-stranded RNA Seq Several reports have put forward the importance of the RNA secondary structure, which plays an essential functional role for maturation and regulation. Zheng et al. [17] reported the study of secondary structures from Arabidopsis flower buds where they only sequenced the double-stranded fraction of the RNAome. The data was cDNA synthesis U A

U A

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dUTP, dATP, dCTP, dGTP U A

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FIG. 1 dUTP-based strand-specific RNA Seq.

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FIG. 2 Bulked segregant analysis (BSA) using RNA Seq.

­Elements of RNA Seq data and analyses

found to be rich in rRNA, tRNA, and snRNA, along with portions of introns, exons 3′, and 5′UTRs. This was achieved by digesting the single-stranded molecules with ribominus treatment prior to library preparation. Interestingly, few regions of the genome appeared to be responsible for producing more dsRNAs than others, with transposable elements representing nearly 60% of these “hotspots” (Fig. 3).

­Differential RNA Seq This approach is generally employed for detecting transcription start sites (TSSs) of operons. The chemistry is simple where a terminator exonuclease removes the processed transcripts by systematic degradation of the 5′ monophosphate RNAs; however, the primary transcripts are not harmed due to the presence of a 5′ triphosphate. The treated and non-treated samples are then compared and analyzed. TSSs of barley chloroplast RNAome has been reported employing this technique [18]. They have identified four categories of TSSs, such as gTSSs situated in the upstream of the annotated genes within 750 nucleotides; iTSSs generating sense transcripts and are intragenic, whereas aTSSs produce antisense transcripts; and orphan TSSs located in the intergenic regions (Fig. 4).

­Elements of RNA Seq data and analyses The following are the essential parameters for evaluation of RNA Seq data [19]:

­Raw read Raw reads need to be analyzed for various parameters, such as quality of sequence, total guanine-cytosine (GC) percentage, presence or absence of adaptors, k-mer overrepresentation, and read duplicates; the entire process is termed as Quality Control (QC). This enables the detection of sequencing errors, if any. Because most study designs use bio-replicates, duplication, k-mer, or GC content levels are generally experiment- and organism-specific.

­Read alignment This is a measure of sequencing accuracy and the presence or absence of DNA contaminations. This is expressed in terms of percentage of mapped reads. The higher the percentage, the better the accuracy.

­Quantification Following the analysis of total transcript values, GC content and length biases should be evaluated so that proper normalization methods can be employed. If the experiment is not de novo, then the design should incorporate the analyses of biotype composition, which indicates the quality of RNA purification.

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CHAPTER 4  Transcriptome-based identification of RNA in plants

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­Elements of RNA Seq data and analyses

Pre-mRNA

PPP P

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FIG. 4 Differential RNA Seq.

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­Transcript identification This step is different for known and de novo approaches. In the former, the focus is mostly toward achieving maximum alignment, thus leading to quantification but sacrificing the chances of novel transcript discovery. In the latter, the contain assembly step ensures the probability of identification of novel transcripts, from a combination of partial alignments and functional annotations.

­Alignment Alignments can be performed with reference genomes or reference annotated transcriptomes as templates. In both scenarios, unique mapping of reads (assignment of a read to a single position in the template) or multi-mapping of reads (reads mapped to multiple positions) is common.

­Differential gene expression analyses De novo assembly is performed when a single reference genome is unavailable. This involves assembly against multiple related genomes or whole transcriptomes to increase the percent annotated transcripts. SOAPdenovo-Trans, Oases, Trans-ABySS, or Trinity are the most used packages. Most packages prefer long reads as they enable better mapping and annotation.

­Alternative splicing identification Differential expression analyses typically follow two approaches. The first estimates the isoform expression of each of the isoforms within the total gene expression cascade. The second approach skips the estimation of isoform expression and directly looks for signals of alternative splicing by focusing on exon junctions. CuffDiff and DEXseq are the two commonly used pipelines for the respective approaches.

­Identifying gene fusions Because we can no longer assume that transcript segments may be arranged colinearly on a single chromosome, the identification of gene fusions presents a challenge. The easiest to identify are read-through chimeras as they involve alternative splicing between adjacent genes. Artifacts that are not true, recurrent fusions tend to appear in unrelated control datasets simultaneously making them detectable. Often, the fusion boundary has been found to coincide with a splice site within each gene, thus making them detectable. A flow chart showing different strategies toward RNA Seq experimentation and analyses is depicted in Fig. 5.

Experimental design

Sequencing design

Library type

Sequencing length

Replicate number and sequencing depth

Single vs paired-end

Longer reads better for isoform analysis

3 replicates or power analysis software

Spike-Ins?

Randomization @ library prep

For quality control and library-size normalization

Genome mapping

Randomization @ sequencing run

Avoids confounding experimental factors with technical factors

Raw reads

Read alignment

Sequence quality, Read GC content, uniformity, K-mers, duplicates GC content

Transcriptome mapping

Quantification

Reproducibility

3′ bias, biotypes, low-counts

Correlation, PCA, batch effects

Reference free assembly

Reads

Tophat star

Gapped mapper

UnGapped mapper

Mapping to genome

Transcript discovery and counting

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BLAST2GO

Functional annotation

FIG. 5 Strategies toward RNA Seq experimentation and analyses.

Trinity

De Bruijn graphs Assembly into transcripts

RSEM Kallisto

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CuffLinks With GFF

Reads

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Map reads back

RSEM HiSeqCount

GTF based Counting

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BLAST2GO

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­Elements of RNA Seq data and analyses

Reads

Transcript identification and counting

Quality control

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­ hallenges and solutions for annotating small RNAs in C plants The role of small RNAs has been well documented over the years, especially in post-transcriptional regulation [3]. Furthermore, techniques have also been developed that enable the identification of sRNA populations. Computational analysis enables the rapid identification of putative sRNA genes, but from an experimental standpoint, RNA Seq technology represents an excellent means for sRNA discovery and validation [19]. Some sRNAs have been found to be expressed in all five tissues, whereas others exhibited tissue-specific patterns, especially in the developmental zones. A large number of RNA Seq studies have revealed that small RNA expression in plants is spatiotemporally regulated. Like miRNAs and other sRNAs, small ncRNAs (sncRNAs) have also been associated with abiotic stresses, and many of these are differentially expressed under phosphate limiting conditions as found in Arabidopsis roots and shoots [20] or under cold conditions [21]. In contrast to the vast literature on plant miRNAs, much less is known about lncRNAs (> 200 nt), especially in plants. Despite major efforts of plant biologists in the last decade, the complete functional characterization still remains elusive [22].

­Empirical toolkits and databases ­FASTX The FASTX Toolkit [http://hannonlab.cshl.edu/fastx_toolkit/] handles Short-Reads FASTA/FASTQ files and preprocess them based on command line interventions. The key processing step is mapping, and the following tools are used to perform the action: Blat, SHRiMP, LastZ, and MAQ by aligning the sequences to reference genomes.

­miRCat miRCat is used for identifying miRNAs from sRNA sequencing data, which does not require pre-identification of any putative precursor sequence, as these are identified from the data itself; however, the loci are selected on the following criteria [URL: http://srna-workbench.cmp.uea.ac.uk/mircat-2/]: • Loci must contain no more than four non-overlapping sRNAs. • Each sRNA in a locus must be within 200 nt from its closest neighbor (this threshold can be adjusted by utilizing the hit dist parameter). • At least 90% of sRNAs in a locus must have the same orientation (this threshold can be adjusted by utilizing the percent orientation parameter).

­Emerging algorithms

­SiloCo This is a tool used for prediction of an sRNA locus. This utilizes a normalization protocol that gives weightage to repetitiveness, and each distinct hit is represented as hits per million matching reads. Each predicted locus must have a minimum of three weighted sRNA hits with gaps no longer than 300 nucleotides.

­miRBASE This is one of the premier databases pertaining to microRNA (miRNA) data. This contains information regarding the actual maturation sites of the sequenced miRNA from their precursors and chromosome (DNA strand) wherever applicable. Users can search for hairpin precursor and mature sequences in the search interface. A text-based search option is also available, and entries can be accessed using name and other keywords. Most of the data is freely downloadable. The miRBase Registry often provides unique names for novel miRNA genes prior to publication of data.

­TAPIR [http://bioinformatics.psb.ugent.be/webtools/tapir/] With increasing information regarding functional imperfect hybridizations leading to translational repression rather than cleavage, identifying candidates using base pair complementarity information is gradually gaining importance. TAPIR server provides scope for searching plant miRNA targets using a functionality based algorithm that emphasizes accuracy and speed. The precise function eases the prediction of target mimics, which are characterized by the presence of a miRNA target duplex, a pattern that goes undetected in traditional tools.

­Emerging algorithms ­Validation of expression using time course data The ordinary differential equation model has been successfully utilized using OpenCL with a Java wrapper for predicting gene networks. In this model developed by Modrák and Vohradský [23], the synthesis of a new mRNA is non-linearly related to the expression of its regulators. This is coerced by keeping the per-gene decay rate of mRNA constant. Presently, there are three interfaces to this Genexpi core, namely CyGenexpi (a Cytoscape plugin), a command-line interface, and an R interface.

­Normalization and log ratio transformation method Most algorithms uses the alignment-based count matrix, followed by quantification to analyze differential expression data that predicts which genes are getting differentially expressed from a statistically favorable standpoint. DESeq and EdgeR are the two most popular algorithms that are in use. Normally, a fraction of the total input is

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sequenced; hence, normalization becomes an essential step that nullifies the constraint imposed by the per-sample output to a fraction of the total number of molecules in the input library. Although choice in normalization can affect the final results of a DE analysis, yet it is necessary because per-sample counts generated by alignment and quantification cannot be directly compared. Sequencing depth is thus defined as the total number of sequences generated. Thus, all RNA Seq data is compositional and is characterized by two key properties: the total summation of the components tend to be artifacts of the procedure of sampling and the differences observed between the components are proportionally important. In contrast to normalizations, compositional data analyses using log ratio transformation methods do not retrieve absolute abundances. The ALDEx2 package (available for the R programming language) uses log ratio transformations and enables the cluster-­independent identification of differentially abundant features using a three-step statistical hypothesis testing: (1) Generation of Dirichlet distribution-based Monte Carlo (MC) instances of the count matrix; (2) Following this, univariate statistical analyses is performed on the generated instances; and finally (3) Calculation of the per-transcript-based adjusted false discovery rate (FDR) p-values across all MC instances.

T­ ools and databases available according to the basic steps of RNA Seq data analyses ­Quality control • dupRadar [24] is an R package that provides functions for plotting and predicts the rates of duplication by taking into account the levels of expression. • FastQC is a quality control tool for high-throughput sequence data and was developed in Java. Data can be imported from FastQ files, BAM, or SAM format. This is considered to be the gold standard for quality control of sequencing raw data and provides important insights regarding specific features that enable assessment of the quality of sequencing run. It is available both online as well as in command line versions. • Kraken [25] is a set of tools like FASTQC and is used for quality control analysis of NGS data. • HTSeq [26] is a Python-based program that uses the htseq-qa script to evaluate the sequencing read file (raw or aligned) and generates a .pdf file, which eases the technical quality of the run. • mRIN [27] is used to asses mRNA integrity directly from RNA Seq data. • MultiQC [28] combines results from various tools, namely FastQC, HTSeq, RSeQC, Tophat, STAR, and others across all samples into a single report. • RNA-SeQC [29] is a tool that deals with raw reads and provides a comprehensive summary of read-specific parameters such as count, mapped reads, etc. along with information regarding coverage, GC bias, and expression

­Tools and databases

levels based on RPKM normalization. The Gene Pattern GUI enables the offline processing of RNA-SeQC, where BAM files are accepted as input. Output is in the form of HTML. • RSeQC [30] accepts SAM, BAM, FASTA, and various other file types as input and performs basic compositional analyses on different aspects of RNA Seq experiments; sequencing depth, GC bias, sequence quality, etc. are the standard outputs. Genome browsers like UCSC, IGB, and IGV are generally used for visualization. • SAMStat [31] enables the detection of poor mapping, which may have been generated due to issues such as inaccurate quality control and processing at different stages.

­Trimming and adapters removal • Condetri [32] focuses on the quality scores of each base, performs read trimming from NGS data, and has the ability to process single-end and pairedend sequencing data of arbitrary read length. • Cutadapt [33] detects and removes adapter sequences from NGS data (Illumina, SOLiD, and 454). This is considered in cases when the average read length happens to be greater than the sequenced molecule, for example, miRNA. • Erne-Filter [34] is a tool for dealing with short reads and comprises of read trimming and contamination filtering (ERNE-FILTER); core alignment tool/ algorithm (ERNE-MAP); bisulfite treated reads aligner (ERNE-BS5); and distributed versions of the aligners (ERNE-PMAP/ERNE-PBS5). • PRINSEQ [35] deals with QC statistics such as GC content, length, log odds ratios, etc., which are also generated along with options for data trimming and filtering. • SnoWhite [36] is a pipeline designed to clean DNA sequence reads prior to assembly. • Trimmomatic [37] accepts FASTQ reads (single or paired end) from Illumina platforms and trims them. It can perform tasks such as cut adapters, cut reads to a specific length, cut bases in optional positions based on quality thresholds, and convert quality scores to Phred-33/64.

­Error correction AmpliconNoise [38] is a software suite that removes noise from 454 sequenced amplicons and also removes sequence chimeras using the Perseus algorithm. Bless [39] is a tool that takes NGS reads as input and employs a bloom filterbased error correction tool used for NGS reads. Blue [40] is a tool that identifies the k-mer consensus sequence and then performs quick and accurate error correction for short reads.

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­Bias correction • Alpine [41] is employed in modeling and correcting fragment sequence bias for RNA Seq. • cqn [42] implements the conditional quantile method for normalization and prevents skewed expression data. • EDASeq [43] is a bioconductor package to normalize GC contents for RNA Seq data. • Peer [44] is a suite of Bayesian approaches based on factor analyses and helps in the inference of hidden determinants and their effects from gene expression profiles. Applications of PEER have: (a) detected batch effects and experimental confounders, (b) increased threefold the number of expression QTL detected, (c) allowed the identification of events such as transcription factor or pathway activations that have long-term regulatory repercussions. • RUVseq [45] is an R package that executes the remove unwanted variation (RUV) method that normalizes RNA Seq read counts. • SysCall [46] is a classifier tool used to identify and correct systematic errors in NGS data.

­Other tasks/preprocessing data • COPE [47] is an accurate k-mer-based tool employed to facilitate genome assembly. • PEAR [48] is a fast and accurate Illumina Paired-End reAdmergeR. • SHERA [49] is a SHortread Error-Reducing Aligner.

­Alignment tools Alignment is one of the key steps after quality control, and this is the step where the reads are aligned to a reference genome when available; otherwise, a transcriptome database is used as reference. • Burrows-Wheeler Aligner (BWA) uses three important algorithms, BWA backtrack, SW, and MEM, to map low divergent sequences against a reference genome that is substantially large. BWA backtrack deals with reads up to 100 bp, whereas SW and MEM can process 70 bp to 1 Mbp.

­De novo splice aligners • De novo splice aligners are used for the identification of novel splice junctions; it does not require any previously annotated reference. • Pass [50] is used for the analysis of bisulfate sequencing data. This includes the option to filter the data prior to the process of alignment. Global and Local Alignment algorithms such as Needleman-Wunsch and Smith-Waterman are used, and a three-stage process is initiated that involves scanning seed sequence position in the genome, identification of contiguous regions, and alignment refinement.

­Tools and databases

• RAZER [51] is used for the analysis and read alignment of SNPs and RNA editing sites. • Subread [52] is a very efficient read aligner and scores over other methods in speed, precision, and scalability. The seed and vote mapping method is used to identify the exact map position in the largest mappable region and chooses the global or local alignment method as a default function. • Subjunc is an upgraded version of Subread; here, exons and exon junctions are identified by scanning all the available mappable regions. Donor and receptor flags are used to locate the exact splice points. • STAR detects chimeric fusion sequences and spice junctions (canonical and non-canonical) by employing the “sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure.” Data from third-generation sequencing platforms producing ultralong reads are easily processed with an average speed of 45 million paired reads per hour per processor [53]. • TopHat [54] locates de novo junctions. There are two steps to the analysis. The first involves Bowtie-based alignment of unspoiled reads followed by Maqbased assembly resulting in island sequences, which are then searched for canonical donor and acceptor sites to predict the splice junctions.

­Normalization, quantitative analysis, and differential expression • The important step of transcript abundance identification using normalization is performed by these tools [55]. RPKM, FPKM, and TPMs [56] are the accepted parameters and units for measuring expression in and across samples. The robustness of the inter-sample comparisons mostly depend on the quality of data generated at the read alignment step. Several reports are available comparing differential expression between samples [57–59]. • GMNB [60] uses a Bayesian method to identify the temporal expression of genes across samples. This naturally accounts for differences in sequencing depths between samples, thus normalization step is not required. Cufflinks/Cuffdiff is one of the most efficient tools for the identification of isoforms as well as to predict and measure transcript expression. Transcripts are assembled first and then estimation of abundances and determination of differential expression (Cuffdiff) is performed, which leads to the prediction of regulation in RNA Seq samples [61]. • DESeq uses a negative binomial distribution and provides a measure of differential expression of genes across samples. This is available as a Bioconductor package. • DEXUS is also a Bioconductor package that is more robust in its identification strategies as it can handle almost all probable study designs, for example, mono samples, data from studies with no sample groups, and even when conditions are unknown [62]. An expectation maximization algorithm is used to predict the possible number of replicates per sample based on length. • DGEclust is a Python package that uses a Hierarchical Dirichlet Process Mixture Model, which utilizes the range of differential gene expressions to

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cluster the transcripts into gene-specific and sample-specific dendrograms for better visualization. Clustered data is also often represented as heatmaps [63]. • GFOLD [64] utilizes a generalized rank abundance algorithm that assembles transcripts from RNA Seq data according to their expression values both within and across samples. • globalSeq [65] is used for studying the association between RNA Seq and other high-dimensional data. • metaseqR [66] is a predictive algorithm that combines six different methods and utilizes a weightage scheme that takes as input the comparative performance of simulated and real data to predict the differentially expressed genes. The user output is a detailed and interactive report with a variety of charts and diagrams, and auto-generated text. • Semisup [67] is a tool that utilizes a semi-supervised learning algorithm mixed with a background database and identifies SNPs having interactive effects on a quantitative trait.

­Open (free) source solutions • BioQueue [68] is a unique tool in an era where analysis speed is of great importance. This tool predicts the efficiency of the system in which the analysis is being performed by evaluating the resources available, both software and hardware, and thus enables the improvement of efficiency and robustness of a pipeline being studied. • BioWardrobe [69] is a GUI-based Web-available tool that can analyze ChIP Seq and RNA Seq datasets. STAR and DESeq analysis packages are used in the user friendly interface.

­Alternative splicing analysis LEMONS [70] helps to identify Splice Junctions in Transcriptomes of organisms having no Reference Genomes to date. RPASuite [71] (RNA Processing Analysis Suite) identifies differentially expressed transcripts by passing RNA Seq data through an analysis pipeline. The data may have originated from multiple tissue or cell lines but are processed simultaneously. SpliceGrapher enables the prediction of novel alternative splicing events from RNA Seq data [60]. SpliceTrap [72] accepts paired RNA Seq data and provides statistical data toward quantifiable exon inclusion ratios.

­Differential isoform/transcript usage • IsoformSwitchAnalyzeR [73] directly supports data from Cufflinks/Cuffdiff, RSEM Kallisto, and Salmon, and its in-built R script identifies isoform switches as well as predicts possible consequences of such events.

­Fusion genes/chimeras/translocation finders/structural variations

• DRIMSeq [74] is another R package that identifies isoform switches from count data profiles by using a generalized linear model (GLM). • BayesDRIMSeq [75] is a standalone R package that enhances DRIMSeq by implementing a Bayesian sampling algorithm. • Cufflinks/Cuffdiff [76] utilizes the biological phenomenon where isoforms often share a transcription start site and maps the changes in this usage by performing a generalized t-test based on the asymptotic of the Jensen-Shannon metric. • rSeqNP [77] is a standalone R package that predicts and tabulates differentially expressed transcripts and their probable splicing events by using a nonparametric approach. • Isolator [78] is a Bayesian hierarchical model-based tool that identifies isoform usage patterns, both uniform and differential, and also quantifies transcripts to predict the differences in expression across samples.

F­ usion genes/chimeras/translocation finders/structural variations The fate of major gene rearrangements leads to deadly diseases such as cancer where translocations and aberrant fusions are very common. Thus, it is very important to identify such locations from RNA Seq data to enhance models of carcinogenesis [70]. • Arriba is an important tool of this genre and is based on the STAR [69] RNA Seq aligner that enhances ultrafast alignments. A meager 2 min is the average post-alignment runtime. In terms of accuracy, this is still the best-performing algorithm in the current DREAM Challenge. Gene fusions, exon duplications, or truncations of genes are also detected by Arriba. • ViReMa (Viral Recombination Mapper) [79] is a tool that identifies recombination or fusion events in viral and host genomes by analysis of data generated from deep sequencing.

­Single-cell RNA Seq Single-cell RNA Seq [80] offers new challenges and, though some old tools, can be used to analyze these type of data. There are quite a few new players in this category, which explores different algorithms and approaches. • CEL-Seq [81] is used for single-cell RNA Seq analysis, and it uses multiplexed linear amplification. • Drop-Seq [82] is widely used for genome-wide profiling of gene expression, and data can be analyzed in parallel, which decreases time and increases specificity achieved at the individual cell level by using nano-liter droplets. • SCUBA [83] is a modeling tool that predicts cell lineages during and between the process of multi-lineage cell differentiation.

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• scLVM [84] is a tool and modeling framework for single-cell RNA Seq data that enables the identification of different sources of data by analyzing their heterogeneity and thus also contributes to the correction of sources of variation. • Sphinx [85] is a tool used for binning. It is efficient as it performs hybrid binning where both the sequence composition and similarity is weighted and analyzed. • TraCeR [86] is a tool used for the reconstruction of paired T-cell receptor from single-cell RNA Seq reads. VDJPuzzle is a similar tool to TraCer, but here the clonotype is linked to the functional phenotype and transcriptome of individual cells.

­Integrated packages • SINCERA [87] is a Profile Analysis Pipeline for Single-Cell RNA Seq data.

­Genome-guided assemblers [88, 89] • Cufflinks is the gold standard for transcript assembly, abundance estimation, and prediction of differential expression from paired RNA Seq data. Initially a parsimonious set of transcripts are assembled, and then their relative abundances are calculated based on the number of supporting reads for each of the transcripts. Library preparation protocols and their biases are also considered while preparation of the parsimonious dataset. • Bridger [90] is a tool developed at Shandong University, and it enhances the approach of Cufflinks by using an approach called minimum path cover, which enables the identification of transcripts with greater efficiency and utilizes lesser time for generating results. • Velvet [91] is a de novo assembler that accepts short read data and performs contig assembly and scaffold construction.

­Co-expression networks • Pigengene is an R package that works on gene expression profiles to provide biological insights. Feature selection is based on the prediction of eigengenes, and the tool effectively uses them to construct decision trees and Bayesian networks [92].

­Visualization tools • BrowserGenome.org [93] is a Web-based RNA Seq data analysis and visualization tool. • Tablet [94] is a tool that is low on system requirements but does not compromise on its performance as a graphical viewer for NGS data assemblies and alignments.

­Fusion genes/chimeras/translocation finders/structural variations

­Functional, network, and pathway analysis tools • GAGE [95] is a de novo assembly specific tool that works despite heterogeneous sources of data as well as variations in sample size, experimental design, or assay platform. • GeneSCF [96] is a tool for real time analyses of gene set enrichment and functional enrichment based on transcript information generated using RNA Seq. • GOexpress [97] utilizes Gene ontology annotations and helps in the visualization of microarray and RNA Seq data. • GOSeq [98] is a gene ontology analyzer for RNA Seq having length bias data. • GSAASeqSP [99] is a tool used for the analysis of association among Gene Set from different RNA Seq Data. • PathwaySeq [100] is a tool that predicts pathways based on enrichment scores from transcript annotation information. • ToPASeq [101] is a standalone R package for topology-based pathway analysis of microarray and RNA Seq data. • TRAPID [102] is a Web browser for rapid analysis of transcriptome data. • T-Rex [103] is used for RNA Seq expression analysis.

­Links to databases used for analysis of plant transcriptome data • Arabidopsis Transcriptome Genomic Express Database (RNA Seq data) URL: http://signal.salk.edu/cgi-bin/atta • RiceGE Japonica: Rice Functional Genomic Express Database (RNA Seq data) URL: http://signal.salk.edu/cgi-bin/RiceGE • RiceGE: Rice (indica) Functional Genomic Express Database (RNA Seq data) URL: http://signal.salk.edu/cgi-bin/RiceiGE • PopGenIE: The Populus Genome Integrative Explorer (cDNA array) URL: http://www.popgenie.org/ • Medicagotruncatula Gene Expression Atlas (Affymetrix data) URL: http:// mtgea.noble.org/v2/ • Maize C3/C4 Transcriptomic Database (RNA Seq data) URL: http://c3c4. tc.cornell.edu/search.aspx • Tomato Expression Database (cDNA array and Affymetrix) URL: http://ted.bti. cornell.edu/ • SRA. The Sequence Read Archive (SRA) is the largest collection of raw sequence data from NGS technologies including 454, IonTorrent, Illumina, SOLiD, Helicos, and Complete Genomics. In addition to raw sequence data, SRA has started storing alignment information as well in the form of read placements on a reference sequence (Table 1). These pipelines have been used extensively in the analysis of numerous plantbased RNA Seq experiments. For example, the current citations for Trinity and its associated suite of tools are 4915, with an average citation per year of 535 i­ ndicating

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Table 1  Raw data repositories of RNA Seq data. Data type

Repository

URL

Raw sequence reads

EBI Array Express

Raw sequence reads

NCBI-SRA

Transcriptome assemblies, annotation, markers, etc. All data generated from the experiment All data generated from the experiment

European Nucleic Archive

https://www.ebi.ac.uk/ arrayexpress/submit/ overview.html https://www.ncbi.nlm.nih. gov/sra https://www.ebi.ac.uk/ena/ submit http://datadryad.org

Dryad digital repository Harvard Dataverse

https://dataverse.harvard. edu

the value of the tool despite emergence of new algorithms. Very recently, it has been used in a study on Clematis finetiana [104]. The CLC Genomics workbench on the other hand is a commercial tool, and it has been used by people involved in RNA Seq experiments 4109 times in the past 10 years [105]. FASTQC, the leading quality control analysis tool, was used 1976 times in 2017 alone, and in the last 5 years has been used for 4056 different works involving RNA Seq, including a very recent work on fungus-insect symbiosis [106]. These data indicate that these gold standard tools shall continue to be of importance in all RNA Seq studies in the future. However, the following areas need more robust algorithms for future workers in RNA Seq: (a) Targeted assembly of reads (b) Proper identification of isoforms from different tissue types (c) Prediction of metabolic pathways with specific comparison with replicates.

­ case study: Transcriptome analyses from Vigna mungo A and identification of miRNAs ­Background of the work miRNAs are a discrete class of small non-coding RNAs involved in multifaceted function in plants. They have been identified to participate actively in gene regulation and gene silencing activities in plants. Activities of miRNA in biotic and abiotic stress have been well documented. Vigna mungo L. Heppar (black gram) is one of the favorite legumes cultivated in south-east Asian countries including India due to its high protein content, which contributes positively to its nutritional importance mainly in people dwelling in lower economic strata of the society. In India, several grain legumes including V. mungo are marred by a very potent Mungbean Yellow Mosaic India Virus (MYMIV), causing yellow mosaic disease. This viral disease

­A case study

results in heavy crop penalty leading to enormous nutritional and economic loss. Kundu and Pal [107] have reported a MYMIV-resistant inbred line of V. mungo, VM84. Paul et al. [108] have demonstrated the tissue-specific expression pattern of both conserved and novel miRNAs in V. mungo. Furthermore, the same group has reported miRNAs involved in regulation of stress-related responses in V. mungo [109]. The methodology adopted for this entire work is represented in Fig. 6.

Sample preparation and RNA isolation

Small RNA library construction and deep sequencing

Raw reads

Quality control

De novo assembly

Removal of all non coding RNA features except miRNAs

Precursor and mature miRNA sequence prediction

Known or estabilshed miRNA

Tissue specific expression analysis (qPCR)

Annotation

Novel miRNA

Gene ontology analysis and qPCR based validation of targets

FIG. 6 Flowchart of miRNA isolation, identification, and expression analyses.

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­Sample preparation and sequencing Inbred lines of MYMIV-resistant V. mungo VM84 and high-yielding susceptible cultivar T9 were used as plant materials for this study. Surface-sterilized seeds were germinated and transferred to sterile and moist vermiculites and grown in greenhouses at 25 ± 1°C. After 15 days, leaves, stem, and root were collected and frozen using liquid nitrogen and stored at − 80°C separately. Two sets of similar material were infected with viruliferous (infected) and non-viruliferous (mock control) white flies; these plants were allowed to grow until 21 days. The infection was carried out by method as demonstrated by Kundu et  al. [109]. Total RNA was isolated from MYMIV-mock and MYMIV-inoculated leaves as well as from the control materials (young leaves, stem, and root) using the Trizol reagent (Invitrogen, USA) following the manufacturer’s protocol. The small RNA libraries were constructed from the total RNA extracted from leaf tissues of both mock-control and MYMIV-inoculated samples using an Illumina Small RNA sample prep kit (Illumina, San Diego, CA, USA) by following the manufacturer’s instructions. Initially, 10 μg of total RNA was resolved in a 15% denaturing polyacrylamide gel, followed by excision of small RNA fragments in a range of 18–30 nt from the gel, and ultimately these fragments were purified, both 5′ and 3′ terminal ends were ligated with specific adapters, succeeded by reverse transcription, and ultimately these cDNA were amplified using PCR. The amplified cDNA product was ultimately sequenced using an Illumina Genome Analyzer II X by Genotypic Technologies (Bangalore, KA, and India). RNA isolated from VM84 mock-control and MYMIV-inoculated samples, and T9 mock-control samples and MYMIV-inoculated tissue samples were then subjected to qPCR analyses.

­Screening and identification of miRNAs from sequenced data Raw sequenced reads obtained from sequencer were subjected to quality control for removal of low-quality reads using SeqQC V 2.2. Low-quality reads were first filtered out, then adapter contaminated sequences were filtered out followed by reads without insert fragments; sequences with polyA tail were last to be removed in the garbage-out step. From processed reads, other non-coding RNAs features such as rRNA, tRNA, snRNA, and snoRNA were screened using Rfam (http://rfam.sanger. ac.uk/) and Genbank (http://www.ncbi.nih.gov/Genbank/) databases, so that remaining processed reads only consisted of small RNA features for further analysis. Filtered sequences were used to identify miRNAs sequences by comparing them against miRNA sequences from miRBase (V.21.0); 1-nt mismatch was selected as ­threshold for comparison of V. mungo miRNAs with known miRNAs. Significant queries were grouped to the particular miRNA family. When known miRNA sequences were segregated into their respective groups, the rest of the sequences were screened for identification of novel miRNA candidates. Because V. mungo genome has not been characterized before, precursor sequences of other legumes in miRBase were used as a reference set for predicting novel miRNA precursors of V. mungo [108]. Secondary structures of screened novel miRNA precursors were

­A case study

predicted using MFOLD server [110] based upon Zuker’s algorithm. Established criteria of Zhang et al. [111] were considered, which includes the following: (i) the secondary structure of precursors should consist of a stem-loop structure, which harbors a mature miRNA sequence within one arm, and lacks any loop or break in mature sequence; (ii) the potential miRNA should not be present at the terminal end of a hairpin loop; (iii) mature miRNA should not harbor more than nine mismatched nucleotides with the opposite miRNA* sequence [112]; and (iv) the predicted stemloop structure should possess higher MFEIs and negative minimum folding free energies.

­Identification of established and novel miRNA sequences After proper data analysis, miRNA belonging to the established and novel class were identified. Established miRNAs were divided into two further categories: conserved and non-conserved. miRNAs belonging to 45 conserved and 19 nonconserved families were identified [108]. All of these miRNAs shared high level of homology with their respective homologs from other plants such as Glycine max, Arabidopsis thaliana, Medicago trunculata, and Phaseolus vulgaris. Another 14 novel miRNAs were identified altogether. They were named from vmu-miRn1 to vmu-miRn14. Sequences of these novel miRNAs have been shown in Table 2. It was revealed from the data that miR 156a, miR157a, miR164, miR 390, and mir 398c had a higher read count in mock control (MC) as compared to MYMIV infected (MI). On the flip side, the opposite trend was observed in cases of miR 166b, miR 159b, miR 1514a, miR160a, and miR1515, whereas miR 159c, miR160c, miR 169a, miR 319a, and miR 393a were exclusively identified in the MC dataset. Table 2  List of novel miRNAs identified from Vigna mungo. Name

Sequence (5′ to 3′)

Length (nt)

vmu-miRn1 vmu-miRn2 vmu-miRn3 vmu-miRn4 vmu-miRn5 vmu-miRn6 vmu-miRn7 vmu-miRn8 vmu-miRn9 vmu-miRn10 vmu-miRn11 vmu-miRn12 vmu-miRn13 vmu-miRn14

AGGAGUGGUGGUGUUGACAGGA GGAGGAAAGUAGGUCUGCUGC AGCAGAAUAUAGAGCUAUGACA GGCUCUCAACGAAAGCACCA AAGCUAUGAGAUCUGAGGGC AAACUAUAUGAACCAAAGACAC AUCUCUAGUCGAUGUGAGAC AAAGGACCAAAUUGAACAAAA UUGGCAAGUGUACCAGAUCG GGGGACGUAGCUCAUAUGGUAG UGUCACUACCUCUGAGGCCA AGAUAUGAUCAAUGUAGUCC UUCGGGUGUUAUUUGGGCCUAC GGUGUUGGUCGAUUAAGACAG

22 21 22 20 20 22 20 21 20 22 20 20 22 21

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­ iRNA target prediction, gene ontology classification, and m quantification of target genes Mature sequences of both known and novel miRNAs were selected for their target prediction. Webserver psRNA Target (http://plantgrn.noble.org/psRNATarget/) [113] was used for this purpose. Genomes of all available legumes in this server were used as the target dataset for prediction of the miRNAs of concern. Gene ontology of miRNA target genes was performed using BLAST2GO tool [114]. The results were categorized under biological process, molecular function, and cellular component at level 2 (http://www.geneontology.org/). Quantification of target gene expression was carried out; five genes were selected and amplified with custom designed primers, and their expression was normalized against actin. It was shown previously that actin is the best reference gene for qPCR normalization to study biotic and abiotic stress responses in V. mungo [115].

­ uantification of miRNAs in different tissues to study their Q tissue-specific expression qPCR analysis of predicated miRNAs established the authenticity of the in silico work. To check the tissue-specific expression levels, identified conserved and novel miRNAs isolated from leaves were estimated in stem and root tissues of V. mungo using qPCR [108]. Out of 13 conserved and non-conserved miRNAs tested, miR 164, mir398, miR 408, and miR 2218 were found to be quite abundant in stems as compared to roots. Among novel miRNAs, vmu-miRn4, vmu-miRn5, vmu-miRn6, vmu-miRn8, and vmu-miRn9 were more abundant in case of roots. It was found that more activity of tested miRNAs was in young stem and root tissues than the leaf tissues [108].

E­ xpression patterns of miRNAs from both mock control (MC) and MYMIV-inoculated (MI) datasets On the basis of comparison of read counts for different miRNA for both MC and MI datasets, it was revealed that different miRNAs showed altered expression patterns for different datasets, and even a few were exclusive for one particular type. For validation of differential expression of miRNAs, 11 established miRNAs based upon their reported role in plant pathogen interaction and 8 novel miRNAs were selected for qPCR-based expression analysis at 3 days post-inoculation (dpi), 7 dpi, and 10 dpi time frames for both resistant (VM 84 MI) and susceptible (T9 MI) background. In case of the resistant background, expression profile of five established miRNA families (miR 159, miR 160, miR166, miR 167, and miR 393) revealed to have enhanced response in later stages of infection, whereas expression profiles of miR 156, miR 164, miR 171, miR 398, and miR1511 presented examples of comparatively lower expression during transition from 3 dpi to 10 dpi. In case of novel miRNA

Cellular process Metabolic process Biological regulation Response to stimulus Multicellular organismal process Localization Cellular component organization Signaling Multi organism process Developmental process Category

Reproductive process Growth Binding Catalytic activity Transporter activity Molecular function regulation Transcription factor activity Electron carrier activity Antioxidant activity Cell Organelle Membrane Macromolecular complex

0

5

10

15

FIG. 7 Bar representation of target genes post-gene enrichment analysis at GO level 2 of target genes of differentially expressed miRNAs. Color scheme: biological process (blue), cellular component (red), and molecular function (violet).

20

­Expression patterns of miRNAs from both MC and MI datasets

Single oraganism process

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vmu-miRn13 and vmu-miRn14, both displayed suppressed level of expression at later stages of infection in the resistant background. On the other hand, in case of susceptible background, six established miRNA families (miR 156, miR 160, miR 164, miR 166, miR 167, and miR 393) displayed an enhanced expression pattern in later stages of infection. The same trend of expression pattern was observed for four novel miRNAs, vmu-miRn7, vmu-miRn8, vmu-miRn13, and vmu-miRn14 [109].

q­ PCR validation of miRNA targets in MYMIV-susceptible and -resistant background A total of 97 target transcripts were identified as targets of predicted miRNAs. Gene Ontology (GO)-based analysis was further carried out for all of these targets at GO level 2 [109]. Summary of the enrichment pattern is represented in Fig. 7. Highest numbers of enriched terms were observed for biological process followed by cellular component, and least categories were enriched for molecular function. Five targets genes - Squamosa promoter binding protein (SPB), Auxin Responsive Factor (ARF), Superoxide dismutase (SOD), Nucleotide binding-leucine rich repeat (NB-LRR), and Basic blue copper protein (BBCP) of resistant control (VM84 MC), MYMIV-infected resistant (VM84 MI), susceptible control (T9 MC) and susceptible infected (T9MI) - were analyzed for differential expression [109]. SPB displayed enhancement in the resistance background, although no considerable change was noted in the susceptible plants upon MYMIV infection. Expression of ARF, which is a target of miR 160, declined in both resistant and susceptible backgrounds upon MYMIV infection. Expression of SOD, NB-LRR, and BBCP were observed to be overexpressed in case of resistant genotype, whereas changes in the susceptible genotype was negligible [109]. The predicted role of these miRNAs in MYMIV stress has been discussed in a paper by Kundu et al. [109].

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[55] L. Pachter, Models for transcript quantification from RNA-Seq, arXiv:1104.3889. 2011. [56] H. Jin, J.W. Wan, Z. Liu, Comprehensive evaluation of RNA-seq quantification methods for linearity, BMC Bioinf. 18 (Suppl 4 (117)) (2017) 117, https://doi.org/10.1186/ s12859-017-1526-y. [57] V.M. Kvam, P. Liu, Y. Si, A comparison of statistical methods for detecting differentially expressed genes from RNA-Seq data, Am. J. Bot. 99 (2) (2012) 248–256, https:// doi.org/10.3732/ajb.1100340. [58] M.A. Dillies, A. Rau, J. Aubert, C. Hennequet-Antier, M. Jeanmougin, N. Servant, et al., A comprehensive evaluation of normalization methods for Illumina high-­throughput RNA sequencing data analysis, Brief. Bioinform. 14 (6) (2013) 1–13, https://doi. org/10.1093/bib/bbs046. [59] C.  Evans, J.  Hardin, D.  Stoebel, Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions, Brief. Bioinform. 19 (5) (2017) 776–792, https://doi.org/10.1093/bib/bbx008. [60] E. Hajiramezanali, S.Z. Dadaneh, P.D. Figueiredo, S. Sze, Z. Zhou, X. Qian, Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain. https://arxiv.org/pdf/1803.02527.pdf, 2018. [61] C. Trapnell, B.A. Williams, G. Pertea, A. Mortazavi, G. Kwan, M.J. van Baren, et al., Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms, Nat. Biotechnol. 28 (5) (2010) 511– 515, https://doi.org/10.1038/nbt.1621. [62] G. Klambauer, T. Unterthiner, S. Hochreiter, DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions, Nucleic Acids Res. 41 (21) (2013) e198. https://doi.org/10.1093/nar/gkt834. [63] D.V. Vavoulis, M. Francescatto, P. Heutink, J. Gough, DGEclust: differential expression analysis of clustered count data, Genome Biol. 16 (2015) 39, https://doi.org/10.1186/ s13059-015-0604-6. [64] J. Feng, C.A. Meyer, Q. Wang, J.S. Liu, X.S. Liu, Y. Zhang, GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data, Bioinformatics 28 (21) (2012) 2782–2788, https://doi.org/10.1093/bioinformatics/bts515. [65] A. Rauschenberger, M.A. Jonker, M.A. van de Wiel, R.X. Menezes, Testing for association between RNA-Seq and high-dimensional data, BMC Bioinf. 17 (118) (2016) 118, https://doi.org/10.1186/s12859-016-0961-5. [66] P. Moulos, P. Hatzis, Systematic integration of RNA-Seq statistical algorithms for accurate detection of differential gene expression patterns, Nucleic Acids Res. 43 (4) (2015) e25. https://doi.org/10.1093/nar/gku1273. [67] A.  Rauschenberger, R.X.  Menezes, M.A.  van de Wiel, N.M.  van Schoor, M.A.  Jonker, Detecting SNPs with interactive effects on a quantitative trait, arXiv:1805.09175 [stat.ME]. 2018. [68] L.  Yao, H.  Wang, Y.  Song, G.  Sui, BioQueue: a novel pipeline framework to accelerate bioinformatics analysis, Bioinformatics 33 (20) (2017) 3286–3288, https://doi. org/10.1093/bioinformatics/btx403. [69] A.V.  Kartashov, A.  Barski, BioWardrobe: an integrated platform for analysis of epigenomics and transcriptomics data, Genome Biol. 16 (1) (2015) 158, https://doi. org/10.1186/s13059-015-0720-3. [70] L. Evin, D. Bar-Yaacov, A. Bouskila, M. Chorev, L. Carmel, D. Mishmar, LEMONS—a tool for the identification of splice junctions in transcriptomes of organisms lacking reference genomes, PLoS One 10 (11) (2015) e0143329. https://doi.org/10.1371/journal.pone.0143329.

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­Further reading

[103] A.  de Jong, S.  van der Meulen, O.P.  Kuipers, J. Kok, T-REx: transcriptome analysis webserver for RNA-seq expression data, BMC Genomics 16 (663) (2015) 663, https:// doi.org/10.1186/s12864-015-1834-4. [104] Z.  Liu, W.  Shao, Y.  Shen, M.  Ji, W.  Chen, Y.  Ye, Y.  Shen, Characterization of new microsatellite markers based on the transcriptome sequencing of Clematis finetiana, Hereditas 155 (2018) 23, https://doi.org/10.1186/s41065-018-0060-x. [105] M. Knopp, D.I. Andersson, Predictable phenotypes of antibiotic resistance mutations, MBio 9 (3) (2018) e00770-18, https://doi.org/10.1128/mBio.00770-18. [106] Y. Wang, M. Stata, W. Wang, J.E. Stajich, M.M. White, J.M. Moncalvo, Comparative genomics reveals the core gene toolbox for the fungus-insect symbiosis, MBio 9 (3) (2018) e00636-18, https://doi.org/10.1128/mBio.00636-18. [107] A.  Kundu, A.  Pal, Identification and characterization of elite inbred lines with MYMIV-resistance in Vigna mungo, Field Crop Res 135 (2012) 116–125, https://doi. org/10.1016/j.fcr.2012.07.006. [108] S. Paul, A. Kundu, A. Pal, Identification and expression profiling of Vigna mungo microRNAs from leaf small RNA transcriptome by deep sequencing, J. Integr. Plant Biol. 56 (2014) (2014) 15–23, https://doi.org/10.1111/jipb.12115. [109] A. Kundu, S. Paul, A. Dey, A. Pal, High throughput sequencing reveals modulation of microRNAs in Vigna mungo upon Mungbean yellow mosaic India virus inoculation highlighting stress regulation, Plant Sci. 257 (2017) 96–105, https://doi.org/10.1016/j. plantsci.2017.01.016. [110] M.  Zuker, Mfold web server for nucleic acid folding and hybridization prediction, Nucleic Acids Res. 31 (2003) 3406–3415, https://doi.org/10.1093/nar/gkg595. [111] B. Zhang, X. Pan, E.J. Stellwag, Identification of soybean microRNAs and their targets, Planta 229 (2008) 161–182, https://doi.org/10.1007/s00425-008-0818-x. [112] W. Yang, X. Liu, J. Zhang, J. Feng, C. Li, J. Chen, Prediction and validation of conservative microRNAs of Solanum tuberosum L, Mol. Biol. Rep. 37 (2010) 3081–3087, https://doi.org/10.1007/s11033-009-9881-z. [113] X.  Dai, P.X.  Zhao, psRNATarget: a plant small RNA target analysis server, Nucleic Acids Res. 39 (2011) W155–W159. [114] A.  Conesa, S.  Götz, J.M.  García-Gómez, J.  Terol, M.  Talón, M.  Robles, Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research, Bioinformatics (2005), https://doi.org/10.1093/bioinformatics/bti610. [115] A. Kundu, A. Patel, A. Pal, Defining reference genes for qPCR normalization to study biotic and abiotic stress responses in Vigna mungo, Plant Cell Rep. 32 (2013) 1647– 1658, https://doi.org/10.1007/s00299-013-1478-2.

­Further reading [116] http://genomebiology.com/content/pdf/gb-2012-13-1-r4.pdf. [117] S. Kumar, A.D. Vo, F. Qin, H. Li, Comparative assessment of methods for the fusion transcripts detection from RNA-Seq data, Sci. Rep. 6 (21587) (2016) 21597, https://doi. org/10.1038/srep21597. [118] M. Guo, H. Wang, S.S. Potter, J.A. Whitsett, Y. Xu, SINCERA: a pipeline for singlecell RNA-Seq profiling analysis, PLoS Comput. Biol. (2015), https://doi.org/10.1371/ journal.pcbi.1004575.

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Role of RNA interference in seed germination

5

Neeti Sanan-Mishra, Anita Kumari Plant RNAi Biology Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India

­Introduction Seeds comprise the dormant form of embryonic plants formed from zygote after the fertilization of ovule by pollen. They protect the embryo by providing a physical barrier for the emergence of the radicle [1]. Therefore, seed dormancy is an essential prerequisite for the survival and viability of embryonic life. Under favorable conditions of temperature and moisture, the quiescent dry seed becomes activated and starts to germinate. This process involves rapid growth of an embryonic radical and its emergence from the seed [2, 3]. In plants, the period of seed dormancy and germination are intricately regulated to cope with adverse environmental conditions. At the molecular level, it involves an intricate coordination of many physiological, cellular, and metabolic dynamic processes. Despite gaining a lot of knowledge on the processes involved, the precise regulations of the series of events are still poorly understood.

­Mechanism of seed germination ­Phases in seed germination The process of germination is initiated upon imbibition of water by the dry seed, and the subsequent stages can be categorized into three phases [4]. The first phase involves rapid water intake along with an increase in seed volume and some physiological and metabolic activities including respiration and protein synthesis [5]. This phase is primarily a physical process, which depends on the permeability of the testa. Although most absorption occurs through the micropylar region of the testa, involvement of water channel proteins and membrane aquaporin has been demonstrated [6]. The hydration of the cells in the seeds leads to a transition of the phospholipid membrane from its gel phase to the liquid-crystalline state [7]. Water uptake also activates the metabolic activities of enzymes present within the dry seed. These are subsequently replaced by new enzymes to achieve the full metabolic status in later Plant Small RNA. https://doi.org/10.1016/B978-0-12-817112-7.00005-5 © 2020 Elsevier Inc. All rights reserved.

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phases. Within a few minutes after imbibition, there is restoration of respiratory activity as the glycolytic and oxidative pentose phosphate pathways are restarted [8, 9]. The mitochondria, present in the dry seed tissues, have enough terminal oxidases and Krebs cycle enzymes needed for the production of ATP to support the metabolic activities even after several hours of imbibition in seeds [10, 11]. However, this is accompanied by a sharp initial increment in oxygen consumption, which lowers the level of cellular oxygen, as there is restriction in gaseous diffusion by the dense internal structure in most of the seeds. The oxygen deficiency creates more pyruvate than can be utilized in the Krebs cycle and electron transport chain. The second phase is marked by reduction in the rate of water uptake and an increase in the accumulation of mitochondria to support the metabolic activities [12]. On the basis of the nature of stored reserves, there seem to be two different patterns of mitochondrial development in cotyledons. In seeds that store starch, there is mainly repair and activation of pre-existing mitochondria, whereas in the seeds that store oil, new organelles are produced [13, 14]. Besides, there is synthesis of new mRNA and proteins, and the stored reserves of lipids and proteins are mobilized [15]. In the later stages, enzymes associated with cell wall loosening are activated resulting in the weakening of endosperm and rupture of testa. Concurrently, there is expansion of the embryonic axis, which paves the way for the emergence of radicle. During root elongation in maize seedling, the activity of enzyme Xyloglucan endotrans-glycosylase (XET), which is capable of reversibly cleaving xyloglucan molecules, increases in the apical root region. Expansins, which have the ability to break the hydrogen bonds between cell wall polymers (e.g., matrix polysaccharides and cellulose microfibrils), are the second major group of enzymes that play an important role in expansion [16]. Expansins have been strongly implicated in the hypocotyl expansion in cucumber [17]. The third phase involves rapid DNA synthesis, cell division, and elongation resulting in the emergence of radicle. It is a transition phase where the physical, biochemical, and molecular changes determine the course of seed germination. Dead seeds and dormant seeds cannot enter phase III, so effectively all the cellular and metabolic events that occur in the first two phases also occur in imbibed dormant seeds. A detailed study to analyze the gene expression profiles during early stages of Arabidopsis seed germination [18] identified the involvement of regulatory transcription factors like MYBs, homeobox leucine-zipper proteins, GATA zinc finger proteins, etc. Comparison of gene expression profiles in dry and germinating Arabidopsis seeds showed that around 1200 different types of transcripts are present in dry seeds, thereby suggesting that these mRNAs are for protein synthesis during early stages of germination [19]. It was also shown that varying transcriptional activities in the dry and germinating states are regularly maintained by the dynamic levels of Gibberellic acid (GA) and Abscisic acid (ABA) [20]. It is assumed that all the essential components for resuming protein synthesis are there in the seeds. The mRNAs are most likely associated with proteins and stored as mRNP complexes in the seed [21]; however, it is also possible that some mRNAs are

­Mechanism of seed germination

securely stored within the nucleus [22]. Within a few minutes after imbibition, the extant single ribosomes get engaged in polysomal protein-synthesizing complexes, and this is rapidly followed by the synthesis of new ribosomes to meet the growing demands of the developing embryo [23]. Many proteomics-based studies have been carried out in different plant species to understand the molecular processes of seed germination. This has identified the involvement of proteins associated with energy metabolism, mobilization of storage reserves, cell cycle regulation, DNA replication, repair of damaged DNA, transcription, splicing, translation, protein-protein interactions, etc. [24, 25].

­Factors regulating seed germination The reports available from studies on model species such as tomato, tobacco, and Arabidopsis give a clear indication on the intricate influence of the endosperm and the embryo in controlling seed germination [26, 27]. The process of seed germination and/or dormancy is also influenced by several environmental factors such as temperature, moisture pH, and nutrient availability. These mainly influence the onset of dormancy or initiation of germination, although no generalizations can be made as their role varies depending upon the species or variety. The influence of both exogenous and endogenous signals is processed by modulation of the gene regulatory networks. Forward genetic studies have identified fundamental signaling networks operative during this process. Endogenous factors, especially phytohormones (ABA, GA, Brassinosteroids [BR], Ethylene and Auxin), also regulate several processes during seed germination [28]. Several excellent reviews are available that detail the mechanism of hormone signaling and action; however, little is known about the mechanistic action during seed germination. It is believed that hormone and environment signals may be key regulators of seed dormancy and germination through interrelated molecular processes. GA is known to release dormancy and promote germination. De novo-synthesized GA plays an important role in promoting the embryo growth and weakening of the surrounding tissues by reducing the inhibitory effects of ABA [26, 29]. Inhibition of GA biosynthesis blocks the rupture of testa and endosperm. It has been shown that temporal and spatial patterns of GA biosynthesis and signaling promotes the expression of enzymes like expansins [30], glucanase [31], xyloglucan endotrans-hydrolase, pectin methylesterase, mannanase, cellulose, etc. [18, 32]. Localization of GA biosynthesis is influenced by environmental cues like light and temperature [33]. GA also cross-talks with cytokinins, ethylene, and BR in controlling germination [34]. ABA has a positive regulatory effect on dormancy and a negative regulatory effect on germination [35]. It has been shown that inhibition of ABA results in precocious germination or viviparous seeds. The overexpression of genes required for ABA biosynthesis lead to increase in the ABA content of seeds, which can enhance seed dormancy or cause delay in seed germination [36–39]. The Arabidopsis mutant aao3, having low levels of seed ABA, exhibits reduced dormancy [40], whereas

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­mutant cyp707a2, having high levels of ABA content, shows enhanced dormancy [41]. Several ABA-insensitive mutants are available like viviparous (vp) in maize [42], sitiens (sit) in tomato, ABA-deficient (aba) in Arabidopsis, and ABA-insensitive (abi) mutants in Arabidopsis, which show reduced sensitivity to exogenous ABA and viviparous germination [43]. Arabidopsis mutants exhibiting hypersensitivity to ABA response, like enhanced response to ABA1 (era1) and supersensitive to ABA and drought (sad1), show enhanced seed dormancy [44–47]. Ethylene hormone promotes seed germination by interacting with molecules involved in ABA signaling [35]. The level of ethylene is lower in dormant seeds when compared to non-dormant seeds [48]. It has been proposed that the basic action of ethylene is to promote the expansion of embryonic hypocotyl, seed respiration and water potential. The ethylene mutants ethylene resistant1 (etr1) and ethylene insensitive2 (ein2) or enhanced response to aba3 (era3) exhibit upregulation of ABA response genes and delayed seed germination [35], although application of ethylene precursor ACC (1-aminocyclopropane-1-carboxylic acid) on wild type seeds results in downregulation of ABA response factors. It has been demonstrated that, when ethylene is not present, ETR1 is triggered, and this activates CTR1, which is a negative regulator of EIN2, a downstream signaling molecule. This increases sensitivity to ABA to promote dormancy. On the other hand, etr1–2 mutant shows accumulation of GA to overcome the ABA-induced delay in seed germination [35]. It was demonstrated that the Arabidopsis ein2 mutant is allelic to the cytokininresistant1 (ckr1) mutant [49]. Cytokinins have been shown to play an important role during embryogenesis and in the early stages of grain filling in cereals. The endosperm seems to be a likely source for the production of cytokinins [50, 51]. In grains of sorghum, the cytokinin content is higher in the embryo than in the endosperm, and it gradually declines during imbibition [52]. Within the embryo, the cytokinin levels seem to concentrate in specific regions to direct root growth. In several species, cytokinin treatment is capable of breaking seed dormancy [53] and subsequently promoting germination by increasing ethylene biosynthesis [54, 55]. Cytokinin biosynthesis is also regulated by auxin [56]. Auxin also affects seed germination through cross-talk with ABA. During early embryogenesis, auxin signals are required for viability and pattern formation. In bean seeds, increasing synthesis of auxin and indole acetic acid (IAA) occurs during germination [57]. Studies in wheat have indicated a connection between IAA, dormancy, and pre-harvest sprouting [58]. However, there is little information on its molecular mechanism [47, 59]. It was shown that auxin regulates expression of catalase in the scutellum of germinating maize kernels [60]. The levels of free IAA decrease in the imbibed grains and during radicle emergence [52]. Fresh IAA synthesis is re-established in the emerged seedling [61]. BRs are also positive regulators of seed germination, although they are mainly associated with stem elongation and leaf unfurling [62]. In seeds of many plant species, the BRs interact with GA and light to promote germination and overcome the inhibitory effect of ABA [63]. Through this route, they activate the expression of different expansins, which are normally cell wall-loosening proteins and can indirectly act to stimulate germination of seeds [16].

­The phenomenon of RNA silencing

­The phenomenon of RNA silencing Studies suggest that many miRNAs are stringent regulators of gene expression in seed germination, development, and stress condition.

­Mechanism of RNA silencing The discovery of RNA-induced gene silencing has added a new paradigm to the regulation of the genetic machinery [64, 65]. Fire, Mello, and colleagues found that small double-stranded RNAs (dsRNAs) are potent triggers for gene silencing [66–68]. The classical genetic and biochemical studies in C. elegans [69], Drosophila [70], and plants laid the foundation for understanding the mechanisms underlying RNA silencing [71]. It was shown that long non-coding dsRNA substrates are processed into small fragments of 22–24 nucleotide, having 3′ overhang of two nucleotides, by the action of DICER, a ribonuclease III protein. The small RNAs then associate with the RNA-induced silencing complex (RISC) to negatively regulate the gene expression during diverse aspects of plant growth, development, and morphogenesis. These can be classified into two main types, viz. microRNAs (miRNAs) and small interfering RNA (siRNAs). They can act at the transcriptional level by influencing chromatin remodeling [72–74] or at the post-transcriptional level by cleaving the target mRNA or blocking translation [75–77].

­miRNAs The miRNAs are encoded by endogenous genes dispersed throughout the genome, and in plants their biogenesis is restricted to the nucleus. The primary transcripts (pri-miRNAs) are produced mostly by RNA polymerase II, and they are conventionally processed into mature miRNAs in two cleavage steps by DICER-LIKE 1 (DCL1) [78–80]. The pri-miRNA is first processed into precursor miRNA (premiRNA), which is about 70–100-nt long, and in the subsequent step(s), it is further processed into mature miRNA duplex. The other essential components of miRNA biogenesis are Hyponastic Leaves 1 (HYL1), Serrate (SE), Cell Division Cycle 5 (CDC5), Pleiotropic Regulatory Locus 1 (PRL1), Negative On Tataless 2 (NOT2), Tough (TGH), and the RNA-binding protein Dawdle (DDL). HYL1 protein binds dsRNA and is localized in the nucleus along with DCL1 and SE [81, 82]. Plants with mutations in HYL1 have elevated levels of pri-miRNA and lowered levels of miRNAs [83]. Serrate (SE) protein contains a C2H2 zinc-finger, and it plays an essential role in processing of pri-miRNAs [84]. In Arabidopsis, SE is expressed in specific tissues, and it functions to control leaf development, meristem activity, inflorescence architecture, and phase transition. SE interacts with DCL1 and HYL1 to regulate the biogenesis of miRNA. It is also implicated in regulating gene expression via chromatin modification. CDC5 protein contains a DNA-binding domain, and its main function is to regulate growth and immunity. Its functional mechanism is still elusive; however

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it has been shown to act as a component of the DCL1 complex to regulate miRNA accumulation. PRL1 is a conserved RNA-binding WD-40 protein, which regulates plant development and immune responses. It has been shown that PRL1 is a positive regulator of miRNA and siRNA accumulation. PRL1 interacts with DCL proteins and is required for stabilizing the pri-miRNA and facilitating DCL1 action. NOT2 interacts with the Piwi/Ago/Zwille domain of DCL1. NOT2b protein has been shown to interact with polymerase II and other miRNA processing factors, including cap-binding proteins (CBP80/ABH1 and CBP20) and SE. Arabidopsis mutants of NOT2 genes exhibit severe defects in male gametophytes and decrease in the accumulation of pri-miRNAs and mature miRNAs. It also affects DCL1 localization in vivo. It is hypothesized that NOT2 proteins act as general factors to promote the transcription of genes and facilitate efficient recruitment of DCL1 during miRNA biogenesis. TGH is an RNA-binding protein, which binds pri-miRNAs and pre-miRNAs and thereby contributes to recruitment of the DCL1–HYL1–SE complex for efficient processing of mature miRNAs. Lack of TGH disturbs many DCL activities and reduces the accumulation of miRNAs and siRNAs. DDL protein contains a conserved forkhead-associated (FHA) domain, and it plays an important role in plant development and immunity. The FHA domain is crucial for the interaction with DCL1. It acts in the biogenesis of miRNAs and endogenous siRNAs derived from sense transgenes and inverted-repeat transgenes, although its function is not well known. HEN1 methylates the 2′OH of the 3′ end of each strand of the duplex, before it is transported to the cytoplasm. HEN1 protein has one putative dsRNA-binding motif and a C-terminal methyltransferase domain. It plays an important role in stabilizing the miRNAs and protecting them from 3′ uridylation and subsequent destruction [78, 85, 86]. In the cytoplasm, the mature miRNA gets associated with Argonaute (AGO) protein containing RISC. The AGO protein family contains four distinct domains: the N-terminal, PAZ, Mid, and PIWI domains. In Arabidopsis, there are 10 AGO proteins that play a crucial role in maintaining genome integrity and controlling protein synthesis [87]. The AGO1, AGO10 double mutant is embryo-lethal [88, 89]. Interestingly, AGO1 mRNA is a target of miR168 [90], which is essential for proper embryo development. AGO1 mutants resistant to regulation by miR168 overaccumulate AGO1 and exhibit a mutant phenotype similar to that of dcl1, hen1, and hyl1 mutants [91].

­Tasi-RNAs Along with miRNA, different kinds of siRNAs also play significant roles in seed germination and dormancy. Biogenesis of siRNAs from the heterochromatic region depends crucially upon some essential factors like RDR2, DCL2, DCL3, or DCL4 [92, 93]. A special class of siRNAs called transacting siRNAs (tasiRNAs) plays an important role in seed germination. The tasiRNAs are produced as a result of miRNA

­Role of small RNAs in seed germination

guided cleavage of special transcripts called TAS [94]. The cleaved transcript is stabilized by suppressor of gene silencing 3 (sgs3) and then converted into a long dsRNA by the action of RNA-dependent RNA polymerase (RDR6) [95–97]. This dsRNA is then cleaved by DCL4 enzyme to produce 21-nt-long phased tasiRNAs, which associate with AGO7 containing functional RISC to negatively regulate gene expression [94]. The sgs3 and rdr6 loss-of-function mutants exhibit elongated and downwardly curled leaves and accelerated vegetative phase change [98]. However, the role of SGS3 in seed germination and seed development has not yet been well studied. In Arabidopsis four TAS transcripts (TAS1, TAS2, TAS3, and TAS4) have been reported so far. The TAS1 family comprises of three loci: TAS1a, TAS1b, and TAS1c, whereas TAS2 is in close vicinity of TAS1c. For their initial processing, TAS1 and TAS2 are dependent on miR173, whereas TAS3 and TAS4 are dependent on miR390 and miR828, respectively. Among these, TAS1, TAS2, and TAS4 require one miRNA binding site, whereas TAS3 requires two miRNA binding sites [99]. The tasiRNA derived from TAS3 locus target the transcripts of Auxin Response Factor2 (ARF2), ARF3, and ARF4 [98]. ARFs constitute a large family of transcription factors involved in the auxin-signaling pathway to regulate different stages of plant growth and developmental.

­Role of small RNAs in seed germination The small RNAs are involved in various aspects of plant development; however, their regulatory role in seed germination is not well known. The cross-talk of miRNA and tasiRNAs with the hormone-signaling cascades provides indications for their involvement in seed germination. With the arrival of new bioinformatics tools and high-throughput next-generation sequencing techniques, there is a significant gain of knowledge in small RNAs and their targets. The miRNAs play important roles in maintaining dormancy as well as promoting embryo growth during seed germination [100–102]. This has been well demonstrated by the mutants of factors involved in their biogenesis [103]. The Arabidopsis dcl1 mutants show early seed maturation phenotype [103]. Mutations in se, hyl1, hen1, and AGO1 display abnormal embryo and seed development [83]. The se-4 mutants display irregular cell divisions, abnormal embryo development, and fail to develop recognizable cotyledon primordium. The homozygous se-3 mutants are lethal for embryo formation [82]. A number of different miRNAs, such as miR156, miR158, miR159, miR160, miR164, mir165/166, miR167, miR172, miR395, miR402, and miR417, regulate the expression of transcripts that encode activators and repressors of seed germination and dormancy [59, 100, 104–107]. During the imbibition phase, there is downregulation of 12 miRNA families: miR156, miR159, miR164, miR166, miR167, miR168, miR169, miR172, miR319, miR393, miR394, and miR397. Members of four families are upregulated during seed germination: miR398, miR408, miR528, and miR529 [108]. A list of the miRNAs with their putative function is given in Table 1.

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Table 1  miRNAs likely to play a role during seed germination. miRNA

Target transcripts

Physiological effect

Reference

miR164

CUC1/CUC2, NAC1

Affect reproductive and root development Maintain auxin signaling during seed development and maturation Determination of abaxial/ adaxial leaf polarity and root development Maintain auxin signaling during seed development and maturation Regulation of flower and embryo development Regulation of seed germination under salt stress and dehydration Regulatory effect on seed germination and seedling growth under salt, dehydration and cold stress Regulation of ABA responsive genes during germination Positive regulators of ABA signaling during seed dormancy and germination Seed development and maturation Negative regulator of seed germination under salt stress Seed development and maturation

[109]

miR166/165

Class III HD-ZIP

PHB, PHV, REV

miR172 miR395

AP2 and other mRNAs ATP sulfurylases, sulfate transporter

miR402

DEMETER-LIKE protein 3

miR160

ARF10, ARF16, ARF17 MYB33, MYB65, MYB101

miR159

miR156

SPL 3, 4,5

miR417

Unknown

miR158

Unknown

[101]

[110–112]

[101]

[113] [104]

[104]

[59] [105]

[101] [106] [101]

The expression of miR159, miR156, and miR172 are intricately regulated during germination [100]. Mir156 promotes dormancy, which is antagonistic to the role of mir172-regulated SPL [100, 101]. Under abiotic stress conditions, miR395 acts both as a positive and negative regulator of seed germination [104]. miR395 is a family of six family members that target APS1, APS3, APS4, and SULTR transcripts. It was observed that, under high salt or dehydration stress conditions, the germination potential of seeds of ath-miR395c overexpressing lines was greatly reduced, whereas those of ath-miR395e overexpressing lines was enhanced under the same stress conditions [104]. This was attributed to a single nucleotide difference between miR395e and miR395c, due to which miR395e was unable to target APS1 and APS4.

miRNA serve as convergence regulatory nodes

Likewise the seed germination potential is enhanced under salt, dehydration, and cold stress conditions by overexpression of ath-miR402 in Arabidopsis [107]. miR402 downregulates its target transcript DML3 (encoding DEMETER-LIKE protein3, which is involved in DNA demethylation) to influence the epigenetic regulatory processes of plants in various stress conditions [107]. miR417 also shows a negative regulatory effect on seed germination under salt stress [114]; however, the molecular mechanism of its action is not completely understood.

­miRNA serve as convergence regulatory nodes The biosynthesis and function of plant small RNAs are affected by different plant hormones and environmental stresses. This knowledge has developed from the analysis of loss-of function mutants of factors involved in their biogenesis [103]. Some of these mutants show high expression or varying sensitivity to ABA and abiotic stresses [115] during seed germination. Like the se mutants show hypersensitivity to ABA [116, 117], two ABA mutants supersensitive for germination, viz. absg1 and absg2, were identified as the alleles of dcl1 and hen1, respectively [115]. This highlights the underlying complexity in the regulatory networks by identifying overlap between the small RNAs and hormones and the environmental cues during embryo and seed development. A central role is played by miR159 in regulating the dynamic process of seed germination by modulating signaling cascades of phytohormones, GA and ABA. It was shown that miR159 expression is upregulated in rdr2 and dcl2,3,4 triple mutants [93, 99]. The miR159 targets MYB33 and MYB101 transcription factors, which are the positive regulators of ABA signaling during seed germination [100, 105]. In Arabidopsis, miR159 regulates MYB33, MYB65, and MYB101, which are required in GA, mediated developmental processes, like flowering under short days, and male fertility, etc. These genes are related to miR159-regulated GAMYB, a gene that activates GA genes during floral development, fertility, and seed germination [105, 118]. Recently, it was shown that GAMYB activates programmed cell death process in alurone during seed germination [35, 119, 120]. The role of auxin in embryo development and seed germination is regulated by the action of miR165/166, miR167, miR164, miR158, and miR160 [100]. It was observed that downregulation of ARF10 by miR160 plays an important role in maintaining auxin homeostasis during seed germination [59]. The miR160 appears to be the converging point of auxin, BR, and ABA signals during seed germination, because mutation in ARF10 results in developmental defects and overexpression of ABA-responsive genes [59]. It was also shown that plants overexpressing miR160 exhibited ABA hyposensitivity during germination [59]. The cross-talk between BR and ABA is possibly involved in the activation of the miR160 regulatory pathway in seed germination [59]. The mutants for BR biosynthetic and signaling pathway are sensitive to ABA, leading to a decrease in the germination potential [35]. Ethylene also cross-talks with ABA and GA to promote seed germination [35]. The study of mutants ethylene resistant1 (etr1) and ethylene insensitive2 (ein2)

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showed increased expression of ABA-responsive genes and delayed seed germination. In etr1, mutant accumulation of GA was also observed [35]. It is thus hypothesized that miR159 and miR160, which have regulatory effects on ABA and GA, may also directly or indirectly control the ethylene responses during seed germination.

­Conclusion The dynamic process of seed germination process involves a series of interrelated physiological, cellular, and metabolic events that are intricately controlled. Although there is much more to be learned about the cellular process of seed germination, there is sufficient evidence to show that small RNAs play crucial roles in regulation of gene expression in developing and germinating seeds [121]. The miRNAs specify critical regulatory nodes during seed germination that serve as converging points for the signals from various phytohormones and abiotic stress factors. However, more studies are required to understand the mechanistic details of their mode of action and interconnection. The functional analysis of small RNAs during germination process will shed light on their role in maintaining viability, overcoming dormancy, and responding to the external environment.

­Acknowledgment Anita Kumari acknowledges fellowship from Department of Biotechnology, Government of India.

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Importance of small RNA in plant seed germination

6

Yingyin Yao, Mingming Xin, Zhongfu Ni, Qixin Sun State Key Laboratory for Agrobiotechnology and Key Laboratory of Crop Heterosis and Utilization (MOE) and Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, People’s Republic of China

Seed germination is not only a critical developmental step in the plant life cycle, it is also important for agricultural production including yield and quality of crops. Rapid and near-simultaneous germination is highly and positively associated with seedling survival rates and subsequent vegetative growth; however, cultivars germinating too readily usually show weak dormancy, which is one of the factors leading to pre-harvest sprouting (PHS). The kernels germinate at spikes before harvest in the rainy season, which cause obvious losses in both yield and quality. In addition, cultivars that germinate too slowly will cause retarded vegetative growth, which decrease yield and quality. Therefore, to improve the performance of agronomic traits, it must be required to investigate the mechanisms of seed germination in plants. Here, we review the importance of small RNA in plant germination.

­Brief introduction of seed germination Seed germination, which determines when the plant enters natural or agricultural ecosystems, is a crucial process in the seed plant life cycle and the basis for crop production. The germination of freshly produced seeds is inhibited by primary dormancy, which helps the seeds equip for environments with unfavorable conditions [1–3]. The seeds will enter a germinating state from the dormant state at an appropriate time when the dormancy is lost through moist chilling (stratification) or after-ripening [4]. Therefore, seed germination is a accurately timed checkpoint to avoid unsuitable weather and unfavorable environments during plant establishment and reproductive growth [5]. Finally, seed germination in crops will affect seedling survival rates and vegetative growth, which are accordingly associated with ultimate yield and quality. Considering agronomic production, crop cultivars must be prepared for rapid and uniform germination at sowing, which will improve the crop yield and quality; however, this selection during crop breeding usually results in Plant Small RNA. https://doi.org/10.1016/B978-0-12-817112-7.00006-7 © 2020 Elsevier Inc. All rights reserved.

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weak dormancy, which is one of the factors leading to PHS in the rainy season, which tends to overlap with the harvest season [6, 7]. Hence, to improve crop agronomic performance, the crop cultivars during breeding must be prepared for uniform and rapid germination at sowing while preventing PHS [7a]. Seed germination is a transit process when an active plant with photosynthesis grows from a quiescent embryo, generated in the fertilized ovule. The process of seed germination includes the following five changes or steps: imbibition, respiration, effect of light on seed germination, mobilization of reserves during seed germination, and role of growth regulators and development of the embryo axis into a seedling. All five of these stages result from a interplay of several metabolic and cellular events, coordinated by a complex regulatory network that includes seed dormancy, an intrinsic ability to temporarily block radicle elongation to optimize the timing of germination. The primary plant hormones including abscisic acid (ABA) and gibberellin (GA) antagonistically regulate seed dormancy and germination [8–10]. ABA is synthesized during seed maturation and decreased before the onset of germination; it plays key roles in inhibiting germination and establishing and maintaining seed dormancy [11]. In contrast to ABA, GA significantly increases to promote germination by causing the secretion of hydrolytic enzymes that weaken the structure of the seed testa [12, 13].

­miRNAs related to seed germination in Arabidopsis Small RNAs (sRNAs) are categorized into a couple of classes, such as microRNAs (miRNAs) and small interfering RNA (siRNAs), based on their mode of biogenesis. The function of sRNAs during seed germination were characterized through the mutants of small RNA biogenesis, such as DCL1, HYL1, HEN1, and AGO1, which displayed defects in embryogenesis and seed development with defects of sRNAs generation [14]. Studies in Arabidopsis show that miRNAs play critical roles in seed germination and dormancy, acting as both activators and repressors [15, 16]. For example, miR159 post-transcriptionally mediates the cleavage of mRNA encoding GAMYB transcription factors, which are important for seed germination through regulating GA-mediated programmed cell death in the aleurone layer of seeds [17, 18]. miR160 is able to negatively regulate AUXIN RESPONSE FACTOR 10 to affect seed germination and post-embryonic development [19, 20]. Both miR156 and miR172 control expression levels of DELAY OF GERMINATION1 (DOG1) to regulate seed dormancy and flowering times in lettuce (Lactuca sativa) and Arabidopsis [21]. The Arabidopsis genome encodes six members of miR395, which regulate the expression of genes encoding ATP sulfurylase (APS) and the sulfate transporter SULTR2;1. Overexpression of miR395c or miR395e, which show a single nucleotide difference, retarded and accelerated, respectively, the seed germination of Arabidopsis under high salt or dehydration stress conditions. Also, the cleavage of mRNA targets APS1, APS3, APS4, and SULTR2;1 was not same in miR395c- and miR395e-overexpressing plants [22].

siRNAs related to seed germination

Some miRNAs play important roles in an adaptive process of Arabidopsis to stress conditions. For example, Arabidopsis thaliana miR163, highly induced by light during seed germination, targets mRNA encoding a methyltransferase that methylates 1,7-paraxanthine. During seed germination, miR163 and its target PXMT1 are predominantly expressed in the radicle, and miR163 mutant or PXMT1-overexpression line shows delayed seed germination under continuous light, compared with WT [23]. Overexpression of miR402 improved the seed germination under salt stress conditions through downregulating DEMETER-LIKE protein3 mRNA, indicating a miRNA-guided regulation of DNA-demethylation during seed germination [24].

­miRNAs related to seed germination in crops Genome-wide dissection of the miRNA expression profile in rice embryo during early stages of seed germination had been determined. A total of 289 miRNA loci, including 59 known and 230 novel miRNAs, were identified, and the dry and imbibed seeds have unique miRNA expression patterns compared with other tissues, particularly for the dry seeds [25]. Similarly, genome-wide survey of wheat miRNAs from 11 tissues including germinating embryos was conducted, and 64 miRNAs preferentially expressing in developing or germinating grains were obtained, which could play important roles in grain development [26]. MiR9678, a wheat species-specific miRNA and uniquely expressed in the scutellum of seeds, plays a key role in affecting seed germination [27]. Overexpression of miR9678 in wheat leads to delayed seed germination and improved resistance to PHS through reducing bioactive GA levels. The promoter of the miR9678 is recognized and bound by components of the ABA signaling pathway, indicating miR9678 might function in GA-ABA balance during seed germination in wheat. A couple of researches had revealed the miRNA transcriptome during seed germination in maize [28–31]. These studies led to the discovery of conserved and maize-specific miRNAs, which resulted in significant enrichment of the repertoire of maize miRNAs and provided insights into miRNA regulation of genes expressed in seed germination. In rice, miR393a/OsTIR1 plays a role in coleoptile elongation and stomatal development via modulation of auxin signaling during seed germination and seedling establishment under submergence. This study provides new perspectives on the direct sowing of rice seeds in flooded paddy fields.

­siRNAs related to seed germination Phased small interfering RNAs (phasiRNAs) belong to a special type of siRNAs in plants, and their biogenesis depends on an initial targeting event and specific cleavage guided by an sRNA (miRNA or phasiRNA) on a primary transcript [32]. The cleaved miRNA target genes are further synthesized into double-stranded RNAs by RNA-DEPENDENT RNA POLYMERASE 6 (RDR6), which are recognized and cleaved by DCL4 from the miRNA-mediated cleavage sites several times, ­generating

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21-nt-long phasiRNAs [32a, 32b, 33, 33a]). These are incorporated into RISCs, where they regulate other genes in cis or trans for target mRNAs cleavage or translational repression [33–36], and these phasiRNAs are involved in regulating plant development and growth [15]. Wheat-specific miR9678, an important player for seed germination, is able to trigger the generation of phasiRNAs through targeting and mediating the cleavage of a long non-coding RNA WSGAR. The functional analysis of phasiRNAs production indicated that they are negatively associated with germination, similar to miR9678 [27]. The Arabidopsis miR390 targets trans-acting siRNA locus TAS3 derived transcripts, mediating the siRNA-ARF (AUXIN RESPONSE FACTORs ARF2/3/4) expression, suggesting the cross-talk between miRNA and ta-siRNA pathways during seed germination [37]. Although phasiRNAs have been implicated to function during plant seed germination, it is still unknown how these phasiRNAs affect plant seed germination, for example, what kind of genes are targeted and regulated by these phasiRNAs.

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[26] F. Sun, G. Guo, J. Du, W. Guo, H. Peng, Z. Ni, Q. Sun, Y. Yao, Whole-genome discovery of miRNAs and their targets in wheat (Triticum aestivum L.). BMC Plant Biol. 14 (2014) 142, https://doi.org/10.1186/1471-2229-14-142. [27] G. Guo, X. Liu, F. Sun, J. Cao, N. Huo, B. Wuda, M. Xin, Z. Hu, J. Du, R. Xia, V. Rossi, H. Peng, Z. Ni, Q. Sun, Y. Yao, Wheat miR9678 affects seed germination by generating phased siRNAs and modulating abscisic acid/gibberellin signaling. Plant Cell 30 (4) (2018) 796–814, https://doi.org/10.1105/tpc.17.00842. [28] D. Ding, Y. Wang, M. Han, Z. Fu, W. Li, Z. Liu, Y. Hu, J. Tang, MicroRNA transcriptomic analysis of heterosis during maize seed germination. PLoS One 7 (6) (2012), e39578. https://doi.org/10.1371/journal.pone.0039578. [29] D. Li, L. Wang, X. Liu, D. Cui, T. Chen, H. Zhang, C. Jiang, C. Xu, P. Li, S. Li, L. Zhao, H. Chen, Deep sequencing of maize small RNAs reveals a diverse set of microRNA in dry and imbibed seeds. PLoS One 8 (1) (2013), e55107. https://doi.org/10.1371/journal. pone.0055107. [30] L. Wang, H. Liu, D. Li, H. Chen, Identification and characterization of maize microRNAs involved in the very early stage of seed germination. BMC Genomics 12 (2011) 154, https://doi.org/10.1186/1471-2164-12-154. [31] M.  Xin, G.  Yang, Y.  Yao, H.  Peng, Z.  Hu, Q.  Sun, X.  Wang, Z.  Ni, Temporal small RNA transcriptome profiling unraveled partitioned miRNA expression in developing maize endosperms between reciprocal crosses. Front. Plant Sci. 6 (2015) 744, https:// doi.org/10.3389/fpls.2015.00744. [32] M.J.  Axtell, Lost in Translation? microRNAs at the Rough ER, 20171878–4372. (Electronic). [32a] Z. Xie, E. Allen, A. Wilken, J.C. Carrington, DICER-LIKE 4 functions in trans-acting small interfering RNA biogenesis and vegetative phase change in Arabidopsis thaliana, Proc. Natl. Acad. Sci. U. S. A. 102 (2005) 12984–12989. [32b] Y.  Nakazawa, A.  Hiraguri, H.  Moriyama, T.  Fukuhara, The dsRNA-binding protein DRB4 interacts with the Dicer-like protein DCL4 in vivo and functions in the transacting siRNA pathway, Plant Mol. Biol. 63 (2007) 777–785. [33] E.  Allen, M.D.  Howell, miRNAs in the biogenesis of trans-acting siRNAs in higher plants. Semin. Cell Dev. Biol. 21 (8) (2010) 798–804, https://doi.org/10.1016/j. semcdb.2010.03.008. [33a] M.A.  Mecchia, J.M.  Debernardi, R.E.  Rodriguez, C.  Schommer, J.F.  Palatnik, MicroRNA miR396 and RDR6 synergistically regulate leaf development, Mech. Dev. 130 (2013) 2–13. [34] E.  Allen, Z.  Xie, A.M.  Gustafson, J.C.  Carrington, microRNA-directed phasing during trans-acting siRNA biogenesis in plants. Cell 121 (2) (2005) 207–221, https://doi. org/10.1016/j.cell.2005.04.004. [35] M.J. Axtell, Classification and comparison of small RNAs from plants. Annu. Rev. Plant Biol. 64 (2013) 137–159, https://doi.org/10.1146/annurev-arplant-050312-120043. [36] H.M.  Chen, Y.H.  Li, S.H.  Wu, Bioinformatic prediction and experimental validation of a microRNA-directed tandem trans-acting siRNA cascade in Arabidopsis. Proc. Natl. Acad. Sci. U. S. A. 104 (9) (2007) 3318–3323, https://doi.org/10.1073/ pnas.0611119104. [37] S. Sarkar Das, S. Yadav, A. Singh, V. Gautam, A.K. Sarkar, A.K. Nandi, P. Karmakar, M. Majee, N. Sanan-Mishra, Expression dynamics of miRNAs and their targets in seed germination conditions reveals miRNA-tasiRNA crosstalk as regulator of seed germination, Sci. Rep. 8 (2018) 1233, https://doi.org/10.1038/s41598-017-18823-8.

­Further reading

­Further reading [38] F.  Vazquez, H.  Vaucheret, R.  Rajagopalan, C.  Lepers, V.  Gasciolli, A.C.  Mallory, J.L. Hilbert, D.P. Bartel, P. Crete, Endogenous trans-acting siRNAs regulate the accumulation of Arabidopsis mRNAs. Mol. Cell 16 (1) (2004) 69–79, https://doi.org/10.1016/j. molcel.2004.09.028.

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Importance of small RNA in plant metabolism

7

Abbu Zaida, Shabir H. Wanib, a

Plant Physiology and Biochemistry Section, Department of Botany, Aligarh Muslim University, Aligarh, India b Mountain Research Centre for Field Crops, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, India

­Introduction Being sessile, plants are exposed to a variety of environmental pressures ranging from temperature, cold salinity, and heavy metals during their life periods. Both abiotic and biotic stress factors impose great limitations to plant growth, metabolism, development, and productivity, thus causing a great loss in agricultural yield and harvest quality. To feed a teeming population, it necessitates breeding stress-resistant crop plants that can stand healthy against dynamic climates and environmental perturbations with higher yield and improved quality. Because conventional breeding practices resulted in marginal success as a result of complexity in stress tolerance mechanisms, the transgenic methods are now emerging widely for breeding stresstolerant crops. There is rising alarm that the environmental challenges and impending climate scenario will pose a major obstacle in the agricultural and allied sectors. Plant stress tolerance mechanisms to biotic and abiotic pressures are multifaceted; during the course of evolution, plants have developed intricate mechanisms at the biochemical and physiological levels for achieving environmental-induced stress tolerance and adaption [1–3]. It has thus arisen as a noteworthy responsibility among scientists across the globe, including plant biotechnologists and breeders, to advance the goal of climate-­resilient crop plants that can stand healthy against a range of environmental pressures and climate fluxes. Advancement in biotechnological tools, likely in the post-genomic era, are building fundamentals for intensification of agriculture in a sustainable way as well as providing new opportunities in engineering flexibility to climate changes in crop plants. To achieve targets in a challenging environmental scenario, methods such as breeding are complemented with new and emerging biotechnological tools including genomics, transcriptomics, and proteomics for developing stress-tolerant crop plants with little negative effect on yield on one hand, yet sustained higher yields on the other hand [4–6]. It is thus necessary to identify and characterize ­critical Plant Small RNA. https://doi.org/10.1016/B978-0-12-817112-7.00007-9 © 2020 Elsevier Inc. All rights reserved.

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genes related to plant stress tolerance to perform an essential role in engineering tolerance against myriad abiotic and biotic stresses in crop plants. In contrast to the identification and manipulation of a single functional gene, engineering specific regulatory gene segments for specific traits have now emerged as an effective practice to control the expression of many stress-responsive genes. To minimize the effects of abiotic stress conditions, plants have advanced with a network of gene regulation systems that involves reprogramming of gene expressions [7]. Many studies have shown that sRNAs play a significant role in gene regulation in a post-transcriptional mechanism to attenuate growth, development, and metabolism of plants under stress conditions [8]. Environmental stress-induced inhibition in development and production of plants is caused through a change in the expression pattern of various genes, which are themselves under the regulation of various regulatory proteins such as transcription factors (TFs) and non-coding RNAs [2, 9–11]. sRNA-mediated regulation of gene expression constitutes a noteworthy component plants utilize to counter the abiotic- and biotic-induced challenges. During the current decade, in contrast to gene regulation at the transcriptional level, post-transcriptional gene regulations have been evidenced to show promising roles during plant response to various biotic and abiotic stress factors as well as during various developmental processes. Nevertheless, the regulatory role played by small non-coding RNAs at the levels of post-transcription have emerged in recent times, principally in response to orchestrate biotic and abiotic stress tolerance as they are versatile performers in controlling expression of genes both in plants and animals [12]. sRNAs comprise a family of short regulatory noncoding RNAs 19-28 nucleotides long, which are highly conserved in plants, with two main types of sRNA viz. microRNAs (miRNAs) and small interfering RNAs (siRNAs). miRNAs, which were not discovered until the past two decades, are the main components of complex networks of gene regulatory networks. miRNAs are endogenously expressed, non-translated RNAs that are 20–25 nucleotides in length and are processed by Dicer (proteins) from regions of longer precursors of RNA. miRNAs and siRNAs are chemically and functionally similar, facilitating the process of RNA interference (RNAi), post-transcriptional gene silencing (PTGS), and transcriptional gene silencing. Additionally, miRNAs regulate gene expression through cleavage of miRNA, translational repression, chromatin remodeling/epigenetic change, and methylation of DNA [13]. Both these types of RNAs are inserted into silencing complexes having Argonaute proteins, where they guide target gene repression [14]. Recent accent on miRNAs, often ascribed to plant stress tolerance in cooperation with climatic adjustment process, were also found as critical regulators at post-­ transcriptional gene expression levels to reduce growth and development of plants under stress condition, and have a potential role for improvement of crops [15]. miRNA play a key part in multifaceted processes in a plant’s molecular life due to their more substantial attention from plant researchers across the globe in recent years. To identify different miRNA in response to solitary or diverse biotic or abiotic stresses, different advanced methods like high-throughput or deep sequencing are available, as well as degradome sequencing together with more progressive tools

­Major types of plant sRNA

and techniques and databases [16]. Recently, the use of artificial miRNA (amiRNAs) technology has significantly and efficiently advanced for RNA silencing of the targeted gene [17]. Also, a new and more advanced genomic editing technique like clustered regularly interspaced short palindromic repeats and associated protein (CRISPR/Cas9) have made cloning easy for specific miRNA without affecting an untargeted region [15]. In this context, research on sRNA is among the few cutting-edge scientific sections explored so far in the surplus literature present on the emerging subject. The study of sRNA biology includes biogenesis of miRNA, their sub-cellular transport, regulation of gene expression, and degradation are presently, which are being researched in various plant species to understand their roles in various biological processes, particularly in metabolism of plants and stress tolerance.

­Major types of plant sRNA sRNAs are acknowledged as fundamental players in plants, contributing toward regulating growth and development, tolerance to abiotic stress factors, and stimuli to biotic stresses in crop plants [18, 19]. sRNAs in plants are mostly classified based on origin and biogenesis; small non-coding RNA has been extensively investigated and are known to govern many biological processes. Accordingly, sRNA is classified into small interfering RNAs (siRNAs) and miRNAs [20], which are further subcategorized as heterochromatic siRNAs (hc-siRNAs), phase secondary siRNAs (phase siRNA), and natural antisense transcript siRNA (NAT-siRNA) [21]. sRNAs are formed from double-stranded precursor of RNA molecule catalyzed by ribonuclease III-like Dicer proteins (DCL) and subsequently integrated into Argonaute family protein to an encoded complementary sequence [22]. miRNAs are the most widely studied sRNAs whose presence have been found in almost all known eukaryotic organisms [23]. Moreover, miRNA have been interlaced to play significant roles in multifacets of the plant developmental process, viz. division of meristematic tissue, organ specification, formation of leaf shape, secondary root growth, induction of flowering, and events of fertility [24]. The endogenous population of siRNAs in plant cells exceeds to a greater extent than that of miRNAs. However, their mode of action is dependent primarily on the complementarity between the target gene and miRNA [25, 26]. Cumulative pieces of proof have revealed the essential and characteristic regulatory roles of siRNAs in plants. The phenomenon of RNA silencing was first discovered in plant tissues as a doctrine to downregulate the invading viruses/transgenes or pathogens through the mechanism of action of siRNAs [27]. However, it was later known that the process of RNA silencing also plays an imperative role in gene regulation expression in an extensively wide range of biological processes [27a]. In recent years, more attention has been paid to experiments focusing on plant sRNAs, more especially on the model plant Arabidopsis thaliana (Syndrilla of plant kingdom). On the basis of these studies, identification and characterization of few genes related to the biogenesis and different functions of sRNA has been carried out [28, 29], as well as new and emerging groups of functional sRNAs [30–36].

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siRNAs were found to cause post-TGS (PTGS) in crop plants (i.e., RNA interference or silencing) but were confirmed later to also take part in TGS. The siRNAs production relies on RNA-dependent RNA polymerase to produce dsRNAs, which further undergoes recognition and cleavage by specific DCLs to yield siRNA of diverse types [21, 37]. The hc-siRNAs types have 24-nucleotide length and are generated by DCL3 from transcripts of transposon and repeat sequences. This type of siRNA regulates TGS by promoting methylation of DNA and histone proteins to cause repression in transposable elements via the process of RNA-directed DNA methylation (RdDM). Nevertheless, the phasiRNAs lead to the formation of a type of a plant-specific endogenous class of sRNAs (trans-acting siRNAs) [38]. The length of phasiRNAs is 21 nucleotides. They are usually formed from an mRNA-derived dsRNA by RNA-dependent RNA polymerase 6 (RDR6), and their processing is carried out by DCL4 or DCL5 (DCL3b) in rare cases [39]. The ha-siRNAs are involved in the degradation of target mRNA in a homology-dependent fashion. Also, the other types of NAT-siRNAs are produced from homologous and interacting mRNAs that are distinct, forming a dsRNA that involves cleavage by DCLs. Among the various types of sRNAs previously mentioned, hc-siRNAs are essential for modification of chromatin; the miRNAs, ha-siRNAs, and NAT-siRNAs roles mostly occur at the post-transcriptional level by cleaved known transcripts or by suppressing the process of translation, although in a few cases there are instances in which they mediate the methylations of DNA [40–42]. The functional known siRNA classes include (1) trans-acting siRNAs (ta-­ siRNAs), which are phased sRNAs formed from the TAS genes with the concern of miRNAs and RNA-dependent RNA polymerase 6 (RDR6) [43]; (2) repeat-­associated siRNAs (ra-siRNAs), which are formed from transposons, heterochromatic, and repetitive genomic regions [44]; (3) natural antisense siRNAs (NAT-siRNAs), which are the precursor of dsRNA produced by the complementary hybridization of independently transcribed RNAs, whose precursor dsRNA is formed by the hybridization of complementary and is transcribed independently [21]; (4) cis-acting siRNAs (ca-siRNAs), which are produced by TAS3 transcript cis-cleavage [33]; and (5) double-strand DNA break-induced small RNAs (diRNAs) produced in response to DNA damage [36]. Besides the previously mentioned groups, there are, however, a large group of unclassified sRNAs with unknown functions. With the advent of highthroughput sequencing techniques, the expression and population of sRNAs are being extensively explored in diverse crop plants, thus clearing the desks for revealing the genomic regions associated with sRNAs [44].

­ iogenesis of small RNA in plants: microRNA and small B interference RNA Both siRNAs and miRNAs are formed from the RNA molecules characterized by double-stranded regions. The difference is that siRNAs are produced from doublestranded duplexes, which are formed by two molecules of RNA, whereas miRNAs

­Biogenesis of small RNA in plants: microRNA and small interference RNA

are processed from the double-stranded stem region of a single RNA molecule having precursors of hairpin shape [24]. There are various steps involved in biogenesis of miRNA molecules, which consists of many enzymatic steps including transcription, processing, modification, and RISC loading [45]. The complete sequence of events is depicted in Fig. 1. First, primary miRNAs (pri-miRNAs) are the original transcripts for miRNA processing, which is typically a long sequence of more than several hundred nucleotides, and are transcribed in most cases by RNA polymerase II (RNA Pol II) [46–48]. In plant cells, pri-miRNAs are derived primarily from intergenic regions, in contrast to a large number of miRNAs in animals, which are programmed by introns of protein-coding genes [49]. In the second step, the primiRNAs are cleaved into precursor miRNAs (pre-miRNAs) with secondary structures of stem-loop shaped by the endonuclease Dicer-like 1 protein (DCL1) [191] or Drosha RNase III endonuclease in animals [25]. In this process, there is a requirement of active participation of many other proteins, such as RNA-binding protein DAWDLE (DDL) [50], the C2H2-zinc finger protein SERRATE (SE) [51], and the dsRNA binding protein HYPONASTIC LEAVES1 (HYL1) [52]. In the third step, these pre-miRNAs are processed in the nucleus by DCL1 proteins into 21-­nucleotides double-stranded miRNA duplex [25]. This double-stranded mature miRNA duplex and its pairing sequence, named *miRNA*, are then exported by the action of the HASTY protein to the cytoplasm, which belongs to the plant ortholog of exportin 5 [53]. Evidence has found that increasing lines of miRNA sequences of some miRNAs have a conditional-based function. The miRNAs are commonly called miRNA-5p and miRNA-3p, r­espectively, MIR gene

Pre miRNA

DCL1

HYL1

SE

HEN1 HASTY

RISC

FIG. 1 miRNA biogenesis pathway in plants.

AGO

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based on their positions on the hairpin-shaped precursor [54–56]. miRNA-5p or miRNA-3p are then methylated at their respective 3-terminal ends by HUA ENHANCER1 (HEN1) [57]. This methylation reaction prevents their degradation by the sRNA-degrading nuclease (SDN) class of exonucleases [58]. In the final step, the functionally formed miRNA (either miRNA-5p, miRNA-3p, or both) will be incorporated into Argonaute (AGO) protein-centered RNA-induced silencing complex (RISC) to accomplishing their functions [59]. The plant miRNAs usually incorporate with AGO1 to induce post-transcriptional gene silencing (PTGS) of RNA by pairing to target genes, which results in transcript cleavage and inhibition at a translational level [47, 48, 60]. So far, AGO1 and AGO10 are the only proteins with known functions that have been identified in translational inhibition [47, 48]. But the underlying mechanism by which miRNA is known to mediate protein synthesis inhibition is still unclear.

­ iverse functions of sRNA in controlling plant metabolism D during stress condition Owing to the sessile nature of plants, sRNA plays an impressive part during plant metabolism in controlling diverse aspects of plants. Recently, advancement in understanding epigenetic regulations including histone modifications, chromosome remodeling, DNA methylation, and sRNA mediated degradation [61–63] has provided an esteemed approach to exploit stress adjustment in crop plants. The fertile validation from functional genomics studies that target the stress responses at the molecular level has unraveled new insights into miRNA epigenetic regulation of gene expression to modulate and respond against the adverse impacts of environmental pressures in crop plants [64]. Due to the abundant nature and diverse functions in plant systems, it is straightforward to interpret that, in plants, an array of biological processes are modulated by the regulatory mechanisms of one or more miRNAs as shown in Table  1. Production of abiotic stress-tolerant plants executes great consideration of gene-regulation mechanisms utilized by the crop plants in response to external abiotic deviations. Thus, research based on unravelling regulations at a posttranscriptional level by non-protein coding sRNAs that mediate specific messenger RNA (mRNA) blockage or affect transcriptional-mediated epigenetic modifications have attained extraordinary interest. miRNAs are ample in diverse crop plants and are ascribed to play foremost roles in regulation at a post-transcriptional level via base-pairing with complementary mRNA targets, mainly with TFs [65]. In crop plants, environmental stress induces expression of various miRNAs or biosynthesizes new and adaptive miRNAs to manage under stress conditions. Many miRNAs showing regulatory roles under stress environments have been verified in model crop plants under various biotic stresses, which include nutrient deficiency [66], drought [67–69], cold [70], salinity [68, 71], exposure to UV-B radiation [72], and mechanical stress [73], as well as abiotic stress-bacterial infection [74] and endogenous signals that define the form of plants in accordance with its genetic

­Diverse functions of sRNA in controlling plant metabolism

Table 1  Representative studies showing the role of conserved plant miRNA families and their target families/genes. miRNA family

Target gene families

miR156

SPL/SPL2 family

miR159

MYB33

miR162 miR164

DCL1 Dicer-like RNase III CUC

miR165/166

HD-ZIP III

miR167

ARF6 and ARF8

miR169

CBF

miR170/171

Scl6-III and related TFs

miR172

AP2 family

miR319

TCP family

miR390

TAS3

miR393 miR394

TIR1 and AFB LCR

miR396

GRF

miR857

LACCASE7

TAS3

ARF3/4 and (only Leaf polarity, vasculature in mosses) AP2-like development

TFs, transcription factors.

Target role

Species

Plastochron length, promoting flowering; tillering and corn development in Zea mays and also related to genes for floral meristem identity in Arabidopsis thaliana Anther, silique, and seed development Biogenesis of MiRNA

Arabidopsis thaliana and Zea mays

Meristem boundary identity, auxiliary meristem formation, and leaf serration Maintaining meristematic cells, the adaxial identity of leaves, lateral root growth, and procambium identity Development of male organ

Arabidopsis, Solanum, and Oryza Arabidopsis thaliana

Enhancer of C homeotic gene transcription Related to genes for root radial patterning and adaptive responses to stress Represses flowering, flower meristem identity, and patterning; carpel and stamen development in Z. mays; flower opening in Hordeum vulgare and tuberization in Solanum tuberosum Control of cell growth and proliferation during development; involved in leaves and petal shape tasiRNA biogenesis for ARF repression and indirect miR165/166 regulation Auxin homeostasis Meristematic identity suppression via WUS down-regulation Cell proliferation in leaves, determining the morphology Secondary growth

Arabidopsis thaliana

Arabidopsis thaliana Antirrhinum majus Arabidopsis thaliana Arabidopsis, Zea mays, H. vulgare, and S. tuberosum

Arabidopsis and Solanum lycopersicum Arabidopsis

Arabidopsis Arabidopsis Arabidopsis, Medicago, and Oryza Arabidopsis and Citrus sinensis All land plants

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makeup. It is noticeable that miRNAs involve differential regulation in response to environmental stress factors. Prerequisites for the developmental processes in plants, among others, include plant growth regulators (PGRs) [75]. A low amount of these PGRs is known to regulate every developmental aspect, in addition to the response of plants to various environmental stresses [76–78]. The intricate signaling pathways of PGRs are efficiently regulated by various mechanisms (positive/negative) during growth and developmental phases in plants. It has been recently proven that sRNAs (which also includes miRNAs) are involved in signal transduction pathways of these regulatory signaling mechanisms known to significantly affect the overall growth and development aspects of crop plants [79–82]. Lu and Fedoroff [83] first reported the hormonal signaling association with miRNAs, wherein the altered response of hyl-1 mutant to cytokinin, auxin, and abscisic acid (ABA) was demonstrated. In addition, modulation between miR159 and gibberellic acid [84] as well as miR164mediated auxin induction [85] was later well recognized. In a similar way, under the regulation of miR160, the expression of auxin-responsive factors (ARFs) is also well documented [86], and ABA-mediated induction of miR159 during germination of seeds in Arabidopsis thaliana has also been reported [66]. The different types of miRNA (miR156, miR159, miR165, miR166, and miR319) have been evidenced to play wide-spectrum roles in regulating the development and morphology of leaves [87, 88], floral organ identity control, and development of flowers (miR172) [89–91]. miR824 plays a crucial role in development of stomata [92]. miRNAs also regulate the phenotype and plant developmental process by regulating the plant cell-division processes [93]. In a study, it has been found that miRNA165 guides specific cells for triggering the exchange of positional information for inducing proper patterning that occurs during development of roots [94]. Carlsbecker et al. [94] found that the overexpression of miR165 increases the number of stele cells, and xylem pioneers develop a peripheral fate differentiated entirely as protoxylem in roots of Arabidopsis wild type, as well in the shr-2 mutant light-inducible. In Arabidopsis, miRNA163 targets PXMT1 transcripts to promote germination of seeds and elongation of primary roots [95]. Xing et al. [96] reported that, in Zea mays plants, miRNAs are also involved in the development of endosperm and embryo. These results indicate that miRNAs are important to establish the identities of plant cells during vital developmental processes in crop plants for conquering specific physiological and morphological statuses [97]. miRNAs are implicated to play an important role during plants’ response to biotic as well as abiotic stress entities. During biotic stress, plants are always said to be involved in the co-evolutionary defense against invading pathogens [98]. In addition, climate change is known to cause pathogen-borne diseases in the plant body resulting in many deleterious effects [99, 100]. Owing to this requirement, interpreting plants’ regulatory responses against such biotic stresses, researchers have evaluated the role of miRNAs during biotic stresses in plants [16]. In spite of the role of miRNAs in plant growth, development, signaling of hormones, and in biotic interactions, miRNAs are also involved in the biogenesis as well as the functioning of other small non-coding RNAs (miRNAs and siRNAs) [43, 101, 102].

­Role of miRNAs in ABA-mediated stress responses

In Arabidopsis thaliana, miR173 and miR390 are known to target five ta-siRNA, which generate known transcripts, and these ta-siRNAs were found to exert a key role to negatively regulate other genes [43]. The effector protein (AGO) is identified that undergoes shuttling (nucleo-cytosolic) for export of molecules of miRNA [103]. The AGO phosphorylation cycle also triggers the silencing, which is miRNAmediated [104]. The AGO protein has also been identified as an miRNA target. In an experiment, Lu et  al. [73] recognized the sequences of miRNA from genome Populus plants and concluded that miRNAs of Populus plants is known to target the genes involved in developmental and stress/defense-related processes. Other authors also found that mechanical stress can also induce plant miRNAs, which may play a critical role in plants’ defense responses in conferring structural and mechanical fitness [73]. Collectively, the vast evidence found the complex association of miRNAs in synthesis and functioning of other small non-coding RNAs. Even though great advancement has been carried out to understand the precise role/function of different miRNA in model plant species, the collective knowledge of the functioning of miRNA still remains obscure. Nonetheless, more emerging results indicate that different miRNAs play a crucial role in almost all biological and metabolic processes in crop plants.

­Role of miRNAs in ABA-mediated stress responses It is well known that plant growth phytohormone-ABA is documented in response to extrinsic abiotic stress. The first clue that miRNA is responsible for ABA-mediated response came to light by hypersensitivity of ABA in a mutant of Arabidopsis, which contained a “recessive Arabidopsis transposon insertion mutation,” hyl1 [83]. Recently, it was documented that treatment of gibberellin (GA) or ABA independently regulates the expression of miR159 [84, 106] and controls development of floral organ [84]. In germinating seeds of Arabidopsis thaliana, the expression of miR159 was upregulated in seedlings of ABA treatment [105]. Similarly, miR393, miR397b, and miR402 expression was upregulated under ABA stress [106] where, in contrast, ABA appears to downregulate miR389a expression [106]. In yet other results in Arabidopsis thaliana, there are reports of the upregulation of miR160 [107, 108] and miR417 [109], and downregulation of miR169 [110] and miR398 [111] in ABA response. In rice plants, under ABA-mediated response, miR319 was found to be upregulated, whereas miR167 and miR169 expression were downregulated [112]. The expression of miR159.2, miR393, and miR2118 under ABA response were induced, whereas miRS1, miR1514, and miR2119 were found to be moderately upregulated in Phaseolus vulgaris plants [113]. In Physcomitrella patens, the necessity of methylation of DNA on levels of miRNA was also shown for an ABAresponsive PpbHLH-miR1026. Nevertheless, the application of ABA resulted an increased expression of miR1026 in contrast to a decrease in PpbHLH-targeted RNA [114].

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­ iRNA-mediated adaptive response to drought and salt m stress conditions Plants are exposed to several kinds of stresses. To cope up with severe abiotic challenges, plants develop various adaptive measures for growth and development [2, 115]. The role of miRNA during salt and drought stress conditions has been well established in Arabidopsis. Accordingly, many different types of stress-related miRNAs have been found through a sequencing library of sRNAs in which seedlings were exposed to variable stress conditions [106]. In Arabidopsis, various differential miRNAs have been identified in tissues having salt stress. miR156, miR158, miR159, miR165, miR167, miR168, miR169, miR171, miR319, miR393, miR394, miR396, and miR397 were found to be upregulated, whereas the miR398 was found to be downregulated in Arabidopsis thaliana, thus showing a synergistic role between miRNAs and a salt stress-adaptive response [68]. Under drought stress, miRNA expression profiling has now been performed in plants like rice, Arabidopsis, and Populus. In Arabidopsis, several miRNAs were found to be drought-responsive [68]. In response to dehydration in Arabidopsis, the expression of miR393, miR319, and miR397 has been reported [106]. In rice, miR169 was found to be specifically expressed by drought-induced stress, whereas miR393 was found to be briefly induced by drought stress conditions [67]. The ­genomic-wide profiling analysis in rice specified that 16 miRNAs (miR156, miR159, miR168, miR170, miR171, miR172, miR319, miR396, miR397, miR408, miR529, miR896, miR1030, miR1035, miR1050, miR1088, and miR1126) were decreased significantly during drought stress conditions. On the contrary, 14 miRNAs (miR159, miR169, miR171, miR319, miR395, miR474, miR845, miR851, miR854, miR896, miR901, miR903, miR1026, and proline dehydrogenase [PDH]) were shown to be oversized in maize plants in response to drought-induced changes [116]. In Populus trichocarpa, miR530a, miR1445, miR1446a-e, miR1447, and miR171l-n were dow-regulated, whereas miR482.2 and miR1450 were oversized in response to salt stress conditions [117]. Later, miR169g and another member of the miR169 family, miR169n, were found to be induced due to excess salinity stress [118]. The researchers have identified cis-acting ABA-responsive element (ABRE) in the upstream region of miR169n-a, which indicates the ABA-mediated regulation of miR169n. Both miR169g and miR169n targeted nuclear factor Y subunit A (NF-YA), a factor reported to be downregulated in drought-affected wheat leaves [119].

­ egulation of cold and heat stress tolerance by miRNAs R expression The miRNA expression during cold stress was reported to be upregulated in Arabidopsis, Populus, and Brachypodium plants, and miR397 and miR169 classes were found in these species whereas miR172 was only identified to be expressed in Arabidopsis and Brachypodium ([68, 106, 120]) and Populus [114]. Also, ­several

­miRNAs expression to hypoxia and oxidative stress

different types of miRNA have been identified to show their upregulation and downregulation under cold stress conditions. In a study using solexa high-throughput sequencing, Xin et  al. [121] found that there is differential expression of miRNAs in wheat leaves exposed to duration of heat stress. Among the 32 miRNA families identified in wheat, nine conserved miRNAs were evident to be heat responsive. For example, miR172 was identified as significantly decreased, and miRNAs (including miR156, miR159, miR160, miR166, miR168, miR169, miR393, and miR827) were found to be upregulated under heat stress conditions [121].

­miRNAs expression to hypoxia and oxidative stress In plants, various abiotic pressures, viz. salinity, drought, heavy metal, and temperature are responsible for accumulation of various reactive oxygen species (ROS) such as superoxide radicals (O2−), hydrogen peroxide (H2O2), and hydroxyl radicals (OH-·) resulting in major loss of crop productivity [11, 122]. Production of ROS is an inherited property produced in the chloroplast, mitochondria, and peroxisomes in plants [123, 124]. In plants, superoxide ion is converted into molecular oxygen and hydrogen peroxide by the catalytic action of superoxide dismutases (SODs). Arabidopsis (Cu-Zn SODs) are encoded by CSD1, CSD2, and CSD3 [124a], and it was predicted that CSD1 and CSD2 are targeted by miR398 [125, 126]. Later, findings from Sunkar et al. [124a] authenticated these results, and it was discovered that, under oxidative stress conditions, miR398 is downregulated. Under the conditions of stress, the negative regulation of CSD1 and CSD2 by miR398 is reduced, which resulted in the upregulation of CSD1 and CSD2, therefore relieving accretion of the extremely toxic O2− [124a]. Redox signaling played a specific role in initiating miR398 during sulfate deficiency in Arabidopsis. The induction of various miRNAs in maize under oxygen deficiency highlights a diverse role of miRNAs in induction of physiological and morphological adaptations in roots, and in oxidative and metabolism of sulfur [81, 82]. miR398 levels were unaffected in mutant defective of redox signaling when compared with wild-type miR398 levels under S-deprivation [126a]. The H2O2-responsive targets of these miRNAs are involved in regulating vital metabolic processes, which includes regulation at transcriptional level, transport of nutrients, homeostasis of auxins, proliferation of cells, and programmed cell death, indicating the diversity in function of miRNAs with regard to responses of plants under oxidative stress conditions [126b]. Hypoxia (low oxygen stress) produces many transcriptome changes that results in a change of respiration from aerobic to anaerobic [127]. Recent results indicated that miRNAs are engaged in responses of plants to hypoxia [19, 81, 82]. Several miRNAs show a change in response to low oxygen stress and found that their action is inversely associated with their specific mRNA targets, and their expression level is increased under hypoxia. Recently, it has been found that the miRNA expressed in response to low oxygen stress was further authenticated using chemicals that control mitochondrial respiration and consequently hypoxia [19].

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­miRNA in response to nutrient homeostasis The growth and development of plants is mainly dependent on essential mineral elements absorbed from the soil. sRNAs play a vital role in nutrient stress conditions, and it is important to understand the expression of different pathways by miRNA will provide the fundamentals of molecular signal transduction during nutrient deprivation [128]. Inorganic phosphate (Pi) is the backbone for nucleic acids and membrane lipids biosynthesis, and is often a limiting factor for nutrition in plants. Nucleases and phosphates are produced by plants to solubilize external Pi, relieve Pi from organic derivatives, or to oversize the expression of genes of a few transporter proteins to adjust phosphate stress. In Arabidopsis, genome-wide profiling by micro- and tiling-arrays revealed a minimal overlap between transcriptomes of root and shoot, which suggests two independent phosphate-starvation regulations. The homeostasis of phosphate is partly controlled by miR399, which is known to target a gene that encodes a putative ubiquitin-conjugating enzyme (UBC24) [106, 129]. In rice, the overexpression of the upstream miR827 (targeting SPX-MFS protein family members) plants impaired Pi homeostasis in the leaves, suggesting OsSPX-MFS1 is a principal actor to maintain the homeostasis of Pi in the leaves, potentially acting as a Pi transporter [129a]. Sulfur (S) is an indispensable mineral nutrient present in the soil environment in the sulfate form. In Arabidopsis, xylem or phloem is involved in the transport of sulfate via transporters, which are cell-specific such as sulfate transporters, SULTR1;1, SULTR2;1, and SULTR2;2, and miR395 that targets both ATP sulfurylases (APSs) and the sulfate transporter AST68 [29, 126]. The deprivation of sulfate triggers the miR395 expression with the reduction of APS1 transcript levels [126]. It has been found that the miR395 abundance in the phloem elevated in Brassica plants, which were deficient in S, and the enhancement was greater in the phloem tissues than in the root, stem, or leaf [130, 131].

­Regulating plant metabolism: Role of sRNAs Plant miRNAs are known to regulate the expression of genes encoding different kinds of proteins, and a noteworthy classification of miRNA target genes comprises of regulatory proteins, TFs that work in plant advancement, or transduction of signals. Plant miRNAs contain a high degree of sequence complementarity to their target mRNAs, which can aid in the bioinformatic prediction of miRNA target genes.

­miRNA-mediated regulation plant phytohormone signaling Phytohormones are a universal mediator in regulating a broad range of physiobiochemical activities in plants. Accordingly, several phytohormones such as GAs, auxin, and ethylene are regulated by the miRNAs. GA is a well-known phytohormone showing regulatory control during germination of seeds, elongation of hypocotyls, flowering, and development of trichomes. GA is known to influence the

­Regulating plant metabolism: role of sRNAs

formation of trichomes in Arabidopsis plants. Results are available for the mutation in the SPINDLY (SPY; the repressor of GA signaling) locus that results in increased trichome formation [132, 133]. The SPY is regulated by three miRNA families, viz. miR156, miR414, and miR5015, and other GAs responsive proteins also showed response toward miR5021 and miR414. Ethylene responsible for branching of trichomes was also reported to be regulated by miR5021 [134]. Different hormones’ mediated signals regulate flowering and consequently the development of fruits [135, 136] Among various phytohormones, auxin is known to play a critical role for growth of plants in regulating the expression of genes within fruit and flower [137]. The local auxin concentration formed by auxin transport (polar) is known to form root stem cells, axis patterning of the embryo, control of primordial outgrowth from meristematic tissue, gravitropic responses, and formation of lateral roots. The AUX/IAA proteins form a dimer with auxin response factor (ARF) family members of transcription activators and repressors, thereby inhibiting the activities of the activating ARFs [138]. A number of genes during auxin signaling are confirmed as potential targets of various miRNAs. In recent times, miR167 is found to be involved in signaling of auxin, which regulates the expression of specific ARF genes for determining the various developmental processes in plants. Out of the 10 MIR167 genes present in rice plants, the structures (precursor) derived from 3 genes including MIR167a, MIR167b, and MIR167c produce miR167 with high efficiency suggesting that miR167 played a significant role during expression of at least 4 OsARFs, which intervene the auxin response, thereby contributing to the healthy growth and advancement of rice plants [107, 108]. It has been found that several ARFs contain potential miRNA binding sites, that is, ARF10, ARF16, and ARF17 possesses miR160-binding sites [139], whereas ARF6 and ARF8 have their own miR167 sites ([140]); their expression leads to pleiotropic developmental abnormalities in plants [86]. These results indicate that miRNA-mediated regulation of ARF is vital for regulating various developmental aspects in plants. Further, ARF17 may trigger the activation of miR167 to coordinate the expression of ARF6 and ARF8 and development of roots. Hence, miR167 and miR160 targets show contrasting roles in regulation of auxin [141]. Among ARF proteins in Arabidopsis that mediate the auxin-induced activation of genes [142], ARF6 and ARF8 are found to regulate the process in the growth in tissues (vegetative and reproductive). It has been found that the single mutants of Arabidopsis ARF6 and ARF8 show mild delays in elongation of stem and growth of flower organ [143]. However, double mutants (ARF6, ARF8) showed severe defects, which indicate the partial overlapping functions of ARF6 and ARF8. In addition, ARF6 and ARF8 promote the production of jasmonic acid (JA), which in turn exert the MYB21 and MYB24 expression required for floral organ identity [144, 145]. In addition to Arabidopsis, orthologues of ARF6 and ARF8 are present in dicots and monocots [146], but their established roles during growth of plants have been authenticated in Arabidopsis only [143–145, 147]. Tomato plants offer an excellent

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model to verify the conserved role of ARF6 and ARF8 in the development of flower as well as their regulation by miR167. In addition to target ARF6 and ARF8, it has recently been shown that miR167 directs the cleavage of IAA-Ala Resistant 3 (IAR3) transcripts in Arabidopsis thaliana [148]. The overexpression of the AtMIR167a gene in the wild species of tomato accession LA1589 were shown to be downregulation of SpARF6 and SpARF8, which are supposed to change maturation of flower as in Arabidopsis, suggesting that ARF6 and ARF8 exert an essential role in dicot flower development [149]. In soybean plants, miR160 and miR167, which target ARF10/16/17 (repressors) and ARF8 (activator), respectively, are known to act as positive and negative regulators of auxin. At the early stages of nodule development, low auxin sensitivity favors nodule primordia formation [150].

­miRNA: Transcription factors in regulating plant metabolism TFs belong to members of multigene families and are essential players for regulating the expression of genes [11, 151]. Generally, TFs are a class of modular proteins that contains a DNA-binding domain that interacts with cis elements of their target genes [152, 153]. The regulation of TFs genes takes place at transcriptional and posttranscriptional levels in plants [11, 154, 155]. TFs are involved in the genetic system through many ways such as control and regulation at developmental level, elicitation of defense, and stress responses by expressing the specified genes in plants [7, 11, 23, 156]. Recently, it’s been discovered through recent observations that a change in triggering transcription of genes is closely related to changes in the expression of TFs [157]. Hence, it is obvious that an alteration in expression pattern of TF genes can result in significant changes in growth and development of plants [158, 159]. Therefore, engineering of TF genes could enable desired trait manipulation in plants [160, 161]. miRNAs tend to regulate many distinct genes, which suggest that these genes are under the control of miRNAs. Many groups of TFs are known to control the development of trichomes in Arabidopsis, which in turn induces the formation of a TF’s trimeric activator complex, MYB-bHLH-WDR (MBW), which regulates the expression of downstream targets, thereby inducing formation of trichome [133]. Researchers find that miR156 and miR5015 regulate the initiation of trichome by exerting control over the expression of bHLH and MYC. Proteins (WD) are involved in the determination of cell fate, and cell cycling and signaling [162] are under the regulatory of miR5015 [134]. Computational predictions along with experimental evidence have shown that many TFs such as MYB, NAC, and WRKY are the targets of miRNAs. MYB plays a diverse role in eukaryotic gene networks. Various MYB proteins act as TFs with different numbers of MYB domain repeats; MYB related, R2R3-MYB, R1R2R3MYB, and a typical MYB family, which has a strong tendency to bind with DNA [163, 164]. MYB has been shown to be involved in the growth and development of various species of plants, that is, in Glycine max, they are engaged in the ­development

­Functional role of miRNA in plant secondary metabolism biosynthesis

of flower color [165], and in signal transduction pathways in Arabidopsis thaliana, Oryza sativa, and cassava [166, 167]. In Arabidopsis thaliana and Medicago truncatula, they are known to show regulation on secondary metabolite biosynthesis [168, 169]. Cup-shaped cotyledon 1 (CUC1) and CUC2 are two essential TFs of the NAM/ ATAF/CUC (NAC) domain, chiefly restricted to plants, that are known to play a significant role in the development of embryogenic and floral parts [169a]. With regard to MiR164-targeted NAC1 in Arabidopsis, overexpression of this miRNA caused the fusion of floral and vegetative organs, unbalanced floral organ numbers, and less emergence of lateral roots [85]. A large number of species-specific miRNAs have been identified in tomato and hypothesized their role in fruit development [170]. Further, the tissue-specific expression of numerous miRNAs have been described by Moxon et al. [171] in S. lycopersicon. miRNAs have also been identified to target metabolic pathways such as carbohydrate and protein metabolism, purine and pyrimidine biosynthesis, cellular transport, and glutathione metabolism.

F­ unctional role of miRNA in plant secondary metabolism biosynthesis In crop plants, it has been found that miRNAs exert control over the expression of genes that encode TFs, stress response proteins, which show a profound impact on diverse biological processes such as genome integrity maintenance, primary and secondary metabolism, transduction of signaling pathways, homeostasis, innate immunity, and also the stress-adaptive responses to various biotic and abiotic pressures [172]. Secondary metabolites are a class of phytochemicals regulating various processes in plants related to their interaction with environment [173]. These compounds, among others, include alkaloids, phenolics, terpenoids, glycosides, saponins, and known to defend plants from various stress factors [174]. Some studies have brought the role of miRNAs into secondary metabolite regulation pathways [174]. Hence, it may be concluded that the production of compounds derived from secondary metabolites could be achieved through the miRNAs. Li et  al. [158, 159] in Nicotiana tabacum have identified four novel tobaccospecific miRNAs that were supposed to target principal genes for biosynthesis and catabolism pathways of the nicotine. In Taxus baccata, Hao et al. [175] found that two paclitaxel biosynthetic genes, taxane 2α-O-benzoyltransferase and taxane 13α hydroxylase, are the main cleavage targets of miR164 and miR171, respectively. Prakash et al. [176] by using in silico analysis in Rauwolfia serpentina observed that miR396b targets kaempferol 3-O-beta-d-galactosyltransferase, the activity of which is required for flavanol glycoside formation. In species of Mentha, computational approach revealed that miR156, miR414, and miR5021 are essential for oil biosynthesis. It has been found that miR156 are involved in the biosynthesis of flavone, flavanol, and terpenoid [134]. Recently, miRNAs have been regarded as a potential biological factor for controlling the production of diverse secondary metabolites in many crop plants [177]. In

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species of Mentha, miR5021 have been evidenced to control the biosynthesis of essential oils by regulating coding of gene expression for enzyme-geranyl di-phosphate synthase involved in pathway of 2-C-methyl-d-erythritol 4-phosphate/1-deoxy-dxylulose 5-phosphate (DOXP) [134]. In Panax notoginseng, plant miR5021 and miR5293 were found to keep a check at the first enzymatic function of the pathway of MVA [178]. Based on the review, some miRNAs have also been found to regulate the pathway of secondary metabolites in C. borivilianum [179]. Unrevealing the regulatory roles of miRNAs is not only confined to primary metabolites but also in secondary metabolism regulation as well [177], and, of this, miR156, miR163, miR393, and miR828 are adjudged as potential tools for regulating Arabidopsis secondary metabolism. On the basis of transcriptome data of C. borivilianum, it has been found that miR9662, miR894, miR172, and miR166 might be engaged to regulate the biosynthetic pathway of saponins [179]. During the last decade, a combination of various studies discovered that TCP were responsible for the biosynthesis of jasmonic acid [180]. The technique of reverse genetic approaches in the interaction of miR319 and TCP4 in the root-knot nematode (RKN) resistance in tomato advocated that the synthetic genes of jasmonic acid and the endogenous jasmonic acid level is found in leaves of plant. These results suggest that the negative interaction between TCP4 and miR319 resulted as a regulator and responder of signal modulating the defensive response, mediated through the response of jasmonic acid to RKN [181].

­Regulatory role of siRNAs in plant stress responses siRNAs, in relation to plant abiotic stress response, was reported first by Sunkar and Zhu [106]. An important role of nat-siRNAs, which is synthesized from natural cis-antisense transcript pairs of 24-nucleotide-long SRO5 (stress-induced gene of unknown functions) and P5CDH (pyrroline-5-carboxylate dehydrogenase) genes, was found significant during osmotic adjustment and oxidative damage under salinity in Arabidopsis plants [182]. Downregulation of P5CDH resulted in the accumulation of proline, which controls the ability of plants to show resistance against excess salt stress [182], which ultimately leads to excess ROS production and intermediate unstable compounds (P5C), which is counterbalanced by SRO5 protein that relieves the salinity stress. Hence, SRO5-P5CDH nat-siRNAs, along with the P5CDH and SRO5 proteins, is a key regulator complex in controlling the production of ROS and response toward salinity [182]. The differential expressions of small non-coding RNA in wheat under abiotic stress environment have also been studied. The downregulation of three siRNA variants, which include siRNA 002061_0636_3054.1, siRNA 005047_0654_1904.1, and siRNA 007927_0100_2975.1, was found, which leads to the assumption about the proteins that may prove helpful under saline condition [183]. The genome-wide identification of heat stress-responsive sRNAs in Festuca arundinacea has been carried out by employing the high-throughput sequencing

­Conclusion and future prospectus

techniques. They subjected the genotypes of heat-sensitive PI234881 and heat-­ tolerant PI578718 to heat-stress regimes at 40°C for 36 h. Searching against the database (miRBase), among 1421 reference miRNAs (monocotyledon), in all samples more than 850 were identified. Among all miRNAs searched, 1.46% and 2.29% were found to be expressed in two contrasting genotypes under conditions of heat stress. Furthermore, the miRNA regulation under heat-stress conditions was revalidated via the quantitative reverse transcription PCR (qRT-PCR) technique. Their results are the first report of genome-wide miRNA characterization in genotypes of tall fescue. miRNAs specific to heat-tolerant genotype (PI578718) showed differential expression profiles under high-temperature stress, which were probably found to be associated with tolerance against heat stress in tall fescue [184]. In recent studies, sweet potato was subjected under a controlled Soil-PlantAtmosphere-Research (SPAR) system to elevated carbon dioxide (CO2) and drought conditions to assess how miRNAs modulate the physiology and storage-root development. The study found that a total of 32 miRNAs belonging to 23 miRNA families were identified. Geneontology and KEGG ontology functional enrichment showed that miRNAs target TFs (MYB, TCP, NAC), signaling regulators of phytohormone (ARF, AP2/ERF), metabolism of carbon (ATP synthase, fructose-1, 6-bisphosphate), factors associated with cold and drought (corA), and photosynthesis (photosystem I and II complex units). This study first identified miRNAs targets under elevated CO2 levels [185].

­Conclusion and future prospectus Gene expression changes during abiotic stresses play a key role in stress tolerance response in crop plants. For various abiotic-stress-related genes, sRNAs principally function as post-transcriptional modulators that control the expression pattern. miRNAs and TFs have important roles during regulation of plant metabolism including signal transduction, tolerance to drought stress, biosynthesis of secondary metabolites, development of floral organs and nodule formation, multiple stresses tolerances, growth of lateral roots, and transition of plants from juvenile to adult stages, physiology, and phenotype. Plant scientists have adjudged miRNAs as potential targets toward the improvement of crops with enhanced yield and stress tolerance against multiple or combinations of stresses via sRNA-directed genetic engineering. The emerging knowledge of miRNAs and TFs will unravel underlying mechanisms that will help plant scientists engineer plants with desired traits with stress-tolerance ability against particular environmental pressures. The engineered plants with enhanced environmental stress-tolerance capacity will help secure the production of food for the ever-increasing human population. Nevertheless, miRNA and different TFs play important roles in the biosynthesis of secondary metabolites and their regulation, which can be used as a tool for the concomitant production of plant-based medicinal biomolecules. Recent technological advancements at molecular levels have established the identification of various sRNAs, which are involved in plant stress

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responses to myriads of environmental pressures. However, most of these studies are based on a few model crop species, and this need to be dissected in detail.

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[146] D.L. Remington, T.J. Vision, T.J. Guilfoyle, J.W. Reed, Contrasting modes of diversification in the Aux/IAA and ARF gene families, Plant Physiol. 135 (2004) 1738–1752. [147] P. Ru, L. Xu, H. Ma, H. Huang, Plant fertility defects induced by the enhanced expression of microRNA167, Cell Res. 16 (2006) 457–465. [148] N. Kinoshita, H. Wang, H. Kasahara, J. Liu, C. Macpherson, Y. Machida, Y. Kamiya, M.A. Hannah, N.H. Chua, IAA-Ala Resistant3, an evolutionarily conserved target of miR167, mediates Arabidopsis root architecture changes during high osmotic stress, Plant Cell 24 (2012) 3590–3602. [149] N. Liu, S. Wu, J. Van Houten, Y. Wang, B. Ding, Z. Fei, T.H. Clarke, J.W. Reed, E. Van Der Knaap, Down-regulation of AUXIN RESPONSE FACTORS 6 and 8 by microRNA 167 leads to floral development defects and female sterility in tomato, J. Exp. Bot. 65 (9) (2014) 2507–2520. [150] T. Suzaki, K. Yano, M. Ito, Y. Umehara, N. Suganuma, M. Kawaguchi, Positive and negative regulation of cortical cell division during root nodule development in Lotus japonicus is accompanied by auxin response, Development 139 (2012) 3997–4006. [151] H. Salih, W. Gong, S. He, G. Sun, J. Sun, X. Du, Genome wide characterization and expression analysis of MYB transcription factors in Gossypium hirsutum. BMC Genet. 17 (2016) 129, https://doi.org/10.1186/s12863-016-0436-8. [152] V.  Boeva, Analysis of genomic sequence motifs for deciphering transcription factor binding and transcriptional regulation in eukaryotic cells, Front. Genet. 7 (2016) 24. [153] Y. Orenstein, R. Shamir, Modeling protein–DNA binding via- high throughput in vitro technologies, Brief. Funct. Genomics 16 (2016) 171–180. [154] K.M. Lelli, M. Slattery, R.S. Mann, Disentangling the many layers of eukaryotic transcriptional regulation. Annu. Rev. Genet. 46 (2012) 43–68, https://doi.org/10.1146/ annurev-genet-110711-155437. [155] J.L. Payne, A. Wagner, Mechanisms of mutational robustness in transcriptional regulation, Front. Genet. 6 (2015) 322. [156] D.C.J. Wong, R. Schlechter, A. Vannozzi, J. Höll, I. Hmmam, J. Bogs, et al., A systemsoriented analysis of the grapevine R2R3-MYB transcription factor family uncovers new insights into the regulation of stilbene accumulation, DNA Res. 23 (5) (2016) 451–466. [157] X. Yan, C. Dong, J. Yu, W. Liu, C. Jiang, J. Liu, et al., Transcriptome profile analysis of young floral buds of fertile and sterile plants from the self-pollinated offspring of the hybrid between novel restorer line NR1 and Nsa CMS line in Brassica napus, BMC Genomics 14 (1) (2013) 26. [158] F. Li, W. Wang, N. Zhao, B. Xiao, P. Cao, X. Wu, et al., Regulation of nicotine biosynthesis by endogenous target mimicry of microRNA in tobacco. Plant Physiol. 169 (2015) 1062–1071, https://doi.org/10.1104/pp.15.00649. [159] J. Li, S. Han, X. Ding, T. He, J. Dai, S. Yang, et al., Comparative transcriptome analysis between the cytoplasmic male sterile line NJCMS1A and its maintainer NJCMS1B in soybean (Glycine max (L.) Merr.), PLoS One 10 (2015) e0126771. [160] A. Pandey, P. Misra, S. Bhambhani, C. Bhatia, P.K. Trivedi, Expression of Arabidopsis MYB transcription factor, AtMYB111, in tobacco requires light to modulate flavonol content, Sci. Rep. 4 (2014) 5018. [161] L. Weng, X. Bai, F. Zhao, R. Li, H. Xiao, Manipulation of flowering time and branching by overexpression of the tomato transcription factor SlZFP2, Plant Biotechnol. J. 14 (2016) 2310–2321. [162] E.J. Neer, C.J. Schmidt, R. Nambudripad, T.F. Smith, The ancient regulatory-protein family of WD-repeat proteins, Nature 371 (1994) 297–300.

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[163] S.  Ambawat, P.  Sharma, N.R.  Yadav, R.C.  Yadav, MYB transcription factor genes as regulators for plant responses: an overview, Physiol. Mol. Biol. Plants 19 (2013) 307–321. [164] X. Wu, D. Ding, C. Shi, Y. Xue, Z. Zhang, G. Tang, et al., MicroRNA dependent gene regulatory networks in maize leaf senescence, BMC Plant Biol. 16 (2016) 73. [165] R. Takahashi, N. Yamagishi, N. Yoshikawa, A MYB transcription factor controls flower color in soybean, J. Hered. 104 (2013) 149–153. [166] B. Bakhshi, E. MohseniFard, N. Nikpay, M.A. Ebrahimi, M.R. Bihamta, M. Mardi, MicroRNA signatures of drought signaling in rice root, PLoS One 11 (2016) e0156814. [167] W.  Liao, Y.  Yang, Y.  Li, G.  Wang, M.  Peng, Genome-wide identification of cassava R2R3 MYB family genes related to abscission zone separation after environmentalstress-induced abscission, Sci. Rep. 6 (2016) 32006. [168] J. Liu, A. Osbourn, P. Ma, MYB transcription factors as regulators of phenylpropanoid metabolism in plants, Mol. Plant 8 (2015) 689–708. [169] N.H. Nguyen, H. Lee, MYB-related transcription factors function as regulators of the circadian clock and anthocyanin biosynthesis, J. Exp. Bot. 66 (2016) 4653–4667. [169a] K.I.  Hibara, M.R.  Karim, S.  Takada, K.I.  Taoka, M.  Furutani, M.  Aida, M.  Tasaka, Arabidopsis CUP-SHAPED COTYLEDON3 regulates postembryonic shoot meristem and organ boundary formation, Plant Cell 18 (11) (2006) 2946–2957. [170] A. Itaya, R. Bundschuh, A.J. Archual, J.G. Joung, Z. Fei, X. Dai, P.X. Zhao, Y. Tang, R.S.  Nelson, B.  Ding, Small RNAs in tomato fruit and leaf development, Biochim. Biophys. Acta 1779 (2008) 99–107. [171] S.  Moxon, R.  Jing, G.  Szittya, F.  Schwach, R.L.  Rusholme Pilcher, V.  Moulton, T.  Dalmay, Deep sequencing of tomato short RNAs identifies microRNAs targeting genes involved in fruit ripening, Genome Res. 18 (2008) 1602–1609. [172] A.T. Djami-Tchatchou, N. Sanan-Mishra, K. Ntushelo, I.A. Dubery, Functional roles of microRNAs in agronomically important plants-potential as targets for crop improvement and protection. Front. Plant Sci. 8 (2017) 378, https://doi.org/10.3389/ fpls.2017.00378. eCollection 2017. [173] V. Mahajan, A. Mahajan, S.S. Pagoch, Y.S. Bedi, S.G. Gandhi, MicroRNA mediated regulation of plant secondary metabolism: an in silico analysis, J. Nat. Sci. Biol. Med. 2 (Suppl S1) (2011) 44–45. [174] O.P. Gupta, S.G. Karkute, S. Banerjee, N.L. Meena, A. Dahuja, Contemporary understanding of miRNA-based regulation of secondary metabolites biosynthesis in plants. Front. Plant Sci. 8 (2017) 374, https://doi.org/10.3389/fpls.2017.00374. [175] D.C. Hao, L. Yang, P.G. Xiao, M. Liu, Identification of Taxus microRNAs and their targets with high-throughput sequencing and degradome analysis, Physiol. Plant. 146 (2012) 388–403. Epub 2012 Jul 25. [176] P. Prakash, R. Rajakani, V. Gupta, Transcriptome-wide identification of Rauvolfia serpentina microRNAs and prediction of their potential targets. Comput. Biol. Chem. 61 (2015) 62–74, https://doi.org/10.1016/j.compbiolchem.12.002. [177] V.P.  Bulgakov, T.V.  Avramenko, New opportunities for the regulation of secondary metabolism in plants: focus on microRNAs, Biotechnol. Lett. 37 (9) (2015) 1719–1727. [178] R. Wei, D. Qiu, I.W. Wilson, H. Zhao, S. Lu, J. Miao, S. Feng, L. Bai, Q. Wu, D. Tu, Identification of novel and conserved microRNAs in Panax notoginseng roots by highthroughput sequencing, BMC Genomics 16 (1) (2015) 835.

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[179] M.  Kajal, K.  Singh, Small RNA profiling for identification of miRNAs involved in regulation of saponins biosynthesis in Chlorophytum borivilianum, BMC Plant Biol. 17 (1) (2017) 265. [180] C.  Schommer, J.F.  Palatnik, P.  Aggarwal, A.  Chetelat, P.  Cubas, E.E.  Farmer, et  al., Control of jasmonate biosynthesis and senescence by miR319 targets, PLoS Biol. 6 (2008) e230. [181] W. Zhao, Z. Li, J. Fan, C. Hu, R. Yang, X. Qi, et al., Identification of jasmonic acidassociated microRNAs and characterization of the regulatory roles of the miR319/ TCP4 module under root-knot nematode stress in tomato, J. Exp. Botany 66 (15) (2015) 4653–4667. [182] O. Borsani, J. Zhu, P.E. Verslues, R. Sunkar, J.K. Zhu, Endogenous siRNAs derived from a pair of natural cis-antisense transcripts regulate salt tolerance in Arabidopsis, Cell 123 (2005) 1279–1291. [183] Y.  Yao, Z.  Ni, H.  Peng, F.  Sun, M.  Xin, R.  Sunkar, J.K.  Zhu, Q.  Sun, Non-coding small RNAs responsive to abiotic stress in wheat (Triticum aestivum L.), Funct. Integr. Genomics 10 (2010) 187–190. [184] H. Li, T. Hu, E. Amombo, J. Fu, Genome-wide identification of heat stress-responsive small RNAs in tall fescue (Festuca arundinacea) by high-throughput sequencing, J. Plant Physiol. 213 (2017) 157–165. [185] T.  Saminathan, A.  Alvarado, C.  Lopez, S.  Shinde, B.  Gajanayake, V.L.  Abburi, U.K. Reddy, Elevated carbon dioxide and drought modulate physiology and storageroot development in sweet potato by regulating microRNAs, Funct. Integr. Genomics 19 (2019) 171–190.

­Further reading [186] J.L.  Bowman, Y.  Helariutta, P.N.  Benfey, Cell signalling by microRNA165/6 directs gene dose-dependent root cell fate, Nature 465 (2010) 316–321. [187] D.  Ghosh, J.  Xu, Abiotic stress responses in plant roots: a proteomics perspective, Front. Plant Sci. 5 (2014) 6. [188] H. Abe, T. Urao, T. Ito, M. Seki, et al., Arabidopsis AtMYC2 (bHLH) and AtMYB2 (MYB) function as transcriptional activators in abscisic acid signalling, Plant Cell 15 (2003) 63–78. [189] J.H.K. Suzanne, R. Greco, A. Agalou, Interaction between the growth-regulating factor and knotted1-like homeobox families of transcription factors, Plant Physiol. 164 (2014) 1952–1966. [190] K.D. Kasschau, N. Fahlgren, E.J. Chapman, C.M. Sullivan, J.S. Cumbie, S.A. Givan, Genome-wide profiling and analysis of Arabidopsis siRNAs, PLoS Biol. 5 (2007), e57. [191] Y. Kurihara, Y. Watanabe, Arabidopsis micro-RNA biogenesis through Dicer-like 1 protein functions, Proc. Natl. Acad. Sci. U. S. A. 101 (34) (2004) 12753–12758. [192] S. Mi, T. Cai, Y. Hu, Y. Chen, E. Hodges, F. Ni, S. Chen, Sorting of small RNAs into Arabidopsis argonaute complexes is directed by the 5′ terminal nucleotide, Cell 133 (2008) 116–127. [193] A. Pereira, Plant abiotic stress challenges from the changing environment, Front. Plant Sci. 7 (2016) 1123. [194] Y. Qi, A.M. Denli, G.J. Hannon, Biochemical specialization within Arabidopsis RNA silencing pathways, Mol. Cell 19 (2005) 421–428.

­Further reading

[195] R. Fan, Y. Li, C. Li, Y. Zhang, Differential microRNA analysis of glandular trichomes and young leaves in Xanthium strumarium L. reveals their putative roles in regulating terpenoid biosynthesis, PLoS One 10 (2015), e0139002. [196] Y. Tabach, A.C. Billi, G.D. Hayes, M.A. Newman, O. Zuk, H. Gabel, Identification of small RNA pathway genes using patterns of phylogenetic conservation and divergence, Nature 493 (2013) 694–698. [197] B.  Zhang, Q.  Wang, Micro RNA, a new target for engineering new crop varieties, Bioengineered 7 (2016) 7–10.

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8

Summi Dutta, Uzma Afreen, Manish Kumar, Kunal Mukhopadhyay Department of Bio-Engineering, Birla Institute of Technology, Ranchi, India

­Small RNA: Discovery, classifications, and biogenesis It has been more than two decades since RNA interference in gene silencing was first discovered. Non-coding RNAs (ncRNAs) derived from the regions of genome that were previously considered JUNK or DARK matter are the common sources for all kinds of small (s) RNAs [1, 2]. Most of the earlier research focused on detecting the protein-coding genes on exonic regions. And so a large part of the genome was neglected by the scientific community for a long time. Unavailability of proper techniques to detect such tiny molecules also contributed to their late discovery. In a normal RNA gel, they are easily washed out due to their small size. Lin-4 was the first sRNA discovered in 1993, which was later categorized as microRNA isolated from Caenorhabditis elegans [3, 4, 4a]. The second one was let-7 [5], again from the same organism. These were put under small temporal RNAs due to their involvement in regulating developmental timing (heterochronic genes) of nematodes. In plants, although the RNA-based silencing of genes was known during a transgenic experiment in Arabidopsis in 1990, proper sRNAs were identified a bit later in animals [6]. It was miR393 in Arabidopsis that was the first plant miRNA identified [7]. It had been predicted that this miRNA was induced by a flagellin-derived peptide of Pseudomonas syringae that restricted its growth. Plant-specific trans-acting siRNA-producing locus (TAS1) was also reported in Arabidopsis [8]. Small RNAs, as the name suggests, are small in size and includes RNAs of 18–26 nucleotide (nt) in length. They are both endogenously and exogenously produced depending on their type [9]. They act as “switches” of gene expression and can suppress transcripts that are not required. The small RNAs once synthesized are incorporated into RNA-induced silencing complex (RISC) which help them in targeting mRNAs on the basis of complementarity and bring about their successive cleavage [10]. In this way, they play a significant role in post-transcriptional gene silencing (PTGS) and regulate gene expression [11]. These complexes are more actively expressed in the presence of certain stress-responsive signals in plants to mediate sRNA-induced silencing of certain genes that might be associated with plant Plant Small RNA. https://doi.org/10.1016/B978-0-12-817112-7.00008-0 © 2020 Elsevier Inc. All rights reserved.

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FIG. 1 Small RNAs and their involvement with stress.

­ etabolic, ­developmental, or highly stress-related traits, thus participating in host– m microbe interactions (Fig. 1; [12, 13]).

­Classification Micro (mi)RNAs and small interfering (si) RNAs are the two domains of sRNA regulatory molecules (Fig.  2). With advancement in sRNA research, the numbers and types of sRNAs detected are increasing every day. miRNAs are derived from a single RNA molecule with characteristic stem-loop structure. Isoforms of miRNA, also called as isomiRs, with a function similar to that of miRNA, are also detected. Recent reports also reveal the presence of miRNA-like molecules (milRs) in several fungal pathogens. The second type, that is, siRNAs with double-stranded (ds) RNA as precursor include both phasi as well as non-phasiRNAs. PhasiRNAs are the ones originated as a result of phasing or sequential cleavage and include trans-acting siRNAs (ta-siRNAs) and cis-acting siRNAs. In plants, the other classes include natural antisense transcript siRNA (NAT-siRNAs), coding transcript-derived siRNAs (ctsiRNAs), and heterochromatin-derived siRNAs (hc-siRNAs) [14, 15].

­MicroRNAs and isomiRs MicroRNA are 18–24 nucleotide (nt) single-stranded ncRNA molecules, endogenous in origin and trans-acting in nature. These are derived from long primary miRNA precursor with regions of self-complementarity that fold backs on itself to give rise to the characteristic stem-loop structure. The features for miRNA isoforms or isomiRs are similar to miRNA sequence, having variables that might have

­Small RNA: Discovery, classifications, and biogenesis

FIG. 2 Classification of small RNAs present in plants.

o­ riginated from imprecise cleavage of precursors or addition/deletion of nucleotides to the canonical miRNAs. They target complementary mRNAs and repress their activity either by translational inhibition or by miRNA-induced silencing complex (miRISC)-mediated cleavage. Differential expression of these miRNA genes under prevailing stress condition indicates their involvement in stress tolerance. In plants, they target mRNA and bind with them with almost 100% complementarity to eventually degrade them [2]. Some modification events producing isomiRs are significant with respect to Argonaute (AGO) preference and target choice. Modifications at 5′ end produced several isomiRs (miR171b-f, miR171h, and miR171i) in rice showing different target gene preferences due to changes in sequences of seed region [16]. IsomiRs with 5′U mostly prefer AGO1, whereas AGO2 is preferred by isomiR with 5′A in Arabidopsis and rice [17].

­Ta-siRNAs The trans-acting small interfering RNA is mostly considered as a plant-specific molecule of endogenous origin. They are exactly 21 nt in length. Their biogenesis is marked by two major events, namely phase initiation and phasing. This makes it different from other siRNAs. They all are phasiRNAs, but all phasiRNAs are not tasiRNAs. They target mRNAs on the basis of sequence complementarity in the same manner as that of miRNAs [10].

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­Nat siRNA A new class of endogenous siRNA is produced from the overlapping region of two genes arranged in a context of convergent transcription [18]. Nat-siRNA are 21–24-nt-long RNA molecules (Katiyar et al., 2006). Nats are categorized into two classes according to their genomic origins: cis- and trans-NATs (nat pipe). TransNATs are transcribed from various genomic loci, but cis-NATs are transcribed from opposite strands of adjacent genes [18a]. It has been observed that nat-siRNA regulate salt stress response in Arabidopsis [18]. In most of the plants, cis-NATs show their role in pathogen resistance [18b], successful fertilization [19, 20], stress tolerance, phosphate homeostasis, and plant fitness [21].

­Heterochromatic-siRNA Heterochromatic-siRNA are endogenous plant small RNA of 23–25 nt in length and are also known as repeat associated siRNA (ra-siRNA). They are derived from heterochromatic DNA, reteroelements, repetitive DNA, centromeric regions, and some methylated DNA regions. Hc-siRNA is associated with chromatin remodeling (5 methyl cytosine, particularly at asymmetric CHH sites) and DNA methylation [22–24]. In Arabidopsis, plant transcriptome analysis revealed the upregulation of heterochromatic siRNA biosynthesis during cold stress [25]. Largely ­RNA-dependent RNA polymerase-2 (RDR2) and Dicer-Like protein-3 (DCL3) are specifically required for their biogenesis [26].

­Pathogen-derived sRNAs and miRNA-like molecules Plant pathogens also contribute to sRNA population to promote pathogenesis. Therefore, plant-pathogen interaction at the sRNA level is incomplete without taking into consideration the pathogen-derived sRNAs [27]. Pathogens are in a constant effort to counter-dominate the host immune system and host immunity for which they also follow a mechanism similar to RNAi. Small RNAs of 18–23 nt from plant eukaryotic fungi, like Magnaporthe oryzae and Sclerotinia sclerotiorum, have already been confirmed [28, 29]. Recent reports have strongly advocated the flow of sRNAs not only from plant to pathogen but also from pathogen to plant [29a]. Bemisia tabaci, the phloem feeding whitefly, and Botrytis cinera mediated cross-kingdom mobility of sRNAs to their host plant tomato has recently been reported [30, 31]. The production of miRNA-like RNA molecules (milR) in fungi has also been studied. MiRNA-like molecules (milR) are no different to miRNAs regarding their function but differ with them in two points. First, they are of pathogen origin, and second, their precursor molecules lack one or two criteria stringently set for a true miRNA [29]. Reports suggest production of milR in fungi, like Puccinia striiformis, and their locations were also mapped on to the fungal genome [32]. They might have been generated from diverse genomic loci, commonly from long terminal repeats (LTRs), and contributed to pathogenesis. Very recently, milRs of Puccinia triticina has also been detected with roles in wheat during leaf rust infection [33, 34].

­Small RNA: Discovery, classifications, and biogenesis

­Biogenesis Biogenesis of all the sRNAs start first with the synthesis of mRNAs in the usual manner by DNA-dependent RNA polymerase II, but then these mRNAs are processed differently to generate different categories of sRNAs. The mRNAs, if transcribed from miRNA-producing locus, will have the property to first fold in a 100–120-ntlong imperfect stem-loop-like structure called primary (pri)-miRNA, which undergoes two rounds of cleavage events before releasing miRNA/miRNA* duplex (Fig. 3). The primary mRNAs after processing are exported to cytoplasm like any other mRNAs when destined to produce phasi-RNAs and undergo targeted cleavage by RISC, proceeded with sequential cleavage or phasing with Dicer molecule to produce phasi-RNA duplexes [36, 37, 37a]. miRNA isoforms also exist called isomiRs that follow the same steps of biogenesis of miRNAs, but the difference is created during the process of dicing by Dicer (e.g., DCL1, DCL3) in case of plant- or enzymemediated nucleotide addition or removal at the end of miRNA [38–41]. Once these sRNAs are released as duplex, one of these are incorporated into RISC whereas the other is generally degraded [42]. These phasiRNAs, when acting in trans, are called trans-acting small interfering RNAs, and when showing cis-acting behavior, they are called cis-acting siRNAs.

FIG. 3 Characteristic stem-loop precursor [35].

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Initially, synthesized as mRNA but successively cleaved to small ­18–24-­nucleotide mi/siRNA incorporated into RISC, the biogenesis of these molecules provides a good example of molecular coordination. These tiny molecules have large impact on gene expression. A mere complementarity of 7–8 nucleotides is enough for miRNAmediated repression of mRNA activity, and the complementarity may be within open reading frame (ORF) or 3′ untranslated region (3′ UTR).

­Methodologies applied for sRNA research Any kind of stress is associated with presence of conditions preventing healthy growth of plants. These stress responses can only be studied if the plant is exposed to that stress. So to search for the molecules over- or underexpressed during stress condition, it is important to grow the plants under stress condition; control plants should be maintained under normal condition for comparative analysis. Once the sample is collected from both types of plants, different strategies can be implemented to detect the miRNA and their expression levels (Fig. 4). To follow this strategy for sRNA studies, a highly efficient method for RNA isolation is required to retrieve such molecules. Several methods and kits exist for successful isolation of RNA from different plant and animal tissues and even from fungal urediniospores, a basic requirement for most molecular biology studies [33, 34, 43–45]. Once isolated, the small RNAs are further separated and converted to cDNA as the stability of RNA limits its long-term storage for further analysis. The doublestranded cDNA molecules can either be subjected to quantitative real-time PCR if primers specific to miRNA stem-loop sequences are available, or they can be used for Serial Analysis of Gene Expression (SAGE) or RNAseq library preparation.

FIG. 4 Pipeline to study stress-responsive sRNA.

­Methodologies applied for sRNA research

FIG. 5 Approaches to detect sRNAs.

The currently used methodologies applied for sRNA detection and validation of their expression comprises coordination of two approaches, that is, experimental as well as computational. The process can be initiated by both ways, i.e., either by experimental or computational approach (Fig. 5). If the search has been initiated with experimental approach, the last step will be sequence analysis using computational validation methods like homology-based search in different databases to obtain the sequences fulfilling criteria for candidate miRNA. If the approach is to first proceed through homology-based search to find conserved microRNAs in different species, then it will end with experimental validation of those sequences in that particular organism. Initially, microRNAs were detected through cloning-based experiments, which are still mainly used to detect new microRNAs. The approach is biased toward RNA, which are overexpressed. This approach is well suited in conditions when prior knowledge of microRNA sequence is not available. Its use is limited when microRNA expression level has to be studied. Illumina-based genome sequencer is the most widely used next generation sequencing (NGS) tool based on the principle of sequencing-by-synthesis, and it provides the quickest method for sequencing. In wheat, 35 miRNAs belonging to 20 conserved miRNA families and 23 novel miRNAs have been identified using NGS approach, and 4 of the novel miRNAs (miR506, miR510, miR514, and miR516) appear to be monocot-specific [46]. In A and B lines of Brassica compestris, 27 pairs of novel conserved microRNAs have also been identified using high-throughput sequencing techniques [47]. Sequencing data generated through NGS techniques needs to be analyzed to select potential miRNAs sequence and their precursors. Only those sequences which fulfill the parameters set for miRNA are considered as potential

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miRNA [48]. Deep sequencing technique was used to identify 497 conserved and 559 novel miRNAs in wheat. In addition, differential expression of these miRNAs under progressing hours post-inoculation with leaf rust spores correlated their involvement with leaf rust development [35]. Also, expression of some conserved miRNAs showed their participation in compatible and incompatible interactions. Many of the target genes were involved in essential cell functions like metabolism and development. Computational methods are now being used for detection of candidate mi/ siRNAs. Search for conserved sRNA using sequence homology of mi/siRNA sequences with available sRNA sequences in the database is the widely used computational approach also to infer novelty of miRNAs. In wheat, using this approach, 68 species-specific and chromosome-specific miRNA were identified [49, 50]. Using homology-based search, 14 novel conserved miRNAs were identified in tomato, and the expression of one of the newly identified conserved miRNA, miR398, was validated using real-time PCR [51]. Using computational approaches, in an earlier study, 51 conserved and 1 novel miRNAs were detected from the wheat genome, of which 22 miRNAs displayed differential expression levels in response to leaf rust [52]. Other techniques used for miRNA study includes Northern blotting, microarray analysis, and real-time RT PCR. Northern blotting was considered as a “Gold standard” for microRNA expression profiling and detection. It accurately depicts microRNA expression level but was restricted by the use of radioactive probe and requirement of a large amount of RNA molecules. This technique has been used to identify 115 mammalian microRNAs from cerebrums, livers, muscles, and lungs [52a]. Microarray analysis is generally used for expression analysis to check the expression level of particular miRNA. These generally involve preparation of readyto-use microRNA microarray using fluorecently labeled microRNAs and then hybridizing the sample microRNA with the labeled oligonucleotide present on the array [52b]. The position of the fluorescence produced can easily be detected and quantified as well. SRNA expression can be best checked by quantitative real-time RT-PCR (qRTPCR). Many vendors designed two-step methodologies to detect miRNA. First, a stem-loop primer is hybridized to a microRNA and reverse transcription is performed, and second, the mature microRNA specific primer is designed and real time PCR is conducted. Use of qRT-PCR has made this method highly sensitive but expensive too. Other methods are available to detect microRNA, but more advancement is still needed to make an easy detection. The expression pattern of miRNA in plants under stress can be revealed by quantifying the miRNA level in samples from both normal and stressed condition. Absolute abundance of a particular transcript expressed in a particular population of cell can best be determined using procedures like Serial Analysis of Gene Expression (SAGE), RNA sequencing by Oligonucleotide Ligation and Detection (SOLiD), or Illumina platform. This technique has been successfully used to obtain potential miRNA sequences from wheat infected with leaf rust [35].

­Parameters applied for sRNA prediction

­Parameters applied for sRNA prediction ­Plant miRNAs and pathogen milRs A number of small ncRNA molecules with similar size exist within cells but are totally different otherwise. So, to predict potential microRNAs and to avoid selection of false positives, stringent criteria are followed [48]. These mainly include features of miRNA uncommon among other small ncRNA molecules such as its secondary structure, free energy of folding, number of bulges allowed in the structure, etc. Stem-loop (hairpin) structure formed by miRNA precursor sequences is the characteristic feature and prime requirement for miRNA biogenesis. There are several softwares available for miRNA prediction like miR-PREFeR (miRNA PREdiction From small RNA-Seq data), miRCat (miRNA categorizor), mirDeep-P, miRanalyze, miRPlant, etc. [53, 53a]. All available online and offline tools and software designed for miRNA prediction basically searches for such regions on reference genomes or EST with the potential to form stem-loop structure as well as presence of miRNA and miRNA* complementary sites. Input required for miRNA prediction include high-throughput sRNA sequencing data (Illumina Solexa high-throughput sequencing) and a reference genome or EST sequence. The raw sequences are first trimmed to remove adapter sequences and the proteincoding ESTs, tRNA, rRNA, or other noncoding siRNA or snRNA available at Rfam database (http://rfam.xfam.org; [53b]) to get clean reads [35]. Trimmed sequences having the length of 18–24 nucleotides are selected for further analysis. Using input sequences, first sRNA mapping sites are searched and then RNAfold program (http:// rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi) is used to verify the characteristic features of a potential miRNA precursor. The stem-loop structure must contain approximately 18 nucleotide mature microRNA sequences located on one of its arms. To increase the stringency of search for putative miRNA, precursors with secondary structures having low minimum free energy of −18 kcal/mol is set as the standard for miRNA. In addition, not more than four bulges can be present in this stem-loop structure. At least 16 nucleotides should be engaged in Watson-Crick base pairing between miRNA/miRNA*, and no more than 4 nucleotide mismatches can be allowed in the duplex. The same criteria is set for the detection of milRs from pathogen,but due to lack of few of these characteristics in pathogen-derived miRNAs, like availability of miRNA* strand or presence of more than 4 bulges in the duplex, they are called miRNA-like RNAs rather than miRNAs [33, 34].

­Plant ta-siRNAs Ta-siRNAs, like other sRNA molecules, are also incorporated into RISC and mediate Argonaute-directed target silencing. Because of these similarities, it is intractable to distinguish ta-siRNAs from miRNAs and other siRNAs once their synthesis step is over. Hence, the parameters used to detect ta-siRNAs are considered on their biogenesis from TAS.

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The primary requirement for any tool used for ta-siRNA prediction is availability of input files like sRNAs, reference genome sequence, or EST, all in FASTA format. Then comes the role of parameters used for prediction of highly accurate ta-siRNA. These parameters strictly look for a proper phasing site or the occurrence of a true phasing event. To select, the size of phased siRNA is strictly set as 21, phasing register is the term used for this by ta-si prediction tool of University of East Angelia (UEA) sRNA workbench (http://srna-workbench.cmp.uea.ac.uk/), and the minimum abundance of that siRNA in the library should be 10. In addition, P-value optimization needs to be done where sRNAs of no other sizes like 19, 20, or 23 are detected [54, 55].

­Plant isomiRs Many user-friendly softwares like IsomiRage [55a], IsomiRex [55b], IsomiR-SEA [41], isomiRID [55c], Miraligner (https://github.com/lpantano/seqbuster/wiki/miraligner), mirMOD [39], and many more are available that have simplified detection of isomiRs [56]. These tools essentially require input sequences like sRNA libraries, miRNA sequences for which modifications have to be detected, and reference genome sequence or sometimes an alignment file of reference genome with sRNAs to detect templated isomiRs like in miRMOD [39].

­Databases available for sRNAs Like nucleotide database at NCBI, miRBase database (Release 22.1: October 2018) makes available all the published microRNA sequences (http://www.mirbase.org; [57]). Thus, facilitating an easy way to match with what already exists and find the novelty of newly discovered miRNAs. A similar database is also available for miRNA target gene prediction. The miRTarbase database (https://bio.tools/mirtarbase; [58]) is an archive for all available miRNA target genes. Almost 253 precursors and 437 miRNA sequences are presently deposited for Caenorhabditis elegans in the miRBase database (http://www.mirbase.org; [57]). Other miRNA sequences derived from other species are also available in the miRBase database. For example, 1917 precursors and 2654 miRNA sequences for humans, and 1234 precursors and 1978 miRNA sequences for mice are deposited in the miRBase database. In Arabidopsis thaliana, 326 precursors and 428 miRNAs have been deposited to date in the miRBase database. MiRNAs have also been detected in most staple crops like wheat, rice, and maize. For rice, 604 precursors and 738 miRNA sequences have been deduced and deposited in the miRBase database, whereas 122 precursors and 125 miRNA sequences have been deposited for wheat in the miRBase database. For Zea mays, 234 precursors and 343 mature miRNA sequences are available in this database. A high number of miRNA sequences are also deposited for eudicots like tomato (Solanum tuberosum); the list includes 224 precursors and 343 mature miRNA sequences.

­SRNA-Mediated biotic stress responses in plants

Likewise, tasiRNAdb (ta-siRNA database) represent freely accessible data for available ta-siRNAs and ta-siRNA-producing loci with their regulatory pathways if available. It stores sequence information of 77 ta-siRNA-producing loci with 457 pair numbers of ta-siRNAs and their targeted sites (http://bioinfo.jit.edu.cn/tasiRNADatabase; [59]). Massively Parallel Signature Sequencing (MPSS) and Arabidopsis Small RNA Project (ASRP) are the databases in which more than 100 nat-siRNAs are present [60, 61].

­SRNA-mediated biotic stress responses in plants Biotic stress is mediated by pathogen infection. Necrosis in infected tissues is the most obvious response. Induction of signaling pathways like jasmonic acid or salicylic acid are mostly observed under biotic stress, which in turn regulates expression of certain stress-responsive genes and help in survival of the plant [62]. Decreased cell growth is the adaptive feature of plants under stress. So there is a need to block the expression of various genes that are not essential for the survival of plant. To achieve this goal, post-transcriptional regulation of mRNA level plays an important role. A vast number of siRNAs are associated with regulation of mRNA during a biotic stress condition in the plant. Production of mi/siRNA molecules are generally induced in response to effector molecules released by a pathogen. They mostly target protein-coding genes and ncRNAs in plants. Differential expression of mi/ siRNA genes under prevailing stress condition indicates their involvement in stress tolerance.

­SRNA-mediated responses against insects Insects do not directly infect plant cells, but they inject certain pathogens which may be bacteria, fungi, or viruses inside plant cells. So, they may be better called as vectors for these pathogens. The first among the list is miR393, which also is the first microRNA identified in plants. It was found to be associated with tolerance against roundworm infection in Arabidopsis and was closely associated with defense against bacterial pathogens, which are released inside a plant by this vector [7].

­SRNA-mediated responses against fungi Fungi traverse through the plant cell wall and form germ tubes that further develop into haustoria and, at this stage, stimulates effector-triggered immunity inside the plant cell. There are abundant reports available that suggest production of certain sRNAs are stimulated or suppressed in the presence of fungal infection [35, 63]. Also, such sRNAs have a wide range of origin, which includes mRNA with coding regions for certain proteins to tRNA cleavage product derived as well as 28S rRNAs [14]. Differential expression pattern of many miRNAs under an infected condition has been observed. For example, 10 out of 11 analyzed miRNAs were significantly

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suppressed in stems of galled loblolly pine (Pinus taeda) infected with Cronartium quercuum fungi [63]. A large number of miRNA genes were differentially expressed under powdery mildew infection caused by Erysiphe graminis f. sp. tritici (Egt) in wheat. Increased expression of miR393, miR444, miR827, miR2005, and miR2013 while repressing expression of miR2001, miR2006, and miR2011 was observed during this infection in wheat [64]. Similarly, when miRNA expression profile was studied in cassava (Manihot esculenta) infected with Xanthomonas axonopodis, the fungus induced expression of miR160, miR394, miR167a, miR167b, miR165, miR171, and miRE while repressing the expression of miR535 and miR408 [64a]. Similarly, expression of miR811 and miR829 in maize confers a high degree of resistance to leaf blight caused by Exserohilum turcicum [65]. The exact pathway of miRNA-mediated stress tolerance is still to be elucidated.

­SRNA-mediated responses against virus Plant viruses infect and multiply inside plant cells. It is during the intermediate steps of replication in RNA viruses that they are triggered, mainly by DCL4, to produce vsiRNA duplexes mostly 21 nt in length, as in the case of ta-siRNA [65a, 65b]. Now these siRNAs can target their own genome. In the case of DNA viruses, these DNA molecules are first transcribed into RNA either by RNA polymerase II if they follow RDR independent pathway or by RDR6 [65c]. They also require SGS3 and a complete set of host DCL molecules. Virus infection in plants stimulates expression of certain DCL and RDR proteins in plants, for instance, RDR1, RDR2, and RDR3 during infection caused by Cucumber Mosaic Virus (CMV) and many of the tomato DCLs by Tomato yellow leaf curl virus (TYLCV, [66, 67]). Advanced techniques of genetic engineering are also employed to mediate resistance against viral stress. For instance, components of dsRNA from bacteriophage, like phi6 are engineered using P. syringae production system, which are implemented to generate dsRNA sequence homologous to tobacco mosaic virus (TMV). These TMV-derived dsRNA can easily restrict growth of TMV in Nicotiana benthamiana plants [68].

­SRNA-mediated responses against bacteria Plants respond to bacterial infection by Pathogen Associated Molecular Pattern (PAMP) Triggered Immunity (PTI), which is considered as a first line of defense followed by Effector Triggered Immunity (ETI; [69]). MiR393 and its role in providing resistance against Pseudomonas syringae is a well-known example of miRNA-mediated defense via PTI. MiR393 expression was predicted to be induced by flagellin-derived peptide, which restricts the growth of P. syringae [69a]. In addition, Arabidopsis mutant plants for miRNA pathway with overexpression of miR393* also shows enhanced resistance against virulent and avirulent strains of P. syringae [70].

­SRNA-Mediated responses against abiotic stress

­SRNA-mediated responses against abiotic stress Expression of miR169 was found to be downregulated in rice and cowpea under a drought condition. This downregulation was correlated with overproduction of NFY A5 protein, as the mRNA of the protein was the target mRNA for miR169. Eventually this was correlated with better survival of the plant under stress [71]. In addition, miR169 was also found to be downregulated during a phosphate deficiency condition in Arabidopsis, rice, and maize [72]. MicroRNA expression level was also correlated with many types of abiotic stresses. Presence of abscisic acid (ABA), a key plant stress hormone, induced the expression of miR159 in Arabidopsis seeds during germination [73]. This infers that miRNA was expressed when stress was imposed. MiRNA correlation with ABA synthesis was first deduced by identification of ABA-hypersensitive mutants defective in any of the genes for microRNA biosynthesis [74]. OsmiR393, the rice homolog of miR393, generally targets mRNA coding for auxin receptors and results in reduced tolerance to salt stress and hyposensitivity to auxin in rice [75]. Downregulation of miR169 in Arabidopsis was also found to be associated with drought-tolerant phenotypes (Fig. 6). Other examples of miRNAs showing conserved expression under the same stress condition in different organisms include miR159, miR171, and miR393, which show elevated expression under high salt concentration in Arabidopsis [76], as well as in wheat [77]. Similarly, miR319 was promoted under cold stress in wheat and Arabidopsis [78].

FIG. 6 SRN- mediated stress tolerance in the plant. ABA, abscisic acid; NFY A5, nuclear factor Y A5; TIR1, transport inhibitor response 1.

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MicroRNAs associated with many other abiotic stresses have also been reported like cold-responsive and phosphorus deprivation-responsive miRNA [79]. For example, miR167 and miR319 are cold-responsive miRNAs showing overexpression and miR1425 showing downregulation during cold condition in rice. MiRNAs associated with phosphate (Pi)-deprived condition in both Arabidopsis and rice include miR399 and miR827, and have been reported to show increased expression under stress [79]. A conserved gene family of plant miRNA miR393 is predicted to play a crucial role in regulating mRNA levels in plants under both biotic and abiotic stress. Its upregulation under stress is almost conserved in many species like rice, Arabidopsis, and sugarcane [80]. Its overproduction is predicted to negatively regulate auxin signaling and induce hyposensitivity to auxin in plants [7], thus, reducing the plant growth. Discrepancies related with microRNA regulation during stress conditions: • The miR396 is downregulated in rice and cowpea, but it is upregulated in Arabidopsis under drought. In addition, miR169 was upregulated in rice, but downregulated in Arabidopsis by drought stress treatment [72]. • The miR167 in rice and Arabidopsis is upregulated in response to ABA [81], but the same is downregulated in maize in response to ABA [82]. • The microRNA level is inversely proportional to its target concentration, but miR820, a rice-specific miRNA, showed reduced level of expression along with its target in rice. The possible reason was found to be the epigenetic modification of its own locus, that is, DNA methylation around miR820 coding region [83]. These are the few examples, but contradictions regarding expression level of conserved miRNA genes under similar conditions in different species do exist.

­SRNAs and agricultural improvement MicroRNA in plants regulates a number of physiological and developmental processes. Correlation of miRNA has been shown with levels of phytohormone expression. For example, miR164 downregulates transcription factors responsible for transducing auxin synthesis [84]. Similarly, miR393 of Arabidopsis has also been observed to induce auxin production [7]. Differential expression of many miRNAs under stress conditions indicates its involvement in biotic and abiotic stress tolerance. Some miRNAs show differential expression under both conditions. For example, miR398 of tomato was underexpressed during pathogen-induced stress but overexpressed during salt stress in wheat [51, 84a]. Reduced expression of 10 out of 11 analyzed miRNAs was found in the stems of galled loblolly pine (Pinus taeda) infected with Cronartium quercuum [63]. Artificial microRNA (amiRNAs)-mediated resistance to plants has been a recent approach. This technique has been used to induce viral (Cotton leaf curl Burewala virus) resistance in cotton by developing amiRNA169, a construct that targets viral DNA [85].

­Conclusion

RNAi has been successfully utilized in virus-induced gene silencing (VIGS) where plant sRNA machinery process viral dsRNA and combine with RISC to silence viral genes [86]. Identification of differentially expressed tomato leaf curl New Delhi virus (ToLCNDV)-responsive miRNAs in a variety of hosts differing in their tolerance would provide better speculations of threats associated with similar invasions [87]. RNAi mechanism mostly mediated by miRNAs may be used as a molecular tool to develop resistant plants without going through the time and exhaustive procedure of plant breeding. But this procedure requires efficient plant transformation protocols. Several studies in the past few years correlate miRNA expression levels with a specific biotic or abiotic stress, and now a few reports on differential expression of isomiRs are also available, but these are mainly associated with abiotic stress. Phosphorus deficiency in Hordeum vulgare has been related to increased expression of isomiRs of miR399 and miR827 family [88]. In Arabidopsis, isomiRs of miR160c are differentially expressed under high and low temperature conditions [89]. TasiRNAs responding to leaf rust pathogen infection has been reported with five differentially expressed target genes making plants more susceptible [14]. Recent studies also reveal the significance of Puccinia triticina-derived milRs to induce host susceptibility against leaf rust in wheat [33, 34].

­Small RNA as a spray Transgenic RNA interference is an attractive technology, and its alternative application is to provide resistance to plants during stress conditions by using pathogenspecific exogenous double-stranded RNA (dsRNA) as a spray [68, 90]. Implementation of RNAi by exogenous application of dsRNA, combined with layered double hydroxide (LDH) to induce resistance for up to 20 days ­post-application in tobacco, is a fascinating advancement [90]. The authors o­ bserved that naked dsRNA provides protection against virus only up to 5 days when sprayed on the leaf’s surface. So, to improve the stability and to extend the protection period in plants, they used nanoparticles (layered double hydroxide: LDH) in a bioclay as a matrix. This kind of Host-induced Gene Silencing through exogenous dsRNAs or sRNAs are referred to as Spray-induced gene silencing (SIGS) for disease control in plants [91].

­Conclusion The world of small (s)RNAs not only fascinates but also provides a tool to fight any abnormal conditions inside plants, if it is explored and well exploited. Discovery of miRNA, which targets stress-responsive mRNA with known functions, have further increased the curiosity to find more such molecules and their expected targets.

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As sRNAs are regulatory molecules, they can suppress unwanted genes while making their mRNAs unavailable for expression. Therefore, these may be considered as future alternative solutions to tackle stress in plants, other than just developing resistant cultivars.

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­Further reading [92] R.  Komiya, Biogenesis of diverse plant phasiRNAs involves an miRNA-trigger and Dicer-processing, J. Plant Res. 130 (2017) 17–23. [93] M. Yoshikawa, T. Ikia, Y. Tsutsuic, K. Miyashitaa, R.S. Poethig, Y. Habue, M. Ishikawaa, 3′ fragment of miR173-programmed RISC-cleaved RNA is protected from degradation in a complex with RISC and SGS3, Proc. Natl. Acad. Sci. U. S. A. 110 (2013) 4117–4122. [94] C. Zhang, G. Li, J. Wang, J. Fang, Identification of trans-acting siRNAs and their regulatory cascades in grapevine, Bioinformatics 28 (2012) 2561–2568.

CHAPTER

Role of small RNA in regulating plant viral pathogenesis

9

Lovepreet Kaura, Mohit Sharmab, Shiwani Guleria Sharmac a

Domain of Molecular Biology and Genetic Engineering, School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, India bDepartment of Biotechnology, DAV University, Jalandhar, India cDomain of Microbiology, School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, India

­Introduction Every year, various crops are affected worldwide due to pathogen attacks on plant species. Plant viral diseases are reported to result in a loss of quality as well as quantity of crops. Virions are plant viral particles present just outside the body of the host, enclosing the core RNA and a capsid in a complete infective form of virus [1]. Viruses have small genomes embraced with RNA or DNA in either single- or doublestranded forms encoded with a protein to complete their life cycle. These proteins also help in transmission of virus in plant species. They replicate only within the living cells of their host organisms using RNA-dependent RNA polymerase, DNA replicase, or reverse transcriptase encoded by their own genome [1, 2]. Viruses spread in plants through infected seeds, wind splashing, pollination, by dripping of sap, nematodes, plasmodiophroids, insects, vegetative propagation, and through wounds. These viruses are not host-specific, i.e., a single virus can infect multiple hosts such as mosaic viruses affecting tobacco, cucumber, tomatoes, and peppers; similarly, brome mosaic viruses affect grasses, grains, and bamboos. The effect of viruses on plants is not directly in the killing of the plant species, but they show some adverse effects that appear in the form of any morphological effect like yellow spots on leaves, distortion, ring spots, necrosis, wilting, rolling of leaves, or white spots over the leaf surface [2]. To protect plant species from virus attack, various antiviral mechanisms have been reported. In more than last two decades, attention has been paid on integrated disease management practices derived from an integrated pest management (IPM) system. This integrated disease management practice is focused on controlling plant diseases with an inexpensive and safe method [3]. Various therapeutic tools based on current molecular biology with use of molecular markers for identification, mapping, cloning of pest- and disease-resistant genes in the plant genome, and development of transgenics [4, 5] have been developed. The inherent risks associated with traditional Plant Small RNA. https://doi.org/10.1016/B978-0-12-817112-7.00009-2 © 2020 Elsevier Inc. All rights reserved.

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transgenics can be mitigated by new strategies such as RNA interference and the use of small RNA (sRNA)-based viruses in regulating plant viral pathogenesis [6]. Viral pathogenesis is basically the study of how viruses cause disease in the target hosts, and pathogenesis is a process in which an initial infection becomes a disease. The sRNA viruses are typically 21–24 nucleotides long, which regulate a diverse array of cellular processes, from developmental patterning and genome rearrangement to antiviral defense. sRNA-based viruses work by targeting specific nucleotide sequences to shut down gene expression [7]. In the case of plants, the main classes of sRNA that play key roles in antiviral defense mechanism are: • • • • •

MicroRNA (miRNA) Short interfering RNA (siRNA) trans-Acting small interfering RNAs (ta-siRNAs) Natural antisense transcript-derived small interfering RNAs (nat-siRNA) Repeat associated interfering RNAs (ra-siRNAs)

Why are plants affected by viruses and viroids more rapidly? The only reason found is that the replicating power of viruses or viroids in the host body is high. Various studies have been done, and some have demonstrated that virus and viroid replication along with RNA genomes and transcripts led to generation of dsRNAs that play a significant role in the RNA silencing machinery. Thus, this chapter intends to present and recapitulate the current knowledge on the roles and importance of sRNA viruses in regulating plant viral pathogenesis.

I­llustrations of siRNA-mediated and miRNA-mediated antivirus pathway mechanisms Once a virus penetrates the cell surface, it starts replicating itself. It infects the initially targeted cell and then moves into adjacent cells, spreading from cell to cell until it enters the vascular system. It enters there, then the vascular system allows for rapid movement of virus infection to distant parts of the plant. In response, a mobile silencing signal is produced by the host plant by initiating siRNAs silencing against the viral RNA. This signal moves along the same route the virus takes. The plant and virus thus enter a race. If the mobile silencing signal reaches the uninfected cells first, the virus will enter those cells only to find itself targeted by RNA silencing. The infection will then fail to become systemic. The virus will try to move ahead of the signal so that it can further generate an infection. Subsequently, just before the viruses invade the cells, miRNAs action has already been produced within cells. Thus, as soon as the viruses enter, they are targeted by miRNA-mediated silencing.

­Role of miRNA in plant antiviral defense During evolution, the primitive plants that were subject to virus infections evolved a series of mechanisms to counteract viral infections. In plants, miRNA play a role

­Role of miRNA in plant antiviral defense

in growth and defense pathways. miRNAs are located in the introns of host RNAs. Presence of non-coding miRNAs of approximately 20–24 nucleotides in length is found in coding as well as in non-coding host RNAs. Understanding the involvement of miRNA in plant defense against viral attack has been recently improved. The interaction of miRNA and the virus genome has been documented. There is a presence of miRNA-binding sites in the viral genome. The number and location of miRNA-binding sites in viral genome may vary from 5′ and 3′ non-translated regions and coding regions of viral proteins. These binding sites in the viral genome can influence the function of miRNA and can repress host mRNA function. Computational validation of binding of miRNA in the viral genome and effects of their interaction need experimental validation [8]. However, the following are the reported effects of interactions of host miRNA and RNA viruses: − − − −

Binding of host miRNA with wide variety of RNA viruses Regulation of level of viral pathogenesis by binding of miRNA Inhibition of translation of viral genome Inhibition of replication of viral genome [8] Cellular miRNAs can function as inhibitors of viral replication:

• Several reports have been documented on tissue-specific attenuation of viruses when the insertion of artificial target sites specifically expressed in that tissue for cellular miRNAs occur [9]. This approach has led to various more developments, such as attenuated viruses’ use in vaccination as well as designing oncolytic viral vectors that only target the transformed tissues and not the normal ones [10]. • Inhibition of viral replication by endogenous cellular miRNA species has also been reported. However, some viruses such as RNA viruses possess a plastic genome sequence, thus they can rapidly evolve by repression from a specific miRNA even if it is a single nucleotide mutation. On the other hand, in the case of cellular miRNA, they are conserved through evolution process such as let-7 [11]. • Rapidly evolving viruses such as HIV-1 lose cognate RISC-binding sites when grown in the presence of artificial siRNAs [12]. • In the case of cell lines, the inhibition of viral replication by endogenous miRNAs was reported. However, this may not be a good model to be used in case studies of infected target tissues in vivo. Inhibition of primate foamy virus replication was documented in human embryonic kidney cell line 293 T by cellular miRNA, i.e., miR-32 [13]. • Cellular miRNAs can inhibit virus replication by repressing some of the expressions such as cellular factors required for the viral replication cycle, which are inhibitors of protective innate immune responses. For this, a virus may follow two possible pathways. One is to inhibit miRNA biogenesis either globally (e.g., the adenoviral noncoding RNA VA1) or selectively. The other is to block the action of miRNA specifically [14]. HIV-1 Tat protein is another viral gene product that inhibits miRNA biogenesis globally by inhibiting dicer function [15].

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• In the case of plants and insects, the viral gene products that block RISC function are common, where innate immune response to viral infections were represented by RNAi [16]. miRNAs are stable having half-life in days whereas lytic viral replication cycle occurs in hours. Once cells differentiate into suprabasal cells, miRNAs establish a latent infection in the skin epithelial that turns to virus replication production. In this case, miRNA is provided by human papillomavirus (HPV) [16]. • Cellular miR-203 represses the transcription factors of p63 family, which is mediated after differentiation results in cell cycle arrest [17]. • It has been reported that the HPV oncoproteins E6 and E7 helps in inhibition of various expressions at the transcriptional level and is also reported to increase the expression of p63 and other target genes, thus facilitating replication of HPV-infected cells. p63 also plays a key role in genome amplification of HPVDNA [18]. • The highly expressed transcripts that contain various tandem sites that are partially complementary to miRNA specifically are sponges. These sponges reduce the stability of the miRNA’s binding [19]. Plant defense against virus attack involves siRNA-mediated gene silencing and another two ways that includes roles of miRNA: miRNA direct and indirect modes of viral defense. • Direct mode involves targeting viral RNAs (Fig. 1). • Indirect mode involves initiation of siRNA biogenesis with an antiviral response (Fig. 2). After decades of research, numerous plant biotechnologies, including antisense suppression, transcriptional gene silencing (TGS), virus-induced gene silencing (VIGS), and RNA interference (RNAi), are currently being used in plant antiviral approaches to create a defense mechanism against various pathogens [20]. As mentioned, numerous studies show the relationship of miRNA and plant viral pathogenesis. New techniques led to the formation of artificial miRNA (amiRNA), which is another robust biotechnology used in plants for silencing of genes, and engineering of amiRNAs has been widely applied for the targeted downregulation of endogenous genes in various plants, e.g., Arabidopsis thaliana and Nicotiana tabacum [21]. Due to efficacy and reliability of miRNA approaches, host-derived endogenous precursor miRNA has been commonly used as a structural backbone to replace the original ∼21-nt-long miRNA sequence with a region complementary to the target viral genome in Arabidopsis [22]. Multiple miRNA can also affect various viruses, more specifically those that have the potential to create resistance to any defensive mechanisms [2]. In 2006, the first miRNA (miR393)-mediated antibacterial resistance was reported in plants [23]. The study documented plant defense against viruses by posttranscriptional regulation mediated by miRNA, such as in Nicotiana benthamiama against begomovirus [24], and in Nicotiana benthamiama against PVX-Potyvirus [25]. Various miRNAs related to plant defense system target different proteins or

Role of miRNA in plant antiviral defense

FIG. 1 Direct interaction between miRNA and virus genome.

viruses of either the same or different host plants, such as miR1-39, miR160-3, and miR408, which targets different proteins, such as mucin-like protein, pathogenesisrelated protein, and electron transporter or viruses, respectively, for same host plant Physcomitrella [26]. Similarly, other miRNAs have defense responses for host plant Populus [27]. miR393 targets auxin signaling in the case of Arabidopsis [23], and miR171e targets RGAI for host plant V. vinifera. In the case of rice and Nicotiana, artificial miRNAs targets TYMV, TuMV, and plum pox virus [28]. Another interesting phenomenon, namely recovery phenotype, has been discovered. In this, the systematic infection has initially occurred in the case of a transgenic

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FIG. 2 Indirect interaction between miRNA and virus genome.

plant; later on, only a few symptoms of the same were observed on each new leaflet. Eventually with time, virus-free leaves emerged that were completely resistant to superinfection. This phenotypic recovery was due to post-transcriptional gene silencing (PTGS) that was either too slowly or poorly induced, thus significantly resulting in inactivation of the virus but eliciting the signal that was amplified in the recipient cells, so that the recipient cells can perform effective silencing.

­Application of siRNA against plant antiviral defense RNA interference is a cellular response in which the introduction of long doublestranded RNAs into the cells results in the post-transcriptional inhibition of endogenous mRNAs that are complementary to the dsRNAs, as stated by Fire et al. in 1998 [29]. These RNAs play essential roles in the stability of genomes and development of genomes, as well as responses to both biotic and abiotic stresses in the case of eukaryotes. In the case of plants, the RNAase class III enzyme DICER-LIKE1 (DCL1) produces miRNAs, which is a subtype/class of sRNAs, and enzymes like DCL2, DCL3, and DCL4 produce various classes of siRNA [30]. The survival or the resistance of plants against pathogens depends upon the ability of plants to elicit a defense mechanism against the invading pathogens or hostile environment. siRNA-mediated gene silencing is now the best strategy of plants against

Regulation of siRNA for plant viral pathogenesis

viral infections [31], whereas miRNAs are involved in plant growth and development, signal transduction, protein degradation, and response to biotic and abiotic stresses. One more great advantage of a siRNA-mediated silencing defense system is that the defensive signal can spread, i.e., when inoculation in one area occurs it can confer immunity to surrounding cells [32]. In the case of plants, they can strengthen their defense capacity with a systematic defense response against viruses before the viruses are transmitted from the site of infection to neighboring cells. It was confirmed by grafting experiments that silencing can be transmitted from silenced stocks to non-silenced scions [33]. Several studies have reported observations that the plant sRNAs are directly involved in bacterial disease responses. Some key characteristics of sRNAs give evidence that they play great roles in plant viral pathogenesis: • There are various different ways of producing these small interfering molecules, including chemical synthesis, in vitro transcription, or vector-based delivery into mammalian cell lines. • The efficiency of the siRNAs is mostly dependent on the successful rational design of the 21-mer sequences. • Advances in the biochemical mechanism of RNA interference and statistical analyses of experimentally verified siRNAs have highlighted new biochemical and biophysical properties of the siRNA molecules. • sRNA classes work in plant defense responses by causing either PTGS or TGS to a set of host or pathogen genes [34]. • Endogenous miRNAs have been shown to play an important role in the suppression of invading viruses in mammals. • siRNAs have a characteristic size of 22±2 nt. Therefore, if an antiviral RNAi response occurs, sRNA deep sequencing will reveal a readily detectable, discrete peak of viral origin in this size range. • The endogenous siRNAs direct the endonucleolytic cleavage of homologous transcripts or promotes the DNA methylation and heterochromatin formation at the genetic loci from which they originate, resulting in TGS [35]. • Up- or downregulations of sRNAs led to suppression of the target site expression in the case of plants. • miRNA393 suppresses auxin receptors via Argonautes (AGO1); these auxin receptors are responsible for the activation of antibacterial immunity [36].

­Regulation of siRNA for plant viral pathogenesis Various discoveries have been to generate an antiviral mechanism, but at the same time, viruses have evolved mechanisms to counteract these antiviral strategies such as the deployment of decoy RNAs, specialized mechanisms of replications, and sequestration of viral RNAs in large protein. In 1998, the transgenic plants expressing viral coat proteins showed resistance against infection by the homologous ­sequence-dependent RNA silencing mechanism in which resistance against

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viral ­infection was by the homologous virus of the same infected virus. It has been observed that the viral protein sense sequence and the non-coding viral sense also conferred high resistance to some extent [37]. Various plant viruses encode viral suppressors of RNA silencing; these VSRs interfere with host RNA silencing by various modes of actions with their functions in viral replication, encapsidation, and movement. In addition to this, miRNA silencing of certain host genes contributes as part of positive selection during evolution in viral genome helping to bring up virus resistance in plants at the genome level. One such observation was made indicating that rapid evolution in a viral genome helps in minimizing host miRNA-directed gene silencing in facilitating viral infection in a specific plant virus interaction [38]. Various virus derived small interfering RNAs (vsiRNAs) are generated in plants; they directly target the viral genomes to initiate defense against viral infections. Basically, the generation of vsiRNAs is for both DNA and RNA viruses. In the case of DNA viruses, the vsiRNAs are processed via the structural region of the transcripts along with the overlapping regions of the bi-directional transcription, whereas in the case of RNA viruses, the structural regions of viruses can be processed through DCL proteins [39]. In both cases, the secondary vsiRNAs are coordinated by RDR1 and RDR6. Once these vsiRNAs are produced, they are loaded into different Argonautes (AGOs) that play roles in virus genome silencing, thus helping to create a defense mechanism by silencing genome of viruses. Previous studies indicated that vsiRNAs are responsible for RNA silencing-mediated antiviral immunity, and the main function of vsiRNAs is to target and degrade viral mRNA through post-transcriptional gene silencing in plants. Recent studies have shown that vsiRNAs also regulate host mRNAs with near-perfect complementarity [40]. Various vsiRNAs play great roles in defense by interacting with host species; they attack a specific target site of the host that results in a morphological impact on plants. Some examples are shown in Table 1. It has been observed that Argonaute proteins taken from Arabidopsis play a great role in plant resistance to viruses [46]. AGO1 is a protein acting as a key component targeted by VSRs to inhibit its cleavage activity to promote its degradation; it also helps to load vsiRNAs and target viral RNAs [47]. Similarly, AGO2 regulated by miR403 in an AGO1-dependent manner plays a significant role for creating an antiviral mechanism for the two major viruses, i.e., TCV and CMV. When infection occurs with these two viruses, the wild plants are suspected to show an increase in AGO2 levels that act as a second layer of defense against viruses. The VSR of the TCV acts directly with the AGO2, thus promoting catalytic activity [37, 48]. Various other AGOs like AGO5, AGO4, and AGO7 also play an important role in antiviral activity, for example, AG07 protein involved in removal of specific viral RNAs that have secondary structures that are less structured [49]. Several advantages of using miRNAs and siRNAs: • • • •

Highly RNA promoter-compatible. Fewer off-target effects. Stable in vivo usage of miRNAs is adapted at low temperature. Environmental biosafety.

siRNA response against bacterial diseases

Table 1  Morphological effect produced by vsiRNA at different target sites. Targeting site

Species

Targeted by

Morphological effect

Chlorophyll biosynthetic gene (CHLI) Eukaryotic translation initiation factor 4A (EIF4A) Seeds and pollen

Nicotiana

CMV Y-satellite (siRNA)

Yellowing of plant

[41]

Nicotiana benthamiana

Rice stripe virus (siRNA)

Leaf twisting and stunting

[42]

Melon

Molting of leaves

[43]

Atlg76950

Arabidopsis

Late flowering

[44]

Leaves and roots

Nicotiana benthamiana

Cucumber green mottle mosaic virus (CGMMV) Cauliflower mosaic virus (CaMV) Beet necrotic yellow vein virus (BNYVV)

Accumulation of vsiRNA in leaves

[45]

Reference

­siRNA response against bacterial diseases sRNAs in plants plays a direct role for creating a defense mechanism against bacterial diseases. miRNAs were the first identified sRNAs involved in plant immunity against various bacterial infections. miRNAs are 21–24 nucleotides long and are generated from RNAs with imperfectly base-paired hairpin structure [50]. They are non-coding RNAs located within the introns of both coding and non-coding host RNAs. miRNAs bind specifically to the miRNA-binding sites within miRNA and viral genomes to mediate miRNA function [51]. Various hormone-related miRNAs contribute to plant defense against pathogens (Table 2); one is miR398 that targets the two copper superoxide dismutases, CSD1 and CSD2, as well as cytochrome-c-oxidases subunit V (COX5). Accumulation of miR398 is dependent upon abiotic and biotic stress such as salinity, increased light, and increased Cu2+ and Fe3+. In plants, these stresses induce early and rapid accumulation of reactive oxygen species (ROS) in the infection zone [53]. It was observed that the overexpression of miR398 reduces callose deposition and moreover, due to gene silencing of CSD1, CSD2, and COX5, these transgenic plants were more susceptible to virulent and avirulent strains of Pseudomonas syringae [54]. There are different types of miRNA acting as sRNA playing a defensive role against same bacteria by targeting different genes. RNAi has shown one great example of bacterial disease management by the process of gene regulation, which was documented by Escobar et al. in 2001 [55]. The process of tumorigenesis by initiating RNAi of the iaaM and ipt oncogenes was a great strategy that targets the crown gall disease management. The expression of

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Table 2  Defensive role of different sRNAs against bacterial species. Type of small RNA

Defensive role in plant species

miR159

Arabidopsis

miR159

Arabidopsis

miR159

Arabidopsis

miR160

Arabidopsis

miR160

Arabidopsis

miR160

Arabidopsis

Bacteria

Target gene

References

Pseudomonas syringae Pseudomonas syringae Pseudomonas syringae Pseudomonas syringae Pseudomonas syringae Pseudomonas syringae

MYB33

[52]

MYB65

[52]

MYC101

[52]

ARF10

[53]

ARF1

[53]

ARF17

[53]

genes iaaM and ipt oncogenes are a prerequisite for the wild type of tumor formation. The transformed RNAi construct was generated for Transgenic Arabidopsis thaliana and Lycopersicon esculentum; the construct targeted the main genes iaaM and ipt genes and, as a result, showed resistance toward crown gall disease [6].

­Role of siRNA to prevent fungal disease Fungal pathogens infect plants via highly specialized cell haustorium and acts as an interface for exchange of various signals, as well as for nutrient uptake. The connection between the host cell and the plant fungal via haustorium also facilitates the uptake of dsRNA or the siRNA from the host plant cells to that of the fungal pathogen, which ends up creating an RNA silencing-mediated resistance, just as the kind of resistance induced by a host cell against its infecting pathogen is termed as host-induced gene silencing. This concept has been applied for the barley powder mildew Blumeria graminis via transgenic expression method using dsRNA-directed approach against Blumeria graminis and, as result, reduced disease symptoms were observed [56]. The roles of sRNA within the relationship between plants and viruses ought to be illustrated. More informative approaches are required to understand the idea of using sRNAs to control viral infection against plants. To some extent, the relationship and effect of sRNA against viruses has been discussed in this chapter. If this approach is used appropriately in the future, then it could solve complex issues of nature and open new approaches of biotechnology in the field of plant defense. However, the sRNA effect is not limited to viruses. It is also effective against bacterial and fungal diseases of plants as discussed, although this strategy is still in its infancy and needs more understanding to achieve the goal of plant defense.

­References

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CHAPTER

Salt stress tolerance and small RNA

10

Titash Duttaa, Nageswara Rao Reddy Neelapua, Shabir H. Wanib, Challa Surekhaa a

Department of Biochemistry and Bioinformatics, Institute of Science, GITAM (Deemed to be University), Visakhapatnam, India, b Mountain Research Centre for Field Crops, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, India

­Introduction Environmental stresses, predominantly salinity, drought, and extreme temperatures, are major hindrances in crop production leading to global economic losses. Being sessile, plants cannot avoid exposure to abiotic stresses; instead they developed intricate mechanisms to counter the detrimental effects of these stress conditions and propel their survival. Salinity, drought, and heavy metal accumulation in soil hinders crop cultivation by altering plant morphology and physiological attributes, deteriorating seed quality and reducing agricultural yield [1, 2]. It was reported that 60 and 10.5 million km2 of arable lands are exposed to drought and salinity, respectively [2a,3]. The loss of agriculturally productive land due to these environmental stresses is a constant barrier toward sustaining food resources for the booming population. The situation is further aggravated by periodic changes in climate patterns such as premature seasons and irregular rainfall, which lead to loss in productivity of economically important crops such as maize, rice, wheat, and barley [4]. Plants are frequently exposed to multiple environmental stresses at any given instance thereby initiating an eccentric stress response that is impossible to characterize when studying individual stress factors [5]. Plants have evolved comprehensive mechanisms for perceiving minute changes in their immediate environment. Once the signals are perceived, plants initiate their protective responses by triggering a cascade of regulatory pathways that involve alteration of gene and protein expression at the transcriptional, post-transcriptional, and translational levels, respectively [6]. These changes are characterized by the up/downregulation of specific genes (osmolytes, aquaporins, antioxidant enzymes, etc.) leading to specific stress responses against these abiotic stresses. So far, a majority of the studies focused on regulation of genetic events at the transcriptional level; however, in recent years, genetic Plant Small RNA. https://doi.org/10.1016/B978-0-12-817112-7.00010-9 © 2020 Elsevier Inc. All rights reserved.

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regulation at post-transcriptional and post-translational levels is being ­extensively explored due to their involvement in re-establishment of plant cellular homeostasis and restoring physiological traits during the recovery phase under abiotic stress conditions [7]. Small non-coding RNAs (sRNAs) comprise a group of RNA involved in regulation of gene expression at the post-transcriptional level by interfering with mRNA translation [8]. sRNAs differ from the conventional RNAs (mRNA, tRNA, and rRNA), which are involved in protein coding. In the last decade, there have been many reports suggesting the involvement of sRNAs in gene regulation at the posttranscriptional and post-translational levels, specifically in response to abiotic stress conditions [9–12]. The length of sRNAs generally ranges between 20 and 30 nucleotides, and so far, the types of sRNAs identified includes microRNAs (miRNAs), short interfering RNAs (siRNAs), and Piwi-interacting RNAs (piRNAs). RNA interference (RNAi) technology, also named RNA silencing, has surfaced as a potential strategy for molecular biologists and plant breeders to facilitate crop improvement. RNAi technology is a molecular phenomenon that inhibits the expression of specific genes by post-transcriptional gene silencing (PTGS) promoted by double-stranded RNAs (dsRNAs) [13]. The advantage of RNAi over other antisense techniques revolves around its efficiency, precision, and stability. All these attributes have made RNAi technology superior to conventional breeding techniques for attaining crop improvement as well as developing genetically modified plants with enhanced stress tolerance [14]. RNAi technology finds successful application in modification of several morphological, biochemical, and physiological traits in plants such as nutritional amendments, reduction of cellular toxic contents, male sterility, accumulation of antioxidants, and enhanced defense response against biotic as well as abiotic stresses [14]. High-throughput next-generation sequencing, along with advancement in computational studies, have been instrumental in unraveling and understanding the role and mode of action of sRNA-mediated salt stress response in plants (Fig. 1). Table 1 enlists the various types of small RNAs involved in abiotic stress responses in plants. Exposure to salinity leads to oxidative stress due to closure of stomata in plants, which decreases the cellular carbon dioxide/oxygen ratio. This decrease in the gaseous ratio triggers cellular reactive oxygen species (ROS) accumulation and damages cellular organelles. Prolonged exposure to salt stress disrupts ionic and cellular homeostasis and enhances oxidative stress, disruption of functional proteins, and cellular structures, thereby significantly affecting plant development and survival [15, 16]. The genes involved in regulation of salt stress include ion channel activators, signal perception, and transduction and growth factors [17]. miRNAs, a type of sRNA, are found to be associated with post-transcriptional regulation of salt stress-mediated gene expression [18]. Several studies have reported varying expression levels of miRNA in response to salt stress in many plant species, for example, Arabidopsis thaliana [19], Zea mays [20], Gossypium hirsutum [21], Raphanus sativus [22], Populusto mentosa [23], Cicer arietinum [24], Triticum aestivum [25], and Oryza sativa [26].

­Introduction

FIG. 1 sRNA-mediated signal perception and regulation of salt stress tolerance in plants.

The advent of molecular technologies and significant research in the field of RNAi has led to the identification of sRNAs and their specific target sites. These findings are pivotal for understanding the mode of action of sRNA-mediated stress response and their precise role in epigenetic regulation. Thus, the focus lies on highlighting the various plant small RNAs, their biogenesis and target genes, their regulatory roles in salt stress responses, and adaptive mechanisms unraveled in plants. Moreover, the possible development of salt-tolerant cultivars of various crop plants harboring these sRNAs, and current and future challenges toward attaining sustainable crop productivity for the booming global population have also been discussed.

193

194

S. no

Class

Full form

Length (nt)

Originating loci

Biogenesis

Function

1

miRNA

microRNA

20–22

MIR genes

The hairpin structures of ssRNA transcripts are cleaved by DCL1 and HYL1

2

siRNA

Short interfering RNA

24–30

RDR-generated dsRNAs are cleaved by DCL-4 and HYL1

3

ta-siRNA

Trans-acting-Short interfering RNA

21

Repeats, transposons, and retroelements (endogenous). Transgenes and viral RNAs (exogenous) TAS loci

Silences expression of target genes by mRNA degradation and translational repression Silences gene expression by RNA-dependent DNA methylation and chromatin modification

4

nat-siRNA

Natural antisense transcript derived siRNA

20–22; 23–26

Loci generating pairs of senseantisense transcripts

miRNAs mediated cleavage of TAS genes, transcribed by RDR into dsRNA, and then processed by Dicers dsRNA derived from overlapping transcripts are cleaved by DCL1 and binds to AGO1

Silences target gene expression through mRNA cleavage nat-siRNA silences target gene expression through mRNA cleavage

CHAPTER 10  Salt stress tolerance and small RNA

Table 1  Types of sRNAs involved in plant response to abiotic stresses.

­Plant sRNAs: Types and biogenesis

­Plant sRNAs: Types and biogenesis sRNAs are made up of small nucleotide (nt) sequences ranging between 20 and 30 nt, and they play an integral role in formation of protein–RNA complexes. These complexes undergo sequence-specific base pairing and aid in the suppression of target gene expression, thereby contributing to PTGS. These RNAs bind to RNA-induced silencing complexes (RISC) and trigger the regulation of gene expression networks by either DNA methylation, chromatin remodeling, or mRNA degradation. The various strategies by which silencing or suppression of target genes can be achieved include (1) minimizing transcription rate, (2) diminishing cellular mRNA stability, or (3) truncating translation of mRNAs into protein. sRNAs are grouped into three distinct categories on the basis of their size, biosynthesis pathway, and mode of action. The three types of sRNAs so far identified are microRNAs (miRNAs), small interfering RNAs (siRNAs), and the piwi-interacting RNAs (piRNAs). Out of these three types of sRNAs, miRNAs and siRNAs are involved in regulation of plant gene expression, whereas piRNAs are associated with regulation of gene expression in animals (metazoans).

­miRNA miRNA genes were first identified in Caenorhabditis elegans [27]. This landmark discovery paved the way for the recognition of miRNA families in plant species as they play a key role in the regulation of gene expression networks during exposure to abiotic stress [28–30]. The first miRNA families in plants were reported in the model plant Arabidopsis thaliana by three distinct groups of researchers about 10 years after it was identified in animals [31–33]. The MIR genes code for miRNA biosynthesis in presence of RNA polymerase II. Single-stranded hairpin structures (64–303 nt) serve as the precursor for miRNA synthesis. The enzyme RNA polymerase II aids the synthesis of primary-miRNA (pri-miRNAs) from the MIR genes. miRNA biosynthesis starts in the nucleus where Dicer-like 1 (DCL1), hyponastic leaves 1 (HLY1), and ribonuclease III enzymes carry out the cleavage and processing of pri-miRNA complex to form a mature miRNA duplex structure [34]. The miRNA duplex is then methylated to maintain its structural integrity. DNA methylation at this stage is catalyzed by a methyltransferase protein named HUA ENHANCER 1 (HEN1) enzyme, which adds a methyl group at the hydroxyl ends of the terminal nucleotides in the miRNA duplex. The methylated miRNA duplex is subsequently exported to the cytoplasm by the action of a nucleocytoplasmic transporter protein, HASTY (HST) [35]. An unknown helicase enzyme catalyzes unwinding of the miRNA duplex and processes them into short mature miRNA fragments (21 nt). Gene silencing is achieved by these mature miRNA fragments. They bind to the RISC complex in association with Argonaute (AGO) protein forming the miRNA-AGO complex, which in turn directs the RISC complex to bind with the target mRNA and induce silencing or suppression of protein synthesis (translation) as well as mRNA degradation [36, 37] (Fig. 2). The AGO proteins are a vital cog in the gene silencing pathway due to its

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FIG. 2 Biogenesis of sRNAs (miRNA and siRNA) and their mode of action in gene silencing. RNA pol II, RNA polymerase II; DCL1, Dicer-like 1; HYL1, hyponastic leaves 1; HEN1, HUA ENHANCER 1; RDR, RNA-dependent RNA polymerase; DCL4, Dicer-like 4; AGO, Argonaute; RISC, RNA-induced silencing complexes.

endonucleolytic activity essential at the post-transcriptional level [38]. AGO proteins have been comprehensively studied in the model plant Arabidopsis thaliana, and Vaucheret [39] reported the presence of 10 diverse types of AGO proteins in Arabidopsis.

­siRNA siRNAs are short nucleotide sequences ranging between 20 and 25 nt and can be found endogenously as well as exogenously. siRNAs and miRNAs perform similar functions but differ on the basis of their mode of action and origin [39a]. The preliminary findings of the presence of siRNA in Arabidopsis was reported in 1999 [40], and gradually researchers identified different types of siRNA of endogenous origins in other higher plants and animals [31, 41–43]. siRNAs can be further ­divided into three major types, namely miRNA-induced trans-acting siRNAs (tasiRNAs), natural antisense siRNAs (nat-siRNAs), and heterochromatic siRNAs (hec-siRNAs) [44].

­Role of sRNAs in salt stress response

­Trans-acting siRNAs (ta-siRNAs)

The ta-siRNAs fall under the siRNA type and are generated from trans-acting siRNA (TAS) genes transcribed by the enzyme RNA-polymerase II. ta-siRNAs biosynthesis takes place in the cytoplasm and is mediated by miRNAs as the TAS gene transcripts are non-coding in origin [45, 46]. To date, eight TAS loci have been identified in plants, and they are divided into four distinct families, namely TAS1, TAS2, TAS3, and TAS4 based on their chromosomal location. miR173 binds to the AGO1 protein and mediates the cleavage of TAS1 and TAS2 genes. On the other hand, miR390AGO7 and miR828-AGO1 protein complexes promote the cleavage and processing of the TAS3 and TAS4 genes, respectively [17, 47]. In the next step, a suppressor of gene silencing 3 (SGS3) mediates the processing of the cleaved RNA transcripts to form double-stranded RNAs by the action of RNA-dependent RNA polymerase 6 (RDR6). The dsDNAs thus formed are cleaved repeatedly in presence of the DCL1, AGO1 protein and HYL1 enzymes followed by methylation by HEN1 enzyme to generate the ta-siRNAs. The ta-siRNAs bound to AGO1 complex attaches to specific sites in the target mRNA and facilitates gene silencing either by mRNA degradation, DNA methylation, or inhibition of synthesis of specific functional proteins at the post-transcriptional levels.

­Natural antisense siRNAs (nat-siRNAs)

Natural antisense transcripts (NATs) also belong to the category of siRNAs and are obtained endogenously. The nat-siRNAs are divided into two types, cis-natsiRNAs and trans-nat-siRNAs, which are based on their origin. Cis-nat-siRNAs are generated from RNA transcripts present on the same loci, whereas trans-natsiRNAs are obtained from RNA transcripts of different loci [47a]. cis-nat-siRNAs are more abundant than trans-nat-siRNAs in plants and have been identified in Arabidopsis, rice, and wheat among the major crop plants [48–50]. NATs have emerged as potential gene silencers as depicted by their role in alternative splicing, DNA methylation, RNA editing, genomic imprinting, and regulation of gene expression cascades [51, 52].

­Heterochromatic siRNAs (hec-siRNAs)

Heterochromatic siRNAs (hec-siRNAs) are synthesized predominantly from transposable elements and are reported to take part in histone modification and suppression of gene expression at the transcriptional level.

­Role of sRNAs in salt stress response Technological advancements in the field of molecular biology enabled plant breeders and molecular biologists to unravel the intricate gene expression regulatory networks triggered in response to exposure to abiotic stresses of plants. RNAi technique has been employed to successfully establish a link between sRNAs (especially miRNAs) and abiotic stress-mediated response in major crop plants [7, 18, 53–55]. Salinity is

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a considerable obstacle for sustainable crop production and is responsible for loss of crop productivity as well as quality [56–58]. Prolonged exposure to salinity (100 mM NaCl onward) leads to deleterious effects on plant physiology, development, and survival rate. Plants are classified as glycophytes (salt-sensitive) and halophytes (salttolerant) based on their salt-tolerance potential; they differ in their sRNAs expression profiling when exposed to salt stress. Under such a scenario, miRNAs expression levels have been found to be altered along with other salt stress-specific responsive genes [18]. A variety of genes is involved in salinity-mediated plant stress response and encompasses the genes coding for ion channel activation, signal transduction, and plant growth-factors [17]. Among the different types of sRNAs, various miRNAs families have been associated with regulation of gene expression in relation to salt stress. In the model plant, Arabidopsis thaliana 13 miRNA transcripts (miR156, miR158, miR159, miR165, miR167, miR168, miR169, miR171, miR319, miR393, miR394, miR396, and miR397) and 1 miRNA transcript (miR398) were upregulated and downregulated, respectively, under salt stress [59]. Expression profiling studies of salt tolerant and sensitive varieties of maize showed differential expression levels of miRNA families where miR396, miR167, miR164, and miR156 were downregulated, whereas miR474, miR395, miR168, and miR162 transcripts were upregulated [60]. Sun et al. [22] studied the expression profiling of miRNAs in radish (Raphanus sativus) under salt stress, and they reported the identification of 22 novel miRNAs families in addition to 49 known miRNAs families [22]. These miRNAs were involved in regulation of plant growth primarily by initiating ROS scavenging and restoring cellular ionic homeostasis. In another set of experiments, microarrays were employed to observe the miRNA expression profile of barley (salt-tolerant and -sensitive varieties). The researchers concluded that the expression levels of four miRNA families (miR156, miR164, miR167, and miR396) family members were significantly downregulated, whereas there was a mild increase in the expression levels of four other miRNA transcripts (miR162, miR168, miR395, and miR474) [61]. In a recent study, transgenic creeping bentgrass (Agrostis stolonifera) varieties harboring miR319 gene (Osa-miR319) isolated from rice were found to be tolerant to prolonged exposure to drought and salinity. The transgenic varieties were characterized by their enhanced water retention capacity, leaf wax content, and low sodium uptake rate. Comparative expression profiling studies revealed low expression of four putative miRNA39-specific target gene transcripts called teosinte branched/ cycloidea/proliferating factors (TCPs), namely AsPCF5, AsPCF6, AsPCF8, and AsTCP14 [62]. Overexpression of the Osa-miR319 gene under salt- and waterdeficient stress altered various morphological traits (leaf size increase, decrease in number of tillers, enlargement of stems, etc.) in the transgenic lines and subsequently promoted plant survival and growth. Similarly, overexpression of the osa-miR393 gene in Arabidopsis thaliana enhanced salt tolerance in the transgenic plants and are involved in restoration of ionic homeostasis [63]. Dolata et  al. [64] also subjected 14-day-old Arabidopsis thaliana plants (T87 lines) to 250 mM NaCl stress for 60 min and studied the expression profiling. They observed that levels of two

­Role of sRNAs in salt stress response

transcripts (miRNA161 and miRNA173) increased by twofold, whereas the expression levels of pri-miRNA161 and pri miRNA173 were downregulated significantly. Moreover, the presence of ARGONAUTE1 protein (AGO1) at transcriptional and post-­transcriptional levels shed light on their participation in regulating the expression levels of the previously mentioned miRNA transcripts in the nucleus. Fu et al. [20] employed high-throughput sequencing and degradome analysis to detect the miRNAs families and their targets genes involved in salt stress (250 mM NaCl) response and adaptive mechanisms in maize varieties. They identified the presence of 1040 known miRNA transcripts (762 from leaves and 726 from roots) in addition to 37 novel miRNA families. Similar high-throughput sequencing was used to determine the miRNA expression profiling of Saccharum sp. cultivars exposed to saline soils. The samples were exposed to 170 mM NaCl, and plants were collected at three different time phases (after 1 h, 6 h, and 24 h). Results confirmed the presence of 98 conserved and 33 novel miRNAs grouped into four different miRNA libraries. The computational analyses were validated by subjecting the 11 abundant miRNAs (in terms of expression levels) to severe salt stress (340 mM). The 11 selected miRNA transcripts were found to be significantly expressed in the test varieties (340 mM NaCl) in comparison to the plants exposed to 170 mM NaCl stress [65]. Likewise, Deng et  al. [66] also employed degradome analysis to predict the salt-responsive miRNA transcripts present in barley. They identified 152 miRNA transcripts (142 known and 10 new) and, out of these, only 30 miRNA transcripts were found to be upregulated. They concluded that miRNAs are actively involved in attenuation of plant growth parameters and suppression of cellular metabolic rates in its quest to initiate salt stress response and enhance the survival rate of the barley cultivars. High-throughput sequencing of two related SR varieties of wheat subjected to salt stress lead to the identification of 98 known and 219 novel miRNA transcripts along with their specific target genes. The authors investigated the function role of these salt stress-responsive miRNAs by virus-induced gene silencing. They reported that the miRNAs target various pathways such as auxin, jasmonate signaling, carbohydrate metabolism, and epigenetic changes to enhance salt tolerance in the stressed plants [67]. miRNAs also target the hormone signaling cascades to induce salt tolerance in plants. For example, overexpression of miR393 gene decreases the expression levels of transport inhibitor response 1 (TIR1) and Auxin signaling F-box 2 (AFB2) leading to hypersensitivity toward salt stress, whereas overexpression of the transgene (miR393-resistant TIR1) enhances salt tolerance [68, 69]. Similarly, plants expressing high levels of miR394 leads to sensitivity to salt stress, whereas plants harboring a miR394-resistant LCR exhibit enhanced salt and ABA tolerance [70]. miRNVL5 and miR417 isolated from cotton and Arabidopsis, respectively, are associated with negative regulation of plant stress response [21], whereas miRNAs transcripts (miR319 and miR528) positively regulate salt stress responses in plants by downregulating the expression of their specific target genes [62, 71]. Apart from miRNAs, nat-siRNAs also play an integral role in inducing abiotic stress tolerance. A classic example in this regard is nat-siRNAs generated from the

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precursor pair of SRO5 and P5CDH. These nat-siRNAs identified in Arabidopsis are involved in osmoprotection and alleviate the deleterious effects of oxidative stress in response to salinity [72]. The pyrroline-5-carboxylate dehydrogenase (P5CDH) gene encodes an enzyme that catalyzes proline degradation, while the salt stressinducible gene (SRO5) is involved in managing ROS production. Introduction of the SRO5 gene generates dsRNA sequences in SRO5-P5CDH overlapping juncture. The dsRNA transcripts are then cleaved by DCL2 enzymes to generate a short24-nt siRNA, which undergoes successive cleavage by DCl1 enzyme producing a shorter 21-nt nat-siRNA transcript. Both of these generated natsiRNAs in turn cleaves the P5CDH gene, thereby suppressing its expression levels and promoting the accumulation of proline. Proline belongs to the class of osmolyte and functions as free radical scavengers thereby aiding salt stress tolerance in plants. As exposure to salinity leads to increased ROS production, downregulation of P5CDH triggers accumulation of proline. This in turn activates the SRO5 genes, which scavenge the excess ROS generated. These findings suggest that nat-siRNA-mediated regulation of P5CDH and SRO5 minimizes ROS accumulation and restores cellular ionic homeostasis in response to salt stress. In the present decade, plant sRNAs have been extensively studied under the effect of salt stress leading to the identification of 217 different sRNA families across major plant species such as Arabidopsis, Glycine max, Glycine soja, Gossypium hirsutum, Medicago truncatula, Nicotiana tabacum, Oryza sativa, Panicum virgatum, Phaseolus vulgaris, Populus euphratica, Saccharu mofficinarum, Triticum aestivum, and Zea mays [36]. A list of these identified sRNAs along with their target genes involved in salt stress responses have been compiled in Table 2.

­Conclusion and future perspective Abiotic stresses are a major hindrance to sustainable agricultural productivity. Being sessile, plants are unable to avoid the adverse effects of these stress conditions, and prolonged exposure to abiotic stresses is detrimental for survival. The situation is further worsened due to global warming, loss of farmable land, faulty irrigation practices, and gradual shift in rainfall patterns. All these factors lead to widespread loss in global crop productivity in terms of crop quality and quantity, and pose a serious challenge for the agricultural industries to adequately feed the growing population, which is expected to breach the 9 billion barrier by 2050 [80]. Hence, the focus lies in developing superior varieties of economically important crops capable of thriving in abiotic stress conditions without compromising crop yield and quality. Since the first discovery of sRNAs in plants, extensive research has been conducted to identify various sRNA families and decipher their exact role in mediating abiotic stress responses. There are many reports highlighting the presence of several conserved as well as novel miRNAs in major crop plants in response to salt stress. High-throughput sequencing, degradome analysis, as well as other computational software facilitated the genome-wide identification of miRNA based on their expression ­profiles with

Table 2  List of sRNAs along with their target genes involved in salt-stress responses in plants. Sl no

sRNAs

Plant species

Regulatory pattern

Target gene

Reference

1 2

miR172 miR156

Arabidopsis Arabidopsis thaliana

AP2 like TFs Squamosa promoter binding

Li et al. [72a] Liu et al. [59]

3

miR156

Zea mays

Overexpression of gma-miR172c Overexpression of miR156, upregulated Downregulated

Ding et al. [60]

4 5 6

miR156 miR395 miRNVL5

Panicum virgatum Nicotiana tabacum Cotton

7

miR393

Oryza sativa

TIRI, AFB2

Barrera-Figueroa et al. [74]

8 9 10 11 12 13

miR394 miR396 miR398 miR474c miR1507 miR530a

Glycine max Populus trichocarpa Arabidopsis thaliana Populus trichocarpa Glycine max Populus trichocarpa

Upregulated Upregulated Downregulation, reduces sodium uptake Upregulated, promotes auxin signaling Downregulated Downregulated Downregulated Downregulated Upregulated Downregulated

SPL-like transcription factor (TF) Squamosa promoter binding ATP sulfurylase GhCHR

Li et al. [75] Zhou et al. [76] Jagadeeswaran et al. [77] Zhou et al. [76] Li et al. [75] Lu et al. [78]

14 15

miR2871 miR169

Oryza sativa Zea mays

Upregulated Upregulated

F-box protein GRL TFs Cu/Zn superoxide dismutase Protein kinase; kinesin NBS-LRR resistance protein F-box domain-containing protein TIRI, AFB2 NFY-A T

Sun et al. [55] Frazier et al. [73] Gao et al. [63]

­Conclusion and future perspective

Barrera-Figueroa et al. [74] Luan et al. [79]

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respect to environmental stresses. These sRNAs are being used to engineer transgenic varieties harboring the specific sRNA transcripts so as to enhance abiotic stress tolerance and crop productivity. RNAi technique has emerged as a potential tool for improving crop viability and productivity in comparison to conventional breeding techniques among plant breeders. RNAi-mediated suppression of gene expression is the potential target for developing novel transgenic lines harboring the specific sRNA transcripts to enhance abiotic stress tolerance. The advantage of RNAi technique over conventional breeding strategy includes its sequence specificity, multiple gene targeting at a single instance, and regulating the level of gene silencing. Another added advantage of RNAi is the minimal biosafety issue associated with development of transgenics. Although RNAi has ample potential to revolutionize genetic engineering of crop plants, there are minute drawbacks associated with this technique. Despite its sequence-specific nature, there are possibilities of off-target binding and unwanted secondary consequences, which may lead to undesired traits in the transgenic varieties. Therefore, comprehensive knowledge of miRNA biosynthesis and their regulatory pathways are essential for development of transgenic cultivars employing RNAi technique. Genetic engineering of crop plants using RNAi approach are accompanied by ethical and biosafety issues concerning hereditary changes due to chromatin and genetic manipulation. Thus, plants engineered with meticulously designed RNAi technique and thorough evaluation of the food safety-related risk factors can solve the biosafety and ethical issues. Despite the limitations, engineering salt-tolerant varieties of economically important crops based on sRNAs have substantial potential to enhance productivity and nutritional attributes of crops under abiotic stress conditions.

­Acknowledgment The authors are grateful to GITAM (Deemed to be University) for providing necessary facilities to carry out the research work and for extending constant support in writing this review. Titash Dutta is thankful for financial support in the form of DST Inspire Fellowship (IF 160964), Department of Science and Technology, New Delhi.

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­Further reading [81] S.  Challa, N.R.R.  Neelapu, Genome wide association studies (GWAS) for abiotic stress tolerance in plants, in: S.H.  Wani (Ed.), Biochemical, Physiological and Molecular Avenues for Combating Abiotic Stress Tolerance in Plants, Elsevier, ISBN: 9780128130667, 2018, pp. 135–150.

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CHAPTER

Small RNAs and cold stress tolerance

11

Deepali Singha, Vartika Sinhab, Abhinav Kumarc, Sachin Teotiad a

School of Biotechnology, Gautam Buddha University, Greater Noida, India Department of Genetics, University of Delhi-South Campus, New Delhi, India c IILM College of Engineering and Technology, Greater Noida, India d Department of Biotechnology, Sharda University, Greater Noida, India

b

­Introduction All living organisms are affected by the environment and surrounding living beings. Sometimes their effects influence the physical or genetic parameters of one or many individuals, which is termed “stress”. Stress is nothing more than a restrained or controlled condition that leaves a negative impact on the affected being. Stress caused by organisms such as bacteria, virus, fungi, parasites, insects, pests, etc. is called biotic stress, whereas stress caused by environmental factors such as high temperature, light, wind, salinity, flood, and drought is called abiotic stress. Stresses affect development and output of crop plants, amounting to significant losses to agricultural productivity worldwide. Cold stress significantly affects crop yield and all other physiological and biochemical aspects of plant functioning [1, 2]. Temperature stress in plants can be classified as high (>40°C), chilling (0–15°C), or freezing temperature (