Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II) 9811629552, 9789811629556

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Anirudh Kumar Rakesh Kumar Pawan Shukla Hitendra K. Patel   Editors

Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II)

Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II)

Anirudh Kumar • Rakesh Kumar • Pawan Shukla • Hitendra K. Patel Editors

Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II)

Editors Anirudh Kumar Department of Botany Indira Gandhi National Tribal University Amarkantak, Madhya Pradesh, India Pawan Shukla Ministry of Textile Central Silk Board, Ministry of Textile, Pampore, Jammu and Kashmir, India

Rakesh Kumar Department of Life Science Central University of Karnataka, School of Life Science Gulbarga, Karnataka, India Hitendra K. Patel Plant-Pathogen Interactions lab Centre for Cellular and Molecular Biology Habsiguda, Telangana, India

ISBN 978-981-16-2955-6 ISBN 978-981-16-2956-3 https://doi.org/10.1007/978-981-16-2956-3

(eBook)

# The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Increasing global population and unpredictable climate change have forced for sustainable growth in crop production. An exploration of various challenges and their possible solution to improve yield and securing food for all, this book entitled “Omics Technologies for Sustainable Agriculture and Global Food Security (Vol I and Vol II)” comprehensively and coherently reviews the application of various aspect of rapidly growing omics technology including genomics, proteomics, transcriptomics, and metabolomics for crop development. It provides a detailed examination of how omics can help crop science and introduces the benefits of using these technologies to enhance crop production, resistance, and other values. In the first volume, we have discussed the recent advances in omics technology, bioinformatics, and database management for crop sciences, plant defense and disease control, application of omics tools in plant tissue culture, improving nitrogen use efficiency, and abiotic stress tolerance in crop plants. In addition, some informative chapters on understanding microbial systems through integrated omics approaches, microbial-mediated environmental contaminants remediation, genome editing for plant improvement, metabolomics-assisted breeding, and safety and ethics in omics biology were also part of the first volume. Importantly, in the second volume, 13 chapters, which were not included in the first volume such as the use of nanotechnology in agriculture, manipulation of plant– microbe interaction signals for yield enhancement, systems biology approach to understand plant physiology and designing future crop, molecular farming, RNAi engineering for crop improvement, biofuel production, and public acceptance for hybrids and transgenic products are covered. The upcoming challenges are also covered, which will surely increase the interest of the readers. There are very few books available, which are mostly covering single topics such as abiotic stress, biotic stress, breeding or having two to three topics, but surely not all the above-mentioned topics. This book provides all the important topics with updated information in the area of crop breeding, abiotic and biotic stress resistance, nanotechnology, RNAi technology, system biology, and biofuel production. These two volumes can be useful for graduate and postgraduate students of life science including researchers who are keen to know about the application of omics technologies in the different areas of plant science/agriculture sciences/botany/life sciences. This book can also be an asset to modern plant breeders and agriculture biotechnologists. The book v

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represents an advancement in technologies that have revolutionized the process and understanding of the whole plant system. The book represents chapters from wellknown scientists and pioneer researchers from various research organizations, plant breeders, and agriculture scientists. Both Vol I and Vol II present a broad view of how omics would help crop scientists to meet the challenges of food security. Conclusively, it is an exploration of the challenges and conceivable solutions to improve yields of the food crops. It also provides a platform to ponder upon the integrative approach of omics to deal with complex biological problems. All the chapters are supplemented with diagram and discussion and arguments are supported by data table with valid references. Sincerely! Dr. Anirudh Kumar Corresponding Editor Amarkantak, India Gulbarga, India Pampore, India Hyderabad, India

Anirudh Kumar Rakesh Kumar Pawan Shukla Hitendra K. Patel

Contents

1

Integrating Phenomics with Breeding for Climate-Smart Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abhishek Bohra, S. J. Satheesh Naik, Anita Kumari, Abha Tiwari, and Rohit Joshi

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Application of “Omics” Technologies in Crop Breeding . . . . . . . . . Rahul Priyadarshi, Pragya Sinha, Aleena Dasari, and Raman Meenakshi Sundaram

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Omics Technologies and Molecular Farming: Applications and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gopalareddy Krishnappa, Krishnappa Gangadhara, Siddanna Savadi, Satish Kumar, Bhudeva Singh Tyagi, Harohalli Masthigowda Mamrutha, Sonu Singh Yadav, Gyanendra Singh, and Gyanendra Pratap Singh

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Omics to Understand Drought Tolerance in Plants: An Update . . . Prasoon Jaya, Alok Ranjan, Arshi Naaj Afsana, Ajay Kumar Srivastava, and Laxmi Narayan Mishra

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Recent Advances in Transcriptomics: An Assessment of Recent Progress in Fruit Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manoj K. Rai, Roshni Rathour, and Sandeep Kaushik

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Harnessing Perks of MiRNA Principles for Betterment of Agriculture and Food Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Anjan Barman, Tarinee Phukan, and Suvendra Kumar Ray

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Potential of Metabolomics in Plant Abiotic Stress Management . . . 193 Nitesh Singh, Aadil Mansoori, Debashish Dey, Rakesh Kumar, and Anirudh Kumar

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Integrating Pan-Omics Data in a Systems Approach for Crop Improvement: Opportunities and Challenges . . . . . . . . . . . . . . . . . 215 Donald James, P. R. Rennya, Mani Deepika Mallavarapu, Ram Chandra Panigrahi, and Hitendra Kumar Patel vii

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Application of Nanobiotechnology in Agriculture: Novel Strategy for Food Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Kamal Kumar Malukani, Namami Gaur, and Hitendra Kumar Patel

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Understanding and Manipulation of Plant–Microbe Interaction Signals for Yield Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 Sohini Deb, Kamal Kumar Malukani, and Hitendra K. Patel

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Next Generation Biofuel Production in the Omics Era: Potential and Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 Sumit Kumar, Naveen Kumar Singh, Anirudh Kumar, and Pawan Shukla

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Multiomics Technologies and Genetic Modification in Plants: Rationale, Opportunities and Reality . . . . . . . . . . . . . . . . . . . . . . . 313 Vilas Parkhi, Anjanabha Bhattacharya, and Bharat Char

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Social Acceptance and Regulatory Prospects of Genomics in Addressing Food Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 S. J. S. Rama Devi and Supriya Babasaheb Aglawe

About the Editors

Anirudh Kumar is a research faculty member in the Department of Botany, Indira Gandhi National Tribal University (IGNTU), Amarkantak, MP, India. He has 10 plus year career as a researcher in the area of plant molecular biology and plant pathology. During 2015–2016, he was working at CCMB, Hyderabad as DBT-Research Associate. He received M.Sc. and Ph.D. from University of Hyderabad, India. His current research interests span from antioxidants studies of medicinal plants to plant pathology. He is author and co-author of several papers on different aspects of plant biology. He also teaches courses for B.Sc., M.Sc., and Ph. D. degree. For the past few years, his research group is trying to study phytochemical profiles of native plants traditionally used by tribal healers of Amarkantak, MP, India. Rakesh Kumar is a plant biologist and researcher, currently working as assistant professor at School of Life Sciences, University of Karnataka, India. He has obtained his PhD in plant sciences from University of Hyderabad and is an alumnus of International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India. During his past 11 years of research, he has worked in the area of molecular plant biotechnology and crop improvement. His expertise includes genomics and OMICS approaches such as mutagenesis, TILLING, Eco-TILLING, transcriptomics, sequencing, QTL-seq, trait and gene discovery, and LC-MS and GC-MS based proteomics and metabolomics. He has published several high-impact research papers, reviews, book chapters and obtained research grants from both national and international funding agencies. Pawan Shukla is scientist in Central Silk Board, Ministry of Textile, Govt. of India, Bangalore since 2015. He obtained his M.Sc. and Ph.D. from the University of Hyderabad. He was Dr. D. S. Kothari postdoctoral fellow at University of Hyderabad during 2014–2015. He is having 12 years of experience working in the area of plant molecular biology and genetic engineering. During his Ph.D., he developed a plant gene-based pollination control system which has practical implication in hybrid seed production. In addition to this, he worked on the development of transgenic plants tolerance to different abiotic and biotic stresses. At present, he is associated with the biotechnology division of Central Sericultural Research and ix

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About the Editors

Training Institute (CSR&TI), Central Silk Board, Pampore, Kashmir, J&K (UT) and his laboratory is working on the development of cold-tolerant mulberry variety. He has published several research papers in reputed international and national Journals and is an editor of three books published by CSR&TI, Central Silk Board Pampore. Hitendra Kumar Patel completed his master degree in biotechnology from Guru Ghasidas Central University, Chhattisgarh and PhD in molecular life sciences from International Centre for Genetic Engineering and Biotechnology, Trieste, Italy. He is currently leading the rice functional genomics group at CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India. His research activities are focused on developing novel rice varieties and also on understanding the mechanisms of the interactions between plants and their pathogens. He was involved in the identification and characterization of several virulence functions including EPS, LPS, cell wall degrading enzymes (CWDEs), and T3SS effector proteins of bacterial pathogen Xanthomonas oryzae pv. oryzae. He was involved in popularization and licensing of improved Samba Mahsuri rice, a bacterial blight disease resistant and low GI (diabetic-friendly) rice for which the team has won prestigious CSIR Technology award and DBT award. He is an associate editor for Science India portal and recognizing his contribution, he has been selected as Associate Fellow of Telangana, India.

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Integrating Phenomics with Breeding for Climate-Smart Agriculture Abhishek Bohra, S. J. Satheesh Naik, Anita Kumari, Abha Tiwari, and Rohit Joshi

Abstract

Increasing food demand, with the burgeoning population worldwide is reaching an alarming condition along with depleting resources and unpredictable climatic vagaries. Thus, twenty-first century agriculture is facing a daunting task of developing high-yielding and multiple stress tolerant plants to ensure food security. Thus, in-depth analysis of crop stress response is essentially required. Therefore, linking phenomics with crop breeding programs can fill the gap between complex targeted traits and genotypic responses. Phenotyping ensures reliable data for predicted trait, during identification and selection of improved varieties in conventional breeding programs. Besides this, high-throughput phenotyping helps in delineating phenotypic and genotypic associations along with characterization of potential genomic regions for forward genetics in molecular breeding. Recent advancements in high-throughput automated imaging techniques provide huge amount of data and high-resolution images. To make precise decisions, specific tools are required to disentangle this huge array of data. We uncovered here a comprehensive overview of (1) phenomic techniques for climate smart agriculture, (2) association between breeding and phenomics, and (3) strategies for big data analysis for crop improvement programs. To conclude, automated plant phenotyping techniques are precise tools for in-depth analysis and identification of traits responsible for crop improvement.

A. Bohra · S. J. Satheesh Naik · A. Tiwari Crop Improvement Division, ICAR-Indian Institute of Pulses Research (IIPR), Kanpur, India A. Kumari · R. Joshi (*) Division of Biotechnology, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India Academy of Scientific and Innovative Research (AcSIR), CSIR-HRDC Campus, Ghaziabad, Uttar Pradesh, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Kumar et al. (eds.), Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), https://doi.org/10.1007/978-981-16-2956-3_1

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Keywords

Phenomics · Breeding · Crop · Abiotic stress · Signalling

1.1

Introduction

Human population is estimated to reach from 6.8 billion to 9.1 billion by 2050, which requires a steep incline in crop production to fulfil the requirements of growing population, despite accelerated urbanization and shrinking cultivable area (Joshi et al. 2016a; Kumar et al. 2020). However, global warming and unpredictable climatic vagaries posed by various biotic and abiotic stresses have challenged worldwide crop growth and productivity (Ramegowda and Senthil-Kumar 2015; Pereira 2016; Pandey et al. 2017), leading to loss of crop productivity worth several billion dollars (Dhankher and Foyer 2018). Unlike animals, plants cannot escape from unfavourable environmental conditions such as drought, salinity, and cold stress. With continuous changes in the global climate, these stresses will further aggravate in future and drastically alter the genotype–environment interaction, resulting in changes of plant’s basic metabolism representing its phenotype (Gupta et al. 2015). The potential of any genotype under any stress can be assessed by examining its phenotypic changes under controlled environmental, or in the field conditions to evaluate the regulatory elements during plant’s stress response (Farooq et al. 2014; Mickelbart et al. 2015). In postgenomic era, efficient utilization of crop genetic resources relies on precise examination of phenotypic response database under particular stress, which are getting more complex with current explosion of OMICS datasets (Singh et al. 2018a; Kumar et al. 2020). Wilhelm Johannsen coined the term “Phenotype” (phainein ¼ show and typos ¼ type) in 1911. From 1949 till 2013, “phenomics” was defined as highthroughput and accurate acquisition and multidimensional analysis of phenotypes (Davis 1949; Houle et al. 2010), while “phenotyping” is a set of methodologies and protocols for accurate measurement of plant phenotype (Fiorani and Schurr 2013). However, next-generation sequencing and genotyping have immensely improved functional genomics (Zhang et al. 2019; Kumar et al. 2020), but lack of phenotypic data acquisition restricts crop breeding studies (Furbank et al. 2019; Yang et al. 2020). Thus, there is great concern regarding precise phenotyping of plant stressassociated traits (Yang et al. 2013). However, traditional methods of crop phenotyping are destructive to plants, labour intensive, time consuming and subjective (Yao et al. 2018). Thus, development and integration of automated phenotyping with stress related traits by generating precise, high-resolution, and high-throughput data are currently required (Maphosa et al. 2016) to help crop breeders to adroitly identify tolerant genotypes for development of climate change resilient crops (Kumar et al. 2020). This chapter focuses on recent advances and challenges in utilizing phenotyping techniques, along with analysing big data and their integration with crop breeding programs in generating climate-resilient crop plants. In addition,

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we discuss high-throughput phenotyping combined with genetic studies to provide our perspectives on phenotyping for crop improvement.

1.2

Prioritizing Climate-Smart Agriculture

Severe environmental constraints adversely affect crop growth and productivity (Umezawa et al. 2006). The 1960’s Green Revolution results in steep inclination in the yield of staple food grains to fulfil the need of burgeoning world population. Increase in food security benefitted agricultural development, reduced poverty and malnourishment in several parts of the world. Future food security will focus on reducing crop losses due to environmental vagaries with transformative advances in improving crop yield. Tremendous advancements in genomics have advanced crop breeding to enhance trait development for improved productivity and climate resilience. Thus, greater understanding of plant mechanisms and designing novel strategies to engineer for stress tolerance in plants under variable environments is essential for crop improvement through genetic diversity, genome-scale breeding, genetic engineering, and precise agronomic practices (Gupta et al. 2015; BaileySerres et al. 2019). High-throughput phenotyping plays key role in crop breeding for development of robust model across multiple environments, due to their cost effectiveness and ease of measurement (Crossa et al. 2017). However, limited advancement in phenomics in comparison to genomics is responsible for its poor application in detecting potential of different crop genotypes. Development of highyielding, climate smart varieties is restricted due to manual, slow, and subjective phenotyping. Thus there is a global need to develop high-throughput, high-resolution, non-destructive plant phenomics approaches in our breeding systems, which allow measurement of nonvisible phenotypic changes and integrate technologies to enable reproducible and regulated image analyses in both controlled and field conditions (Singh et al. 2018a). Precision phenotyping of abiotic stress-associated traits and their interrelationships provides a holistic approach for precise understanding of environmentally regulated traits, to precisely elucidate the adaptive mechanisms (Yang et al. 2013). It can also identify trait–SNP relationships and allelic variations germplasm for breeding or introgression through crossing or genetic engineering (Bortesi and Fischer 2015; Furbank et al. 2019). Several QTLs have been characterized in different crops based on automatic plant phenotyping centres established in various countries (Zhang et al. 2017).

1.3

Phenomics of Abiotic Stress Tolerance

Abiotic stresses arising due to climatic changes are the primary causal factors affecting crop biomass and growth, resulting in global annual yield loss (Verma and Deepti 2016). Abiotic stresses are complex and dynamic and demonstrate osmotic and metabolic crosstalk. Therefore, detailed analysis for improved understanding in multifaceted responses of crop species against abiotic stresses requires an

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integrated approach of phenomics and genomics (Kumar et al. 2020). Phenotypic effects depend on both the organ and tissue affected by the stress and the duration and level of stress (Joshi et al. 2016b; Singh et al. 2018b). Thus, visualization of abiotic stress-responsive traits and its correlation with associated phenotypes is crucial for breeding tolerant plants, e.g., differences between green and yellow leaf areas (salinity stress); canopy temperature differences (drought stress); relative chlorophyll content and thermal imaging (Na+ exclusion and osmotic tolerance) (Munns et al. 2010; Weirman 2010). The vegetation indices are also interrelated with different traits, i.e., photosynthetically active biomass, pigment content, water status, and stomatal closure which correlates total yield in various crops (Din et al. 2017; Singh et al. 2018b). Similarly, phenotyping of roots still depends upon traditional methods, such as root excavation to determine density and length (Araus and Cairns 2014) which can be determined using advance root phenomics. Ex-situ evaluation exploits agar medium, hydroponics, aeroponics, and rhizotrons for capturing and visualizing images along with digital scanning and computerized image analysis for rapid root morphology evaluation (Vale and Fritsche-Neto 2015; Borianne et al. 2018; Kumar et al. 2020). Besides root phenomics, phenotyping in field, using non-destructive phenotyping tools such as thermal imaging and infra-red thermometry (IRT) are necessary to perceive and quantify the spectral reflectance, which is key factor towards breeding stress tolerant varieties (Fahlgren et al. 2015; Kumar et al. 2020).

1.3.1

High-Throughput Integrated Phenotyping

Several large-scale, high-throughput and automated phenotyping platforms were established to screen different crops for quantitative genetics, reverse genetics, and forward genetics (Table 1.1). Automated weighing and red–green–blue (RGB) imaging systems, PHENOPSIS (2003) was initially used to analyse water deficit response in Arabidopsis (Granier et al. 2006), but lacks data integration. However, PHENOPSIS DB developed in 2011 can store several hundred GB of metadata and images for data analysis modules (Fabre et al. 2011). Quantification of seedling growth dynamics under different light conditions was performed either through colour imaging device of GROWSCREEN (Walter et al. 2007) or chlorophyll fluorescence imaging system, GROWSCREEN FLUORO (Jansen et al. 2009). For large-scale phenotyping of shoot growth and water content, phenoscope rotating platform was designed to compensate environmental heterogeneity (Tisné et al. 2013) and phenovator utilizes high-speed system on rails to capture data on photosynthesis and plant growth parameters (Flood et al. 2016; Yang et al. 2020). Traitmill, a bioinformatics tool was developed by CropDesign (Belgium) for highthroughput phenotyping to determine morphometric yield related traits (Reuzeau et al. 2005). For analysis of phenomics data, Scanalyzer3D platform (LemnaTec GmbH, Germany) was developed. Similarly, to access nitrogen/water deficiency and salinity stress, plant accelerator (Australia) developed computer-regulated conveyor

Phenopsis DB (Arabidopsis thaliana) GROWSCREENFLUORO (Arabidopsis thaliana) PHENOSCOPE (Arabidopsis thaliana) Plant screen (Arabidopsis thaliana)

Organ level (shoot) Organ level (shoot) Organ level (shoot)

Organ level (shoot) Pocket phenotyping

Plant to sensor Plant to sensor Plant to sensor

Plant to sensor Plant to sensor

Field level (shoot)

Sensor to plant

Phenobox/PhenoPipe

Organ (shoot)

Plant to sensor

Imaging, photosynthetic, colour and growth

Phenotyping and experimental analysis

Plant imaging and phenotyping evaluation in ideal or stress condition Plant imaging and visualization of environmental data of experiment Chlorophyll fluorescence and growth

Yield

Biomass and other traits

Growth, transpiration, growth rate, transpiration, biomass, 3D architecture, leaf area

Organ (shoot)

High-throughput Rice phenotyping facility (HRPF) Trait mill (specifically designed for Rice)

Plant height, leaf width, and leaf area values

Remarks Shoot and biomass production

Organ (shoot)

Application level Field

Plant to sensor

Mode of operation Plant to sensor

MVS-Pheno (3D scanner) PHENOARCH

Platform Scanalyzer 3D (imaging)

Integrating Phenomics with Breeding for Climate-Smart Agriculture (continued)

Fabre et al. (2011) http://bioweb.supagro.inra.fr/phenopsis/ Jansen et al. (2009) https://www.fz-juelich.de/ibg/ibg-2/EN/methods_ jppc/GROWSCREEN_fluoro/_node.html Tisné et al. (2013) https://phenoscope.versailles.inra.fr/ Awlia et al. (2016) https://fz-juelich.de/ibg/ibg-2/EN/methods/Plant_ Screen_Mobile_PSM/psm_node.html

Reuzeau et al. (2005) https://www.quantitative-plant.org/software/ traitmill Czedik-Eysenberg et al. (2018)

Brichet et al. (2017) http://bioweb.supagro.inra.fr/phenoarch (https://www6.montpellier.inra.fr/lepse/M3P/ plateforme-PHENOARCH) Yang et al. (2014)

References Hairmansis et al. (2014) https://www.lemnatec.com/customized-solutions/ field-scanalyzer/ Wu et al. (2020)

Table 1.1 A non-exhaustive list high-throughput phenotyping platforms with their modes of operation and application levels

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Crop phenology recording system (CPRS)

Hyper spectral imaging (NMR and X-rays) Digital imaging of root traits (DIRT) Field Scanalyzer

Root reader 3D

RhizoTubes and Rhizocaps GROWSCREEN-Rhizo

Rhizoponics (Arabidopsis thaliana) RADIX

Rhizoslides

PlaRoM

Platform RhizoVision crown

Table 1.1 (continued)

Sensor to plant

Plant to sensors Plant to sensors Plant to sensors Sensor to plant

Mode of operation Plant to sensor Plant to sensor Plant to sensor Plant to sensor Plant to sensor Plant to sensors Sensor to plant

Field level

Organ level (root specific) Organ level (root specific) Organ level (root specific) Field level

Application level Organ level (root specific) Organ level (root specific) Organ level (root specific) Organ level (root specific) Organ level (root specific) Organ level (root specific) Organ level (root specific)

High level automated setup at a ground level with movable apparatus for high-throughput phenotyping Fixed tower covering whole ground in one go

Plant traits

3D imaging in a transparent box (Rhizotrons) from its embryonic stage till vegetative stages Physio-chemical root zone properties

Non-destructive and growth analysis of root along with environments

RSA and shoot development from seedling to adult stage Differential response of crown roots to splitnutrient application for selection experiments Roots under biotic and abiotic stress

2D imaging of root in laboratory environments Non-destructive rapid RSA

Remarks Root crowns

Fukatsu et al. (2012)

Das et al. (2015) http://dirt.iplantcollaborative.org/ Sadeghi-Tehran et al. (2017) http://www.rothamsted.ac.uk/field-scanalyzer

Bodner et al. (2018)

Nagel et al. (2012) https://www.lemnatec.com/growscreen-rhizo-thefuture-of-root-phenotyping/ Clark et al. (2011)

Jeudy et al. (2016)

Le Marie et al. (2016)

Mathieu et al. (2015)

Le Marie et al. (2014)

References Seethepalli et al. (2020) https://doi.org/10.5281/zenodo.2585881 Yazdanbakhsh and Fisahn (2009)

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Drones (remote sensing)

Unmanned aerial systems (UAS) X-RAY CT/micro-CT

PhenoSeeder

Seed evaluation accelerator (SEA) P-TRAP; PANorama

Vitis berry

Pocket plant 3D

Phenome

PhenoApps

BreedVision

Sensor to plant Plant to sensor Sensor to plant

Sensor to plant Sensor to plant Sensor to plant Sensor to plant Sensor to plant Camera to sample Camera to sample Sample to sensor

Field level

Aerial field level Organ level

Pocket phenotyping Pocket phenotyping Pocket phenotyping Pocket phenotyping Organ level (seed) Organ level (seed) Organ level (seed)

Field level

Visualize multi-temporal dataset in experimental fields

Plant tissues

Canopy level traits

Crowell et al. (2014)

Quantifying traits from the imaging and spectral Measuring 3D traits or seed biomass

Kefauver et al. (2017)

Gomez et al. (2018)

Jahnke et al. (2016) https://www.lemnatec.com/products-and-solutions/ phenoseeder/ Maes and Steppe (2019)

Duan et al. (2011)

Aquino et al. (2018)

Vankadavath et al. (2009) https://www.phenome-emphasis.fr/phenome_eng/ Confalonieri et al. (2017)

http://phenoapps.org/apps/

Busemeyer et al. (2013)

Seed related traits during threshing

Berries counting

Canopy and other leaf traits

Phenotyping at canopy level and leaf traits

Multiple sensors on a movable vehicle and obtain full trait analysis in field –

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platform with automated weighing-watering and imaging (Neilson et al. 2015; Atieno et al. 2017). HTPheno is an open-source colour image-processing plugin ImageJ software, developed by the Leibniz Institute of Plant Genetics and Crop Plant Research (Hartmann et al. 2011). Similarly, Integrated Analysis Platform (IAP) is a Java based open-source program to derive images and models for analysis of drought response (Chen et al. 2014) and determination of genetic variation regulating growth dynamics (Muraya et al. 2017). Similarly, Bellwether Phenotyping Platform developed a Scanalyzer3D system and PlantCV, opensource library (OpenCV, NumPy, and MatPlotLib) based platform-independent software for RGB, fluorescent, and NIR image processing (Fahlgren et al. 2015). A High-throughput Rice Phenotyping Facility (HRPF) was developed by Huazhong Agricultural University and Huazhong University of Science and Technology, China to analyse automatic controls, X-ray CT, colour imaging and analysis for plant architecture, leaf traits, and genetic variation (Guo et al. 2018; Duan et al. 2018; Zhang et al. 2017; Xiong et al. 2017; Fang et al. 2016). For cost-effective phenotyping and hyperspectral imaging of grass species under salt stress, an open-source and flexible phenotyping system, PhenoBox, was developed (Czedik-Eysenberg et al. 2018). Further to quickly acquire data and high-resolution spatial images of large areas, drones (or UAVs) enabled with computer vision approaches, multi-view stereopsis, and photogrammetric techniques to provide a flexible platform. Thus, remote sensing is used to get detailed information on drought stress response, canopy colour and texture, leaf area index estimation, nutrient status, growth, weeds and pathogen detection, QTL identification (Yue et al. 2019; Yao et al. 2017; Wang et al. 2019; Madec et al. 2019; Zhao et al. 2018), seedling performance, and yield (Maes and Steppe 2019). In addition, advanced deep learning (DL) techniques can analyse millions of remote sensing images with high speed and accuracy (Li et al. 2020). For geometric morphometric analysis related to aboveground biomass and plant height, 3D canopy modelling generates higher resolution (Maimaitijiang et al. 2019). Structure for motion (SFM) algorithms produce geometrically specific 3D point clouds at the subpixel level using 2D images generated through RGB sensors with quality and accuracy similar to LiDAR (Hassan et al. 2019). Thus, remote sensing using UAVs has demonstrated immense potential for high-throughput phenotyping to improve crop functional genomics and breeding (Yang et al. 2020).

1.3.2

Pocket Phenotyping

Since long physiologists are using dedicated handheld portable tools for phenotyping and integral data standardization, multiple sensors with data analysis software combined with artificial intelligence and fifth generation mobile network for robust analysis under field conditions (Yang et al. 2020). Thus, next-generation “wearable” or “pocket” phenotyping gadgets will profoundly accelerate phenotyping. In addition, broad range of optical sensors will greatly enhance the portability and connectivity of phenotyping tools. PocketPlant3D is used to analyse

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whole canopy architecture (Confalonieri et al. 2017). Similarly, chlorophyll, a fluorescence kinetics can be measured with handheld Handy PEA—plant efficiency analyser(Hansatech Instruments, UK) under control and stress conditions in greenhouses and fields (Wungrampha et al. 2019).

1.3.3

Belowground Phenotyping

In contrast to shoots, in situ phenotyping in underground root system architecture (RSA) is limited, which are crucial for water and nutrient absorption, directly affecting yield and biomass (Xiong et al. 2020). Broad range growth mediums such as transparent (gel-based, aeroponic, and hydroponic) and PlaRoM, enabled with RGB cameras were used to analyse root hair development and growth dynamics (Yazdanbakhsh and Fisahn 2009). Initially for destructive analysis of the root system architecture at the field level, the roots were dig out, washed and analysed; which is termed as “shovelomics” (Trachsel et al. 2010). Later on non-destructive methods are also developed such as RADIX (a rhizoslide platform used to screen both roots and shoots; Le Marie et al. 2016), rhizoslides (a paper-based observation system for root growth; Le Marie et al. 2014), GROWSCREEN -Rhizo (for simultaneous imaging of roots and shoots in transparent soil-filled rhizotrons; Nagel et al. 2012), rhizoponics (a hydroponic rhizotron; Mathieu et al. 2015), and RhizoTubes (an automated “plant-sensing” platform; Jeudy et al. 2016). Further, semiautomatic (RootNav, Pound et al. 2013; SmartRoot, Lobet et al. 2011; RootTrace, French et al. 2009) and automatic (EZ-Root-VIS, Shahzad et al. 2018) softwares were developed for 2D image analysis of root system architecture. In addition, for 3D image reconstruction and analysis GiARoots (Galkovskyi et al. 2012) and RootReader3D (Clark et al. 2011) have been developed. Similarly, an open-source software RooTrak was developed for differential X-ray attenuation between roots and soil to recreate 3D root system architecture (Mairhofer et al. 2015; Yang et al. 2020). Alternative techniques to reconstruct 3D root system architecture and root morphology include magnetic resonance imaging (MRI; Schmittgen et al. 2015) and for non-destructive scanning of roots and carbon transportation positron emission tomography (PET; Metzner et al. 2015; Atkinson et al. 2019). Non-destructive root phenotyping remains a challenge till date and there is still need to develop breakthrough technologies for belowground imaging (Yang et al. 2020).

1.3.4

Post-Harvest Phenotyping

Economically important harvested part of the crop can be assessed for yield and quality through various sensors and GPS (global positioning system) installed in tractors and (combine) harvesters such as cotton (Pelletier et al. 2019), grain (Li et al. 2016) and blueberries (Farooque et al. 2013) yield determination. Various smart farming machines such as advanced farming system (AFS, Case IH, US) have been commercialized for quality assessment in the seed and milling industry, i.e., seed

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phenotyping using 2D imaging technologies (Gegas et al. 2010). Fully automatic machines for phenotyping of rice panicles, scoring yield traits (Duan et al. 2011), maize stalks (Mazaheri et al. 2019), kernels (Miller et al. 2017), and tassels (Gage et al. 2017) have been developed. In addition, user friendly and free-use image analysis software such as SmartGrain is used to analyse seed shape and size (Tanabata et al. 2012), PANorama (Crowell et al. 2014) for efficient quantification of traits related to rice panicle and grain, P-TRAP (AL-Tam et al. 2013) and GrainScan, for traits related to colour and size (Whan et al. 2014). Further, android based mobile application, SeedCounter, is developed to measure grain size and number (Komyshev et al. 2016). Ultimately, combination of multidimensional seed traits with genetic analysis tools and developing cost-effective photonicsbased phenotyping tools will improve the extendibility and reliability of future crop research (Yang et al. 2020).

1.4

High-Throughput Phenotyping to Strengthen Trait Mapping

Genetic mapping, also as linkage mapping, is a process to fix the locus of a gene and the relative distances between the genes on a chromosome (https://www.genome. gov). Genetic mapping aims to locate a group of DNA markers onto their respective positions on the genome, so that the genes in relation to phenotype can be earmarked. In 1911, Morgan first explained the concept of linkage map in Drosophila melanogaster (Morgan 1911). Later on, during meiosis, identification of recombinant gametes due to crossover enables genetic distance calculation between two loci (Sturtevant 1913). Genetic mapping requires the heritable trait, availability of polymorphic DNA markers and mapping populations segregating for the trait of interest. A variety of mapping populations are deployed to map and locate the genes or QTL in concert with phenotyping of the population. There are two main types of populations deployed to map the gene(s), viz. naturally available individuals of one species also called germplasms and/or synthetic population created by involving bi- or multi-parents differencing for the traits to be studied. The construction of synthetic population is advisable, where the controlled breeding and shorter life cycle in animals/plants are amenable. It is always preferred to take the extreme phenom expressing parents for the gene under target to construct mapping population. Synthetic population in plants is also depends on the mode of reproduction, i.e., self naturally/manually and on the other hand selfincompatible and inbreeding sensitive. Hence the kind of mapping population depends on the availability of plant material types. The mapping populations appropriate for self-fertilizing plants are F2 generation plants, recombinant inbred lines and introgression lines are popular. While, inbreeding sensitive or self-incompatible heterozygous parents are used only to derive mapping populations such as F1, double haploid lines and backcross populations are applicable to both the aforementioned types.

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In case of barley, high degree of correlation between images of HTP platform [“Plant Accelerator”, Adelaide, Australia] and actual biomass accumulation was demonstrated in 47 juveniles (6 weeks old) barley introgression lines during drought. Subsequently, 44 QTL for 14 traits such as water use efficiency and growth rate were mapped and revealed that juvenile plant phenotyping can predict the performance of adult plant in barley (Honsdorf et al. 2014). SmartGrain software for high-throughput measurement of seed shape in backcrossed inbred lines obtained by crossing japonica rice cultivars Nipponbare and Koshihikari detected several QTLs for six shape parameters (Tanabata et al. 2012). Similarly, using rice automatic phenotyping platform (RAP), Zhang et al. (2017) quantified 106 traits (ten plant morphological, 64 growth-related traits, six histogram texture, three biomass-related, one plant colour, and 22 leaf architecture) in maize RILs (n ¼ 167) from sixteen developmental stages. For all investigated traits, 988 QTLs were identified including three QTL hotspots. The power of highthroughput phenotyping enabled near precision prediction of yield and biomass, using early stage trait combinations in maize.

1.5

Trait Associations

Breeding is a process of trait’s value addition in an individual through recombination and selection. Having conspicuous phenotype at field is difficult for most of the quantitative traits specifically for nutritional and stress tolerant traits. Therefore, breeding prioritization of such traits stands after yield. Nevertheless, the present advancement in genomics and phenomics guide in selection and improvement of such traits by studying various other associated traits. Recently, various researchers have developed successful varieties/breeds in different crops and dairy animals by using trait association models. Longitudinal traits express throughout the life cycle of an individual (Oliveira et al. 2019a, b). Hence, measurement of such traits helps to build the genotype specific phenotypic expression model for various stress responses and developmental milestones. This information can also be used to uncover the time specific expression of complex polygenic traits (Collet and Fellous 2019). In barley, various sensors captured images on daily basis over 58 days of variable stress treatments using visible light, near infra-red (NIR) and fluorescence spectrum to dissect phenotypic mechanism of drought (Chen et al. 2014). Likewise, Neilson et al. (2015) explored the growth dynamics and fertility in sorghum under different levels of water stress and capture images using NIR, RGB, and laser scanning cameras for traits such as leaf greenness, tiller number, height, shoot biomass, and leaf area. Tillering in rice was modelled using about 700 associated traits derived from a computed tomography (CT) based Red–Green–Blue (RGB) imaging system during vegetative period (Wu et al. 2019).

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Next-Generation Phenotyping and Genome-Wide Association Studies

Free accessibility of whole genome sequence data across a number of plant species has opened exciting avenues to perform genome-wide scans for trait analysis. Advances in next-generation sequencing (NGS) technologies facilitated to harness the immense potential of reference genome sequence information. In the context, whole genome resequencing (WGRS)/skim sequencing and genotyping-bysequencing have facilitated sequence-based genome-wide association study (GWAS) for gene or QTL discovery across various crops (Bohra et al. 2020). WGRS based studies in different crops have enabled access to large-scale sets of DNA markers including SNPs, InDels, etc. Potential of large-structural variations such as presence-absence variation (PAV) and copy number variation (CNV) was explored to identify marker-trait associations (MTAs) for significant plant traits (Varshney et al. 2017). Phenotyping plant traits in an accurate, precise, and high-throughput manner have been a great stumbling block for progress of genetic studies and crop breeding alike. However, recent availability of next-generation phenotyping platforms particularly based on remote sensing technologies has emerged as a great tool to underpin gene discovery in plants (Cobb et al. 2013). Besides being efficient in terms of both cost and time, high-throughput phenotyping (HTPP) platforms have also uncovered the inaccuracy associated with the measurements on plant phenotypes recorded manually or based on visual scoring (Araus et al. 2018). In humans, as demonstrated by Neumann et al. (2010), live imaging technique can successfully uncover the realtime cell division on genome-wide scale by using advanced image analysis tools. Yang et al. (2014) combined high-throughput rice phenotyping facility (HRPF) with GWAS for analysis of a diverse collection of 533 rice accessions including landraces and elite genotypes. RAP and yield traits scorer (YTS) constitute the two component of HRPF facility. The study elucidated a total of 141 significant associations for 15 traits. The HRPF allowed HTP recording of both traditional traits as well as novel traits, i.e., plant compactness and grain-projected area, which could not be scored using conventional phenotyping tools. Recently, Sun et al. (2019a) performed phenotyping of 80 cultivated rice accessions for Normalized Difference Spectral Index (NDSI) using hyperspectral technology (ASD FieldSpec4 Hi-Res Spectroradiometer). The panel was re-sequenced with Illumina platform. GWAS analysis on the panel for hyperspectral data elucidated a total of known 43 genes and 22 genes that were not reported previously. A strong correlation of NDSI with protein content encouraged authors to use NDSI as a HTP measure alternative to a biochemical trait-like protein content. Owing to the growing availability of multi-omics platforms, high-throughput analysis has now been possible for RNA transcript abundance, metabolites, proteins, and epigenome. GWAS has been combined with transcriptomics (TWAS; Kremling et al. 2019), metabolomics (MWAS; Zhou et al. 2019), proteomics (PWAS; Brandes et al. 2020), and epigenomics (EWAS; Quadrana and Colot 2016) to offer highresolution genetic dissection of complex traits via analysis of these endophenotypes.

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As evident from the growing literature, the non-destructive high-throughput phenotyping has emerged as a great strength to leverage GWAS for functional genomics and gene discovery studies.

1.7

Enhancing Accuracy of Genomic Selection

Twenty years ago, Meuwissen et al. 2001 proposed the concept of using genomewide marker information to predict the genetic worth of individuals. The concept has accelerated genetic gains in animal breeding, and results from plant breeding are encouraging (Crossa et al. 2019). Genomic selection (GS) harnesses genome-wide linkage disequilibrium in comparison to the marker-assisted selection methods that rely upon selection of superior individuals based on a set of DNA markers significantly associated with the phenotypes. “Training population” and “Breeding population” constitute the two key components of the GS strategy. Individuals from “Training population” are scored at both genotypic and phenotypic levels. The genotypic and phenotypic information obtained from training population are then used to compute genomics-estimated breeding values (GEBVs) that form the basis of the selection of individuals from “Breeding population” that are having only genotypic data (Varshney et al. 2019). Therefore, the process of GS saves the time and cost invested on repeated phenotyping of the populations. Recent years have witnessed a surge of GS studies not only in major crops like rice (Huang et al. 2019), maize (Crossa et al. 2014), wheat (Velu et al. 2016; Sukumaran et al. 2017, 2018) but also lesser studies legume crops like chickpea (Roorkiwal et al. 2016, 2018), pea (Annicchiarico et al. 2017), etc. Accurate phenotypic data remains crucial to inform GS models, and hence HTPP assumes a pivotal role to accelerate GS applications in plant breeding. A recent study in wheat reported 146% increase in prediction accuracy for grain yield when GS was applied with secondary traits such as canopy temperature and normalized difference vegetation index (NDVI) (Sun et al. 2019b).

1.8

Bioinformatics and Big Data Analysis

With the varying imaging sensors, the imaging data formats also vary widely, thus it is challenging to conclude from image analysis (Yang et al. 2020). According to the workflow, preprocessing of the raw image, image segmentation, feature extraction, and trait selection to develop dynamic mechanistic models using deep neural networks or recurrent neural networks, huge amount of data is generated. In another way, major challenge of functional phenomics is acquisition of huge phenotypic data besides analysing it for knowledge generation (Tardieu et al. 2017; York 2019). To solve this challenge, on the one hand, we have to reduce/distill phenotypic data to traits of interest through available computational algorithms for mapping these traits with genotypic data (Fig. 1.1). On the other hand, retaining only processed information limits our current ability to convert data into knowledge (Furbank et al. 2019). Breeding in the “omic” era is a multidisciplinary blend of phenomics of various

Fig. 1.1 Diagrammatic representation of the integration of phenomics with genomics for climate-smart agriculture. Phenomics is performed by running large set of multiple phenotypic assays on genetic variants along with environmental changes, quantitative trait loci (QTLs) analysis and their integration using bioinformatics tools. The big data generated is validated using algorithmic analysis, online databases, metadata analysis, and computational modelling. This results in prediction of regulatory gene expression of related phenotype leading to precision breeding for crop improvement

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abiotic stresses with high-density genomics and scripting software, i.e., PYTHON and R processing pipelines (Furbank et al. 2019). Another challenge is to manage and share this “Big Data”. To address this question, in 2016, Wilkinson and co-workers coined FAIR (findable, available, identifiable, and reusable) principle, to help in metadata analysis such as CropSight (Reynolds et al. 2019) and PHENO PSIS DB (Fabre et al. 2011). Similarly, to facilitate reuse of metadata, development of OPEN data infrastructures is equally importantly such as iPlant cyber infrastructure (CI), which provides high-performance computing, and large data access across multidisciplines (Goff et al. 2011). Further, CGIAR proposed “The Crop Ontology of the Generation Challenge Program”, aiming towards online data management tool to integrate genotypic and phenotypic data of various plants (Shrestha et al. 2012). Several other integrated genomics and phenomics open-source softwares and databases were developed, such as BrAPI (an application programming interface for plant breeding; Selby et al. 2019), Phenobook (open software for plant breeding data collection; Crescente et al. 2017), and Planteome database (an integrated ontology resource; Cooper et al. 2018). Although much of the data available in the analytics domain of the commercial sector remains inaccessible, but few publicly funded agencies provide freely accessible analysis pipelines through web portals (Furbank et al. 2019). Dynamic and curated trait ontologies are essential to enable cross referencing of multiplicity in descriptions for either single trait or process. PODD (Phenomics Ontology-Driven Database) is an ontology-based method to manage and link images with phenomics metadata (Li et al. 2010). Phene networks are determined using network analysis (Bartsch et al. 2015; York 2019) and structural equation modelling (Tétard-Jones et al. 2018). Thus, synergistic efforts are required to develop data infrastructure, multivariate statistics, generation of funds, integration of resources with bioinformatics to expedite crop breeding and functional genomics (Yang et al. 2020).

1.9

Conclusion and Future Directions

In an era of accelerated global climate change and global population, developing “climate smart” crop varieties have become the need of the hour, which requires meticulous efforts in identifying and characterizing genes and translating the proof of concept to the farm gate. Therefore, ultimate desirable outcome of agriculture biotechnology is to develop “all-inclusive” crop varieties to ensure higher yield and nutritional security. Thus, integrative plant biology approach requires to correlate canopy performance, environmental responses, and gene function with highresolution and rapid pace. The next-generation phenotyping platforms will help in widening their knowledge on the multitude of diverse plant stress responses. Automated phenotyping tools capture structural, functional, and phenotypic expression data of various genotypes under varied abiotic stresses and thereafter organize, analyse, and store information in various datasets and finally perform modelling on plant’s performance under varied environmental scenarios. Research advances in plant phenomics tools and techniques hold immense potential in the development of

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Table 1.2 Some of the national and international networks established worldwide as community resources for high-throughput phenotyping Network Australian plant phenotyping facility (APPF)

German plant phenotyping network (DPPN)

PHENOME

European plant phenotyping network (EPPN)

Nord American plant phenotyping network

European infrastructure for multiscale plant phenotyping and simulation for food security in a changing climate (EMPHASIS)

International plant phenotyping network (IPPN)

Description First institutional programme, started in Australia. It provides a platform for large-scale phenotyping, data mining and collaborative network for sharing and research innovation programme on a national scale Started in 2012, which targeted a shift in in-house phenotyping platform to field level for better results Started in 2013, in France

A community network started to access 23 small individual crop specific installation under Europe such as (AGRON-OMICS, SPICY, EURooT, DROPS). After successful experiments, demand increased, and EPPN 2020 an advanced community has provided a cost-effective phenotyping platform A collaborative network to connect all scientist of the phenotyping area in North America EMPHASIS works on five pillars that are: Controlled experimental conditions; intensive controlled conditions in fields; lean field; visual modelling platforms; data and computational services Till date, approximately 40 organizations ranging from academic to industry are working in different domains of plant phenotyping at international level

Websites https://www. plantphenomics.org. au/)

https://dppn.plantphenotyping-network. de/index.php? index¼6) https://www. phenome-emphasis.fr/ phenome_eng/ http://www.plantphenotyping-network. eu/ EPPN 2020 (2017–2021) https://eppn2020. plant-phenotyping.eu/

http://nappn.plantphenotyping.org/

https://emphasis. plant-phenotyping.eu/

https://www.plantphenotyping.org/)

crop genotypes with improved resilience. Timely investments with collaborative efforts have provided innovative opportunities to deploy resilience mechanisms to provide solutions for improved sustainability, nutritional value, and crop yield (Table 1.2). Greater investment in high-throughput phenomics is required to fill the gap between genotyping and phenotyping to accelerate mapping genes and downstream mechanisms for “speed breeding” of climate-resilient and stress tolerant

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varieties. With rapid advancement in artificial intelligence (DL), cloud based technologies, fifth generation mobile networks, automation technology, mechanized vision, and sensor technologies, phenotyping have shown immense potential in fundamental research as well as breeding. Acknowledgements RJ gratefully acknowledges the Director, CSIR-Institute of Himalayan Bioresource Technology, Palampur, for providing the facilities to carry out this work. CSIR support in the form of project MLP0201 and GAP0254 for this study is highly acknowledged. This manuscript represents CSIR-IHBT communication no. 4790.

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Application of “Omics” Technologies in Crop Breeding

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Rahul Priyadarshi, Pragya Sinha, Aleena Dasari, and Raman Meenakshi Sundaram

Abstract

A significant increase in genetic gains of different crops can be achieved by the pragmatic use of modern breeding techniques. The improvement in “omics” technologies makes an opportunity to generate different datasets for several crop species. The “omics” approach coordinates information gathered from the genome, transcriptome, proteome, and metabolome into a solitary informational collection which can rapid the recognizable proof of doubtful qualities and their administrative organizations related with metabolic pathways of interest. The analysis of genetic and phenotypic data using genomics and functional omics together is the main method to identify genes and pathways responsible for important phenotypic traits. The screening of large number of germplasm pools to identify unique alleles from various sources through high-throughput genotyping technologies. This approach enhances the availability of variation for breeding. The various omics tools and approaches like high-throughput genotyping platforms such as whole genome re-sequencing, proteomics, and metabolomics give more significant prospects to explore molecular phenomenon and the exposure of important genes for developing ideal genotypes which can survive in the rapidly changing climate. This book chapter provides an introduction to the core omics technologies, their relevant tools and methodologies, and application for developing and screening of breeding lines to accelerate precision breeding efforts in crops.

R. Priyadarshi International Rice Research Institute (IRRI) – India, Guwahati, Assam, India P. Sinha · A. Dasari · R. M. Sundaram (*) Department of Biotechnology, ICAR-Indian Institute of Rice Research (IIRR), Hyderabad, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Kumar et al. (eds.), Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), https://doi.org/10.1007/978-981-16-2956-3_2

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Keywords

Genomics · Transcriptomics · Proteomics · Breeding · Marker-assisted selection · Next-generation sequencing · Speed breeding

2.1

Introduction

Ensuring adequate food and nutritional security is an important global agenda and is a key sustainable development goal set by the United Nations. The agricultural production and productivity has shown remarkable progress over the past 50 years. Increasing and sustaining crop productivity amidst decreasing land, water resources, and other inputs is one of the key concerns in the realm of a rapidly changing climate. Three aspects have strengthened the pace of crop improvement: (i) improvement of new varieties through plant type-based breeding approaches; (ii) increase in the irrigation facilities; and (iii) increase in the use of fertilizers and chemical fungicides and insecticides. Among these, only the first approach can be considered as sustainable and needs to be adequately focused. Conventional plant breeding is based on phenotypic observations, but it is time consuming and its contribution remains insignificant in case of crop improvement programs that involve complex traits like yield, abiotic stress tolerance. To maintain good rates of genetic gain in cereals, breeders evaluate a million of germplasm, pre-breeding and breeding lines each year in their breeding programs. The availability of molecular markers has provided an opportunity to identify genetic position of key genes/ QTLs in crop genomes and improve them through target-oriented breeding. The existence/availability of genetic variation is key factor for crop improvement. The incidence of genetic gain in plant breeding process can be increased by utilization of available natural variation and also by using best selection method to develop new varieties speedily (Cobb et al. 2019). Genomics-assisted breeding can provide support and basis for both the methods. It has been observed that several “omics” platforms improve our capacity to unravel the genes/QTLs controlling various agronomically important traits, their allelic variation and also the pathways that control specific traits of interest and offer various high-throughput platforms, which are helpful in the selection and breeding process. “Omics” technology is helpful in identification of genes/QTLs underlying apparent and also hidden traits. It is also known as large-scale data rich biology, which can be used for heavy data mining. With the increasing availability of huge array of genomic, transcriptomic, proteomic information in databases, geneticists and breeders are facing a daunting challenge on how to exploit the information about functions of each gene/QTL to develop better crop varieties and hybrids. To overcome this challenge in functional genomics, the “era of omics” has been ushered in (Kandpal et al. 2009). The different disciplines of omics which are molecular biology and biotechnology such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, physiognomics, phenomics, etc., have emerged as key components of the comprehensive molecular studies for identification of candidate

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genes underlying agronomically important traits and their utilization in breeding process (Varshney et al. 2019). The omics methods are transforming plant breeding process by accelerating its throughput and precision. These technologies support a direct and equitable observation of the reasons which affecting plant growth, biotic and abiotic stress tolerance and ultimately, become the holy grail for breeder’s and geneticists, for increasing crop yield. These studies additionally give information, how to explore the complex interaction between the plant, its metabolism process, and moreover the information about abiotic stress and biotic stress or threats by insects, fungi, or other biological agents. These technologies also help in find out the cause of better performance of agronomic traits at the biochemical levels, molecular levels, and physiological levels. Ultimately, this will be helpful in improvement of crop production (www.genedata.com). The initial draft of the genome of the first plant (Arabidopsis thaliana) to be sequenced took approximately ten years to be developed. Nowadays, the next generation of DNA sequencing technology (e.g., Ion Torrent, Ion Proton, Oxford Nanopore, and PacBio RS, among others) along with powerful bioinformatics and computational modeling programs can be used for genomes sequenced, assembled, and correlated sequence variations with the phenotype of targeted traits specific to each genotype within a few weeks. This capability, combined with the drastic cost reduction of sequencing process, has enabled the generation of an ever-increasing volume of data, thus enabling the comprehensive study of genomes and also assisting the development of informative molecular markers. This approach is helpful in understanding of genetic factors of complex traits and generating a huge amount of electronic information that can be used in the selection of superior genotypes of targeted traits in the different crop (Pérez-de-Castro et al. 2012). RNA interference (RNAi) or post-transcriptional gene silencing (PTGS) approach has helped in the development of transgenic plants in different crop which are able to suppress the expression of endogenous/foreign genes and resulted in manipulation of several recalcitrant traits (Vaucheret et al. 2001). Such integrated studies enhance our knowledge and understanding of the candidate gene(s) and develop strategies to gainfully utilize this information to improve the crop varieties and hybrids. Recently, elite mega varieties of rice were improved against bacterial blight resistance using CRISPR/Cas9 technology by creating editing and developing promoter mutants of SWEET gene(s) and simultaneous introgression of five promoter mutations into varieties IR64, Ciherang-Sub1, and Kitaake (Oliva et al. 2019). This chapter covers recent examples of the applications of “omics” technologies for crop improvement and discusses future developments in the improvements of different crop end-use quality.

2.2

Components of “OMICS” Technology

Omics is the advanced approach in the fields of biotechnology which includes the following tools, viz., genomics, transcriptomics, proteomics, metabolomics, phenomics, and physiognomics, among others. The main components of “omics” technology are outlined as follows (Fig. 2.1).

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Fig. 2.1 A graphic representation of flow of information and application of Omics technologies

2.2.1

Genomics and Plant Breeding

Genomics is fields of which is concerned with the structure, function, evolution, and mapping and editing/manipulation of genomes. In plant sciences, genomics involves in the large-scale analyses of structural and functional information of genome, which allows the detection of evolutionary and functional dynamics in plant of different crop. Genomics investigates how variation in gene affects protein structure, protein function, and non-coding RNAs throughout the life of a cell. The broad prospects of genomics include DNA sequencing, development of sequence-based markers, genetic mapping, and genome-wide selection (GWS). These measures allow the molecular breeders and plant biologists to develop designer crop varieties and hybrids, which are resilient to biotic and abiotic stresses and vagaries of a rapidly changing climate. Further, these tools and techniques can also be used for improvement of quality related traits and yield to ensure sustainable food and nutritional security in the future. The term genome sequencing refers to elucidating the pattern of arrangement of nucleotide bases in the entire genome (including its organellar genomes). Once the genome sequence is available, it should be annotated with the help of computer programs, based on several algorithms. This in-silico analysis relies extensively on the similarity of sequences of the new genotype to the already available gene sequences in sequence databases, such as GenBank, EMBL, DDBJ, etc. The most widely used similarity detection tool is the BLAST program (Altschul et al. 1997). All the above comprise structure genomics. These efforts are followed by functional genomics, wherein researcher attempts to elucidate the function of each and every

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gene annotated in the genome through various approaches and this is called as functional genomics. The knowledge of available reference genomes of different crop species and the high-throughput nature of sequencing projects have provided innovative informations for plant breeder to study inter-relationship between different genomes through comparative genomics. Genome-wide selection (GWS) can be a useful approach to the improvement of any plant species. The information about the location of marker in reference genomes provides further utilization in the examination for fundamental regions and the estimation of genomic values. With the development of automated sequencing technologies, new markers progressively accessible in public databases. Now, different molecular markers are available which differ from each other in their ability to detect DNA polymorphism, reliable cost, easiness of use, and robustness. The DNA markers have been used in the study of genetic diversity, DNA fingerprinting, seed genetic purity, genetic mapping, gene identification, and molecular marker-assisted breeding (Borem 2009). Among different applications, one can explain the marker-assisted breeding, which deals with identification of linked markers of targeted genes that control important phenotypic traits and gainfully utilizing this information in breeding process. Among the DNA markers, the latest advent, single nucleotide polymorphism (SNP) molecular markers are gaining predominance in plant breeding programs due to their abundance and amenability for high-throughput analysis (Jafar et al. 2012). The genomics-assisted breeding is used for identification of unique genes for yield and quality traits for sustainable production of legume crop (Afzal et al. 2020).

2.2.2

Transcriptomics

Transcriptomics is the study transcribed genes, their expression and functions. It includes the study of the expression and quantum of set of transcripts (RNAs), including messenger RNAs (mRNA) and non-coding RNAs (ncRNAs), produced by any cell, tissue, or organism. Transcriptome analysis has been broadly used to identify the global expression pattern of genes. Various techniques used for studying a transcriptome can be divided into two types, viz., (i) sequencing-based approach, which includes expressed sequence tags (EST) sequencing, serial analysis of gene expression (SAGE), RNA-seq, massive parallel signature sequencing (MPSS), and (ii) hybridization-based approach, which includes tiling arrays and microarray technology (Jiang et al. 2015). EST sequencing has been widely used to survey the transcriptome of many plant species. ESTs are short sequence reads derived from the partial sequencing of complementary DNA (cDNA) sequences. The construction of a cDNA library and the generation of ESTs involve: the isolation of total RNA and purification of mRNAs; synthesis of cDNA using reverse transcriptase enzyme; cloning of cDNA fragments and sequencing of randomly selected clones using the Sanger method (Sanger et al. 1977). After generating ESTs, the next step is sequence analysis, which can be divided into pre-processing, clustering/assembly, and EST annotation.

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Generally, the annotation of contigs can be performed by sequence similarity searches comparing DNA or protein sequences placed in public databases, such as the GenBank. BLAST algorithms from the NCBI (National Center for Biotechnology Information) are used for similarity searches of ESTs against nucleotide (BLASTN) or protein (BLASTX) sequence databases (Altschul et al. 1990). Collections of ESTs generated by the sequencing of cDNA libraries are stored in EST database repositories. The first database created for EST sequences was dbEST (Boguski and McCormick 1993). Other public databases include EMBL, which also archives all available ESTs, and UniGene (Boguski and Schuler 1995; Schuler et al. 1996), which contains clustered EST sequences retrieved from dbEST. The separation of either DNA or cDNA molecules that distinguish two related samples was done by suppression subtractive hybridization (SSH). In particular, the SSH technique can be applied to study transcriptomics, as it is a comparative method that examines the relative abundance of transcripts of a sample of interest in relation to a control sample (Lukianov et al. 1994; Gurskaya et al. 1996; Diatchenko et al. 1996). Among all the diverse methods, Microarray technology has become one of the preferred transcriptomic approaches for many plant biologists. This technology consists of an array of single-stranded DNA molecules, called probes (fragments of genomic DNA, cDNA, or oligonucleotides) chemically linked onto a solid surface, usually glass slides, which are called chips (David et al. 2010). In a single DNA microarray, it is possible to analyze the differential expression of about 10,000–40,000 targets per chip (Mockler et al. 2005). Thus, this technique allows the instantaneous investigation of thousands of genes at gene expression levels. Another approach to this technique is genomic analysis, using high-density oligonucleotide-based whole genome microarrays. This platform permits the analysis of alternative splicing, characterization of the methylome, comparative genome hybridization, genotyping, polymorphism detection, and genome re-sequencing (Mockler et al. 2005). Although microarrays have the ability to evaluate a large number of transcripts simultaneously, the greatest disadvantage of this method is that they detect only the transcripts of genes that have been previously characterized and is an indirect method to measure the transcript abundance. RNA-seq is another revolutionary tool of transcriptomics which is used for the high-throughput sequencing of cDNA, and is based on the direct sequencing of transcripts (Wang et al. 2009). In this technique, a library of cDNA fragments with adaptors attached to one or both ends were developed from total RNA. The short sequences from one end (single-end sequencing) or both ends (pair-end sequencing) were done through the high-throughput sequenced of with or without amplified each molecule. The size of reads varies from 30–400 bp which depends on the type of the DNA sequencing technology used. A reference genome or assembled de novo without the help of the reference genome sequence or reference transcripts were used for sequencing, the resulting reads for either aligned to produce a genome-scale transcription map. This map consists of the transcriptional structure as well as level of expression for each gene information.

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This technique is more dynamic, reproducible, and affords a better assessment of the complete expression levels (Nagalakshmi et al. 2008; Fu et al. 2009). Another advantage is that the analysis of RNA-seq allows us to identify isoforms of a gene, which are not easily detected using microarrays (Wang et al. 2009). Genome-based crop development studies and the availability of genomics data as well as decisionmaking gears look be specific for breeding programs.

2.2.3

Proteomics

It is defined as the large-scale description of the whole protein component of an organism or cell or tissue. The proteomic survey involves total protein extraction methods, sub-proteomics, post-translational modifications (PTMs), etc. Therefore, this technology is helpful in accumulating all the proteins in a cell to generate a complete three-dimensional (3-D) map of the cell close-fitting the structural and functional aspects. Proteomics is efficient in learning the huge number of proteins in enormous number of biological samples, all at once, rapidly, reproducibly, constantly, and faster analysis. There are three mains steps of proteomics which include (i) extraction and separation of proteins (ii) identification of proteins, and (iii) verification of protein (Liu et al. 2014a). All these steps will certainly require the involvement of different disciplines of biology such as biochemistry, bioinformatics, and molecular biology (Graves and Haystead 2002; Chen and Harmon 2006). The initial proteomics analysis was done with electrophoresis gel-based method for the resolution of proteins by using one-dimensional electrophoresis in polyacrylamide gel (SDS-PAGE), which separates the proteins on molecular mass basis. Later, the focus shifted to two-dimensional electrophoresis in polyacrylamide gel (2D-PAGE), which separate proteins in two dimensions, based on molecular mass and isoelectric point (Liu et al. 2014b; Marin-Garnica 2007). These approaches were still considered as the touchstone of proteomics and do have various limitations (O’Farrell et al. 1977). But, the progresses in gel-free methods the Mass spectrometry (MS) is a new powerful approach for the identification of combination of different proteins. This technology uses a bottom-up approach where first digestion of proteins with a proteolytic enzyme, and the mixture of complex peptide obtained is analyzed by mass spectrometry, which may also be preceded by a separation step with the help of liquid chromatography (LC). There are many types of proteomicsbased MS including; LC-MS, 2-DE/MS, and Matrix-assisted laser desorption ionization MALDI-TOF. MALDI-TOF/MS is a set of analytical techniques which are based on ionization of charged molecules as well as mass to charge ratio and has sensitivity and selectivity compared to other MS techniques. Hence they are considered as most appropriate, high-throughput proteomics-based MS technique (Wang et al. 2015; Mellon, 2003; Cao and Limbach 2017; Roy et al. 2019; Byrum et al. 2010). It has the automation of the analysis channel and analytical tool to the highthroughput platform for the study of genomic (Gygi et al. 1999; Zhou et al. 2002;

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Gygi et al. 2002). The primary objectives of plant proteomics are to get insight into the physiology and biochemical mechanisms in different crop plant species, crop varieties and their presentation to development factors, yield and yield related traits, biotic stress resistance, abiotic stress tolerance, etc., and finally to develop better and harmless crops to meet the objectives of food security. This approach promotes sustainable agriculture practices.

2.2.4

Metabolomics Approach in Crop Breeding

The phenotype of any trait is associated with metabolites as they are the final products of all the cellular processes. The interaction of protein–protein, protein– RNA, and protein–DNA is associated with the metabolic constituent of the cell either directly or indirectly through cellular feedback mechanisms. The study of the qualitative and quantitative outline of metabolites existing in the organism is called metabolomics (Hall 2006). These techniques along with other technology such as transcriptomics and proteomics try to present the detailed pictures of the complete cellular procedures. The complete study of plant metabolism is possible using different technology because of the presence of a high degree of diversity of Plants metabolites. The recent advances in mass spectrometry (MS) technology accelerate the metabolics process to achieve the goals of functional genomics. Several techniques for separation and quantification of such metabolites can be used independently, or in combination which helps in the identification of a particular group of metabolites (Dunn and Ellis 2005). The gas or high-pressure liquid chromatographic or electrophoretic methods coupled with mass spectrometry are the initial step in metabolomics studies for the separation and quantification of metabolites which are often used for deciphering the metabolic profile of organisms (Lisec et al. 2008). In addition, nuclear magnetic resonance (NMR) spectroscopy is used to study and classify different metabolites (Fernie and Schauer 2008). Along with these tools, information technology is a requirement for metabolomic investigation (Wishart 2007). Recently, various online web-based courses have been intended to aid in data calculation, data processing, data explanation, and data mining correlated to metabolomics (Gardinassi et al. 2017). The use of metabolomics approach in crop breeding incorporates the investigation of various types of metabolic pathways (Saito and Matsuda 2010), engineering of metabolite in plants of different crop (Rischer and Oksman-Caldentey 2006), the secondary metabolism in plants of different crop (Keurentjes 2009), plant nutrition in different crop (Keurentjes 2009), plant development of different crop (Peluffo et al. 2010), phenotypic study of different crop (Riedelsheimer et al. 2012). The use metabolomics in future may comprise identifying metabolic markers to study plant metabolism and to understand the nature and development of biotic/abiotic stress tolerance (Sharma et al. 2021). To develop high-yielding crop varieties, stresstolerant crop varieties/hybrids, and also to develop climate change resilient crops

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will be used metabolomics-assisted breeding (Fernie and Schauer 2008; Kumar et al. 2017; Sharma et al. 2018, 2021).

2.3

Phenomics and Physionomics

It is defined as the collection of whole multi-dimensional phenotypic data in an organism (David et al. 2010). It is a fast developing area of science which aims at describing phenotype in a laborious and proper way which gives information about genes/alleles of targeted different traits (Close et al. 2011). The different techniques under phenomics are used for speed up phenotyping, which include automated hightech sensors, imaging system, and computing power such as visible light imaging, thermographic imaging, hyperspectral imaging, X-ray, etc., and they are used at different stages of plant growth and development. One of the earliest phenotyping tools widely used to increase accuracy of phenotype was the infrared gas analyzer (IRGA). Next-generation phenomics approach aims at rapid discovery of genes as well as significant improvement in selection of targeted trait in a plant breeding program. This approach is useful in the characterization of germplasm, aids in genomic selection (GS) and QTL identification. In order to increase the precision, phenomics studies are often combined with trait information, gene sequence, transcripts, proteins, and metabolites data as they are key component which act together with the environment to produce the biodiversity and drive the generation of new diversity. The approach of next-generation phenomics will be a key component in our efforts to overcome the effect of climate change and other global challenges in food production. Physiology is used for study of functional processes which ranging from germination of seed to development of flower and fruit. It integrates different information which is anatomy and morphology, along with biochemical mechanisms. Hence physiology is a topic of knowledge which is advanced by the approach of omics. Fundamentally, physionomics aims to understand how plant functions under different conditions. Physionomics introduces to use of different omics approach for functional studies and plant breeding in different crop. The close approach to physionomics is called as “phenomics” (Furbank and Tester 2011; Berger et al. 2010; Tisné et al. 2013). The knowledge of phenomics can be directly used for the decision-making in crop breeding programs.

2.4

Next-Generation Sequencing (NGS)

This technology is also called high-throughput sequencing which revolutionizing the science of “omics” in many ways (Suma and Sivakumar 2016). These technologies include Pyrosequencing, Sequencing by synthesis (Illumina), Sequencing by ligation (SOLiD; ABI), single molecule real time sequencing (Pacific Bio), etc. (Ambardar et al. 2016). Many of the important crops like pigeon pea, tomato, and rice of economic importance traits have been sequenced through NGS technology.

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The application of Next-generation sequencing broadly comprises both genome (i.e., DNA) sequencing and RNA sequencing (which has been discussed briefly in an earlier section). With the recent advancement in sequencing technologies, whole genome sequencing has enhanced our understanding of the interactions between sequence variations in the genome in an organism and their phenotype (Ambardar et al. 2016). Whole genome sequencing along with long read sequencing is used to assemble unique genome sequences (which is referred to as DeNovo sequencing) and also to rapidly identify and characterize the genetic markers associated with different traits of agronomic importance. One of the most common applications of NGS in genome sequencing is SNP discovery, whose downstream applications like linkage map construction, molecular mapping, genetic diversity analyses, phylogenetic analyses, genome-wide association mapping, and marker-assisted selection and this has been very well demonstrated in several crop species like rice, sugarcane, maize, wheat, barley, etc. (Kumar et al. 2012). The application of NGS techniques in studies of transcriptomics is known as RNA-seq (Jain 2011). It helps in transcriptome analysis as well as expression profiling for a particular gene or set of genes. RNA-seq is replacing the use of microarray methods, due to its great simplicity of implementation, facilitating analysis of large number of known and unknown genes and also splice variants. The main use of RNA-seq is profiling of gene expression levels and to find differentially expressed transcripts among groups of different samples, in addition to identification of splice variants. With fast advancements in the next-generation sequencing techniques, the profiling of small RNA molecules like miRNA and siRNA has been accelerated through high-throughput techniques like RNA-seq (Baker 2010). MicroRNAs (miRNAs) are key post-transcriptional regulators that affect protein translation by targeting mRNAs. The major application of the miRNA profiling is in differential gene expression as well as target predictions. The important application of NGS are chromatin immunoprecipitation sequencing (ChIP-Seq) to study protein–DNA interactions and crosslinking immunoprecipitation (CLIPSeq) to study protein–RNA interactions (Johnson et al. 2007).

2.5

Application of “Omics” Technology

The deployment of omics tools in the plant breeding reduces the time, efforts, and cost of producing additional quantity of food crops in addition to making them resistant to stresses and ensures that they have better nutritive values. Omics has provided understanding of the molecular level cause of resistance and adaptation reaction against biotic and abiotic stress factors. It also allows deployment of a systems biology method to understand the complex interactions between genes, proteins, and metabolites. Omics relies deeply on molecular biology procedures, computational analysis, bioinformatics, and other different disciplines of life sciences. Due to fast advancement in omics technologies, it is possible to create comprehensive omics datasets for various crop species. The status of omics technology for the “Top 20” different crop species in global production is presented in Table 2.1.

Zea mays

Oryza sativa

Triticum Aestivum

Solanum Tuberosum Beta vulgaris

Maize

Rice

Wheat

Potato

Glycine Max Solanum Lycopersicum

Hordeum vulgare

Soybean

Barley

Tomato

Manihot Esculenta

Cassava

Sugar beet

Scientific name Saccharum Officinarum

Genotype Sugarcane

Diploid, 5 Gbp

Diploid, 1.1 Gbp Diploid, 950 Mbp

Polyploid, 850 Mbp Diploid, 758 Mbp Diploid, 760 Mbp

Hexaploid, 17 Gbp

Diploid, 389 Mbp

Diploid, 2.3 Gbp

Ploidy, Genome size Polyploid,10 Gbp,

A

A

A

>1.2 M >1 M >230 000 >26 000 >1.5 M

>501 616

>1.4 M ~300 000

A

>2 M

A

A

A

A

Transcriptomics L

ESTb >230 000

A

A

A

A

A

A

R

A

A

Proteomics

A

A

A

R

R

A

A

Metabolic Omics R

A

A

A

A

R

A

A

A

LIL R

Table 2.1 Review of utilization of Omics technology for the top 20’ different crop species in global production

A

R

A

A

A

A

A

A

HTG A

A

A

A

A

A

WGS

Application of “Omics” Technologies in Crop Breeding (continued)

http://www. phytozome.net/ cassava.php#D2 http://soybase.org/; Schmutz et al. (2010) http://solgemics.net/ gemes/Solanum_ lycopersicum/ Schulte et al. (2009)

References http:// sugarcanegeme.org/; Dillon et al. (2007) http://www. maizegdb.org/; Schnable et al. (2009) http://rice. plantbiology.msu. edu/; IRGSP (2005) http://wheat.pw.usda. gov/GG2/index. shtml http://potatogeme. net/ Catusse et al. (2008)

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Allium cepa

Brassica rapa subsp. pekinensis Sorghum Bicolor Malus x Domestica

Vitis vinifera

Brassica Napus

Onion

Cabbage

Sorghum

Grape

Rapeseed

Tetraploid, 1.1 Gbp

Diploid, 487 Mbp

Diploid, 730 Mbp Diploid, 750 Mbp

Diploid, 16 Gbp Diploid, 529 Mbp

Diploid, 424 Mbp Diploid, 600 Mbp

Hexaploid

Ploidy, Genome size Tetraploid, 2.5 Gbp,

A

A

>640 000

A

A

A

A

>360 000

>200 000 >320 000

>20 000 ~150 000

>11 000

A

>22 000 ~8000 A

Transcriptomics A

ESTb >260 000

A

A

R

R

R

R

Proteomics A

A

A

R

Metabolic Omics

A

A

A

A

A

A

A

A

LIL A

A

A

A

A

A

HTG A

HTG high-throughput genotyping platform, WGS Whole genome sequence, LIL Long insert library, R Restricted, A available

Apple

Banana

Ipomoea Batatas Citrullus Lanatus Musa acuminata AAA Group

Scientific name Gossypium Hirsutum

Sweet Potatoes Watermelon

Genotype Cotton

Table 2.1 (continued)

A

A

A

WGS

http://www.rosaceae. org/; Han and Korban (2008) http://www.vitaceae. org/; Jaillon et al. (2007) http://brassica.bbsrc. ac.uk/welcome.htm

http://www.brassica. info/; Kim et al. (2009) Paterson et al. (2009)

http://www. musagemics.org/; Hippolyte et al. (2010) Jakse et al. (2008)

References http://www. cottonmarker.org/; Van Deynze et al. (2009) Srisuwan et al. (2006) Joobeur et al. (2006)

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Gene Discovery

One of the exciting applications of omics technologies has been gene discovery. This can be done through many approaches such as forward genetics, reverse genetics, or positional cloning approach. Combination of “omics” data, genetic mapping, and modeling has promoted rapid cloning and discovery of function of many plant genes. The data of metabolomics, proteomics, and transcriptomics can offer straight help for positional cloning by providing information genes location in the genome. The profiling of transcriptome is helpful in the mapping of genetic maps in the segregating population which allow the fast identification of QTLs associated having targeted phenotypic variation and also possibly the candidate genes underlying the variation. Simple sequence repeat multiplexing, single nucleotide polymorphism (SNP) genotyping, and diversity array genotyping are high-throughput genomic approaches that have revolutionized the mapping of QTLs for complex trait (Appleby et al. 2009). These approaches are used to screen large populations and aid in quick identification of genes underlying QTLs for trait of interest (Varshney et al. 2009).

2.5.2

Modeling of Plant Response

The molecular plant breeding techniques are based on the prediction of phenotype based on their genotype through marker-assisted selection. The reliability of these predictions is based on the phenotypic observation in huge segregating populations which is monitored by the use of statistical procedures based on quantitative genetics principle (Hammer et al. 2006). The study to association of physiological and molecular knowledge to the conventional hereditary and phenotypic trait is expanded by omics platforms. The objective of this technology is to make interaction models in agronomy in relation to genetic structure, which in gives better data of targeted trait, resulting in the highest genetic gain. This approach can be useful to produce a “guide of the climatic environment” and this is helpful in breeding for drought tolerance in sorghum and wheat (Chapman 2008).

2.5.3

Utilizing Gene Information

The gene (i.e., the favorable allele of the gene) identified to control a particular trait of interest is further used to enhance plant breeding in many ways. The first is the use of DNA markers designed targeting the gene for screening and to study the difference in the expression of targeted agronomic trait. The differential expression of targeted trait might be due to different expression in the protein level. The cause of different expression is due to changes in gene duplication or variations in the promoter site of the gene, (thus resulting in altered promoter activity), or might be due to the presence or absence of a particular allele of the gene (Song et al. 2007). The identified gene also gives information about new variation or additional alleles

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at targeted locus through allele mining, resulting in identification of novel allelic variants (Ramkumar et al. 2010). In the recent past, advanced high-throughput screening methods have facilitated rapid screening of mutants and other populations. (Barkley and Wang 2008). Herbicide-tolerant maize lines were developed by specific insertions in a target gene through gene editing (Shukla et al. 2009). The genetic engineering approach of gene editing is most effective for transfer of slow or difficult in breeding perennial crop species of the unique alleles where the different crop is strongly out-breeding or clonally propagated.

2.5.4

Identification of Complex Genetic Structure

Detailed omics data provide the complete information on the structure, function as well as behavior of plant genomes. This information provides opportunity to the plant breeders for precise, rapid, and successful introgression of targeted alleles from wild species and land races into cultivars and also for the study of genotype and environmental interactions. The complete omics dataset provides knowledge about interactions of environment with particular genomic regions and also between the genes with respect to traits of agronomic importance. This analysis can give detailed information about the environmental features which are related to changes in the expression levels of a targeted metabolite or stimulation of expression of a targeted gene or group of genes. This sort of investigation offers the possibility to undertake specific studies to targeted for a specific climate, and find out responses that offer the greatest prospect for the genetic gain. For example, under ideal condition, grain yield in different cereals crop is increased by long vegetative phases. But in the stressed environmental condition, early maturity in the different crop regularly allows the escape of terminal drought stress (Kuchel et al. 2007).

2.5.5

Identifying Areas of Small Hereditary Impact: Genomic Selection

The weakness of basic molecular marker-assisted selection is the introgression of only major QTLs/genes. The complex trait such as yield, abiotic stress tolerance, and broad-spectrum disease resistance is generally controlled by a huge number of genes, each gene with small or minor effect on the targeted phenotypic traits. In the GWS selection molecular marker selection, there is no information with reference to marker-trait associations are required. In the GWS selection, the influence of specific molecular marker alleles at each locus across the whole genome can be found which will explain the entire genotypic variation for targeted traits. The identified molecular marker is afterward used in the enhancement of breeding value which depends on association of molecular marker with targeted phenotype of genotypes. A genomic estimated breeding value (GEBV) is genotype of each character in a segregating large mapping population and the sum the breeding values

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of all molecular markers. It is used for the identification of targeted trait (Heffner et al. 2009).

2.5.6

Precise Genetic Engineering

The information generated by omics tools has provided the base for the perfection of genetic engineering (GE) approaches to increase grain yield and tolerance to biotic and abiotic stresses in plants of different crop. The development of site-directed alteration in the specific DNA sequences to introduce unique varation in the phenoyte of different crop plants by precision genetic engineering (PGE). The gene editing techniques like CRISPR/cas have been widely used nowadays in crops like rice, tomato, etc. For instance, Li et al. (2012) changed the part of the promoter region of OsSWEET14 gene and changed the binding region of the Xanthomonas effectors without affecting the TATA box of innate promoter. In this approach large number of mutant lines were developed, some of which was resistance to the bacterial blight pathogen. The resistant plants were examined and found to be homozygous or heterozygous in the mutation at the targeted site.

2.5.7

Improving Biotic Stress and Abiotic Stress Tolerance in Crops

Omics technologies are helpful in understanding the defense mechanism in plants of different crop or protection mechanisms in the different crop against biotic stresses and abiotic stresses condition. These mechanisms depend on association of molecular dynamics with different disciplines, i.e. anatomy, biochemistry, development, evolution, genetics, and physiology. The use of model plant of Arabidopsis and rice should be used for understanding the plant defense against biotic stresses and abiotic stress condition with the help of identification of associated representative candidate genes, associated quantitative trait loci (QTLs), associated proteins, and associated metabolites. The availability of genome sequence of different crop allows the identification and development of genome-wide molecular markers, through molecular mapping with SNP. The development of different mapping populations is near isogenic lines (NILs), introgression lines (ILs) or chromosome segment substitution lines (CSSLs), and recombinant inbred lines (RILs) which are being used for identification of candidate gene for the targeted traits. The immortal or permanent mapping populations for accurate QTL mapping and for direct or indirect use in development of different varieties in the plant breeding are multi-parent advanced generation inter-cross (MAGIC) and heterogeneous inbred family (HIFs). The omics tools have been already successfully applied to wheat, maize, rice, barley, sesame, and other different crop plants. GWA used to the “breeding by design” approach, leads to genomic selection where the outcome of a set of crosses in the plant breeding on the basis of molecular markers information alone.

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Speed Breeding

The speed breeding method is defined as an alternative to facilitate the simple and fast generation of new crop cultivars (Watson et al. 2018). It is a short way to reduce the generation time and speeds up breeding and research programs. For speed breeding purpose plants were grown in completely closed chambers with controlled environmental conditions which speed up the growth and development of plant. The use of speed breeding along with conventional field-based tools together with modern tools like high-throughput SNP markers and CRISPR-Cas9 gene editing, can be an important approach to permit the depth of understanding the genetics of different crops and also to breed for novel traits in a fast and precise manner. This technology is capable to accelerate the advancement in different plant research areas including crossing, generation of mapping populations, and phenotyping of plants for specific traits (Watson et al. 2018). Additionally, backcrossing and traits pyramiding can be accelerated by speed breeding (Hickey et al. 2017).

2.5.9

Molecular Farming

Plant molecular farming can be defined as large-scale production of recombinant proteins in plant system for the purpose of their use as biotechnological products with various applications. Through the application of various omics tools along with genetic engineering techniques, plants can be modified as synthetic chemical factors for the production of various recombinant proteins. The recombinant proteins include edible vaccine, antibodies, and other biological compounds such as human growth hormone, serum albumin, cytokines, etc. (Buyel 2019).

2.5.10 Development of Herbicide and Insect Resistant Plants Omics has enabled the study the depth of the evolution of herbicide and insect resistance in plants along with the biochemical and molecular mechanisms underlying resistance. Plants have been modified using date derived from different omics studies in such a way to make different proteins or target site to provide herbicide resistance to targeted plants of different crop. For example, resistant plant to glyphosate has a modified host target protein which is not suppressed by the herbicide in crops like soybean, cotton, and corn. The toxic protein produced by soil bacterium Bacillus thuringiensis (Bt) is specific to different insect pests like European corn borer, corn rootworm, corn earworm, and cotton bollworm. The improved corn and cotton plants with this gene provide resistant from these pests in the entire lifespan of plants and latest tools of omics and expanding the spectrum of insect resistance in plants (James 2014).

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2.5.11 Maintaining Quality of Food Omics tools can be used at each step from raw resources to end foods product in the food-processing industry to increase and preserve quality of end product, improve shelf life of end product, and limit microbial growth and its contamination in the real time. It can be helpful to improve capacity health and safety guarantee. These tools based on DNA and protein markers, allergen detection (peptide markers), biomarkers can be used for rapid food validity and contaminations tests for meat tenderness, and profiling of protein product (Mamone et al. 2009).

2.6

Conclusion

OMICS, a composite of high-throughput genome analysis tools is very useful to study the structural and functional aspects of plant cellular mechanism on a global scale and these tools are opening wider vistas in plant genetic studies, trait characterization and manipulation. Integrating different omics tools would also be a valuable strategy for investigating the regulation of the relationship between plant metabolism and physiology. The potential of genomics, transcriptomics, proteomics, and metabolomics as tools for crop genetic analysis is well realized and they are now routinely used to understand genotype–phenotype relationships, thus helping to improve the quality and productivity of crop plants for the food and nutritional security of the global population. Even though technological and software advancement are fast evolving, omics strategy still faces many challenges, some of which were discussed earlier. Hence, to overcome the challenges, the different aspects of omics such as genomics, transcriptomics, proteomics, and metabolomics need to align with different basic disciplines of plant physiology, biochemistry, genetics, and plant breeding to formulate strategies to work on problem-oriented and processoriented goals, thus leading to sustainable crop improvement.

References Afzal M, Alghamdi SS, Migdadi HH, Khan MA, Nurmansyah, Mirza SB, El-Harty E (2020) Legume genomics and transcriptomics: from classic breeding to modern technologies. Saudi J Biol Sci 27:543–555 Altschul S, Gish W, Miller W, Myers E, Lipman D (1990) Basic local aligment search tool. J Mol Biol 215:403–410. https://doi.org/10.1016/S0022-2836(05)80360-2 Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman WDJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl Acids Res 1;25(17):3389–402. https://doi.org/10.1093/nar/25.17.3389 Ambardar S, Gupta R, Trakroo D, Lal R, Vakhlu J (2016) High throughput sequencing: an overview of sequencing chemistry. Indian J Microbiol 56(4):394–404. https://doi.org/10. 1007/s12088-016-0606-4 Appleby N et al (2009) New technologies for ultra-high throughput genotyping in plants. Methods Mol Biol 513:19–39

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Omics Technologies and Molecular Farming: Applications and Challenges Gopalareddy Krishnappa, Krishnappa Gangadhara, Siddanna Savadi, Satish Kumar, Bhudeva Singh Tyagi, Harohalli Masthigowda Mamrutha, Sonu Singh Yadav, Gyanendra Singh, and Gyanendra Pratap Singh

Abstract

“Omics” is a broad area mainly deals with the analysis of biological information obtained from the genome, transcriptome, proteome, and metabolome profiling, together with other relevant –omes. Various omic technologies are focused to unravel the putative markers, overall gene, protein, and metabolite expression in a very functionally relevant context, and provide insights into the molecular basis of different fundamental processes involved in growth and development of plants. New gene(s) discovery and their expression profiling provide ample opportunity for breeders to introgress economically important traits from new sources. The omic technologies have been found useful in decoding the complexity of abiotic and biotic stresses through genome sequences, cell and tissue specific transcripts, protein and metabolite profiles and their dynamic changes, and interactions. Plant molecular farming (PMF) is an emerging branch of plant biotechnology, wherein large quantities of industrial proteins and recombinant pharmaceuticals produced by engineered plants. Many biopharmaceuticals like monoclonal antibodies, recombinant vaccine antigens, and other commercially viable proteins are produced in plants, a number of which are in the clinical and pre-clinical stages. Keywords

Omics · Genomics · Transcriptomics · Proteomics · Metabolomics · Plant molecular farming

G. Krishnappa · K. Gangadhara · S. Savadi · S. Kumar (*) · B. S. Tyagi · H. M. Mamrutha · S. S. Yadav · G. Singh · G. P. Singh ICAR-Indian Institute of Wheat and Barley Research, Karnal, Haryana, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Kumar et al. (eds.), Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), https://doi.org/10.1007/978-981-16-2956-3_3

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3.1

G. Krishnappa et al.

Introduction

A holistic approach with genomics, transcriptomics, proteomics, and metabolomics inferences helps researchers to classify and prioritize the genes of economically important traits for their improvement in crop plants. Omics research further broadens to investigate the linked regulatory steps like post-transcriptional and translational, interatomic, and epigenetic regulation. The interactome complex investigations aim to unravel molecular interactions among biomolecules such as nucleic acid, proteins, amino acids, carbohydrates, lipids, etc. and further deepen knowledge about the genotype-phenotype relationship (Vadivel 2015). An overview of OMICs in agriculture is presented in Fig. 3.1. First phase of omics technologies directed at random or non-targeted discovery of transcripts, proteins, and metabolites, whereas second phase involves complex nature of data mining and eventually leading to the dissection of the qualitative and quantitative part of biological systems. Understanding the relationships of genes, proteins, and

Fig. 3.1 An overview of OMICs in agriculture

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Fig. 3.2 Commonly used OMICs in agriculture (complexity increases with the progression of arrow)

metabolites is essential in relative comparison of the networks that ultimately help in knowing the regulatory mechanisms. Similarly, approaches like reverse and forward genetics, overexpression, and knockdowns through transgenic can assign the function of a gene to the particular phenotype, however, omic technologies help in the unraveling of all the gene functions of the genome, which together contribute toward deciphering the networks and better understanding of the whole plant phenotype (Hecker et al. 2008). Continuous technological advancements led omics tools accessible at an affordable price, which together with large candidate gene discovery, metabolites, proteins, and their databases from profiling efforts in model systems and crop plants have accelerated the analysis of biological functions in different plant stress responses (North et al. 2010). Commonly used OMICs in agriculture is presented in Fig. 3.2. Plants have been used in various ways ranging from food, fiber, and medicine since the time of human civilization. Molecular farming could be able to produce important molecules of industrial interest at larger volumes to meet the ever growing demand. Plants generally considered as cost-effective and high scalable compare to present industrial standardized production systems like animal and microbial cells. However, plants may not replace already standardized systems like Chinese hamster ovary cells and E.coli for efficient protein manufacturing. The key factors considered

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for large scale industrial molecular farming are downstream processing coupled with upstream production. The first intentional use of plants as host for the production of recombinant antibody was attempted in transgenic tobacco by Hiatt et al. (1989) to produce immunoglobulins and assemble of functional antibodies. Later, Sijmons et al. (1990) used a modified CaMV35S promoter to enable the expression of chimaeric genes encoding human serum albumin in tobacco and potato transgenic plants. These initial studies produced human proteins with medical importance provide new enthusiasm in research fraternity to use plants as biopharmaceutical producers. One of the major players of molecular farming at industrial level was Prodigene Inc. and was investigated the utilization of maize for the production of industrial enzymes and reagents apart from pharmaceuticals. Researchers at the company studied the economic viability of molecular farming by considering downstream processing and upstream production. Accordingly, they established that maize-derived recombinant avidin was commercially viable compared to the already existing commercial avidin derived from hens’ eggs (Hood et al. 1999) and β-glucuronidase derived from maize was also commercially viable compared to existing enzyme isolation from bacteria at commercial levels (Witcher et al. 1998). The early researchers of pharmaceutical molecular farming learned lessons including breaching of environmental regulations apart from production and processing. Therefore, molecular farming research fraternity now generally avoids using whole field grown plants unless it is confirmed that there is minimal risk of outcrossing or admixture. This led to the consolidation of molecular farming in the most promising production systems like transgenic tobacco and close relative Nicotiana benthamiana, plant cell suspension cultures and clonally propagating aquatic plants such as duckweed and moss. Although transgenic based molecular farming was gained much importance during the early phase due to their high scalability, but in reality all of the commercial breakthroughs in the pharma industry were realized with plant cell suspension cultures or similar contained systems, as they were easier to accommodate under existing regulations. Earlier molecular farming mainly dominated by tobacco and rice cells and these two systems still widely used even today. Dow AgroSciences used tobacco cells in 2006 to produce the first veterinary vaccine and approved by USDA (Schillberg et al. 2013). Similarly, Protalix Biotherapeutics used carrot cells to produce pharma recombinant protein from plants for human use in 2012 (Mor 2015).

3.2

Genomes and Genomics

The term “genome” was coined by Winkler in 1920 to represent a haploid set of chromosomes. With the molecular genetics’ advancements, the sense genome term has changed and it represents the total genetic material present in an organism (Zelenin et al. 2001). Studying of structural and functional organization of genomes is referred to as genomics. The study of genomes began with the use of karyotype analysis, chromosome banding, in situ hybridization of chromosomes and then, determination of DNA content, restriction profiles, collinearity of molecular markers

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were initiated and recently, determination of complete sequence and their functions are being studied in genomics (Zelenin et al. 2001). Genomics has been further divided into structural and functional genomics. Structural genomics deals with the primary structure of the whole genome, i.e. organization of nucleotide sequence into genic and nogenic regions, whereas functional genomics deals with the function of the genic and non-genic regions of the genome. In the recent years, a rapid progress in the structural and functional genomics of crop plants has been observed. There is an increase in the number of annotated genome sequences, transcriptomes, molecular markers, and genetic maps which are all useful resources for devising tools and strategies for crop improvement. With the development of next-generation sequencing (NGS) technologies, the generation of whole-genome and transcriptome sequences has become more inexpensive. This has resulted genome sequencing projects for large number of crop plants including the minor and orphan crops. NGS technologies can also be effectively utilized for high-throughput genotyping of crop plants.

3.2.1

Genome Sequencing

The major steps in genome sequencing projects are quality DNA isolation, sequencing and assembly of genome sequences, and annotation and deposition of genome sequences into a database. Over the years, tremendous advances have occurred in DNA sequencing methods due to major breakthroughs in sequencing technologies. In the last decade many next-generation sequencing (NGS) platforms such as Illumina (Solexa), SOLiDTM (Applied Biosystems), and Ion Torrent (Life Technologies) have become popular. These platforms generate short but accurate read sequences. Recently, long read sequencing technologies referred to as PacBio and Nanopore technologies have been developed which provide very long sequence reads but are less accurate compared to short read sequence technologies. A combination of these platforms enables fast and accurate genome sequencing and assembly of crop genomes and at very less cost. As a result, numerous crop genome sequencing projects have been completed and many more are in pipeline (Kersey 2019). Furthermore, the reduced genome scale sequencing costs have enabled large scale resequencing of plant genomes which is useful for understanding of genetic variations in germplasm accessions of crop species as well as the related species. In the last few decades, both structural and functional genomics on crop genomes have allowed understanding of both basic and applied aspects of plant biology (Edwards and Batley 2010). Genomics studies in the number of species are providing insights on the dynamics, complexity, and evolution of genomes and also, provide an understanding of how biological pathways work. Genomics allows development of molecular markers and genetic maps, detection of QTLs (quantitative trait loci), gene orthology, colinearity, and also the functions of the genes and gene networks. Thus, genomics as whole has enhanced understanding about gene networks governing different traits of economic interest and their improvement in crop plants. Accessibility to a large number of molecular markers and genotyping

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technologies has facilitated marker-assisted selection (MAS), genomic selection (GS) and association mapping for understanding the genetics of complex agronomic traits and improvement in many of the crops plants (Edwards and Batley 2010; Bolger et al. 2014).

3.2.2

Marker-Assisted Selection (MAS)

MAS is an indirect plant selection method using the molecular markers tagged to traits of interest at an early stage of the plant (Collard et al. 2005). It is used for selection plants for improved traits and it helps in speeding up breeding process compared to the traditional plant breeding (Francia et al. 2005). MAS has been employed to transfer a large number of genes and QTLs governing important traits such as abiotic and biotic stresses tolerances by employing the linked molecular markers (Francia et al. 2005; Gupta et al. 2010; Khan et al. 2017; Devi et al. 2017; Sun et al. 2020). Although MAS has great potential for improvement of traits in crop plants, improvement of complex traits has been difficult and no substantial improvement has been reported in case of complex traits.

3.2.3

Association Mapping (AM)

AM is an alternative to biparental mapping approach to identify marker-trait associations for applications in MAS. AM is based on linkage disequilibrium (LD) and is carried out in the natural populations/germplasm accessions. It is more powerful than linkage mapping for detecting the genes/QTLS governing complex traits (Gupta et al. 2014). AM takes into consideration all the recombinations that have occurred over long period of time during species evolution. Thus, only genes/ QTLs that are tightly linked tested markers are only identified. The recombinations are not uniformly distributed across the genomes and hence, the genetic distance between markers and candidate genes differs. The degree of LD decodes the resolution of association analyses and the markers required to cover the entire genome (Ersoz et al. 2007; Gupta et al. 2014). AM allows identification of QTLs governing not one or few but a large number of traits. However, association analyses may be impacted by the population structure existing in the germplasm used for analysis resulting in biased marker-QTL associations. Hence, this has to be taken into consideration while analyzing the marker-trait associations. AM has been employed in number of crops for QTL detection (Gupta et al. 2010; Sajjad et al. 2014; Iquira et al. 2015; Descalsota et al. 2018; Tamisier et al. 2020).

3.2.4

Challenges in Applications of Genomics

Although rapid progress in next-generation genomics technologies presents a huge scope for genetic improvement of crops, crop genomics faces certain challenges

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while deploying them for crop improvement. Some of the challenges for future crop genomics include advancing genomic studies to have comprehensive high resolution genetic and physical maps of crops for deployment in mapping and utilizing of genes/QTLs more precisely for crop improvement, functional analysis of genes and regulatory sequences using high-throughput tools for developing transgenics with improved traits, and developing high-throughput phenotyping under field conditions for the target traits. As genome and transcriptome sequences are becoming easily available to researchers in number of crop plants, devising genomics-based approaches for improvement of crops has attracted lot of attention. The advances in genomics have increased breeding effectiveness to improve crop plants to meet the food, fiber, and fuel needs of growing populations.

3.3

Transcriptomics

Transcriptomics is a branch of omics which aims in the study of expression profiles of genes that are involved in various trait expressions and cellular responses. Furthermore, it helps to study global mRNA expression profile of a particular tissue. Unraveling global variations in gene expression could allow a comprehensive insights of genes and pathways involved in different biological processes; gene stacks exhibiting quantitative and qualitative expression similarity are functionally related and this is likely due to a common switch for regulation of these genes (North et al. 2010). Plant genome sequencing through high-throughput sequencing platforms opens the door for better understanding of the function of the genes, structure and evolution of the genome. However, precise annotation of all the predicted genes in most of the sequenced genomes has remained incomplete and it has been major challenging task for the researchers across the globe. Transcriptomics can be used to understand the physiological and molecular basis of complex phenomenon like abiotic and abiotic stress response. Common platforms used for high-throughput genome-wide gene expression are microarrays, massively parallel signature sequencing (MPSS), serial analysis of gene expression (SAGE), and next-generation sequencing platforms (NGSPs). Candidate genes influencing important economical trait can be identified by in silico analysis of entire set of gene transcripts or mRNAs expressed during a particular situation and/or tissues.

3.3.1

Gene Expression Analysis: Northern Blotting to RNA-Seq

The analysis of global gene expression which can provide biological information needed to understand the molecular basis of economical traits in crops, resulting in discovery of gene targets for manipulating these traits (Edwards and Batley 2010). Initially, gene expression studies were based on the classical Northern blotting and then, qRT-PCR emerged and it has been used to quantify expression of number of genes. However, expression analyses in these experiments are very limited. Other methods that overcome the limitation of number of genes analyzed simultaneously

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are the serial analysis of gene expression (SAGE) and massively parallel signature sequencing (MPSS) (Tuteja and Tuteja 2004; Brenner et al. 2000). Later, global transcript profiling method based on high-throughput platforms referred to as microarrays (Fryer et al. 2002). These expression arrays have many advantages such as it allows quantification of tens of thousands of different gene transcripts in the single hybridization experiment and also allow semi-quantitative and sensitive detection of even low-abundant transcripts. NGS technologies also allow global scale RNA sequencings, which has resulted generation of large number transcriptome sequencings in different crop plants. Deep RNA-seq is emerging as an alternative to microarray-based global gene expression profiles (Garg and Jain 2013). RNA-seq is useful to expand and accelerate gene expression analyses in crop plants. RNA-seq is also useful for improving genome annotation, the discovery of rare transcripts and splicing variants and identification of exon/intron boundaries. RNA-seq overcome biasness in hybridization of microarrays, and has more sensibility and reproducibility than microarrays (Schliesky et al. 2012; Garg and Jain 2013). RNA-seq is emerging as technology of choice for large scale transcriptome sequencings projects. Transcriptomic technology has been extensively utilized for explaining the variations in transcriptome data during seed germination, growth, development, and various stresses (Poole et al. 2007). Functional genomics is a combinational approach that utilizes the genomic data along with expression analysis to identify putative genes associated with particular biological trait. Functional techniques such as RNA interference (RNAi), mutagenesis, and epigenetics can be applied as gene silencing tools for economically important traits. Molecular switches are sequence specific DNA binding transcriptional factors, which regulate the biological processes. The transcriptional factors (TF) of newly sequenced plant species could be identified by comparing their genome sequence with TF sequences of model plants (Arabidopsis thaliana) (Riechmann et al. 2000). Limited availability of molecular markers has restricted the ability for functional analysis of target traits. Limited access to high-throughput and cost-effective phenotyping platforms and lack of suitable infrastructure are the barriers for whole transcriptome analysis. The data of transcriptome is highly variable with respect to time and tissue; therefore, the sample collection and analysis should be done carefully. Currently, another limitation of microarray technology is that its species coverage limited. This bottleneck has partially been addressed by advent of high-throughput next-generation sequencing (NGS) technologies which have allowed to study the transcriptomes to a great extent in a large number of plants.

3.4

Proteomics

Proteomics deals with collective identification and quantification of protein expression in a particular cell, tissue or organism at a given time and certain conditions (Tan et al. 2013). Although genome sequencing generated vast amount of information, it is insufficient for elucidating biological functions (Vanderschuren et al.

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2013). Moreover, cells generally depend on various metabolic and regulatory pathways for growth and development. Proteomes and different processes of biological systems are related in nature, therefore, the study of these proteome profiles through proteomics would give better understanding of such related metabolic processes and their interactions with other regulatory pathways (Esteve et al. 2013). Quantitative and qualitative estimation of proteomes in specific cell and organelle during particular physio-developmental stages aids in study of many regulatory pathways. The full potential of proteomics in crop plants yet to harness, although lot of progress has been made (Tan et al. 2017). Nevertheless, recent technological advancements in both developing novel methodologies and improvement of already available methodologies provided new opportunities for large scale protein quantification through high-throughput studies and also errors in protein quantification are reduced to a great extent.

3.4.1

Proteomics Applications

Although genomics and gene expression analysis are useful to understand genetic architecture, genotypic information alone may not precisely reflect the cellular molecular state at a specific time and stage (Tan et al. 2013). For example, genome-wide gene expression profile through transcriptomics may not always correlate with the quantity of protein produced (Vanderschuren et al. 2013; Kumar et al. 2014). Accordingly, proteomics gaining importance which enable improvement of crops through biotechnological interventions, including annotation of mechanisms related to growth and development, biotic and abiotic stresses responses in crop plants. Proteomics is a powerful tool for understanding protein changes induced by various stress conditions. Complex association between stress tolerance and crop yields were elucidated with the help of recent advancements in proteomic investigations and which would augment in the formulation of new breeding strategies to enhance the crop yields (Beyene et al. 2016). Somatic embryogenesis (SE) is a process by which embryogenic potential of haploid or diploid cells enable them to differentiate into whole plants by different differentiation patterns. Usefulness of SE as a plant propagation system made it as most widely studied in different crop species. Proteomic analyses focusing on SE development have been studied in maize by Sun et al. (2013), and rice by Xu et al. (2012) and suggested that proteins response to stress are the most abundant proteins in SE. Proteomic investigations of biotic and abiotic stresses have been studied in major food crops including rice, wheat, and soybeans by the comparison between stressed and control genotypes. Proteins responsible for energy, nitrogen, and carbohydrate metabolism are generally identified in plants exposed to abiotic stresses. Understanding the change in proteins in crops plants caused by drought can provide information regarding key proteins response to drought with the help of genotypes exposed to various levels of tolerance. For example, Ge et al. (2011) used two wheat varieties contrasting for drought tolerance to study protein expression and found that minimum two-fold variation in 152 protein spots, further 96 proteins were

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responsible for various metabolic processes including tolerance due to various stresses, defense mechanisms, photosynthetic ability, carbohydrate metabolism, and storage. The authors also identified two differentially expressed proteins, i.e. triosephosphate isomerase and the oxygen evolving complex and inferred that they could be responsible for drought tolerance. Many researchers have investigated ripening genes and have profiled their expression during fruit development and maturation. Different modifications including post-transcriptional, post-translational, and degradative modifications may affect gene products. Therefore, comparative proteomics has become more attractive for analyzing fruit development and ripening processes in various commercially important crops, including bananas (Toledo et al. 2012) papayas (Nogueira et al. 2012), strawberries (Song et al. 2020), apples (Marondedze and Thomas 2012), and peaches (Jiang et al. 2014).

3.4.2

Proteomics Challenges

One of the basic challenges for proteomic studies is the high-throughput quantification of proteins and to allow comparative proteomics analysis (Schulze and Usadel 2010). This will help in the elucidation of patterns of global protein expression during various processes like growth and development of plants or responses to different stresses. Presently, complexities associated with measurement of whole protein complement coupled with technical hindrances in whole protein quantification are protracting the advancements in comparative proteomics. Proteomics is still long way to go and has many technical challenges including dynamic resolution, protein purification, protein quantification, separation, visualization, and identification of hydrophobic membrane proteins. The problem is further complexed when big proteomes containing large number of proteins are studied, in general only most abundant proteins will be detected due to difficulties associated with resolution. Another important challenge is to obtain proteome in pure form and which is very critical step for obtaining precise results and reducing further verification steps, as they are time consuming procedures. Generally, hydrophobic membrane proteins quantification is more tedious than hydrophilic proteins, due to their ability to aggregate and adsorb to the surface of tubes and vials very easily. The problem is further aggravated while handling proteins with nanomelia to femtomole quantities, as these minute quantities could lead to losses or complete disappearance of some proteins or peptides.

3.5

Metabolomics

Biological systems complexity can be understood better with the advancements made in metabolomics science. Metabolome consists of a set of metabolite molecules in small quantities in a specific cell, organ or organism (Kumar et al. 2017; Sharma et al. 2018; Sharma et al. 2021). Metabolites influence the plant architecture and biomass, as they are indispensable part of metabolism (Turner

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et al. 2016). Recently, advancements in metabolomics have made it as one of the salient breakthroughs in science, which enabled in the precise profiling of metabolites in crops (Heyman and Dubery 2016). Metabolomics enables to identify a wide range of metabolites from a single extract, thus enabling fast and precise metabolites analysis. Metabolomics study generates in-depth information of metabolites with very small compounds at cellular levels, which involved in various cellular activities and therefore they reflect the physiological state of a cell exactly. With the rapid advancement of metabolomics science, the study of lines derived from mutation and transgenic provides new insights into the metabolic pathways and locating the candidate genes underneath (Hong et al. 2016). Furthermore, study of metabolites helps in the determination of gene(s) function, i.e. how a specific gene effects the metabolic pathway and suggests the various levels of regulation and interception among associated pathways (Wen et al. 2015), which otherwise is difficult to achieve by conventional assays like microarray (Kusano and Saito 2012).

3.5.1

Metabolomics Applications

Metabolomics in agriculture is related to study of metabolite content in relation to developmental and differentiation processes, resistance to biotic and abiotic stresses, and various quality attributes. Table 3.1 summarizes the recent metabolomics studies in different crop plants for various traits of interest. One of the major objectives of metabolomics studies in agriculture is to know the metabolic responses towards various abiotic tolerances in different crop plants. Cereals have been widely studied to quantify metabolites variations and their association with sequence variation. Metabolomics investigations have provided great insights in fruit biology, especially related to ripening and quality. Characterization and functional metabolomics which assign functions to the characterized proteins through recent advancements would help in crop breeding by specific biochemical composition.

3.5.2

Metabolomics Challenges

One of the major modern challenges for metabolomics is the discovery of biomarkers, especially in the field of diseases (Armitage and Barbas 2014). Currently, the problem that researchers face, is the difficulty associated in the identification and precise quantification of large number of metabolites at a given time with reliable methods. The issue is further complexed for the metabolites with very small quantities and large number but they have some biological function in the crop plants. Recent technological advancements led in the development of new analytical techniques are to address the metabolomics associated problems including broadening the detectable range of metabolites based on the in-depth studies of structural characterization of metabolites, and developing robust methods which are sensitive to minute concentrations of chemical compounds. Another bottleneck of the metabolomics science is that, the information obtained from analyzed samples

Crop Wheat Wheat Wheat Wheat Wheat Wheat Wheat

Wheat

Rice Rice Rice Rice Rice

Rice Maize

Sorghum Soybean Soybean

Tomato

Tomato

S. no. 1 2 3 4 5 6 7

8

9 10 11 12 13

14 15

16 17 18

19

20

Heat stress

Salt stress

Waterlogging Drought tolerance Drought stress Waterlogging Heat stress

Drought stress Salt stress Salt stress Salt stress Salt stress

Heat stress

Trait Drought stress Drought stress Salt stress Salt stress Waterlogging Heat stress Heat stress

GC/MS and NMR RP/UPLC -MS/MS FT-IR and GC/MS NMR LC-MS, GC-MS UHPLC-ESI/QTOFMS LC-QTOF-MS

GC-MS GC/MS GC-MS NMR GC-MS

Platform GC-MS GC/MS GC/MS HPLC GC/MS and LC/MS LC-HRMS LC-MS/MS HPLC GC-MS

Flavonoids

Heat shock proteins (HSPs) and dehydrins (DHNs) Isoflavones and kaempferol Ferulate, naringenin-7-O-glucoside, Genistein, glycitein, and apigenin Sesquiterpene lactones, alkaloids, and polyamines

Melibiose, serine, lysine, glycine, malic acid, mannitol, xylitol, inositol, Fructose, proline, and glutamic acid 4-hydroxycinnamic acid and ferulic acid Mannitol and sucrose Leucine, isoleucine, valine, proline, and GABA Acetic acid, GABA, and sucrose non-polar metabolites Vanillic acid, 4-hydroxybenzoic acid, palmitic acid, stearic acid, L-tryptophan, and pyruvic acid 6-phosphogluconate, phenylalanine, and lactate Metabolism of lipids, carbohydrates, and glutathione cycle

Key metabolites produced Tryptophan, valine, citric acid, fumaric acid, and malic acid Glutamine, serine, methionine, lysine, and asparagine Proline, lysine, alanine, and GABA Malic acid, proline, fructose, mannose, glycine, glutamic acid Lysine, proline, methionine, and tryptophan Pipecolate and L-tryptophan G1p and sucrose

Table 3.1 Recent applications of metabolomics to understand abiotic stress tolerance in major crop plants

Paupiere et al. (2017)

Rouphael et al. (2018)

Ogbaga et al. (2016) Coutinho et al. (2018) Chebrolu et al. (2016)

Locke et al. (2018) Yang et al. (2018)

Ma et al. (2016) Chang et al. (2019) Gayen et al. (2019) Ma et al. (2018) Gupta and De (2017)

Qi et al. (2017)

Reference Kang et al. (2019) Yadav et al. (2019) Che-Othman et al. (2019) Borrelli et al. (2018) Herzog et al. (2018) Thomason et al. (2018) Wang et al. (2018)

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may not be interpreted correctly due to its complexity. Lack of robust data bases related to chemical compounds for their identification and their structural elucidation is another shortcoming in the metabolomics, unlike genomics.

3.6

Plant Molecular Farming (PMF)

Ever-growing demand for biopharma products are related to the difficulties associated with the already established industry stalwarts’ methods to produce pharma products with bacteria, microbial eukaryotes, mammalian and insect cells, and transgenic animals. Therefore, much global attention towards uses of transgenic plants as the bioreactors for the production of antibodies, vaccines, enzymes, growth factors, and other human proteins has increased in recent past, as they provide flexibility for scalability and processing (Gul et al. 2018). Recently, PMF is gaining much importance due to some early success and gaining knowledge with earlier failures which made the technology more optimistic, particularly due to recent technological advancements (Priyadarshi et al. 2018). Some of the important factors of plants to be utilized as a bioreactor as against conventional production systems are cost-effectiveness, scalability, flexibility, versatility, and robustness. The claim is supported by many biopharma compounds already in developing pipelines and at different stages of pre-clinical and clinical testing stages, these compounds include vaccine antigens, monoclonal antibodies, and other commercially viable human proteins. PMF can be defined as the method by which utilization of genetically modified plants as bioreactors for the production of useful molecules at larger quantities to meet the ever-growing demand. Molecular farming is the cost-effective production of important pharma proteins in a modified expression system of plants, other than their natural production source (Franken et al. 1997). Most common applications of PMF are represented in the Fig. 3.3 (Table 3.2).

3.6.1

PMF: Considerations

The production cost of recombinant proteins is one of the most important factors considered for commercial viability of the molecular farming. Production based on stable nuclear transformation found to be the most useful strategy because of stable expression of the inheritable transgene after its integration into the nuclear genome. The limitation with prokaryotic bacterial expression systems is the lack of proper folding and assembling of protein complexes and lack of post-translational modifications. For an efficient plant based recombinant antibodies production, suitable plant expression machinery with right combination of transgene expression is necessary and that depends on sequence of events including regulatory pathways, post-translational pathways, and effective product recovery methodologies. Selection of appropriate gene design and promotors is important for the production of biopharma products to be expressed at relatively higher levels for vaccine antigen

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Fig. 3.3 Most common applications of plant molecular farming

production against human and animal pathogens. Product authenticity is another essential criterion for successful molecular farming.

3.6.2

Products Developed

In case of all the approved biopharmaceuticals one third comprised of only glycoproteins (Walsh and Jefferis 2006) and the biological activity of most of the therapeutic glycoproteins (antibodies, blood factors, and interferons) are influenced by glycosylation. Considerable number of bio-products has been developed; among them pharmaceutical antibodies have been expressed in plants and undergoing different phases of clinical trials. Some of the products are: CaroRx from tobacco (Planet Biotechnology) for dental caries against Streptococcus mutants. Similarly, BLX-301 an anti-CD20 optimized antibody for the treatment of non-Hodgkin B-cell lymphoma expressed in the aquatic plant Lemna minor by Biolex Inc. Plant derived vaccines including both veterinary and medical purpose have also been developed. Vaccine for Newcastle disease vaccine for poultry was approved (Twyman et al. 2005). Tacket et al. (1998) performed trial on transgenic potatoes expressing the enterotoxigenic E. coli (ETEC) labile toxin B-subunit (LTB), one of the most potent known oral immunogens. A wide range of plant molecular products ranging from plant-produced therapeutic proteins (Polio viral protein 1, Human Adenosine deaminase, Avian influenza H5N1 antigen and Hemophilia B coagulation factor IX); Vaccines (H5N1 influenza HA VLP, Cholera CTB, E. coli LT-B and Norwalk virus CP); Antibodies (ZMapp™ a cocktail of highly purified monoclonal antibodies, Hepatitis B surface antigen, Norovirus Capsid protein, and Hepatitis B surface antigen), and therapeutic and dietary proteins (Intrinsic factor, Glucocerbrosidase,

c2A10G6

6D8

HSV8

Recombinant protein/ vaccine CHKV mab

Zika virus

Pathogen Chikungunya virus Herpes simplex virus Ebola Tobacco

Tobacco

Tobacco

Expression system Tobacco

Table 3.2 Recent applications of plant molecular farming Reference Hurtado et al. (2020) Diamos et al. (2019) Diamos et al. (2019) Diamos et al. (2019) Hepatitis B surface antigen Hepatitis B surface antigen Anthrax protective antigen

Antibodies/antigens LO-BM2

N. Tabacum, Daucus carota

N. tabacum, S. tuberosum N. tabacum

Expressed in plants N. tabacum

Reference De Muynck et al. (2009) Kostrzak et al. (2009) Kostrzak et al. (2009) Brodzik et al. (2009)

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Gastric lipase, and Lactoferrin) under different levels of development and clinical trials (Singhabahu et al. 2017) were developed.

3.6.3

Biosafety Considerations

The introduction of genetically modified crops and contamination of the food chain with plant made pharmaceuticals is one of the important concerns. Transgene contamination could be possible by using the equipment for harvesting and processing of transgenic and food crops without proper cleaning and growing food crops in the same field where a transgenic crop was grown previously (Rigano and Walmsley 2005). Also, other concerns consist of risks of co-mingling with feed crops, spread of transgenes through pollen, seed or fruit dispersal and horizontal gene transfer. The contamination of the food chain can be prevented by the use of non-food and non-feed crops, such as tobacco, lemna, arabidopsis, microalgae or mosses (Decker and Reski 2008; Tremblay et al. 2010). Other measures include growing in small restricted and isolated areas, crop destruction and post-planting field cleaning, the use of non-commercial varieties or plant containing visible marks (Biemelt and Sonnewald 2005), and the use of dedicated machineries and facilities. The problem of gene spread and unintended exposures could be prevented by use of closed isolated containment facilities, such as greenhouses (Menassa et al. 2001), glasshouses, hydroponics (Drake et al. 2009), chloroplast transformation (Svab and Maliga 2007), and transgene excision (Gidoni et al. 2008). Regarding the regulation of transgenic crops, there are many challenges because diversity of molecular farming products and individual host requirements. Efforts have been made in past to develop safe regulations (Chen et al. 2005; Sparrow and Twyman 2009), however; these regulations still need improvement.

3.7

Conclusion

Technological advancements in “omics” along with precise phenotyping data generated through high-throughput phenotyping methods create new avenues for integration of these resources in crop research and breeding. This integrated approach of all the omics including phenomics will help in the dissection of complex traits with considerable environmental influence. This will also help in the pinpointing of candidate genes, gene products, and in turn deciphering the functional association of genotype and observed phenotype. Therefore this integrated approach allows a system-based study from genome to phenome for precise trait mapping, transfer of desirable alleles and also in certain cases cloning of major genomic regions (QTLs) controlling complex traits like biotic and abiotic stress tolerance. The methods of development of transgenic plants have considerably advanced in the recent past, due to which PMF gained a momentum. Utilization of plants as bioreactors has several advantages in terms of scalability, cost-effectiveness, and safety considerations, etc. for the production of biopharma molecules. Therefore,

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some of the important plant based pharma proteins and antibodies produced in recent past. However, many difficulties associated with PMF including acceptance, biosecurity, clinical and commercialization, transgene escape, etc., which has made it a challenging area.

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Omics to Understand Drought Tolerance in Plants: An Update Prasoon Jaya, Alok Ranjan, Arshi Naaj Afsana, Ajay Kumar Srivastava, and Laxmi Narayan Mishra

Abstract

Drought is a major threat to many plants and especially with rice. Rice is a staple food for more than 3.5 billion people worldwide. There is a need to increase its productivity to make up the ever-increasing demand. However, drought during flowering stage reduces the crop yield significantly. Advances in omics technologies such as transcriptomics, genomics, proteomics, and metabolomics have provided an opportunity to study the drought-responsive genes and their functional product at genome-wide level. In recent years, these state-of-the-art techniques have improved our understanding of the complex drought tolerance mechanisms significantly. However, there are still challenges in generating a drought resistant rice variety having good yield. In this chapter, we have discussed about the application of different omics technologies to improve the drought resistant varieties with special reference to rice.

Prasoon Jaya and Alok Ranjan contributed equally with all other contributors. P. Jaya Department of Developmental and Molecular Biology, Albert Einstein College of Medicine, Bronx, NY, USA A. Ranjan ICAR-Indian Institute of Agricultural Biotechnology, Ranchi, Jharkhand, India A. N. Afsana University Department of Botany, Ranchi University, Ranchi, Jharkhand, India A. K. Srivastava Department of Botany, St. Xavier’s College, Ranchi, Jharkhand, India L. N. Mishra (*) Department of Cell Biology, Albert Einstein College of Medicine, Bronx, NY, USA # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Kumar et al. (eds.), Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), https://doi.org/10.1007/978-981-16-2956-3_4

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Keywords

Plant physiology · Drought stress · Transcriptomics · Single-cell transcriptomics · Genomics · Metabolomics · Proteomics · Glycomics · Phenomics

4.1

Genomics Approaches for Drought Tolerance

Omics is a multidisciplinary approach to study an organism’s genome, transcriptome, proteome, metabolome, epigenome, etc. in order to understand the mechanism of cellular processes. There has been a stark increase in the use of multiple omics tools such as high-throughput sequencing (genomics), mass spectrometry (proteomics, metabolomics), nuclear magnetic resonance (metabolomics), etc. to understand the physiology of plants. Two most commonly used model plants are Arabidopsis thaliana and Oryza sativa whose genome sequences are available. In this chapter, we have covered the omics of drought (an abiotic stress) tolerance in rice. Rice is a part of daily diets and livelihoods for more than 30% population worldwide (Lampe 1995). However, due to its morphological, anatomical, and physiological characteristics such as thin cuticular wax, swift stomatal closure, and the absence of deep root system, rice is considered as one of the most droughtsusceptible plants especially during flowering stage resulting in low yield (Pantuwan et al. 2002). Genes responsible for drought tolerance have been identified using molecular, genetic, and morphological approaches (Sasaki and Ashikari 2018). Few of those genes are DRO1, OsNAC5, and OsGL1-6. Taking advantage of this information, it is possible to develop rice varieties with drought resistance. However, one potential problem that might come on the way is polygenic inheritance of drought resistance. Recent advances in genomics technologies have enabled the identification of genes/quantitative trait locus (QTLs) for yield and yield-related traits under drought condition (Lanceras et al. 2004; Li and Zhang 2013; Swamy et al. 2017). A list of all QTLs identified for drought condition can be found in Table 4.1 of Oladosu et al. (2019) (Oladosu et al. 2019). Two subspecies of rice, japonica (cv. Nipponbare) and indica (cv. 93–11) are having their whole genome sequenced. Their genome size is of ~390 Mb, smallest among domesticated cereals. All the information regarding the genomics of model plants including rice can be found at http://rice.plantbiology. msu.edu, https://rapdb.dna.affrc.go.jp, or http://iric.irri.org/resources/rice-databases or in Table 4.1. Rice is rich in genomic information such as genetic map, single nucleotide polymorphisms (SNPs), insertion–deletion polymorphism (IDPs), and more than 20,000 single sequence repeat (SSR) markers (McNally et al. 2009; Salvi and Tuberosa, 2005). The availability of functional genomics tools such as expressed sequence tags (ESTs), cDNA libraries, transcriptomics, microarrays, RNA-sequencing, real-time PCR (qPCR), massive parallel signature sequences (MPSS), serial analysis of genome expression (SAGE), genome-wide association

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Table 4.1 Omics (genomics, proteomics, transcriptomics, metabolomics) analysis database with description and URL Database Description Genome analysis database Gramene Provides data resource for comparative functional genomics in crops MOsDB It provides gene sequences and gene annotation of japonica and indica rice subspecies MSU-RGAP Available genome sequence of the Nipponbare subspecies of rice and annotation of 12th rice chromosomes O. sativa It provides genome sequence view genome DB for Oryza sativa (ssp. japonica) Oryzabase Oryzabase provides strain stock and mutant information, chromosome maps RAP-DB It provides annotation of the rice genome sequence and comprehensive analysis of the genome structure and function Rice Phylogenetic approach used in phylogenomics comparative genomics to predict database the biological functions of members of a large gene family RiceGE It is Rice functional genomic express database TIGR DB Rice genome annotation database

RiTE

Rice GT

Rice GH

Rice TF

It provides information of transposable elements (TEs) of several species of the Oryza (rice) genus Database provides information on 793 putative rice GTs (gene models). It includes an interactive chromosomal map showing the positions of all rice GTs Database provides information on 614 putative rice GHs (gene models)

It provides functional genomic information for all putative rice transcription factors (TFs) and other transcriptional regulators

URL

References

http://www.gramene. org

(Monaco et al. 2014)

http://pgsb.helmholtzmuenchen.de/plant/ rice/ http://rice. plantbiology.msu.edu

(Karlowski et al. 2003)

http://www.plantgdb. org/ http://shigen.nig.ac.jp/

http://rapdb.dna.affrc. go.jp/

http:// ricephylogenomics. ucdavis.edu/ http://signal.salk.edu/ cgi-bin/RiceGE http://blast.jcvi.org/ euk-blast/index.cgi? project¼osa1 http://www.genome. arizona.edu/cgi-bin/ rite/index.cgi

(Kawahara et al. 2013)

(Duvick et al. 2008) (Kurata and Yamazaki 2006) (Ohyanagi et al. 2006)

(Jung et al. 2015)

(Ouyang et al. 2007) (Copetti et al. 2015)

http:// ricephylogenomics. ucdavis.edu/cellwalls/ gt/genInfo.shtml

(Cao et al. 2008)

http:// ricephyloucdavis. genomics.edu/ cellwalls/gh/genInfo. shtml http:// ricephylogenomics. ucdavis.edu/tf/ genInfo.shtml

(Sharma et al. 2013)

(Gao et al. 2006)

(continued)

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Table 4.1 (continued) Database Description Transcriptome analysis database Gene It provides information of expression atlas normalized expression levels of in RiceGE nine Affymetrix dataset (GPL2025) microarray and fold change for an Agilent Rice 22K dataset (GSE661 for ABA) Genevestigator Expression visualization tool which assists in prediction and analysis of genes FitDB Provides statistical modeling of transcriptional dynamics in field Rice genome Available high-quality annotation annotation for genes found in rice RiceXPRO

It is a repository of gene expression profiles derived from microarray analysis of tissues/ organs encompassing the entire growth of the rice plant under different conditions ROAD Rice oligonucleotide array database Proteome analysis database Phospho Rice Meta-predictor of rice-specific phosphorylation sites Oryza PG-DB

Rice proteome database based

PRIN

Predicted rice interactome network First detailed database to describe the proteome of rice

Rice proteome database RiceRBP

Facilitate the study of plant RNA-binding proteins

DIPOS

Complete rice protein interaction database Database of physicochemical and structural properties and novel functional regions in plant proteomes It is a database on gene identifiers, functional and proteomic annotations, subcellular localization, phenotypes, and upstream transcription motifs

Plant-PrAS

RiceDB

URL

References

http://signal.salk.edu/ cgi-bin /RiceGE5

(Sun et al. 2017)

https://www. genevestigator.com/ gv/index.jsp http://fitdb.dna.affrc. go.jp http://rice. plantbiology.msu.edu/ expression.shtml http://ricexpro.dna. affrc.go.jp

(Hruz et al. 2008)

(Sato et al. 2013)

http://www.ricearray. org/index.shtml

(Cao et al. 2012)

http://bioinformatics. fafu.edu.cn/ PhosphoRice http://oryzapg.iab. keio.ac.jp/ http://bis.zju.edu.cn/ prin/ http://gene64.dna. affrc.go.jp/RPD/ main_en.html http://www. bioinformatics2.wsu. edu/RiceRBP http://csb.shu.edu.cn/ dipos http://plant-pras.riken. jp/

(Que et al. 2012)

http://ricedb. plantenergy.uwa. edu.au

(Izawa 2015) (Ohyanagi et al. 2006)

(Helmy et al. 2011) (Gu et al. 2011) (Helmy et al. 2011) (Morris et al. 2011) (Sapkota et al. 2011) (Kurotani et al. 2015)

(Narsai et al. 2013)

(continued)

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Table 4.1 (continued) Database Description Metabolome analysis database MassBank Metabolite annotation, MS/MSdatabases METLIN

LipidBlast

ReSpect KNApSAcK MetaCyc KEGG

PRIME Golm metabolite database RiceCyc DB

Repository of metabolite information and tandem mass spectrometry data designed to facilitate metabolite identification in metabolomics Tandem mass spectral (MS/MS) database dedicated to annotate and identify hundreds of lipids Collection of literature and in-house MSn spectra data Comprehensive species metabolite relationship database Curated database of experimentally Facilitate understanding of highlevel functions and utilities of the biological system, such as the cell, the organism, and the ecosystem, from molecular-level information Metabolite annotation, plant metabolomics Metabolite annotation, plant metabolomics Rice metabolic pathways

URL

References

http://www.massbank. jp/index.html? lang¼en http://metlin.scripps. edu/index.php

(Horai et al. 2010)

http://fiehnlab. ucdavis.edu/projects/ LipidBlast/ http://spectra.psc. riken.jp http://kanaya.naist.jp/ KNApSAcK/ http://metacyc.org http://www.genome. jp/kegg

http://prime.psc.riken. jp/ http://csbdb.mpimpgolm.mpg.de/csbdb/ gmd/gmd.html http://pathway. gramene.org/ricecyc. html

(Tautenhahn et al. 2012)

(Kind et al. 2013) (Sawada et al. 2012) (Nakamura et al. 2014) (Caspi et al. 2020) (Kanehisa and Goto 2000)

(Sakurai et al. 2013) (Hummel et al. 2007) (Monaco et al. 2014)

mapping (Zhao et al. 2011), proteomics, and metabolomics platforms have facilitated gene expression and proteome analyses to identify and characterize candidate genes and their functional products (proteins) for drought tolerance (Ranjan et al. 2012a, b; Swamy et al. 2013; Li et al. 2015; Han et al. 2020).

4.1.1

The Genetic and Genomic Basis of Drought Tolerance

Drought tolerance is controlled by polygenes with low to medium heritability (Manickavelu et al. 2006). The availability of complete rice genome sequence, linkage maps, association mapping, marker-aided recurrent selection (MARS), and genome-wide selection (GWS) has made it possible to map and introgression of QTLs for drought tolerance (McCouch et al. 2010; Chen et al. 2014).

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Epigenomics of Drought Tolerance

Histone posttranslational modifications (PTMs) such as methylation, acetylation, phosphorylation, etc., DNA methylation, and microRNAs are considered as epigenetic regulators of drought tolerance. Under drought conditions, combinatorial histone PTMs such as lysine acetylation and/or lysine and arginine methylation pattern within the promoters, enhancers, or coding regions of genes cause gene activation or repression, results in drought tolerance (Chinnusamy and Zhu 2009; Lämke and Bäurle 2017). Changes in histone PTM levels can be studied by chromatin immunoprecipitation followed by sequencing (ChIP-sequencing). The level of DNA methylation and demethylation causing silencing or expression of certain genes also helps in coping up with stress in higher organisms including plants (Polosoro et al. 2019). A recent study compared the genome-wide DNA methylation level of two rice varieties with different level of drought tolerance capacity (DK151 and IR64) and found significant differences in their level of genome-wide DNA methylation (Wang et al. 2011b) and it accounted for 12.1% of the total sitespecific methylation differences. Most of these DNA methylations were reversible and showed developmental and tissue-specificity. Drought-susceptible varieties have DNA hypomethylation and drought-tolerant varieties have DNA hypermethylation. Changes in DNA methylation have been observed lesser in roots compared to leaves. Changes in DNA methylation level can be studied by genome-wide bisulfite sequencing (WGBS), methylated DNA immunoprecipitation (MeDIP), reduced representation bisulfite sequencing (RRBS), etc. MicroRNAs (miRNAs) are 22–24 nucleotides in length and control the posttranscriptional silencing of genes. They are also involved in the epigenetic process of drought stress. They control the gene expression post-transcriptionally. In rice, miR159, miR396, etc. regulate drought response (Zhou et al. 2010).

4.1.3

Genetic Engineering for Drought Resistance in Rice

Agrobacterium-mediated and bio-ballistic-based gene transformation methods have been used to develop transgenic rice expressing drought-responsive genes which are involved in histone PTMs, cell signaling, production of secondary metabolites, etc. (Seo et al. 2011; Fang et al. 2014; Cheng et al. 2018). During drought condition, several genes related to MAP kinase, LEA, heat shock proteins, NAC, ABA, DREB (DREB1/CBF, DREB2), AREB and calcium-dependent protein kinase show differential expression (Lenka et al. 2011; Sinha et al. 2011; Todaka et al. 2017). Transgenic plants expressing these genes either singly or in combination showed greater drought tolerance in rice (Hu et al. 2006). Recently, genome-wide profiling and analysis of mRNAs between drought-challenged and normal rice plants identified OsAHL1 gene responsible for drought avoidance and drought tolerance. This gene regulates the root development under drought condition and also regulates the content of chlorophyll in rice leaves (Zhou et al. 2016).

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Transcriptome Analysis of Plants to Understand Physiological Mechanisms

Transcriptome defines all the DNA (deoxyribonucleic acid) complement which are expressed as RNA transcripts, including coding (mRNA) as well as non-coding (e.g., tRNA, miRNA, snRNA) RNAs at a fixed time in a cell under a certain condition (Wang et al. 2009). Traditional transcriptional analyses comprised of single gene transcript by northern blots and real-time quantitative polymerase chain reaction (qRT-PCR). The advent of next-generation sequencing (NGS) technologies have enabled us to unravel the complete transcriptome of plant cell (Wang et al. 2009). In the early 1990s, transcriptomics analysis was mainly based on hybridization-based microarray technologies and had provided a basic information of diverse RNA molecules that are expressed from genomes over broad levels (Schena et al. 1995). A vast number of bioinformatics tools have developed in the microarray-based research in plant such as Genevestigator (Hruz et al. 2008), NASCArrays (Craigon et al. 2004), Microarray Analysis and Retrieval System (MARS), Stanford Microarray Database (Maurer et al. 2005), and Bioconductor (Huber et al. 2015). The use of high-throughput next-generation sequencing (NGS) technologies by Solexa around the year 2005 reformed transcriptomics by using RNA analysis through cDNA sequencing at huge scale (RNA-seq). NGS platforms used for RNA-seq are commercially available from many companies such as Illumina, SoLiD, Ion, PacBio, Roche 454, Helicos, Nanopore, and Solexa. These new technologies for RNA-seq analysis have been providing an increasingly in-depth knowledge of both the quantitative and qualitative views of gene expression in plant biology. Due to unavailability of reference genome of new plant species the de novo assembly is used for transcriptional analysis (Robertson et al. 2010). In many case for RNA-seq analysis Arabidopsis thaliana and rice genomes have been utilized as a reference for the identification of splice variants and transcriptional profiling (Filichkin et al. 2010; Lu et al. 2010). Apart from messenger RNA (mRNA) based transcriptome studies, NGS technologies were also used to focus on non-coding small RNA studies in several plants like Arabidopsis (Gustafson et al. 2005), wheat (Wei et al. 2009), Solanum lycopersicum (Liu et al. 2018), Medicago sativa (Long et al. 2015), and rice (Nobuta et al. 2007). Various bioinformatics tools support the de novo assembly in RNA-seq based applications like CAP3 (Huang and Madan 1999), CLCbio Genomics Workbench (CLC Bio), gsAssembler (454 Life Sciences), Trinity (Grabherr et al. 2011), Velvet (Zerbino and Birney 2008), etc. Similarly, there are many other bioinformatics tools available for reference-guided assembly in RNA-seq like, BLAT (Kent 2002), Bowtie (Langmead 2010), and RaGOO (Alonge et al. 2019). Bioinformaticians are working on many ways to find out new methods and applications of NGS in plant science for several years. NGS have made crucial breakthrough improvements to sequence and dissect the genome-wide transcriptomic and their differential expression pattern in specific cells, tissues, organs, mitochondria, and chloroplast in plants at different developmental stages and also analyzes the influence of stress conditions (Wang et al. 2017; Rich-Griffin et al. 2020). The improvement of NGS technologies has made progress

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in the discovery and functional characterization of microRNA (miRNA). It has been identified to play essential roles in various development process in plants and has a regulatory role in different abiotic stress conditions including salt, drought, heat, heavy metal, UV, dehydration, and mineral deficiency and also in several biotic stresses (Deyholos 2010; Liu et al. 2018). Although it is the application of genomewide transcriptome analyses in Arabidopsis, rice, wheat, cotton, soybean, chickpea, maize, poplar, etc. in combination with functional approaches that has made unprecedented remarkable advances in the study of plant physiology possible in recent years. These transcriptomic studies have significantly contributed to our understanding of fundamental principles of plant physiological mechanism on a genome-wide scale, including how plant physiological mechanisms are coordinated by gene regulatory networks, the hierarchical action of many transcription factors, and crosstalk between different hormonal pathways. A comparative transcriptome analysis has furnished new insights into the regulatory mechanisms of flowering and fruit development. Recent advances in transcriptomics have allowed researchers to describe the metabolites, transcription factors, and stress-inducible proteins involved in different level of stress tolerance in plants which may be useful in producing stress tolerant crops. RNA-Seq study in medicinal plants is gaining a strong foothold and has become the most active tool in medicinal plant genome research. It offers a complete understanding of gene expression and its regulation which has great implication in solving the questions of genetic evolution, genetic breeding, and ecology of medicinal plants. Transcriptomic analysis allowed us to study additional properties about the genes and metabolic pathways that are essential for health benefits, increasing basic nutrition and into the development of enriched foods (Kim et al. 2016). Refinement in heart-healthy omega-3 fatty acids (Kim et al. 2016) and protein quality for better human nutrition increased vitamin and mineral levels to cure nutrient deficiencies, and reduction of allergens and of anti-nutritional substances that decrease food quality can all be explored through omics technologies (Ramesh et al. 2016). Drought is the major abiotic stress to agricultural food production, mostly in the cultivation of rice. Rice drought gene expression studies have been done by transcriptomic as well as proteomics approaches. These approaches decipher differential gene expression between contrasting genotypes along with SNP marker identification, especially by exposure of transcripts over known QTL map. Such methods are able to discover new candidate genes controlling drought and other stress tolerance in rice. The transcriptomic approach has been used successfully in many crops to obtain an insight of drought stress response and linked candidate genes along with their markers. Numerous studies by NGS in rice have described changes in the transcriptomes under drought stress. More than 11,000 differentially expressed genes are reported in rice under drought stress (Maruyama et al. 2014). Wang et al. (2011a) reported 5284 drought stressresponsive genes in rice. The α-linolenic acid metabolic pathway genes were mainly upregulated in the drought-tolerant rice genotype. By using expression datasets, the marker transcripts were also identified for selection of drought tolerance in a wide range of rice germplasm resources through integrated analyses of gene expression and stress tolerance (Degenkolbe et al. 2013). These large expression datasets have

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provided much information on drought responses in rice. Many drought-responsive genes encoding both regulatory proteins, like transcription factors and functional proteins, such as galactinol synthases and late embryogenesis abundant (LEA) proteins, were found to be upregulated during drought stress using large-scale transcriptomic tools (Maruyama et al. 2014). Identification of drought-responsive transcription factors, like AP2/EREBP-, bZIP-, NAC, WRKY and MYB-type, in shoot, flag leaf, and panicle of rice was successfully carried out using RNA-seq (Wang et al. 2011a; Maruyama et al. 2014). Therefore, advances in RNA-sequencing and genomic analysis have provided an opportunity to identify important genes and genetic network for drought tolerance. However, transcriptomics provides a stage for high-quality research on plants for health benefits of their dietary components for both animals and humans and in the process provides a vital link to relate agricultural and animal products use.

4.2.1

Single-Cell Transcriptomics to Understand Cellular Mechanisms in Plant

For the past 15 years, NGS has been a popular method to study global gene expression changes in tissues. The limitation of this method is the confounding of heterogenous cell type. James Eberwine et al. (1992) first introduced sequencing of whole transcriptome of single-cell (Eberwine et al. 1992). These technologies were initially available as a high-density DNA microarray chips (Klein et al. 2002) and were later used for single-cell RNA sequencing (scRNA-seq). The first result of single-cell transcriptome sequencing by using next-generation sequencing platform was published in 2009, and it reported the characterization of cells from early developmental stages (Tang et al. 2009). Recent improvement in microfluidicsbased approaches to single-cell RNA-seq (scRNA-seq) provided us with a special opportunity to study transcriptional changes of thousands of cells in a single experiment. Single-cell separation and isolation is the first step in obtaining transcriptome data from a specific cell. Recently, 10X genomics platform for single-cell technologies have developed efficient protocols for plant tissue lysis and capture efficiency of single-cell analyses irrespective of tissue properties (Rich-Griffin et al. 2020). The Drop-seq, inDrop, and 10X are droplet-based methods of single-cell RNA-seq (Rich-Griffin et al. 2020). All these three methods have specific features and differ in terms of cell capture, efficiency, and doublet rate, and the ideal platform is experiment dependent. Till now, single-cell RNA-seq based studies have mainly been done in Arabidopsis root system. By using10X Genomics platform, JeanBaptiste et al. (2019) analyzed scRNA-seq data from Arabidopsis root and transcriptionally profiled over 3000 cells from whole root samples (Jean-Baptiste et al. 2019). Recently, scRNA-Seq utilized to study germinal cells in maize anthers and analyzed the gene expression changes during cell division in both mitosis and meiosis (Nelms and Walbot 2019). The role of endoderm cells in root growth was studied using scRNA-Seq analysis and it has been demonstrated visually as well (Jain et al. 2007). In recent single-cell transcriptomic study of root xylem cells, Shulse et al. (2019)

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identified several new candidate marker genes. These findings opened up the new approach to study the transcriptional role of newly found genes in root cell differentiation. In the same study, they also showed the functional response to environmental stimuli by sucrose treatments on root development. They reported that sucrose application does not significantly change the cell identity, but alter the cell proportion and gene expression in different tissue types (Shulse et al. 2019). By using Drop-seq-based single-cell transcriptomics, Turco et al. (2019) identified a new regulatory network which is controlled by VASCULAR-RELATED NAC DOMAIN7 (VND7) transcription factor and is involved in terminal differentiation of xylem cells. Another study by Hossain et al. (2017) reported differentially methylated regions in soybean root hairs under the control and heat stress treatments. Root hairs exhibited hypermethylation pattern under heat stress under control conditions. By the use of single-cell transcriptomics, Gould et al. (2018) investigated the circadian rhythm activity in Arabidopsis thaliana and results were found in spatial waves of gene expression of the plant circadian clock. These studies indicate the significance of scRNA technologies to characterize plant systems. Although many challenges still exist in using the single-cell transcriptomics in complex systems in plants.

4.3

Metabolomics Application in Plant Physiology

Metabolomics is defined as the systematic, quantitative, and qualitative analysis of all metabolites within cells or tissues (Sharma et al. 2018). Metabolomics is a supportive tool for “omics” technologies for high-throughput data acquisition. Due to the complexity and diversity of plant metabolites, single analytical technique is not sufficient for the identification and quantification of the metabolome and, therefore, various technologies are needed for a comprehensive study (Saito and Matsuda 2010; Hong et al. 2016; Kumar et al. 2017; Sharma et al. 2021). Highresolution 1H NMR (Kim et al. 2010), gas chromatography–mass spectrometry (GC-MS) (Halket et al. 2005), liquid chromatography–mass spectrometry (LC-MS) (Tugizimana et al. 2018), and capillary electrophoresis–mass spectrometry (CE-MS) (García et al. 2017) are the most widely used technologies today to study metabolome. The selection of suitable technology for metabolite analysis is based on time, selectivity, sensitivity, and accuracy. Metabolomics analysis produces complex datasets that are difficult to interpret. Therefore, several multivariate data analysis (MVDA) and bioinformatics tools are used to obtain meaningful information and interpret (Krumsiek et al. 2016; Chong et al. 2019). Several web-based applications like MetaGeneAlyse (Daub et al. 2003), metaP-Server (Suhre et al. 2011), MetiTree (Rojas et al. 2012), MetaMapp (Barupal et al. 2012) are available for data analysis and interpretation. Various well curated databases for metabolic pathway analysis in plants are available such as KEGG (Okuda et al. 2008), MetaCrop (Schreiber et al. 2012), UniPathway (Morgat et al. 2012), SMPDB (Jewison et al. 2014), MapMan (Thimm et al. 2004), MetaboAnalyst (Xia et al. 2015), MSEA (Xia and Wishart 2010), MetPA (Xia et al. 2011), and AraPath (Lai

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et al. 2012). Approximately 200,000 metabolites are present in plants, with 7000–15,000 found in single plant species (Patti et al. 2012). Metabolomics has been carried out on a large number of plant species (Arabidopsis, tomato, potato, rice, wheat, etc.) (Oikawa et al. 2008; Grennan 2009; Fukushima et al. 2014; Shepherd et al. 2014; Cavanna et al. 2020). The metabolomic profiling of a cell, tissue, or organism is very crucial because metabolites are diverse molecules that chemically change during metabolism processes (Goodacre et al. 2004; Yang et al. 2019). Different environmental factors lead to altered gene expression particularly in plants, which results in qualitative changes in the metabolite pool. Therefore, metabolite identification and characterization become more critical. The important traits in plants, such as drought, heat, flood, metal stress resistance, and postharvesting dependence upon the metabolite composition can be used for the manipulation of the metabolic phenotype through traditional breeding method (Das et al. 2017; Chen et al. 2018; Coutinho et al. 2018). The metabolite profiling studies identify the combination of metabolites that can predict tolerance for drought (Wu et al. 2017), salt (Sazzad Hossain et al. 2017), heat (Das et al. 2017), and cold (Song et al. 2017) in several plant species. Several candidate genes were also characterized for stress tolerance by metabolomics study (Kage et al. 2017; Song et al. 2017). Metabolomics was also used for the identification of genes and metabolic pathways which are affected by nutrient starvation or limitation of macronutrients in plants. For example, by metabolomic profile analysis, 134 metabolites were identified under sulfur stress (Hoefgen and Nikiforova 2008). Based on metabolite analysis result, a coordinated network of metabolic regulation was reconstructed under sulfur stress. Metabolite profiling under phosphorus deficiency in wheat showed decrease in levels of phosphorylated intermediates (glucose-6-P, fructose-6-P, inositol-1-P, and glycerol-3-P) and organic acids (2-oxoglutarate, succinate, fumarate, and malate) (Nguyen et al. 2019). Rice accumulates metabolites to protect itself against several abiotic and biotic stresses, Degenkolbe et al. (2013) analyzed metabolomic profiles in leaf from both indica and japonica rice varieties that varied in drought tolerance. In drought stress, in comparison with control plants, indica accumulated lower amounts of metabolites such as aconitate hydratase, AMP deaminase, and asparagine synthetase while lower amounts of asparagine synthase and AMP deaminase correlated negatively with performance parameters in drought stress. Sakuranetin, a phytoalexins, is synthesized biochemically from naringenin. Low level of sakuranetin is present in healthy rice leaves, which rapidly increases under biotic and abiotic stress (Shimizu et al. 2012).

4.4

Proteomics: Applicability and Challenges

For the last two decades, proteomics have been used as a discovery tool to uncover mechanisms related to plant physiology such as biotic and abiotic stress (Kumar et al. 2014; Zipfel and Oldroyd 2017). This is because mass spectrometry has the power to identify the spatial proteome and posttranslational modifications (PTMs) of

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Extract prepara on

Separat i-on of protein s by SDSPAGE or 2D PAGE

Digeso n of proteins of interest by protease s

Ionizaon by MALDI/ESI

Separaon of ions based on m/z , MS1 spectra

Collision induced dissociaon to fragment the MS1 pepdes

Separat ion based on m/z

MS2 spectra Detec o-n

Fig. 4.1 Steps involved in a typical mass spectrometry experiment

proteins that directly participate in the physiological processes. Need for proteomics arises because the functions of proteins do not depend on their individual properties, but it depends on the complex network of signals and dynamic regulation of cellular processes which largely depends on protein–protein interaction. Furthermore, the function of a protein does not depend only on their individual structures, but also on their subcellular localizations and any covalent modifications taking place posttranslationally. PTMs are critical for many processes such as reprogramming of cells, cellular homeostasis, defense mechanisms, etc. (Withers and Dong 2017). The need to do a proteomic study is further strengthened by the fact that in many studies the correlation between mRNA levels and protein levels is not great (Ingolia 2014; Feussner and Polle 2015). In simple words, proteomics is able to determine the identity of proteins, detect the PTMs, and quantitate proteins in a given sample (Gemperline et al. 2016). In a typical proteomics experiment involving liquid chromatography–mass spectrometry, proteins are extracted from the plant tissue by biochemical fractionation and then are processed directly to digestion with proteases or are separated on SDS-PAGE or 2-dimensional (2-D)-PAGE, and the band of interest is excised and then digested with proteases (Fig. 4.1). Most commonly used protease is trypsin that digests at the C-terminus of lysine or arginine resulting in smaller peptides. Peptides are then separated further by high-pressure liquid chromatography (HPLC) in very fine capillaries and are eluted into an electrospray ion source which results in protonated peptides that enter the mass spectrometer. Based on the mass-to-charge ratio, detector yields a mass spectrum called MS1 spectrum. These MS1 spectra are further analyzed and top 20–50 spectra are further fragmented by energetic collision with gas in a process called tandem mass spectrometry and MS/MS (MS2) spectra is obtained (Newton et al. 2004). In proteomics, before choosing the type of mass spectrometry to be used for sample analysis, four parameters that should be considered are sensitivity, resolution, mass accuracy, and mass range of the sample. Mass spectrometric analysis is performed in the gaseous phase on ionized analytes. A typical mass spectrometer consists of an ion source, a mass analyzer, and a detector. For most of the biological samples, ion source can be electrospray ionization (ESI) or matrix-assisted laser desorption/ionization (MALDI). ESI ionizes the analytes in the liquid phase, while MALDI ionizes the samples in solid phase. MALDI-MS is used to analyze relatively simple peptide mixtures or total mass of the protein, whereas ESI-MS systems are the method of choice for the analysis of complex samples (Ho et al. 2003; Singhal et al. 2015). Most commonly used mass analyzer is the ion trap, time-of-flight (TOF), quadrupole, Fourier transform ion cyclotron (FT-MS),

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and orbitrap (Haag 2016). Analyzers separate the ionic peptides based on the massto-charge ratio (m/z), while detectors measure the abundance of ions at each m/z value. Following are the characteristics of common mass analyzers: • Ion-trap analyzers: Uses a combination of electric or magnetic fields to capture ions for a certain time interval and are then subjected to MS or MS/MS analysis (sequential trapping and fragmentation). Advantages: Robustness, high sensitivity, and low expense Limitation: Relatively low mass accuracy • Time-of-flight analyzer: This is usually associated with MALDI. It analyzes the flight time of every ionized peptide. Flight time starts when voltage is applied to the ionized peptide and ends when the peptide hits the detector. Ions with lower masses reach the detector fast and thus, for each peptide with different masses, time-of-flight is different. Advantages: Faster turnaround time, high mass accuracy, moderate to high resolution Limitation: Low sensitivity • Quadrupole analyzer: This contains four parallel cylindrical rods of circular or hyperbolic cross section which can scan or filter sample ions based on the stability of their flight trajectories through an oscillating electric field applied to the rods. This analyzer is able to stay tuned to a single ion for extended periods of time and therefore, it is helpful in studying particular ions of interest. They need low vacuum and can be coupled to GC-MS or LC-MS. Advantages: Low cost, ability to perform both qualitative and quantitative analyses, fast scan rate, compact size Limitations: Low resolution, low mass accuracy, limited mass range • Fourier transform ion cyclotron resonance (FT-ICR) analyzer: It determines the m/z of ions based on the cyclotron frequency of the ions in a fixed magnetic field. Advantages: High mass accuracy and resolution Limitation: High cost • Orbitrap: This is based on the orbital trapping of ion in an electrostatic field consisting of two outer electrodes and a central electrode, which enables it to act as both an analyzer and a detector. Advantages: Highest resolving power, high sensitivity, high accuracy Limitation: High cost

4.4.1

Comparative Quantitative Proteomics

One of the purposes of using proteomics is the precise quantification and comparison of the proteomes of cells or tissues in different physiological conditions. This can be done by quantifying protein abundance using a label-free method or derivatizing the peptides from different conditions or time points with isobaric reagents that label the primary amines of peptides and proteins and yield different reporter groups after

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fragmentation in a technique called isobaric tags for relative and absolute quantitation (iTRAQ) (Ross et al. 2004). In metabolic labeling also known as stable isotope labeling of cells in culture (SILAC), newly synthesized proteins incorporate non-radioactive amino acids such as heavy lysine and arginine such as 13C or 15 N. After few cell doubling cycles (4–5), light and heavy cells are mixed, and relative abundances of peptides are determined by mass spectrometry (Ong et al. 2002). Proteomics have been used to study the abiotic stress in plants. In general, proteomes are analyzed by comparing stressed plants with unstressed (control) ones. Stress tolerant genotypes have been found to have increased oxidative stress, metabolism, and the scavenging capacity of reactive oxygen species (ROS) and also show enhanced carbohydrate metabolism, and more efficient photosynthesis (Hajheidari et al. 2007; Su et al. 2019). In this connection, embryo proteome of six different rice genotypes (with contrasting responses to drought, salt, and cold) was studied and they showed differences in the posttranslational status of an LEA rice Rab21 (Farinha et al. 2011). This stress protein was relatively more phosphorylated in the embryos of sensitive genotypes compared to tolerant ones.

4.4.2

Proteomic Responses Under Drought Stress

Drought is an abiotic stress associated with reduced water availability for the use in metabolism leading to cellular dehydration. Drought causes altered carbon and nitrogen metabolism and also leads to reduced photosynthesis (Hajheidari et al. 2007). Comparing the proteomic changes between drought sensitive and drought resistant plants may provide some clues as to how plants respond to this stress. Ge et al. (2012) compared the proteomic changes in the developing grains under drought condition of two spring wheat varieties: Ningchun 4 (good tolerance to abiotic stress) and Chinese Spring (poor tolerance to abiotic stress). They found 152 protein spots showing at least two-fold difference in abundance by using 2-DE followed by MALDI-TOF and MALDI-TOF/TOF. Of them, 96 proteins belong to stress tolerance, defense, detoxification, carbohydrate and nitrogen metabolism, photosynthesis, and storage. Comparative proteomic analysis identified four proteins, ascorbate peroxidases, Rubisco large subunit, triosephosphate isomerase, and oxygenevolving complex that might be involved in drought resistance. Comparative proteomic analyses by 2D-PAGE combined with MALDI-TOF and MALDI-TOF/ TOF of extracts from leaves and roots of drought sensitive and tolerant varieties of Barley have shown that drought resistance is provided mainly by energy-related proteins and chaperones (Chmielewska et al. 2016). In an interesting study, droughttolerant rice plants were generated by overexpressing Arabidopsis DREB1A protein (Paul et al. 2015). Comparative proteome analysis between the roots of transgenic DREB1A overexpressing homozygous plants and wild type plants under drought stress condition by 2-DE coupled with MS identified proteins expression level change related to carbohydrate and energy metabolism. Stress and defense related genes were upregulated under drought condition in both WT and transgenic rice plants. Roots of transgenic rice had overexpression of a novel protein, R40C1

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indicating its potential role in the generation of drought-tolerant plants. In another study, Zhao et al. (2018) performed the proteomic analyses of drought-responsive proteins in Agropyron mongolicum Keng, a drought-tolerant perennial forage grass. Using iTRAQ, they identified 4105 unique proteins with 255 and 280 proteins upand downregulated, respectively. Upregulated proteins during drought stress included storage proteins, dehydrins, cell wall structural proteins, amylase, and trypsin inhibitors. These upregulated proteins were mainly involved in galactose hydrolysis, proline biosynthesis, pentose phosphate pathway, and antioxidant systems. Downregulated proteins mainly included chloroplast function-related proteins involved in the pathways of chlorophyll biosynthesis, aromatic amino acid biosynthesis, flavonoid and iso-flavonoid biosynthesis, etc. Pandey et al. (2010) studied the extracellular matrix (ECM) proteome in rice shoots under dehydration stress by 2-DE followed by ESI-LC MS/MS or MALDI-TOF MS/MS and their study revealed 100 differentially regulated proteins presumably involved in carbohydrate metabolism, cell defense and rescue, cell wall modification, cell signaling and molecular chaperones to be responsible for dehydration tolerance. Nucleus controls the changes in the gene expression based on environmental stress response. The study on drought-treated chickpea identified a total of 147 differentially expressed nuclear proteins involved in different cellular processes (Pandey et al. 2008).

4.5

Glycomics of Plants in Stress Management

The integral part of all the organisms and most abundant bio-molecule on earth, present as structural, functional, and shielding component, are the carbohydrates also called as saccharine or sugars (Showalter 1993). These are as diverse as diversity of plants. A plant cell’s integrity and functionality depend on what carbohydrate it synthesizes. They are so dynamic in nature that even after being synthesized, they undergo various sorts of modifications and then finally fabricated. A cell type is a reflection of what its genome expresses, but now recent studies in the field of glycan biology have uncovered the fact that the glycan biosynthesis, structural arrangements, and function become much more complex through modifications as compared to their naive forms. Hence, a new area of the study of the entire contingent of sugars in an organism evolved that helps to analyze the association of sugar with other macromolecules like lipids, proteins, and nucleic acids, known as glycomics (Shahzad et al. 2016; Okekeogbu et al. 2019). Unlike the DNA, RNA, and proteins, carbohydrates (glycans) are difficult to analyze because of their unique stereochemical properties. The formation of these glycans as well as the glycoconjugates is very onerous to study. The reason behind this is their structural diversity and stereochemistry, where the possibility of glycosidic linkage is random because of multiple hydroxyl groups, thus baffling the researcher to understand the joining of two monosaccharide units and makes it unpredictable (Shahzad et al. 2016). The glycoconjugates, namely the glycolipids and glycoproteins exist in all organisms from tiny bacteria to higher plants (Borner et al. 2002). The glycan

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moieties are attached to the glycoproteins during posttranslational modifications which are the key step in advancing these proteins into cell specific information carriers (Crowell et al. 2009). There are two basic kinds of glycosylation, namely N-linked glycosylation in which the glycans are linked to the nitrogen atom of asparagine residue within an Asn-X-Ser/Thr sequence (where X is any amino acid other than proline and threonine is more common than serine) and O-linked glycosylation in which the glycans are linked to the serine or threonine and rarely to hydroxyproline and hydroxylysine (Shahzad et al. 2016). There are other different types of glycosylation also such as C-linked and S-linked. Glycosylation and linking of additional groups upon glycoconjugates are carried out on specific sites which are arbitrary even after the predicted sites according to the consensus sequences have been worked out (Shahzad et al. 2016). Current research advances have opened new prospects of obtaining pure and chemically defined glycan moieties. Some important tools for glycomics are MALDI-TOF mass spectrometry, ESI LC-MS/MS mass spectrometry, NMR spectroscopy, and carbohydrate/lectin microarray. The differentially expressed genes for glycosylation are studied through glycogene microarrays and they aid in the study of glycan biosynthesis, structure, and function (Shahzad et al. 2016).

4.5.1

Significance of Glycomics in Plant Stress

The polysaccharides comprising the cell wall as well as extracellular matrix are critical in modulating various processes, including signaling and cell–cell interaction. They carry and store vital biological information about virtually all cellular processes and pathways (Okekeogbu et al. 2019). A normal glycome differs from abnormal glycome indicating that something anomalous is going on in a cell or tissue type which enables us to distinguish between normal and stressed plants. Resolving the enigma of glycomics would be very advantageous as sugars play key role in many biological processes such as cell signaling, stress tolerance, and providing immunity. A major study on effect of spaceflight on the glycome of Arabidopsis thaliana has been carried out using the Biological Research in Canisters (BRIC) hardware during Space Shuttle mission STS-131 which reveals that biosynthesis of xylan and pectic components of the walls may be impacted by microgravity resulting in a compositional difference in the cell wall matrix (Ferl and Paul 2010; Nguema-Ona et al. 2014). The genes involved in cell wall modification, including the arabinogalactan protein AGP31 (AT1G28290), the xyloglucan XTH9 (AT4G03210), the glycosidase XTH32 (AT2G36870), and the glycosyl hydrolases, are upregulated in spaceflight as determined by the BRIC-16-Cyt microarray (Johnson et al. 2017). The cellular responses to abiotic and biotic stress depend upon the regulation of vesicle trafficking to make sure the right localization of proteins specialized in sensing and responding to stress stimuli. However, the underlying molecular mechanisms are poorly understood. Further, the identity, specialization, and stress-relevant cargo transported in stress and under extreme circumstances also need extensive glycomic studies (Rosquete and Drakakaki 2018).

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Phenomics in Plants

With the advent of various tools in phenomics, it is now possible to annotate plant characters like leaf characters, canopy temperature, and automated imaging of plants without entering into the fields anytime and anywhere as compared to the traditional phenotyping which is time consuming and a laborious task, using different equipment (Furbank and Tester 2011; Tardieu et al. 2017). This includes far infrared imaging for canopy temperature; fluorescence imaging for the physiological state of photosynthetic machinery, high-throughput imaging technologies, including color imaging for biomass, leaf characters, and plant structure; near infrared imaging for measuring tissue and soil water contents and automated weighing and watering systems have also been developed for water usage imposing drought/salinity (Pieruschka and Schurr 2019). The automated analysis of root capacity and functioning made easier (Yang et al. 2013). Various 2D and 3D imaging software has been developed so far that helps in monitoring growth and development through imaging of the leaf angles, area, and length; mineral and water uptake; photosynthetic activities of plants grown in large areas, hence predicting the minor differences among their phenotypes (Pieruschka and Schurr 2019). This ultimately aids the plant breeders in the selection of plants with desirable traits and features at an early stage of their development and among large populations (Furbank and Tester 2011). Meanwhile the changes taking place in their phenotypes are also monitored in real time. The phenomics of crop plants are very important as they find many applications in crop improvement as it helps in understanding morphology of the plants, biomass prediction, grain yield prediction, understanding the genetic basis of quantitative traits, etc. (Furbank and Tester 2011). The plants can be monitored and the best plant varieties can be selected at their early stage of development without the need of waiting till their maturity. The traits with low heritability can be amplified using phenomics tools by finding putative genes for a superior trait. So, phenomics complements genomics in a wider prospect and is going to prepare the next generation of crops being more sustainable, abiotic and biotic stress tolerant, and less susceptible to damage by climate change taking place in this world, hence ensuring food security to enormously growing population worldwide.

4.7

Conclusions

Making sure of food security for an ever-increasing global population under the changing climate is the topmost priority for science policy makers across the globe. The conventional research efforts and old technologies will not be able to provide sufficient food for the global population, necessitating the incorporation of modern science, tools, and techniques into the current plant science research. Biotechnology has great potential in bridging the demand of food through developing improved agricultural technologies. In plant research transcriptomics, genomic, proteomics, glycomics, phenomics, etc. are witnessing a rapid pace of development due to integration of new robotics and automation technologies. These technologies have

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improved our understanding of genome, transcriptome, and proteome and its complexity in plants. The plant and environmental stress interactions complicate the expression of genes and protein functions. This chapter covers these important areas of research concerning to plant biotechnology, which are key for achieving advanced molecular level understanding in plant physiology and crop development. The information provided in this chapter will be of great value to students, researchers, and academicians.

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Recent Advances in Transcriptomics: An Assessment of Recent Progress in Fruit Plants Manoj K. Rai

, Roshni Rathour, and Sandeep Kaushik

Abstract

Fruits are an excellent source of nutrition and a key component of the human diet. Many indigenous fruits are also the source of income of local people of poor and developing countries from the export of fresh fruits and processed fruit products. However, a number of horticultural and agronomic problems discussed in this chapter are associated with fruit plants which affect significantly on its production and crop improvement programs. The recent advances in new biotechnological tools including cisgenics/intragenics, gene editing coupled with the latest omics technologies may be able to solve or reduce the problems faced by the indigenous fruit plants. With the advent of next generation sequencing (NGS) technology, genomic and transcriptomic research for many fruit plants has accelerated dramatically in recent years. The enormous sequence data generated through RNA sequencing have enabled to generate large-scale transcriptomic resources for gene discovery and their expression, comparative analysis and identification of novel single nucleotide polymorphism (SNPs) and simple sequence repeat (SSR) markers in many model and non-model plant species included fruit plants also. In the last 5–8 years, RNA-seq based transcriptome analysis of many fruit plants has been focused mainly on the identification of key functional and regulatory genes associated with many horticultural and agronomic traits like plant disease response and resistance, abiotic stress tolerance, fruit nutritional value, quality, ripening and flowering. In this chapter, we provide an overview of recent successes achieved in the field of transcriptomics of major fruit plants and their involvement in the discovery of trait specific genes and the role of these genes in

M. K. Rai (*) · R. Rathour · S. Kaushik Department of Environmental Science, Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, India e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Kumar et al. (eds.), Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), https://doi.org/10.1007/978-981-16-2956-3_5

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specific biological processes. Challenges and prospects for the deployment of transcriptome analysis in many indigenous fruit plants are also discussed. Keywords

Biotic and abiotic stress · Food security · Fruit quality · Genomics · Next generation sequencing · Omics · Transcriptome

5.1

Introduction

The global climate change, water scarcity, reduction in the agricultural land and increasing food demand due to the fast growth of human population, crop yield losses caused by the severity of diseases and soil infertility are some crucial factors affecting food security in many regions of the world, particularly in developing and poor countries (Tyczewska et al. 2018). Low income, hunger and childhood malnutrition are also some major challenges to meet this food security goal (Priyadarshi et al. 2018). Attaining food security needs not only the investment in advanced agrotechnology based research but also the policy reforms in many areas like rural infrastructure, agricultural, human and natural resources management. To accelerate food security, it is essential to reform a progressive action plan and more investment in rural areas. Such investment and progressive action will help in high crop production and boost income of people of rural areas (Rosegrant and Cline 2003). Since olden times, plants have been domesticated by human being for food security. However, among more than 4,00,000 plant species, only a few hundred contribute significantly to our food supplies (Moshelion and Altman 2015; Tyczewska et al. 2018), and the proportion of domesticated plants is only about 0.3% (Leakey and Tomich 1999). During domestication, genomic and phenotypic characteristics of crop have been altered and species is gradually transformed from wild to elite and finally in high-yield cultivars (Khan et al. 2019). As a result of domestication and successfully exploitation over millennia, three major cereals wheat, rice and maize have been considerably used as staple food and it has a share of more than 70% among all food crops (Tyczewska et al. 2018; Kumar et al. 2020). Other than these three-staple foods, some other important crops like different types of pulses, oil yielding crops, sorghum, millet, sweet potato, etc. also occupy the main component of human diet and have the potential to meet the food security goals (Khan et al. 2019). These basic staple food crops are key components of diets of people of rural and low-income backgrounds. If these staple crops are threatened by biotic and abiotic stresses like diseases or pests, drought, or salt or nutrient-poor soils, poverty and hunger can rise dramatically. Other than these staple foods, people of different regions of the world also rely on complementary foods like vegetables and fruits as well as animal-based products milk and meat (Kilian 2012). Indigenous species native to a specific region play a significant role in food security because they are naturalized in that particular area and become the part of the culture of a community (Muthoni and Nyamongo 2010). Although, many developed

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countries are now slightly shifting away from traditional foods due to their high economy, increase in per person income and flexibility in trade and export (Kilian 2012). But, due to high cost of exotic food crops and high poverty level, the farmers of most developing and poor countries are still forced to continue producing and consuming traditional food crops.

5.1.1

Fruit Crops and Food Security

Fruits are a key component of the human diet and recommended as an excellent source of vitamins, minerals, phytochemicals, especially antioxidants and dietary fibres (Matas et al. 2009). Fruit not only provides healthy and nutritious food but also generates employment and income for farmers (Rai et al. 2009; Joosten et al. 2015). Fruit crops have a specific nutritional constituent which cannot be obtained from cereals or other staple foods. Therefore, fruits contribute as a dietary replacement for people who consume nutritionally poor diets (Fentahun and Hager 2009). Indigenous fruit trees have long been exploited for its nutritional values and played a vital role in household food security, particularly at times of food and nutrient scarcity (Priyadarshi et al. 2018). In comparison to exotic and conventional farmed fruit crops, indigenous fruit crops have more genetic diversity and potential to adapt to local climatic and edaphic conditions and able to address food insecurity problem in most of countries. Due to their wide availability and nutritional composition, most of the indigenous fruits also have the potential to alleviate vitamin and micronutrient deficiencies in rural population. Fruits may also contribute a significant role in food security as most of fruit crops are perennial tree species and they are usually less affected by climate change and other biotic and abiotic stresses than annual cereal and other staple crops. In addition, household harvesting, utilization and marketing of indigenous fruits can generate more rural income and employment for farmers as compared to traditional staple crops (Chivandi et al. 2015). Despite the importance of fruit plants and its contribution in food security and rural income, current state of utilization of fruit is very low in many countries due to cereal based dietary habit of peoples. Moreover, the importance of fruits as food supplements has largely been disregarded (Fentahun and Hager 2009).

5.1.2

Need of New Biotechnology Tools (NBTs) and ‘Omics’ in Fruit Plants

Food insecurity and childhood malnutrition are serious public health issues in many developing and poor countries. In the perspective of fast population growth and global climate change, requirement of alternative food products with high nutrition value other than traditional staple foods becomes urgent now (Tyczewska et al. 2018). Owing to their nutritional and commercial values, many indigenous fruit crops may contribute a substantial role in food security. However, most of the indigenous fruits remain categorized as underutilized because of unawareness of their

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nutritional importance in local peoples, limited commercial exploitation of processed food products from fruits and their low marketing and advertisement (Rai and Shekhawat 2014). In addition, the selection of improved quality of fruits has also much impact on fruit industries. The desirable fruit quality traits include the fruits with high phytonutrients, longer postharvest shelf life, low seed content, flavourful, attractive in size and free from contaminants. Many biochemical and regulatory mechanisms also influence the different physiological processes like flowering, fruit development and ripening, fruit softening after storage (Handa et al. 2014). Resistance to several pest and diseases and tolerance to various abiotic stresses are other important desirable crop improvement trait of fruit plants which directly impacted on fruit production (Rai and Shekhawat 2014). Therefore, there is a need to focus on improvement of fruit plants with these all desirable agronomic and horticultural traits. The last 2–3 decades have witnessed the development of many biotechnological tools that allow the modifying genomes of many commercially important crops including fruit crops by incorporating new traits into elite genotypes (Francis et al. 2017). Through the application of this GM (genetically modified) technology, many new and improved fruit crop varieties with novel agronomic and horticultural traits have been generated. Transgenic approaches in fruit plants are aimed mainly to the development of crops with abiotic stress tolerance, resistance against pests and diseases, fruits with high nutritional levels and improved postharvest shelf life and reduced generation time (Gambino and Gribaudo 2012; Litz and Padilla 2012; Rai and Shekhawat 2014; Limera et al. 2017). Although traditional transgenic technology has achieved some success in fruit plants, but due to unpredictable risks to the environment and food safety, commercialization of GM crops is a subject of debate (Kanchiswamy et al. 2015). Alternative to traditional GM technology, recently developed new biotechnology tools (NBTs) like cisgenesis/ intragenesis or genome editing based new breeding techniques are more promising tools to manipulate the plant genome without involving foreign DNA (Kanchiswamy et al. 2015; Limera et al. 2017; Tyczewska et al. 2018). Genetically modified plants developed using these tools contain no transgenes, therefore the safety concerns that have been raised due to transfer of transgene in traditional GM crops can be neglected, which should increase consumer’s acceptance of GM crops (Limera et al. 2017). Although these techniques have been mostly established in cereals, legumes and other commercially important crops, but these are successfully applied in some fruit plant also (Rai and Shekhawat 2014; Kanchiswamy et al. 2015; Karkute et al. 2017; Limera et al. 2017). Genomic editing tools like CRISPR-Cas9 provide an optimized method to engineer desirable traits in less-studied crops including indigenous fruit crops in a very short time, particularly fruit quality related traits that can address food security issues (Ma et al. 2018; Khan et al. 2019). In the last decade, omics-based technologies have gained immense popularity among plant biotechnologists because of its ability to identify and characterize key genes related to a specific trait. Omics provide an inclusive picture of cellular physiology to understand the response mechanisms under specific conditions and

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to reveal unknown metabolic pathways (Mochida and Shinozaki 2011; Witzel et al. 2015). There are four fundamental omics components which include genomics, transcriptomics, proteomics and metabolomics. With advances in omics technology, many other omics fields are also derived from these four fundamental components, namely phenomics, hormonomics, epigenomics, ionomics, glycomics, physiomics, microbiomics and cellomics, etc. (Rai et al. 2017a). In the couple of decades, next generation sequencing (NGS) technology based whole genome and transcriptome sequencing has accelerated genomic research in a number of model and non-model plant species (Varshney et al. 2009; Unamba et al. 2015). The sequence data generated from whole genome and RNA-seq could be served as valuable genomic resources for the identification of candidate genes associated with a specific trait. In addition, the development of large-scale genomic resources like transcripts, molecular markers, genetic and physical map generated through NGS technology has also been used in a variety of genomic research like comparative genetics, gene expression, genetic diversity and phylogenetic analysis and marker assisted breeding (Varshney et al. 2009). In recent years, genome sequence projects on various fruit plants have been successfully completed and some projects are in progress (Gapper et al. 2014; Rai and Shekhawat 2015; Badenes et al. 2016; Chen et al. 2019). Another omics approach, i.e. transcriptomic research in fruit plants has also accelerated using microarrays-based technology or NGS based RNA sequencing technology (Sonah et al. 2011; Rai and Shekhawat 2015; Badenes et al. 2016; Shiratake and Suzuki 2016; Tang and Tang 2019). However, with the advancement of NGS technology, the microarrays-based transcriptome research is now being replaced by RNA-seq. Over the last few years, RNA-seq based transcriptome analysis of various plant species has attracted a lot of interest for generating genetic resources for gene discovery, gene expression, comparative analysis, molecular marker development and underlying the mechanism of different biological processes. Transcriptome analysis of any plant allows us to understand the plant response mechanisms in response to various pathogens, abiotic stresses or during many physiological processes and targets the genes that are expressed in a tissue at that particular time (Unamba et al. 2015; Chaudhary and Sharma 2016; Rai et al. 2017a, b). Moreover, NGS based transcriptome sequencing facilitated the discovery of novel single nucleotide polymorphism (SNPs), Expressed sequence tags (ESTs) and simple sequence repeat (SSR) markers, which are the foundation of molecular breeding of any plant species (Varshney et al. 2009; Kalia et al. 2011). In this chapter, we present recent studies involving RNA-seq based transcriptome analysis in fruit plants to understand the biosynthesis and regulation of specific metabolites during many physiological processes or in response to various pathogens or abiotic stresses and identification of genes associated to crop improvement traits.

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RNA Sequencing-Based Transcriptome Analysis in Fruit Plants: Recent Progress

In the last 5–8 years, RNA sequencing-based transcriptome analysis has been applied in fruit plants for characterizing pathogen infection and resistance pathways, investigating transcriptome response to abiotic stresses and different physiological and development processes like fruit ripening, flowering, postharvest physiology, fruit size, colour, flavour, firmness as well as biosynthesis of secondary metabolites (Table 5.1).

5.2.1

Disease Resistance Response

The sustainable production of many fruit crops faces a challenge of worldwide fruit crop damage due to attack of many pathogens and pests. After pathogen attack, plants have developed an immune system to defend themselves against these pathogens that are triggered by recognition of pathogens and further activation of several metabolic pathways (Gomez-Casati et al. 2016; Kankanala et al. 2019). Depending on plant–microbe interaction and their susceptibility or resistance to pathogens, plants have evolved several mechanisms involving defense response like signal transduction, pathogen-derived resistance (PDR), changes in physiological and biochemical processes or synthesis of several pathogenesis-related proteins (PR proteins), antimicrobial metabolites or peptides (Rai and Shekhawat 2014; Kumar et al. 2014). In recent years, RNA-seq based transcriptome analysis not only helps understanding of the basic mechanism of disease susceptibility or resistance but is also useful in identification of several genes that respond in pathogen attack (Gomez-Casati et al. 2016; Shiratake and Suzuki 2016; Kankanala et al. 2019). One of the main applications of this technique is to measure the expression level of genes involved in plant–pathogen interaction without prior knowledge. In the last 8–10 years, availability of large-scale RNA-seq based transcriptome database has enabled understanding of the various plant disease-resistance mechanisms of fruit plants against fungal, bacterial and viral pathogens and insect and pests (Table 6.1). Owing to rapid adaptation in changing environments, their genetic flexibility and ability to infect almost all parts of plant, fungi is one of the most challenging plant pathogens (Kankanala et al. 2019). The major fungal diseases of fruit plants include rots, rusts, wilts, mildews, blights, scabs. A number of fungal pathogens like Botrytis cinerea, Colletotrichum spp., Monilinia spp., Penicillium spp. and Aspergillus spp. are main cause of postharvest disease in many fruit plants during storage and transport of fruits (Gatto et al. 2011). Transcriptome analysis in response to several fungal pathogens identified many candidate or regulatory genes associated with defense-related mechanisms in a number of fruit plants like apple (Ballester et al. 2017; Zhu et al. 2017a, b; Tian et al. 2019; Tao et al. 2020), avocado (Reeksting et al. 2014, 2016), banana (Bai et al. 2013a; Li et al. 2013; Sun et al. 2019), Citrus (Ajengui et al. 2018), grapevine (Li et al. 2015; Fröbel et al. 2019), kiwifruit (Zambounis et al. 2020), mango (Hong et al. 2016; Liu et al. 2016a) and

Species Malus domestica

Malus sieversii and Malus domestica Prunus armeniaca

Persea americana

Fruit plant Apple

Apricot

Avocado

Abscission of the lateral pedicels Biotic and abiotic stresses Defense system against pathogen Pythium ultimum Defense system against pathogen Alternaria alternata Floral transitions Drought, cold and high salinity Anthocyanin biosynthesis at low temperatures Powdery mildew (Podosphaera leucotricha) infection Defense system against the rust fungus Gymnosporangium yamadae Proanthocyanidin biosynthesis during fruit development Blue mold (Penicillium expansum) resistance and susceptibility Plum pox virus (PPV) susceptibility/ resistance Drought stress Fruits infection with Colletotrichum gloeosporioides Defense system against pathogen Phytophthora cinnamomi and flooding

Identification of genes associated with trait/mechanism/response Anthocyanin content

(continued)

Ballester et al. (2017) Rubio et al. (2015) Liu et al. (2020a) Djami-Tchatchou et al. (2012) Reeksting et al. (2014)

Li et al. (2020a)

Tao et al. (2020)

Li et al. (2018) Li et al. (2019) Song et al. (2019a) Tian et al. (2019)

Zhu et al. (2017b)

References El-Sharkawy and Xu (2015) Heo et al. (2016) Zhou et al. (2017) Zhu et al. (2017a)

Recent Advances in Transcriptomics: An Assessment of Recent Progress in. . .

Root

Leaf Unharvested and harvested fruits

Leaves

Mature fruit

Fruits of different developmental stages

Leaf

Leaf

Terminal buds Leaf Leaf

Leaves

Plant tissue used for transcriptome analysis Yellow-skin mutant and red-coloured mature fruits Pedicels Roots Roots

Table 5.1 Recent progress in transcriptome analysis in some fruit plants

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Six-leaf stage seedlings

Musa itinerans

Pooled tissues of roots, pseudostems, leaves, floral organs, and developing fruits Ripe and unripe fruit tissue Xylems of banana roots

Musa sp.

Musa acuminata

Banana

Fruit mesocarp

Fruit

Fruits

Seeds, roots, stems, leaves, aerial buds, flowers and fruits Root

Plant tissue used for transcriptome analysis Mesocarp

Roots, leaves, flowers and fruits at different developmental and ripening stages Root

Species

Fruit plant

Table 5.1 (continued)

Plant–pathogen interaction and plant hormone signal transduction pathways against Fusarium oxysporum f. sp. cubense Cold-sensitive banana and cold-tolerant plantain subjected to the cold stress

Defense system against pathogen Fusarium oxysporum f. sp. cubense Fruit ripening process Defense-related metabolites in response to Fusarium wilt Synthesis of ovate family proteins (OFP)

Fatty acid biosynthesis during fruit ripening process Ungrafted Phytophthora root rot tolerant rootstock in response to flooding and Phytophthora cinnamomi Disease reduction in fruits treated with chitosan Carotenoid biosynthesis during the mesocarp and seed developmental stages Fruit development process

Identification of genes associated with trait/mechanism/response Oil (fatty acid) biosynthesis

Yang et al. (2015)

Bai et al. (2013a)

Zhang et al. (2020)

Asif et al. (2014) Sun et al. (2019)

Vergara-Pulgar et al. (2019) Li et al. (2013)

Xoca-Orozco et al. (2017) Ge et al. (2019)

References Kilaru et al. (2015) Ibarra-Laclette et al. (2015) Reeksting et al. (2016)

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Grapevine

Citrus

Citrus sinensis and Citrus aurantium Vitis vinifera

Citrus reticulata

Citrus sinensis

Water stress Chilling temperature Berry weight determination Bud dormancy

Leaves and berry (pericarp plus seeds) Shoots Pericarp and mesocarp tissues Bud

Leaves and roots

Flowers/berries

Pooled plant tissues

Fruits of different developmental stages

Inedible epicarp (EP) and albedo (AL) and the edible segment membrane (SM) and juice sac (JS) Cambial tissue enriched with xylem

Defense response against Xylella fastidiosa bacterial infection Sugar and organic acid metabolism in fruit during fruit maturation Susceptibility and defense mechanism against Phytophthora nicotianae Berry development in a seeded grape variety and its seedless somatic variant Drought stress

Early fruit development between seedy genotypes and their seedless mutants Fruit development and ripening

Fruit

Endocarps Fruit pulp

Fruit pulp Leaf midribs

Fruits

Anthocyanin biosynthesis genes in the formation of purple peel Huanglongbing disease caused by Candidatus Liberibacter asiaticus (CaLas) Fruit development and ripening Defense system against Candidatus and two strains of Citrus Tristeza virus Citrate accumulation Fruit development and ripening

Epidermal part of the peel

Recent Advances in Transcriptomics: An Assessment of Recent Progress in. . . (continued)

Ajengui et al. (2018) Nwafor et al. (2014) Corso et al. (2016) Santo et al. (2016) Kim et al. (2016) Muñoz-Espinoza et al. (2016) Min et al. (2017)

Rodrigues et al. (2013) Lin et al. (2015)

Lu et al. (2016) Wang et al. (2017a) Zhang et al. (2017a) Feng et al. (2019)

Martinelli et al. (2012) Yu et al. (2012) Fu et al. (2016)

Deng et al. (2010)

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Kiwifruit

Fruit plant

Actinidia deliciosa

V. vinifera  V. labrusca Vitis amurensis

Species

Table 5.1 (continued)

Defense response against early downy mildew (Plasmopara viticola) Heat acclimation Changes in fungal endophyte lifestyle responsible for bud necrosis Copper stress Defense mechanism against downy mildew in grapevine Armoured scale insect (Hemiberlesia lataniae) feeding Waterlogging stress

Leaves

Pseudomonas syringae pv. actinidiae resistance and susceptibility Defense mechanism against Botrytis cinerea

Shoots Fruits

Flesh

Development of fruits and quality of fruits stored at room temperature Fruit ripening traits

Fruits

Roots

Bark

Leaves Leaf

Leaves Buds

Salt tolerance

Identification of genes associated with trait/mechanism/response Cluster compactness, berry number and berry size Salt stress

Leaves

Leaves

Plant tissue used for transcriptome analysis Flowers/berries

Zhang et al. (2018) Song et al. (2019b) Zambounis et al. (2020)

Zhang et al. (2015) Luo et al. (2017)

Hill et al. (2015)

References Grimplet et al. (2017) Upadhyay et al. (2018) Das and Majumder (2018) Fröbel et al. (2019) Liu et al. (2020b) Ribeiro et al. (2020) Leng et al. (2015) Li et al. (2015)

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Litchi chinensis

Mangifera indica

Litchi

Mango

Transformation from a vegetative meristem (VM) into an inflorescence meristem (IM) by low temperatures and reactive oxygen species (ROS) Flower induction in response to low temperature High temperature-induced floral abortion

Apical meristem

Fruit peel

Unripe and mid ripe stages of fruit

Axillary and apical buds

Peels of fruits

Mesocarp

Shrinking panicles (SPs) and developing panicles (DPs) Fruit peel

Leaves

Chilling treatment during postharvest cold storage

Defense system in postharvest mango fruit against Colletotrichum gloeosporioides Defense mechanism against pathogen Fusarium mangiferae Fruit ripening

Postharvested fruit treated with hot water brushing (HWB) Fruit ripening

Seed size of two genotypes

Ovules

Pericarp

Floral initiation and expression of numerous flowering-related genes Fruit maturation processes Interaction of drought and low temperature on floral initiation Light-induced anthocyanin biosynthesis

Entire buds including floral meristems and their surrounding juvenile leaves Pericarp Leaf

Recent Advances in Transcriptomics: An Assessment of Recent Progress in. . . (continued)

Srivastava et al. (2016) Sivankalyani et al. (2016)

Liu et al. (2016a)

Dautt-Castro et al. (2015) Hong et al. (2016)

Luria et al. (2014)

Zhang et al. (2017b) Liu et al. (2019)

Zhang et al. (2016a) Pathak et al. (2016) Lu et al. (2017)

Zhang et al. (2014) Lai et al. (2015) Shen et al. (2016)

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Carica papaya

Leaves, sap and roots

Leaf

Fruits

Leaf

Leaf Base of stem cuttings, root (induction stage) and root (formation stage) Root, stem and leaf Leaf

Papaya

Flavonoid biosynthesis Root formation in softwood cuttings

Leaf

M. indica, M. notabilis and M. laevigata Morus spp. Morus alba

Salt stress responses 1-deoxynojirimycin biosynthesis, a polyhydroxylated alkaloid Stress tolerance and pathogen resistance in papaya ring spot virus (PRSV) transgenic plant Fruit ripening in ethylene/1-MCP treated papaya Defense mechanism against papaya sticky disease (PSD) caused by papaya meleira virus (PMeV) Drought stress

Transcriptome analysis of mulberry under drought stress Stress and hormonal treatments

Leaf

Regular bearing and alternate bearing mango

Leaf/fruit bud

Morus multicaulis

Flowering and floral malformation

Healthy or malformed buds

Mulberry

Identification of genes associated with trait/mechanism/response Cuticle biogenesis

Plant tissue used for transcriptome analysis Peels from ripe and overripe fruit

Species

Fruit plant

Table 5.1 (continued)

Gamboa-Tuz et al. (2018)

Madronero et al. (2018)

Shen et al. (2017)

Liu et al. (2017a) Wang et al. (2018a) Fang et al. (2016)

References Tafolla-Arellano et al. (2017) Yadav et al. (2020) Mahato et al. (2016), Sharma et al. (2020) Wang et al. (2014) Baranwal et al. (2016) Li et al. (2020b) Du et al. (2016)

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Pineapple

Pyrus communis

Pear

Annona squamosa

Five Pyrus species

Pyrus pyrifolia

Prunus persica

Peach

Sucrose biosynthesis Terpenoid and phenylpropanoid biosynthesis Ethephon mediated stimulation of floral transition

Fruit ripening in response to low temperature Flower buds transitioning Anthocyanin accumulation Ethylene-induced postharvest senescence Fruit development and maturation

Low temperature mediated enhancement of ripening Sugar metabolism and accumulation during postharvest ripening of fruit Bud dormancy

Defense mechanism against Xanthomonas arboricola pv. pruni Late stage of fruit ripening Low temperature stress Dormancy progression in seeds after rinsing and chilling Anthocyanin biosynthesis Identified genes that regulate ripening, softening and fruit texture after storage Fruit maturation and ripening

(continued)

Liu and Fan (2016)

Socquet-Juglard et al. (2013) Pan et al. (2016) Jiao et al. (2017) Kanjana et al. (2016) Cao et al. (2018) Wang et al. (2018b) Nham et al. (2015) Nham et al. (2017) Wang et al. (2017c) Gabay et al. (2019) Mitalo et al. (2019) Bai et al. (2013b) Bai et al. (2017) Xu et al. (2018) Zhang et al. (2016b) Lü et al. (2020) Ma et al. (2015)

Recent Advances in Transcriptomics: An Assessment of Recent Progress in. . .

Shoot apical meristems

Fruit Leaves

Flower buds Fruit peel Fruits Fruit flesh

Fruits

Vegetative buds

Fruit

Fruit

Fruit flesh Fruit flesh

Fruit flesh Stigma Embryonic axes of dry seed

Leaf

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Annona squamosa

Citrullus lanatus

Sugar apple

Watermelon

Fragaria vesca

Achenes and receptacles (flesh) Inflorescent meristem Normal and a malformed unopen flower Flesh and mesocarp Flesh

Fruits

Fruits Leaf

Fruits

Regulation of the anthocyanin pathway in red- and white-fleshed fruit Fruits under osmotic stresses Defense mechanism against powdery mildew (Podosphaera aphanis) infection Unripe white and ripe red fruits infected with Botrytis cinerea Hormonal regulation in fruit ripening Floral transition and flower development Normal and malformed flowers Fruit development Cucumber green mottle mosaic virus (CGMMV) infection

Fruits Fruits

Fragaria  ananassa

Strawberry

Fruits

Fruits

Punica granatum

Pomegranate

Defense in response to plum pox virus infection Soft seed formation Acquisition of chilling tolerance during postharvest of fruit Flavonoid synthesis in ripe fruit Postharvest fruit in response to exogenous auxin and abscisic acid Fruit ripening

Leaves

Prunus domestica

Ethylene-induced flowering

Leaves and stem apex

Plum

Identification of genes associated with trait/mechanism/response Cold stress tolerance

Plant tissue used for transcriptome analysis Leaves

Species

Fruit plant

Table 5.1 (continued)

Gu et al. (2019) Liu et al. (2016b) Liu et al. (2017b) Guo et al. (2015) Li et al. (2017)

Galli et al. (2019) Jambagi and Dunwell (2015) Haile et al. (2019)

References Chen et al. (2016a) Wang et al. (2017b) Rodamilans et al. (2014) Xue et al. (2017) Kashash et al. (2019) Pillet et al. (2015) Chen et al. (2016b) Sanchez-Sevilla et al. (2017) Lin et al. (2018)

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strawberry (Jambagi and Dunwell 2015; Haile et al. 2019). Other than fungal diseases, transcriptome analysis of fruit plants focusing on bacterial diseases and their defense mechanism have also been reported (Martinelli et al. 2012; Rodrigues et al. 2013; Socquet-Juglard et al. 2013; Song et al. 2019b). As a result of the plant fungal or bacterial infection process in all these studies, mostly genes involved in cell wall modification, phytohormone signaling, synthesis of defense-related proteins like pathogenesis-related (PRs) or antimicrobial metabolites and transcription factors, i.e. WRKYs, ZIPs, Hsfs, NACs, and MYBs were activated. After fungal diseases, plant viruses are second largest group of plant pathogens which cause significant crop damage and economic losses. During plant–virus interactions, viruses enter the host cell and manipulate the host genetical and cellular machinery and finally leads to pathogenesis by disrupting cellular homeostasis and encoded some proteins that interacted with the transcription machinery of host plant (Singh et al. 2019). The defense mechanism in plants against virus is mostly based on the concept of PDRs in which plant host–virus interaction triggers antiviral response (Rai and Shekhawat 2014). Therefore, transcriptome analysis of the host plant infected with a specific virus provides valuable information on pathogen-derived resistance responses against that particular virus and leads to the plant protection strategies. Transcriptome analyses in response to viruses have been reported in many fruit plants like apricot (Rubio et al. 2015), Citrus (Fu et al. 2016), papaya (Fang et al. 2016; Madronero et al. 2018), plum (Rodamilans et al. 2014), watermelon (Li et al. 2017).

5.2.2

Abiotic Stress Response and Tolerance

Several abiotic environmental stresses such as salinity, water scarcity (Drought), high temperature, cold, waterlogging and metal toxicity are some major limiting factors that adversely affected the plant growth and productivity (Rai et al. 2011). In order to adapt such adverse conditions, plants have evolved many integrated and complex biochemical, cellular, physiological and molecular mechanisms and a variety of genes, belonging to diverse regulatory and functional groups, are expressed in many plant species which products have a great impact on stress response and tolerance (Estravis-Barcala et al. 2020). In the last decade, the availability of extensive genomic and transcriptomic databases in public domain has opened new paths to genome-wide analysis of plant stress responses (Rai et al. 2017b). RNA sequencing-based transcriptome analysis is now widely used to identify candidate and regulatory genes involving in single/multiple abiotic stress response and tolerance of many fruit plants (Table 6.1). For example, transcriptome analysis of leaf of apple subjected to drought, cold and high salinity enabled to identify five upregulated genes PP2C-37b, PP2C-77a, ABI5-5b, SAPK3, and HPt3a and these genes were involved in multiple abiotic stress tolerance (Li et al. 2019). There are other several reports on transcriptome analysis of fruit plants in response to various abiotic stresses like drought (Wang et al. 2014; Corso et al. 2016; Shen et al. 2016; Santo et al. 2016; Gamboa-Tuz et al. 2018; Liu et al. 2020a), salt and osmotic

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(Liu et al. 2017a; Upadhyay et al. 2018; Das and Majumder 2018; Galli et al. 2019), cold or chilling (Yang et al. 2015; Chen et al. 2016a; Kim et al. 2016; Jiao et al. 2017), waterlogging (Reeksting et al. 2014, 2016; Zhang et al. 2015), metal toxicity (Leng et al. 2015), heat (Liu et al. 2020b) or multiple stresses (Baranwal et al. 2016; Zhou et al. 2017; Li et al. 2019). In response to abiotic stresses, many genes or groups of genes identified though transcriptome analysis are either upregulated or downregulated. For example, C-Binding Factor (CBF) transcription factors, which belong to the AP2/ERF family are upregulated under cold stress while most of genes related photosynthesis are downregulated in response to drought stress (EstravisBarcala et al. 2020). In most of these studies, genes involved in electron transport chains, oxidation–reduction processes, reduction of stomatal conductance, plant hormone signaling or genes regulating the synthesis of reactive oxygen species (ROS) scavengers, osmolytes, late embryogenesis abundant (LEA) proteins, aquaporins, ion transporters are identified and expressed.

5.2.3

Fruit Development, Ripening, Quality and Biosynthesis of Metabolites

The initiation and progression of fleshy fruit have several stages including development, maturation, ripening and finally senescence. During fruit development and ripening, several physiological and biochemical processes including attractive colour of fruits due to accumulation of coloured pigments, cell wall metabolism for fruit softening, synthesis and accumulation of sugars, acids and other metabolites for nutrient composition, flavour and tastes take place (Bapat et al. 2010; Gapper et al. 2013; Seymour et al. 2013; Priyadarshi et al. 2018). Further, a good quality of fruit has some desirable characteristics like high nutritional value, attractive colour and shape, good aroma and taste, softness but not excessive, and longer shelf life of fruits (Handa et al. 2014). The application of most modern omics-based tools has now been employed to understand the molecular processes involved in fruit development, ripening and quality. Although researches on biochemical, molecular and genetic regulation of fruit development, ripening and quality have focused mostly on model fruit tomato due to short life cycle, consumer demand, easy to handling and a wellknown history of biochemical and physiological studies, and availability of largescale genomic and transcriptomic database in public domain (Bapat et al. 2010; Gapper et al. 2013). But, in recent years, molecular basis research emphasis on fruit ripening and quality has now shifted towards other commercially important fruit plants also. Regarding fruit ripening and quality, RNA-seq based transcriptome analysis in fruit plants mainly focused on ethylene synthesis and signaling, hormone regulation, cell wall modification and biosynthesis of pigments and metabolites like carotenoids, anthocyanins, ascorbates, citrates, as well as sugar metabolism (Table 6.1). In addition, transcriptomes of post-harvested fruits were also analysed and related genes were identified and characterized in many fruit plants. Ethylene, a gaseous plant hormone, plays a key role in ripening of climacteric fruits. The physiological role of ethylene in fruit ripening, senescence and other

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developmental processes is well known and the biosynthesis pathways of ethylene and their biochemical and molecular characterization are well established in model plant tomato (Bapat et al. 2010; Gapper et al. 2013; Seymour et al. 2013). To better understand the role of exogenous ethylene in ripening of fruits at molecular level, transcriptome analysis of other fruit plants has also been attempted. For instance, Shen et al. (2017) compared the differential gene expression in exogenous ethylene and 1-methylcyclopropene (1-MCP) treated papaya fruit and identified numerous genes related to cell wall, chlorophyll and carotenoid metabolism, hormone signal transduction pathway, proteinases and their inhibitors and senescence. They identified two major genes Pg and Gal-b, which are involved in pectin solubilization during ripening and softening of fruits, and one gene Chy-b which plays a key role in the yellow colour of papaya fruit. In another study, Xu et al. (2018) identified one novel ethylene responsive factor (ERF) gene (Pbr022708.1) related to ethyleneinduced postharvest senescence in pear fruit. Fruit softening is a major attribute of a good quality fruit and is highly dependent on regulation of hormones, changes in cell wall structure and texture (Bapat et al. 2010; Handa et al. 2014). Transcriptome analysis of fruit flesh of peach carried out by Wang et al. (2018b) suggested that lignification is one of the major factors impacting the texture and softening of peach fruit. From the consumer’s perspective, the best-quality fruit should have an appropriate composition of amino acids, carotenoids, organic acids, sugars or other antioxidants in consumable fruit (Handa et al. 2014). During ripening process, there is involvement of the synthesis and substantial accumulation of these constituents. NGS based transcriptome analysis provided a platform to elucidate the metabolic pathways of carotenoid, flavonoid, sugar and organic acid synthesis in fruits during ripening phase, which is vital in the determination of fruit quality. Recently, Ge et al. (2019) used RNA-seq technology to explore carotenoid metabolism in mesocarp and seed of avocado. Result of this study showed the identification of 17 unigenes involved in the carotenoid biosynthetic pathway and expression level of these genes was higher in mesocarp than in seed. In another study, Yu et al. (2012) studied transcriptome changes during fruit development and ripening of two varieties of sweet orange and found that most of the genes differentially regulated during fruit development and ripening were related to cell wall biosynthesis, carbohydrate and citric acid metabolism, carotenoid metabolism. In banana, Asif et al. (2014) identified that differentially regulated genes during fruit ripening that were associated with cell wall degradation and synthesis of aromatic volatiles. Similarly, transcriptome analysis of mesocarp of avocado during fruit development also identified candidate genes linked to lipid metabolism, ethylene signaling pathway and auxin signaling pathway (Vergara-Pulgar et al. 2019). Anthocyanin, a type of flavonoid, has a lot of beneficial effects on human health due to its antioxidant activities, scavenging of free radical and inhibition of lipid peroxidation (Deng et al. 2010). Anthocyanin is synthesized in different parts of many fruit plants via flavonoid pathways. In the last 4–5 years, RNA-seq technology has been widely applied to identify candidate genes involved in anthocyanin/flavonoid biosynthesis pathways during fruit development in a number of fruit plants like apple

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(El-Sharkawy and Xu 2015; Li et al. 2020a), banana (Deng et al. 2010), litchi (Zhang et al. 2016a), peach (Cao et al. 2018), pear (Bai et al. 2017) and strawberry (Pillet et al. 2015; Lin et al. 2018). Other than different parts of fruits, anthocyanin or other metabolites synthesized in leaf or other parts of plants has also been analysed using RNA-seq technology (Ma et al. 2015; Wang et al. 2018a; Song et al. 2019a; Li et al. 2020b). Some other major target traits of fruit transcriptomes linked to fruit development, metabolite biosynthesis or fruit quality are fatty acid biosynthesis in avocado (Kilaru et al. 2015; Ibarra-Laclette et al. 2015), synthesis of ovate family proteins (OFP) in banana (Zhang et al. 2020), sugar metabolism in mandarin orange and pear (Lin et al. 2015; Wang et al. 2017c; Lü et al. 2020), citrate accumulation in sweet orange (Lu et al. 2016), berry development and berry size, number and weight determination in grapes (Nwafor et al. 2014; Muñoz-Espinoza et al. 2016; Grimplet et al. 2017), starch degradation in kiwifruit (Zhang et al. 2018), regular and alternate fruit bearing in mango (Sharma et al. 2020; Alkan and Kumar 2018), cuticle biogenesis in mango (Tafolla-Arellano et al. 2017), postharvest senescence of fruits in pear (Xu et al. 2018), soft seed formation in pomegranate (Xue et al. 2017) and postharvest fruit of strawberry in response to exogenous auxin and abscisic acid (Chen et al. 2016b).

5.2.4

Flowering Related Traits

Flowering is an important physiological and developmental process of a plant’ life cycle. The floral transition, the progression from vegetative to reproductive phase occurs only in favourable environmental conditions. This transition is determined by various endogenous and exogenous signals like photoperiod, vernalization and gibberellic acid (GA) (Srikanth and Schmid 2011). Light is one of the main factors that regulate flower transition through the photoperiod and the regulation of flowering through photoperiod pathway mainly depends on the day length and quality of light. Temperature also plays a significant role in flowering through the process of vernalization that accelerates flowering on exposure of cold. Light and temperature often affect flowering process by stimulating changes in endogenous hormone levels (Zhang et al. 2014). During the floral transition, these endogenous and exogenous signals induce the expression of a number of flowering-related genes (Li et al. 2018). In the last two decades, many flowering-related genes, i.e. FLOWERING LOCUS T (FT), LEAFY (LFY) and APETALA1 (AP1) and many more and their role in flower development have been investigated in the model plant Arabidopsis (Srikanth and Schmid 2011; Lu et al. 2017). As most of fruit plants are woody perennials which have long juvenile period, therefore the regulation of flowering in these plants is relatively different than herbaceous species. In recent years, NGS based RNA-seq technology has now been widely used to identify genes that are differentially expressed in flowering process of many fruit trees (Table 6.1). Most of transcriptome analysis related to flowering related traits performed in fruit plants have focused on floral initiation and transition (Bai et al. 2013b; Zhang et al.

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2014, 2017b; Liu and Fan 2016; Liu et al. 2016b, 2017b; Lu et al. 2017; Wang et al. 2017b; Li et al. 2018; Yadav et al. 2020). In addition, some other traits like floral abortion (Liu et al. 2019), floral malformation (Yadav et al. 2020; Liu et al. 2017b), bud dormancy (Min et al. 2017; Gabay et al. 2019) have also been studied. Li et al. (2018) reported the floral transition in apple was linked to sugar and cytokinin signaling pathways. They identified many sugar biosynthesis genes, i.e. SUS3, RFS5, AGAL1 and SSY1/2 expressed in two cultivars of apple during flower transition. In another study, RNA-seq based transcriptome analysis was used to investigate the control of flowering in litchi tree in response to chilling and reactive oxygen species (ROS) (Lu et al. 2017). In this study, they identified a total of 48 chilling and ROS responsive genes (CRRGs) associated with flowering.

5.3

Conclusions

In the last 4–5 years, NGS based transcriptomic technology has been utilized to accelerate the trait improvement programs in several fruit plants, particularly its involvement in the identification of novel regulatory and functional genes associated with many horticultural and agronomic traits like plant disease response and resistance, abiotic stress tolerance, fruit nutritional value, quality, ripening and flowering, etc. Moreover, with the discovery of NGS technology, transcriptome-based research has speeded up in many non-model species which helps not only the identification of trait specific candidate genes but also in the development of SNPs, ESTs, SSR markers and identification of quantitative trait loci (QTL). However, the progress made in the area of fruit transcriptome is mostly restricted to some major temperate and tropical fruit crops and most of the indigenous fruit plants growing in poor or developing countries are still considered as less-studied or underutilized in reference to genomic or transcriptomic studies. Therefore, there is a need for more transcriptome-based research in these minor and indigenous fruit plants so that the latest omics techniques can be used for crop improvement of these plants. Ultimately, transcriptome and other omics-based research will help in fruit crop improvement programs particularly, in biotic/abiotic stresses and fruit quality which will lead to a profitable return on fruit production and minimize the challenges of food insecurity and malnutrition in many poor and developing countries. Acknowledgments The authors (MKR and SK) are thankful to University Grants Commission (UGC) and Department of Science and Technology (DST), India for funding in the form of UGC-BSR Start Up Project and State Science & Technology Programme (SSTP), respectively.

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Harnessing Perks of MiRNA Principles for Betterment of Agriculture and Food Security Anjan Barman, Tarinee Phukan, and Suvendra Kumar Ray

Abstract

Micro-RNAs (miRNAs), usually 19–24 ribonucleotides in length form a special class of small RNA that are limited to eukaryotic world (except for some DNA viruses) including plants and engrossingly, they serve diverse regulatory roles. Within two decades of their discovery, plant miRNA exploration has established as a new and evolving domain in plant genomic research arena. Crucial findings relating biogenesis, architecture, functional roles of miRNAs have outpoured copious details in the preceding years and the trend is still continuing. MiRNA mediated regulation generally takes place at post-transcriptional level. These non-coding short ribonucleic acid molecules first emerge as double stranded RNAs which undergo sequential processing via dedicated participation of multi-protein complexes. One of the mature miRNA strands associating with the special multi-protein conjugate called RNA induced silencing complex (RISC) binds to mRNAs of specific genes by virtue of their near-perfect complementary pairing and leads to either post-transcriptional degradation of the target mRNAs or halting of its translation. The wide array of regulatory roles miRNA executes in plant’s life starting from seed germination, seedling to adult plant transition, inflorescence, metabolism, reproduction till senescence demonstrates the very significance of these entities. Profuse studies further highlight miRNA’s critical role in plant’s response toward numerous abiotic as well as biotic stresses

A. Barman (*) Department of Biotechnology, Pandu College, Guwahati, Assam, India T. Phukan Department of Bioscience and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India S. K. Ray Department of Molecular Biology and Biotechnology, Tezpur University, Tezpur, Assam, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Kumar et al. (eds.), Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), https://doi.org/10.1007/978-981-16-2956-3_6

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encountered in varying environmental settings. In regard to the mode of epigenetic regulation via mRNA silencing/repression attributed to miRNA, emergence of concept as well as development of RNA interference (RNAi) tools for manipulation of beneficial agronomic traits in agronomy has gained more prominence in recent times. For instance, amalgamation of features like drought resistance, resistance to herbivore mediated afflictions (e.g. insect pests) as well as resistance to lethal pathogenic agents (including virus, bacteria, fungi, nematode) in important agronomic crops via RNAi technology has emerged successful in various experimental milieus. Further, as limitations in sustainability of conventional strategies to counteract abiotic as well as biotic stresses in agricultural crops have been evident, RNAi (RNA interference) technology has indeed brought great promises. Whereas advanced genome editing techniques such as CRISPR/ Cas have begun to assure distinct impact, RNAi technology accompanying the former could undeniably reinforce enduring effects on agronomical crop manipulation strategies for significant benefits. This will be in the large interest of securing food plants and their outputs to meet ever growing demand of global population. Here in this chapter, we are focussing on updated utility of miRNAs as vital tools in economically important food crop plants to counteract physiological, developmental as well as environmental barriers (both abiotic and biotic) to render conspicuous survival fitness and accelerate productivity in them. Besides, we are also mentioning in brief about biogenesis, functional role plant miRNAs play in plant’s life-processes at the onset. Keywords

Plant epigenetics · miRNA · miRNA in crop stress · RNAi · Crop improvement · Agriculture

6.1

Introduction

Plants including all food crops are robust and dynamic living systems that endure immense hardship for their survival and sustenance in their natural habitat. Considering the numerous abiotic and biotic factors impressing up on their growth, productivity and life-processes, plants inevitably require prudent genetic (Lata and Prasad 2011; Qin et al. 2011a; Grativol et al. 2012; Verma et al. 2016; Yang and Guo 2018) as well as epigenetic regulatory networks (Mirouze and Paszkowski 2011; Sahu et al. 2013; Grativol et al. 2012; Roy 2016; Lämke and Bäurle 2017; Thiebaut et al. 2019) that can afford them successful adaptations wherever they thrive. Abiotic factors such as light, temperature, water, humidity, minerals, pH, salinity, etc. can influence crop plant health in positive as well as negative ways (reviewed in Wang et al. 2003; Gourdji et al. 2013; reviewed in Mittler and Blumwald 2010; Yang and Guo 2018). Similarly, biotic factors comprising all other living beings from unicellular microbes, fungi to metazoans (including Arthropods) significantly affect food crop’s survivability either favourably or in derogatory ways (van Dam 2009;

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Compant et al. 2010; Scholthof et al. 2011; Mansfield et al. 2012; Dean et al. 2012; Jones et al. 2013; Myers and Sarfraz 2017; Schwelm et al. 2018; Fernandez and Burch-Smith 2019). Crops direly need to confront all these propositions wisely to assure their fruitful existence. It needs no explaining why significance of food plants for very existence of humans and the organisms on which human direly depend on is right on top. This subsequently call for appropriate measures to secure food crops/plants and simultaneously also to improve their traits like productivity, longevity, etc. Enhancing productivity of food crops is by far of utmost priority looking into the fact that human population is escalating unprecedentedly (United Nations 2017). In the past, prior to molecular genetic principles being uncovered, our ancestors selectively bred food crops/plants on the basis of their useful properties that could meet necessities to certain extent (Mickelbart et al. 2015; Voss-Fels et al. 2019). With the gradual rise in global population alongside concomitant reductions in arable land and climatic fluctuations, traditional measures to enhance food production have met with several shortcomings (Mickelbart et al. 2015; Voss-Fels et al. 2019). Striking impact of abiotic and biotic stress on crop productivities has been apparent from the fact that growth in yields of some of the major crops across the globe (e.g. rice, maize, wheat) has not really picked up along the line of human population growth (United Nations 2017; Alexandratos and Bruinsma 2012). Insufficient diversity in crop species (which is perhaps owing to fewer choices available for extensive cultivation) is one of the major factors thought to make these crops more vulnerable to both biotic and abiotic distresses (Nelson et al. 2018; Ewel et al. 2019). Subsequently, demand for improved crop varieties that could withstand climatic fluctuations as well as resist diseases/pest infestations and eventually meet goals of global food security has gained momentum (Bailey-Serres et al. 2019). Inevitably, such quests entail crucial understanding of crop plant’s biology and their ecological kinships (Miflin 2000; Tirnaz and Batley 2019). In the post-Green revolution era, growing perceptions on genetic basis of inheritance, immune functions, inter-kingdom as well as inter-specific interactions, biochemical communications, etc. (Flor 1971; Dixon and Lamb 1990; Hewezi et al. 2012; Nürnberger et al. 2004; Schaefer et al. 2004; Strauss and Irwin 2004; Heil and Ton 2008; Basso et al. 2019) in several plants including food crop species have immensely contributed to advancements toward effective crop breeding, agronomic practices gradually. Concomitant refinements in DNA based technologies, decisive developments like creation of transgenic plants (Fraley et al. 1983; Zambryski et al. 1983; Horsch et al. 1985), progresses in plant genetic manipulation expeditions (Hood et al. 1997; Kusnadi et al. 1997) had further augmented the impact. Considering overwhelming fundamental impediments in agronomy and food crop breeding practices such as physiological and developmental barriers, want for immunity and resistance mechanisms towards biotic as well as abiotic stresses, manipulation of aforementioned scientific advancements has afforded crucial breakthroughs (Wang et al. 2005; Xu et al. 2006; Qin et al. 2011b; Gamuyao et al. 2012; Munns et al. 2012; Cletus et al. 2013; Park et al. 2013, 2015; Uga et al. 2013;

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Quilis et al. 2014; Schoonbeek et al. 2015; Collinge 2016) although quest for longlasting alternatives are still on. In recent times, advent of genome editing tools such as oligonucleotide-directed mutagenesis (ODM), CRISPR-Cas, TALEN, zinc finger nuclease, etc. has brought newer avenues toward genome tailoring mediated crop improvement strategies (Abdallah et al. 2015; Rodríguez-Leal et al. 2017; Yin et al. 2017; Zhang et al. 2018a; reviewed in Van Vu et al. 2019). In fact, CRISPR aided plant genome editing process especially has shown great possibilities (Scheben et al. 2017; Jaganathan et al. 2018; Zhang et al. 2020a). Meanwhile, one of the entrancing discoveries during the early and late 1990s, when small RNA molecules deciding fate of vital genetic attributes in the model nematode, Caenorhabditis elegans (Lee et al. 1993; Wightman et al. 1993; Reinhart et al. 2000) was thorough, windows for manipulation of this fascinating RNA molecule in plethora of scientific endeavours was just unfurling. Crucial disclosures relating small RNA (sRNA) mediated gene silencing/repression (via RNA interference (RNAi)) in myriad of organisms (Pal-Bhadra et al. 2002; Volpe et al. 2002; Hannon 2002; He and Hannon 2004; Zimmermann et al. 2006) were beginning to accumulate subsequently. Ability of sRNAs to alter fates of cellular phenotypes/ functions in eukaryotic systems provided background for numerous future investigations (Elbashir et al. 2001; McManus and Sharp 2002; Vaucheret et al. 2004; Zeng and Cullen 2004; Cifuentes et al. 2010; Ha and Kim 2014) that further reinforced applicability of these molecules in innovative expeditions. In plants, expanding investigations has provided fascinating clues to small RNA (sRNAs) mediated regulatory mechanisms in both model and non-model species (Chen 2009; Yang et al. 2013; Borges and Martienssen 2015; You et al. 2017; Chen et al. 2018; Ramachandran et al. 2020). sRNAs, ranging ~20–30 nucleotides in length, are endogenously generated non-coding transcripts which initially born as double stranded RNA precursors, undergo sequential processing through unique ribonuclease enzymes called Dicer (Bernstein et al. 2001; Carthew and Sontheimer 2009). Mature sRNAs generated as single stranded entities in nucleus are transported to cell cytoplasm (Meister and Tuschl 2004), where they induce transcriptional or post-transcriptional gene silencing via their near-perfect complementary binding to specific mRNA/DNA sites (Bühler and Moazed 2007; Moazed 2009) with necessary involvement of specified multi-protein composites constituting the RISC (RNA induced Silencing Complex) (Hammond et al. 2000). By now, sRNAs as master epigenetic regulators determining almost every aspect in plant’s life-processes have been well perceived. On the basis of biogenesis and functions, sRNAs are further categorized into two broad types: small interfering RNA (siRNA) and micro-RNA (miRNA) (Axtell 2013; D’Ario et al. 2017). Animal cells have another unique sRNA type called Piwi Interacting RNA (piRNA) solely restricted to them which are thought to originate from single stranded RNA precursors (Ghildiyal and Zamore 2009). Both siRNAs and miRNAs constitute crucial armoury of RNAi mediated endogenous gene silencing mechanism (Hannon 2002; Tomari and Zamore 2005; Novina and Sharp 2004). Further, they utilize similar machinery for RNAi repression, even though their targets might differ.

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Generally, miRNAs regulate endogenous genes, whereas siRNAs largely ensure chromosomal integrity by protecting genome from intrusive foreign genetic materials, viz. viruses, transposons as well as transgenes (Carthew and Sontheimer 2009). siRNAs can further have three sub-groups: heterochromatic siRNAs, secondary siRNAs and natural antisense transcript siRNAs (Kim 2005a; Sunkar and Zhu 2007; Axtell 2013). Exhaustive details on various siRNAs and their functional attributes are documented elsewhere (Kim 2005a; Mallory and Vaucheret 2006; Sunkar and Zhu 2007; Axtell 2013). Here we will be concentrating primarily on plant miRNA based principles. For ample accounts on animal miRNA species, several rich literatures can be referred (Ambros 2004; Williams 2008; Chekulaeva and Filipowicz 2009; Alberti and Cochella 2017; O’Brien et al. 2018). Besides, some DNA viruses do encode viral miRNA candidates, some of which might help them escape eukaryotic host immune system recognition and may further modulate other cellular processes, details of which can be acquired from several recently published works (Plaisance-Bonstaff and Renne 2011; Grundhoff and Sullivan 2011; Ahmad et al. 2020; Mishra et al. 2020). Plant miRNAs have been seen as crucial epigenetic drivers of multitude of genetic regulatory pathways at post-transcriptional level (Grativol et al. 2012; Baulcombe and Dean 2014; Ríos et al. 2014; Mahdavi-Darvari et al. 2015). Their ability to modulate cellular functions during myriad of instances like physiological and developmental transitions, growth regulations, senescence, etc. has provided potential grounds for their effective manipulation in designing strategies to generate improved crop cultivars (Zhang and Wang 2015, 2016; Zhang 2015; DjamiTchatchou et al. 2017; Tang and Chu 2017). Besides, miRNAs performing pivotal regulatory roles in plant’s response to varied kind of environmental stresses (both biotic and abiotic) have inspired several crop genetic improvement programs aiming at creating resilient crop cultivars (Zhang 2015; Djami-Tchatchou et al. 2017; Tang and Chu 2017). Apart from diverse functional attributes, sundry of features relating plant miRNAs such as mode of biogenesis, short size, ability to undergo interspecies transfer and executing regulation at multiple targets, etc. make them fascinating entities for exploration. Newer details in regard to plant miRNAs are still being added. More extensive attention toward these regulatory RNA molecules is expected to reveal important clues to several perplexing gene regulatory networks, which are yet to be realized. These facets might prove instrumental in overcoming the existing challenges in the path of crop genetic improvement programs targeted at meeting global food demand. In view of multitude of functional attributes, here in this chapter, we discuss on our updated knowledge regarding utilization of plant miRNAs in genetically improving important food crop plants to infuse stress resilient traits and also enhance important physiological traits in them. Moreover, we first begin with a glance on plant miRNA biogenesis process, features of plant miRNA targets along with known general functional role these molecules play in plants.

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Plant miRNA and Its Biogenesis

Plant miRNAs are largely 21–22 nucleotides long non-coding sRNAs (Ma et al. 2014; Axtell and Meyers 2018; Meyers and Axtell 2019). These entities have significant conservation across species, families and even within subdivisions of flowering plants (like Monocot or Dicot specific), which suggests conspicuous evolutionary convergence to their common ancestors (Xie et al. 2005b; Zhang et al. 2006a, b; Yao et al. 2007; Sunkar and Jagadeeswaran 2008). Nevertheless, species specific miRNAs as well as novel miRNA members have also been reported in the analysed plants (Xie et al. 2005a; Zhang et al. 2006a; Yao et al. 2007). For instance, a study conducted on wheat (Triticum aestivum L.) (Yao et al. 2007) identified 58 miRNA members that included 35 miRNAs from 20 conserved miRNA families. Left over 23 miRNAs were found to represent novel families in wheat which also included four monocot-specific miRNAs (namely, miR506, miR510, miR514, miR516) (Yao et al. 2007). Ever since the first report of miRNAs from C. elegans in 1993 (Lee et al. 1993; Wightman et al. 1993), list of newly added miRNA family members has continued to grow. More precisely in regard to plant miRNAs, PmiREN (http://www.pmiren.com/; Guo et al. 2020) database currently has a record of 20,388 miRNA loci (MIRs) identified from 88 plant species ranging from chlorophytes to angiosperms. It needs mention that the first plant miRNA to be formally described was from Arabidopsis (Reinhart et al. 2002), about nine years after documentation of the first animal miRNA in nematode. In respect of important food crop varieties cultivated across the globe, crucial information regarding miRNA composition in them has been revealed in the last two decades. These revelations have given important directions in the improvement prospects of agronomic traits in food crops (Xia et al. 2012; Zhang et al. 2013a; Wang et al. 2014; Gao et al. 2016; Tang and Thompson 2019). Plant miRNA biogenesis involves definitive steps and findings suggest numerous factors aids in the process (Xie et al. 2005b; Megraw et al. 2006; reviewed in Yu et al. 2017). Within plant genomes, MIR genes are generally considered to be intergenically located (Chen 2008; Megraw and Hatzigeorgiou 2009; Cui et al. 2009). However, presence of intragenic MIR genes has also come into light (Voinnet 2009; Meng and Shao 2012). Plant MIRs are transcribed by RNA polymerase II enzymes as is the case for animal MIRs (Xie et al. 2005b; Megraw et al. 2006; reviewed in Rogers and Chen 2013; reviewed in Yu et al. 2017). Intergenic MIR genes are generally independently transcribed as single transcription units owing to their possession of TATA box related sequences, transcription factor (TF) binding elements, termination signals, etc. (Xie et al. 2005b; Megraw et al. 2006; reviewed in Rogers and Chen 2013; reviewed in Yu et al. 2017). Polymerase II transcripts of MIRs are called primary miRNAs (pri-miRNAs). Intergenic MIRs can exist singly, sometimes as assembly of homologs or group of unrelated MIRs (which may produce polycistronic miRNA transcripts) (Li and Mao 2007; Budak and Akpinar 2015). Intragenic MIRs can be again of two kinds: intronic and exonic (Colaiacovo et al. 2012; reviewed in Budak and Akpinar 2015; Liu et al. 2019a). Intragenic MIRs are generally co-transcribed along with their host genes (Liu et al. 2019a). In case of

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intronic MIRs which can persist as ‘intron’ between adjacent protein encoding genes, undergoes a special kind of processing and are generally referred to as—‘mirtrons’ (reviewed in Budak and Akpinar 2015). By now ‘mirtrons’ have been reported in both model as well as non-model plant species (Meng and Shao 2012; Joshi et al. 2012; Yi et al. 2013; Patanun et al. 2013). Exonic MIRs co-transcribed as integral part of their host genes are said to regulate expression of host gene mRNA (Li et al. 2011; Colaiacovo et al. 2012; Liu 2012; Liu et al. 2019a). Primary miRNA (pri-miRNA) transcripts are added with a 50 end cap (7-methyl guanine) and a poly-(A) tail at the 30 end, similar to polymerase II transcribed mRNAs in eukaryotes (Xie et al. 2005b; Szarzynska et al. 2009; reviewed in Stepien et al. 2017). Pri-miRNAs have the ability to fold back upon themselves to generate hairpin-like imperfect stem-loop configurations. The structures of these hairpins can be variable and so can be their sizes (i.e. between 50 and 900 nucleotides) (Bologna et al. 2009; Cuperus et al. 2010) which eventually influences pri-miRNA processing steps (Zhang et al. 2015a). The pri-miRNA hairpin-like arrangements generally comprise a terminal loop, an upper stem, the miRNA/miRNA* region, a lower stem and two arms (Wang et al. 2019). In the hairpin, miRNA/miRNA* region generally exists as transient imperfect duplex comprising of a prospective mature miRNA strand (referred to as guide strand) and a passenger strand (usually the miRNA* (miRNA star) strand partner) (Lau et al. 2001; Bartel 2004; Kim 2005b). The multi-domain enzyme dicer-like1 (DCL1) plays crucial role in plant miRNA biogenesis processes (Jones-Rhoades et al. 2006). DCL1 is a relative of the dicer (DCR) or DCL family proteins which bears an N-terminal helicase domain, a central PAZ domain, and two RNase III domains with a dsRNA-binding motif at the C-terminal region (Xie et al. 2010). Notably, PAZ (Piwi/Argonaute/Zwille) domain is a conserved feature of protein families mediating RNA silencing pathways in eukaryotes which necessitate RNA binding properties (Cerutti et al. 2000; Yan et al. 2003). DCL1 accompanied by a dsRNA-binding protein Hyponastic Leaves1 (HYL1) and a zinc finger protein Serrate (SE) forms the dicing complex that engages in pri-miRNA processing (Park et al. 2002; Reinhart et al. 2002; Kurihara and Watanabe 2004; Fernandez and Burch-Smith 2019). In Arabidopsis, presence of other DCL family members has also been known (Gasciolli et al. 2005; Xie et al. 2005a; Bouche et al. 2006; Mlotshwa et al. 2008; Fernandez and Burch-Smith 2019). However, among all, DCL1 is primarily involved in miRNA trimming from their precursors (Park et al. 2002; Reinhart et al. 2002). Nevertheless, involvement of other components like CAP binding complex (during splicing), CPL phosphatases, dawdle (a DCL1 interacting protein), tough (an RNA-binding protein), sickle, hasty (a homolog of the animal Exportin 5), etc. has also been implicated in the miRNA biogenesis process (Bollman et al. 2003; Gregory et al. 2008; Laubinger et al. 2008; Yu et al. 2008; Manavella et al. 2012; Ren et al. 2012; Zhan et al. 2012). Genesis of matured plant miRNA from early precursors follows a sequential order: beginning with pri-miRNAs, followed by precursor-miRNAs (pre-miRNAs), and lastly miRNAs/miRNA* duplexes (Yang et al. 2010). Like MIR transcription,

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processing events of pri-miRNAs as well as pre-miRNAs releasing miRNAs/ miRNA* duplexes conclude within the nucleus (Bologna et al. 2013a; Stepien et al. 2017). The same dicing complex with core components DCL1, HYL1 and SE executes these events (Kurihara et al. 2006; Fang and Spector 2007; Dong et al. 2008). Unambiguous dissection of the mature miRNA from their antecedents is an essential requirement during miRNA biogenesis process. In this regard, distinct structural facets of the precursor miRNA transcripts have been shown to direct appropriate trimming via dicing complex at specific sites (Bologna et al. 2013b; reviewed in Zhang et al. 2015a). A lot of miRNA precursors may carry short RNA duplexes (~15 to 17 bp size) over an inner loop and beyond the miRNA/miRNA* double stranded region, allowing DCL1 to make first excision in the vicinity of the precursor (Mateos et al. 2010; Song et al. 2010a; Werner et al. 2010; Zhu et al. 2013a). This mode of processing is coined as base-to-loop type. A double stranded RNA region over the miRNA/miRNA* site and beyond a loop at the extreme end can also be featured in some precursors (Addo-Quaye et al. 2009; Bologna et al. 2009, 2013b; Chorostecki et al. 2017). In such molecules another mode of processing prevails which is termed as loop-to-base type, where dicer first cleaves to excise out the loop at the extreme end, and proceeds to release the miRNA/ miRNA* duplex from the base end of the precursor (Addo-Quaye et al. 2009; Bologna et al. 2009, 2013b). Interestingly, in both the events, DCL1 performs the ultimate trimming ~21 bp apart from the first excision point, subsequently liberating the guide/passenger miRNA duplex (miRNA/miRNA*) (Kurihara and Watanabe 2004; Zhu et al. 2013a). Besides, depending on structural attributes of the antecedents, other patterns of miRNA precursor processing have gradually unfolded and some with peculiar outcomes (Kurihara and Watanabe 2004; Zhang et al. 2010; Bologna et al. 2013b; reviewed in Zhang et al. 2015a). Very recently, apart from secondary structure of precursors, even sequence composition (Rojas et al. 2020), necessary N6-methyladenosine (m6A) methylation (Bhat et al. 2020) of pri-miRNAs has been correlated with efficient plant miRNA biogenesis process in Arabidopsis. Prior to formation of mature miRNA, within the nucleus, pri-miRNAs undergoing processing via dicer complex gets their hairpins excised out and gives rise to an intermediate precursor called pre-miRNAs ranging from 80 to 300 nucleotides in length (Song et al. 2010a). Pre-miRNA molecules are least likely to get accumulated as these usually undergo rapid trimming to produce mature miRNAs (Zhu 2008) and this might be a reason hindering their exact structural investigations. The final trimming of pre-miRNAs by dicer complex liberates intermediate miRNA molecules as miRNA:miRNA* duplexes (Voinnet 2009; Song et al. 2010a). At the 30 end, each strand of the released miRNA/miRNA* (guide strand/ passenger strand) duplex bears two nucleotide overhangs, an obvious feature in the miRNA genesis process (Voinnet 2009). Further, the last nucleotide at 30 end of miRNA/miRNA* duplex is modified via addition of a methyl group at 20 -oxygen of the hydroxyl group in the ribose sugar by an enzyme called Hua enhancer 1 (HEN1) methyltransferase within the nucleus (Yu et al. 2005). Methylation step of miRNA/ miRNA* terminal nucleotides is said to provide protection to the former and has

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been found to be crucial for mature miRNA stability (Li et al. 2005; Yu et al. 2005; Jones-Rhoades et al. 2006). Once methylated miRNA/miRNA* duplex is generated, it is loaded onto argonaute1 (AGO1) proteins through assistance of heat shock protein 90 (HSP90), transportin1 (TRN1) proteins, etc. within the nucleus (Iki et al. 2010; Cui et al. 2016; Bologna et al. 2018). Argonautes are ubiquitous proteins prevalent in all kingdoms of life (Höck and Meister 2008) that binds with small RNAs and executes numerous genetic regulatory tasks at transcriptional as well as post-transcriptional level (Hutvagner and Simard 2008). Arabidopsis genome encodes about ten AGO proteins, of which AGO1, AGO2, AGO4, AGO7, AGO10 are instrumental in regulation of various sRNAs (Mi et al. 2008; Takeda et al. 2008; Montgomery et al. 2008; Zhu and Helliwell 2011). Of all these members, AGO1 seems to be primarily meant for miRNAs (Zhang et al. 2015b) baring exceptions (Montgomery et al. 2008; Zhu and Helliwell 2011). The recent revelation that plant AGO1s carry a nuclear-localization signal (NLS) and nuclear-export signal (NES) in their N-termini might account for movement of AGO1s between nucleus and cytosol (Bologna et al. 2018). The possibility of AGO1 bound miRNA duplex being transported from nucleus to cytoplasm as AGO1: miRNA conjugate has thus been emphasized in a recent finding (Bologna et al. 2018). Previous work suggested role of the animal Exportin 5 (EXPO5) homologous protein Hasty (HST) in nuclear shipping of methylated miRNA/miRNA* duplexes (Park et al. 2005) although that may not be the case for all miRNA shuttling events (Bologna et al. 2018). Very recently, Arabidopsis nuclear pore complex (NPC) associated protein complex, TREX-2(transcription-2) has been positively correlated with efficient nuclear export of AGO1 and miRNA couples (Zhang et al. 2020b). Notably, thorough understanding on mechanisms governing nuclear to cytosolic transport of plant miRNAs had been largely elusive (Bologna and Voinnet 2014; Gao et al. 2020). During loading process, AGO1 selectively binds to guide miRNA strand of the miRNA/miRNA* duplex (Iki et al. 2010). Protein factors such as HSP90, ENHANCED MiRNA ACTIVITY1 (EMA1), TRN1, etc. are said to regulate this event (Iki et al. 2010; Wang et al. 2011; Cui et al. 2016). The passenger strand of the miRNA/miRNA* duplex is then meant to be degraded (Iki et al. 2010; reviewed in Yu et al. 2017) which probably occurs after AGO1: miRNA conjugate reaches the cytoplasm. Selective retention of miRNA guide strand by AGO1 is perhaps rendered by action of miRNA processing factors; the 50 end nucleotide features as well as configuration of the miRNA duplex region (Mi et al. 2008; Eamens et al. 2009; Zhu et al. 2011; Manavella et al. 2012; Ren et al. 2014). The composite formed by the single strand guide miRNA bound to AGO1 is also termed as RISC (RNA Induced Silencing Complex) (reviewed in Nakanishi 2016). Prior to findings of Bologna et al. (2018), RISC assembly was thought to be an event strictly taking place in cytosol (Park et al. 2005). In cytosol, the RNA induced silencing complex (RISC) comprising AGO1 bound miRNA (guide strand) executes sequence-specific inhibition of target gene expression by inducing mRNA degradation and/or translational repression (Baumberger and Baulcombe 2005; Pratt and MacRae 2009; Iwakawa and Tomari 2013). The loaded RISC is escorted to the target mRNAs by the guide

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miRNA strand and the 30 untranslated region (30 UTR) of target mRNAs usually bear sequence complementarity to the guide miRNA strand which significantly assists in the interference process (reviewed in Nakanishi 2016). Generally, AGO1 is said to function as a ‘slicer’ to make endonucleolytic split in target RNAs (Qi et al. 2019). Micro-RNA biogenesis pathways in plants (as in animals) may follow a canonical path or sometimes non-canonical routes (reviewed in Budak and Akpinar 2015). Most of the abovementioned details are related to canonical pathway. In non-canonical route, miRNA precursor trimming may follow a different order (e.g. in case of miR319 and miR159 processing in Arabidopsis) (Bologna et al. 2009). Some bi-directional processing routes yielding variable products have also been reported (Zhu et al. 2013a). The plant ‘mirtrons’, a special class of plant miRNA, follow a DCL1 independent first excision event compensated by splicing, leading to formation of pre-miRNAs (Axtell et al. 2011; Meng and Shao 2012) exemplifying another example of non-canonical pathway. Some miRNA processing events deploying other DCL members, e.g. DLC2, DCL3 and DCL4 are also known to fall in this category (reviewed in Budak and Akpinar 2015). The mature product of miRNA precursors irrespective of their belongingness to plant or animal origin are key players of post-transcriptional gene regulatory pathways. However, biogenesis processes of plant miRNAs have distinct differences from animal counterparts (Axtell et al. 2011; Bologna et al. 2013b; Fig. 6.1). Requirement of DROSHA (unlike only DCL1 in plants) for pri-miRNA trimming, translocation of pre-miRNAs from nucleus to cytoplasm, RISC loading on miRNA primarily occurring in the cytoplasm, etc. are some of the conspicuous dissimilarities seen in animals from that of plants (Axtell et al. 2011; Bologna et al. 2013a, b). For elaborated description on more of these distinctions, readers may refer to several other relevant literatures (Millar and Waterhouse 2005; Axtell et al. 2011; Bologna et al. 2013a, b; Moran et al. 2017; Li et al. 2018a; Zhang et al. 2018b).

6.3

Plant miRNAs: Targets and Modes of Action

MiRNAs can control target gene expression via different modes such as mRNA decay, suppression of translation and in certain instances they may activate gene expression (Huntzinger and Izaurralde 2011). Generally, plant miRNAs can repress their specific targets by inducing mRNA depletion and also by inhibition of translational events (Brodersen et al. 2008; Bologna and Voinnet 2014; Yu et al. 2017). In earlier work, the degree of sequence complementarity amidst miRNAs and their target mRNA species has been said to command mode of miRNA mediated repression pathways (Hutvagner and Zamore 2002). Plant miRNAs are usually known to bear near-perfect complementarity to their target mRNAs, which is considered to trigger target mRNA destruction as miRNA’s most prevalent repressive measure (reviewed in Chen 2005; Jones-Rhoades et al. 2006; Chen 2009; Voinnet 2009). However, this does not seem to be a rule. MiRNAs with near-perfect complementarity to mRNAs, executing translational inhibition of gene transcripts have also been reported (Brodersen et al. 2008; Yang et al. 2012; Li et al. 2013). Examples of

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Fig. 6.1 Schematic diagram showing distinct routes of miRNA biogenesis process in plants and animals, redrawn from Li et al. (2018a). Major details of plant miRNA biogenesis process have been elaborated in the text of this chapter. Important points relating animal miRNA biogenesis are follows. Like in plants, animal miRNA genesis begins within nucleus. Animal pre-miRNAs are generated in nucleus via activity of Drosha enzyme which needs assistance of DGCR8 protein (in mammals) or Pasha protein (in flies). Unlike in plants, animal pre-miRNAs are transported to cytoplasm where its further processing resumes. Animal miRNA duplexes (miRNA/miRNA*) are generated through processing activities of Dicer and TRBP (in mammals). Exportin-5 is said to aid in animal miRNA/miRNA* duplex shuttling from nucleus to cytoplasm (as pre-miRNAs). The mechanism of RISC loading to miRNA/miRNA* duplex (along with AGO) follows similar route in animals as in plants. Notably, plant miRNAs require near-perfect complementarity to their target

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the latter include translation repression of respective genes executed by miRNAs such as miR172, miR171, miR398, miR156, etc. (Aukerman and Sakai 2003; Chen 2004; Gandikota et al. 2007; Brodersen et al. 2008). Instances of miRNAs inducing degradation of similar miRNA species have also been evident suggesting sequence complementarity may not just be the only determinant of miRNA’s mode of action (Li et al. 2013; Hou et al. 2016; Yu et al. 2016). Plant miRNAs usually couple with their target mRNA species via near-perfect complementary sequences residing at Open Reading Frames, in few occasions at 50 -UTRs or 30 -UTRs of the target mRNA strand (Allen et al. 2005; Addo-Quaye et al. 2008). In regard to complementary pairing with target mRNAs, 50 -end of plant miRNA (specifically 2nd to 13th ribonucleotide positions) has been said to be critical for target repression (Mallory et al. 2004a; Parizotto et al. 2004; Schwab et al. 2005). Wrong base sequences at 30 -end of miRNAs can still be tolerated although not in the 50 -end and in the mid-region (Mallory et al. 2004a; Parizotto et al. 2004; Schwab et al. 2005; Lin et al. 2009). AGO1 (of RISC loaded on to guide RNA), by virtue of possessing a homologus RNAse H domain performs endonucleolytic cleavage in the target mRNA strand at position between 10th and 11th ribonucleotide facing the miRNA molecule (Ameres and Zamore 2013; reviewed in Iwakawa and Tomari 2015). Catalytic PIWI domains of AGO1 are said to assist in the cleavage process (Llave et al. 2002; Baumberger and Baulcombe 2005; Poulsen et al. 2013; Rogers and Chen 2013). Plant miRNARISC complex can directly cleave or translationally repress target mRNAs without requirement of a deadenylation step (Iwakawa and Tomari 2013) which is in contrast to the case observed in animals (Braun et al. 2012). In Arabidopsis, the RISC: miRNA mediated excision of target mRNA may generate a 30 -end and a 50 -end segment (reviewed in Iwakawa and Tomari 2015). Then, the 30 -end section is depleted by cytosolic 50 -30 exoribonuclease 4 (XRN4) enzyme activities (Souret et al. 2004). XRN4 enzyme can also decimate the 50 -end segment in 50 to 30 end direction and the catalysis is expedited by addition of uridine residues (called uridylation process) at 30 -end of the cleaved segment mobilized by the enzyme hua enhancer 1 (HEN1) suppressor 1 (HESO1) (Ren et al. 2014). Plant miRNA mediated translation repression of target mRNA species has also been documented (Chen 2004; Schwab et al. 2005; Gandikota et al. 2007; Brodersen et al. 2008; Li et al. 2013) although concrete mechanistic details are still wanting. Several genetic factors, organelle associated factors are implicated in the repression process (Brodersen et al. 2008; Yang et al. 2012; Li et al. 2013; reviewed in Iwakawa and Tomari 2015). Importantly, unlike in animals, for translation repression too, plant miRNAs need to have near-perfect complementary pairing with target mRNA species (Iwakawa and Tomari 2013; Tang et al. 2003). Some important conclusions

Fig. 6.1 (continued) transcripts for efficient repression. For animal miRNAs that is not the case. Even with imperfect complementarity animal miRNAs can inhibit target transcripts flawlessly. Importantly, plant and animal miRNAs both are capable of silencing gene transcripts either through mRNA degradation or via halting mRNA translation events (Li et al. 2018a)

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have been drawn from an in vitro study (Iwakawa and Tomari 2013) relating probable mechanism of translational repression. In that study involving an Arabidopsis cell-free system (Iwakawa and Tomari 2013), AGO1–RISC composite deficient for cleavage function was observed to inhibit translation initiation although deadenylation and mRNA depletion were not associated events. Further, plant AGO1–RISCs can associate with translating regions of mRNAs, subsequently hindering ribosomal movement. Requirement of several target locations for effective translation repression from the 30 UTR region was also envisaged (Iwakawa and Tomari 2013; reviewed in Iwakawa and Tomari 2015). Plant miRNAs might further rely on some organelle bound factors to orchestrate mRNA translation inhibition (Li et al. 2013). For instance, an Endoplasmic Reticulum (ER) membrane associated protein altered meristem program1 (AMP1) was shown to be essential for Arabidopsis miRNA mediated translational inhibition of mRNAs in ER, without induction of transcript cleavage (Li et al. 2013).

6.4

Plant miRNAs: Functional Role

Micro-RNAs have been denominated as ‘master-regulators’ of eukaryotic genomes, influencing almost every aspect of an organism’s biology as well as ecology (Bushati and Cohen 2007; Jones-Rhoades et al. 2006; Willmann and Poethig 2007; Zhang et al. 2007; Leung and Sharp 2010; Manavella et al. 2019). And the fact is indifferent for plant miRNAs as well. With gradual progress made in genetic and biochemical screening procedures, molecular cloning, structural and bioinformatics analyses, detection techniques, etc. plethora of information relating functional role of plant miRNAs have continued to emerge which in turn suggests adroit regulatory roles they perform. A number of conserved miRNA species across plant kingdom (like miR156/157, miR399, miR847, miR159, miR165/166, etc.) inevitably regulate key developmental pathways in plants, e.g. seed germination, leaf morphogenesis, shoot meristem regulation and maintenance, root development, floral patterning and its temporal control, transition from vegetative to reproductive phase, organ development, etc. (Reyes and Chua 2007; Zhu and Helliwell 2011; Li and Zhang 2016; DjamiTchatchou et al. 2017; reviewed in Liu et al. 2018). Apart from the conserved miRNA species, non-conserved miRNAs from different plant species have also been implicated in their crucial developmental processes. For instance, in Oryza sativa, miR166 regulates grain size and weight, miR4376 of Solanum lycopersicum influences their floral development and fruit productivity. Likewise, in Brassicaceae, miR824 plays a decisive role in ascertaining flowering time. These non-conserved miRNA members and several others play vital regulatory part in developmental paths of respective plant species (Djami-Tchatchou et al. 2017; reviewed in Liu et al. 2018). Generally, myriad of evolutionarily conserved miRNA members are known to regulate mRNAs encoding transcription factors (TFs) deciding developmental fates as well as hormonal signalling networks in plants and importantly miRNA inaction

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is manifested as conspicuous growth defects in them (Manavella et al. 2019). For instance, a number of important TF families across food crop and non-food crop plants form an indispensable regulatory component that determines crucial developmental pathways and also render plant’s adaptation to varied stress environments, are in turn regulated by various plant miRNAs (reviewed in Tang and Chu 2017). Some of these pivotal TF families regulated through plant miRNAs include: Growth Regulating Factors (GRFs) (Jones-Rhoades and Bartel 2004; Bologna and Voinnet 2014), APETALA2/Ethylene-Responsive-Factors (AP2/ERFs) (Chen 2004; Kavas et al. 2015), basic-domain Leucine-Zipper (bZIP) (Baloglu et al. 2014; Baldrich et al. 2015), MYBs (Du et al. 2012; Zhang et al. 2014a), WRKY (Giacomelli et al. 2012; Zhang et al. 2017a), NAC (Mallory et al. 2004b; Wang et al. 2020), bHLH (Debernardi et al. 2012; Kavas et al. 2016), TCP (Zhang et al. 2016b, 2018c), SPL (Jiao et al. 2010; Ding et al. 2012), MADS-Box (Lu et al. 2008; Li et al. 2010), Zinc Finger TFs (Muthamilarasan et al. 2014), etc. Interestingly, miRNAs along with TFs form enormous families of genetic regulatory modules in multi-cellular eukaryotes redefining fundamental biological aspects at cellular level (Hobert 2008). Moreover, miRNAs also remain under stringent control of varied TFs indicating critical coordination between both to be inevitable for host of adaptive cellular functions (Xie et al. 2005b; Hobert 2008; Baek et al. 2013; reviewed in Meng et al. 2011). TFs and miRNAs can sometimes involve in complicated inter-regulatory loop deciding fundamental developmental aspects in plants. For instance, in Arabidopsis, regulation of miR172 transcription by SPL9 and SPL10 TFs (members of squamosa promoter-binding protein-like (SPL) TFs), and eventual regulation of SPL9, SPL10 by miR156, along with miR172 mediated control of APETALA2-LIKE (AP2-like) TF expression, is said to be critical for vegetative phase transformations (Wu et al. 2009). Besides developmental transitions, miRNAs are also instrumental in regulating important plant physiological processes such as fruit ripening, defining fruit weight, seed dormancy, etc. As for example, Csi-miR164 (in sweet orange), miR156/157 (in tomato and pear), miR1917 and miR396 (in tomato), etc. are shown to have notable impact on fruit ripening phases (Zuo et al. 2012; reviewed in Curaba et al. 2014; reviewed in Cedillo-Jimeneza et al. 2020). These miRNAs selectively target expression of vital enzymatic components/factors involved in Ethylene (ET) biosynthesis pathways as well as regulate fruit softening associated factors necessary for ripening (Moxon et al. 2008; Dalmay 2010; reviewed in Curaba et al. 2014). Phytohormones being critical mediator for coordinated transitions in the physiological and developmental courses in plants require efficient functioning and flawless regulation (Curaba et al. 2014). Studies indicate that miRNAs play decisive roles in balancing phytohormonal activities through post-transcriptional control mechanisms at crucial junctures (reviewed in Curaba et al. 2014). In respect of phytohormone auxin homeostasis, from earlier works, miRNAs are known to target members of auxin response transcription factor (ARF) family (e.g. RF10, ARF16, ARF17) thereby influencing auxin mediated pathways in plants (Mallory et al. 2005). More specifically, miR160 targeting ARF17 and subsequent observation of striking developmental defects in Arabidopsis when ARF17 could no longer be regulated by miR160 demonstrated

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stringent miR160 mediated control of auxin induced developmental course (Mallory et al. 2005). Likewise, Gibberellic Acid (GA) and Cytokinin (CK) hormonal pathways were found to be affected by miR396 activities in Arabidopsis (Hewezi et al. 2012; reviewed in Curaba et al. 2014). MiRNAs have been further implicated in management of varied kind of stresses in plants. Alterations in abiotic variables, aggressive negative biotic interactions in environment significantly impact miRNA mediated responses in them (Lu and Huang 2008; Ruiz-Ferrer and Voinnet 2009; Khraiwesh et al. 2012; Sunkar et al. 2012; Zhang 2015; Huang et al. 2016). Importantly, miRNA profile apparent during the plant’s growth and developmental trajectories conspicuously differs from what is observed throughout stress conditions, implying crucial role of stress responsive miRNAs in regulating plant growth and development. As for example, in several of the studied plants, abiotic stress due to drought or salinity conditions has been found to induce different miRNA species (e.g. miR393, miR160 and miR167), which in turn might suppress growth and development in them (Sunkar et al. 2012). Interestingly, across different plant species, some set of conserved miRNA members further manifest differential expression profiles in response to stress conditions (Zhou et al. 2010), adding more complications to the phenomenon. In responding to nutritional stress in plants, involvement of miR398 (in Arabidopsis), OsmiR399 (in O. sativa), miR395 (in maize) along with other miRNA pathways has been implicated (Hu et al. 2011; Zhao et al. 2011; Xu et al. 2011; reviewed in Sun et al. 2019a). Likewise, association of miR156, miR162, miR528 and miR535 members with cold stress responses in different plant species has been observed (reviewed in Sun et al. 2019a). In the same manner, a host of miRNAs have been shown to modulate plant’s responses towards biotic stress agents. Aggregation of miR168 in soy bean plant during infection with Soybean mosaic virus strain G7 (Pacheco et al. 2012; Chen et al. 2015a), altered expression levels of varied kinds of miRNAs (like miR156, miR171, miR398 and miR 168) upon infection of Nicotiana benthamiana with Potato virus X (PVX), Plum pox virus (PPV), Potato virus Y (PVY) (Pacheco et al. 2012), etc. indicates plant’s responses to respective viral pathogenic agents with specific outcomes. Likewise, induction of varied miRNA species as a result of plant’s interaction with several bacterial pathogens (Navarro et al. 2006; Wong et al. 2014; Snyman et al. 2017; Jodder et al. 2017), fungal pathogens (Pinweha et al. 2015; Zhang et al. 2016a, 2017a; Hua et al. 2018; Jin and Guo 2018), nematode parasites (Hewezi et al. 2012; Li et al. 2012a; Medina et al. 2017; Lei et al. 2019; Pan et al. 2019) as well as insect pest species (Jeyaraj et al. 2017; Wu et al. 2017; Moné et al. 2018; Zeng et al. 2019; Nanda et al. 2020) has been well documented. In certain instances, plant miRNAs may modulate host defence responses towards biotic agents positively or sometimes in negative manner. In occasions, some plant miRNAs can themselves be targets of one group of pathogenic effectors (Qiao et al. 2013, 2015) while they might resist another group effectively. The illustrations mentioned above regarding miRNA functions in plants during developmental transitions and in response to varied kind of stress factors are not exhaustive, and readers are advised to refer innumerable published documents available in the public domain for further details (Bartel and Bartel 2003; Dugas

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and Bartel 2004; Chen 2005; Kidner and Martienssen 2005; Cuperus et al. 2011; Li et al. 2012b, 2017; Luo et al. 2013; Staiger et al. 2013; Spanudakis and Jackson 2014; Ferdous et al. 2015; Iwakawa and Tomari 2015; Akdogan et al. 2016; Baldrich and San Segundo 2016; Song et al. 2019). Apart from numerous regulatory commitments, plant miRNAs have also been shown to function by activating a special class of siRNAs called trans-acting siRNA (ta-siRNA) and thereby assist in triggering ta-siRNA-pathways. Distinct miRNAs aid in ta-siRNA biogenesis via RISC mediated slicing of ta-siRNA precursors. The sliced fragments derived from miRNA directed cleavage of ta-siRNA precursors are next acted upon by RDR6 (RNA directed RNA polymerase 6) to generate long dsRNAs. These long dsRNAs are then trimmed into 21-nucleotide long siRNAs through action of DCL4 proteins (Peragine et al. 2004; Xie et al. 2005a; Yoshikawa et al. 2005; Adenot et al. 2006; Hiraguri et al. 2005). ta-siRNAs undergo methylation via HEN1, alike miRNAs (Li et al. 2005). They guide target mRNA transcript cleavage by associating with AGO1 or AGO7 proteins. As an illustration, juvenile to reproductive stage transition in Arabidopsis requires ta-siRNA mediated regulation of auxin response factors (Fahlgren et al. 2006), and hence implying indirect role of distinct miRNAs. Whereas most canonical plant miRNAs function through post-transcriptional gene silencing route (as in all instances cited above), further, a novel group of plant miRNAs (ranging in size of 23–27 nucleotides) processed by DCL3, that associates with AGO4 proteins have been found to dictate DNA methylation at cytosine residues of their own gene loci (in cis) as well as in their target gene loci (in trans), thereby mediating transcriptional gene regulation (reviewed in Jia et al. 2011). These facets along with others, notably reiterate immense functional diversity plant miRNAs carry.

6.5

Applications of Plant miRNA Principles for Crop Improvements

MiRNAs have evolved as natural epigenetic modulators in plants, rendering conspicuous advantages to the latter during (1) growth and survival, (2) while adapting and interacting with varied components of the environment. It needs mention that induction of large number of miRNAs for post-transcriptional regulation of numerous genetic pathways governing fundamental (developmental programming) as well as adaptive ramifications (interaction with environment-comprising abiotic and biotic components) in plants are obvious natural events. Lessons learnt from studies of miRNA repertoires in Arabidopsis, rice, maize, wheat, etc. have immensely helped in realizing these aspects (Sunkar et al. 2005; Yao et al. 2007; Zhang et al. 2009; Li et al. 2010; Xu et al. 2018a). In respect of important food crops grown across the globe, a large number of miRNA members of different families have been discovered that are generated in crop’s natural life course, executing pivotal role in deciding development, product yield, responding to abiotic as well as biotic stresses across these crops species (reviewed in Tang and Chu 2017). Large number of

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published works document miRNAs from different crop plant species under natural milieu along with their predicted/proven functionalities (Sunkar et al. 2005; Yao et al. 2007; Xie et al. 2007; Yin et al. 2008; Zhang et al. 2009, 2011d, 2013b; Li et al. 2010; Pantaleo et al. 2010; Song et al. 2010b; Schreiber et al. 2011; Yi et al. 2013; Budak et al. 2015; Djami-Tchatchou et al. 2017) and all those can be referred for more detail descriptions. Hereafter, we are limiting our discussion relating miRNA’s natural regulatory roles in crop plants. We are rather emphasizing more on the applicability of miRNA’s natural regulatory attributes for improving vital agronomic traits in some food crops utilizing different strategies. Table 6.1 enlists some of the important experimental outcomes based on miRNA principles across different food crops.

6.5.1

Strategies for Harnessing Basic Principles of miRNA Directed Gene Regulation

The proven notion that miRNAs can regulate factors governing expression of genes that are required to be post-transcriptionally repressed indispensably for appropriate developmental transitions in crop plants provide windows for their strategic utilization in crop improvement programs. For instance, overexpression of specific miRNAs can significantly suppress target gene transcripts, rendering essential qualitative development of specific agronomic traits in crops (although at the cost of few other features). In certain cases, miRNAs might themselves pose as hindrance as they might target transcripts of factors which are in essence necessary for appearance of particular agronomic traits. In such instances, overexpression of miRNA target factors or strategies to suppress/inhibit those miRNAs might prove suitable. A host of important methodologies presently being followed (including some of which we are mentioning here) in regard to manipulation of plant miRNA principles for deriving useful outcomes has been outlined in some of the rich literatures (Molesini et al. 2012; Reichel et al. 2015; Basso et al. 2019) and readers may refer to these. Concept of artificial miRNAs (amiRNAs) to silence specific target gene transcripts has also been greatly appreciated (Warthmann et al. 2008). AmiRNA constructs are generally designed by manipulating endogenous plant miRNA precursors (e.g. rice Osa-MIR528 precursor), within which 21 nucleotide long sequence of miRNA complementary to specific target transcript(s) is inserted by replacing the original miRNA sequence (Warthmann et al. 2008; Gasparis et al. 2017) (An illustration on amiRNA design and its function is depicted in Fig. 6.2). Polycistronic amiRNAs can also be constructed to downregulate multiple transcripts by incorporating several amiRNA sequences specific to targets (Fahim et al. 2012). Unlike natural miRNA/siRNA biogenesis process, amiRNA constructs usually generate single amiRNA specific to a target and thus off target downregulation is avoided (Warthmann et al. 2008; reviewed in Basso et al. 2019). Very recently, a modified amiRNA construct design has been forwarded to target insect endogenous gene mRNA transcript by expressing these in transgenic crop plants (as a measure to control specific insect pest infestation on crops) (Bally et al. 2020). This system

miR408

OsmiR393

miR398a

miR171

OsmiR535

miR156

miR396

OsmiR1432

3

4

5

6

7

8

9

10

Rice

Rice

Rice

Rice

Rice

Rice

Rice

Rice

Rice

Rice

Rice

Rice

Rice

Rice

Rice

Rice

Rice

OsmiR397

2

OsACOT (OE)

GRF6 (OE)

Cu/Zn superoxide dismutases (CSD1 and CSD2) OsHAM Transcription Factors OsSPL7/12/16 TFs OsSPL14/IPA1

OsTIR1, OsAFB2

Photosynthesis related genes

OsLAC

Laccase

Banana

Rice

Target gene/locus

Expressing host

SL miRNA/ No amiRNA Source A. Physiological and phenotypic traits 1 Musa-miR397 Banana

DR (via TM) DR (via STTM)

Target OE

OE

OE

OE

OE

OE

OE

OE

Approach

Enhanced grain weight as well as overall increments in grain yield

Elongated grain length along with great influence on panicle architecture Enhanced rice grain yield via increments in panicle branch numbers, increased number of grains per panicle, superior grain size; resistance to Xoo infections Increase in grain yield

Enhanced photosynthetic activity, increase in grain size, grain weight and yield Enhanced tiller numbers and early flowering time Increase in crop height, expanded panicle size accompanied by increment in grain number per panicle Enhanced tiller numbers, increase in plant height

Increased rice grain yield, grain size

Enhancement in plant growth

Outcomes

Table 6.1 Outcomes from various strategic miRNA based experimental studies in different food crops

Gao et al. (2016) Zhao et al. (2019)

Sun et al. (2019b) Liu et al. (2019b), Lian et al. (2020)

Fan et al. (2015)

Xia et al. (2012) Zhang et al. (2017b)

Patel et al. (2019) Zhang et al. (2013a) Pan et al. (2018)

Reference

140 A. Barman et al.

miR1917

miR858

OsmiR164b

MIR396e, MIR396f

amiRNA

amiRNA

SlmiR159

VvmiR159c

13

14

15

16

17

18

19

20

12

OsmiR167; OsmiR1432 miR396a, miR396b

11

Grapevine

Tomato

AS

AS

Rice

Rice

Tomato

Tomato

Tomato

Rice

Grapevine

Tomato

Rice

Rice

Rice

Rice

Tomato

Tomato

Tomato

Rice

VvGAMYB

SlGAMYB1/2

OsBADH2

SBEIIb

GRFs

SlMYB7-like, SlMYB48-like OsNAC2

SlCTR4

GRFs

OsARFs; OsACOT

GA induced OE

OE

OE

DR (via STTM) mTarget OE DR by CRISPR/ Cas editing OE

DR (via STTM)

DR (via STTM) DR (via STTM)

Two-fold increase in amylose content, pronounced alteration of starch grain structure bearing implications for more resistant starch content, lower glycaemic index as well as improved digestibility Generated aroma in rice grain; enhanced proline content Obligate parthenocarpic fruits without altering tomato shape Parthenocarpic grapevine fruit development

Increased biomass, enlarged floral organs accompanied by gain in fruit and seed sizes Upregulation of anthocyanin biosynthesis and accumulation Improve plant architecture, enhance grain number and yield Enhanced grain size (and hence yield), accompanied by shoot architectural alterations

Enhanced grain length, size, weight and thickness Enlargement of flowers, sepals and fruits

Harnessing Perks of MiRNA Principles for Betterment of Agriculture and. . . (continued)

Chen et al. (2012) da Silva et al. (2017) Wang et al. (2018b)

Butardo et al. (2011)

Jia et al. (2015) Jiang et al. (2018a) Miao et al. (2020)

Peng et al. (2018) Cao et al. (2016), Peng et al. (2018) Yang et al. (2020)

6 141

Osa-miR319b

osa-miR319

OsmiR156

OsmiR535

3

4

5

6

Rice

Rice

Rice

Rice

Rice

Rice

Rice

Rice

Rice

OsmiR528

2

Rice

Soybean

Brinjal

B. Abiotic stress related traits 1 Gma-miR1508a Soybean

SlymiR157

23

AS

Expressing host Grapevine

Tomato

amiRNA

22

Source Grapevine

Tomato

miRNA/ amiRNA VvmiR160a/b/c

SL No 21

Table 6.1 (continued)

OsSPL7/12/16

OsSPL3

OsPCF5, OsPCF8

OsPCF6, OsTCP21

MYB TFs

PPR/growth related genes

LeSPL-CNR

Some GTFs

Target gene/locus VvARF10/16/17

DR by CRISPR/ Cas, STTM

OE

OE

OE

OE

OE

DR via VIGS

Approach GA induced OE OE

Increased cold stress tolerance, widening leaf blades, delayed development Improved cold tolerance (4  C) after chilling acclimation (12  C) in seedling stages Increased cold stress tolerance, improved cell viability and growth rates Enhanced tolerance of seedlings toward Abscisic acid (ABA) treatment, salinity and drought induced stresses

Cold stress tolerance during seed germination and young seedling stages Cell viability, growth rate, antioxidant content, cold stress tolerance

Parthenocarpic brinjal fruit development Delayed ripening

Outcomes Parthenocarpic grapevine fruit development

Yue et al. (2020)

Zhou and Tang (2019)

Yang et al. (2013)

Tang and Thompson (2019) Wang et al. (2014)

Sun et al. (2020a)

Toppino et al. (2011) Chen et al. (2015c)

Reference Zhang et al. (2019a)

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Osa-miR169o

miR396e, miR396f

Os-miR166

miR408

Sly-miR169

sha-miR319d

amiRNA

miR1916

8

9

10

11

12

13

14

15

Tomato

Solanum habrochaites AS

Tomato

Chickpea

Rice

Rice

Rice

Rice

C. Biotic stress related traits 1 sly-miR319c Tomato

OsmiR164b

7

Arabidopsis

Solanum lycopersicum Solanum tuberosum Tomato

Tomato

Chickpea

Rice

Rice

Rice

Rice

TCP29

Not well defined

StProDH1

GAMYB-like1

SlNF-YA1/2/3

DREB TFs

OsHB4

OsGRFs

Nuclear factor Y (NF-YA)

OsNAC2

OE

DR via STTM, amiRNA

OE

OE

OE

OE

OE

DR by CRISPR/ Cas

mTarget OE OE

Improved resistance to B. cinerea infection

Enhanced drought and salinity tolerance Under nitrogen scarcity: increased plant height, gain in biomass deposition, rise in root nitrate, total amino acid composition; more susceptible to bacterial blight Under nitrogen scarcity : enhanced grain produce, plant height with concomitant rise in above-ground biomass Enhanced Cd-stress tolerance in rice with apparently reduced Cd accumulation in grains Enhanced tolerance to drought stress conditions Enhanced drought tolerance accompanied by reduced transpiration rate Resistance to cold and heat stress, higher chlorophyll content Enhanced drought tolerance, chlorophyll content Enhanced tolerance to drought stress

Harnessing Perks of MiRNA Principles for Betterment of Agriculture and. . . (continued)

Wu et al. (2020)

Shi et al. (2019) Li et al. (2020b) Chen et al. (2019)

Hajyzadeh et al. (2015) Zhang et al. (2011c)

Ding et al. (2018)

Zhang et al. (2020d)

Jiang et al. (2019) Yu et al. (2018)

6 143

miR482b, miR482c

miR172a, miR172b miR160a, miR398b osa‐miR7695

Osa‐miR1873

Osa-miR162a

miR156

miR482/2118

miR811, miR829 Md-miR156ab, Md-miR395 amiRNA

3

4

7

8

9

10

11

13

12

6

5

miRNA/ amiRNA miR482e

SL No 2

Table 6.1 (continued)

AS

Apple

Maize

Tomato

Rice

Rice

Rice

Rice

Solanum pimpinellifolium Rice

Tomato

Source Potato

Rice

Apple

Maize

Tomato

Rice

Rice

Rice

Rice

S. lycopersicum Rice

Tomato

Expressing host Potato

MdWRKYN1, MdWRKY26 CP (Coat Protein) genes of RSV and RBSDV

Putative TFs

NBS-LRR defence genes

Dicer-like 1 (DCL1) genes IPA1, OsSPL7

OsNAC4, OsPR1a

OsNramp6

ARF, CSD

AP2/ERF

Target gene/locus NBS-LRR defence genes NBS-LRR defence genes

DR (via STTM) OE

OE

DR (via STTM)

Target OE

DR by TM OE

OE

OE

DR by CRISPR/ Cas OE

Approach OE

Resistance to Alternaria alternaria f. sp. mali Significant resistance towards Rice stripe virus (RSV) & Rice black streaked dwarf virus (RBSDV)

significant resistance to P. infestans and Pseudomonas syringae pv. tomato Resistance to E. turcicum

Enhanced M. oryzae resistance; a bit reduced grain yield Enhance Bacterial Blight resistance

Enhanced M. oryzae resistance

Enhanced resistance to P. infestans infection Enhanced resistance toward M. oryzae infections Enhanced M. oryzae resistance

Significant resistance towards P. infestans infection

Outcomes Enhanced susceptibility to V. dahliae

Wu et al. (2014) Zhang et al. (2017a) Sun et al. (2016)

Luan et al. (2017) Li et al. (2014) Campo et al. (2013) Zhou et al. (2020) Li et al. (2020c) Liu et al. (2019b) Canto-Pastor et al. (2019)

Reference Yang et al. (2015) Hong et al. (2020)

144 A. Barman et al.

Polycistronic amiRNA

amiRNA

amiRNA

amiRNA

amiRNA

amiRNA-14

amiRNAs (csu-miR260-16, csu-miR260-18) Tae-miR1023

15

16

17

18

19

20

21

Wheat

AS

AS

AS

AS

AS

AS

AS

AS

Fusarium graminearum

Rice

Rice

Rice

Rice

Tomato

Soybean

Wheat

Wheat

FGSG_03101 in Fusarium graminearum

Chilo suppressalis Endogenous genes CsSpo, CsEcR (in Chilo suppressalis) cytochrome P450 in C. suppressalis

Xa13

WDV Rep transcripts Conserved genomic regions of WSMV Gene transcripts J15, J20, J23) of SCN HaEcR (H. armigera)

HIGS

OE

OE

OE

OE

OE

OE

OE

OE

Delay in C. suppressalis larval pupation Pronounced resistance characterized by high morbidity and developmental defects in C. suppressalis Distinctive lethality against RSB larval stages and affected significantly impaired growth in the pest Reduced susceptibility to Fusarium graminearum

Resistance to cotton bollworm; Defective growth and compromised survivability of the cotton bollworm feeding on transgenic tomato plant Strong resistance to Xoo infection

Resistance to soybean cyst nematode (SCN)

Resistance to Wheat dwarf virus (WDV) Resistance to Wheat streak mosaic virus (WSMV)

Jiao and Peng (2018)

Zheng et al. (2020)

Li et al. (2012c) Jiang et al. (2017) He et al. (2019)

Yogindran and Rajam (2020)

Tian et al. (2016)

Kis et al. (2016) Fahim et al. (2012)

amiRNA artificial micro RNA, AS Artificially synthesized, OE overexpression, HIGS host induced gene silencing, mTarget modified target resistant to miRNA repression, DR down regulation, STTM short tandem target mimic, TM target mimic, VIGS virus induced gene silencing

22

amiRNA

14

6 Harnessing Perks of MiRNA Principles for Betterment of Agriculture and. . . 145

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A. Barman et al.

Fig. 6.2 Illustration of ‘Artificial MIR’ (amiRNA) transcript mediated repression of target gene mRNA in specific host, adapted from Basso et al. (2019). In a nutshell, the amiRNA gene (with all elements included in the vector construct) is transcribed inside the nucleus and it follows the canonical pathway of miRNA biogenesis to generate 21 nt amiRNA duplexes. These duplexes then undergo an important modification, i.e. methylation and next collaborate with RISC complexes

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Fig. 6.3 Schematic outline of a Short Tandem Target Mimic construct, adapted from Peng et al. (2018). The 48 base pair length sequence is placed between two target miRNA binding sites. The nucleotide sequences in the two target miRNA binding sites are chosen according to the specific miRNAs to be sequestered. Further, binding sites are incorporated with a tri-nucleotide sequence in each to produce a bulge in the middle and are cleavage resistant upon binding to respective miRNAs. Simultaneous sequestration of two specific miRNAs with sufficiently conserved sequences can be achieved through this construct. Besides, STTM sequence is provided with a promoter sequence [constitutive (e.g. 35S) or inducible (β-estradiol) as per necessity] and a terminator element so that it can be effectively expressed in target plant species

referred to as plant–insect chimeric artificial miRNA (Plin-amiR) construct contains an endogenous insect pre-miRNA backbone embedded with the miRNA/miRNA* sequence precise for a target gene transcript in the insect and is expressed under a strong plant promoter/terminator element (Bally et al. 2020). Lately, an important strategy called Short Tandem Target Mimic (STTM) deployed to downregulate specific or multiple miRNA species at a time for analysing subsequent effect on crops and model plant development dynamics has provided striking outcomes (Yan et al. 2012; Zhang et al. 2017b; Peng et al. 2018; CantoPastor et al. 2019) (Fig. 6.3 shows a design of STTM construct). Manipulation of gene editing tools (such as CRIPR/Cas9) for generating loss-of-function mutations in target miRNA species has also been found to be effective while analysing transgenic plants for suitable agronomic traits (Miao et al. 2020). Moreover, modified CRIPR/Cas9 system fused with an efficient base editor for creating mutation in target genes forwarded recently (Ren et al. 2017; Hao et al. 2019) may be extended for modifying miRNA target genes without disabling their normal functions or even specific MIR gene sequences may be altered for obtaining effective outcomes.

 ⁄ Fig. 6.2 (continued) to execute RNAi mechanisms via binding to target transcripts. This eventually result in post-transcriptional silencing of target mRNA. Importantly, apart from DCL1, other DCL members like DCL4 may be involved in the amiRNA biogenesis process as per findings of Niu et al. (2006)

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Advances in Understanding of miRNA Influenced Crop Agronomic Traits

Physiological transitions are hall marks of crop plant developmental processes, simultaneously, conferring important qualitative traits. Distinctive agronomic traits segregate superior crop cultivars from their counterparts and these ultimately determine their selective advantages over inferior ones. Useful agronomic traits gain selective advantages owing to the fact that they are anticipated to meet global food demands along with possession of desired qualities. The important parameters which distinguishes superior crop cultivars from rests can comprise: crop height, harvest index, number of productive tillers, grain number, grain size, grain weight, grain nutrient content, photosynthetic efficiency, aroma, etc. (for cereal crops) (Li et al. 2019a; reviewed in Tshikunde et al. 2019); fruit texture, colour, size, shape, comparatively non-perishable, delayed ripening, nutrient content, parthenocarpy, etc. (in regard to fruit as well as vegetable crops) (Pandolfini 2009; Nicola and Fontana 2014; Wang et al. 2018a). Apart from these, stress (both abiotic and biotic) resistant crop cultivars are likely to gain more preference over susceptible ones (Dhankher and Foyer 2018; Zhang et al. 2018d).

6.5.2.1 Gain in Crop Phenotypic and Physiological Traits Interestingly, besides other molecular determinants, miRNAs too have been implicated in deciding several morphological and physiological traits in crop plants via their inherent regulatory properties. For instance, in Oryza sativa (rice), osa-miR156 influences important attributes such as height of plant, size of grain, grain output, quality of grain, etc. (Jiao et al. 2010; Wang et al. 2012a, 2015; Si et al. 2016). In case of potato tuber formation, miR172-1, miR172-5, miR193 and miR152 were found to be involved (Lakhotia et al. 2014). Likewise, for fruit size determination in apple, role of miR172 was ascertained (Yao et al. 2015). Pertinently, these miRNAs largely target TFs (e.g. osa-miR156 regulates expression of OsSPL13, OsSPL14, OsSPL16; miR172 control APETALA2 family of TFs, etc.) committed to vital developmental pathways in these and other crop plants. Appropriate manipulation of these miRNA candidates in respective crop species may be expected to deliver favourable outcomes. On experimental basis, overexpression (OE) of specific miRNA members in respective crop plants has provided useful insights into their potential applications in agronomy. Several encouraging findings have crop up in recent times. In banana roots and leaf tissues under copper deficient condition, upregulation of MusamiR397 species was recorded that represses its target genes for laccase (Patel et al. 2019). Patel et al. (2019) overexpressed Musa-miR397 in banana and observed enhancement in plant growth (and hence plant biomass) as compared to wild type (WT) plants. Interestingly, natural responsiveness of Musa-miR397 toward copper deficiency remained unaltered (Patel et al. 2019). Rice (O. sativa) being an essential global food crop has attracted extensive investigations relating to miRNA manipulations in it. In O. sativa, OsmiR397 regulates OsLAC (Laccase gene of O. sativa) expression in response to brassinosteroid signalling pathway thereby

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involving in plant growth and development (Zhang et al. 2013a). A pioneer study involving OE of OsmiR397 demonstrated increase in rice grain yield, grain size as well as induction of panicle branching in the transgenic rice as compared to the wild type plant (Zhang et al. 2013a). Subsequently, it has been realized that accelerating photosynthetic activities in crops can aid significantly in improving their biomass as well as yield (Ort et al. 2015). In O. sativa (along with Arabidopsis and tobacco), OE of conserved miRNA species miR408 led to enhanced photosynthetic activity, increase in grain size, grain weight and yield (Pan et al. 2018). Increased miR408 expression was correlated with higher CO2 fixation in these plants (Pan et al. 2018), suggesting crucial regulatory role of this small RNA. In an earlier study, role of miR408 in affording copper supply to plastocyanin and thus regulation of plant photosynthesis has been proposed (Zhang et al. 2014b). Likewise, OE of OsmiR393 resulted in enhanced tiller numbers and early flowering time in transgenic rice (however, with associated negative impact on stress responsiveness to salinity and drought) (Xia et al. 2012). Further, in regard to enhanced expression of miR398a in rice, increase in crop height, expanded panicle size accompanied by increment in grain number per panicle are recent attractive findings in crop improvement efforts (Zhang et al. 2017b). In another study, transgenic rice overexpressing well conserved miR171 species resulted in enhanced tiller numbers, increase in plant height quite considerably (Fan et al. 2015). Sun et al. (2019b) have reported that OE of OsmiR535 in their study led to elongated grain length along with great influence on panicle architecture in transgenic rice. Repression of OsSPL7/12/16 TFs in the transgenic rice was evident from the author’s work (Sun et al. 2019b). Interestingly, though plant height was reduced in the transgenic plant (Sun et al. 2019b). MiRNAs may not always positively regulate crop beneficial traits. Some miRNAs pressing negative impact on consequent trait developments have also been uncovered (Jiao et al. 2010; Liu et al. 2014a; Gao et al. 2016; Chandran et al. 2019). In such cases, sequestering respective miRNAs by enhancing miRNA’s target transcripts may prove useful. For instance, miR156 was determined to be a susceptible factor for successful infection of a bacterial phytopathogen (Xanthomonas oryzae; Xoo) in rice (Liu et al. 2019b). A member of squamosa promoter-binding protein-like transcription factors (SPLs), OsSPL14/IPA1 (ideal plant architecture1) is known to influence important grain quality as well as productivity traits in rice (Jiao et al. 2010; Miura et al. 2010; Zhang et al. 2017c) that is but, negatively regulated by miR156 (Jiao et al. 2010). In a recent report, inducible OE of IPA1 in rice (and thus downregulation of miR156 through sequestering) enhanced rice grain yield via increments in panicle branch numbers, increased number of grains per panicle, superior grain size, etc. (with concomitant increased resistance to Xoo infection) (Liu et al. 2019b) forwarding a prominent incentive to crop improvement program. Adding to this, very recently, Lian et al. (2020) have communicated about conspicuous growth improvements accompanied by increments in grain quality, hormonal constituents and other important features observed in transgenic rice plant overexpressing OsSPL14, stressing potential of this candidate gene for biotechnological crop improvement schemes.

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In rice, various Growth Regulating Factors (GRFs) including GRF6 are known to be negatively controlled by miR396 (Liu et al. 2014b; Gao et al. 2016; Chandran et al. 2019). An experiment aiming at downregulation of endogenous miR396 in rice by OE of the target mimic miR396 (MIM396) resulted in significant increase in grain yield (Gao et al. 2016). In addition, OE of GRF6 (a target of miR396) in transgenic rice also enhanced yield corresponding to the WT plants (Gao et al. 2016). Likewise, rice OsmiR1432 targets expression of Acyl-CoA thioesterase (OsACOT) gene that in turn affects normal auxin/ abscisic acid biosynthesis pathways in the host (Zhao et al. 2019). Elimination of OsmiR1432 transcript generation via STTM expression and OE of OsACOT (OXmACOT) (resistant to OsmiR1432 directed cleavage) in independent transgenic rice lines indicated enhanced grain weight as well as overall increments in grain yield (Zhao et al. 2019). Additionally, endosperm tissue specific downregulation of OsmiR167 and OsmiR1432 in rice via STTM mediated strategy was shown to enhance grain length, size, weight and thickness considerably in the transgenic plant (Peng et al. 2018) reinforcing applicability of these loci for crop improvement. With respect to tomato, Cao et al. (2016) recorded enlargement of flowers, sepals and fruits in transgenic tomato plants, when miRNA pair—miR396a and miR396b were suppressed using STTM strategy. Manipulating same STTM technology to suppress expression of miR396, Peng et al. (2018) have documented enlarged size of the transgenic tomato plant bearing flowers, leaves as well as fruits with bigger sizes. In a more recent work, suppression of miR1917 in transgenic tomato plant using the same STTM technology manifested increased biomass, enlarged floral organs accompanied by gain in fruit and seed sizes in contrary to the WT (Yang et al. 2020). Anthocyanin being an important group of flavonoids confers decisive characteristics to flowers, fruits, seeds besides involving in plant growth, development, stress resistance mechanisms (He et al. 2011; Tanaka and Ohmiya 2008; Buer et al. 2010; Christie et al. 1994; Sarma and Sharma 1999). In tomato, miR858 was linked to negative regulation of anthocyanin biosynthesis via degradation of SlMYB7-like and SlMYB48-like transcripts (Jia et al. 2015). Blocking miR858 through expression of STTM858 in transgenic tomato plant resulted in upregulation of anthocyanin biosynthetic pathway followed by anthocyanin accumulation in several tissues (Jia et al. 2015). Considering advantages of anthocyanins, miR858 can be a potent target for tomato crop improvement with anthocyanin enrichments. As an alternative to downregulation of specific miRNAs, OE of its targets resistant to miRNA directed cleavage has also been tested to determine important outcomes in crops. Rice TF OsNAC2, a target of OsmiR164b, has been implicated in the former’s crucial developmental pathways (Mao et al. 2007; Fang et al. 2014; Chen et al. 2015b). OE of mutated OsNAC2 (resistant to OsmiR164b directed cleavage) in transgenic rice was found to improve plant architecture, enhance grain number and yield considerably in comparison to the wild type (WT) rice (Jiang et al. 2018a). Deployment of precise gene editing approaches to eliminate specific miRNA function can be another way for screening important agronomic traits in crops. Miao et al. (2020) recorded enhanced grain size (and hence yield), accompanied by shoot

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architectural alterations in transgenic rice plants upon mutating gene pairs MIR396e and MIR396f, utilizing CRISPR/Cas9 gene editing technology. Importantly, miR396 controls various Growth Regulating Factor (GRF) genes thereby orchestrating numerous aspects of plant growth and development (Miao et al. 2020). Enhanced amylose content in grains is hailed as a superior agronomic trait in cereal crops (such as rice) owing to their better health benefits and efficient industrial use (Jobling 2004; Morell and Myers 2005; Rahman et al. 2007). In order to maximize amylose content in rice grain endosperm, a well perceived strategy is to repress the enzymes involved in amylopectin synthesis and redirect starch biosynthesis towards genesis of amylose component (Morell and Myers 2005; Regina et al. 2006, 2010; Rahman et al. 2007). Among several others, starch branching IIb (SBEIIb) enzyme plays a major role in biosynthesis of amylopectin in rice grains (Yamanouchi and Nakamura 1992; Ohdan et al. 2005; Yamakawa et al. 2007). Accordingly, Butardo et al. (2011) used an amiRNA construct (along with hairpin RNAs) to target repression of SBEIIb enzyme expression in transgenic rice. Transgenic rice significantly inactivating SBEIIb enzyme expression manifested two-fold increase in amylose content in comparison to WT. Further, rice transgenic lines carrying amiRNA mediated downregulation of SBEIIb, demonstrated pronounced alteration of starch grain structure bearing implications for more resistant starch content, lower glycaemic index as well as improved digestibility (Butardo et al. 2011). Fragrance in rice grain has been perceived as a superior agronomic feature that also uplifts its economic importance (Bhattacharjee et al. 2002; Fitzgerald et al. 2009). The compound that confers aroma in rice is 2-acetyl-1-pyrroline (2AP) which is basically lacking in non-fragrant rice. Homozygous recessive state of alleles for a gene called BADH2 that consequently codes for non-functional betaine aldehyde dehydrogenase enzyme has been correlated with genesis of 2-acetyl-1-pyrroline (2AP) in fragrant rice (Berner and Hoff 1986; Ahn et al. 1992; Bradbury et al. 2008; Chen et al. 2008). In attempts to generate aroma in non-fragrant rice, Chen et al. (2012) designed an amiRNA construct (taking osa-MIR528 precursor backbone) to target and repress OsBADH2 gene expression in transgenic rice plants. AmiRNA driven suppression of OsBADH2 in transgenic rice lines showed elevated levels of 2AP concentration as against the WT. Proline (precursor of 2AP) content was also observed to rise in the leaves of transgenic rice (Chen et al. 2012). Utilization of amiRNA technology for infusing such qualitative agronomic trait in rice is promising. Parthenocarpy is the phenomenon of fruit formation independent of fertilization/ pollination events in crops/plants (Joldersma and Liu 2018), which has reasonable value in fruit market as well as in food industry (Schijlen et al. 2007). This important agronomic trait can prove instrumental in generating quality fruit crops (e.g. seedless fruits) that can further contribute to global food security considerably (Schijlen et al. 2007; Pandolfini 2009; Knapp et al. 2017). Critical role of phytohormones, viz. Auxin, Gibberellic Acid (GA) and Cytokinins, rendering parthenocarpy induction in several fruits has been already established (Gustafson 1939; Nitsch 1950; Prosser and Jackson 1959; Davison 1960; Hayata and Niimi 1995). These phytohormones

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(i.e. auxin, gibberellin, cytokinin) are further known to engage in complicated crosstalks with different plant miRNA species that modulate former’s functional aspects in plant development and stress responses quite distinctively (Damodharan et al. 2016; Li et al. 2020a; Yu and Wang 2020; Zhang et al. 2020c). As such, in some recent reports, plant miRNAs have also been implicated in parthenocarpic fruit development (da Silva et al. 2017; Wang et al. 2018b; Zhang et al. 2019a). In tomato, OE of SlmiR159 in floral organs led to downregulation of SlGAMYB1/ 2 transcription factor expressions (which are responsive to auxin and GA phytohormones) and generated obligate parthenocarpic fruits without altering tomato shape (da Silva et al. 2017). Likewise, in another study involving GA induced grapevine (Vitis vinifera), Wang et al. (2018b) have worked out important role of miRNA159 family members which targets VvGAMYB TFs during fruit development. Post GA-induction, VvmiR159c showed accelerated expression with simultaneous reduction in expression of its target VvGAMYB (Wang et al. 2018b) indicating VvmiR159 mediated downregulation of VvGAMYBs to be crucial for parthenocarpic grapevine development. Recently, Zhang et al. (2019a) have demonstrated repression of VvARF10/16/17 expression via VvmiR160a/b/c to be crucial during GA induced parthenocarpy in grape fruits. These miRNA candidates may have useful impact on efficient molecular breeding programs. Artificial miRNA (amiRNA) technology has also been recruited to generate parthenocarpic crop. For instance, Toppino et al. (2011) used an inducible amiRNA incorporated genetic system to generate transgenic Solanum melongena, which eventually produced parthenocarpic fruits. They used the amiRNA construct to silence some general TF (GTF) genes (Toppino et al. 2011). Besides, in earlier works, RNAi strategies involving small RNA/RNA hairpins have been used to downregulate important genes of fruit developmental pathway to generate parthenocarpic fruits of crops such as tomato (Schijlen et al. 2007; Molesini et al. 2009; Mounet et al. 2012; Martínez-Bello et al. 2015) with fair amount of success. Softening and ripening are important transitions in fleshy fruit (e.g. tomato, banana, watermelon, etc.) developmental processes. MiRNAs have also been associated with fruit ripening/softening pathways. Examples include Sly-miR156/ Sly-miR157 (Chen et al. 2015c), miR172 (Chung et al. 2020) in tomato, cme-miR393 in Cucumis melo (Bai et al. 2020), miR164 in Actinidia deliciosa (Wang et al. 2020), etc. Softening and ripening features are also crucial for determining quality of fruits. Excessive softening and ripening can lead to early decay and senescence, thus posing a yet unresolved challenge for post-harvest preservation of fruits (reviewed in Wang et al. 2018a; reviewed in Seymour et al. 2013). Various efforts are on to mitigate these challenges employing biochemical as well as molecular level know-how including miRNA strategy (Payasi and Sanwal 2010; Chen et al. 2015c; Minoia et al. 2016; Jincy et al. 2017; Li et al. 2018b; Silva et al. 2018). In their work, Chen et al. (2015c) have found silencing (via virus induced gene silencing (VIGS) strategy) of SlymiR157 species to delay ripening in transgenic tomato fruits locally although fruit softening might be further controlled separately by SlymiR156 (Zhang et al. 2011a). SlymiR157 is said to control expression of

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LeSPL-CNR gene involved in key steps of ripening transitions (Chen et al. 2015c). Both these miRNAs (i.e. SlymiR156 and SlymiR157) can be suitable targets for tomato ripening/softening trait manipulations in this fruit. In regard to banana, using deep sequencing, bioinformatics analysis combined with quantitative RT-PCR results, Bi et al. (2015) have predicted about enhanced expression of miR395 species to possibly delay ripening process in this climacteric fruit, however, any experimental validation is still awaited. Cold temperature treatment for post-harvest fruit preservations is a regular practice. Upregulation of miR167, miR164, miR172 observed in strawberry during cold storage indicates miRNA mediated protective function (Xu et al. 2015). Likewise, downregulation of miR398 and miR159 in response to UV-C treatment in strawberry reflected negative role of these in the fruit preservation (Xu et al. 2018b). Generally, fruit browning in long term cold storage is an obvious hurdle during preservation. For instance, enzyme polyphenol oxidase (PPO), a leading cause of browning, is expressed in high amount when banana is stored under cold temperature (Zhu et al. 2020). MiR528, which is downregulated in response to cold stress in banana, controls PPO expression in normal circumstances, thereby preventing browning (Zhu et al. 2020). These miRNA candidates can certainly prove effective while designing suitable strategies for fruit preservation. These findings (and several others which we could not mention here) are tempting considering the crop improvement goals.

6.5.2.2 Progress in Understanding of miRNA Influenced Stress Responsive Traits In Crops A well perceived fact is plant growth must be compensated for the cost of mitigating varied stresses experienced by plants in their life time (reviewed in Chapin 1991). Stress can be both abiotic and biotic and the duo affects plants significantly. Fluctuations in abiotic parameters such as temperature, light, soil pH, water availability, soil salinity, etc. are obvious facets in the environment. In the same manner, biotic stress due to varied kind of pathogenic microbes (viruses, bacteria, fungi), eukaryotic parasites (nematodes), herbivorous consumers (insect pests, mammals), etc. threatens crop plant survivability enormously. In order to mitigate these impediments, plants have evolved several sophisticated stress response regulators encoded genetically, e.g. transcription factors (TFs) (Singh et al. 2002; Agarwal et al. 2006; Golldack et al. 2011; Nakashima et al. 2012), protein kinases (Romeis et al. 2001; Pedley and Martin 2005; Ye et al. 2017), protein phosphatases (Schweighofer et al. 2004; País et al. 2009; Durian et al. 2016; Singh et al. 2018; reviewed in Barman and Ray 2020), phytohormones (Fahad et al. 2015; Eremina et al. 2016; Podlešáková et al. 2019), secondary metabolites (Bennett and Wallsgrove 1994; Edreva et al. 2008; Akula and Ravishankar 2011; Bednarek 2012), etc., and also varied epigenetic arms (e.g. chromosome modifiers, non-coding sRNAs (miRNAs, siRNAs), etc.) (Sunkar et al. 2007; Chinnusamy and Zhu 2009; Kumar 2018; reviewed in Mozgova et al. 2019; Huang et al. 2019). Importantly, under varied stress responsive conditions, biosynthesis, signalling or function of most of these components such as TFs, secondary metabolites,

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immune receptor kinases, hormones, etc. are regulated by plant miRNAs posttranscriptionally (Robert-Seilaniantz et al. 2011; Zhang et al. 2011b; Li et al. 2012b; Shivaprasad et al. 2012; Ehya et al. 2013; Liu et al. 2014c; Gupta et al. 2017; reviewed in Ng et al. 2018). Numerous miRNAs have been known to express in crop plants while responding to fluctuations in abiotic environments (Kantar et al. 2011; Zhao et al. 2011; Sunkar et al. 2012; Ding et al. 2013; reviewed in Sun et al. 2019a) as well as varied biotic stresses (Pacheco et al. 2012; Snyman et al. 2017; Hua et al. 2018; Lei et al. 2019; Nanda et al. 2020). These stress responsive miRNAs function to balance defence mechanisms with physiological and developmental processes in crops to mitigate regressive transitions to a great extent. Here, we highlight some of the conspicuous improvements observed in crop agronomic traits via manipulation of miRNA principles responsive to varied abiotic and biotic stresses. 6.5.2.2.1 Gain in miRNA Mediated Abiotic Stress Resilience in Crops Experimental manipulation of some of the abiotic stress responsive miRNA candidates via overexpression (OE), heterologous miRNA expression within compatible species, miRNA resistant target expression, silencing through artificial miRNAs/STTMs (Short Tandem Target Mimics), etc. in important crops (such as rice, soybean, tomato, chickpea, potato) have provided some noteworthy insights (Zhang et al. 2011c; Wang et al. 2014; Hajyzadeh et al. 2015; Ding et al. 2018; Yu et al. 2018; Chen et al. 2019; Jiang et al. 2019; Shi et al. 2019; Tang and Thompson 2019; Zhou and Tang 2019; Li et al. 2020b; Sun et al. 2020a; Yue et al. 2020). These observations are heartening from the view of their potential applicability in breeding improved abiotic stress tolerant crop varieties. Here are some of the important findings gained from different experimental studies. In soybean (Glycine max (Linn.) Merr.), miRNA species ‘Gma-miR1508a’ is generated in response to cold stress, but is inhibited under drought condition (Sun et al. 2020a). OE of Gma-miR1508a in transgenic soybean conferred cold stress tolerance during seed germination and young seedling stages with subsequent enhancement in soluble sugar content under cold stress as compared to WT plant (Sun et al. 2020a). However, transgenic soybeans were more susceptible to drought stress than the WT and had short stature with thick cell wall (Sun et al. 2020a). OsmiR528, a cold stress responsive miRNA candidate in rice, negatively regulates expression of many stress-associated MYB TFs (Tang and Thompson 2019). In a recent finding, OE of OsmiR528 enhanced cell viability, growth rate, antioxidant content as well as synthesis of other factors which eventually positively influenced cold stress tolerance in transgenic rice as compared to the WT (Tang and Thompson 2019). In response to cold stress, Osa-miR319b was found to be downregulated in rice (Wang et al. 2014). OE of Osa-miR319b was however shown to impart increased cold stress tolerance to the transgenic rice plant with accompanied widening of leaf blades. OE of Osa-miR319b was shown to repress expression of two TFs, namely, OsPCF6 and OsTCP21 in the transgenic rice, which is generally induced under cold stress in the WT plant (Wang et al. 2014). Importantly, transgenic rice had delayed

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development in comparison to WT plant (Wang et al. 2014), illustrating an instance of stress resistance at the cost of development. In an earlier study on the same line, OE of osa-miR319 improved cold tolerance (4  C) after chilling acclimation (12  C) in seedling stages of transgenic rice (Yang et al. 2013). Authors further reported about another two TF targets—OsPCF5 and OsPCF8 for osa-miR319 (Yang et al. 2013). Cold responsive miRNA candidate ‘OsmiR156’ was recently found to enhance cold stress tolerance in rice plant by downregulating expression of its target gene OsSPL3 (Zhou and Tang 2019). OsSPL3 induces OsWRKY71 expression which in turn suppresses expression of OsMYB2 and OsMYB3R-2 TFs (Zhou and Tang 2019). OsMYB2 and OsMYB3R-2 TFs are instrumental in conferring resistance to cold stress via induction of several stress responsive genes (Zhou and Tang 2019). Zhou and Tang (2019) recently overexpressed OsmiR156 in transgenic rice plants and reported increased cold stress tolerance, improved cell viability and growth rates in the transgenic lines as compared to WT demonstrating positive regulatory role of OsmiR156 in rice cold stress tolerance. Another miRNA, OsmiR535, which was earlier shown to negatively regulate cold stress tolerance (Lv et al. 2010; Sun et al. 2020b) has recently been determined to be responsive towards drought and salinity stress in rice plant (Yue et al. 2020). Subsequent repression of OsmiR535 by gene editing tool (using CRISPR/Cas9) and via STTM strategies has revealed enhanced tolerance of transgenic rice seedlings toward Abscisic acid (ABA) treatment, salinity and drought induced stresses (Yue et al. 2020). Also cited in earlier section, rice miRNA candidate OsmiR164b negatively regulates OsNAC2 transcription factor implicated in several crucial developmental pathways in rice (Jiang et al. 2018a). In a recent study conducted by Jiang et al. (2019), OE of mOsNAC2 (a modified form of OsNAC2 TF resistant to OsmiR164b mediated repression) in transgenic rice lines significantly enhanced drought and salinity tolerance in the latter. Germinating seeds of mOsNAC2 overexpressing rice lines accumulated high conc. of ABA hormone which was correlated with relatively slower germination rate in seeds but also with appearance of improved resistance traits towards drought and salinity stresses (Jiang et al. 2019). Mineral nutrient limitations pose serious hindrances in normal growth and development of crop plants. Nitrogen is one of the indispensable nutrients that significantly influence plant health and yield (Tilman et al. 2002). Crop miRNA candidates responsive to nitrogen limitations have been deciphered (Wang et al. 2013; Zhao et al. 2013; Yan et al. 2014; Yu et al. 2018; Hou et al. 2020). In rice, miR169o is one such candidate that was shown to have overlapping roles. In transgenic rice, OE of Osa-miR169o resulted in increased plant height, gain in biomass deposition, conspicuous rise in root nitrate and total amino acid composition than the WT plant (Yu et al. 2018). However, transgenic rice (OE-Osa-miR169o) lines were found to be more susceptible to bacterial blight (due to Xanthomonas oryzae pv. oryzae) than WT (Yu et al. 2018). Under nitrogen scarce condition, transgenic rice lacking functional miRNA pair—miR396e and miR396f was demonstrated to significantly enhance grain produce, plant height with concomitant rise in above-ground biomass (Zhang et al. 2020d). Zhang et al. (2020d) generated transgenic rice containing loss-

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of-function mutation in MIR396e and MIR396f genes via CRISPR/Cas9 mediated gene editing technology which has subsequently revealed some notable agronomic traits in transgenic rice under nitrogen starvation condition as compared to WT. Some elemental constituents in environment can have detrimental effect on plant health. For instance, Cd (cadmium) is a highly toxic element significantly affecting most living organisms including plants. In rice, miR166 (Os-miR166) is known to be a Cd-stress responsive candidate (Ding et al. 2018). Ding et al. (2018) had found OE of Os-miR166 to substantially enhance Cd-stress tolerance in transgenic rice with apparently reduced Cd accumulation in grains and decreased translocation of Cd from root to shoot. Overexpressed Os-miR166 significantly downregulated expression of its target homeodomain containing protein4 (OsHB4) gene encoding a conserved class-III homeodomain-Leu zipper (HD-Zip) protein in rice (Ding et al. 2018). On the contrary, an earlier study demonstrated OE of Os-miR268 to reduce Cd-stress tolerance in transgenic rice that further inhibited seedling growth under Cd-stress (Ding et al. 2017). In this case, Os-miR268 was shown to repress NRAMP3 (natural resistance-associated macrophage protein 3) gene expression (Ding et al. 2017). Both Os-miR166 and Os-miR268 which regulate different gene targets can be potential candidates for Cd-stress resistance trait improvement in rice. An important nutrient rich legume, Chickpea (Cicer arietinum L.) is mainly cultivated in regions receiving low precipitations, implying drought stress posing as an obvious hindrance to this crop (Hajyzadeh et al. 2015). A drought responsive chickpea miRNA candidate miR408 was shown to repress expression of a copper containing gene called plantacyanin that eventually regulated DREB transcription factors in the host plant (Hajyzadeh et al. 2015). OE of miR408 in transgenic chickpea lines conferred enhanced tolerance to drought stress conditions (Hajyzadeh et al. 2015), further suggesting a potential miRNA candidate for useful agronomic applications. Tomato miRNA candidate ‘Sly-miR169’ is drought stress responsive and downregulates expression of nuclear factor Y subunit genes (SlNF-YA1/2/3) and a multidrug resistance-associated protein gene (SlMRP1) under similar condition (Zhang et al. 2011c). Constitutive OE of Sly-miR169c (a Sly-miR169 family member) manifested enhanced drought tolerance accompanied by reduced transpiration rate in transgenic tomato plants in comparison to WT counterpart (Zhang et al. 2011c). Sly-miR169 can reasonably account for its future manipulation in efficient breeding of drought tolerant tomatoes. Transgenic inter-species transfer and expression of candidate miRNAs between phylogenetically compatible members have also been attempted to analyse useful crop agronomic traits. MiRNA319d (sha-miR319d), a candidate from wild tomato Solanum habrochaites was overexpressed in cultivated tomato cultivar ‘Micro-Tom’ (Shi et al. 2019). The resulting transgenic tomato manifested useful traits such as resistance to cold and heat stress, higher chlorophyll content, etc. as compared to WT (Shi et al. 2019), suggesting meaningful implications. Artificial miRNAs (amiRNAs) have also been deployed to study apparent effects of miRNA principles on crops under abiotic stress conditions (Li et al. 2020b). Li et al. (2020b) have reported about expression of an amiRNA construct in transgenic

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potato that represses expression of StProDH1 gene (encoding Solanum tuberosum Proline Dehydrogenase enzyme 1). Transgenic potato expressing the amiRNA candidate was shown to be significantly resistant to drought stress as compared to WT (Li et al. 2020b). AmiRNA mediated repression of StProDH1 resulted in enhanced accumulation of proline in the transgenic potato plant thereby improving drought tolerance (Li et al. 2020b). Transgenic potato was also manifesting enhanced chlorophyll content along with accumulation of some other compounds than that of WT (Li et al. 2020b). A non-conserved miRNA candidate ‘miR1916’ has been reported from different crop plant species such as tomato, tobacco, potato, eggplant, etc. (Chen et al. 2019). However, exact targets of this miRNA candidate are still not known. Chen et al. (2019) have downregulated miR1916 species via STTM and amiRNA technologies to generate transgenic tomato plants. Interestingly, the authors (Chen et al. 2019) recorded transgenic tomato plants with downregulated miR1916 expression had manifested enhanced tolerance to drought stress as compared to WT. Authors also generated transgenic tomato with overexpressed miR1916 that however displayed opposite feature (Chen et al. 2019).

6.5.2.2.2 Advances Toward miRNA Mediated Biotic Stress Resilience in Crops Some Forewords on Plant Immune System and Significance of miRNA in it

Plants are bestowed with sophisticated protective mechanisms conferred by inbuilt innate immune system at individual cellular level to counteract numerous pathogenic intrusions (Dodds and Rathjen 2010). Plant innate immunity has two distinct branches called PTI (PAMP triggered immunity) which usually begins at cell surfaces and ETI (Effector triggered immunity) that is basically an intracellular defence cascade (Muthamilarasan and Prasad 2013; Thomma et al. 2011; Macho and Zipfel 2014). Both these branches involve prudent arsenals to distinguish pathogenic elicitations; PTI senses extracellular pathogenic components via PAMPs/MAMPs/DAMPs/NAMPs recognizing receptors (called PRRs), whereas ETI recognizes pathogenic effectors through intracellular NBS-LRR receptors to activate plant defence responses (Felix et al. 1999; Newman et al. 1995; Meyer et al. 2001; Kunze et al. 2004; Gust et al. 2007; Miya et al. 2007; Holbein et al. 2016; Sidonskaya et al. 2016). This eventually signifies inevitable roles of extracellular and intracellular immune receptors for prudent plant defence mechanisms (Macho and Zipfel 2014). Both PTI and ETI driven pathways are adroit mechanisms which can effectively forbid numerous pathogenic assaults rendered by pathogenic bacteria, fungi, nematodes, etc. (reviewed in Pumplin and Voinnet 2013; reviewed in Miller et al. 2017). In order to counteract viral pathogens, besides recruiting varied defensive strategies (Calil and Fontes 2016), plants also deploy RNA silencing approach quite regularly (Eamens et al. 2008; Pumplin and Voinnet 2013). Plants usually deploy gene silencing mechanism via siRNA approach to defend against most viral agents (Wang et al. 2012b; Pumplin and Voinnet 2013; Ghoshal and Sanfacon 2015; Khalid et al. 2017).

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Intriguingly, in several occasions, plant miRNAs have been observed to regulate intracellular NBS-LRR receptors quite readily during pathogenic invasions leading to compromised plant defence responses (Ouyang et al. 2014; Yang et al. 2015; Hong et al. 2020). These NBS-LRR receptors (a crucial component of ETI arm in plant immunity) are also referred to as products of resistance (R) genes in plants (reviewed in Liu et al. 2017; reviewed in Miller et al. 2017). siRNAs are said to modulate plant defence against viruses via regulation of these R genes (reviewed in Liu et al. 2017). As per recent documentations, plant miRNAs are involved in downregulation of ‘R’ genes somewhat in an indirect manner through induction of secondary siRNA (i.e. phased/tasiRNAs) mediated repression, and notably pathogenic bacterial/viral infection events are said to disable such indirect miRNA regulatory functions in plants (Zhai et al. 2011; Li et al. 2012b). For instance, miR482 in tomato was observed to downregulate NBS-LRR (R gene) expression via induction of siRNAs (Shivaprasad et al. 2012). Figure 6.4 outlines the mechanism of miRNA mediated regulation of plant immunity and resistance to viral pathogens. Similar kinds of observations were made in other plants too and interestingly, NBS-LRR expression (via suppression of miR482 activity) could be induced back after treatment of these plants with pathogenic bacterial and viral agents effectively (Li et al. 2012b; Shivaprasad et al. 2012; Zhu et al. 2013b; Yang et al. 2015). In a way, impediment of such miRNA functions can strengthen host resistance towards pathogenic afflictions (via expression of R gene products) quite readily. However, plant viruses have in turn evolved various counter measures to dampen host resistance mechanisms (Ghoshal and Sanfacon 2015; Nie and Molen 2015; reviewed in Liu et al. 2017). Quite often, plant viruses express viral suppressors of RNA silencing (VSRs) which not only assists in completing viral life-cycle but also target host RNA silencing mechanisms via multiple modes such as sequestration of RNAi machinery, usurping components of host sRNA (miRNA/ siRNA) biogenesis processes or even by altering host miRNA expression profile (reviewed in Liu et al. 2017). Figure 6.5 illustrates predicted miRNA mediated responses during plant host–viral pathogen interaction. Not in all cases pathogen wins neither the host remain unconquered. And whenever plants fail to defend, either survivability or productivity or both in the former is at stake. That is where human intervention becomes indispensable. Besides viruses, non-viral pathogen elicited plant immune system is also influenced by alterations in expression profile of several miRNA candidates in plants. For instance, suppression of wheat miRNA candidate ‘miR156’ with subsequent induction of its target SPL transcription factor (Ta3711) upon Erysiphe graminis (a fungal pathogen) infection of wheat plants indicates negative role of miR156 in plant defence (Xin et al. 2010). Occasions when suppression of certain miRNA families could promote expression of receptor kinases involved in PTI and ETI responses upon pathogenic elicitation of host plant have also been uncovered (Lu et al. 2007; Guo et al. 2011). In other words, it appears like plants which are normally devoid of any pathogenic elicitations may have their PTI and ETI arms suppressed through action of miRNAs and would be activated only after pathogen infects, via downregulation of those miRNAs (Pumplin and Voinnet 2013).

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Fig. 6.4 Schematic diagram depicting a route of miRNA mediated plant defence control via regulation of Resistance genes, adapted from Liu et al. (2017). A conserved miRNA familymiR482 is known to downregulate NBS-LRR class of defence genes (‘R’ genes) in several plants via induction of secondary siRNAs (in fact tasiRNAs) [reviewed in Liu et al. (2017)]. Interestingly, upon infection by viral pathogens, which suppresses miRNA mediated tasiRNA biogenesis process at several steps, on the other hand allows ‘R’ gene expression to commence. Accumulation of ‘R’ gene product eventually elevates plant’s defence towards viral pathogens leading to increased plant resistance (reviewed in Liu et al. 2017). Abbreviations in the diagram (adapted from Liu et al. 2017): AGO1 Argonaute 1, DCL4 Dicer-like 4, RDR6 RNA-dependent RNA polymerase 6, SGS3 suppressor of gene silencing 3, DRB4 double stranded RNA-binding protein 4

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Fig. 6.5 A speculative pictogram depicting miRNA function during plant host–viral pathogen interaction, drawn from Liu et al. (2017). Plant miRNAs induced by viral entry can downregulate host gene expression as well as silence viral gene transcripts. Viruses in turn may recruit VSRs (viral suppressors of RNA silencing) to dampen host RNAi strategies. VSRs are capable of inhibiting host miRNAs as well as these can also repress host resistance gene functions. Even though plant viral miRNAs has not been reported yet, theoretically if they exists, they might be able to downregulate host gene transcripts as well (Liu et al. 2017)

A. thaliana miRNA candidate ‘miR393’ was found to positively regulate host defence towards bacterial phytopathogen Pseudomonas syringae pv. tomato (Pst) in a pioneer study (Navarro et al. 2006). flg22 (Pst PAMP) induced expression of miR393 downregulates an auxin signalling receptor (TIR1) and contributes positively towards PTI in the host to dampen Pst infection (reviewed in Pumplin and Voinnet 2013). Auxin signalling is a negative regulator of A. thaliana defence towards Pst and miR393 regulatory function prudently interlinks hormonal crosstalk in plant defence. Cross-kingdom transfer of miRNA candidates (e.g. miR159, miR166, pst-milR1, etc.) from host plants to fungal pathogens or vice versa

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modulating reciprocal host–pathogen interactions has also been illuminated (reviewed in Gualtieri et al. 2020). In the same manner, instances of plant miRNA transfer to insects feeding on plants (Zhang et al. 2019b) as well to parasitic nematodes (Wang et al. 2017) have also been realized. It has been envisaged, engineered plant miRNA driven silencing of crucial genetic factors governing developmental fates in insect pests, parasitic nematodes can effectively control crop infestations mediated by them (reviewed in Gualtieri et al. 2020). Persistence of miRNA mediated regulation of plant immune responses towards varied phytopathogens and also targeted at insect pests under natural circumstances has inspired scientists to design novel experiments anticipating sanguine outcomes. Here we are illustrating some of the experimental conclusions based on plant miRNA principles conducted via deployment of strategies such as STTM, amiRNA, CRISPR/Cas, etc. which have also been mentioned in earlier sections. Necrotrophic fungus Botrytis cinerea is the causal agent of stem rot disease in tomatoes (Jin and Wu 2015). In an earlier study, Jin and Wu (2015) had found abundance of sly-miR319c expression along with other miRNA species in tomato after stimulation with B. cinerea, indicating sly-miR319c to be one of the candidates responsive to the pathogenic elicitation. A recent finding has indicated role of sly-miR319c in resistance response toward the stem rot causal agent in tomato (Wu et al. 2020). Authors could confirm their discovery by overexpressing sly-miR319c heterologously in Arabidopsis and observed transgenic plants to gain improved resistance trait to B. cinerea infection (Wu et al. 2020). Significant downregulation of TCP29 (a member of TCP family TF) expression, which is the target of sly-miR319c has also been revealed in the same study. On the contrary, OE of TCP29 was shown to augment susceptibility of transgenic Arabidopsis towards B. cinerea infection (Wu et al. 2020). It appears sly-miR319c-TCP29 regulatory module can be an effective target for manipulation to infuse stem rot resistance in tomato. Potato early dying disease is afflicted by the soil-borne fungus Verticillium dahliae (Bhat and Subbarao 1999). Generally, under natural circumstances, V. dahliae infection resistant potato cultivars are found to have suppressed miR482e functions and consequently its target genes that primarily encode defence proteins (belonging to nucleotide binding site (NBS) and leucine-rich repeat (LRR) families) are upregulated (Yang et al. 2015). In an experiment, OE of miR482e manifested enhanced susceptibility to V. dahliae in transgenic potato with concomitant silencing of NBS-LRR defence genes (Yang et al. 2015). MiR482e can genuinely serve as susceptible marker for screening V. dahliae resistant potato cultivars with potential implications in agronomy. Fungal pathogen Phytophthora infestans causes serious Late Blight (LB) disease in tomato thereby affecting its crop productivity immensely (Fraley et al. 1983). Studies have indicated involvement of tomato miRNA candidates—miR482b and miR482c in the immune responses of tomato towards P. infestans infection (Jiang et al. 2018b; Hong et al. 2019, 2020). MiR482b and miR482c are said to modulate tomato immune responses considerably via regulation of NBS-LRR defence genes (Hong et al. 2020). Very recently, simultaneous inactivation of miR482b and

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miR482c functions using CRISPR/Cas9 gene editing system has been shown to confer significant resistance in transgenic tomato plants towards P. infestans infection (Hong et al. 2020). Disabling both miR482b and miR482c however augmented expression of NBS-LRR immune function genes in the transgenic tomato (Hong et al. 2020). Looking at this facts, miR482b and miR482c can reasonably stand out as important candidates for developing LB tolerant tomato cultivars. P. infestans also stimulates another miRNA pair in wild tomato, namely miR172a and miR172b, which was found to positively regulate Solanum pimpinellifolium immunity against the LB pathogen (Luan et al. 2018). OE of both miR172a and miR172b precursor candidates from S. pimpinellifolium (a wild tomato species) in the transgenic tomato (S. lycopersicum) significantly enhanced resistance to P. infestans infection in the latter (Luan et al. 2018). MiR172a/miR172b pair is said to downregulate expression of AP2/ERF transcription factors considerably in the transgenic tomato thereby allowing accumulation of crucial defence proteins to counteract P. infestans proliferation (Luan et al. 2018). Thus, effective inter-species heterologous expression of miRNAs such as miR172a/miR172b can further provide useful alternatives for incorporating LB resistance traits in susceptible tomato cultivars. Rice blast pathogen Magnaporthe oryzae is an ascomycete fungus that severely impacts rice cultivation globally (Wilson and Talbot 2009). Li et al. (2014) had determined miRNA candidates in rice which positively regulate rice counter-defence towards M. oryzae. The author’s work revealed two important miRNA species: miR160a and miR398b in the resistant rice cultivars that were examined (Li et al. 2014). The same group further overexpressed miR160a and miR398b in a susceptible Asian rice cultivar (‘Kasalath’—an indica accession) and recorded enhanced resistance toward M. oryzae infections in the transgenic rice (Li et al. 2014). A recent study has unveiled miR398b mediated superoxide dismutase (SOD) function leading to enhanced H2O2 activity in transgenic rice playing a key role in improving M. oryzae resistance (Li et al. 2019b). In an earlier work, Campo et al. (2013) correlated M. oryzae resistance in transgenic rice with overexpression of miRNA candidate osa-miR7695 in the latter. The authors reported significant downregulation of OsNramp6 (Natural resistance-associated macrophage protein 6) gene transcripts (Campo et al. 2013) which encodes important rice metal (i.e. iron and manganese) transporters (Peris-Peris et al. 2017). Interestingly, suppression of OsNramp6 contributes to rice immunity towards blast pathogen under elevated iron supplies in resistant rice cultivars (Peris-Peris et al. 2017). Osa-miR1873 is another candidate implicated in rice—M. oryzae interaction that coordinates both defence responses and developmental routes in rice plant (Zhou et al. 2020). Osa-miR1873 was shown to negatively regulate O. sativa immune responses to M. oryzae via downregulation of a novel locus whose functional annotation is yet to be made (Zhou et al. 2020). In fact, OE of Osa-miR1873 in rice was further connected to reduced expression levels of OsNAC4 and OsPR1a (Pathogenesis-Related 1a) immune marker genes (Zhou et al. 2020). Recently, Zhou et al. (2020) expressed a target mimic (MIM1873) to repress Osa-miR1873 expression in transgenic rice lines and observed significant reduction in susceptibility of those plants towards M. oryzae infections. Defence related proteins were also

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upregulated in the transgenic rice lines (Zhou et al. 2020). Importantly, besides enhanced resistance to M. oryzae, authors further recorded no significant alteration in grain yield for transgenic rice lacking functional Osa-miR1873 (Zhou et al. 2020). Targeting Osa-miR1873 can certainly be a valuable alternative towards development of ‘Rice blast’ resistant improved rice lines without compromising much on the productivity end. Corresponding to a new study, OE of Osa-miR162a (another rice miRNA candidate) was correlated with enhanced immunity against M. oryzae in the transgenic rice (Li et al. 2020c). On the contrary, suppression of Osa-miR162a (via expression of a target mimic) was found to render rice plants more susceptible to the blast pathogen (Li et al. 2020c). It is noteworthy that transgenic rice overexpressing Osa-miR162a had a bit reduced grain yield though (Li et al. 2020c). Bacterial Blight (BB) of rice is caused by Xanthomonas oryzae pv. oryzae (Xoo), has lethal impact on agro-economy of rice-growing regions around the globe (NiñoLiu et al. 2006). In rice, miR156 was correlated with downregulation of Ideal Plant Architecture1 (IPA1) and OsSPL7 gene expressions which eventually enhanced susceptibility of rice plant to Xoo infections (Liu et al. 2019b). OE of IPA1 under an inducible promoter (that senses Xoo infection) was found to boost BB resistance in transgenic rice significantly (with concomitant improvement in grain yield which we have already quoted in the previous section) (Liu et al. 2019b). In tomato, 22 nucleotide length miRNA family—‘miR482/2118’ is known to negatively regulate NBS-LRR proteins executing plant immune functions (CantoPastor et al. 2019). Unlike canonical 21 nt miRNAs, this miRNA family members induce secondary siRNA generation post-cleavage of their target mRNAs (CantoPastor et al. 2019). Canto-Pastor et al. (2019) have recently inactivated miR482/ 2118 species in transgenic tomato lines using STTM technology. Interestingly, transgenic tomato with disabled miR482/2118 expression (via STTM expression) was found to be significantly resistant to P. infestans and Pseudomonas syringae pv. tomato DC3000 (PtoDC3000) infections (Canto-Pastor et al. 2019). Although authors recorded little NBS-LRR transcript accumulation in transgenic lines, however, elevated resistance towards both the pathogens was prominent in the transgenic tomato plants (Canto-Pastor et al. 2019). A single miRNA manipulation simultaneously conferring resistance to two serious phytopathogens (a fungal and a bacterial pathogen) is a notable achievement in the lines of crop molecular breeding. Importantly, Canto-Pastor et al. (2019) also stated STTM manipulation to have less significant impact on transgenic plant growth and developmental pathway, reinforcing its wide applicability further. Exserohilum turcicum (Pass.) is a fungal agent that causes the lethal Northern leaf blight disease in maize (Zea mays L.) (Bentolila et al. 1991; Wu et al. 2014).Wu et al. (2014) had reported about candidate miRNA species that are expressed in response to E. turcicum infection in maize. OE of two miRNA species, i.e. miR811 and miR829, was shown to significantly reduce E. turcicum pathogenicity responses in transgenic maize (Wu et al. 2014), indicating potential of these miRNA candidates for improved maize agronomic manipulations. Alternaria alternaria f. sp. mali (ALT1) is a pathogenic fungus responsible for leaf blotch and fruit spot disease in apple (Zhang et al. 2017a). Sequencing assays

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have indicated generation of Md-miRNA156ab and Md-miRNA395 candidates in apple when infected by ALT1 (Zhang et al. 2017a). Both these miRNA species represses expression of two WRKY transcription factors, namely MdWRKYN1 and MdWRKY26 which are instrumental in synthesis of immune function proteins in apple to counteract ALT1 (Zhang et al. 2017a). In their study, Zhang et al. (2017a) either overexpressed MdWRKYN1 and MdWRKY26 or inactivated Md-miRNA156ab/Md-miRNA395 (via STTM procedure) to generate transgenic apple plants that manifested significant resistance toward ALT1 infections. Transgenic apple had upregulated expression levels of defence related proteins (Zhang et al. 2017a) suggesting Md-miRNA156ab/Md-miRNA395 downregulation can rationally be utilized to generate ALT1 resistant apple cultivars. Ascomycete fungus Fusarium oxysporum f. sp. lycopersici (Fol) is the pathogenic agent of vascular wilt disease in tomato (Ouyang et al. 2014). A study involving susceptible (Moneymaker) and resistant (Motelle) cultivars of tomato post-infection with Fol showed expression of two candidate miRNA species, viz. slmiR482f and slmiR5300 in both the cultivars to vary distinctly (Ouyang et al. 2014). Authors found slmiR482f and slmiR5300 to be downregulated in the Fol resistant (Motelle) cultivar that accompanied upregulated expression of two miRNA target genes belonging to plant defence protein family (containing NB-related domains). On the other hand, findings in regard to the susceptible cultivar (Moneymaker) were opposite (Ouyang et al. 2014). This study has highlighted slmiR482f and slmiR5300 to be negative regulators of tomato resistance toward the vascular wilt pathogen in susceptible cultivars and thereby forwards a suitable biomarker for screening of Fol resistant tomato cultivars. In barley (Hordeum vulgare L.), destructive powdery mildew disease is caused by the fungal pathogen Blumeria graminis f. sp. hordei (Bgh) (Liu et al. 2014a). Specific barley defence genes carrying different alleles (Mla alleles) coding for NB-LRR class of proteins are induced to counter Bgh infection in the host (Liu et al. 2014a). Interestingly, members of miR9863 family in barley were shown to repress expression of Mla1 alleles of defence genes (thus abolishing NB-LRR expression) and subsequently made the host susceptible to Bgh infection (Liu et al. 2014a). It will be interesting to observe how downregulation of miR9863 or overexpression of Mla1 would impact barley-Bgh interaction although no report with respect to this has appeared yet. A positive outcome relating barley resistance to Bgh with concomitant growth improvements may have been useful from the view of barley crop improvement goal. Artificial miRNAs (amiRNAs) are attractive molecular regulators which have been successfully manipulated in plants for generating desired phenotypes (Alvarez et al. 2006; Warthmann et al. 2008). One of the challenging aspects in food crop breeding is amalgamation of pathogen resistance/tolerance trait in them so that they can withstand varied pathogenic afflictions in natural milieu. AmiRNA technologies have been recruited in several crop plants in attempts to mitigate pathogenic as well as nematode and insect pest challenges. Here we forward few illustrations in this regard.

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Rice stripe virus (RSV; with single stranded RNA genome) and Rice black streaked dwarf virus (RBSDV; harbouring double stranded RNA genome) are lethal pathogenic agents infecting and affecting rice cultivation globally (Sun et al. 2016). Sun et al. (2016), generated transgenic rice plants expressing mature amiRNA candidates specifically targeting CP (Coat Protein) genes of RSV and RBSDV. AmiRNA construct was prepared based on naturally expressing Osa-MIR528 precursor of rice. Transgenic rice expressing amiRNAs were found to manifest significant resistance towards both the virus mediated infections (Sun et al. 2016). Interestingly, amiRNAs together with activated siRNA pathway could synergistically repress viral transcripts leading to amplified viral resistance in transgenic rice (Sun et al. 2016). Wheat dwarf virus (WDV), a single stranded DNA virus belonging to Geminiviridae family causes dwarf disease in barley (Kis et al. 2016). This viral agent is transmitted by leafhoppers to respective hosts (Kis et al. 2016). In barley (Hordeum vulgare), a polycistronic amiRNA construct (based on natural hvuMIR171 precursor structure) containing three customized amiRNA/amiRNA* sequences generating amiRNA precursors which could target WDV Rep transcripts were used to create transgenics (Kis et al. 2016). Transgenic barley plants highly expressing the customized mature amiRNAs were found to be resistant to leafhopper transmitted WDV infections even under low temperature conditions in the experimental settings (Kis et al. 2016). This is an encouraging outcome cropping up from use of a prudent amiRNA technology. Wheat streak mosaic virus (WSMV; member of Potyviridae family) is a persisting threat to global wheat cultivation without an enduring control measure yet (Fahim et al. 2012). Using the backbone of rice miR395 precursor, Fahim et al. (2012) designed a polycistronic amiRNA (FGmiR395) precursor construct containing five amiRNAs targeting conserved regions of WSMV. The polycistronic amiRNA construct was used to generate transgenic wheat expressing the former constitutively (Fahim et al. 2012). Among the successful transformants, some transgenic wheat lines stably expressing FGmiR395 manifested complete immunity to WSMV and the resistance trait could be inherited to successive generation accompanying the transgene (Fahim et al. 2012). Heterodera glycines, also known as soybean cyst nematode (SCN), is a parasitic pest that affects soybean cultivation worldwide quite significantly (Tian et al. 2016). Polycistronic amiRNA construct targeting three important gene transcripts (i.e. J15, J20 and J23) associated with SCN reproductive function was used to generate transgenic soybean plants. Under experimental conditions, amiRNA expressing transgenic soybean plants manifested conspicuous resistance to SCN infections in hairy roots via reduction of SCN densities (Tian et al. 2016). AmiRNA strategy with potential to deter plant nematode parasites thus carries immense potential for crop improvement goals. Cotton bollworm (Helicoverpa armigera) is a major insect pest of tomato including several other crop plants which sternly affects crop yield globally (Yogindran and Rajam 2020). Its effective control measures are still a challenging issue. Ecdysone receptors (EcR) are essential components in insect developmental

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pathways (Riddiford et al. 2000; Thummel 2001, 2002). A customized amiRNA construct targeting EcR transcript of H. armigera (HaEcR) was expressed in transgenic tomato which resulted in defective growth and compromised survivability of the cotton bollworm post-feeding on transgenic tomato leaves (Yogindran and Rajam 2020). AmiRNA mediated downregulation of HaEcR further impeded downstream signalling cascade of ecdysone pathway in H. armigera (Yogindran and Rajam 2020), forwarding a promising alternative control measure for the pest. Naturally persisting recessive state of certain genes in crop plants may contribute to striking agronomic characters which are otherwise inconceivable in their dominant condition. For instance in O. sativa, grain aroma (Chen et al. 2008), semi-dwarf stature (Spielmeyer et al. 2002), resistance to bacterial blight pathogen (Chu et al. 2006), etc. are important agronomic features conferred by recessive state of respective gene loci. In rice, resistance to race specific Xoo (bacterial blight pathogen) is conferred by recessive state of xa13 gene which in its dominant state assists in pollen development (Chu et al. 2006). In order to incorporate bacterial blight resistance in transgenic rice, Li et al. (2012c) used two amiRNA constructs (manipulating natural osa-MIR528 precursor) to silence dominant allele of Xa13 gene. Authors (Li et al. 2012c) strictly expressed the amiRNAs in leaves of transgenic rice and achieved strong resistance to Xoo infection. Due to tissue-restricted expression of amiRNAs, no significant derogatory effect on rice pollen development was discernible (Li et al. 2012c) which is generally observed in naturally occurring xa13 mutants (Chu et al. 2006). This particular accomplishment of amiRNA repression leading to a useful recessive trait inspires extension of this strategy in the molecular breeding programs for other beneficial traits in crops due to recessive gene loci. Chilo suppressalis, also known as striped stem borer (SSB) or rice stem borer (RSB) is an important insect pest that negatively impact rice crop productivity globally (Jiang et al. 2017; He et al. 2019). Manipulating endogenous novel miRNA sequence information from SSB for generating 13 amiRNA candidates and their subsequent expression in transgenic rice, Jiang et al. (2017) have identified two potential amiRNA species (i.e. csu-novel-miR15 and csu-novel-miR53) which could significantly impair growth of SSB larvae post-feeding on stems of transgenic rice plant. Specifically, feeding on expressed csu-novel-miR15 in transgenic rice plant delayed SSB larval pupation by at least 96hrs along with conspicuous alterations in expression profiles of certain genes in the insect (Jiang et al. 2017). Interestingly, these expressed amiRNAs had no significant lethal effect on SSB. However, delay in SSB larval pupation phase is anticipated to slow-down SSB generation cycle and thus might minimize effect of severe infestation on rice plants (Jiang et al. 2017). Recently, He et al. (2019) have identified another endogenous miRNA, i.e. Csu-miR-14 in RSB (that regulates Spook (CsSpo) and Ecdysone receptor (CsEcR) genes of the ecdysone signalling cascade in RSB), for generating transgenic rice lines significantly resistant to C. suppressalis. Taking sequence information of RSB endogenous Csu-miR-14 and rice Osa-miR-528 precursor backbone as basis (Warthmann et al. 2008), authors (He et al. 2019) designed an amiRNA candidate (amiRNA-14) and expressed it in transgenic rice. Interestingly, one of the transgenic rice lines expressing amiRNA-14 manifested pronounced resistance

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characterized by high morbidity and developmental defects in RSB that fed on transgenic plant stems (He et al. 2019). It is evident that unlike csu-novel-miR15 (Jiang et al. 2017), amiRNA-14 (He et al. 2019) showed distinct lethal impact on C. suppressalis. However, simultaneous manipulation of both the amiRNA candidates for generating RSB resistant rice cultivars may have lasting and favourable consequences. Adding to the endeavour, more recently Zheng et al. (2020) have communicated about successful utilization of one more RSB endogenous miRNA (csu-novel-miR260) based amiRNA construct to introduce RSB resistance in transgenic rice. Even under field trials, two transgenic rice lines expressing slightly modified csu-miR260 based amiRNAs (i.e. csu-miR260-16 and csu-miR26018), exhibited distinctive lethality against RSB larval stages and affected significant growth impairment in the pest (Zheng et al. 2020). Importantly, Csu-novel-miR260 based amiRNAs were used to repress a cytochrome P450 enzyme that is essential for ecdysteroid biosynthesis pathway in RSB (He et al. 2017; Zheng et al. 2020). Host Induced Gene Silencing (HIGS) is one of the novel strategies that have recently gained attention for its potential applicability in targeting phytopathogens (Ghag 2017; Qi et al. 2019). Instances of plant miRNA transfer to phytopathogens which in turn represses expression of crucial gene products in the latter and affects virulence/successful colonization of hosts by the pathogens have been uncovered (Jiao and Peng 2018). For instance, wheat miRNA candidate Tae-miR1023 was shown to inhibit Fusarium graminearum (Fg), the fungal agent responsible for lethal head blight or scab disease in wheat, barley, etc. (Jiao and Peng 2018). Reportedly, Tae-miR1023 suppressed expression of a gene (FGSG_03101) encoding alpha/beta hydrolase in Fg, that affected host infectivity of the fungal pathogen conspicuously (Jiao and Peng 2018). The abovementioned instances and several old and new advances which we failed to enumerate here are noteworthy achievements that will surely push crop molecular breeding programs targeted at obtaining pathogen/pest resistant food crops, towards pragmatic end.

6.6

Conclusions

Plant miRNAs are master epigenetic regulators of almost all events ensuing in plant’s life processes. With the advent of advanced and efficient functional genomics tools, miRNA profiles of numerous plant species have been determined and the exploration has presently gone unabated. As such, ever growing disclosures relating numerous regulatory aspects of plant miRNAs have opened windows for innovative plant biology research. Undeniably, extension of miRNA based novel expeditions towards important plant species, e.g. food crops has gained much priority for good. Sustainable cultivation of food crop plants is an indispensable liability before human, for survival needs of mankind direly relies on the former. Food crop cultivations across the globe encounter unprecedented hurdles from both environmental and biotic perturbations. Despite plethora of interventions forwarded so far, a long-lasting measure to eliminate those obstacles has still remained a challenge.

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Interestingly, within two decades of first plant miRNA member’s discovery (Reinhart et al. 2002), relentless efforts in this context have generated affirmative harbinger. This is supported by several enthralling discoveries with potential applications in agronomical practises. Here, in this monogram, we have tried to enumerate some of the important achievements regarding miRNA based expeditions which are definitely not exhaustive. Newer aspects of miRNA based regulatory pathways have continued to appear, which will certainly contribute refined methods of their application in crop improvement programs. Some of the prolific advancements like target mimics, STTMs, artificial miRNAs (amiRNAs), CRISPR/Cas based editing of plant MIRs or their targets, HIGS (Host Induced Gene Silencing), etc. have considerably renovated conventional genetic studies. Nevertheless, numerous challenges are yet to be resolved. Balancing growth and stress management in food crops without hampering essential genetic functions has remained a big challenge before molecular breeding (Huot et al. 2014). In roads to address those facets, contributions from evolving knowledge of epigenetic post-transcriptional regulators like miRNA (as well as siRNAs) are going to be crucial. In days to come, we hope much newer applications of miRNA principles will intensify the research arena largely aiming at generating sustainable food crop cultivars which will be able to offer and secure food demand of the ever growing global population uninterruptedly. Acknowledgements Barman sincerely acknowledges Early career Project with (Ref: NECBH/ 2019-20/127) under North East Centre for Biological Sciences and Healthcare Engineering (NECBH) Twinning Outreach Programme hosted by Indian Institute of Technology Guwahati (IITG), Guwahati, Assam funded by Department of Biotechnology (DBT), Ministry of Science and Technology, Govt. of India with number BT/COE/34/SP28408/2018 for providing necessary financial support. Phukan humbly acknowledges financial support received from the DBT-RA Program in Biotechnology and Life Sciences sponsored by DBT, Govt. of India. Ray acknowledges the students and research scholars of MPMI laboratory (MBBT Department, Tezpur University, Assam, India), with whom he discusses plant–microbe interaction studies.

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Potential of Metabolomics in Plant Abiotic Stress Management Nitesh Singh, Aadil Mansoori, Debashish Dey, Rakesh Kumar, and Anirudh Kumar

Abstract

Metabolomics reveals the metabolite profile of biological systems, its various chemical compositions, its physiology, and complexity at a point in time. Metabolites are the main working horses to drive for any phenotype as compared to their corresponding DNA or RNA component by itself alone. This omics tool integrated with mass spectrometric analysis can be applied to study the molecular responses in plants against biotic and abiotic stresses. Changes in the flux of both primary and secondary metabolites are observed against several stress conditions. The tools of metabolomics help the scientists to understand the core-metabolite profile conferring stress-resistance in plants and to develop core-metabolite profile in any crop species for resilience against climate change and various biotic and abiotic stresses. Several key metabolites may play roles against multiple stress-resistance mechanisms in plants. Integration of multi-omics and metaomics studies may reveal a globalized overview of biochemical, physiological, and molecular processes of stress response in plants. It will help in the studies for the improvement of quality and yield as well as development of stress-resistance in commercially important crops.

N. Singh · A. Mansoori · A. Kumar (*) Department of Botany, Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, India e-mail: [email protected] D. Dey School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India R. Kumar Department of Life Science, Central University of Karnataka, Kalaburagi, Karnataka, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Kumar et al. (eds.), Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), https://doi.org/10.1007/978-981-16-2956-3_7

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Keywords

Metabolomics · Abiotic stress · Mass spectrometry · Metabolite profiling · Crop improvement · Agriculture

Abbreviations ABA APX CAT DHAR MS NO t-ZOG UPLC

7.1

Abscisic acid Ascorbate peroxidase Catalase Dehydro-ascorbate reductase Mass spectrometry Nitric oxide Trans-zeatin-O-glucoside Ultra-performance liquid chromatography

Introduction

“Omics” is broadly classified into five different groups: genomics, transcriptomics, proteomics, metabolomics, and phenomics. Till date, the genomics, transcriptomics, and proteomics have become principal technologies in the field of research. However, the aspect of metabolomics in “Omics” is still inadequate, particularly in the field of crop sciences that includes trait identification and its utilization through metabolomics. In the past few decades, the metabolomics has emerged as one of the major disciplines for metabolite profiling in plants and animals. The metabolomics provides a complete image of cellular metabolites and represents the entire phenotype of a cell by studying the minute organic molecules involved in diverse cellular and molecular processes. The ability of metabolomics to recognize several metabolites from a given extract has immensely increased the speed of analysis of extensive metabolites. Indeed, the fast growth of high-throughput metabolomics technology has led to the study of mutants and transgenic lines intensely. This offers an opportunity to discover and determine the candidate genes and their metabolic networks. The metabolomics approach provides information on gene function: how genes affect the metabolic pathways, and the levels of regulation and interception between multiple pathways, which solely could not be achieved by genomics approaches like microarray. Moreover, the comprehensive functional genomics approach: integration of metabolomics with genomics, transcriptomics, and proteomics allows researchers for cataloguing genes and identification of specific traits in crop species. Besides tomato, metabolomics has been comprehensively used in many transgenic and non-transgenic crop species to establish functions of genes (Oikawa et al.

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2008; Kumar et al. 2017; Sharma et al. 2018, 2021). The aspects of metabolomics have immense ability for the selection of better traits and metabolomics-assisted breeding. The availability of whole genome sequences, huge mapping markers, and cost-effective strategies in metabolomics provides a huge opportunity to integrate metabolomics as an indispensable component in crop breeding programs (Lin et al. 2014). With the prevailing demands in the last decades, the involvement of mass spectrometry (MS) tools in the metabolomics studies like nuclear magnetic resonance (NMR) and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) has gone high. These instruments are often used for metabolomics research in direct infusion (DI) mode as their greater mass precision ensures proper separation (Allwood et al. 2011). Such techniques can be used to classify metabolic components accurately (Shulaev et al. 2008; Meng et al. 2011; Barding Jr et al. 2013). These metabolic platforms generate huge sets of metabolite profiles that consist of both known and unknown metabolites; therefore, annotation of metabolites is a major challenge in the metabolomic studies (Lei et al. 2011; Matsuda et al. 2011). However, the recent advancements in bioinformatics tools and the developing metabolomics databases together have great potential that can make annotation of metabolites an easier task (Afendi et al. 2012). Metabolomics is gaining momentum in different sectors of plant sciences including basic and applied scenario. Previous studies have proven that the variation in chemical compositions provides detailed information about the physiological and biochemical changes occurring in plants and their effect on phenotypes. In this chapter, we briefly describe the major techniques employed to investigate plant metabolites and the application of metabolomics in crop breeding program.

7.2

Analytical Techniques for Metabolomics platforms

Metabolomic studies involve utilization of two important techniques NMR and MS together with a statistical tool like principal component analysis (PCA). This chapter provides brief knowledge about different analytical techniques (NMR and MS based) being used for the metabolomic analysis of biological samples.

7.2.1

NMR

NMR detects metabolites using the magnetic properties of the nuclei of an atom in the magnetic field. It is an analytical method which is non-destructive in nature used for metabolic fingerprinting along with metabolic profiling and flux determination (Zhang et al. 2012). It is largely used to detect low molecular weight metabolites and its structural information in the given samples (Zhang et al. 2010). NMR can be used to find the structural transformation of a metabolite that occurs during metabolism, which can help to acquire a deeper understanding of several biological processes (Winning et al. 2009). Even though it has many advantages, the poor sensitivity of NMR restricts its uses over MS to investigate important biological samples to

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identify those biomarkers or compounds whose concentrations are below detectable limits.

7.2.2

MS

The high sensitivity and wide range coverage of metabolites have allowed MS to gain preference over NMR in the metabolomics studies. Recent advancements in mass accuracy and its ability to analyze extremely complex molecules dramatically improved the range of metabolites that can be identified in any biological sample. The direct injection of biological samples to the MS is a fast and rapid technique; it is used to analyze huge numbers of metabolites during metabolite fingerprinting. In the last two decades, MS has been established as a key technique for metabolomics studies that can profile plant metabolites against inhibitors, environmental stresses, and different nutritional status (Zhang et al. 2012). The success of MS allowed researchers to use this technique to investigate the metabolic biomarkers and discover molecules that can enable metabolic pathways renovation and networks designing. Unlike NMR, MS allows a researcher to achieve high coverage (with accuracy) of metabolome data, which consists of huge numbers of metabolites that are present in various biological samples. For high-throughput metabolomics studies, MS is often integrated with various techniques like Fourier transform ion cyclotron resonance (FT-ICR), gas chromatography (GC), field asymmetric waveform ion mobility spectrometry (FAIMS), liquid chromatography (LC), and capillary electrophoresis (CE). However, two most common standard mass spectrometry techniques that are used for the metabolomics studies are LC-MS and GC-MS. The MS platforms like GC-MS and LC-MS have become much accurate and robust with the developments in the MS instrumentation such as APCI, ESI, MALDI-TOF, and scanning rate (Issaq et al. 2009).

7.2.2.1 GC-MS The GC-MS based metabolomics can be used for both targeted and non-targeted identification of volatile components as well as both hydrophilic and lipophilic compounds (Sun et al. 2017; Tsugawa et al. 2011; Kamthan et al. 2013). With GC-MS, it is possible to separate and quantify a variety of compounds such as alkanes, amino acids, alcohols, aromatic amines, fatty acids, organic acids, polyphenols, and sugars. For GC-MS, derivatization of samples is required to make compounds volatile; however, this limits its applicability because the underivatized compounds get ignored during the study. Implementation of GC X GC-TOF-MS has remarkably increased the scan time and efficient fractionation of co-eluting peaks (deconvoluted peaks) that also enabled better sample throughput (Kalinová et al. 2006; Ralston-Hooper et al. 2008). 7.2.2.2 LC-MS LC-MS based metabolomics is powerful and has gained attention for marker-based studies. It is also used for targeted and non-targeted detection of primary and

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secondary metabolites using mostly ESI and APCI (Shuhai et al. 2011; Saika et al. 2012; Heuberger et al. 2014). The main advantage of LC-MS over GC-MS is that it offers identification of broader range of compounds; does not require extensive sample preparations and provides list of m/z values and the relative abundance of metabolites. Additionally, the combination of UPLC with QTOF-MS has augmented the resolution of peak drastically, speedy identification of hundreds of metabolites within a short period time with greater mass accuracy. However, compared to GC-MS, LC-MS suffers ion suppression (reduced ionization) due to co-eluting peak; hence, this approach requires efficient ionization strategy.

7.2.2.3 CE-MS It is used in both targeted and non-targeted metabolomics wherein it separates various groups of analytes like polar, neutral, charged, etc., with better resolution (Ramautar et al. 2013; Ramautar and de Jong 2014). The foremost advantages of CE-MS over LC-MS are fast and high-resolution chromatography and consumption of small volume of solvent with negligible amount of organic solvent, which minimizes solvent wastage. In the recent years of metabolomics research, the use of CE-MS has been increased and more than 150 metabolites have been identified (Timischl et al. 2008; Ramautar and de Jong 2014). 7.2.2.4 FT-ICR-MS FT-ICR-MS lacks chromatography and is solely dependent on high-resolution mass analysis, which allows discovery of abundances of metabolites (Aharoni et al. 2002). The identification of accurate mass of metabolites using FT-ICR-MS allows researchers to identify transformants formed during biological reaction such as oxidation/reduction or conjugation of secondary moieties during acetylation, glucuronidation, and sulfation that helps to understand and reconstruct the metabolic pathways (Brown et al. 2005). However, the application of FT-ICR-MS is limited because it lacks chromatographic separation. 7.2.2.5 Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS-MS) It is an ion based electrophoretic technique which is attached with MS. It is mostly used to identify volatile compounds generated during the bacterial growth (Zrodnikov and Davis 2012).

7.3

Plant-Based Metabolic Databases: Reference Database for Metabolism

Plants are indispensable components of the ecosystem. The crop plants are important to achieve the hunger demands of growing human population. In comparison to human and microbes, the efforts applied to uncover the plant metabolic pathways for crop improvement are insufficient. In fact, compared to human or microbes, the plant-based metabolic databases are less diverse and fewer in number. However, in

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the recent years, after the human genome sequence got completed, the research community geared up to retrieve the genome sequence of many plants including crop species. The combined information gathered from “omics” provided vital information which has propounded the plant metabolic pathways and gene discovery. An overview of omics approaches to identify candidate genes is demonstrated in Fig. 7.1. Till date, exclusively, several plant-based databases are available which provide an online portal to explore metabolic pathways and allow researchers to download metabolic data for different plant species. The metabolic server MetaCyc consists of wide range (2400) of experimentally verified biochemical pathways elucidated from 2600 different organisms (Zhang et al. 2005; Caspi et al. 2015). It is the largest curated metabolic pathway collection derived from more than 46,000 publications. The MetaCyc was used as a reference database to develop or predict new pathways in plant and other organisms. For example, plant specific database AraCyc was the first computationally derived platform by using MetaCyc as a reference database. Metabolomics databases and services are useful for the identification of plant-based metabolites (Sharma et al. 2021). Among plants, the model plant Arabidopsis is rich in available information resources. The Arabidopsis Information Resource (TAIR) platform provides comprehensive information on Arabidopsis genome, proteome, and metabolome, which can be retrieved from the web servers (Huala et al. 2001; Lamesch et al. 2012). The first plant metabolic pathway database, AraCyc allows the researchers to browse the information about compounds, pathways, enzymes, etc., from Arabidopsis and other species through the Plant Metabolic Network (PMN) (Dreher 2014). Originally the AraCyc database was developed by TAIR, but now it is maintained by PMN. PMN represents a meta-database consisting of a core database (PlantCyc) and the other species-specific databases like AraCyc, BarleyCyc, CassavaCyc, CornCyc, PapayaCyc, etc. Altogether, PMN provides a platform for 22 plant species-specific databases that provide access to manually curated and computationally foretold facts about enzymes and pathways. The PlantCyc provides metabolic references for 1214 pathways, 152,416 enzymes, 6200 reactions, 5138 compounds, and represents 300 plant species (http://www.plantcyc.org/). On the other hand, the species-specific portal exclusively hosts the metabolic pathways and provides computational prediction of enzymes in many crop species or their ancestral wild relatives. Recently, Arabidopsis metabolite profiling database for knock-out mutants (MeKO) was buildup, which allows studying the impact of mutations on plant growth and metabolism (http://prime.psc.riken.jp/meko/). The databases contain huge datasets derived from the GC-MS based metabolomic study of 50 Arabidopsis mutants, and provides a free downloadable platform for non-processed data chromatograms, mass spectra, and experimental metadata (Fukushima et al. 2014). The MeKO server is useful for hypothesizing gene function and upgrading gene annotation. The Sol Genomics Network (SGN) is a web portal exclusively for the Solanaceae family and for the close relatives. SGN provides genomics, transcriptomics, proteomics and phenotypic data, and analytic tools to access the “omics” of Solanaceae

Fig. 7.1 Schematic presentation of different steps of metabolomics from sample preparations to interpretation (adopted from Kumar et al. 2017)

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members (Fernandez-Pozo et al. 2015). SGN database also provides an interface for SolCyc, a collection of metabolic information on Solanaceae species (http://solcyc. solgenomics.net/). SolCyc currently provides metabolic data for six plant species: tomato, pepper, petunia, potato, and tobacco (including Nicotiana benthamiana), one species from the Rubiaceae family (coffee) and one species of animal kingdom (C. elegans). The portal is convenient and dedicated to search compounds, enzymes, reactions, pathways, etc., and is useful for the comparative analysis of omics data.

7.4

Metabolic Responses of Plants to Stress

Plant growth is influenced by climatic and environmental conditions such as global warming, extreme heat waves, etc., which poses serious impacts on plant growth and development. Extreme summer temperature variations have significant impacts on global agricultural productivity and food security. Metabolism is profoundly participated in signaling, regulating physiology, and responding to defense (Sharma et al. 2021). Activation of early metabolic process in a proper way leads to reinstate stress-imposed chemicals and imbalance energy which is very decisive for the acclimatization and existence (Kumar et al. 2021). In stress metabolic response comes faster when compared to transcriptional responses in time bound experiments. The qualitative and quantitative study of metabolites has enriched the understanding about dynamics of adaption and tolerance under diverse kinds of stresses. Plant accumulates compatible solutes such as osmoprotectants in stress condition as a part of defense mechanism which includes amines, amino acids, and Gamma aminobutyric acid (GABA). In addition, carbohydrates like trehalose, polyols like myo-inositol, D-pinitol and antioxidant such as glutathione accumulate in response to osmotic pressure (Krasensky and Jonak 2012; Banu et al. 2010; El-Shabrawi et al. 2010; and Phang et al. 2008).The aggregation of compatible solutes in other organisms like animals, microbes, and algae along with plants in response to stress indicates the evolutionary consequences. Compatible osmoprotectants play very important role starting from balancing ROS to redox metabolism, stabilizing cellular structure by protecting proteins, and restoring osmotic equilibrium during stress conditions (Empadinhas and da Costa 2008; Rathinasabapathi 2000; Yancey 2005). In the mid-1960s, it was demonstrated in Bermuda grass that a correlation exists between stress tolerance and amino acid accumulation, especially proline during water stress. Henceforth, it has been proven that proline serves as osmoprotectant, cryoprotectant, and ROS scavenger in response to stresses. Once stress got over, plants need a lot of energy to restart the development and reproduction and in such case, energy is provided by oxidation of proline (Barnett and Naylor 1966; Verslues and Bray 2006; Verbruggen and Hermans 2008; Mattioli et al. 2009). Plants maintain an efficient subcellular compartmentation, a broad metabolizing range, and a fast developmental period (Allwood et al. 2011). Plants have been documented to flexibly modulate stoichiometry and metabolism to maintain optimum fitness in processing defense and reproduction mechanisms under different temperature and water availability conditions (Rivas-Ubach et al. 2012).

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The implicit goal in metabolomics is to categorize and enumerate all probable metabolites during any kind of stress in a cellular system over a specific duration to accurately characterize the metabolic outline (Jones and Scott 1983). But there are some methodological drawbacks in metabolome research. It is important to remember that only a piece of information can be accurately detected and quantified overall of metabolites in a given sample. A complementary metabolite profiling approach based on both NMR and MS can be used to increase the breadth of detection of primary and secondary metabolites in any given sample (Abd Ghafar et al. 2020).

7.5

Role of Secondary Metabolites in Abiotic Stress

Plants often experience episodes of abiotic pressures; for instance, drought, high irradiation, salinity, submergence, and shortages of nutrients in today’s climate change scenarios. Such stresses restrict the overall production of crops. Breakthrough in physiology, genetics, genomics, and molecular biology in recent years has greatly enhanced our understanding of the response of crops to these stresses and the basis of tolerance differences. In recent decades renewed interest was shown on the study of abiotic factors particularly related to secondary metabolism during plant growth. When plants detect stress at the cellular level, a stress reaction is caused. In case of dryness, plants may have a tendency to reduce the leaf surface area in order to manage water loss through respiration. Plants also boost leaf abscission and expand the network of its root system. When plants are exposed to oxidative stress, several secondary metabolites are synthesized and concentration of many other enzymes like ascorbate peroxidase, glutathione reductase (GR), superoxide dismutase (SOD), catalase (CAT) got elevated.

7.5.1

Drought Stress

Drought is considered as one of the most important abiotic stresses that cause adverse effect on the growth of plants (Xu et al. 2010). Physiological machinery of plants directly get affected due to dryness condition which changes and interfere the morphology, anatomy, and biochemical activity including sugar content, protein content as well as catalytic activity. Drought is also reported to induce oxidative stress and increase the levels of flavonoids and phenolic acids in willow leaves (Larson 1988). Free radicals generated by drought pressure cause oxidative damage of lipid and plant membrane which subsequently create imbalance between antioxidants and oxidants, resulting in ROS mediated oxidative stress (Islam et al. 2011). In addition to drought tolerant morphological structures, plants also have developed a numerous biochemical and physiological mechanisms to alleviate drought stress (Wang et al. 2009). Recent work has shown that ABA (Abscisic acid) intermediated stomatal closure and persuaded RNS synthesis such as nitric oxide (NO) responsible for calcium ions induction and pH along with succeeding initiation of guard cells S-type anion channels for shutting the stomata (Sun et al.

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2019; Shunya and Nobuyuki 2019). ABA mediated ROS accumulation, induced stomatal closure by activating calcium ions channels in the plasma membrane (Orozco-Cárdenas et al. 2001). This research shows the complex mechanism of ROS in drought stress condition. Enlightening the content of certain active molecules in plants is the utmost significant pharmaceutical requirement and possibly will be improved in certain vegetables by various environmental stresses (Cisneros-Zevallos 2003). Many stress-inducible enzymes (such as Phenylalanine ammonium lyase (PAL) and Glutathione-S-transferase (GSTs)) perform an imperative role in plant immunity during adverse stress effects (Marrs 1996). PAL, with 4-hydroxylase cinnamates, is a group of enzymes that extract and allocate substantial amounts of carbon from phenylalanine to several secondary metabolites for biosynthesis of active compounds (Singh et al. 2009). Drought stress exposing plants are noted to produce more secondary metabolites such as terpenes, phenols as well as substances like cyanogenic glucosides, gluco-sinolates (sulfur containing) or alkaloids (nitrogen containing) (Khan et al. 2011).

7.5.2

Salinity Stress

Salination (high pH) is well known to be a key environmental threat to farming systems which is responsible for about 20% of arable land and 50% of irrigated land globally (Munns and Tester 2008; FAO 2009). For salination, K+, Mg2, Na+, Ca2+, Cl , NO3 , HCO3 , CO32 , and SO42 ions are found primarily in natural salty soils among others (Cui et al. 2019). Plants in alkaline soils have to deal with physiological dryness and ions toxicity, maintain the balance of intracellular ions, and control the pH outside the roots. Plant responses to salinity stress can include change in pathways such as transportation of ions, photosynthesis, osmotic solutes accumulation, and synthesis of hormones. (Muchate et al. 2016) Metabolic solutes including betaine, proline, polyhydric alcohol, and polyamines contribute to the resistance to salt pressure. Metabolic components may be involved in plants towards salinity tolerance; however, there is limited information on the metabolic components associated with alkali tolerance. Besides the upper parts of the plant, alkaline stress also affects physiology and eventually nutrient uptake in roots due to their distorted growth. The result of salinity stress can lead to cellular membranes being disorganized, photosynthesis inhibition, toxic metabolites accumulation, and declined nutrient absorption, which ultimately propelling plants towards necrosis (Mahajan and Tuteja 2005). In order to survive under salt pressure, plants exhibit certain morphology, anatomy, and physiology or biochemical based pliable features that allow them to maintain and survive under saline stress conditions. Physiological aggregation of some compatible solutes such as sucrose, glycerol, trehalose, proline, pinitol as well as quaternary ammonium compounds (glycine, betaine) is a popular process in plants (Ashraf and Harris 2004). Such metabolites typically shield plants from stress grievance by various course of action, including the inside defense mechanism of cytoplasm and

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chloroplasts from salt-induced impairment and as a ROS scavenger, protein stabilizer, and general physiological stability of plants under stress (Galinski and Trüper 1994; Ashraf and Harris 2004). Plants under salt stress can reduce the CO2 accessibility and obstruct carbon fixation, by divulging chloroplasts to unwarranted excitation energy, which ultimately could increase ROS production (Gill and Tuteja 2010). According to previous reports, ROS is overproduced upon facing salinity stress, which eventually causes membrane impairment, is a major cause of cell toxicity (Mittova et al. 2004; Hossain et al. 2011; Hasanuzzaman et al. 2011a, 2011b). Under salinity, plants use an antioxidant mediated (enzymatic and non-enzymatic) scavenging mechanism to alleviate ROS production (Demiral and Türkan 2005) and their activity is highly correlated with the tolerance of antioxidants and salt stress. The behaviors, however, differ with plant cultivar, period of pressure, and dosage. Several plant studies recorded the ROS production and increased action of several antioxidants against ROS response in salt stress, suggesting the potent action of antioxidants of salt-tolerant genotypes, while it observed vice-versa in saltsensitive species (Hasanuzzaman et al. 2011a, 2011b; Hossain et al. 2011).

7.5.3

High and Low Temperature

Heat stress caused by temperatures that are quite enough to harm plant tissues shows a substantial impact on development and metabolism (Balla et al. 2009). Although there is temperature variability for different species of plants such as temperatures between 35  C and 45  C are sufficient to develop heat stress in plants (Hall 1992). On the other hand, melting stress resulting from low temperatures is sufficient to cause cold impairment in plant tissues without ice formation, while ice is formed in plant tissues upon encountering freezing stress. Together, both freezing and melting pressures come under cold pressure. Very high temperatures can lead to cell damage and death responsible for catastrophic collapse of the cell infrastructure and their association (Schöffl et al. 1999). At moderate temperatures, cell damage and death can happen only after prolonged exposure. Ultimately, these damages result in malnutrition, impaired growth, aborted ion flux, and accumulation of lethal compounds along with ROS (Howarth 2005). The foremost symptoms of these injuries are scorching twigs and leaves, bronze sunburn on the skin, senescence, and abscission of leaves (Ismail and Hall 1999). High temperatures delay germination and cause energy loss as well as reduced emerging plants (Egli et al. 2005). High temperature causes lowering shoot dry weight, net assimilation, relative growth as well as discoloration and fruit damage which ultimately hamper the production (Wahid et al. 2007). There are two ways by which low temperature may expose stress in plants: (1) by the effects of low temperature individual, and (2) due to cells and their tissues dehydration when cellular water freezes. Low temperatures have a more serious effect on seedlings than on mature seedlings with noticeable plant symptoms including surface lesions, water soaking appearance, drying, coloring, breaking of

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tissues, accelerated sensation, and rapid decline due to plant metabolite leakage (Solanke and Sharma 2008). Chilling pressure also impacts plant root growth such as restricting the root potential and consequently the overall plant growth (Farooq et al. 2009). Low temperatures reduce the production of dry matter in crops (Verheul et al. 1996). According to number of research project, plants face oxidative damage due to high temperatures (Yin et al. 2008). Several scientists have recorded that high temperature pressure dramatically increases lipid peroxidation (Wu et al. 2008). With higher temperatures, gas solubility increases which results in higher oxygen levels and therefore enhances the risk of a high-temperature mediated oxidative damage, which results in increased ROS generation (Guo et al. 2006). Before declining at 50  C, CAT, APX, and SOD initially increase, whereas POX and GR activities are noticed to decline between 20  C and 50  C. Therefore, all-over antioxidant activity was optimum at 35–40  C and 30  C in resistant and susceptible varieties, respectively. A rise in temperature results in substantial upsurge in antioxidant enzymes till a particular temperature after which they start reduction. Tolerant varieties can withstand higher-temperature activity than susceptible (Chakraborty and Pradhan 2011). Comparative experimental study of species has shown that plants with low temperature sensitivity have more activity in antioxidant enzymes compared to sensitive ones as Huang and Guo (2005) reported the effectiveness of antioxidant enzymes in chilling-tolerant rice cultivars is higher equated to chillingsusceptible cultivars.

7.5.4

Waterlogging Stress

There are many direct (inappropriate irrigation methods) and indirect methods which lead to waterlogging stress. Direct method includes inappropriate irrigation, whereas indirect method includes results of anthropogenic activities like global warming. Waterlogging stress brings several changes in plants starting from plant metabolism to architecture, physiological changes to ecogeographical spreading as per the plants innate responses. There is decreased gaseous exchange between the upper atmosphere and soil during waterlogging conditions. With the decrease in diffusion of gas in water, the levels of oxygen in soil decline rapidly which in turn make soil anoxic or hypoxic not beyond few hours (Malik et al. 2002). ROS are formed during in between when either a plant or its any parts are hypoxic/anoxic or return to aerobic environments under normoxic conditions (Irfan et al. 2010). Water logging stress in wheat during the generative stage of development can be improved effectively. The higher activity of ROS scavenging related enzymes like SOD, CAT, and APX may damage the flag leaf (Li et al. 2011).

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Heavy Metals (HMs) stress

Naturally, the two-core source of HMs is original parent material and atmosphere. The quantity of HM in soils mainly relies on rock weathering and metallic discharges in the atmosphere. The production and accumulation of these substances in habitats were caused by volcanic eruptions and dusts, the anthropogenic activities such as mining, metallurgical processes, agrochemical goods, waste disposal, energy houses, fossil fuel combustion, etc. (Angelone and Bini 1992). HMs are also causing a number of adverse effects on plants and their parts such as chlorosis, leaf rolling, necrosis, root growth inhibition, arrested growth, different stomatal responses, reduce water holding potential, alterations in membrane features, photosynthesis inhibition, alteration in metabolism, and several key enzymes’ activities and much more (Sharma and Dubey 2007). The germination of seeds also faces serious consequences from HMs (Ahsan et al. 2007). Usually, higher level of HM reduces the photosynthesis, breathing rate, carbohydrate metabolism (Heckathorn et al. 2004; Llamas et al. 2000; Vinit-Dunand et al. 2002). In response to HM pressure, the plants are physiologically adapted to generate various sorts of organic solutes including small compounds such as proline and betaine; which help them to defend from stress by maintaining membrane integrity and the stabilization of the enzymes through cellular adjustment (Hossain et al. 2010). The plasma membrane-localized HM-dependent NADPH oxidase activation is also responsible for ROS production (Nordzieke and Medraño-Fernandez 2018). There are some redox-active HMs such as Fe, Cr, Cu, V, and Co which require redox reactions in the cell. However, some HMs like Zn2+ and Cd2+ are physiologically non-redox-active HM. Cd is the most extensively studied in plants among HMs. Excessive ROS development in cells resulted in oxidative stress caused by Cd. The major damage associated are membrane peroxidation, protein oxidation, inhibition of enzymes activity, and degradation of nucleic acids (Gill and Tuteja 2010; Hossain et al. 2010; Gill et al. 2011). Plants are using both non-enzymatic and enzymatic antioxidant defense system for restoring the inhibitory effects of ROS (Gill and Tuteja 2010; Hossain et al. 2010; Gill et al. 2011).

7.5.6

UV Radiation Stress

In recent decades, the stratospheric layer has been decreased due to the production and emissions of anthropogenic halogen-containing compounds. Extended UV-B radiation exposure is particularly damaging for all photosynthetic organisms (Sinha et al. 2003). UV-B radiation is detrimental and leads to reduction in photosynthesis, decrease in protein synthesis and biomass, chloroplast impairment, and DNA damage (He et al. 2003). Exposure to UV-B radiation leads to retards the plant height, leaf surface area and upsurges the thickness of leaf (Ren et al. 2007). An increased thickness of the leaves suggests that UV-B radiation could penetrate deeper mesophyll cells layers of leaves (Bornman et al. 1991). Uncontrolled development of ROS in UV-B-induced plant cells also adversely affects the enzymatic activity and

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genetic expression, contributing to cellular and Programmed Cell Death (PCD) mediated injuries (Mackerness et al. 2001). In response to UV-B stress, plants comprehend an intricate biochemical protection system (Liang et al. 2006). So far, the available UV-B radiation impact and antioxidant reactions show significant variances between different plant tissues among several plant species (Rao et al. 1996; Mackerness et al. 2001). Both non-enzymatic and enzymatic antioxidants develop immune responses against oxidants.

7.5.7

Ozone Stress

Plants’ reaction to ozone exposure comprises many physiological and biochemical changes that arise directly from selective increase or decrease in gene expression and the consequent change in the build-up of the related protein products. Ozoneinduced protein groups contain antioxidants and a variety of stress-related proteins linked to other abiotic stresses. Ozone is a water polluting agent which is the most harmful to plants (Ashmore 2005). The consequences of ozone exposure include significant increases in protein carbonylation, decreased lipid peroxidation, and cell permeability changes (Gillespie et al. 2011). Increasing endogenous antioxidants such as ascorbic acid or flavonoids can limit ozone-related deleterious effects (Vickers et al. 2009). ROS generation, metabolism, and detoxification are vital processes for plant growth, adaptation, and survival. Generation of ROS and its scavenging are critical components of plant defense and control mechanisms and the overexpression of new isoforms of these genes improves the resistance to environmental stresses.

7.6

Metabolomic Studies in Plant Science

The major goals of plant enhancement through biotechnology include: (1) high yield potential; (2) early maturation; (3) resistance to break, (4) resistance to hostile environments; (5) resistance to diseases; (6) resistance to insects; (7) improved quality of grain; and (8) improved nutritional components (Newton et al. 2011). Metabolomics can help in understanding the chemical machinery operating in plants that respond to various stresses as well as increasing plant resistance and thereby minimizing crop yield loss during stress conditions. Over the past few decades, researchers tried to manipulate the biosynthetic pathways to enrich target metabolites that can significantly enhance the agronomical traits. Arabidopsis has long served as a model plant to gain knowledge in “omics.” The overall idea of omics technology has been summarized in Fig. 7.2. The integration of genomics and metabolomics has enabled identification of genes and the metabolic networks present in Arabidopsis. Till date, around 20% of genes from Arabidopsis have been characterized and their functions are well known. However, the function of the rest 77% of the genes from Arabidopsis was predicted by computational approaches including homology based searches (Lamesch et al.

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Fig. 7.2 Schematic representation of integrated omics approach used for plant improvement (adopted from Kumar et al. 2017)

2012). Recently in Arabidopsis, the POLARIS (PLS) gene was demonstrated to be involved in the cross-talk among auxin, ethylene, and cytokinin (Casson et al. 2002; Liu et al. 2010). The PLS encodes for the short peptide that is mandatory for normal root growth and vasculature (Casson et al. 2002). The targeted hormone analysis of pls mutant revealed that PLS regulates the auxin content of the root and downregulates the cytokinin levels (Liu et al. 2010). Hormones play important role in the shoot apical meristems (SAM) development and differentiation. Improved growth of SAM and the ectopic meristem have been observed due to overexpression of Brassica STM gene in Arabidopsis. It resulted in lobed leaves and augmented reproductive organs such as flowers and siliques (Elhiti and Stasolla 2012). These altered phenotypes in the Arabidopsis STM overexpression line were related to reduced levels abscisic acid (ABA) and cytokinin’s, isopentenyladenosine (iPA), etc. The hybrids of Arabidopsis (C24/Col) is an evident example of role of phytohormones in the tissue and organ differentiation where higher levels of IAA enhance the numbers of leave cells while reduced levels of salicylic acid (SA) have been seen to promote photosynthetic cells size (Groszmann et al. 2015). However, the increased level of SA conferred enhanced resistance in the parents or the hybrids of Col/Sei due to elevated defense responses (Groszmann et al. 2015). The GC-MS based metabolite profiling of the root exudates of Arabidopsis coumarin biosynthesis mutant revealed the importance of coumarins in Fe uptake from soil (Schmidt et al. 2014). The mutant grown in the Fe-limited and alkaline (high pH decreases Fe

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uptake) conditions revealed the importance of coumarins in the Fe solubilization that is needed for its uptake (Schmidt et al. 2014).

7.7

Conclusions and Future Perspectives

Metabolomics is an emerging field for the characterization of complete metabolites present in a given biological sample. It has immense potential for its application in various fields such as biology, agriculture, food industry, health and medicine, forensics, etc. With the evolution of new detection tools and newly developed software and databases, it has been possible to unravel and characterize novel metabolites in a given sample which was not possible earlier. The tools of metabolomics have been crucial in revealing the large diversity of metabolites that gets accumulated in plants against aforesaid abiotic stresses. The change in the metabolic profiles in different stress conditions indicates the corresponding changes at the molecular, physiological, biochemical levels due to various adaptive mechanisms. In most of the cases, there is variation in the levels of primary metabolites observed in most of the abiotic stress conditions. However, the variations in secondary metabolites level differ according to the nature of abiotic stress in a given species. An integrated-omics approach including genomics, transcriptomics, and proteomics in combination with metabolomics will provide deeper insights on the molecular changes happening at respective levels. These approaches will help us to select superior crop species based on the storage of key metabolites which are indicators of abiotic stress tolerance. Through multi-omics approaches we can develop climate resilient crops with higher yields to meet the growing global hunger demands and to guarantee food security.

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Integrating Pan-Omics Data in a Systems Approach for Crop Improvement: Opportunities and Challenges Donald James, P. R. Rennya, Mani Deepika Mallavarapu, Ram Chandra Panigrahi, and Hitendra Kumar Patel

Abstract

Ensuring food security for the growing world population amidst the vagaries of nature is an unrelenting challenge for plant scientists. The utilization of modern tools such as genome wide association studies (GWAS), CRISPR based genome editing (GenEd), and genomic selection (GS) augmented by multi-scale “panomics” data will be vital in achieving rapid crop improvement necessary to ensure improved productivity even under adverse biotic and abiotic stresses. Constant advances in the technology driving high-throughput “omics,” accompanied by reducing costs, have led to the rapid generation of “omics” data of immense proportions. The collation and utilization of this vast data for crop improvement would require an interdisciplinary approach utilizing bioinformatics, statistics, “Big-data” science, and systems biology. Integration of the data from the various “omics-spaces,” and correspondingly with other non-omics “Big-data,” is often the limiting step in realizing this goal. In this chapter we attempt to classify the various approaches in data integration and discuss the opportunities and challenges involved in data integration from “pan-omics” studies. We also highlight the potential of “pan-omics” data integration in driving next-generation precision breeding. Keywords

Pan-omics · Systems biology · Data integration approaches · Crop improvement

D. James · P. R. Rennya · M. D. Mallavarapu · R. C. Panigrahi · H. K. Patel (*) Plant Microbe-Interactions Lab, Centre for Cellular and Molecular Biology, Habsiguda, Hyderabad, Telangana, India e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Kumar et al. (eds.), Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), https://doi.org/10.1007/978-981-16-2956-3_8

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Introduction

The challenge before plant scientists of this era is to achieve food and nutritional security under the vicissitudes of nature. The global population is expected to reach beyond 10 billion by 2050 and the food demand is expected to double, with the bulk of this demand deemed to be met by substantial increases in yield and productivity of major cereal crops such as rice, wheat, and maize (Rötter et al. 2015). However, decreasing arable land due to urbanization, increasing scarcity of irrigation water resources, compounded with soil salinity, extreme heat and drought conditions, and pest and disease incidences pose major impediments in crop production. The current pandemic also highlighted the importance of ensuring nutritional security for health and immunity. Hence, it is imperative to design and develop climate resilient smart crops which will not only have higher yield under resource limited and adverse conditions, but also enhanced nutritional quality. Contemporary breeding and crop improvement methods alone are insufficient to meet the current and future demands of food and nutritional security in a sustainable and cost-effective manner (Langridge and Fleury 2011; Weckwerth et al. 2020). Rapid technological advances in the various “omics” levels have led to the generation of an enormous amount of biological data which has yet to be utilized to its full extent in applied crop science. Precision breeding strategies designed utilizing data from multi-omics studies in an integrated systems approach would be crucial to meeting the challenge of feeding the future population.

8.2

The “Omics-Spaces” and Pan-Omics

The suffix “-omics” has been appended to technologies which help in characterizing constituent components of the biological organisms such as DNA (genomics), epigenome (epigenomics), RNA (transcriptomics), proteins (proteomics), metabolites (metabolomics), lipids (lipidomics), carbohydrates/glycans (glycomics), ions (ionomics), and other distinct biomolecules, their modification, regulation (regulomics) as well as interactions (interactomics) between them (Fig. 8.1). Characterization and analyses of each level of the “omics-space” have furthered our understanding of the complexity of the plant response to its environment. Advances in “next-generation” sequencing (NGS) and “third generation sequencing” (long read) technologies and availability of new NGS platforms (Illumina TruSeq, PacBio SMRT; BGI Nanoball sequencing; ThermoFisher IonProton; and Oxford Nanopore sequencing based MinION) have improved sequencing efficiency and reduced the cost of sequencing by several orders of magnitudes well outpacing Moore’s law (Muers 2011). As of now, “representative” genome assemblies of close to 600 higher plants are available at the NCBI genome database (https://www.ncbi.nlm.nih.gov/genome/ browse#!/eukaryotes/). Several ambitious projects envisage sequencing of thousands of more plants as a part of the aim to sequence and catalog the genomes of all eukaryotes over a period of 10 years (Earth BioGenome Project; 2018) (Lewin et al.

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Fig. 8.1 The “Omics-spaces” and the concept of pan-omics

2018). Similarly, the 3K rice genomes undertook the resequencing of around 3000 cultivated rice accessions from 89 countries for developing an information database to assist plant breeding (3K Genomes Project; Li et al. 2014). A recent study reported the successful sequencing of transcriptomes of 1000 plants under the 1KP Project (One Thousand Plant Transcriptomes Initiative; Leebens-Mack et al. 2019). Furthermore, high-throughput advances in quantitative and qualitative proteomics and metabolomics including techniques like ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), etc., have contributed to the rapid biological data generation. Currently vast amounts of tera- or petabyte sized datasets are being generated almost daily from various NGS-based genomics, high-throughput proteomics and metabolomics studies (Misra et al. 2019). An accurate and robust methodological scheme to collate and critically analyze multiple omics datasets, utilizing resources optimally, is crucial in determining its translatability to crop improvement. The rapid explosion of omics data has led to the establishment of the systems biology approach to integrate multi-dimensional data to deduce biologically relevant observations, as well as predict system behavior under different environmental conditions.

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Studies attempting to integrate data from at least two of the three major “omicsspaces,” viz. transcriptomics, proteomics, and/or metabolomics come under the ambit of “pan-omics,” and have been interchangeably named as multi-omics, trans-omics, poly-omics, integrated omics, or shortened to just “omics” in literature (Fig. 8.1) (Misra et al. 2019). The generation of omics data in biology is a multi-step process comprising of the following steps: (1) data generation, (2) data processing, (3) data integration, and (4) data analysis (Palsson and Zengler 2010). Of these, data integration followed by analysis are the key bottlenecks limiting the full utilization of the vast heterogenous omics data available at our disposal (Pinu et al. 2019). It would be a herculean task to manually annotate each transcript to its respective protein or metabolic pathway, and therefore allocation of human and machine resources for multi-omics data processing and integration is also vital (Palsson and Zengler 2010; Jamil et al. 2020). Suggested reading on approaches in multi-omics data integration (Ebbels and Cavill 2009; Fukushima et al. 2009; Fukushima et al. 2014; Ritchie et al. 2015; Buescher and Driggers 2016; Bersanelli et al. 2016; Cavill et al. 2016; Ebrahim et al. 2016; Hasin et al. 2017; Huang et al. 2017; Lin and Lane 2017; Dihazi et al. 2018; Li et al. 2018; Tarazona et al. 2018; Yu and Zeng 2018; Zeng and Lumely 2018; Pinu et al. 2019; Pierre-Jean et al. 2020; Pucher et al. 2019; Weighill et al. 2019; Wu et al. 2019; Zeng et al. 2019; Baldwin et al. 2020; Canzler et al. 2020; Chauvel et al. 2020; Jamil et al. 2020; Lee et al. 2020; Nguyen and Wang 2020; Subramanian et al. 2020).

8.2.1

Current Approaches in Pan-Omics Data Integration

Many studies have attempted to classify multi-omics data integration methodologies (Fig. 8.2). Multi-omics integration methodologies were initially classified into “biology-driven” and “data-driven” approaches (Thomas and Ganji 2006) based on prior biological knowledge being available for analysis. Later, these were classified as “conceptual,” “statistical,” and “model-based” integration approaches (Ebbels and Cavill 2009; Cavill et al. 2016; Rai et al. 2017). Ritchie et al. (2015) further categorized statistical integration approaches into “multivariate,” “concatenation-based,” and “transformation-based” approaches. Concatenation-based integration combines multiple omics data into a single matrix and uses it as an input for low-rank approximation or latent factor analysis to integrate it into a single low-dimensional space (Tini et al. 2019). An example of the concatenation-based method is the Multiple Co-Inertia Analysis (mCIA) which was used to integrate metabolome and proteome in a study of herbicide tolerant transgenic maize (Mesnage et al. 2016). In the transformation-based approach the omics data are transformed into an intermediate form like a graph or a kernel matrix before integration and further analysis. This helps to preserve individual characteristics of data from the individual “omics-spaces” that can be lost otherwise (Tini et al. 2019). Tarazona et al. (2018) classified integration methods into “supervised” or “unsupervised” approaches. Supervised approaches will help in predicting a given response variable using the omics data, whereas unsupervised approaches provide an

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Fig. 8.2 Various approaches in multi-omics data integration

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exploratory overview of the data and possible relations in-between the “omicsspaces” (Canzler et al. 2020). Pinu and co-workers (Pinu et al. 2019) categorized various approaches involved in multi-omics data integration into three main categories, namely, (1) “post-analysis” data integration, (2) “integrated” data analysis, and (3) “systems modeling” approaches (Pinu et al. 2019). In the post-analysis approach, multiple omics data sets are initially analyzed separately, and then vital features are “networked” or associated together through construction of a model pathway. Whereas, in the integrated data analysis approach data from various omics-spaces are collated and merged before further analysis (Pinu et al. 2019). This approach has the advantage of finding shared similarities between each omics-space through statistical derivation and hence negates any human bias in interpreting the data. The first two categories of this classification are comparable to the classification given by Bersanelli et al. (2016) as “sequential” and “simultaneous” approaches. The most comprehensive approach is the systems modeling approach which relies on pre-existing data from several genomic, transcriptomic, and metabolomic studies to develop system models which can be trained to predict response of the complex biological system. These model systems usually incorporate differential or partial differential solving kinetic models, Petri-Net models, inverse stochastic Lyapunov matrix equations, or steady state models such as flux balance models (Pinu et al. 2019; Weckwerth et al. 2020). However, model-based integration has been limited until now in most plant systems due to lack of pre-existing genomic, transcriptomic, proteomic, and especially high-quality quantitative metabolic reference data. “Fusion based” methods are a specific type of integration methodology that applies uniform processes such as scale normalization across data blocks or dimension reduction for analysis of multi-omics assays for knowledge discovery. Baldwin et al. (2020) categorized fusion based methods into “data fusion,” “model fusion,” and “mixed fusion” methods. “Data fusion” method is similar to concatenation-based approach while “model fusion” is akin to model-based approaches as described by Ritchie et al. (2015) in their review. Whereas, transformation-based approaches can be regarded as either data fusion or model fusion depending on whether the intermediate form is considered as data or a model. Lastly, “mixed fusion” is a novel approach that accesses omics datasets directly and builds a comprehensive model across multiple blocks (Baldwin et al. 2020). A recent critical review has reclassified the multi-omics data integration approaches into three levels based on increasing complexity (Jamil et al. 2020). The authors argued that “conceptual” integration which is similar in which multiple omics datasets are separately analyzed, may miss many valid correlations which may otherwise be found when they are analyzed together. Further, since it is a biologydriven approach, there is a chance for human bias in the analysis, and it was not included in the new classification. “Statistical” integration in which statistical associations between elements from multiple data sets were analyzed was reclassified as “Element-based” integration. As per their classification, unbiased “Element-based” integration is the first level (Level 1) which is further divided into three subclasses, viz. correlation, clustering, and multivariate analyses. The

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second level (Level 2) includes the knowledge-based “Pathway-based” integration and comprises the subclasses co-expression and mapping based approaches. Lastly, Level 3 is defined as “Mathematical-based” integration and comprises differential and genome-scale analyses.

8.2.2

Element-Based Approaches

Element-based approaches based on correlation utilize simple correlation analysis such as Pearson’s or Spearman’s correlation coefficients to assign linear or ranked associations (Schober and Schwarte 2018) although other methods such as Goodman and Kruskal’s gamma statistic are also available (Cavill et al. 2016) to study correlation. Most correlation-based studies have analyzed the straight-forward association between transcripts and their respective proteins. However, the assumption that changes in transcript levels will be seen in the respective protein levels has been usually found to be unsubstantiated, as the dynamic nature of the transcriptome may not necessarily be mirrored at the translational level. In fact, many examples have shown weak or at times even negative correlations between the two omics levels. For example, a study analyzing the transcriptomic, proteomic, and metabolic changes in Arabidopsis thaliana leaves post illumination, observed negative correlation between transcripts and protein abundance of the components of the photosystem (Liang et al. 2016). Similarly, in an integrative analysis of transcriptome and proteome to study the gene regulation of leaf vascular specification and development in the rice midribless (dl2) mutant, only a modest concordance between transcript and protein levels was seen (Peng et al. 2015). A weak correlation between proteins and their cognate mRNAs was also observed in a study on proteome and transcriptome profiling in developing grains of a notched-belly rice mutant undertaken to elucidate the molecular pathways behind grain chalkiness (Lin et al. 2017). One of several reasons, including half-lives of transcripts, rate and volume of protein translation, protein degradation and turnover rates, post-transcriptional and post-translational modifications, and even technical errors while assaying can lead to such discrepancies between mRNA and protein abundances (Liang et al. 2016). Interestingly, under specific conditional treatments such as abiotic or biotic stress, a significant correlation between upregulated proteins and their corresponding transcripts in resistant phenotypes has been observed. For example, a multi-omics study of the rice-Fusarium fujikuroi disease interaction showed a positive correlation between the proteome and transcriptome for tolerant genotype 93-11 and a negative correlation (R ¼  0.097) for the susceptible genotype Nipponbare (Ji et al. 2019). Comparative analyses of transcriptome and metabolome when performed using correlation studies should take into consideration the biochemical pathways before drawing conclusions. For example, Glaubitz et al. (2017) performed correlation analysis between transcripts and metabolites in rice in response to elevated night temperatures to identify sensitivity and tolerance-related profiles. The study revealed a highly activated TCA cycle under high night temperature (HNT) conditions which was corroborated by enzyme activity measurements.

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The second sub-classification under level 1 “element-based” approach is clustering analysis which performs grouping of data with comparable characteristics such as expression levels to infer fundamental patterns of associations. Clustering analysis can be performed using either hierarchical methods (such as hierarchical cluster analysis; HCA) or non-hierarchical methods (k-means clustering or Random Forest) or using Self-Organizing Maps algorithms. Non-hierarchical methods are viewed to be more appropriate for use in machine learning algorithms and hence the preferred choice when comparing multi-omics data (Silva et al. 2019; Jamil et al. 2020). kmeans clustering is a method by which multiple observations are subdivided into clusters in which each cluster group has similar means (Larsen et al. 2016). Whereas, Random Forest is a classification algorithm which utilizes ensemble learning method and creates several association trees and the consensus is achieved by a voting algorithm that outputs the final tree (Silva et al. 2019). In a multi-omics study to understand plant–mycorrhizal interaction in roots, kmeans clustering was utilized for identifying co-regulated clusters of genes which are important for symbiotic interaction (Larsen et al. 2016). Similarly, k-means clustering was used to identify markers of stress in a transcriptomic and metabolomic analysis of response of Medicago to combinatorial drought and Fusarium infection (Dickinson et al. 2018). Acharjee et al. (2016) used Random Forest regression analysis for integrating transcriptomic, proteomic, and metabolomic data for determining a small set of omics variables for accurate prediction of quality traits such as flesh color and tuber shape in potato. Interestingly, traits related to flesh color had greater correlation with metabolite data (carotenoid contents), while tuber shape was strongly correlated to transcripts for corresponding genes involved in shape and size determination. This clearly shows the importance of using pan-omics approach to understand variation in such important agronomic and quality parameters as single omics level studies may not necessarily reveal them. The third subclass in “element-based” classification is multivariate analysis. Multivariate analysis appears to be the most appropriate method to handle more complex omics studies in which measurements from multiple data sets and their corresponding metadata and parameters of experimental design are to be considered. It allows more flexibility in experimental design, can ascertain influence of non-biological (metadata) on the results of the analysis, and allows predicting trends, variance, or covariance associations among datasets, as well as finding dynamic relationships and topological networks between the omics elements (Rai et al. 2017; Weckwerth 2019; Jamil et al. 2020). The various multivariate data analysis methods for multi-omics integration include Principal components analysis (PCA), partial least squares (PLS), PLS-DA (partial least square discriminant analysis), OPLS (orthogonal projections to latent structures), ICA (independent component analysis) and CCA (canonical correlation analysis), MCIA (multiple co-inertia analysis), LASSO (least absolute shrinkage and selection operator), GFLASSO (graph-guided fused least absolute shrinkage and selection operator), and DIABLO (Data Integration Analysis for Biomarker discovery using a Latent component method for Omics studies) of which PCA and PLS are very commonly used (Rai et al. 2017) A few examples using of multivariate analysis methods to integrate multi-omics studies in

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plants have been reported for maize (Mesnage et al. 2016; Xu et al. 2017; de Abreu E Lima et al. 2018), poplar (Obudulu et al. 2018), and avocado (Uarrota et al. 2019). The following articles might help as a starting point for novice users in choosing the optimal method amongst these multivariate analysis methods (Bylesjö et al. 2006; Worley and Powers 2013; Meng et al. 2014; Uarrota et al. 2014; Bersanelli et al. 2016; el Bouhaddani et al. 2018; Saccenti and Timmerman 2016).

8.2.3

Pathway-Based Approaches

While “element-based” analysis is simple, direct, and intuitive, they do not take into consideration the underlying biological pathways and interacting protein/gene partners in a biological context. Hence, pathway-based approaches are utilized to map the multi-omics data onto experimentally well characterized metabolic pathways and statistical tools are utilized to examine the over-representation and enrichment of these pathways in response to a stimulus (Cavill et al. 2016). Several databases as well as web-based and standalone software are available for pathwaybased multi-omics data integration in plants (Table 8.1). Some of the databases cover several species and are not limited to one organism such as MetaCyc, KEGG (Kyoto Encyclopedia of Genes and Genomes), and PlantCyc database at Plant Metabolic Network site (which includes metabolic pathway data from 125 plants). Whereas, several others such as OryzaCyc (v6.0), and RiceCyc for rice, AraCyc for Arabidopsis, CitrusCyc for citrus, VitisCyc for grapevine, and SolCyc for Solanaceae species are organism specific plant metabolic databases which aid in pathway-based analysis. Several bioinformatics tools such as KaPPA-View4, MapMan4, ImPaLA, PaintOmics3, OmicsAnalyzer, OmicsPLS PathView, PathVisio3, MixOmics, COMBI, and VANTED are available for plant scientists to integrate, analyze, and visualize multi-omics data (Table 8.1). Several comprehensive databases integrating multi-omics data are also available for plants such as rice (IC4R, CARMO, UniVIO, MBKBase), maize (MODEM, ZEAMAP), wheat (GrainGenes2), and others (Plant Reactome; GourdBase; SoyKB). It is noteworthy to mention that annotations and enrichment results generated automatically by such tools must be manually curated and experimentally validated to prevent false representations or reaching inaccurate conclusions. Co-expression analysis approach is another subclass under “Pathway-based” analysis and is based on using statistical correlation to assess associations between expressed transcripts which are then converted into a weighted network. These associations can be visualized, or further enrichment analysis can be performed using tools such as Weighted Gene Co-expression Network Analysis (WGCNA) or Gene Set Enrichment Analysis (GSEA) in R program, Cytoscape, and Gephi. Co-expression network analysis integrated with metabolic pathway databases are powerful tools in plant systems biology and help in grouping and visualizing multiomics data in highly connected metabolic hubs for further analysis. However, pathway approaches do not allow changes in experimental design or perturbation to be incorporated, or for cross species translatability, and hence are not useful in

Database/software CARMO

COMBI (Compositional Omics Model Based visual Integration)

Grimon (Graphical interface to visualize multi-omics networks) ImPaLA

InCroMAP

KaPPA-View4

Knowledge Base Commons (KBCommons v1.1)

S. no. 1.

2.

3.

4.

5.

6.

7.

Multiple species

Multiple species

Multiple species

Multiple species

Multiple species

Multiple species

Target organism Rice

Brief description A comprehensive annotation platform for functional exploration of rice multi-omics data A flexible model-based R package for multi-omics data integration which allows visualization through multiplots to improve interpretation An R package which helps visualize and analyze high-dimensional multi-layered omics data explore multiple inter-layer connections A tool for pathway over-representation and enrichment analysis with expression and/or metabolite data A freely available data integration, analysis, and visualization tool for integrated enrichment analysis and pathway-based visualization of multiomics data A metabolic pathway database for representation and analysis of correlation networks of gene co-expression and metabolite co-accumulation and omics data A universal and all-inclusive web-based framework providing generic functionalities for storing, sharing, analyzing, exploring, integrating, and visualizing multi-level omics data to support biological discoveries for all species via a common platform

Table 8.1 List of databases, software, and tools aiding multi-omics data integration

http://kbcommons.org/

http://kpv.kazusa.or.jp/ kpv4/

http://www.ra.cs.unituebingen.de/software/ InCroMAP/index.htm

http://impala.molgen.mpg. de/

https://github.com/mkanai/ grimon

https://bioconductor.org/ packages/release/bioc/html/ combi.html

Link http://bioinfo.sibs.ac.cn/ carmo/

Zeng et al. (2019)

Sakurai et al. (2011)

Eichner et al. (2014)

Kamburov et al. (2011)

Kanai et al. (2018)

Hawinkel et al. (2020)

Reference Wang et al. (2015)

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MapMan4

MBKBase

mixOmics

MODEM

Omicade4

Omics discovery index (OmicsDI)

OmicsAnalyzer

8.

9.

10.

11.

12.

13.

14.

Multiple species

Multiple species

Multiple species

Maize

Multiple species

Rice

Multiple species

A refined protein classification and annotation framework applicable to multiomics data analysis An integrated omics knowledgebase which integrates rice germplasm information, multiple reference genomes with a united set of gene loci, population sequencing data, phenotypic data, known alleles, and gene expression data An R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction, and visualization A comprehensive database of maize multidimensional omics data, includinggenomic, transcriptomic, metabolic, and phenotypic information from the cellular to individual plant level An R/Bioconductor package that uses multiple co-inertia analysis (MCIA) to identify co-relationships between multiple high-dimensional omics datasets An open-source platform for knowledge discovery and dissemination of publicly available heterogeneous multi-omics data A Cytoscape plug-in suite for modeling omics data https://apps.cytoscape.org/ apps/omicsanalyzer

https://www.omicsdi.org/

https://bioconductor.org/ packages/release/bioc/html/ omicade4.html

http://modem.hzau.edu.cn

www.mixOmics.org

http://www.mbkbase.org/ rice

https://mapman.gabipd.org/

(continued)

Xia et al. (2010)

Perez-Riverol et al. (2017)

Meng et al. (2014)

Liu et al. (2016)

Rohart et al. (2017)

Peng et al. (2020)

Schwacke et al. (2019)

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OmicsPLS

OpenCOBRA Toolbox v3.0 (constraint-based reconstruction and analysis) PaintOmics3

PathView

PathVisio3

Plant metabolic network (PlantCyc)

Plant MetGenMAP

Plant Reactome (Gramene pathways)

16.

17.

19.

20.

21.

22.

23.

18.

Database/software OmicsPipe

S. no. 15.

Table 8.1 (continued)

Rice, maize, wheat, Arabidopsis, barley etc.

Arabidopsis, tomato

Multiple species

Multiple species

Multiple species

Multiple species

Multiple species

Multiple species

Target organism Multiple species

A freely available web-based resource for the integrated visualization of multiple omic data types (including epigenomics data) onto KEGG pathway diagrams A R package for integrated pathway analysis of multiple omics data A free open-source standalone pathway editor, visualization and analysis software PMN currently houses one multi-species reference metabolic pathway database called PlantCyc and 125 species/taxonspecific databases A web-based system to comprehensively integrate and analyze large-scale gene expression and metabolite profile datasets A freely accessible database of plant metabolic and regulatory pathways and provides tools for visualization, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modeling, systems biology

Brief description Omics pipe is an open-source, modular computational platform that automates best practice multi-omics data analysis pipelines An open-source package in R which uses O2PLS method for analyzing multidimensional omics datasets efficiently An open-source code for constraint-based metabolic reconstruction and analysis

https://plantreactome. gramene.org/

http://bioinfo.bti.cornell. edu/tool/MetGenMAP

https://plantcyc.org/

https://pathvisio.github.io/

https://pathview.uncc.edu/

http://www.paintomics.org/

https://cran.r-project.org/ web/packages/OmicsPLS/ index.html https://opencobra.github.io/

Link https://pypi.org/project/ omics_pipe/

Naithani et al. (2020)

Joung et al. (2009)

Luo et al. (2017) Kutmon et al. (2015) Schläpfer et al. (2017)

HernándezDe-Diego et al. (2018)

el Bouhaddani et al. (2018) Heirendt et al. (2019)

Reference Fisch et al. (unpublished)

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PlantSEED

UniVIO

VANTED

ZEAMAP

24.

25.

26.

27.

Maize

Plants

Rice

Multiple plant species

A comprehensive database incorporating multiple reference genomes, annotations, comparative genomics, transcriptomes, open chromatin regions, chromatin interactions, high-quality genetic variants, phenotypes, metabolomics, genetic maps, genetic mapping loci, population structures, and populational DNA methylation signals within maize inbred lines

A tool which allows automated annotation, metabolic reconstruction from transcriptome data and simulate metabolic activity under various conditions using flux balance analysis Multiple omics database with hormonome and transcriptome data from rice A tool for integrative visualization of -omics data for systems biology applications http://www.zeamap.com/

https://www.cls.unikonstanz.de/software/ vanted/

http://univio.psc.riken.jp/

http://modelseed.org

Kudo et al. (2013) Hartmann and Jozefowicz (2018) Gui et al. (2020)

Seaver et al. (2018)

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prediction of metabolic changes as a response to different environmental stimuli of interest (Jamil et al. 2020).

8.2.4

Mathematical-Based Approaches

Mathematical-based approaches, the final level of classification according to Jamil et al. (2020), are the most complex integration method amongst the three levels and prior requirement of large-scale and well characterized omics data is indispensable for creating the functional model. Mathematical approaches are classified into “differential analysis” and “Genome scale analysis.” Differential analysis utilizes mathematical differential equations to create models which can accurately identify response of the system to any perturbations. Differential analysis has been applied in plants such as tomato (Belouah et al. 2019), poplar (Wang et al. 2018), grapevine (Soubeyrand et al. 2018), and Arabidopsis (Koç et al. 2018). Several tools such as COBRA (constraint-based reconstruction and analysis), E-flux, and MADE (Metabolic Adjustment by Differential Expression) are available for flux balance analysis based modeling in plants. In contrast to differential analysis, genome-scale modeling is a bottom-up approach which builds a model from extensive curation before its experimental validation (Goh 2018; Jamil et al. 2020). Initial genome-scale metabolic models are draft models and undergo several rounds of repeated curation and bootstrapping to remove errors. The prerequisite for developing a genome-scale metabolic model is to have a high quality and well annotated genome assembly. The recent rapid increases in de-novo and whole genome sequencing efforts in plants will help in the reconstruction of their genome-scale models. While several well characterized genome-scale models have been developed for microbial organisms and have been put to good use in enhancing production of valuable metabolites, metabolic models of plant species pose greater challenges. Larger size and complexity of metabolic networks, multiple levels of regulation, polyploidy, and compartmentalization of metabolic processes in different organelles have posed challenges in developing genome-scale models for plants (Rai et al. 2017; Jamil et al. 2020). However, genome-scale metabolic reconstruction models have been developed in several plants such as in rice, soybean, corn, rapeseed, Setaria, and Arabidopsis (AraGEM), among others (Seaver et al. 2018). For a comprehensive review of the process of genome-scale metabolic model reconstructions, see (Fondi and Liò 2015; Voit 2017; Goh 2018; de Oliveira Dal’Molin and Nielsen 2013; Seaver et al. 2018; Gu et al. 2019). Several tools and resources such as PlantSEED, ModelSEED, C4GEM, Kbase, COVRECON, and PMR (Plant/Eukaryotic and Microbial Metabolomics Systems Resource) are available to plant scientist for genome-scale metabolic model development (Mendoza et al. 2019; Jamil et al. 2020). The PlantSEED and ModelSEED tools not only collate genome annotation and generate genome-scale metabolic

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models, but also permit validation, predict new functions, and discover errors and gaps in the models and comparison between models (Rai et al. 2017; Seaver et al. 2018, 2020). Draft genome-scale metabolic models lack accuracy because of variation in metabolic processes in various spatial, temporal, and developmental stages as well as under different environmental and physiological conditions. Hence, it is imperative to validate the draft genome-scale models using high-throughput omics data from time-resolved experiments to further improve the models. In this regard, constraint-based modeling (CBM) and specifically flux based analysis (FBA) using isotopic labeling techniques have an important role to play in developing a realistic reconstruction of metabolic models of plant systems (Rai et al. 2017). Advances in machine learning, deep learning, and artificial intelligence have furthered the development of metabolic models (Rai et al. 2019). Further development of these models to facilitate multi-tissue and whole plant metabolic modeling will help in rational design and synthetic biology approaches for crop improvement (Libourel and Shachar-Hill 2008; Shaw and Cheung 2020).

8.3

Challenges in Pan-Omics Data Integration and Analysis

Numerous multi-million-dollar funded projects based on pan-omics data generation have resulted in accumulation of redundant heterogenous data in different databases which are often not processed, have low integrability, and later remain unused. Therefore, advancing data integration and analysis and addressing the challenges is of key importance in the era of “Big data” biology. Data pre-processing, dimensionality reduction, data storage, archiving, and management, statistical validation and precise data analysis, along with optimization of computational space, power and efficiency requirements are particularly challenging problems in multi-omics studies (Misra et al. 2019). Lack of scalable parallel computational infrastructure, unavailability of skilled statisticians/data scientists, and absence of a unified management scheme for big data are also challenges that need to be addressed (Rai et al. 2017). However, advances in “exascale” computing, “Big Data” management and mining, Machine and Deep learning and explainable AI (artificial intelligence) are bound to help in addressing many of the challenges in agriculture (Ma et al. 2014; Harfouche et al. 2019; Tantalaki et al. 2019; Streich et al. 2020; Wang et al. 2020). While automation of the data integration process is inevitable, irregularities between data from different omics-spaces due to fundamental molecular mechanisms may be missed out by curators only trained in data science and not having sufficient background in biological sciences (Palsson and Zengler 2010). Another aspect which needs immediate consideration is the lack of adherence to international standards and guidelines for omics data acquisition to guarantee reproducible and biologically pertinent results from multi-omics studies. For instance, metadata containing a minimum set of key experimental design parameters and other

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relevant information when provided with the original omics data can help in accelerating data integration and interpretation from multiple studies with different experimental conditions. Several details such as tissue type and age, time of sampling, and comprehensive plant growth conditions are needed to improve reproducibility and transferability of results among labs and further identify the most biologically suitable data for integration (Argueso et al. 2019). It is recommended that the minimum standards recommended by community initiatives such as MINSEQE (minimum data for NGS experiment), MIAME (microarray data), MIQE (gene expression data), MIAPE and HUGO (proteomics data), CIMR (metabolomic data), MIQAS (genetic linkage and association studies), and MIMIX (interaction studies) should be universally followed before submission to databases to ensure compatible data integration (Rai et al. 2017; Argueso et al. 2019). Similarly, for improving reproducibility, it is recommended to adhere to FAIR (Findability, Accessibility, Interoperability, and Reusability) principles while archiving data of single and multi-omics data (Misra et al. 2019). Another challenge in integrating multi-omics studies for crop improvement is the problem of identifying a proper “scaling law” while integrating multi-dimensional data sets from multi-omics studies with the high-throughput, heterogenous, “high dimension” information from plant phenomics or other non-omics studies (Fukushima et al. 2009; Tardieu et al. 2017; do Amaral and Souza 2017; de Maturana et al. 2019). The use of static steady state models for metabolic modeling might be an oversimplification and not be enough for accurate prediction of the phenotype of complex plant systems. Whole plant signaling and intercellular cross talk further complicate the analysis. Hence, newer methods which consider the dynamic aspects of large-scale models need to be developed and utilized, and taking the layered structure (organelles, cells, tissues, and organs) into context will help in advancing the modeling efforts on a whole-crop physiology level (Yin and Struik 2008; do Amaral and Souza 2017). In their recent review, Nguyen and Wang (2020) discuss the problems associated with multi-omics data integration such as noisiness, inconsistency, heterogeneity, multimodality, and “high-dimensionality” associated overfitting. They propose a “multi-view” machine learning based on an empirical risk minimization (MV-ERM) approach for multi-omics data integration to address such problems. For detailed reviews on challenges and advances associated with data integration in omics studies, see (Rajasundaram and Selbig 2016; do Amaral and Souza 2017; Tarazona et al. 2018; de Maturana et al. 2019; Misra et al. 2019; Canzler et al. 2020; Jamil et al. 2020; Schwartz 2020).

8.4

Pan-Omics and Systems Biology Approaches in Crop Improvement

Omics studies are a classic example of the reductionist approach to understanding biology. Dividing the biological entity into several “-omics spaces,” followed by the functional characterization and analysis of each of these individual constituent

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components, has led to many unknowns in biology being unraveled. However, it has become evident that biological systems are very complex and are known to have “emergent” properties which cannot be predicted or explained only by such approaches (Van Regenmortel 2004). Hence, a systems biology strategy of integration of data from multiple pan-omics studies is essential for a holistic understanding of the biological phenomena. Systems biology approaches, such as integrative and predictive ones, utilize mathematical modeling and graph theory to aid in collating, interpreting, and analyzing this vastly heterogenous data and help in understanding the molecular mechanisms of complex traits linked to crop improvement and facilitate rational/precision breeding programs. Advances in modeling of whole gene regulatory networks (GRN), interaction networks, signal transduction networks, and network engineering help in anticipating systems behavior and can be used to identify key network players, thus discovering important breeding targets which are ordinarily not revealed through common reverse and forward genetic approaches for gene discovery. The utilization of time-series data sets will further help dynamic modeling of the network and predict network behavior and discover the minimum nodes (targets) to be genetically re-engineered to drive the system towards the phenotype of interest, hence aiding rational/knowledge-based precision breeding programs (Lavarenne et al. 2018). Thus, manipulation of key candidate genes identified from topological analysis of systems can help in designing smart crops with superior agronomic traits (Kumar et al. 2015; Lavarenne et al. 2018). Such systems-based approaches have led to the identification of some key genes and proteins involved in networks of pathways involved photosynthetic efficiency, in abiotic and biotic stress resistance, plant architecture, and nutrient use efficiency and mobilization (Kumar et al. 2015; Lavarenne et al. 2018). A recent example of integrated multi-scale models driving crop improvement is the “e-photosynthesis model” that helped predict that higher relaxation rates of nonphotochemical quenching (NPQ) enhance photosynthetic efficiency and yield (Zhu et al. 2013). The directed genetic engineering of tobacco plants with faster NPQ relaxation led to a 15% increase in yield under field conditions (Kromdijk et al. 2016). Systems approaches can be applied to study the plant system at different scales including at the micro (molecular, cellular) or macro (tissue, organ, whole plant, or ecological cropping systems) levels (Benes et al. 2020). However, the scale differences between whole plant crop growth models and molecular or cellular models that are on different biological, temporal, and computational scales pose the biggest challenge to such an endeavor (Benes et al. 2020). Hence, developing a robust and dynamic computational framework for connecting independently generated multi-scalar models is a key goal for several initiatives which aim at creating 3D “Virtual plant” models, such as the Arabidopsis Framework Model (FM) and the Crops in silico consortium (Chew et al. 2017; Benes et al. 2020). The Crops in silico initiative enables multi-scale modeling across molecular, cellular, tissue, organ, and ecosystem levels using a 3D graphical user interface as a bridging

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platform. Some of the tools available for such systems approaches in plants are Virtual Plant (Katari et al. 2010), ePLant (Waese et al. 2017), and Plaza 4.0 (Van Bel et al. 2018). Furthermore, for accurate predictions of G x E x M (genotype vs environment vs management) interactions at the crop systems level, integration of pan-omics data with “Big data” from numerous field experiments such as high-throughput phenomics and agronomic data including yield parameters, genotypes, soil characteristics, input application rates, irrigation practices, planting density, spatial and imaging data (satellite, drone and remote sensing imagery, GIS, etc.), and meteorological data (precipitation, temperature, solar radiation intensity, etc.) will be necessary (Christensen et al. 2018; Benes et al. 2020). Various reviews have covered the methodologies of systems biology approaches at different scales of plant science (Yuan et al. 2008; Sheth and Thaker 2014; Ogura and Busch 2016; Zhu et al. 2016; Chew et al. 2017; Lavarenne et al. 2018; Álvarez-Buylla et al. 2016; Chang et al. 2019; Coruzzi et al. 2018; Benes et al. 2020).

8.5

Pan-Omics in Precision Crop Breeding

Traditional plant breeding is based on the process of phenotypic selection and is successfully used to combine simple traits having high heritability. However, conventional breeding methods are time consuming and labor-intensive and have limitations when combining complex traits which are usually polygenic, have genetic/epigenetic interactions, and are environmentally influenced. Hence, modern crop improvement and breeding techniques have shifted to the use of molecular markers for aiding the selection process based on genotyping traits of interest. Several molecular breeding techniques such as marker-assisted selection (MAS)/ marker-assisted backcrossing (MABC), marker-assisted recurrent selection (MARS), and genomic selection (GS) have been successfully used for accelerating the breeding process. However, several limitations such as lethal alleles, redundant genes, multigenic interactions, and environmental interactions (GxExM) can complicate these approaches (Weckwerth et al. 2020). Traits of genes which are influenced by environment show varying phenotypes under varying environmental stimuli due to complex interactions which may not be understood by simple forward or reverse genetics approaches (Weckwerth et al. 2020). A systems biology approach to integrate data from different molecular “omics-spaces” of the plant system to understand the flow of information from the gene to metabolism to phenotype in an environmental context will be essential for its translational applications (Choi 2019). Weckwerth et al. (2020) proposed a “PANOMICS” platform which seeks to statistically and mathematically integrate data arising from genomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenomics to aid crop improvement by identifying new traits, genes, networks, and pathways for “precision breeding.”

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Genomic selection (GS) involves developing a prediction model by integrating genotypic and phenotypic data of a training population (TP) and using genome-wide markers to derive genomics estimated breeding values (GEBVs) for rapid selection in crop breeding programs. Integrated pan-omics data can help in improving phenotypic prediction accuracy of models which is the major limiting factor in GS based breeding (Wang et al. 2018; Weckwerth et al. 2020). For example, genomic and metabolic data, as well as transcriptomic data have been utilized to predict complex traits in maize (Riedelsheimer et al. 2012; Guo et al. 2016; Azodi et al. 2020). Schrag et al. (2018) utilized an integrative approach combining genomic, transcriptomic (mRNA and small RNA) as well as metabolomic data of parental lines to evaluate hybrid performance for important agronomic traits in maize. Similarly, prediction of heterosis in rice hybrids was significantly improved by including parental metabolic data (Gärtner et al. 2009). Hence, integrating multi-omics data can play an important role in improving GS based breeding approaches (Wang et al. 2018). A recent technique termed Speed Breeding which facilitates rapid advancement of generations has made possible growing four generations of crops such as bread wheat, chickpea, and barley in a single year under controlled conditions with incessant light for ~20 h per day (Ghosh et al. 2018; Watson et al. 2018). When speed breeding is combined with Genomic Selection (SpeedGS), the potential to accelerate genetic gain in a short period of time is increased tremendously (VossFels et al. 2019; Jighly et al. 2019). Genome-wide association study (GWAS) is based on natural linkage disequilibrium, and hence has higher mapping resolution when compared to conventional QTL mapping which is based on linkage-based mapping in structured populations. GWAS has led to the identification of thousands of unique associations between SNPs and complex traits in several plants (Liu and Yan 2019). In several organisms, complex traits associated SNPs are seen to be localized in non-coding regions and often have minor effects, thus making their functional connections to complex traits difficult to study (Peng et al. 2018; Gleason et al. 2020; Anderson et al. 2019). GWAS studies in crops have been shown to account only for ~40% of phenotypic variance and integration of GWAS with data from complementary pan-omics approaches is anticipated to help in explaining the rest (Weckwerth et al. 2020). Hence the focus is shifting to Omics Wide Association (OWAS) or trans-OWAS based approaches which aim to integrate multi-omics data with GWAS to elucidate mechanistic understanding of genotype-phenotype relations beyond the genomic hints provided by current techniques (Yugi et al. 2016; Xiao et al. 2017; Liu and Yan 2019). A few examples of OWAS studies are available in animal systems, but in plants such studies are likely be limited to model systems like Arabidopsis, rice, and maize where extensive omics data are readily available (Gupta et al. 2019). Transcriptomics data can be integrated with QTL mapping to discover eQTLs (expression QTLs) which gives an of idea of the regions influencing transcriptional abundance of genes (Varshney et al. 2005). Recent studies on such genetic effects on omics traits have led to several novel QTLs being defined based on their effects on

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gene expression (eQTLs), protein abundance (pQTLs), metabolites (mQTLs), alternate splicing (sQTLs), DNA methylation (meQTLs), histone modification (hQTL) and have revealed an abundance of such quantitative trait loci (QTLs) for molecular traits (omics QTLs/molQTls) throughout genomes of several organisms (Knoch et al. 2017; Khokhar et al. 2019; Ye et al. 2020). Such integrative studies have several advantages which can be complementary to QTL mapping studies in that they include both genetic and epigenetic variation effects and can help to fine map QTLs to higher resolution (Weckwerth et al. 2020). In such an example, eQTL analysis in peach identified candidate genes for fruit softening rate in peach (Carrasco-Valenzuela et al. 2019). Similarly, integrative study in rice revealed metabolite QTLs (mQTL) responsible for C/N partitioning (Li et al. 2016). Kuroha et al. (2017) used expression QTL (eQTL) analysis for understanding the rapid internode elongation phenotype in response to flooding in rice. These approaches will help to reveal the whole organism level response of an omics-space to specific environmental conditions like drought, heat, flooding, nutrient deficiency, pathogen attack, etc., and provide valuable markers for incorporation into molecular breeding programs for precision breeding (Rodziewicz et al. 2019). Furthermore, mGWAS studies (metabolic GWAS) have also helped in revealing the global genetic determinants of metabolic diversity (Fang and Luo 2019). For instance, a combination of eQTL mapping and mGWAS resulted in identification of two metabolite markers correlated with kernel weight in maize (Wen et al. 2014). Therefore, incorporation of multi-omics data with omics QTLs and mGWAS studies show significantly higher potential to elucidate complex traits when compared to individual approaches (Gleason et al. 2020; Weckwerth et al. 2020). Pan-genomics is another approach coming under the ambit of pan-omics and plays a critical role in characterization and identification of genetic variation in the germplasm. Pan-genomics is the study of the pan-genome (defined as the “full complement of genes of a biological clade, such as a species, which can be partitioned into a set of “core” genes that are shared by all individuals and a set of dispensable genes that are partially shared or individual specific”) (Tao et al. 2019). The growing recognition that a single reference genome is incapable of fully representing the diversity in genetic variation within a species, which can be in the form of single nucleotide polymorphisms (SNPs) as well as structural variations like presence/absence variation (PAVs) and copy number variations (CNVs), has led to the pan-genome concept (Tettelin et al. 2005; Tao et al. 2019). Technical advances and lowering costs of whole genome resequencing (WGRS) have made the genome sequences of multiple individuals of the species available thereby facilitating pan-genomic studies (Tao et al. 2019). A pan-genome reference for major crops species will help in variants other than SNPs, such as CNVs and PAVs to be accessible for GWAS studies thus increasing resolution and improving candidate discovery (Pourkheirandish et al. 2020). Pan-genomic studies have been reported in many important crop species such as rice (Wang et al. 2018; Zhao et al. 2018; Zhou et al. 2020), maize (Hirsch et al. 2014), and wheat (Montenegro et al. 2017). Fueled

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by reducing costs in NGS sequencing, techniques like Genotyping by sequencing (GBS) are now commonly used for discovering and genotyping SNPs in large genomes and populations, and have been successfully applied to molecular marker discovery, genome-wide association studies (GWAS), genomic diversity studies, and genomic selection (GS) in various plants (He et al. 2014). Advances in NGS sequencing have also helped high-throughput analysis of the transcriptome (RNASeq), regulome (DNase-seq; ATAC-seq; FAIRE-Seq; Bisulfite-Seq; 2P-Seq), and interactome (ChIP-Seq; ChIA-PET; PLAC-Seq; HiChIP; RIP-Seq; CLIP-Seq) and these omics data can also be integrated to understand gene regulation and interaction in determining phenotypes (Jiang and Mortazavi 2018). A large part of plant genomes consists of genes encoding regulatory non-coding RNAs (ncRNAs), including ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), and small RNAs like microRNAs (miRNAs), small nucleolar RNAs (snoRNAs), as well as long non-coding RNAs (lncRNAs) (Pourkheirandish et al. 2020). Of late, long non-coding RNAs (lncRNAs) are becoming valuable targets for crop improvement programs due to several insights gained on their potential involvement in regulation of a wide range of plant processes such as flowering time, stress tolerance, and gamete formation (Golicz et al. 2018). Developments in NGS-based RNASeq and advanced bioinformatics have enabled global characterization of lncRNAs transcripts. Several characterized lncRNAs affect important agronomic traits, and hence are valuable targets for crop improvement through genome editing. Ding et al. (2012) reported a lncRNA, namely LDMAR to be regulating photoperiod-sensitive male sterility (PSMS) which is an important trait in hybrid rice production. Further functional characterization of these vast lncRNAs will require integrative multiomics studies, as well as initiatives such as the RiceLncPedia which is a comprehensive database of rice long non-coding RNAs integrated with multi-omics data (Zhang et al. 2020). Phenomics is an important component of the pan-omics approach and involves accurate and systematic characterization of the plant phenotype at different levels of organization, from cellular to whole plant level. Swift and precise phenotyping is crucial for plant breeding to link the genotype to the plant trait and further selection. Phenotyping in greenhouse/growth room level usually do not give accurate “real time” predictions when compared to field conditions. Moreover, manual phenotyping of thousands of germplasm is impractical considering modern plant breeding requirements. Hence, high-throughput phenotyping/phenomics platforms (HTP) are being utilized to help in advance large-scale field level phenotyping. The techniques utilized for HTP are usually based on noninvasive and nondestructive approaches like remote sensing using multispectral, hyperspectral, fluorescence, thermal sensors and imagers mounted on platforms like satellites, drones, UAVs, air zeppelins, autonomous ground vehicles/rovers, phenomobiles/tractors, mobile field scanners, etc. (Shakoor et al. 2017). Advances in machine learning, deep learning, and AI have facilitated raster-data (video and images) based nondestructive phenotyping which helps in a holistic approach. Several comprehensive reviews

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addressing advances in HTP based field phenotyping of crop plants are available (Pauli et al. 2016; Rebetzke et al. 2016; Shakoor et al. 2017; Araus et al. 2018; Moreira et al. 2020). Integrating HTP data with pan-omics data is essential in crop modeling and simulation studies as well as for understanding gene function (Zivy et al. 2015; Moreira et al. 2020). New breeding techniques like CRISPR based genome editing allows precise manipulation of the genome sequence in vivo and are revolutionizing functional genomics and precision crop breeding. CRISPR technology has been rapidly redesigned to aid in a suite of applications including generating point mutations (deletion or insertion), knockouts, as well as activation or repression of genes (CRISPRa and CRISPRi) (Moradpour and Abdulah 2020). CRISPR has been successfully applied for trait improvement in several crops (Belhaj et al. 2015; Jaganathan et al. 2018). The pan-omics platform integrated with tools for complex trait elucidation as discussed above will help identify numerous putative candidate genes which can be applied to genome editing pipelines for functional validation as well as accelerating crop improvement. Moreover, single cell pan-omics data integrated with CRISPR based genetic screens has the potential to map high resolution gene regulatory networks and test gene function at a genome scale, which will push the limits of functional genomics and synthetic biology (Gaillochet et al. 2020).

8.6

Conclusions

The faithful and seamless integration of data from multi-dimensional studies, such as pan-omics data (genome assemblies, transcriptome data, epigenome data, gene regulatory networks (GRNs), metabolic flux models, and protein–protein interaction networks (PPI)), non-omics “Big data” from crop growth models and simulations, agronomic studies, environmental studies, genetic studies (GWAS—genome-wide association studies and QTL—quantitative trait loci mapping), as well as data from high-throughput phenomics (HTP), in a multi-scale modeling framework can help direct future crop-ideotype design and also to predict crop response to future climates (Fig. 8.3). Pan-omics data integration when complemented with modern breeding techniques like CRISPR based genome editing, de-novo domestication, and speed breeding assisted genomic selection will help in the rapid precision breeding of high yielding, nutrient-dense, disease resistant, climate-smart crops with additional novel and valuable traits that meet our ever-evolving demands (Li and Yan 2020; Weckwerth et al. 2020).

Fig. 8.3 Multi-scale pan-omics and Big Data Integration in driving precision breeding

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Acknowledgements The authors are thankful to the Science and Engineering Research Board (SERB), India for NPDF grant, and the Council for Scientific and Industrial Research (CSIR), India and CSIR-Centre for Cellular and Molecular Biology for funding.

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Application of Nanobiotechnology in Agriculture: Novel Strategy for Food Security Kamal Kumar Malukani, Namami Gaur, and Hitendra Kumar Patel

Abstract

The growing population had quadrupled over a century now. The agricultural food supply of an unprecedently growing population aggravates the situation. Factors such as climatic change coupled with water scarcity, biotic stress, and urbanization make it challenging to produce enough food. Nanobiotechnology is a newly emerging field that links nanostructures with crop sustainable intensification to increase productivity. Many studies indicate that nanoscience can be employed in various agricultural aspects such as to understand the soil quality and composition, enhance crop yield, and to provide tolerance to the plants against biotic and abiotic stress. In this chapter, we discuss the novel strategies where nanomaterials are used in agriculture to minimize the usage of fertilizers, reduce contamination of natural resources, monitoring of soil quality, disease diagnosis and management, increase crop yield, and crop fortification. Careful interpretation and examination of pre-existing data and better future planning are needed to understand the true impact of nanobiotechnology as a boon or bane on the environment, agriculture, and human health.

9.1

Introduction

It is estimated that the world population may reach above 9 billion by 2050 (Gerland et al. 2014). With a growing population, we need more food with continuously shrinking agricultural land (Alexandratos and Bruinsma 2012). So, we need a gradual increase in yield from agriculture fields. A large amount of farmers produce

K. K. Malukani · N. Gaur · H. K. Patel (*) CSIR-Centre for Cellular and Molecular Biology (CCMB), Hyderabad, India e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Kumar et al. (eds.), Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), https://doi.org/10.1007/978-981-16-2956-3_9

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is lost due to environmental stress, diseases, and post-harvest mismanagement. So, we also need new technologies to control the loss at various levels. Nanotechnology is the field of science that deals with small particles of less than 100 nm dimension (Bhushan 2017). The field of nanotechnology is continuously growing and finding new applications in various streams such as technology development, communication system, healthcare, and agriculture as well. In this chapter, we will discuss some recent findings that indicate possible applications of nanotechnology in the field of agriculture. Richard Feynman, who is considered the pioneer of nanoscience once said: “There is plenty of room in the bottom” (Toumey 2008). This in general term is considered that there is a lot of scopes to learn and develop when we deal with a small structure. But in literal terms it also means that the finer the particles are, the more surface area per volume is available that can lead to higher efficiency and productivity. The concept of application of a small scale is not new to agriculture. We have been doing it for generations without realizing the basic technical details behind it. For example, ploughing a field before sowing breaks large soil pieces into small pieces which are more accessible for plant roots. In many places, agriculture waste after harvesting is partially burnt and mixed back in the soil. According to Prof. Mainak Das, this burning breaks down biomolecules into smaller components which makes it more accessible for the next crop (https://youtu.be/hdDBvC7kop8).

9.2

Why Nanotechnology

With a growing population and shrinking agriculture lands, we need new technologies to increase yield coming from our fields. Nanotechnology is a relatively new field of science that deals with tiny particles having a size of less than 100 nm in at least one dimension. The advantage of the application of such tiny nanoparticles includes (Shawon et al. 2020): 1. A high surface-to-volume ratio which leads to more contact with plant cells leading to higher adsorption. 2. Application of relatively less quantity. 3. Relatively higher mobility and penetration into the soil. 4. Particle size can be synchronized, i.e. similar size and properties of all particles. 5. Slow- and long-term release of elements and/or encapsulated chemicals. 6. Easy to manage chemical properties. 7. Relatively higher sensitivity and faster results. 8. Low background noise as compared to other sensors. 9. Fewer chances of particles clustering in soil or water so uniform distribution in the field. These features can be very useful in agriculture. For example, leaching of pesticides and fertilizer from agricultural soil to water resources leads to big environmental problems. If we use nanoparticles, we can reduce the quantity of applied

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fertilizers by 100 to 1000-fold or more (Kheyri et al. 2019). This will lead to lower chances of extra material leaching into water bodies. For example, nitrogen leaching from traditional fertilizers into groundwater is a big environmental concern. Pandorf et al. observed that up to 70% NPK fertilizer can be replaced with only 1% of its weight of graphite carbon nanoparticles without any negative effect on the yield of lettuce in the field (Pandorf et al. 2019). This leads to a 70% reduction in nitrogen leaching. This indicates nanoparticles can replace a significant amount of fertilizers used in the field. This will also help in easier transportation of these particles instead of transporting tonnes of fertilizers. Sometimes fertilizers form clusters when added in soil or water and do not distribute uniformly in the field. This can also be tackled by the application of nanoparticles. Many times, fertilizers are effective only for a few days as they leach out. Nanoparticles can release fertilizers for a longer time. Xhi et al. developed superhydrophobic biopolymer-coated slow-release fertilizers that contain magnetite nanoparticles (Fe3O4) (Xie et al. 2019). This coating leads to an adequate slow release of nanoparticles which can last up to 100 days. These were stable in the soil also and could sustain various environmental conditions. The application of nanotechnology can address many problems of agriculture (Shawon et al. 2020). The benefits may include: 1. 2. 3. 4. 5. 6. 7. 8. 9.

Large uncultivated lands can be modified for cultivation. Better management of farms. Maintaining the fertility of the soil. Increase in productivity of the agriculture field. Enhanced tolerance towards biotic and abiotic stresses. Less application of fertilizers and pesticides. Reduction of wastage of the agriculture field. Increase storage time of seeds. Better management of crops after harvesting.

In the end, nanotechnology can also help in or goal towards precision farming (Duhan et al. 2017). Precision agriculture involves a detailed analysis of the field and application of specific fertilizers and agricultural practices required in that specific agriculture field (Gebbers and Adamchuk 2010). In other words, we can say that it is a personalization of agricultural practices for each field and crop depending on field and environmental conditions.

9.3

Application of Nanotechnology in Agriculture

As mentioned above nanotechnology can be used in agriculture in various aspects (Fig. 9.1). This includes for the detection of soil quality by nanosensors, enhancement of soil quality and plant growth by nanofertilizer, application to enhance plant tolerance against abiotic and biotic stresses, and its use for the fortification of fruits, vegetables, etc. Here we will discuss each of these aspects in detail.

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Fig. 9.1 Various applications of nanomaterials in agriculture

9.3.1

Application of Nanomaterials as Nanosensors

Sensors are molecules that are used to detect the presence of specific target molecules or alteration of environmental conditions. This could include alteration of pH, salinity, moisture content, soil water retention, excess presence of particular soil minerals, etc. (Table 9.1) These can also be used for the detection of microbes such as pathogens or beneficial microbes. If the reporter molecules are tiny nanomaterials, these are called nanosensors. If the nanosensor also includes a biological component such as nucleic acid, protein, or carbohydrate it is called a nanobiosensor (Bellan et al. 2011). The advantages of nanobiosensors over common sensors include higher efficiency and sensitivity due to higher surface-to-volume ratio and small size. They also provide a high signal-to-noise ratio and usually have a relatively low effect on the biological system. In most of the cases, a nanobiosensor need to have three components (Rai et al. 2012). The first part is a sensor that detects the target molecules. These could be nucleic acid sequences, antibodies, receptors, or carbohydrate molecules. The second part is the transducer that helps in the relay of the signal from the probe to the detector. These are generally nanoparticles and could act as electrodes, transistors, or conductors. The third part is the detector, which reads output from the probe or transducer and converts it into digital or visible form for analysis, display, and storage. In agriculture, mainly two types of nanosensors are being used, electrical and nanobiosensors (Dubey and Mailapalli 2016). Nanotubes, nanoparticles,

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Table 9.1 Some examples of nanomaterials used as nanobiosensors Nanoparticles Gold nanobiosensor

Applied on Oilseed rape

Gold nanoparticles

Soil

Gold nanoparticles coated on carbon nanotubes (CNTs) Gold nanorods

Soil and vegetable samples Leaf saps of orchid leaves

Copper nanoparticle

Oilseed rape

Carbon nanotubes

Lettuce, arugula, spinach, strawberry blite Sorrel and Arabidopsis thaliana Soil and water

Carbon nanotubes

Carbon nanotube-based nanosensors

Application Detection of the salicylic acid level Detection of organophorous pesticide Detection of pesticide triazophos

References Wang et al. (2010) Wu et al. (2011) Li et al. (2012)

Detection of cymbidium mosaic virus or odontoglossum ringspot virus Detection of the salicylic acid level Wound-induced H2O2 fluctuation in the leaves

Lin et al. (2014) Wang et al. (2010) Lew et al. (2020)

Wound-induced H2O2 fluctuation in the leaves

Lew et al. (2020)

Detection of bismuth

Mortazavi and Farmany (2017) Yao et al. (2009) Chen et al. (2016) Koman et al. (2017)

Silica nanoparticles

Tomato

Silver nanocluster

Soil

Detection of Xanthomonas axonopodis Detection of nitrite

Nanoparticle-based plant compatible conducting ink

Peace lily

Measure stomatal aperture

nanoprobes, and nanowires are different types of nanosensors being used in agriculture (Omanović-Mikličanina and Maksimović 2016, Yang et al. 2013). Wireless compatible nanosensors are being used for the detection of pesticides in food material. Organophosphorous pesticide was detected by acetylcholinesterase coated nanocomposite of gold nanoparticles and poly(dimethyldiallylammonium chloride) protected Prussian blue (Wu et al. 2011). Gold nanoparticles coated on carbon nanotubes (CNTs) modified glassy carbon electrodes were used for the detection of pesticide triazophos in vegetable samples (Li et al. 2012). Nitrite is a major component of many preservatives, but it is also a carcinogen. Hyperbranched polyethyleneimine protected silver nanocluster based nanosensor was proposed for the detection of nitrite in food samples (Chen et al. 2016). Aptasensors are nanosensors that consist of a probe (a single-stranded nucleic acid or peptide molecule) attached to a nanomaterial that acts as a transducer. These probes then can detect different proteins, nucleic acid sequences, microbes, viruses, or toxic compounds. The transducers can be nanoparticles, nanoclusters, nanotubes, semiconductor nanoparticles, etc. (Sharma et al. 2015). Yao et al. used antibody

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conjugated fluorescent silica nanoparticles to detect Xanthomonas axonopodis that causes bacterial spot on tomato (Yao et al. 2009). Lin et al. used antibody-coated gold nanorods for detection of cymbidium mosaic virus or odontoglossum ringspot virus in leaf saps of orchid leaves (Lin et al. 2014). They also observed that this approach is relatively faster and up to 30 times more sensitive than traditional ELISA based detection. Nanotechnology can be used for real-time detection of plant hormones, signalling molecules, and metabolites in the living plants. Salicylic acid (SA) is a key phytohormone involved in immune responses. Gold electrode modified with copper nanoparticles was used for precise detection of the salicylic acid level in oilseed rape plants infected with the fungal pathogen Sclerotinia sclerotiorum (Wang et al. 2010). Nanosensors are also applied for the detection of other plant hormones such as ethylene, jasmonic acid, abscisic acid (Giraldo et al. 2019). Nanotechnology has also helped in the measurement of intracellular fluctuation of secondary signalling messengers such as hydrogen peroxide (H2O2) and calcium (Ca2+) (Jin et al. 2010; Son et al. 2011). Recently Lew et al. used single-walled carbon nanotubes to detect wound-induced H2O2 fluctuation in the leaves of six plant species, namely lettuce, arugula, spinach, strawberry blite, sorrel, and arabidopsis (Lew et al. 2020). Interestingly, the output can be monitored in portable electronic devices which increase the ease of plant health monitoring in the field. Nanosensors are also developed for realtime measurement of glucose and sucrose in the Arabidopsis root tip (Chaudhuri et al. 2008). Nanoparticles can also be used for detection of plant physiological processes. Koman et al. developed nanoparticle-based plant compatible conducting ink to measure stomatal aperture in peace lily plants (Koman et al. 2017). Nanosensors are also used for the detection of pH not just at the cellular level but at the level of individual cell organelle (Shen et al. 2013). Zheng et al. developed carbon dot nanoparticles that can be used for the detection of chromium (IV) (Zheng et al. 2013). Bismuth is a common soil contaminant that affects the life of soil microbes, plants as well as human health (Wang et al. 2019). Mortazavi et al. developed rubinic acid mixed carbon nanotube-based nanosensors for the detection of bismuth in water and soil (Mortazavi and Farmany 2017). Graphene-based nanosensors have also been useful for the detection of various heavy metal ions (Zhang et al. 2018). As we can see from the examples above, nanosensors can be used for real-time detection of a large variety of molecules and environmental conditions.

9.3.2

Application of Nanomaterials to Increase Soil Quality

Nanoparticles can also enhance soil quality for the growth of plants. Iron is a key component of agriculture soil but it is easily leached or is unavailable in different pH conditions (Table 9.2). Some chelates and common iron salts are used as an alternative but they affect phosphorus availability of soil. Das et al. observed application of iron oxide (Fe3O4) nanoparticles drastically increased soil availability of iron in a wide pH range (Das et al. 2016). They also observed certain

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Table 9.2 Application of nanomaterials for soil quality enhancement Applied on Soil

Nanoparticles Iron oxide (Fe3O4) nanoparticles Fe2O3 nanoparticles

Application Increase soil quality

References Das et al. (2016)

Soil Soil

Reduces the emission of carbon dioxide Soil water retention

Zinc oxide nanoparticle

Soil

Enhanced soil enzymatic activity

Copper oxide nanoparticle NPK loaded chitosan nanoparticles Urea nanoparticles

Soil

Enhanced soil enzymatic activity

Soil

Substitute for NPK fertilizer with less quantity Substitute for urea with less quantity

Rashid et al. (2017) Kumari et al. (2020) Asadishad et al. (2018) Asadishad et al. (2018) Ha et al. (2019)

Zeolites and nano-clays

Soil

Salama and Badry (2020)

concentrations of these nanoparticles increase organic carbon, nitrogen, and phosphorus level as well as the enzymatic activity of soil. These also had no negative effect on beneficial soil microbes and germination of black and green gram seeds. Kumari et al. observed nanoparticles such as zeolites and nano-clays can be used for water or liquid agrochemicals retention in the soil. In this way, they will be slowly released in the plants (Kumari et al. 2020). Asadishad et al. looked for changes in soil enzymatic activity and microbial community after the application of various concentrations of different nanoparticles (Asadishad et al. 2018). They observed zinc oxide (ZnO) and copper oxide (CuO) nanoparticle have a positive impact on soil enzymatic activity while no effect on microbial community structure. NPK nanofertilizer was recently developed by loading N, P, and K on chitosan nanoparticles. The application of NPK nanofertilizer on coffee plants leads to an increase in growth, photosynthesis, and nutrient uptake in the coffee plant (Ha et al. 2019). Abdelsalam et al. applied the same fertilizer to wheat plants in the field and observed increased yield (Abdelsalam et al. 2019). Recently Salama and Badry replaced half of the urea with urea nanoparticles and applied in the field of wild type maize variety (Salama and Badry 2020). They observed no significant difference in yield and a nutritive value indicating a significant amount of fertilizers can be replaced by nanofertilizers. Application of Fe2O3 nanoparticles to the soil sequesters organic carbon from the soil and reduces the emission of carbon dioxide. Hence, it helps in reducing global warming (Rashid et al. 2017). Pandorf et al. were also successful in replacing 70% of NPK fertilizer with just 1% of graphite carbon nanoparticles without any effect on lettuce yield in the field (Pandorf et al. 2019).

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Application of Nanomaterials to Increase Plant Yield

Increasing yield from a crop field is a major goal of modern agriculture practices. This could also be achieved by a higher yield per plant, faster growth, more biomass, etc. Nanoparticles are applicable in this field as well. Nanoparticles made of different elements have been used for this purpose (Table 9.3). Lahiani et al. observed that long-term (20 weeks) exposure of carbon nanotubes to barley, soybean, and corn increased plant photosynthesis without any negative effect on plant growth and Table 9.3 Application of nanomaterial for higher yield from crops Nanoparticles Carbon nanotubes Carbon nanotubes Graphene and carbon nanotubes Zinc oxide nanoparticles Zinc oxide nanoparticles Zinc nanoparticles Zinc and silicon nanoparticles Zinc nanoparticles Zinc nanoparticles

Studied in Barley, soybean, and corn Tomato Switchgrass seeds, sorghum seeds

References Lahiani et al. (2017) McGehee et al. (2017) Pandey et al. (2018)

Rice

Increased growth, yield, and zinc content Increased dry and fresh weight Increased yield and quality of fruit Increased grain yield

Pearl millet

Increased plant yield

Tarafdar et al. (2014)

Rice, maize, potato, sunflower, or sugarcane Rice

Increased plant yield

Monreal et al. (2016)

Increased grain yield

Kheyri et al. (2019)

Increase in plant growth and life span

Boutchuen et al. (2019)

Increase in root and shoot biomass

Meier et al. (2020)

Potato, chickpea, and peanuts

Increases yield

Iron oxide nanoparticles

Wheat grains

Boron nanoparticles

Pomegranate plant

Increases seed germination rate and length of the shoot Increased yield and quality of fruit

Roozbahani and Pour Ali (2015), Rui et al. (2016), Pawar et al. (2019) Sundaria et al. (2019)

Silicon nanoparticles Haematite (Fe3O4) nanoparticles Calcium borate nanoparticles Iron nanoparticles

Grains of maize

Implications Assessment of growth yield Increased fruit production Increased seed germination rate

Coffee plant Pomegranate plant

Chickpea, green gram, black bean, and red bean Lettuce and zucchini

Subbaiah et al. (2016) Rossi et al. (2019) Davarpanah et al. (2016) Kheyri et al. (2019)

Davarpanah et al. (2016)

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development (Lahiani et al. 2017). Similarly, Pandey et al. observed that the application of graphene or carbon nanotubes to switchgrass seeds increases the seed germination rate (Pandey et al. 2018). They also observed that the same treatment led to the early germination of sorghum seeds. Graphene application also increased the biomass of switchgrass. McGehee observed application of carbon nanotubes increases fruit production in tomato plants in a hydroponics system (McGehee et al. 2017). Subbaiah et al. observed that ZnO nanoparticles significantly increase growth, yield, and zinc content in grains of maize in the field studies (Subbaiah et al. 2016). Foliar spray of relatively low concentration of boron and zinc nanoparticles on the pomegranate plant led to an increase in yield and quality of fruit (Davarpanah et al. 2016). Kheyri et al. applied zinc and silicon nanoparticles in the rice field and compared changes in grain yield with respect to field treated with traditional fertilizer form of zinc and silicon (Kheyri et al. 2019). All four applications increase grain yield in the field. Interestingly, 300 g/hectare of zinc or silicon nanoparticle application gave equivalent or higher yield than 9 kg/hectare of zinc fertilizer or 392 kg/ hectare of silicon fertilizer. Foliar spray of ZnO nanoparticles in coffee plants leads to an increase in the growth of the plants leading to an increase in dry and fresh weight as compared to traditionally used fertilizer form of zinc sulphate (Rossi et al. 2019). Tarafdar et al. observed foliar spray of biologically synthesized zinc nanoparticles on pearl millet in the field significantly increases the yield of the plants as compared to ordinary zinc fertilizer (Tarafdar et al. 2014). A significant increase was observed in shoot length, root length, root area, chlorophyll content, total soluble leaf protein, plant dry biomass, and various enzyme activities. The application of zinc nanoparticles in rice, maize, potato, sunflower, or sugarcane also increases yield (Monreal et al. 2016). Treatment of wheat grains with iron oxide nanoparticles increases the germination rate of seeds and length of the shoot (Sundaria et al. 2019). Recently Boutchuen et al. soaked seeds of four different legume species, i.e. chickpea, green gram, black bean, and red bean in haematite (Fe3O4) nanoparticles before germination (Boutchuen et al. 2019). They observed a two to eight-fold increase in plant growth in all plant species. They also observed an increased duration of growth of plants and the number of fruits per plant as well as faster development of fruits. They also obtained healthy offspring from nanoparticle treated plants indicating no negative side effects on next generation if plants are treated with iron nanoparticles. Meier et al. observed an increase in root and shoot biomass after the application of calcium borate nanoparticles in lettuce and zucchini plants in boron deficient conditions (Meier et al. 2020). The growth of plants in nanoparticle supplemented soil was higher than that in soil supplemented with normal boron or commercial boron fertilizer. This indicates that calcium borate nanoparticles can act as a substitute for boron in the field. Similarly, the application of iron nanoparticles increases yields in potato, chickpea, and peanuts (Roozbahani and Pour Ali 2015; Rui et al. 2016; Pawar et al. 2019). These pieces of evidence indicate nanoparticles could be very useful tools to increase the yield of agricultural fields.

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Application of Nanomaterials for Stress Tolerance in Plants

Nanoparticles can also be used to enhance plant tolerance to various biotic and abiotic stress. Abiotic stress includes high temperature, salinity, drought, cold, heavy metals, etc., whereas biotic stress is caused by infections from biological agents such as bacteria, viruses, fungi, parasites, insects, etc. In this section, we will discuss some applications of nanoparticles to enhance the tolerance of plants against various stresses.

9.3.5

Application of Nanomaterials for Abiotic Stress Tolerance

There are a large number of environmental conditions that can lead to abiotic stress in the plant. This includes drought, submergence, excess or lack of some soil components, soil pH, temperature, etc. Nanoparticles have been useful in addressing some of these problems (Table 9.4). For example, application of cerium oxide Table 9.4 Applications of nanomaterials for plant stress tolerance Nanoparticles Abiotic stress Cerium oxide nanoparticles Cerium oxide nanoparticles Cerium oxide nanoparticles Selenium nanoparticles Iron oxide nanoparticles Carbon nanotubes or graphene Biotic stress Copper oxide and manganese oxide nanoparticles Copper oxide and manganese oxide nanoparticles Silver nanoparticles Fe3O4 Silicon nanoparticles

Crop

Application

References

Brassica napus Arabidopsis

Enhanced salt stress tolerance Enhanced salt stress tolerance

Rossi et al. (2017) Wu et al. (2017)

Rice

Enhanced nitrogen stress tolerance

Sorghum

Increases seed yield under hightemperature condition Drought tolerance

Brassica napus Switchgrass and sorghum

Increases salt stress tolerance

Wang et al. (2020) Djanaguiraman et al. (2018) Palmqvist et al. (2017) Pandey et al. (2018)

Tomato

Tolerance against Fusarium

Elmer and White (2016)

Eggplants

Tolerance against Verticillium

Elmer and White (2016)

Rice

Tolerance against Rhizoctonia solani, Xanthomonas oryzae pv. oryzae, Helminthosporium oryzae Tolerance against tobacco mosaic virus Tolerance against parasitic week broomrape

Adak et al. (2020)

Nicotiana benthamiana Tomato

Cai et al. (2020) Madany et al. (2020)

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(CeO2) nanoparticles increased the salt stress tolerance in Brassica napus plants (Rossi et al. 2017). This is achieved by increasing photosynthesis and modification of root apoplastic barriers in stressed plants. CeO2 nanoparticles were also reported to increase salt tolerance in Arabidopsis by increasing the scavenging of reactive oxygen species (ROS) (Wu et al. 2017). Recently CeO2 nanoparticles were reported to alleviate low and high nitrogen stress tolerance in rice (Wang et al. 2020). Interestingly, under normal soil nitrogen level, CeO2 affects plant growth indicating the condition-dependent applicability of these nanoparticles. Foliar spray of low concentration of selenium nanoparticles increases seed yield of sorghum plants under high-temperature condition (Djanaguiraman et al. 2018). In sorghum, high temperature increases ROS levels such as superoxide radicals and hydrogen peroxide, while it also reduces the level of antioxidants enzymes such as superoxide dismutase, catalase, peroxidase, and glutathione peroxidase. Selenium nanoparticles increase the activity of these antioxidant enzymes and thus reduce the ROS level in plants. Drought also leads to an increase in the level of hydrogen peroxide and lipid peroxidation in Brassica napus plants. Application of iron oxide nanoparticles reduces these ROS levels and also helps the plant survive in drought condition in the pods (Palmqvist et al. 2017). Application of carbon nanotubes or graphene increases salt stress tolerance in switchgrass and sorghum plants (Pandey et al. 2018).

9.3.6

Application of Nanomaterials to Control Diseases

Nanoparticles can also be used to control the growth of pathogens and to improve plant health in conditions of pathogen infestation (Table 9.4). Kanhed et al. observed antifungal activity of copper nanoparticles against four plant pathogens, namely Phoma destructive, Curvularia lunata, Alternaria alternata, and Fusarium oxysporum (Kanhed et al. 2014). Elmer and White observed Fusarium infected tomato plants or Verticillium infected eggplants performed better if those were sprayed with CuO nanoparticles in a soilless medium in the greenhouse (Elmer and White 2016). They also observed CuO and manganese oxide nanoparticles increase tolerance of tomato against Fusarium fungus. They observed these nanoparticles do not affect fungus in the in vitro studies indicating this tolerance was achieved likely by inducing plant immune responses. Recently Adak et al. synthesized silver nanoparticles using rice leaf extract (Adak et al. 2020). They applied micromolar concentration of these biologically synthesized nanoparticles on different rice pathogens and observed it can reduce the growth of three pathogens Rhizoctonia solani, Xanthomonas oryzae pv. oryzae, and Helminthosporium oryzae in in vitro assays. They also observed it can control the in planta growth of R. solani, which is very difficult to manage rice pathogen. Silver nanoparticles are also reported as an antifungal agent against wheat Fusarium head blight pathogen Fusarium graminearum. A high concentration of silver nanoparticles is considered harmful to soil microbial activity (Hänsch and Emmerling 2010) but they observed the effect of silver nanoparticles on soil

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microbiome is similar to another common fungicide. Kim et al. observed in vitro antifungal activity of silver nanoparticles against 18 plant pathogens (Kim et al. 2012). Interestingly they observed different levels of fungal growth inhibition in three different media used indicating different soil components also play a role in the efficiency of these nanoparticles. Nanoparticles of zinc, copper, and silver have antimicrobial activities that could be useful for agricultural purposes (Dinesh et al. 2012). Foliar application of Fe3O4 nanoparticles induces immune responses in Nicotiana benthamiana (Cai et al. 2020). Recently Madney et al. observed the application of silicon nanoparticle to tomato leads to cell wall fortification and ROS modulation (Madany et al. 2020). This in turn provides tolerance against weed Orobanche ramose. All these pieces of evidence indicate nanomaterials can be used in agriculture to control the spread of pathogens.

9.3.7

Application of Nanomaterials for the Fortification of Crops

Biofortification is the process of improvement of the nutritional quality of food and crops. The common methods involve nourishment of the soil, development of new breeding lines, or genetic modification of crop plants. Some findings indicate nanoparticles can be useful for the biofortification of crops (Emeka et al. 2019) (Table 9.5). Zinc is an essential nutrient for human health and plays important role in many biological activities. But zinc deficiency is very common in a large number of people (Maxfield and Crane 2020). Nanoparticles can be used for the fortification of zinc in food products. ZnO nanoparticles are reported to increase zinc content in maize grains (Subbaiah et al. 2016). Foliar spray of ZnO nanoparticles in coffee plants leads to a higher amount of zinc in the leaves as compared to the traditional fertilizer form of zinc sulphate (Rossi et al. 2019). Zinc nanoparticles are also used recently for the fortification of zinc in wheat and maize (Dimkpa et al. 2020; Umar et al. 2020). Selenium is an essential microelement for all forms of life. Crops are rarely enriched in selenium. Golubkina et al. enriched spinach plants with selenium by application of selenium nanoparticles (Golubkina et al. 2017). This also leads to an Table 9.5 Application of nanomaterials for biofortification Nanoparticles Zinc oxide nanoparticles Zinc oxide nanoparticles Zinc oxide nanoparticles Selenium nanoparticles Iron oxide nanoparticles

Crop Maize grains Coffee leaves Wheat Spinach leaves Wheat grains

Application Increases zinc content in grains A higher amount of zinc in the leaves A higher amount of zinc in grains Increases selenium content Increased iron level in grains

References Subbaiah et al. (2016), Umar et al. (2020) Rossi et al. (2019) Dimkpa et al. (2020) Golubkina et al. (2017) Sundaria et al. (2019)

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increase in plant growth. Similarly, Sundaria et al. observed the priming of wheat seeds with iron oxide nanoparticles increased the iron level in grains (Sundaria et al. 2019). These methods can be used for the enrichment of micronutrients in edible parts of plants in geographical areas where those deficiencies are prevalent.

9.4

The Risk Associated with the Application of Nanoparticles in Agriculture

No technology is completely safe. The same applies to nanotechnology. Nanoparticles can replace a heavy dosage of traditional fertilizers. But a high concentration of nanoparticles in soil or plants may also affect the health of the plant, soil microbial community, and sometimes the health of consumers. McGehee et al. observed a dramatic change in the fruit metabolome of carbon nanotubes exposed tomato plants (McGehee et al. 2017). They also observed an accumulation of nanotubes in fruit. The same group also observed the accumulation of nanotubes in different plant organs (such as leaves, stems, roots, and seeds) of barley, soybean, and corn plants when applied in the hydroponic system (Lahiani et al. 2017). When the concentration of carbon nanomaterials exceeds the threshold applicable concentration in soil, nanoparticles are engulfed by these microorganisms through endocytosis which subsequently demolish the microbial community, thus disturbing the ecological balance (Kang et al. 2019; El Badawy et al. 2011). Application of engineered nanomaterials such as TiO2, Al2O3, Ag, ZnO, and CuO can have a negative impact on soil microbiota and soil organic matter dynamics (Usman et al. 2020). Asadishad et al. observed higher concentrations of silver oxide nanoparticles lead to a reduction in some enzymatic activities in the soil while also severely changing the microbial community structure of soil (Asadishad et al. 2018). Nanoparticles not only affect microbial community but they can also affect plant physiology as well. Nanoparticles can damage the plant cell wall, disrupt the electron transport system of mitochondria, and generate reactive oxygen species (Hossain et al. 2015). ZnO nanoparticles inhibit chlorophyll synthesis and reduce photosynthesis efficiency in wheat (Ramesh et al. 2014). CuO nanoparticles inactivate PS-II centres, halt the electron transport chain, and also reduce the photosynthetic rate and pigment production (Perreault et al. 2014). High dose of silver nanoparticles in rice generate reactive oxygen species and decrease mitochondrial membrane potential (Nair and Chung 2014). Over dosage of Ag nanoparticle leads to a higher concentration of cytokinin in Capsicum annuum, whereas a decrease in auxin and abscisic acid levels has been observed in cotton plants. This indicated that nanoparticles affect hormonal balance in plants as well. Al2O3 nanoparticles reduce root length and seedling growth in Nicotiana tabacum (Burklew et al. 2012). Colloidal suspensions of titanium oxide nanoparticles hinder leaf transpiration and growth processes in maize (Asli and Neumann 2009). As discussed before, NPK nanofertilizer can increase the yield of wheat plants (Abdelsalam et al. 2019). However, they also observed various chromosomal aberrations in root tip cells of plants. This indicates we should be a bit more cautious before applying new

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technologies on a large scale. Similarly, in onion, even a low dosage of Ag-nanoparticles causes chromosomal aberrations in both meiotic and mitotic cells (Saha and Dutta Gupta 2017). Industrialization is leading to an increase in atmospheric CO2 concentration. The behaviour of some nanoparticles and their interaction with other molecules depends on environmental conditions. Du et al. observed that titanium oxide nanoparticles show a significant negative effect on biomass, grain yield, total fat in grain, and total sugar in the grain of rice plants when exposed to 50% higher concentration of CO2 (Du et al. 2017). They also observed alteration in the structure of the microbial community in such conditions. The application of nanoparticles should be concentration-dependent and particle dependent. Some nanoparticles alter the microbial community of soil and plant, while some others have no severe effect on the same. For example, gold nanoparticles do not have any effect on plant growth-promoting rhizobacteria (Shashi Kant et al. 2015). Conversely, silver nanoparticles exhibit antimicrobial activity in a diverse set of microbes in a concentration-dependent manner (Arya et al. 2019). So, adequate precautions need to be taken for using these in agriculture. There are few other disadvantages of using nanoparticles in agriculture such as its transmission through different trophic levels of the food chain which is becoming a major concern for humans.

9.5

The Way Forward

Nanoscience is still a budding field of science and more large-scale studies need to be performed for its implementation in agriculture. The application of nanoparticles certainly shows many advantages but also exposes some risk factors. There are many questions yet to be answered in the field. Some of the major questions that need to be addressed include: 1. Potential side effects on the health of consumers if nanoparticles are carried forward in the food chain (Rajput et al. 2020, Lombi et al. 2019). 2. Effectiveness of nanoparticles in different kinds of soil and environmental conditions. 3. Effectiveness of nanoparticles in different crops. 4. The synergy between different nanoparticles. 5. Deep analysis of the compatibility of different nanoparticles with soil microbial communities. 6. The longevity of different nanoparticles in the agricultural field. 7. Effect of nanoparticle application on the next generation of crops. This area needs more exploration as every new component added in the soil can significantly change soil microbial community, enzymatic activity, and nutrient availability of soil. The same nanoparticles can have a different effect on different types of soil. The output also depends on environmental conditions such as

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temperature, soil pH, water availability, and the microbial community at that time. Enough studies are emphasizing the effect of nanoparticles on soil microbial communities. But Hänsch and Emmerling observed the effect of silver nanoparticles is similar to the effect of commercial fungicide. This indicates the application of nanoparticles may not have a more severe effect than other commonly used practices and could be used as an alternative (Hänsch and Emmerling 2010). More such studies need to be conducted before finally concluding anything.

9.6

Conclusion

Nanotechnology is becoming an integral part of multiple facets of science. It has a tremendous application in agriculture as well. It can be used for the detection of soil quality, composition, plant physiological condition, and pathogens. It can also be used for increasing the soil quality as well as for increasing the yield of the field. We can also use nanoparticles for increasing tolerance of plants against various plant pathogens and various environmental stresses. Nanoparticles can also be used for the enrichment of micronutrients in grains or fruits. But a relatively higher concentration of nanoparticles can harm soil, microbes, plants as well as consumers. So, large-scale detailed studies can address these issues and help in establishing the application of nanoparticles in the agriculture field.

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Burklew CE, Ashlock J, Winfrey WB, Zhang B (2012) Effects of aluminum oxide nanoparticles on the growth, development, and microRNA expression of tobacco (Nicotiana tabacum). PLoS One 7:e34783 Cai L, Cai L, Jia H, Liu C, Wang D, Sun X (2020) Foliar exposure of Fe3O4 nanoparticles on Nicotiana benthamiana: evidence for nanoparticles uptake, plant growth promoter and defense response elicitor against plant virus. J Hazard Mater 393:122415 Chaudhuri B, HÖrmann F, Lalonde S, Brady SM, Orlando DA, Benfey P, Frommer WB (2008) Protonophore-and pH-insensitive glucose and sucrose accumulation detected by FRET nanosensors in Arabidopsis root tips. Plant J 56:948–962 Chen C, Yuan Z, Chang H-T, Lu F, Li Z, Lu C (2016) Silver nanoclusters as fluorescent nanosensors for selective and sensitive nitrite detection. Anal Methods 8:2628–2633 Das P, Sarmah K, Hussain N, Pratihar S, Das S, Bhattacharyya P, Patil SA, Kim H-S, Khazi MIA, Bhattacharya SS (2016) Novel synthesis of an iron oxalate capped iron oxide nanomaterial: a unique soil conditioner and slow release eco-friendly source of iron sustenance in plants. RSC Adv 6:103012–103025 Davarpanah S, Tehranifar A, Davarynejad G, Abadía J, Khorasani R (2016) Effects of foliar applications of zinc and boron nano-fertilizers on pomegranate (Punica granatum cv. Ardestani) fruit yield and quality. Sci Hortic 210:57–64 Dimkpa C, Andrews J, Fugice J, Singh U, Bindraban PS, Elmer WH, Gardea-Torresdey JL, White JC (2020) Facile coating of urea with low-dose ZnO nanoparticles promotes wheat performance and enhances Zn uptake under drought stress. Front Plant Sci 11:168 Dinesh R, Anandaraj M, Srinivasan V, Hamza S (2012) Engineered nanoparticles in the soil and their potential implications to microbial activity. Geoderma 173-174:19–27 Djanaguiraman M, Belliraj N, Bossmann SH, Prasad PV (2018) High-temperature stress alleviation by selenium nanoparticle treatment in grain sorghum. ACS omega 3:2479–2491 Du W, Gardea-Torresdey JL, Xie Y, Yin Y, Zhu J, Zhang X, Ji R, Gu K, Peralta-Videa JR, Guo H (2017) Elevated CO2 levels modify TiO2 nanoparticle effects on rice and soil microbial communities. Sci Total Environ 578:408–416 Dubey A, Mailapalli DR (2016) Nanofertilisers, Nanopesticides, nanosensors of Pest and Nanotoxicity in agriculture. In: Lichtfouse E (ed) Sustainable agriculture reviews, vol 19. Springer, Cham Duhan JS, Kumar R, Kumar N, Kaur P, Nehra K, Duhan S (2017) Nanotechnology: the new perspective in precision agriculture. Biotechnol Rep 15:11–23 El Badawy AM, Silva RG, Morris B, Scheckel KG, Suidan MT, Tolaymat TM (2011) Surface charge-dependent toxicity of silver nanoparticles. Environ Sci Technol 45:283–287 Elmer W, White J (2016) The use of metallic oxide nanoparticles to enhance growth of tomatoes and eggplants in disease infested soil or soilless medium. Environ Sci Nano 3:1072–1079 Emeka E, Uzoh I, Onwudiwe D, Babalola O (2019) The role of nanotechnology in the fortification of plant nutrients and improvement of crop production. Appl Sci 9:499 Gebbers R, Adamchuk VI (2010) Precision agriculture and food security. Science 327:828–831 Gerland P, Raftery AE, Ševčíková H, Li N, Gu D, Spoorenberg T, Alkema L, Fosdick BK, Chunn J, Lalic N, Bay G, Buettner T, Heilig GK, Wilmoth J (2014) World population stabilization unlikely this century. Science 346:234–237 Giraldo JP, Wu H, Newkirk GM, Kruss S (2019) Nanobiotechnology approaches for engineering smart plant sensors. Nat Nanotechnol 14:541–553 Golubkina NA, Folmanis GE, Tananaev IG, Krivenkov LV, Kosheleva OV, Soldatenko AV (2017) Comparative evaluation of spinach biofortification with selenium nanoparticles and ionic forms of the element. Nanotechnol Russia 12:569–576 Ha NMC, Nguyen TH, Wang S-L, Nguyen AD (2019) Preparation of Npk nanofertilizer based on chitosan nanoparticles and its effect on biophysical characteristics and growth of coffee in green house. Res Chem Intermed 45:51–63 HÄnsch M, Emmerling C (2010) Effects of silver nanoparticles on the microbiota and enzyme activity in soil. J Plant Nutr Soil Sci 173:554–558

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Understanding and Manipulation of Plant– Microbe Interaction Signals for Yield Enhancement

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Sohini Deb, Kamal Kumar Malukani, and Hitendra K. Patel

Abstract

Plants encounter a large number of organisms in everyday life, many of which are potential pathogens. They counteract these using their inducible immune responses. Plants can perceive the presence of pathogens by recognition of conserved molecular signatures of the microorganisms and elicit immune responses, which acts as a first line of defense. These elicitor molecules are called pathogen-associated molecular patterns (PAMPs) and the immune responses activated following perception of PAMPs is called PAMP-triggered immunity (PTI). Successful plant pathogens suppress this immune response by secreting effector molecules directly into the plant cell via their type III secretion system. This leads to effector-triggered susceptibility (ETS). Plants, in turn, recognize the presence of these effectors and mount immune responses that are referred to as effector-triggered immunity (ETI). Here we provide a broad overview of plant innate immune responses and also delve into how this understanding of plant immune responses can help engineer for yield enhancement. Keywords

Plant innate immunity · Pathogens associated molecular patterns (PAMPS) · PAMP-triggered immunity · Disease-resistant crops · Plant cell signaling

S. Deb · K. K. Malukani · H. K. Patel (*) Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Kumar et al. (eds.), Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), https://doi.org/10.1007/978-981-16-2956-3_10

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Introduction

Plants, being sessile organisms, are constantly under attack by various stresses in their surroundings. These consist of abiotic stresses like heat, cold, drought, salinity, etc., and biotic stress like an attack from pathogenic organisms and pests, which consist of bacteria, nematodes, fungi, and insects or herbivory. Hence, in order to survive, plants have to fight against these factors. Plants have evolved to perceive these individual stresses using specific receptors, which then activate downstream pathways essential for its survival. The plant immune response is two-layered. The first line of defense includes recognition of conserved microbial signature molecules, which can be flagellin, lipopolysaccharides, peptidoglycan, fungal chitin, etc. These are called pathogenassociated molecular patterns (PAMPs) (Jones and Dangl 2006). As a result of an attack by a potential pathogen, certain plant-derived signaling molecules are also released, which can also elicit plant immune responses. Such molecules are called damage-associated molecular patterns (DAMPs). Examples of DAMPs are cell wall degradation products, extracellular ATP, and plant elicitor peptides. These signals are recognized by pattern recognition receptor (PRR) proteins which are localized on the cell surface of the plants. Immunity resulting from the recognition of PAMP is called PAMP-triggered immunity (PTI) (Boller and Felix 2009) and immunity triggered by DAMP is called as DAMP-triggered immunity (DTI) (Fig. 10.1). A successful pathogen can suppress this immune response using effector proteins secreted through its type III secretion system. This leads to effector-triggered

Fig. 10.1 The plant immune response is multi-layered. Pattern associated molecular pattern (PAMPs) and damage-associated molecular pattern (DAMPs) elicit pattern triggered immunity which is suppressed by pathogen effectors (Jones and Dangl, 2006). These, in turn, are recognized by nucleotide-binding leucine-rich repeat proteins (NB-LRRs), leading to effector-triggered immunity. Pathogens often evolve to evade this by using different strategies to avoid recognition. ETS: effector-triggered susceptibility; HR/PCD: hypersensitive response/programmed cell death

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susceptibility (ETS) (Hogenhout et al. 2009). Recognition of these effector proteins leads to the activation of the second layer of defense. Effector recognition generally takes place by nucleotide binding-leucine rich repeat (NB-LRR or NLR) proteins that are also termed as resistance (R) genes. This layer of the immune response is called effector-triggered immunity (ETI) (Schreiber et al. 2016). This usually leads to a hypersensitive response (HR) and localized cell death to avoid the spread of the pathogen to adjacent plant cells. In the course of evolution, a putative pathogen can evolve to suppress ETI either by avoiding recognition by the R-gene or by suppressing downstream ETI signaling after effector recognition. Only pathogens that can overcome both PTI and ETI can successfully cause disease in plants. The current attempts of using our knowledge about PTI and DTI to manage plant diseases and further improve yield have been discussed here.

10.2

Signal Perception in Plant Immune Response

Recognition of the pathogen attack is the first and key step in the activation of the plant immune responses. The defense response is an energy-intensive process as it involves the activation and deactivation of many molecular pathways, triggering of multiple signaling events, synthesis of antimicrobial molecules, etc. In many cases, basic metabolic processes are compromised in order to redirect the plant’s resources to counteract the pathogen. Therefore, a clear recognition of danger is a very crucial step. Plants have evolved multiple ways to recognize danger. In the extracellular milieu, plants can recognize key structural components of pathogens [e.g. chitin, flagellin, lipopolysaccharides (LPS), peptidoglycans (PGN), etc.], pathogen secreted molecules (e.g. Ef-Tu, RaxX), self-cell wall degradation products [e.g. oligogalacturonan (OG), cellobiose], plant secreted signaling molecules [e.g. plant elicitor peptides (Peps)], extracellular ATP, etc. and activate PTI. In the cytoplasm, plants can sense the presence of effector molecules and activate ETI.

10.2.1 Recognition of PAMPs Plants can perceive the presence of potential pathogens by recognition of certain molecular signatures of microorganisms, which are usually conserved in the pathogen but are absent in hosts. These are called as PAMPs (Buttner and Bonas 2010; Keshavarzi et al. 2004), alternatively also known as microbe associated molecular patterns (MAMPs) (Boller and Felix 2009). Here we will discuss a few well-studied MAMP’s and how they are perceived by plants.

10.2.1.1 Flagellin Perception in Plants One of the well-studied PAMPs is the bacterial flagellin derived 22-amino-acid long peptide called flg22. The N-terminal portion of flagellin is highly conserved and acts as a potent elicitor of immune responses (Felix et al. 1999). In Arabidopsis, flg22 also induces immune responses like callose deposition, accumulation of the marker

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defense protein PR1, and strong inhibition of seedling growth (Gomez-Gomez et al. 1999). Flagellin-sensing 2 (FLS2), the receptor for flg22, was identified in a screen for mutants that were insensitive to flg22. FLS2 encodes an LRR receptor kinase (LRR-RK) (Felix et al. 1999). The extracellular N-terminal LRR repeat domain of FLS2 is required for binding with flg22, while the C-terminal region, which contains the kinase domain, is required for induction of immune responses in Arabidopsis (Meindl et al. 2000; Sun et al. 2013). Ligand binding leads to heterodimerization of FLS2 with another receptor-like kinase BAK1 via flg22 (brassinosteroid-associated kinase 1) (Chinchilla et al. 2006; Schulze et al. 2010; Sun et al. 2013).

10.2.1.2 Recognition of Elongation Factor in Plants Another protein which strongly elicits the plant immune responses was identified as the elongation factor EF-Tu. It is perceived by the EF-Tu receptor (EFR) which also belongs to the receptor-like kinase family (Kunze et al. 2004; Shiu and Bleecker 2003). Acetylated N-terminal fragments of EF-Tu, called elf18 (18-amino acid long peptide) and elf26 (26-amino acid long peptide) triggered immune responses in Arabidopsis (Kunze et al. 2004). Interestingly, even though EF-Tu perception is restricted to only the Brassicaceae family (Kunze et al. 2004; Zipfel et al. 2006), the rice genome also contains many genes which encode similar LRR-RKs, suggesting that they may function as pattern recognition receptor (PRRs) of yet to be identified MAMPs (Boller and Felix, 2009). Rice has also evolved to recognize another 50-amino acid long peptide sequence EFa50 that corresponds to the middle portion of EF-Tu (Furukawa et al. 2014). 10.2.1.3 Recognition of Microbial Cell Wall by Plants Peptidoglycan (PGN) is a highly conserved and major component of Gram-negative bacterial cell walls (Erbs et al. 2008; Gust et al. 2007; Erbs and Newman 2012), whereas chitin is a major component of the fungal cell wall. PGN is made up of glycan strands that contain alternating N-acetylmuramic acid (MurNAc) and N-acetylglucosamine (GlcNAc) which are linked by β-1,4-glycosidic bonds (Erbs and Newman 2012), whereas chitin is a polymer of N-acetyl-D-glucosamine (GlcNAc). Both PGN and chitin are recognized in plants through receptors containing the LysM motif (Lysine motif), which is a small 40-amino acid long globular domain (Kaku et al. 2006; Miya et al. 2007; Wan et al. 2008). PGN was shown to trigger immune responses such as induced ROS production, increase in extracellular pH, PR1 gene expression, and callose deposition in Arabidopsis (Gust et al. 2007; Erbs et al. 2008). In Arabidopsis, the receptor-like proteins AtLYM1 and AtLYM3 have been shown to bind to peptidoglycan and induce an immune response through AtCERK1 (chitin elicitor receptor kinase), as cerk1 mutant Arabidopsis plants do not show peptidoglycan-mediated immune responses (Willmann et al. 2011). CERK1 also recognizes 7-8 residues long chitin oligomers, which leads to homodimerization and transphosphorylation of CERK1, that activates downstream signaling cascades and induces immunity in Arabidopsis (Liu et al. 2012). Chitin recognition in rice takes place via a GPI-anchored receptorlike protein CEBiP (chitin elicitor binding protein) that lacks a cytoplasmic kinase

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domain (Kaku et al. 2006; Kouzai et al. 2014). Ligand binding leads to homodimerization of OsCEBiP (Hayafune et al. 2014). This dimer binds to OsCERK1 that activates downstream signaling (Shimizu et al. 2010). The rice proteins OsLYP4 and OsLYP6 also interact with PGN and chitin oligomers (Liu et al. 2013).

10.2.1.4 Bacterial LPS and its Recognition by Plants The extra-polysaccharides produced by certain bacterial pathogens, e.g. Xanthomonas, consist of two components: exopolysaccharides (EPS) and lipopolysaccharide (LPS). Both these components are extracellularly secreted by the bacterium and act as the first layer of defense of the bacterium. LPS, which is unique to Gram-negative bacteria, consists of a lipid A, a core oligosaccharide and repeating units of a polysaccharide side chain called as O-antigen (Raetz and Whitfield 2002). Plants have evolved to sense LPS of the bacterial cell wall as PAMP, which induces a strong immune response (Erbs et al. 2010). Recently, a bulb-type (B-type) lectin S-domain (SD)-1 RLK (receptor-like kinase) LORE (lipooligosaccharide-specific reduced elicitation) was identified as a putative receptor of LPS in Arabidopsis, wherein LORE mutants were observed to be insensitive to LPS (Ranf et al. 2015).

10.2.2 Recognition of Plant-Derived Molecules (DAMPs) The first barrier faced by the pathogens is the plant cell wall. In order to gain access to the plant cells, pathogens secrete cell wall degrading enzymes (CWDEs). These act as double-edged swords for the pathogen (Jha et al. 2005), as plants have evolved to recognize the cell wall degradation products that are released by the action of these enzymes and mount immune response. This class of molecules that can be recognized by plants are those which may originate from the plant itself as a result of the damage caused by the microbes. These signatures were earlier known as endogenous elicitors (Darvill and Albersheim 1984) and are now described as damage-associated molecular patterns (DAMPs) (Bergey and Ryan 1999; Smith 2001; Jha et al. 2007). Molecules such as oligogalacturonides (OGs) (D’Ovidio et al. 2004), monomers of cutin (Schweizer et al. 1996; Kauss et al. 1999), cellobiose (Souza et al. 2017) are such cell wall derived DAMPs. Additionally, plants also secrete some DAMPs that alert neighboring cells and also amplify the immune responses. Such DAMPs include extracellular ATP (eATP) (Wu et al. 2008; Weerasinghe et al. 2009), plant elicitor peptides (PEPs), small peptides, etc. (Bartels et al. 2013; Klauser et al. 2015). DAMPs are mostly perceived by wall-associated receptor-like-kinases (WAKs). For example, the extracellular domain of an Arabidopsis RLK named WAK1 has been shown to have high affinity to OGs, indicating that it might be a putative receptor for DAMP (Cabrera et al. 2008; Brutus et al. 2010). AtWAK1 and AtWAK2 of Arabidopsis have also been shown to recognize pectin and pectin degradation products (Decreux and Messiaen 2005; Decreux et al. 2006). In rice,

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WAKL21.2 likely perceives damage done by LipA, a Xanthomonas secreted cell wall degrading enzyme as downregulation of OsWAKL21.2 leads to attenuation of LipA induced immunity (Malukani et al. 2020). In Arabidopsis, another receptor, DORN1 (does not respond to nucleotides 1) has been shown to be a receptor for eATP, which binds to the extracellular domain of DORN1 (Choi et al. 2014). PEPs are recognized by plants via LRR-RLKs called PEP-receptors (PEPR), which activate plant immune responses (Krol et al. 2010).

10.2.3 Perception of Pathogen Effectors Plant pathogens suppress the host immune responses by secreting effector molecules directly into the plant cells. Plants have evolved to sense these effectors and mount a robust immune response (Cui et al. 2015). There are two ways in which plants can sense effector molecules: direct recognition of effector molecule and indirect recognition of effector activity. LRR domain-containing proteins are one of the major class of proteins involved in this. These proteins are nucleotide-binding (NB)-LRR or NLR proteins. Most of the NLR proteins contain either coiled-coil (CC) or toll/ interleukin-1 receptor (TIR) domains (Cui et al. 2015; Schreiber et al. 2016). The effector AvrPto, which is secreted by various plant pathogenic strains of P. syringae, was first identified to bind to multiple receptors, e.g. FLS2 and EFR, to suppress PTI (Ronald et al. 1992; Shan et al. 2008; Xiang et al. 2008). The Prf/Pto protein complex of Arabidopsis recognizes AvrPto, where Prf is an NLR. In the absence of effectors, Pto and Prf form a large macromolecular complex that keeps the immune response in a suppressed state (Ntoukakis et al. 2013). AvrPto directly interacts with Pto at the Prf binding site of Pto, thus removing it from Prf/Pto complex, leading to Prf-mediated activation of ETI (Dong et al. 2009).

10.3

Cytoplasmic Events Following Perception of Immune Responses

Perception of phytopathogens leads to an intricate network of signaling pathways. Initial perception of the pathogen involves recognition by receptors at the surface of the cell (Zipfel 2008). This leads to a subsequent elaboration of immune responses that help the plant counter a broad range of pathogens. This induction of immune responses leads to multiple plant responses. The early responses consist of rapid calcium flux across the plasma membrane which leads to activation of calciumdependent protein kinases (CDPKs), an oxidative burst and activation of mitogenactivated protein kinase (MAPK) cascades (Malinovsky et al. 2014; Lin et al. 2014; Lu et al. 2010). This leads to induction of transcription factors, which further upregulate defense-related genes (Zipfel 2008; Zipfel and Rathjen 2008; Felix et al. 1999). The late response includes fortification of the cell wall by deposition of callose and lignin, synthesis of antimicrobial compounds and PAMP induced resistance (Boller and Felix 2009). In case of severe stress, initiation of programmed

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cell death (PCD) is also observed (Petrov et al. 2015). Hormones also play a key role as signaling intermediates. In this section, we will discuss some such cytoplasmic events that occur after the perception of danger.

10.3.1 MAP Kinase Signaling Cascade Rapid activation of MAP kinases is a hallmark of PTI. Phosphorylation events initiated by ligand binding to receptor kinases are usually conveyed by MAP kinase signaling cascades in plants. MAPK cascade consists of three sequential kinases (Rasmussen et al. 2012): MAP kinase kinase kinase (MAPKKK) phosphorylates MAP kinase kinase (MAPKK) which can phosphorylate MAP kinase (MAPK). MAPK then phosphorylates downstream transcription factors and other signaling components. Two MAP kinase signaling components which are majorly known to be involved in plant immune responses are MPK3/MPK6- and MPK4-mediated immune responses (Meng and Zhang 2013). Phosphorylation and dephosphorylation go hand in hand in the regulation of plant immune responses. Protein phosphatases (PPs) such as PP2C and PP2A are known to be involved in the dephosphorylation of receptors and other intermediate kinases in signaling. For example, XB15, a rice PP2C dephosphorylates the receptor XA21 (Park et al. 2008). Arabidopsis orthologs of XB15, PLL4, and PLL5 negatively regulate EFR induced immunity (Holton et al. 2015). Similarly, another PP2C, the kinase-associated protein phosphatase (KAPP), suppresses FLS2-mediated immune responses (Gómez-Gómez and Boller 2000).

10.3.2 Calcium Signaling Calcium ions serve as a key secondary messenger in plant signaling in eukaryotes (Lecourieux et al. 2006; DeFalco et al. 2009). A transient increase in cytosolic Ca2+ level is observed after pathogen infection, treatment with a DAMP/PAMP and after ETI activation (Grant et al. 2000; Lecourieux et al. 2006; Poovaiah et al. 2013; Souza et al. 2017). This change is sensed by the EF-hand motif-containing calciumbinding proteins (CBPs) such as CDPKs (DeFalco et al. 2009), which mediate further downstream signal transduction appropriate to the specific stimuli (McAinsh and Pittman 2009; Hetherington and Brownlee 2004; Schulz et al. 2013). Ca2+dependent protein kinases (CDPK) are known to sense calcium via 4 EF-hand motifs (Harmon et al. 2001) resulting in ROS production, defense gene activation, and hormone regulation in plant immunity (Asano et al. 2012; Schulz et al. 2013). For example, the four functionally redundant Arabidopsis CDPKs, CPK4, CPK5, CPK6, and CPK11 are required for flg22 induced PTI responses (Boudsocq et al. 2010). A Nicotiana tabacum CDPK, NtCDPK2, has also been shown to be activated by phosphorylation upon exposure to the elicitor Avr9, indicating its role in HR upon pathogen recognition (Romeis et al. 2000). Another class of Ca2+ sensors is the Calmodulin (CaM) protein. CaMs also recognize Ca2+ via an EF-hand motif, which

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binds to downstream interacting partners which are called CaM-binding Proteins (CaMBPs) (Ishida and Vogel 2006).

10.3.3 Hormone Signaling Hormones such as salicylic acid (SA), jasmonic acid (JA), and ethylene play a crucial role in immune response signaling. The key hormones are important components of immune responses. However, recent studies indicate the role of other hormones such as abscisic acid (ABA), gibberellic acid (GA), auxin, cytokinin, and brassinosteroids in plant defense responses. Salicylic acid (SA) is a phenolic hormone, which is known to be involved in flowering, germination, abiotic as well as biotic stress tolerance (Metwally et al. 2003; Clarke et al. 2004; Norman et al. 2004; Rajjou et al. 2006; Shigenaga and Argueso 2016). SA is known to enhance resistance towards various hemibiotrophic, biotrophic, and viral infections but also enhances susceptibilty towards necrotrophic pathogens (Malamy et al. 1990). SA is also necessary for the activation of various PR genes (Dodds and Rathjen 2010; Shigenaga and Argueso 2016). Genetic screens indicate that non-expressor of PR genes 1 (NPR1) is a key regulator of SA-mediated immune responses (Cao et al. 1994). NPR1 binds to TGA (TGACG-Binding) transcription factors and activates expression of defense-related genes which also include PR genes (Kesarwani et al. 2007). The SA pathway is targeted for interference by many pathogens as it is a key pathway in immune responses. Pseudomonas HopM1 and AvrE, Xanthomonas XopJ, Hyaloperonospora HaRxL44, and Phytophthora PsIcs1 are some examples of pathogen effectors that target the SA pathway to suppress plant immune responses (DebRoy et al. 2004; Caillaud et al. 2013; Liu et al. 2014). Jasmonic acid (JA) is a lipid-derived phytohormone which is known to be involved in many developmental and defense responses (Santino et al. 2013). Exogenous JA application leads to enhanced expression of defense-related genes including some PR genes (Epple et al. 1995; Penninckx et al. 1996). JA is known to accumulate in response to wounding, necrotrophic attack, and herbivory. JA is perceived by coronatine insensitive 1 (COI1), an E3 ubiquitin ligase which binds to transcription factor JAZ1 (Jasmonate ZIM-domain 1) (Yan et al. 2009; Sheard et al. 2010). In the absence of JA, the JAZ1 protein serves as a transcriptional repressor for JA-responsive genes (Pauwels et al. 2010). Upon pathogen perception or wounding, JA binds to COI1 which then ubiquitinates and degrades JAZ1 via the 26S-proteasome. Degradation of JAZ1, a negative regulator, activates expression of downstream JA-responsive genes such as PDF1.2, PR3, PR4, etc. (Thines et al. 2007). Some effectors such as HopX1 and HopZ1 of Pseudomonas sp. have evolved to activate the JA pathway to enhance plant susceptibility (Jiang et al. 2013, Gimenez-Ibanez et al. 2014). Ethylene (ET) is a gaseous plant hormone which is mainly known to have its role in fruit ripening but is also involved in plant immune responses. Like JA, ET also appears to be involved in enhancing resistance towards necrotrophic pathogens and

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susceptibility towards biotrophic pathogens (Lawton et al. 1994; Lawton et al. 1995). There are many common components between the JA and ET pathway. The JAZ1 protein which is degraded after JA perception is also a negative regulator of ET pathway genes. JA pathway activation degrades JAZ1 which ultimately activates ET-responsive genes as well (Penninckx et al. 1998; Zhu et al. 2011). Like SA and JA, ET is also a common target of many pathogens for suppression of plant immune responses. AvrPto and AvrPtoB of Pseudomonas sp. and XopD of Xanthomonas sp. suppress plant immune responses by manipulation of the ET response pathway (Cohn and Martin 2005; Kim et al. 2013).

10.4

Plant Immune Responses

After signal recognition, plants mount immune responses to restrict the growth and spread of the pathogen. These responses include strengthening of the cell wall, secretion of antimicrobial compounds, localized cell death, etc. (Fig. 10.2).

10.4.1 Stomatal Closure Foliar pathogens infect plants through wounds, hydathodes, or stomatal openings in the leaves. Hence, closure of the stomatal pore is an important hallmark of plant immune response. Plants are known to show stomatal closure after perception of various MAMPs and DAMPs such as flg22, elf18, elf26, LPS, chitin, and oligogalacturonan (Arnaud and Hwang 2015; Murata et al. 2015). This leads to

Fig. 10.2 Plant responses that are activated upon pathogen perception

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activation of defense responses like MAP kinase cascade, Ca2+ flux, ROS and NO production (Melotto et al. 2006; Desclos-Theveniau et al. 2012; Arnaud and Hwang 2015). As the stomatal opening is a crucial factor in infection, pathogens also have evolved various mechanisms to suppress stomatal defense. Bacterial pathogens secrete various molecules such as coronatin (COR) (Bender et al. 1999) and syringolin A (Groll et al. 2008) are secreted by P. syringae that keeps the stomata open. Pathogens also suppress stomatal defense using effector molecules that are injected into the host cells. P. syringae suppresses stomatal closure by HopM1, HopF2, HopZ1, HopZ1a, HopX1, and AvrB in various plant species (Jiang et al. 2013; Gimenez-Ibanez et al. 2014; Lozano-Durán et al. 2014; Hurley et al. 2014; Zhou et al. 2014; Zhou et al. 2015). The Xanthomonas oryzae pv. oryzae effector XopR is also reported to suppress flg22 induced stomatal closure (Wang et al. 2016).

10.4.2 Reactive Oxygen Species (ROS) Production Reactive oxygen species (ROS) includes hydrogen peroxide (H2O2), hydroxyl radical (HO.), superoxide anion (O2
), and singlet oxygen (1O2) (Das and Roychoudhury 2014). ROS burst is an early response that is triggered within a few minutes after MAMP (flg22, elf18) treatment (Jabs et al. 1997; Torres et al. 2006). ROS is known to interact with proteins, DNA, lipids, carbohydrates, fatty acids, guanine, certain amino acids and Fe-S centers of metalloproteins to activate various signaling components such as kinases, transcription factors, and redox responsive proteins (Foyer and Noctor 2013; Kimura et al. 2017). ROS are also involved in various hormone signaling pathways such as SA, JA, auxin, ABA, etc. and their signaling (Kwak et al. 2003; Hu et al. 2009; Foyer and Noctor 2013; Wrzaczek et al. 2013; Herrera-Vásquez et al. 2015). Peroxidases and NADPH oxidases are known to be involved in MAMP-induced ROS burst (Nühse et al. 2007).

10.4.3 Callose Deposition Callose is a β-1,3 glucan polymer that is deposited on the cell wall to strengthen it and serve as a barrier for pathogen entry (Schneider et al. 2016; Voigt 2016). Various MAMPs and DAMPs such as flg22, elf18, oligogalacturonan, chitin, and cell wall degrading enzymes induce callose deposition in the host plant (Luna et al. 2011). Callose synthesis is usually observed at penetration sites of many fungal pathogens of plants and is composed of many antimicrobial compounds such as hydrogen peroxide, phenolic compounds, and thionins (McLusky et al. 1999). Callose is mainly synthesized by callose synthases called as GSL (glucan synthase-like) or CalS (callose synthases) (Ellinger and Voigt 2014; McFarlane et al. 2014). Various type III secreted effectors of plant pathogens are known to suppress callose

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deposition induced after treatment with strains of Xanthomonas that activate plant immune responses (Deb et al. 2020; Deb et al. 2019).

10.4.4 Hypersensitive Response Programmed cell death (PCD) or hypersensitive response (HR) is a well-regulated cellular process and is involved in various developmental processes and stress responses (Pennell and Lamb 1997). PCD also plays an important role in the plant immune response. It is a process of regulated suicide of affected cells, generally at the site of perception, to restrict further growth of the pathogen. PCD is a common response observed after recognition of an effector molecule. The signal is usually initiated by NB-LRR (R genes) which senses the presence of an effector and involves many signaling intermediates such as activation of MAP kinase cascade, production of SA or ROS, nitrous oxide accumulation, increase in cytosolic Ca2+, etc. (Kadota et al. 2004, Kärkönen and Kuchitsu 2015). In many plants, HR can be observed as a necrotic lesion at the site of potential pathogen infection or 2-3 days after elicitor treatment. Plant pathogens have evolved to suppress PCD using their type III effectors (Sinha et al. 2013; Deb et al. 2020; Deb et al. 2019). Conversely, necrotrophic pathogens that utilize dead plant tissue have evolved to kill plant cells by inducing PCD through ROS production by hijacking the plant immune system (Shetty et al. 2008).

10.4.5 Defense Gene Activation Pathogenesis-related (PR) proteins are key components of the plant immune response which are upregulated in plants after pathogen infection or induction of immune responses. The PR proteins are diverse in function and have been classified into 17 families (van Loon et al. 2006). Four PR proteins, such as PR3, PR4, PR8, and PR10 are chitinase by nature and can degrade fungal cell walls. PR2 is a β-1,3-glucanase. Some PR genes encode small peptides such as the PR6 family which consists of proteinase inhibitors (Green and Ryan 1972), PR12 family proteins are cysteine-rich defensins (Terras et al. 1995), PR13 gene family members encode thionins (Epple et al. 1995) and PR14 gene codes for lipid transfer proteins (LPT) (García-Olmedo et al. 1995). AtPR1, AtPR2, and AtPR5 of Arabidopsis are SA-responsive genes and enhance tolerance towards biotrophic and hemibiotrophic pathogens. Conversely, Arabidopsis PR3 and PR4 are JA-responsive genes and enhance tolerance towards necrotrophic pathogens (van Loon et al. 2006).

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10.4.6 Lignin Deposition Lignins are cross-linked phenolic polymers usually deposited in the secondary cell wall to provide mechanical strength. Lignin is deposited in response to both biotic and abiotic stresses (Malinovsky et al. 2014). Lignin is synthesized via the phenylpropanoid pathway in plants (Sattler and Funnell-Harris 2013). Lignin serves as a mechanical barrier against pathogens. Knockdown or mutations in many genes responsible for lignin synthesis make plants such as wheat, melon, alfalfa, etc. more vulnerable to pathogens (Miedes et al. 2014).

10.4.7 Secondary Metabolite Production Plants secondary metabolites are low molecular weight compounds produced in response to various environmental stimuli (Piasecka et al. 2015). The secondary metabolites that are produced in response to a pathogen are called phytoalexins (VanEtten et al. 1994). The various kinds of phytoalexins are camalexins, phenylalanine derived phytoalexins, and terpenoids. Camalexins are sulfur-containing tryptophan-derived secondary metabolites mainly produced by plants of Brassicaceae family including Arabidopsis (Bednarek 2012). Terpenoids are the most recognized class of secondary metabolites (Schmelz et al. 2011). Rice is known to produce a variety of terpenoids in response to pathogen infection, such as momilactones, oryzalexins, and phytocassanes (Schmelz et al. 2014).

10.5

Disease Management for Yield Enhancement

Generation of disease-resistant plants involves breeding of various genes involved in immune perception and signaling. These genes can be both resistance-related genes or susceptibility factors. For genes directly conferring tolerance, transgenic plants can be made by incorporating the transgene by gene knock-in methods or by breeding these genes into susceptible lines from resistant lines. Conversely, for susceptibility related genes, tolerance can be achieved by either silencing gene by gene editing or knockdown or by incorporating the tolerant allele by breeding. Here we will discuss some approaches by which signaling related genes have been utilized to generate disease tolerant plants (Fig. 10.3).

10.5.1 Marker-Assisted Selection (MAS) to Develop Disease-Resistant Lines Marker-assisted selection is the most common approach which has been used to generate disease-resistant lines (St.Clair 2010). It is based on breeding between a commercially cultivated but disease susceptible plant variety and a disease-resistant variety that does not contain agronomically important traits. After breeding, the

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Fig. 10.3 Strategies to generate disease tolerant plants and induce immune responses

plants are selected by PCR for disease-linked quantitative trait loci (QTL). Repeated back-crossing with the parent containing agronomically important traits is carried out to reduce the ratio of wild type background and after a few generations of such crossing, new disease-resistant varieties can be achieved. Being a well-established technique, it is used in many plant species such as rice, wheat, soybean, maize, etc. to generate disease-resistant lines. Major advantages of this technique are (1) more than one QTL can be selected by MAS and (2) introgressed genes are from same plant species, which bypasses the ethical considerations of using trans-genes. One of the biggest advantages of breeding is that multiple genes can be incorporated into a single variety as long as they are present in any other variety of the same species. This approach is called gene pyramiding. These genes can be either a set of genes against a single disease or a different gene providing tolerance against different pathogens. An example of this is the development of a Xoo resistant elite rice variety Improved Samba Mahsuri (ISM), which has been made by crossing between the commercial variety Samba Mahsuri and a disease-resistance donor variety SS1113 (Sundaram et al. 2008). SS1113 contains three R genes Xa21, xa13, and xa5 that provide resistance against Xoo, whereas Samba Mahsuri is susceptible to Xoo infection. ISM contains the good agronomic traits of SM and also resistance to most of the Indian isolates of Xoo. Similar MAS and gene pyramiding techniques have been used to make rice resistance lines against bacterial blight, blast, gall midge, brown planthopper, etc. (Hasan et al. 2015). More recently, He et al. (He et al. 2019) have developed bacterial blight and brown pest hopper tolerant rice lines by incorporating the Xa21, Bph14, and Bph15 genes in a commercial variety Yuehui9113. Zhu et al. have incorporated nine defense-related genes in maize, namely Chi, Glu, Ace-AMP1, Tlp, Rs-AFP2, ZmPROPEP1, and Pti4, to generate maize lines resistant to different necrotrophic pathogens (Zhu et al.

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2018). Similarly, Gautam et al. also incorporated multiple resistance-related and high grain yield-related QTLs in one wheat line which showed higher yield as well as tolerance against rust disease (Gautam et al. 2020). However, a drawback with breeding is that it is a time-consuming process. The F1 lines need to be back-crossed for 6–10 generations to remove the background of the wild variety parent and get a stable phenotype. This process takes many years as usually 1–2 generations can be achieved each year. To circumvent this, Ghosh et al. developed a new strategy to perform the breeding faster by manipulating color and duration of light in the greenhouse (Ghosh et al. 2018). This was achieved by providing light for 22 h at 22  C and dark for 2 h at 17  C. Using this approach, they were able to cycle 4–6 generations in a year. Speed breeding is being successfully implemented in various long day crops such as bread wheat, durum wheat, barley, oat, various Brassica species, chickpea, pea, grass pea, quinoa, and Brachypodium distachyon (Ghosh et al. 2018; Watson et al. 2018). However, this technique still needs to be established in short-day plants such as rice, soybean, onion, etc.

10.5.2 Xa21 Mediated Immune Responses Genus Xanthomonas contain some of the deadliest plant pathogens that can cause disease in hundreds of plant species including many cultivated plants such as rice, banana, citrus, pomegranate, tomato, bean, etc. Given the importance of Xa21, it is also being used in other species. Cross-species expression of rice Xa21 in banana, sweet orange, or citrus enhances resistance against respective pathogen Xanthomonas campestris pv. musacearum and Xanthomonas axonopodis pv. citri (Tripathi et al. 2014; Mendes et al. 2010; Li et al. 2014). Interestingly expression of rice Xa21 in transgenic tomato enhances resistance to bacterial wilt caused by Pseudomonas solanacearum (Afroz et al. 2011). This result is very surprising as Xa21 is known to enhance resistance only against Xanthomonas having functional RaxSTAB gene cluster. But it also indicates Xa21 can provide resistance against other pathogens as well. Resistance genes like Xa21 or EFR can be used to make transgenic plants that can either naturally be resistant to Xanthomonas or can be primed by treatment with elicitors (RaxX21-sY or elf18, respectively). Interestingly, a similar approach was applied by Holton et al. when they expressed a fusion protein having an EFR extracellular domain and Xa21 intracellular domain in Arabidopsis transgenic plants (Holton et al. 2015). In this case, they treated EFR-Xa21 transgenic plants with elf18 and observed an induction of immune responses by the kinase domain of Xa21. The other way around was also true when they expressed Arabidopsis EFR in rice and observed immune response after elf18 treatment (Schwessinger et al. 2015; Lu et al. 2015). Schwessinger et al. observed enhanced tolerance towards two weakly virulent Xoo isolates and partial resistance to PXO99A in Kitaake rice EFR transgenic lines after elf18 treatment. Conversely, Lu, Wang et al. observed enhanced tolerance towards Xoo and Acidovorax avenae but not to Magnaporthe oryzae in Zhonghua

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17 (ZH17) rice EFR transgenic lines after elf18 treatment. These observations indicate that cross-species expression of defense-related genes can be used to make more disease-resistant varieties.

10.5.3 Induction of Immune Responses Using MAMPs or DAMPS Most of the plant species can perceive the self as well as pathogen cell wall degradation products as elicitors. The activity of microbial cell wall degrading enzymes leads to release of cell wall degradation products such as oligogalacturonans that are perceived by the plants (Cantu et al. 2008; Bellincampi et al. 2014). In response, plants mount a potent immune response to avoid growth of pathogen (Jha et al. 2005; Jha et al. 2010; Aparna et al. 2009). Interestingly treatment of only cell wall degradation products or oligogalacturonans also induces plant immune responses (Ferrari et al. 2007; Galletti et al. 2009). So, the prior spray of such cell wall degrading enzyme or elicitors can be used to induce immune responses. Similarly, chitin, peptidoglycan, and lipopolysaccharides serve as a potent MAMP to induce immune responses. Treatment of plant tissue with chitin (or chitin oligomers), PGN, or LPS leads to plant immune response and prime the plant for subsequent pathogen infection (Sánchez-Vallet et al. 2015; Liu et al. 2013; Willmann et al. 2011). So, these MAMPs can be used as universal elicitors to activate plant immune responses. For example, the spray of chitin oligomers into the field before a potential disease period may provide enhanced tolerance towards diseases and subsequently more yield.

10.5.4 Genome Editing to Create Disease-Resistant Lines Another approach to boost plant innate immunity is by genome editing. The target of effector proteins, negative regulators of the immune response, or susceptibility factors can be modified using genome editing. Plant genome can be edited by various techniques such as artificial zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALEN), and CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 (CRISPR-associated protein-9 nuclease) (Kim et al. 1996; Christian et al. 2010; Jinek et al. 2012). In ZNF and TALEN, the probe is a nuclease fused protein which binds to target DNA sequence. Conversely, in CRISPR/Cas9, the probe is an RNA molecule complementary to the target DNA sequence. CRISPR/Cas9 system can also be used to make multiple modifications in the genome at the same time (Cong et al. 2013). These techniques are used in many crops to make disease-resistant varieties such as rice, wheat, Arabidopsis, apple, tomato, nicotiana, corn, etc. (Das et al. 2019).

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10.5.5 Regulated Expression of Defense-Related Genes for Reduced Yield-Penalty A constitutively active immune system comes with an added cost of agronomically important traits such as growth defects, low yield, or lesion mimic phenotypes (Gurr and Rushton 2005). So, in normal condition, plant immune response is kept at the dormant phase and is activated only during attack by a potential pathogen (Gurr and Rushton 2005). It was recently reported for the first time in plants that elf18 induced plant immune response is not only regulated at the transcriptional level but also the translational level (Xu et al. 2017a). This phenomenon has been used to develop disease-resistant Arabidopsis and rice transgenic plants (Xu et al. 2017b), wherein it was observed that transgenic rice plants expressing AtNPR1 showed enhanced tolerance towards Xanthomonas oryzae pv. oryzae, Xanthomonas oryzae pv. oryzicola, and M. oryzae, the causal agents of bacterial leaf blight, bacterial leaf streak, and fungal blast, respectively. However, if expressed constitutively, the plants show fitness penalties such as less height, less grain number, and less grain weight. However, if AtNPR1 was regulated transcriptionally or translationally, and expressed only after PTI induction, transgenic rice plants show reduced fitness cost to these traits. Thus, making transgenic plants with the target gene being transcriptionally or translationally regulated could prove to be a promising method for developing disease-resistant crops that have minimal yield penalties.

10.6

Conclusions

One of the primary factors for reduction in crop yield is the loss incurred due to diseases in crops. The innate immune response of plants plays a major role here and it is effective against multiple pathogens. Research in the last decade has revealed a number of functions and mechanisms that participate in innate immune responses. These include new findings of receptors and signal transduction mechanisms that are a part of the plant immune response cascade. This new knowledge has opened up new possibilities for disease management, and further, yield improvement. Several such options have been discussed here. Furthermore, recent advances in genomics have rejuvenated mutation breeding as they facilitate the identification of causal mutations and their application in the development of newer disease-resistant varieties using marker-assisted selection. Genome editing is another recent tool that permits the development of novel disease-resistant varieties by targeted mutation of susceptibility factors. Collectively these approaches can lead to a sustainable model of development of tolerant crop varieties and agricultural practices which can help us control constantly evolving pathogens without application of chemical pesticides. Acknowledgements SD and KKM acknowledge fellowship from the Council of Scientific and Industrial Research (CSIR), Government of India. The authors acknowledge Vishnu Narayanan Madhavan for assistance in image preparation.

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Conflict of Interest The authors declare that no conflict of interest exists.

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Next Generation Biofuel Production in the Omics Era: Potential and Prospects

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Sumit Kumar, Naveen Kumar Singh, Anirudh Kumar, and Pawan Shukla

Abstract

The surging global population has led to an ever-increasing demand for fossil fuel, and as a result, fossil fuel reserves are depleting sharply and negatively impacting environmental health. The quest for sustainable environmental friendly fuels has presented us renewable energy sources like wind, solar, and biofuels after two decades of research. The usage of biofuels as an alternative fuel has picked up pace in many countries with an aim for sustainable development. However, global production remains a big challenge due to various reasons associated with their sustainable production. Furthermore, omics technology like genomics, transcriptomics, proteomics, and metabolomics has generated quantum of information of biological organisms and their associated pathways in differential interaction with their environment. The deciphering of molecular information associated in biofuel-producing organisms with these technologies is still in its incipient stage. A sustainable production of biofuels might be accomplished by the contribution of information generated with the application of these technologies. The researchers are on course for utilizing this information for understanding molecular mechanism involved in biofuel production. This chapter

S. Kumar Department of Botany, Nalanda College, Patliputra University, Patna, Bihar, India N. K. Singh Department of Life Science, Central University of South Bihar, Gaya, Bihar, India A. Kumar Department of Botany, Indira Gandhi National Tribal University (IGNTU), Amarkantak, Madhya Pradesh, India P. Shukla (*) Seribiotech Research Laboratory (SBRL), Kodathi, Carmelram Post, Bangalore, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Kumar et al. (eds.), Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), https://doi.org/10.1007/978-981-16-2956-3_11

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presents the status of global biofuel production and application of different omics technologies. Keywords

Biofuel · Omics · Biodiesel · Bioethanol

11.1

Introduction

Industrial revolution unravelled a whole new energy resource like fossil fuels (coal, oil, and gas). The fossil fuel energy has been a key player in technological advancement in the world. These resources have not only reaped rewards in terms of economic progress but also have presented several negative impacts due to its uninhibited anthropogenic use. One of the significant impacts on human and non-human populations is the global climate change owing to rise in greenhouse gases (GHGs) such as carbon dioxide (CO2), methane, and nitrogen dioxide concentrations associated with the combustion of fossil fuels. As a result, the average surface temperature of earth has been increasing with each passing year, which can be termed as global warming or climate change (IPCC 2014). The unprecedented usage of fossil fuels due to industrialization led to depletion of these reserves. Moreover, it is negatively affecting environmental health. Therefore, the need for achieving sustainable progress to drive the world with efficient and eco-friendly energy carriers is paramount. This has emphasized the importance to tap into alternative and renewable sources of energy. One such measure in practice is the use of environment friendly biofuels which is a renewable energy source, generated from organic matter (biomass), or wastes including plant materials and animal waste that can be crucial for abating carbon dioxide emissions. In the transport industry, they are blended with existing fuels such as diesel and gasoline. Moreover, biofuels are attracting attention from worldwide industries which seek to reduce their CO2 emissions and dependency on fossil fuels (Hassan and Kalam 2013). These initiatives have been the outcome of international agreements and regional responses, primarily by focusing on reductions in fossil fuel combustion. The collective effort of international community is keeping the check on rise of global average temperature by transitioning to low carbon economy. However, the choice of the sector either energy production or transportation depends on the policies framed by countries individually with respect to their carbon budget. Transportation sector is a major consumer of fossil-derived fuels energy. Therefore, keeping in view for reconciling the worsening global environmental conditions, biofuels are required as a renewable and carbon neutral energy source for an alternative to the fossilderived fuels in near future for sustainable economic development (Patade et al. 2018). The advancements in next generation sequencing technologies have made a significant impact in basic and applied biological research. These sequencing technologies termed as “omics” technologies aim at holistic view of the molecules

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that make up a cell, tissue, and organism which is now extended to both biotic and abiotic interactions. Omics sciences primarily target large-scale detection of genes (genomics), mRNA (transcriptomics), proteins (proteomics), and metabolites (metabolomics) in a fast, reliable, and inexpensive way. The application of these technologies has further evolved to being transdisciplinary addressing various biological aspects like variations in the phenotype (phenomics), total elemental composition (ionomics), quantitative cell analysis and imaging (cellomics), culture studies in evolutionary biology (culturomics), and others. The promise of these powerful omics techniques enables us to generated qualitative and quantitative information at molecular level thereby interpreting the dynamics of structure and function of an organism or organisms. Globally, these technologies are being applied in newer avenues of biological research. Biofuels are potentially one such avenue which can benefit from information generated by these omics technologies. The sole aim will be to promote, develop, and create, if needed, the use of environmental friendly and cost effective biofuels for sustainable development. The amalgamated information of molecules associated with the biological component of biofuels will aid in understanding the mechanistic function and subsequent improvement strategies for its development and usage. Moreover, the added power of multiomics projects, though challenging, complement the existing tools which can be extremely valuable in deciphering potential genetic signatures in biofuel development. The focus of this chapter is on present status of global biofuel production and usage. Furthermore, the chapter discusses primarily on recent applications of omics technologies in understanding of various molecular mechanisms involved in biofuel production.

11.2

First Generation Biofuels

Conventional biofuels (biodiesel, bioalcohol, and biogas) generated from sugar, starch or vegetable oils, are considered as first generation biofuels (Fig. 11.1). The source of these organic matters is plants, mostly food crops like corn, sugarcane, wheat, rapeseed, and soybean (Niphadkar et al. 2018). Bioalcohols and biodiesel are manufactured through well-known processes like distillation, fermentation, and transesterification. Ethanol is the primary bioalcohol produced through fermentation of sugars and starches either by enzymes or microorganisms, with smaller quantities of butanol and propanol. Bioethanol blending with gasoline has been used in spark ignition engines. The production of first generation bioethanol was primarily from sugarcane or corn (Lee and Lavoie 2013). The 2007 U.S. Energy Independence and Security Act set its goal of generating 36 billion gallons of biofuels as renewable fuel by 2022 and this bill has created a momentum for investment in corn-based ethanol plants (Behera and Varma 2019). The ethanol production from sugarcane requires only fermentation of sugar into ethanol which is an easy process. However, ethanol production from corn requires an additional step of hydrolysis of starch by an inexpensive enzyme, α-amylase to get the sugars which finally fermented to ethanol.

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Organic Maer Starch

Technology Hydrolysis

End Product

First Generaon Biofuel (Ethanol)

Fermentaon

Lignin Cellulose

Enzymac Hydrolysis Second Generaon Biofuel Pyrolysis, Gasificaon (Ethanol, Biodiesel, Char) Extracon

Triglycerides Lipid Genecally Engineered Organism

Transesterificaon

Third Generaon Biofuel (Algal Oil, Biodiesel)

Enzymac Hydrolysis, Extracon

Pyrolysis, Gasificaon, Transesterificaon

Fourth Generaon Biofuel (Algal Oil, Biodiesel)

Fig. 11.1 Different types of biofuels, their feedstock and technology used in production

The global production of first generation bioethanol is quite significant. For instance, the largest producers of first generation bioethanol are U.S. (from corn), China (from corn), Brazil (from sugarcane), India (from sugarcane molasses), and the EU (from sugar beet, wheat) (Niphadkar et al. 2018). The Brazil produces bioethanol exclusively from sugarcane. The sugarcane based brazilian bioethanol production is considered as the most efficient system and this has been used in 80% of vehicles and also some jet engines. Sugar and starch are the commonly used substrates for ethanol production and the process includes the pretreatment, enzymatic digestion of starches into sugars and subsequently fermentation, and distillation. The pretreatment process involves the washing of raw material, size reduction, juice extracting, and separation of the bagasse. The fermentation steps include the use of microorganisms for conversion of fermentable hexose sugars to ethanol. During the fermentation process, yeast Saccharomyces cerevisiae is employed for the fermentation of sucrose to ethanol, while the bacteria Zymomonas mobilis is used for glucose to ethanol conversion. Typically, the temperature required for the fermentation is below 32  C and pH between 4 and 5, while concentration of sugar need to be less than 16  Bx. Generally, fermentation medium possesses 7–7.5% (w/w) of ethanol which go

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through a several steps of distillation processes for final bioethanol production (Bertrand et al. 2016; Soccol et al. 2019). The production of biofuel from vegetable oil is a century old method. However, biodiesel production from vegetable oil need to go through transesterification process with methanol in order to obtain fatty acid methyl esters (FAME) having lower viscosity and better volatility. The physical properties of FAME are very much similar to the petroleum-derived diesel fuel. Many countries are producing vegetable oil drive biodiesel such as the USA (from soybean oil), Canada (from canola), Malaysia (from palm oil), Indonesia (from palm oil), and Europe (from rapeseed). In India, there is a huge demand for vegetable oil, therefore, being the largest importer of edible oil, India cannot produce edible oil based biofuel. However, first generation biofuel production from nonedible oil seeds such as Karanja (Pongamia pinnata) and Jatropha (Jatropha curcas) have great future in India (Kumar and Sharma 2011). In addition, other plants like Simarouba, Camelina have also displayed potential source of nonedible oil seeds drive biofuel in India (Kumar and Sharma 2011; Patade et al. 2018). Furthermore, the biodiesel (B100) characteristics should be as per the standards specified by ASTM D 6751, EN 14214, or IS 15607. The transesterification process is a crucial step in biodiesel production from oil especially the catalyst used in this process (Patade et al. 2018). For instance, alkali catalyst such as hydroxides or alkoxides of sodium and potassium require moistures and minimum level of free fatty acid during transesterification process for biodiesel production in industry while acid catalyst suited for vegetable oils having high free fatty acid. However, the acid-catalyzed transesterification process did not get much attention due to slow reaction speed and corrosion of the reaction vessel by liquid acid. In addition, there is an environmental concern on disposal of liquid acids. Furthermore, many biocatalyst like lipases from Candida antarctica, Candida rugosa, Mucor miehei, Pseudomonas cepacia, Thermomyces lanuginosus, etc. have been used to carry out transesterification of vegetable oil (Patade et al. 2018). During this process, lipases are used repetitively for synthesis of biodiesel by immobilization of lipase with solid support. In addition, stepwise addition of methanol is required for transesterification because excess of short-chain alcohol may hinder the activity of the lipase. The lipase identified from C. antarctica catalyze the transesterification of the vegetable oil without acyl migration (Hsu et al. 2002).

11.3

Second Generation Biofuels

The biofuel prepared from starch, sugar or vegetable oil by using food material like corn, sugarcane, wheat, rapeseed, and soybean is considered as first generation biofuel (Patade et al. 2018). Utilizing food material for producing first generation biofuel has allowed its producer to look for alternative choice as it competes for food intake. Therefore, researchers have developed the second generation biofuels by using cellulosic material as a feedstock from straw, long grass, wood, or wood waste (Fig. 11.1) (Sims et al. 2010). The important feature of second generation biofuels is

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the complete plant as raw material for biofuel production instead of using just small part of plant as in the case of first generation biofuel. In addition, it can fulfill the fuel demand in eco-friendly manner. Production of ethanol from lignocellulose material of plant broadly involves biochemical and thermochemical process (Menon and Rao 2012). Biochemical process is typically a process to accelerate the hydrolysis processes by using enzymes as well as heat and other chemicals for releasing sugar molecules from cellulose and to separate out the lignin, hemicellulose, and cellulose (Patade et al. 2018; Philbrook et al. 2013). Later, cellulose is fermented into alcohols. Lignin is generated as by-product which can be burned for producing heat and power for industry as a carbon neutral fuel (Limayem and Ricke 2012). Thermochemical process includes the thermal decomposition and chemical modification of biomass. During this process, biomass is heated at very high temperature in the presence or absence of oxygen or steam. In general, thermochemical treatment involves pyrolysis and gasification by which biomass is converted into combustible gas and solid char. The gas can be further fermented or chemically synthesized into a range of fuels, including ethanol, synthetic diesel, or jet fuel. The important feature of this process over biochemical process is the higher productivity and yields the range of organic ingredient like bio-oil, biochar, syngas (Foust et al. 2009; Zhang et al. 2010). In conclusion, second generation biofuel production still need to cross many barriers like suitable infrastructure, government policies, and social acceptance and establish their market share. Moreover, it has bright future ahead as government is started taking necessary step to utilize wheat straw for producing second generation biofuel instead of burning it which is major concern for air pollution.

11.4

Third Generation Biofuels

The third generation biofuel is produced from microalgae (Fig. 11.1). Production of biofuel using algae has many advantages. For instance, it can produce higher lipid yield due to its rapid growth rate compared to any other plants. It needs small space to grow and do not need fertile land compared to tree as well as crop plants. Therefore, it does not compete with agriculture crops for production (Munoz and Guieysse 2006). In addition, it can be cultivated in pond or closed photo-bioreactors under different environmental conditions with high rate of photosynthetic efficiency (Patade et al. 2018). Operational cost in pond cultivation is fairly low but associated with many disadvantages (Patade et al. 2018). For instance, pond cultivation depends on weather conditions and high risk of contamination with low reproducibility. In addition, harvesting cost is quite high in case of pond cultivation. In contrast to this, cultivation in closed photo-bioreactor operating cost is fairly high but less risk of contamination, high biomass yield and low harvesting cost make it well suitable for biofuel production (Patade et al. 2018). Microalgae production needs approximately 1.8 units of CO2 per unit of algae biomass. Therefore, sufficient amount of

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carbon dioxide as carbon source is required in photo-bioreactor. Moreover, available CO2 in natural water may not be sufficient. Hence, bubbling of water is required to improve the dissolution of CO2. Alternatively, flue gases can also be connected with bioreactor as an alternate source of CO2 having 4–15% CO2 (Doucha et al. 2005). Furthermore, solar radiation is also required for photosynthesis of algae and its growth. Artificial lights providing photosynthetic active radiations (PAR) (400–700 nm) have to be fixed in the closed photo-bioreactors (Gao et al. 2007). In addition to carbon source and light, microalgae rely on nutrient supplements for its growth. These nutrient supplements contain micro as well as macro-elements, especially nitrogen and phosphorus. Synthetic media such as CHU, BG 11, and BBM can be provided for cultivation of microalgae (Patade et al. 2018). Moreover, these media increase the input cost of cultivation. Alternatively, low cost commercially available agricultural fertilizers or waste water effluents can also be used. In addition, optimum temperature for microalgae cultivation is 25  C even though it can sustain wider range of temperature and other environmental conditions (Patade et al. 2018). After cultivation, it is important to use suitable method of harvesting of algae cells. The simple technique of harvesting is gravity settling which is slow and time consuming process and also requires additional space. Another method of harvesting is filtration of cells using permeable filter having suitable aperture size that holds the algae cells when liquid medium pass through it using a pressure of compressed air or vacuum (Patade et al. 2018). However, this method is costly and requires large surface area and frequent maintenance. Harvesting through centrifugation is also commonly used technique but an expensive method. Furthermore, gravity settling combined with centrifugation can be cost effective method of harvesting (Patade et al. 2018). In addition, flocculants need to be added to neutralize the surface negative charge of microalgae. While flocculants treatments make the cells adhere each other and formed the aggregates/flocs, which make them to settle easily. Aggregation of microalgae cell by applying electric charge has also been tried (Poelman et al. 1997). The only drawback of this method is fouling of cathodes. Recently, Ndikubwimana et al. (2016) reported the bioflocculation of nonflocculating microalgae as an alternative, cost effective, environment friendly harvesting method. After harvesting of microalgal cells, lipid extraction is to be carried out. This step requires effective technique as co-extraction of protein and carbohydrates needs to be minimized and acylglycerols preferably to be extracted compared to other lipid fraction which reduces further downstream processing. Lipid extraction through organic solvents is commonly used method in which basic chemistry concept of “like dissolve like,” use to follow (Patade et al. 2018). Furthermore, microwave assisted and supercritical organic solvent extractions have been included with this method to accelerate the extraction process. Supercritical solvent extraction method accelerated the extraction kinetics and cellular disintegration. Even though, new upcoming technique has shown many benefits over organic solvent extraction method, biofuel industry is still facing challenges to find an economical way to produce third generation biofuel (Patade et al. 2018).

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Alternatively, Yadugiri 2009 reported the concept of milking or in situ extraction in which harvesting/dewatering the algal cells are not required for biofuel production. In this method, the biocompatible organic solvent (e.g., n-heptane) is added for continuous production of the compounds of interest without sacrificing the cells. Continuous exposure of microalgae with biocompatible organic solvent maintains the metabolic activity of the cells to produce the compound of interest. Zhang et al. 2011 demonstrated the feasibility of milking and re-milking of microalgae cells for extraction of lipids or other important forms of biofuels. A milking method called Live Extraction developed by Origin Oil, Inc. in which electric stimulation is applied on algal cell for continuous oil extraction. This is a chemical-free method therefore, solvent recovery step get omitted and does not need dewatering of biomass which save the time and make this method energy efficient. Moreover, this method has a wide range of feedstock applicability and highly scalable. Transesterification of lipids is to be performed along with extraction process. Transesterification involves the addition of acid catalyst (sulfuric acid/acetyl chloride) and methanol to the algal biomass. Furthermore, traditional transesterification needs to be followed for downstream processes (Patade et al. 2018).

11.5

Fourth Generation Biofuels

Fourth generation biofuels address both sustainable energy and carbon sequestration for the mitigation of anthropogenic CO2 emission. This group of biofuels also known as synthetic fuels includes electrofuels and solar fuels. Electrofuels are potential future carbon neutral fuels produced from renewable sources using electrical energy and stored in the form of chemical bonds in liquids or gases including butanol, biodiesel, and hydrogen, other alcohols and carbon-containing gases such as methane and butane. Similarly, in solar fuels, light energy is stored into chemical fuels typically by reducing protons to hydrogen or CO2 to organic compounds. The technological design for the production of fourth generation biofuels is currently in young phase at the basic level of biofuel research with significant inputs from synthetic biology, a strongly evolving research field (Scaife et al. 2015). The development of these biofuels will be a concerted effort towards global initiative for potential carbon capture and storage (CCS) which has been proposed for meeting climate change targets worldwide (Bui et al. 2018). The production technologies should be rationally designed in such a way that the organic raw materials should be capable of increased CO2 assimilation for efficient capture of CO2 during growth stage and storage in oil and gas during exploitation with limited emission to the atmosphere. Furthermore, the raw materials should be essentially inexhaustible, cheap, and widely available. These biofuels are a progressive step towards carbon negative bioeconomy rather than simply carbon neutral as it “locks” away more carbon than it produces. The amalgamation of metabolic engineering with carbon capture and storage technique has added a new avenue in synthetic biology (Aro 2016).

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The photosynthetic mechanism, wherein light energy splits water (oxidation) into its constituents and stored as chemical energy, is harnessed for the design and production of solar fuels. The feedstock for this generation of biofuels includes bio-engineered plants or algae as a carbon capture machine with enhanced photosynthetic capabilities to lock more carbon in different chemical forms into their different parts resulting in highly efficient fuels displaying high photon to fuel conversion efficiency (Saha et al. 2019). The genetic complexity of plant systems for bio-engineering is a major roadblock for their usage as a feedstock for fourth generation biofuels for which significant information is being added to the existing literature. Microalgae, as discussed in third generation, with low cultivation costs, ability to adapt to varying conditions and efficient fixation of CO2 are a promising prospect for this generation. In fact, the fourth generation biofuels are a technological upgradation to algae-based third generation biofuels to eliminate their limitations. The raw materials involve organic matter based from genetically modified algae for the production of enhanced biofuels. The genetic manipulation of algae is targeted towards maximization of algal biomass including lipid or carbohydrate content. The lipid can be in any form of polar or neutral lipids. This may involve either modifying processes associated with photosynthesis or storage product biosynthesis or both. The aim is to significantly increase the efficiency of yield of algae biomass (Mat Aron et al. 2020). Synthetic biology has the tools for engineering selected strains of algae to critically maximize the growth and production of compounds for fuels and other industries in the present era of global economic competitiveness. The diverse range of compounds including lipids and other alkanes produced by different algal strains make them a viable option for fourth generation biofuels (Gronenberg et al. 2013). However, only selected algal group like cyanobacteria and green algae are capable of photosynthesis, therefore, strain selection, and identification become an important step before designing any genetic and metabolic engineering strategies (Sajjadi et al. 2018). One of the critical aspects was observed with algae growth in nutrient limited conditions and stress is the overproduction of lipids as reported in earlier studies (Hu et al. 2018). But slow growth rate of algae in these stress conditions is a hindrance for any biochemical channelling for production of lipids through this strategy. This further emphasizes the need for identification of suitable algal strain adapted to fast growth in poor nutrients environments and sufficient yield of lipids for fuel. This limitation can be minimized by employing genetically engineered algal strains to increase the growth rate and lipid production for which already some studies have been published (Chlamydomonas reinhardtii sp., Phaeodactylum tricornutum sp., and Thalassiosira pseudonana sp.) (Abdullah et al. 2019; Deng et al. 2014; Yang et al. 2016). The different strategies employed for genetic engineering of algae include genome editing, gene knockout, and introduction of desirable gene (Banerjee et al. 2018). Furthermore, apart from genetic and biochemical strategies, transcription factor engineering is also being employed for the production of desired constituent in algae. This involves regulating concentration of intracellular metabolites by constructing many biosensors for in vivo monitoring the function of enzymes associated with the production of desired metabolite (Li et al. 2020).

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However, modification of not all photosynthetic algal species is possible through genetic and metabolic engineering as the genetic complexity of the transgenic process, non-availability of genomic data, and difficulty in maintaining a balance between metabolic and energy storage pathways pose a major constraint towards production of sustainable fourth generation biofuel. This again highlights the importance of strain selection and identification as a pre-requisite process for the efficient execution of selected strategy for fuel production (Shokravi et al. 2019a, b). The inclusion of cyanobacteria in algal based fourth generation biofuels addresses some concerns highlighted above. The efforts on “cyanofactories” for the production of biofuels have gained interest in recent years. The simple genetic structure of cyanobacteria with genome data available for some strains, broad environment tolerance, and low nutrient demand is a way forward in the direction of sustainable fourth generation biofuel development programs (Xie et al. 2017). The cultivation and harvesting of genetically modified microalgae for the production of fourth generation biofuels has to be designed considering economic feasibility and minimum environmental impact (Fig. 11.1). The strategies should eliminate the limitations of the previous generations. Progressive research on some cyanobacterial and other algal strains to some extent addressed economic concerns which will be more feasible with the identification of more strains after a thorough screening. The mechanism of cultivation can be either open or close depending on the pros and cons of the system associated with the strain in question. Also, lipid production in genetically modified algae was observed to be enhanced in mixotrophic mode of cultivation. The mixotrophic mode combines both autotrophic and heterotrophic cultivation for carbon and energy sources which has been observed to be operative in many algal species mainly in nutrient-poor habitats as a mechanism for augmenting nutrient supplies (Burkholder et al. 2008; Lowrey et al. 2015). Algal harvesting can be done either through chemical, biological, mechanical or electrical depending on the type and physical properties of algae and the chemical nature of final product (Mathimani and Mallick 2018). Similar to solar fuels, the costs of distribution, propulsion, and storage systems have to be considered if electrofuels are to be employed as a future transport fuel relative to alternatives other than biofuels. A major concern is emission of GHGs is higher in some production technologies of electrofuels in comparison to fossil fuels (Hansson et al. 2017). The discussion on economic sustainability will divert the focus of this chapter from its objective. Hence, we will discuss in detail the current status of application of omics technologies in biofuel production at any stage in the next sections.

11.6

OMICS for Enhancing Biofuel Production

In the last two decades, quantum of data have been generated through omics platform for understanding pathways associated with biofuel production. This led to the development of genetically modified organism for fourth generation biofuel production. The omics platform has great potential to extract the biological

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information for cost effective production of biofuel for future use. The development of improved algal strains with better cultivation methods, low-energy harvesting, and high yield extraction-conversion technology is the focus areas for biofuel production (Mishra et al. 2019). The sequencing of many algal genomes has generated large amount of data about genes associated with lipid production pathway. This information is helpful for the genetic manipulation for higher algal biomass including lipid or carbohydrate content. For instance, overexpression of DGAT-2 gene in Phaeodactylum tricomutum and NoD12 gene in Nannochloropsis oceanica resulted in the increase in lipid production (Kaye et al. 2015; Niu et al. 2013). Furthermore, omics tools provide insight information about the mechanism of lipid accumulation at the level of transcript, protein, and metabolite level for efficient biofuel production. C. vulgaris transcriptomic data under nitrogen depletion condition revealed the increase of FAME by 50% (Guarnieri et al. 2011). Similarly, transcriptomic analysis of Neochloris oleoabundan under nitrogen-limiting conditions displayed five folds increase in triglyceride production (Rismani-Yazdi et al. 2012). Transcriptomics as well as lipidomic analysis in C. reinhardtii under heat stress led to identification of phospholipase A2 homolog and DAG acyltransferase DGTT1 genes associated with TAG synthesis pathway (Légeret et al. 2016). In addition, transcriptomic analysis of biofuel producing tree plants like Styrax tonkinensis (Pierre) Craib ex Hartwich, Jojoba was also carried out for understanding fatty acid biosynthesis pathways (Alotaibi et al. 2020; Wu et al. 2020). The recent transcriptomic study in biofuel plants/algae is summarized in Table 11.1. Proteomics analysis of biofuel algae provides useful information about expression of proteins associated with lipid accumulation. Proteomic profiling of Phaeodactylum tricornutum algae under nitrogen deprivation led to upregulation of proteins associated with nitrogen assimilation and fatty acid biosynthesis. This was also accompanied by reduce rate of photosynthesis and lipid catabolism (Yang et al. 2014). Proteomic analysis in Nannochloropsis oculata showed that upregulation of proteins related to fatty acid synthesis which favors the algal biodiesel production (Tran et al. 2016). Proteome investigation in Nannochloropsis oceanica displayed that under nitrogen deplete condition, increased in proteins accumulation related to TCA cycle and lipids takes place (You et al. 2020). Recent proteomic study in biofuel algae is summarized in Table 11.2. Metabolomics of biofuel producing organism provide biological insight about the metabolic flux associated with target pathways. For instance, metabolomics investigation of microalgae Aurantiochytrium sp. after treatment with gibberellin revealed the 43.6% increase in lipid production and accelerated the rate of glucose utilization and other metabolites of fatty acid biosynthesis pathway (Yu et al. 2016). Metabolomic study in Phaeodactylum tricornutum showed betaine lipids as key source for triglyceride formation and accumulation of sedoheptulose under nitrogenstarvation condition (Popko et al. 2016). Metabolomics of C. thermocellum revealed that ethanol inhibits C. thermocellum metabolism at glyceraldehyde 3-phosphate dehydrogenase (GAPDH) reaction (Tian et al. 2017).

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Table 11.1 Recent study of transcriptomic technologies in biofuel Plants/Algae Biofuel Plants/ Algae Microalga

Species Chlorella protothecoides sp. 0710

Sequencing platform Illumina HiSeq 2000 and Roche 454 GS FLX platforms Illumina HiSeq 100 bp single end sequencing platform Illumina HiSeq 2000

Microalga

Neochloris oleoabundans (UTEX # 1185)

Microalga

Nannochloropsis oceanica IMET1

Green algae

Chlamydomonas reinhardtii wildtype strain CC125

Illumina seq

Woody tree plant

Styrax tonkinensis (Pierre) Craib ex Hartwich Jojoba (Simmondsia chinensis, link)

Illumina Hiseq 4000 Sequencing platform Illunina NovaSeq 6000 system

Woody tree plant

Camelina sativa L.

Woody tree plant

Camelina sativa L. doubled haploid genotype DH55

Illumina GAIIX platform Illumina HiSeq 2000 platform

Woody tree plant

Pongamia pinnata (TOIL 1)

Illumina NextSeq 500 sequencer

Halophilic green alga

Dunaliella parva

Illumina Hiseq 2000 platform

Woody tree plant

Major Results Three Chlorella-specific hexose-proton symporter (HUP)-like genes involve in glucose consumption were identified Identified gene associated with excess triacylglycerol (TAG) production under nitrogen stress condition Identified seven putative diacylglycerol acyltransferase (DGAT) genes involve in triacylglycerol (TAG) synthesis under N-depleted (N-) condition Identified phospholipase A2 homolog and the DAG acyltransferase DGTT1 involve in triacylglycerol (TAG) synthesis Transcriptome change during oil accumulation was observed. Transcriptome analysis revealed the lipid biosynthesis during seed development Gene associated with fatty acid biosynthesis and its metabolism Transcriptome of 12 different developmental stages was carried out and identified genes associated with developmental, and growth especially 3.1% gene related seed development Identified fatty acid biosynthetic related genes associated with flowering to seed maturity Transcriptome analysis resulted in identification of transcription factor wri1 gene associated with lipid biosynthesis

References Gao et al. (2014)

RismaniYazdi et al. (2012) Li et al. (2014)

Légeret et al. (2016)

Wu et al. (2020)

Alotaibi et al. (2020) Mudalkar et al. (2014) Kagale et al. (2016)

Sreeharsha et al. (2016) Shang et al. (2016)

Species (Common name) Nannochloropsis oceanica IMET1

Microalga Nannochloropsis oculata (droop) green (strain CS-179)

Microalga Chlamydomonas reinhardtii

Dunaliella parva

Chlorella sp. FC2 IITG

Scenedesmus acuminatus

Clostridium acetobutylicum ATCC 824

S. No. 1

2

3

4

5

6

7

iTRAQ

iTRAQ

iTRAQ

iTRAQ

LC/MS/MS

LC/MS/MS

Proteomic technique LC-ESI-MS/ MS

583

1705

200

1427

248

1487

Identified proteins 4114

Table 11.2 Recent examples of proteomic approaches performed on biofuel algae Regulated function Proteome data showed that under nitrogen deplete condition, accumulation of proteins related to TCA cycle and lipids occur. Proteomic analysis revealed the upregulation of proteins related to into fatty acid biosynthesis & downregulation of proteins associated with photosynthesis, chlorophyll synthesis and carbon fixation. Proteome data displayed that 33 proteins were associated with lipid metabolism Proteome analysis revealed that nitrogen limitation could induce differential expression of proteins related to lipid, carbohydrate and nitrogen metabolism including photosynthesis and stress associated proteins Result showed the increased expression of hydroxyacylACP dehydrogenase and enoyl-ACP reductase enzymes associated with lipid accumulation under nitrogen starvation. Proteome result highlight that downregulation of proteins related to Calvin cycle chlorophyll, and ribosome while upregulation of photosynthetic proteins under high nitrogen supply, in contrast low nitrogen supply leads to accumulation of proteins involved in glycolysis, and TCA cycle and fatty acid biosynthesis. Results showed that downregulation of several proteins associated with glycolysis and fermentation pathways in the presence of lignin concomitantly with lower ATP production.

Next Generation Biofuel Production in the Omics Era: Potential and Prospects (continued)

Raut et al. (2016)

Zhang et al. (2018)

Rai et al. (2017)

Nguyen et al. (2011) Shang et al. (2017)

Tran et al. (2016)

References You et al. (2020)

11 305

Species (Common name) Phaeodactylum tricornutum (CCAP 1055/1

Phaeodactylum tricornutum

Synechocystis sp. PCC 6803

S. No. 8

9

10

Table 11.2 (continued)

iTRAQ — LC-MS/MS

2DE

Proteomic technique iTRAQ

1452

42

Identified proteins 1043 Regulated function Proteome study revealed that downregulation of lipid catabolism and photosynthetic pathway related proteins, while favoring the nitrogen scavenging under nitrogen starvation. Results displayed that nitrogen deprivation led to upregulation of genes associated with lipid accumulation Results highlights that 303 proteins were differentially regulated by butanol

Tian et al. (2013)

Yang et al. (2014)

References Longworth et al. (2016)

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Future Perspectives

Omics techniques present a promising tool which can lead to significant improvement in next generation biofuel production and subsequent concerted efforts in managing financial, technical and regulatory constraints will help its integration in

Genomics

Carbohydrate Biosynthesis Transcriptomics

M Metabolomics

Proteomics Photosynthesis OMICS Data Integraon

IDENTIFICATION OF REGULATORY NETWORK Gene Eding/ Inseron Development of GM Algae and biofuel plants Cultivation Biomass Harvesting Extraction

Enhanced Producon of Biofuel Fig. 11.2 Schematic representation of OMIC techniques for enhancing biofuel production

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industrial setups. The application of “omics” techniques has been used to understand the complexities of the lipid accumulation pathway. The combination of different omics has facilitated the group of gene identification which are involved in regulating the different steps of fatty acid biosynthesis pathway. The integration of genomics, proteomics, metabolomics, and transcriptomic studies data can be used for identification of regulatory network and thereby improved strain will be developed through genetic manipulation or genome editing to enhance biomass production, better tolerance, and efficient production of biofuel for industrial use (Fig. 11.2). Conflict of Interest The authors have no competing interest to declare.

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Multiomics Technologies and Genetic Modification in Plants: Rationale, Opportunities and Reality

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Vilas Parkhi, Anjanabha Bhattacharya, and Bharat Char

Abstract

Satisfying the food demand of the increasing population is a challenge, especially when availability of land and water is decreasing. Since the inception of agriculture, continuous efforts were made to increase the food production. Farmers have kept on selecting superior genotype from the population, while breeders with fertilization expertise developed improved varieties and hybrids. Traits such as biotic and biotic stress resistance were incorporated into cultivating varieties from the wild sources. Furthermore, the breeders have enriched germplasm pool with induced mutations, which are used to develop improved cultivars. However, the efforts to improve crop production by breeding have its limitations. To increase the speed and accuracy to plant breeding, various omics technologies are playing a crucial role. With the advancement of molecular biology, transgenic technology has made inter species gene transfer possible. Trait like insect resistance developed by transgenic technology is unbeatable, where the genes soil microbes overexpressed in plants. As the knowledge of genome sequence and gene functions are becoming more available for most of the crop species, technologies such as TILLING and EcoTILLING are helping in developing new traits. Latest new breeding tools such TALENs and CRISPR have changed the dimensions of crop breeding. Precise and targeted mutagenesis has increased the speed of new trait development and plant breeding. Epigenetics modifications are also being used in breeding new varieties. In future, NBTs along with bioinfomatic resources will be used to practice predictive breeding more routinely. More crops with the less drops of water and less piece of land are possible using these edge cutting omics technologies.

V. Parkhi (*) · A. Bhattacharya · B. Char Mahyco Pvt. Ltd., Jalna, Maharashtra, India e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Kumar et al. (eds.), Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), https://doi.org/10.1007/978-981-16-2956-3_12

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Keywords

Mutations · TILLING · Eco-TILLING · New breeding techniques (NBTs) · TALLENSs · ZFN · CRISPR · Cas · Genetic engineering · Epigenetics

12.1

Introduction

World human population is nearing 7.8 billion and the demand to feed them can be only satisfied by breeding new high yielding varieties and hybrids, at the same time human nutritional requirements need to be taken care while increasing yield of varieties and hybrids. This is possible by deploying effective plant breeding methodologies which can satisfy growing food demand and nutritional requirements. Genetic variations are the key to the success of plant breeding. The farmers are the original plant breeders, who kept on selecting and propagating superior genotypes over the thousands of years. This natural selection of superior genotype keeps the need of the growing human populations satisfied since introduction of agriculture. The selection of plants for human consumption has begun before 13,000 years in various parts of the World (Schlegel 2018). However, only selection the superior genotype could not satisfy the human need. Plant scientists with the expertise in making cross fertilization of plants called breeders, played significant role in creating man made genetic variations. Discoveries of Gregor Mendel and other plant geneticist have established the role of hereditary factors or genes in deciding plant characters. Geneticists have started applying various methods to induce heritable mutations in plant and animal species. A chemical or a radiation that creates heritable changed in the hereditary material called as mutagen and the process is mutagenesis. With availability of X-ray radiations, researchers have started using it as a mutagen. Mutation rate in drosophila was shown to increase by 15,000 time using X-ray radiations (Muller 1927) and, the variations and advancement in radiation mutagenesis have begun thereafter. Pioneer work in the chemical mutagenesis has been done by Auerbach and Robson (1946), Auerbach (1949) and others, which demonstrate that the exposure of chemical mutagen called Mustard gas (War gas), can increase the mutation frequency in Drosophila. This led to the inventions of methanesulfonates other modern chemical mutagens for plant and animal mutagenesis (Westergaard 1957). Plant scientists have made considerable progress in mutation breeding using various omics technologies. International Atomic Energy Agency (IAEA) database has documented 3320 officially release of mutant varieties from 228 crops as reported by 73 different countries (FAO/IAEA database 2020). This comprises of 49.5%, 35.5% and 15% belongs to cereal, ornamentals and legume crops, respectively. Classical and targeted mutagenesis need to be discussed in great details. Furthermore, the omics technologies, those are being used to generate variations in the germplasm, such as TILLING (Targeting Induced Local Lesions in Genomes), transgenesis, EENs (engineered nucleases) driven targeted mutagenesis and epigenomics have made significant progress based on the advanced knowledge of the plant genome and its functionalities.

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12.2

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Mutagenesis and Gene TILLING

DNA double helix model of Watson and Cricks has given birth to the modern plant science and subsequently to the field of biotechnology. The precise knowledge of genes and gene sequences has helped in introduction of traits in various crops using gene TILLING methodologies. EcoTILLING identifies single nucleotide polymorphism (SNPs) that associated with the distinct trait in the natural population, while high-throughput TILLING is identification of distinct traits of breeding interest in mutagenized population. Later one is done by stepwise manner; the first step is the development of mutation populations using mutagens followed with identification of mutants in the population using biochemical, physiological or genetic methodologies (Sikora et al. 2011). EMS (ethyl methanesulfonate) is the most commonly used mutagen to develop TILLING population. Number of mutations per genome and the size of the TILLING population decide the possibility of mutated alleles of genes. Considering the large numbers of TILLING population, screening it with biochemical or physiological parameters becomes cumbersome. Screening of TILLING population at DNA level becomes new norms with advancement of mutation detection techniques and; the speedy and low-cost sequencing. The discovery of PCR technique and restriction enzymes which cut the hetero duplex DNA has change the paradigm of TILLING technology. PCR amplification of desired genomic region followed with subsequent denaturation and re-alignment of the DNA creates the loop where mismatch between bases occurs. Special restriction enzymes such as Cel-1 or Endo-1 can cut the DNA at loop site and makes two pieces of the amplified region (Colbert et al. 2001). Latest gel separation system like Li-COR or MALDI-TOF can detect the changes between mutated and untreated plants. Furthermore, high resolution melting (HRM) assay, which is based on the release of fluorescence of DNA intercalating fluorescent dye was also proven useful in detection of mutations in TILLING populations (Wittwer et al. 2003; Dong et al. 2009). Advancement and quality in next generation sequencing (NGS) made the detection of mutations with the scanning of whole genome sequence affordable and transformed the today’s biology (Schuster 2008). EcoTILLING by sequencing (EcoTbyS) or TILLING by sequencing (TbyS) is in reality due to advancement of low-cost genome sequencing. Diagrammatic representation of detection of mutants in EcoTILLING and TILLING is depicted in the Fig. 12.1. Both the TILLING methodologies are contributing in future molecular breeding by identification of new allelic variations (natural or induced). EcoTILLING was used to evaluate 192 diverse accessions of desi and kabuli chickpea for the trait seed weight (Bajaj et al. 2009). Furthermore, natural variations was evaluated and identified by EcoTILLING for powdery mildew resistance genes mlo and mla in Barley (Mejlhede et al. 2006) and for drought tolerance in rice (Kadaru et al. 2006). Identifications of allelic variations by EcoTILLING in various crops are thoroughly reviewed by Irshad et al. (2020). Similarly, TILLING was used to identify allelic variations for powdery mildew resistance in wheat. The mlo, loss of function allele for TaMLO gene identified from the mutagenized and natural population demonstrate the durable resistance to powdery mildew disease in wheat (Acevedo-Garcia

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Fig. 12.1 EcoTILLING and TILLING process flow

et al. 2017). Apart from disease resistance, TILLING was used to identify the allelic variations for yield related traits. Identification of mutant Allele, Tagw2-A1 from TILLING population is resulted from G to a transition in spice acceptor site in the wild type gene. This mutation in wild type TaGW2-A1 Gene led to increase in thousand grain weight (TGW), grain length and width of tetraploid and hexaploid wheat (Simmonds et al. 2016). The quantitative variations in maize kernel row number trait are governed by FASCIATED EAR2 locus and the mutated fea2 alleles with loss of function of wild type were identified by TILLING (Bommert et al. 2013). In rice, SLAC1 gene determines stomatal conductance and photosynthesis rate. Four mutant lines were identified from TILLING population carrying mutations in the open reading frame of SLAC1 gene. The rice lines with mutant slac1 gene demonstrate increased stomatal conductance and photosynthesis rate in the well water conditions (Kusumi et al. 2012). Grain quality traits of cereals were also identified using TILLING technologies. In one of the studies, high amylose wheat was identified in TILLING population. Allelic variations were identified in waxy homologues of SBEIIa gene sequence. These mutated alleles were used in breeding of bread and durum wheat to get 47–55% more amylose compared to wild type (Slade et al. 2012). Another gene for starch branching enzyme, SBEI was found to be involved in governing amylose content in rice. Mutagenized population of rice cultivar, Nipponbare was used to identify 37 SBEI mutant lines with higher amylose and protein content (Kim et al. 2018)

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EcoTILLING and TILLING in combination with the advanced high-throughput sequencing tools such as NGS can be applied in detecting allelic series of functional genes in agricultural crops. Knowledge of genome sequence of various crops species in coupled with phenotypic correlations with the predicted genes can help to use diverse and large germplasm base for breeding quality as well as yield related traits. New analytic tools make will play very critical role in the applied genomics-based crop breeding

12.3

Transgenics and Genetically Modified Crops

Biotechnology is a modern technology which comprises of set of tools which deals with living organism for welfare of human being. This technology works for the value addition in plants, animal and microbes by continuous improvement in the desired qualities. Genetic makeup is the key for such improvements in the desired qualities of the living organism. The chronology of genetic modifications was discussed in details by Zhang et al. (2016). The genetic modifications in the living organism becomes possible after, landmark discovery of double helical structure of DNA by Watson and Crick in 1954 and the enhancement of the knowledge around the nucleic acid thereafter. It has started with genetic modification the micrograms in 1970s especially after discovery of recombinant DNA technology by Cohen et al. (1973) and followed with plant and animals in 1980s. The first genetically modified mouse was developed by Gorden et al. (1980). Similarly, first transgenic tobacco plant carrying foreign chimeric genes was developed using Ti-plasmid derived vector (Herrera-Estrella et al. 1983). Another important discovery that has changed the pace of the genetic engineering research was invention of polymerase chain reaction (PCR) in 1983 which helps to multiply DNA template in in vitro conditions (Kary Mullis, 1990). In plant genetic engineering, the model plants such as tobacco and Arabidopsis were used extensively to study the ectopic expression of various genes from diverse living organisms. Reporter genes such as uidA encoding glucoronidase from E. coli (Jefferson 1987; Jefferson et al. 1987) and gfp encoding green fluorescent protein from Aequorea victoria (Chalfie et al. 1994) have helped to standardized plant transformation process in many crop species. In 1994, FlavrSavr™ tomato was the first transgenic crop commercialized in the USA, the gene for polygalacturonase enzyme responsible of fruit ripening was silenced resulted in delayed ripening of tomato fruits. Commercially successful crops such as Bt-cotton, herbicide bromoxynil herbicide tolerant cotton, herbicide glyphosate tolerant soybean and Bt-Potato were commercialized in 1995 (Bawa and Anilakumar 2013). Since then, a wide variety of GM crops developed throughout the world. In India, the first and only GM crop, i.e. Bt-cotton (Boll Gourd I) was commercialized in 2002. Cry1Ac gene is expressed in BGI. To provide improved resistance against lepidopterans, the subsequent commercial release of BGII cotton has happened in 2004, which carry two Bt genes, Cry1Ac and Cry2ab. Despite huge success of Bt-cotton in India, no other GM crops were commercialized.

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One of the promising GM traits is Golden Rice™ developed by two German scientists Dr. Ingo Potrykus and Dr. Peter Beyer in a mega research program “Golden Rice Project” led by Syngenta in collaboration Institute for Plant Sciences, Swiss Federal Institute of Technology, Zurich, Switzerland; University of Freiburg, Center for Applied Biosciences, Freiburg, Germany and International Rice Research Institute, Philippines (Ye et al. 2000; Parkhi et al. 2005). Golden rice is an effective source of Vitamin-A which has ability to eradicate malnutrition in the children (Tang et al. 2009). The Philippines has released golden rice for commercial cultivation in 2019. Same may be followed by Bangladesh and other developing countries where Vitamin-A deficiency in children is a serious problem. Similar impact can be made by other nutrition rich GM crops developed by keeping in view of eradicating iron and zinc deficiency in children and women. Regulations involved in commercializing GM crops are the key hurdles in yielding maximum benefits for the mankind. Activists have strong reservations about the GM crops. One of the objections of the GM crop opponents is a forceful transfer of genes to the crops from unrelated species (Maghari and Ardekani 2011). They believe that the presence of GM crops may disturb the balance of the nature as the traditional breeding and natural selection helped to evolve the elite varieties of many crops species. However, this concern is not very true if one consider the evolution history of the plant species. Species with diverse gene pool will have better chances of flourishing. Another objection is the adverse effects of GM crops on human and other organisms. This is ruled out before release of any GM crop. It is compulsory for any GM product to go through the toxicological and allergenicity studies on animals. It was claimed that rat feeding on roundup ready GM maize to have hepatorenal toxicity (Seralini et al. 2012). This research paper was retracted as research studies have used flawed methodologies and was not reproducible. Various studies have demonstrated that GM food have no adverse effects over non-GM on animals from nutritional health related issues (Flachowsky et al. 2012, Kilic and Akay 2008, Koch et al. 2015; Szymczyk et al. 2018). One of the environmental benefits is reduction in pesticides use after introduction of Bt-crops (Benbrook, 2012). This should be taken as positive impact of GM crop on health and environment.

12.4

Engineered Endonucleases and Genome Editing

Random mutagenesis creates lesions in the plant genome and further transferring of desired traits from the mutant plant to recipient genotype is the time consuming process and it required robust marker assisted background selections to avoid donor genomic content (DGC). Another approach to avoid DGC is precise mutagenesis using engineered endonucleases (EENs). EENs in the presence of a sequencespecific DNA-binding domain (DBDs) or short RNA have the capacity cleave DNA in a sequence-specific manner. These nucleases can efficiently and precisely introduce double-strand breaks (DSBs) through the recognition of specific target DNA sequence stimulating the cell’s endogenous DNA repair mechanisms. The

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DNA repair mechanisms, homology-directed repair (HDR) or the error-prone non-homologous end joining breaks (NHEJ) have capacity to repair these DSBs leading to gene modification at the target sites (Sung et al. 2013). NHEJ repair process is error prone and can introduce mutations in the nucleotides sequence such as the insertion, deletion or substitution, while HDR requires template for repair and hence can be more error free than NHEJ and précised. Taking advantage of insertion of errors by NHEJ or precise nucleotide change by HDR, genome editing technologies are being used for trait development or to understand the gene function on crop species (Belhaj et al. 2015). Over the last one decade various gene editing tools have been evolved. These are engineered homing endonucleases/meganucleases (EMNs), oligodeoxynuclotides (ODNs), Zinc Finger Nuclease (ZFN), Transcription activator-like effector nucleases (TALENs) and CRISPR-Cas technology. Among these, ZNFs, TALENs and CRISPR-Cas are the favourite gene editing tools for crops improvement. ZFN is the first DNA editing tool used for targeted mutagenesis. The modern engineered nucleases designed to carry out précised targeted mutagenesis have two separate protein domains, one is zinc finger (ZF) DNA-binding domain which required for DNA recognition and other is FolkI endonuclease that gives cleavage in the target DNA (Carroll 2011). The ZF DNA-binding domain with alpha helices insert into major groove of the DNA double helix and recognize 3 contiguous bases (Pavletich and Pabo, 1991). Cys2-His2 zinc finger consists of approximately 30 aa in a conserved beta-beta-alfa configuration with zinc atom. Each ZF domain binds to 3 bases of target DNA. Computation of aa combinations in ZFP helps to design the ZFN GE tool. ZFPs (Left and Right) recognized and bind to DNA sequences flanking to the target region. FolkI endonuclease fused with ZFP undergo dimmerisations and makes DSB within 5–7 bases between ZFPs (Osakabe and Osakabe 2015). Cell undergo DNA repair in NHEJ or HDR fashion and error in the repairs process insert mutations. Gene alterations using ZFNs have been demonstrated in various plants like Arabidopsis (Osakabe et al. 2010), maize (Shukla et al. 2009), tobacco (Townsend et al. 2009). However, gene editing using ZFN is time consuming process and it can cause cell toxicity (Cathomen and Joung 2008; Hsu and Zhang 2012). Furthermore, due to large construct size, multiplexing (targeting multiple genes or DNA sites) is difficult. Transcription activator-like effector nucleases (TALENs) is the popular gene editing tool and it works in similar fashion like ZFNs. It is evolved from the defence system of the plant pathogenic bacteria Xanthomonos (Boch and Bonas 2010). While infecting, it gets associates with plant cell and releases its TAL effector proteins inside nucleus. Upon entering into nucleus, TAL effector proteins bind to the regulatory elements of host genes and modulate defence response. TALENs is developed by fusions of DNA-binding domains of TALE proteins and the cleavage domain FokI endonuclease. TALE repeats are arranged in tandem fashion. Individual TALE repeat (33–35 aa) recognize a single base in DNA. TALE specificity decided by two hyper variable residues (repeat-variable diresidues; RDV) at 12 and 13th position. RDV nucleotide recognitions are as AsnAsn:Guanine; AsnGly: Thymine; AsnIle: Adenine and HisAsp: Cytosine (Mak et al. 2012, Deng et al. 2012).

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Similar to ZFNs, left TALE and right TALE binds to flanking DNA of target site. FolkI endonuclease attached to TALE proteins undergoes dimmerization and gives DSB within 8–15 bases in the target site. DNA repair mechanisms using NHEJ or HDR insert mutations that may leads to development new trait in the plant. Variations in the TALLENs were developed by researchers, which include highly active platinum TALENs with repeating pattern of non-RDV in DNA-binding module increases its efficiency (Sakuma et al. 2013). This variant of TALLENs was used in potato genome editing (Yasumoto et al. 2019) In the recent advancement, the bicistronic TALENs with reporter molecules have been developed which increases genome editing efficiency (Martin-Fernández et al. 2020). TALENs mediated genome editing have been demonstrated in various crops species such as Arabidopsis, rice, wheat, sugarcane, soybean, corn and potato and; much of that is attributed for crop improvement (Reviewed in Zhang et al. 2018, Martínez-Fortún et al. 2017). TALENs GE tool was employed to disrupt susceptibility gene OsSWEET14, which led to the development of bacterial blight disease resistance in rice (Li et al. 2012). In another development, TALENs mediated knockout of three TaMLO homologues confers powdery mildew resistance to wheat (Wang et al. 2014). This tool was also used for improvement of quality traits. Oil quality in soybean was improved by disruption of FAD genes. Fatty acid content of healthier oleic acid was increased while less healthy linoleic acid decreased (Haun et al. 2014; Demorest et al. 2016). Storage of potato in cold conditions can lead to the accumulation of undesirable reducing sugars in the tubers. Negligible reducing sugars were observed in the vacuolar invertage (VInv) gene edited potato when stored in the cold conditions (Clasen et al. 2016). Expression of betaine aldehyde dehydrogenase (BADH2) gene negatively correlates to level of aroma in rice, its disruption using TALENs increases the fragrance in the grain (Shan et al. 2015). CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) /Cas is the most popular genome editing tools currently being used for crop improvement. CRISPR/Cas is RNA-Protein recognition system evolved to identify and cleave target DNA. It is most frequently used Cas9 protein in the CRISPR/Cas genome editing tool was isolated from Streptococcus pyogenes. Targeted genome editing in human cells using RNA guided Ca9 endonuclease is the simplified version of RNA-mediated prokaryotic adaptive defence against viruses or plasmids (Wiedenheft et al. 2012; Cho et al. 2013). Four components are involved in this DNA editing tool; those are Cas9, SgRNA, donor DNA (optional) and host DNA. CRISPR Cas9/SgRNA assembly recognizes the 16–20 bp target DNA with Protospacer Adjacent Motif (PAM), i.e. NGG. Cas9 nuclease in association with sgRNA introduce ds break or ss nick three bases away from PAM site. (Cho et al. 2013, Mali et al. 2013a, b). Editing the DNA segment showing sequence similarity with targeted DNA by CRISPR/Cas machinery is called as the off target effect. This is one of the main drawbacks of CRISPR/Cas genome editing (Pattanayak et al. 2013). However, this can be taken care by two to three back crosses of edited line with its non-edited counterpart. Applications of this genome editing tool have been demonstrated in several crops species (Ma et al. 2015), including horticulture crops such as Potato (Wang et al. 2015), Tomato (Brooks et al. 2014; Cermak et al. 2015; Pan et al. 2016;

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Soyk et al. 2017; Parkhi et al. 2018), Apple (Nishitani et al. 2016), Sweet Orange (Jia and Wang 2014) and Cabbage (Lawrenson et al. 2015) and; field crop species such as rice (Xu et al. 2014; Li et al. 2016), wheat (Singh et al. 2018), cotton (Janga et al. 2017; Peng et al. 2020) and maize (Char et al. 2017; Barone et al. 2020). Cpf1 or Cas12a is another variant of Cas9 protein have different PAM sites, i.e. TTN/TTTN/TTTV (N ¼ A/T/C/G; V ¼ A/C/G), site of cleavage and cleavage pattern. It is RNA guided Type V CRISPR system isolated from Prevotella and Francisella bacteria. Cpf1-SgRNA assembly is much smaller, 42 nt long compared with Cas9 (92 nt) long. It has high multiplexing capability compared to Cas9. It induces sticky overhangs of 5 bp, at 18–23 bases away from PAM site (Zetsche et al. 2015). This system is preferably used in HDR, gene knock out and gene insertion or replacement. It is a versatile tool which is being used for advancement of crops (as reviewed by Bandyopadhyay et al. 2020). Base editor is another CRISPR gene editing tool in which dCas9 (cas9 with deactivated nuclease activity) is fused to DNA deaminases (Komor et al. 2016; Lu and Zhu 2017). It can alter the target DNA sequence without inducing DSBs. It is designed for only base substitution. Cytidine deaminase-Base editor (CBE) takes part in conversion of G-C to T-A, while, Adenine deaminase-based editor (ABE) is used in A-T to G-C conversion. This tool was in development of imidazolinone herbicide tolerance in Arabidopsis. CBE was used for C to T substitution at 653 aa position of ALS gene (Dong et al. 2020). Development of herbicide tolerance using base editing is also reported in rice (Liu et al. 2020) and wheat (Zhang et al. 2019). In another research development, base editor was used for development of poty virus resistance, where allelic variants of eILF4E1 gene were generated (Bastet et al. 2019). DNA-free gene editing using ribonucleoprotein (RNA)-guided endonuclease (RGEN) RNPs is another way to use all above mentioned CRISPR/Cas genome editing tools without gene integration (Kanchiswamy 2016). Recently, RNA-virus based vector was also used for DNA-free plant genome editing (Ma et al. 2020). Apart from above-mentioned Cas9 variants, RNA editor is the RNA editing tool. CRISPR cas13 (C2c2) targets RNA in a sequence-specific manner. It does not require PAM site and can be used in non-dividing cells (Abudayyeh et al. 2016). Cas13a, which cleave RNA can be used in therapeutics. Cas13b enzyme fused to an RNA adenosine deaminase (adenosine deaminase acting on RNA type 2) that can convert adenine to inosine (guanine) (Cox et al. 2017). This can create point mutations in RNA which could rescue known pathogenic alleles or introduce a premature stop codon to render RNA non-functional. Overall CRISPR/Cas technology is going change perceptions of plant breeding and trait development.

12.5

Epigenetic Modifications

Epigenetics plays important role in gene functions and regulations. Epigenetic modifications are stable and heritable changes in the gene expression (including complete turn on/off of gene function) without any underlying changes in the genomic DNA (Fujimoto et al. 2012). The epigenetic modifications of plants that

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occur during developmental process or in response to the environmental changes are due of one of these three actions. These are posttranslational histone modifications, DNA methylation and gene regulation by noncoding RNA molecules, i.e. small RNAs (siRNAs and miRNAs) or long noncoding RNAs (long ncRNAs). Upon activation of one of the pathways lead to changes in chromatin structure (open, i.e. permissive or closed, i.e. repressive) and can impact the gene expression in either way (details as reviewed by Kapazoglou et al. 2018). External (environmental or induce) or internal (developmental) stimuli can produce epigenetic changes that alters chromatin structure. The altered chromatic structure can impact the gene expression. These changes in the chromatin and impact on the gene expression could be heritable and the resultant alleles are called epialleles. The epialleles which produce considerable phenotypic changes can be used for epibreeding. These epialleles can be further introduced into crop breeding. In one of such study, rice seeds were treated with DNA methylation inhibitor, 5-aza-deoxycytidine. The survived progenies were propagated for over decade, the resultant line shown acquired resistance to bacterial pathogen Xanthomonas oryzae pv. Oryzae. Further molecular dissection demonstrated that the acquired resistance is due to substantial methylation of promoter of Xa21G gene and expression was high in developed line (Akimoto et al. 2007). The methylation mark of promoter of Xa21G gene was heritable and maintained for nine generations. Epi-Recombinant Inbred Lines (epiRILs), which differs only on DNA methylation and generated from the same parental strain can be used for plant breeding purpose. These homozygouse lines are only differs on DNA methylation pattern and can govern traits such as plant height, yield and stress tolerance (Johannes et al. 2009; Reinders et al. 2009). Chemical modifications such as methylation, acetylation, phosphorylation, ubiquitination, biotinylation and sumoylationon on the N tail of nucleosomal histone are considered as Histone modifications. The classical example of histone modifications is regulation flowering locus (FLC), a repressor of flowering (Dean and Whittaker 2017). Small noncoding RNAs also involved in regulations of flowering by suppressing FLC locus. Furthermore, CRISPR/Cas technology can be used to develop epigenetic changes in the plant, which can help to increase the yield or biotic or abiotic stress tolerance in plant (Khan et al. 2017). Methylation of regulatory elements in the Arabidopsis was demonstrated using dCas9-SunTag system (cas9 with deactivated nuclease activity and VP64 transcription activator) along with catalytic domain of Nicotiana tabacum DRM methyltransferase (Papikian et al. 2019). Similarly, epimodification system, MS2-CRISPR/dCas9 were developed using various effector domains including transcriptional activator VP64, the H3K27 acetyltransferase p300 and the H3K9 methyltransferase. Lee et al. (2019) have used this system to show the early flowering in Arabidopsis by activation of Flowering Locus (FT). Further research is being done to use this intervention in crop improvement.

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Conclusion

Continuous research in the area of plant breeding is required to feed the growing human population. In fact, since the introduction of agriculture, it is the ongoing process with continuously improvement. The farmers kept on selecting and propagating superior genotype or land races to fulfil the food demand. Later, plant breeders with the expertise in cross fertilization developed high yielding, disease resistant varieties and hybrids. Conventional plant breeding has some limitations such as transfer of genes/traits from different species and lack of techniques to avoid of genetic linkage or donor’s genetic content. Mutagenesis approach has assited the breeders to generate variations in the germplasm and with availability of gene sequences and their functions, gene TILLING was helpful to complete the task with more precise way. With advancement of molecular biology, in the late twentieth century, transgenic technology has emerged as miracle technology, as it has the capacity to transfer the gene/genes across species. Furthermore, the genes can be overexpressed or can be silenced. However, public perceptions on this technology are mixed, some of them are supporter of this technology and others strongly opposed it. In the last decade, precise targeted mutagenesis have emerged as more promising and better acceptable to the scientific community as well as general public. Although, traits such as insect resistance developed through GM technology is unbeatable. However, new plant breeding technologies (NBT) such as TALENs and CRISPR/Cas are being use to developed various traits without transfer of foreign gene/genes into the crop species. NBTs are being used to developed novel traits in considerably less time. Future plant breeding will be driven by advanced NBTs with the utilization of genome knowledge of the crop species; in fact crop breeding will be more predictive using bioinformatics knowledge.

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Social Acceptance and Regulatory Prospects of Genomics in Addressing Food Security

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S. J. S. Rama Devi and Supriya Babasaheb Aglawe

Abstract

The global population is expected to raise 9 billion by 2050 and 10 billion by 2100 or may exceed up to 13 billion. Ensuring food security for the growing global population is a big challenge for the breeders and agricultural scientists on the face of global warming and climate change. In order to achieve this, it is essential to fulfill the three factors like availability, accessibility, and adequacy. Plant breeding has been playing a great role in crop improvement and food security. Green Revolution was one of the important landmarks in this aspect. As a result of green revolution, tremendous gains in wheat productivity occurred between the 1960s and 1970s. After the 1980s molecular breeding and genomics assisted breeding contributed in this direction. Further plant tissue culture and genetic engineering overcome the barriers associated with conventional and molecular plant breeding and took crop improvement to the new heights. The intervention of genomics and molecular biology has revolutionized the field of crop improvement; however, there was a huge rejection for genetically modified (GM) or genome edited crops across the globe due to different regulatory policies adopted by countries. Also, the factors like willingness to purchase, taste, and nutrition aspects from consumer’s perception coupled with the questions raised by the environmentalists about the impact of the GM on the existing genetic pool or cross species have discouraged the GM/technology driven crops or their derived products for commercial cultivation. Here we have discussed the social acceptance of the GM or genome edited food crops and their derived products; its

S. J. S. R. Devi (*) Center for Cellular and Molecular Biology (CCMB), Hyderabad, India S. B. Aglawe Professor Jaya Shankar Telangana State Agriculture University, Hyderabad, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Kumar et al. (eds.), Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), https://doi.org/10.1007/978-981-16-2956-3_13

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related challenges, limitations, and promising approaches to tackle the same for assure the global food security. Keywords

Crop improvement · CRISPR · Food security · Genomics · Genetically modified and genome edited crops · Plant breeding · Marker assisted selection · Social acceptance

13.1

Introduction

Food security means ensuring sufficient, safe, and nutritious food to meet the dietary requirement for all the people at all the times to lead a healthy and active life. In order to achieve this, it is essential to fulfill the three factors like availability, accessibility, and adequacy. This means, there must be availability of the food for all; further available food must be accessible to all with affordable prices. In addition, the consumed food has to be adequate to meet the daily nutritional requirements of an individual (Tyczewska et al. 2018). Ensuring food security for the current growing global population is a big challenge. According to the survey and the statistical data reported by United Nations and University of Washington, the global population is expected to raise 9 billion by 2050 and 10 billion by 2100 or may exceed up to 13 billion by 2100 (Murphy 2016; Tyczewska et al. 2018); to feed the raising human population the global food production has to be doubled (over 2.4% increase per annum) for the next 30 years (Ray et al. 2013). Importantly, at present the percentage increase in principle staple food crops (like rice, wheat, and maize) is less than 2% per annum (Tyczewska et al. 2018). Several factors like climate change, soil deterioration, unusual floods, drought, extreme heat, and salinity are responsible for less percentage increase in crop production and productivity. According to world economic forum 2010, climate changes have adverse effects and they may lead to10–40% loss in the crop production per annum across the globe. Importantly, India accounts for 40% loss in total crop production which is a quite alarming. Similarly, the shrinking arable land and the inadequate supply of the water for agriculture purpose are the one of the major concerns (Verheijen et al. 2009; Vörösmarty et al. 2010). It is estimated that approximately one-third of arable land had lost in last 40 years (https://www.theguardian.com/environment/2015/dec/02/arable-land-soilfood-security-shortage). Ensuring food security is one of the 17 sustainable development goals (SDG) adopted by the world. The 2030 agenda for sustainable development goals target to achieve no hunger, without food insecurity and malnutrition in any forms across the world (https://www.un.org/sustainabledevelopment/sustainable-development-goals/). World statistics indicate that more than 830 million people are facing hunger today in Africa and Asia. Approximately 2 billion people are facing food insecurity across the globe as on date, making them undernutrition with poor health. In every continent, the prevalence of malnutrition is higher among women than men.

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Malnutrition may result in low birth weight among the new born, stunting (low height for age), wasting (low weight for height), overweight, obesity, and anemia (in women of reproductive age) (Table 13.1). In this context there is an urgent need to explore the possibilities of increasing food production across the world to meet the requirements in near future. Efforts are being made by the researchers worldwide for the bio-fortification of the staple food through conventional and molecular breeding (Swamy et al. 2016; Saini et al. 2020; Khulbe et al. 2020); however, this approach is very laborious and time consuming. One of the promising ways to address malnutrition effectively and precisely in developing and under developed countries is to bio-fortify the staple food so as to make sure the sufficient dietary nourishment. Scientists are successful in bio-fortification of beta-carotene for vitamin A deficiency in children (Kettenburg et al. 2018); folate bio-fortification (Della Penna 2007; Strobbe and Van Der Straeten 2017); iron bio-fortification (Boonyaves et al. 2017; Narayanan et al. 2019). Nonetheless, the bioavailability of the bio-fortified food is also a subject to be taken for consideration by the researchers since it is an ultimate parameter which plays a role in overcoming malnutrition. However there exists a huge research gap in this direction, due to lack of proper funding to the nutritionalists which has to be encouraged by the funding agencies (Hirschi 2020). Though GM crops initially focused on herbicide and insect resistance (first-generation GM crops) (Alemu 2020; Ranjan et al. 2020); in the subsequent second generation of the GM technology, it was widely adopted for agronomically important traits improvement like grain yield in wheat and soybean, which are at the last stage of evaluation and expected to be released probably in 2020 for Argentina (González et al. 2020). Furthermore, genetically modified major cereals (rice, wheat, and maize) are superior in nitrogen use efficiency as indicated by meta-analysis conducted by Li et al. (2020). In addition, Brookes and Barfoot (2020) had recently analyzed and summarized the statistics of farm income and production impacts of GM technology between years 1996 and 2018 for four crops (soybean, canola, cotton, and corn) globally. It is estimated that the technology has significant impact in gaining the economic benefits for their investment in GM crops (including India for insect resistance cotton). Besides the discussed above recent advances, as of 2017, a total of 24 (19 developing and five industrial) countries are growing GM crops and 43 countries are importing GM crops for food, feed, and processing and there is a significant increase in the area of GM cultivation which is about 189.83 million hectares (ISAAA 2017). International service for the acquisition of Agri-Biotech Application (ISAAA) maintains an up-to-date worldwide biotech/GM crop approvals database for public use. The user can find the various GM plants; genome editing resource; country wise approved GM events; developer details and important traits for which the GM plants were developed. (http://www.isaaa.org/ gmapprovaldatabase/default.asp). Although GM/technology driven crops were developed for different economically and nutritionally important traits across different crops, in reality there exist many troubles for its commercial success. One such fact is an enormous difference in the investments towards GM production between public and private sectors (Shukla et al. 2018). This difference in expenditure may turn out to be monopoly by the

Year of survey India# World#

25.2 49.5

253.9 940.5

194.4 809.9

Prevalence of wasting in children (under 5 Years of age) 2018

Prevalence of undernourishment in the total population 2004–06 2016–18 46 149

Prevalence of stunting in children (under 5 Years of age) 2018

Statistical survey of different aspects of global food security accountability

2.9 40.1

Prevalence of overweight in children (under 5 Years of age) 2018

Table 13.1 Statistical survey of different aspects of global food security accountability

24.1 563.7

32.8 672.3

Prevalence of obesity in the adult population (18 Years and older) 2012 2016

165.6 552.2

175.6 613.2

Prevalence of anemia among women of reproductive age 2012 2016

11.4 49.7

13.4 56.6

Prevalence of exclusive breastfeeding among infants 0–5 months of age 2012 2018

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MNCs in agriculture which is a great concern by social activists (Shiva and Crompton 1998) and is opined that public good should not be compromised for corporate profits (Shand, 2002). Alternatively, it is suggested to establish public private partnership for the deployment of GM crops (Potrykus 2010). The additional reasons include asynchronous; strict and varied regulatory aspects of nations make it difficult for their marketable success. For instance, in Europe, there is a strong opposition for GM and genome editing crops (Schulman et al. 2020; Kwak et al. 2020; Woźniak et al. 2020). However countries like the USA, Argentina have defined regulatory frameworks for the release and cultivation of GMOs (https:// www.fda.gov/food/agricultural-biotechnology/how-gmos-are-regulated-food-andplant-safety-united-states; https://www.loc.gov/law/help/restrictions-on-gmos/usa. php; Burachik 2020). For instance, in India there is a huge opposition for commercial cultivation of GM crops and many are still under trails (Shukla et al. 2018). Mane (2015) had critically reviewed the acceptance of GM rice in Indian perceptive and summarized the various public and private GM rice testing centers across India. According to the author, in Indian scenario the involvement of the different regulatory agencies at various levels had become a bottleneck and is a time-consuming procedure for the GM approval. Golden rice though to be more promising solution for Vitamin A deficiency in children there are reports in several countries where alternatives for golden rice can still solve the problem (https://www.downtoearth. org.in/news/agriculture/commercialisation-of-gm-rice-turns-out-to-be-a-dud-report62218); Bt-Brinjal is opposed since India is a center for biodiversity and may lose the indigenous germplasm if permitted for commercial cultivation (https://www. thehindu.com/opinion/op-ed/serious-concerns-over-bt-brinjal/article28022577.ece). Bt-mustard has faced the consequences from environmental activities for its implications towards developing super weeds (https://www.thehindu.com/opinion/ op-ed/say-no-to-gm-mustard/article18573107.ece). Furthermore, GM/technology driven crops are being very controversial issue between the environmentalists, governing bodies, general public, and farmers for their acceptance since their development. It is important to note that lack of implementation of the technology driven agriculture in vulnerable populations of the world can never achieve food security (Hirschi 2020). With the above perspectives, the current chapter primarily aims in discussing the social acceptance of the genomics derived food crops and their derived products; its related challenges, limitations, and possible approaches to address the same for ensuring global food security.

13.2

Various Techniques Involved in Crop Improvement

Human learned to select good seeds from best genotype for next sowing to secure good yield. Plant breeding has been playing a great role in crop improvement and ensuring global food security. Green Revolution is one of the important milestones in this aspect. Remarkable gains in wheat productivity occurred between the 1960s and 1970s as a result of green revolution. Dr. Norman Borlaug and his colleagues had developed dwarf and semi-dwarf varieties of wheat that are high-yielding,

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Fig. 13.1 Percentage area under cultivation of top four GM crops compared with non-GM crops across the globe (in million hectares) (Source: ISAAA, 2018)

short-stature, disease-resistant, and fertilizer-responsive (Acevedo et al. 2018). Similarly, from the start of the Green Revolution, world rice production also increased strikingly by almost 140% (Mohanty et al. 2013). In due course of time, Marker Assisted Selection (MAS) which is an advance version of traditional plant breeding technique using molecular markers has been employed in crop improvement for introducing many agronomically important traits like quality, biotic and abiotic stress resistance in staple food crops like rice, wheat, and maize (Das et al. 2017; Gupta et al. 2010; Hossain et al. 2018). MAS is more advantageous in increasing the yield than conventional breeding since it is easy to develop new varieties in a minimum time period. Genetic engineering has the potential to address all the issues associated with food security. Crops developed with genetic engineering are known as genetically modified crops (GM crops). This is the most adopted technique in the history of agriculture, since it has a great role in overcoming the limitations of the conventional plant breeding; however, many concerns regarding GM crops were raised. Despite great hue and cry the area under GM crop is still continued to increase till date. In 1994 first GM crop approved for commercial cultivation and from that time the area under GM crops has increased nearly 100-fold (from 1.7 million ha to 191.7 million ha in 2018). The USA is the largest producer of GM crops followed by Brazil, Argentina, Canada, India, etc. The most planted biotech crops in 2018 were soybean, maize, cotton, and canola (Fig. 13.1). Beside from these major GM crops, GM papaya, eggplant, potato, apple, safflower, pineapple, and sugarcane are also under cultivation in different countries. Although India is among the top 5 GM growing countries, Bt cotton is the only GM crop approved for commercial cultivation in India. In addition to the GM, many of the genomics assisted techniques have been developed. Table 13.2 summarizes the list of novel food technologies used in crop improvement. A comparison of various technologies with respect to their pros and

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Table 13.2 Summary of different crop improvement techniques Technique Genetically engineered organisms (GMO)

New breeding techniques (NBTs)

a. Cis genesis

b. Intra genesis

c. Genome editing techniques are as follows Site-directed nucleases (SDNs) [also called molecular or enzymatic scissors]

Transcription activator-like effector nuclease (TALEN); zinc-finger nucleases (ZFN); clustered regularly interspaced palindromic repeats (CRISPR); mega nucleases

Oligo-directed mutagenesis (ODM)

Definition Introduction of foreign DNA into target crops to make them insect resistant or herbicide tolerant or combination of both Methods that allow for the development of new varieties in a faster and with more precision than conventional breeding techniques, by modifying the DNA. Introduction of intact genes, together with associated promoter/terminator from one species are inserted into the genome of the same or a closely related species (which are sexually compatible). Introduction of the functional gene(s) may be partial. The promoter/terminator may not be associated with the functional gene(s) in the native plant, although all components are derived from the same or a closely related species Introduction of small precision modifications (SDN 1 and 2) of larger fragments of DNA or introduction of complete genes at a predetermined genomic location (SDN3). All these are protein-based systems that utilize engineered proteins to target the site and cleave it, i.e. act as DNA nuclease. These techniques have been largely used by the CRISPR/Cas system. CRISPR/Cas uses RNA (in its natural bacterial system encoded on CRISPR sequences) to guide the protein nuclease (Cas) to the DNA site to be cleaved. Short (or DNA-RNA) fragments (oligo nucleotides) would be introduced into cells where the cell will be triggered to modify its own DNA to match the introduced DNA fragments

cons was presented in Table 13.3. The following sections discuss the status of technological developments of three major staple crops: rice, wheat, and maize.

13.2.1 Rice Even though rice is the most important food grain of the world (Kumar et al. 2020a), few GM rice varieties are approved for cultivation. Nevertheless lot of work is going on development of GM rice for various traits all over the world. The first GM rice variety (herbicide tolerant) approved for cultivation in the USA in the year 2000

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Table 13.3 Pros and cons of some of the important crop improvement techniques Crop improvement technique Conventional breeding

GMOs

Pros Majority of the conventional breeding products are inbred lines with high yielding, stress resistance, good grain, and cooking quality which are a combination of the farmer accepted as well as consumer accepted traits The availability, cost, and the quality of inbred seed are reachable to farmer. In many cases, the farmers can maintain their own seed banks without the involvement of the third party Varieties developed with conventional plant breeding are safe and easily acceptable by farmers as well as consumer

Function of trans gene is well understood prior to its introduction Risk assessment of the food safety can be achieved Based on Cartagena protocol on biodiversity GMOs can be regulated in many countries

Genome editing

Right to know can be implemented via proper legislation Genetic engineering offers to incorporate genetic material from any background to the plant. It can cross inter species, inter genus barriers even kingdom barriers The technique is very precise and can get the desired change at the genomic locus with adequate accuracy There will not be any foreign DNA introduced into the plant genome; hence has more probability of public acceptance than GMOs

Cons It can be achieved only between sexually compatible plant species. This may eventually result in narrow genetic base which make them more susceptible to many biotic and abiotic stresses making less durable Using conventional breeding along with the trait of interest many undesirable traits also may transfer to the progeny which may ultimately result in yield penalty It is time intensive (minimum 10–12 years are required to release any variety) To achieve progeny with the combinations of the desirable genes/ alleles breeder needs to generate huge population which makes this process too much laborious and also need more natural resources Transgenic plants developed via RNAi technology would be difficult to understand Trans gene flow is possible between compatible cross species Food safety concerns do exist in consumption of food derived from GM crops producing BT toxins; about allergenicity, toxicity, and horizontal gene transfer In some countries voluntary labeling makes difficult to identify the GM derived food

Due to in-vitro condition there is need to regulate it separately than mutation breeding product There exists asynchrony among different nations regarding the regulatory policies (continued)

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Table 13.3 (continued) Crop improvement technique

Pros Genome editing followed by segregation studies are efficient in development of Cas free plants A better strategy to develop novel traits among staple crops which could meet the future food demand

Cons Voluntary labeling is of more public concern for genome edited driven food products

(GM Crop Database of the Center for Environmental Risk Assessment, (LLRICE06, LLRICE62).). Later on, GM rice developed for different traits were approved in countries like Canada, Mexico, Australia, etc. China approved pest resistant GM rice in 2009 (Chen et al. 2011). From all these GM varieties none of the GM variety is commercially cultivated. Golden rice is the GM rice developed by Ingo Potrykus and his colleagues (Ye et al. 2000). It is engineered for the development of more amount of beta-carotene in the endosperm. It was developed more than 20 years ago and predicted to solve the vitamin A deficiency problem at global level but still it is pending for approval in most of the countries. Recently Canada and the USA approved golden rice for cultivation (Andy 2018). Many scientists all over the world working on the development of GM rice with different traits such as herbicide tolerance, insecticide resistance, improved nutritional quality, increase yield, and conversion of C3 rice in to C4 (Schuler et al. 2016). Rice has been proven as a promising model system for CRISPR/Cas studies. Rice is extensively exploited for CRISPR/Cas studies by many researchers. To date more than 80 research papers have been published on CRISPR in rice. Disease resistance and herbicide tolerance are the mostly studied traits with CRISPR/Cas (Wang et al. 2016; Sun et al. 2016). Efforts also have been diverted to improve yield related traits in rice using genome editing (Wang et al. 2016; Li et al. 2016a). Li and coworkers used CRISPR/Cas system for development of photo and thermo sensitive male sterile line of rice (Li et al. 2016b). However, the success of GM or genome editing in rice depends on consumer acceptance which is still a debatable question to be answered in near future.

13.2.2 Wheat Wheat is the second most important food grain in the world despite it is among the least exploited crop for genetic modifications because of its polyploidy nature and difficulty in obtaining regeneration protocol. The first GM (herbicide tolerant) wheat was developed by Monsanto, despite its approval by FDA Monsanto, withdrawn the product in 2004 (Biosafety Clearing-House Living Modified Organism identity database). To date only one GM wheat variety resistant to glyphosate that is MON71800 has been registered, but due to economic reasons it is not being

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cultivated by the farmers (Heller 2005). Certain extent of work is going on the development of GM wheat with traits like insect resistance, herbicide tolerance, virus resistance, abiotic stress resistance, and quality improvement. There is lot of scope to work on different traits of wheat like grain quality and bio-fortification. Increased or decreased content of glutenin, increased protein content, stability of the phytase enzyme, increased content of the water-soluble dietary fibers, increased zinc and iron content of the seed, etc., are the important traits to explore further (Bowerman et al. 2016; Ral et al. 2016; Brinch-Pedersen et al. 2006). Difficulties in standardization of regeneration protocol and polyploidy nature of wheat make it difficult for genome editing too compared to other diploid food crops. In spite of that excellent work has been going on towards wheat improvement for traits like disease resistance, quality and yield related traits, etc. (Wang et al. 2018; Nalam et al. 2015; Zhang et al. 2016). Wang et al. (2014) used CRISPR/Cas based genome editing to modify powdery mildew resistance in wheat. Similarly, Wang et al. (2018) targeted TaGW2 gene (which is a negative regulator of seed size) in order to improve yield. Recently Jouanin et al. (2020) used CRISPR/Cas genome editing to reduce gluten content in wheat kernel. However, the market performance of these improved varieties is still questionable.

13.2.3 Maize Among the most important staple food crops for global food security, maize occupied highest share in commercially cultivated GM crops. During 2018 GM maize occupied 58.9 million hectares all over the world with 31% of the total maize production of the globe. To date many GM maize varieties are available in market with different traits. Maize is among the crops which has genetic modification for maximum number of traits. Bt-corn and Roundup Ready Corn are among the first GM corns approved for commercialization in 1996 (https://www.nature.com/ scitable/knowledge/library/use-and-impact-of-bt-maize-46975413/). Bt-corn was resistant to American corn borer, whereas roundup ready corn was resistant to glyphosate herbicide. Bt-corns are the most famous among all the available GM corns which are transformed with cry genes from Bacillus thuringenesis. Bt-corn varieties are available with single gene event as well as with stacked and pyramided gene events. DroughtGard is drought tolerant GM maize launched by Monsanto and approved by the USDA in 2011 (McFadden et al. 2019). GM insect resistant sweet corn is also available in market for cultivation, whereas maize streak virus tolerant GM varieties are released in Africa. GM maize with many other traits like insect resistance, virus resistance, abiotic stress tolerance, and for increased nutritional quality is in pipeline and hope will soon be available for commercialization. Genome editing has been proven an efficient technique for enhancing nutritional quality in maize. The quality traits of maize such as Quality Protein Maize (QPM), tryptophan content, high pro-vitamin A, etc., are multi gene regulated and are well characterized. Many studies are available in maize where nutritional quality traits have been improved using CRISPR/Cas tool (Liang et al. 2014; Char et al. 2017;

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Zhu et al. 2016). For more details about the use of CRISPR/Cas for maize improvement it is suggested to refer book chapter of Chilcoat et al. (2017).

13.3

Social Acceptance of Technology-Based Crops

When compared to the conventional breeding derived or MAS derived plant varieties, there was a huge rejection for GM or technology-based crops across the globe. This might be due to factors like willingness to purchase, taste, and nutritional value of the developed products from consumer’s perception. In adding together, environmental concerns from environmentalists which signify to hamper the existing genetic pool or cross-species gene transfer leading to crops more susceptible. Public acceptance of modern food technologies depends majorly on their perception towards benefits versus risks assessments (Bearth and Siegrist 2016); familiarity with the biotechnology and perceptions of safety (Shew et al. 2018). Lusk et al. 2018 conducted a survey among the US consumers for their perception towards technology driven crops. It is interesting to note that, majority of the consumers were opined to regulate the innovative plant breeding technologies based on health and environmental concerns rather than the process by which it is created. Bearth and Siegrist (2016) have conducted a meta-analysis (using data from 26 independent studies) on the influence of risk and benefit perceptions of public acceptance towards novel food technologies. They concluded that a change in people thinking is essential and need of the hour; rather than a debate on acceptance, risk or benefit opinion among the public. It is worthwhile to note that this meta-analysis has been conducted based on the research available from the developed countries rather than underdeveloped or developing countries. In reality, food security is more essential to address the hunger prevailing among the people from the underdeveloped countries. The information about the new technologies-based products influences the consumer decision making. Many statistical surveys have been conducted for the consumer acceptance about the GMO and non-GMO derived products (Delwaide et al. 2015; Kumar et al. 2020b). The consumer perceptions of genomics derived tomatoes were surveyed based on the consumer preferences and their beliefs from Netherlands (a receptive European country for non-traditional production technologies). The study was conducted based on several parameters like credence attributes, color, taste and flavor expectation, quality, naturalness, nice look, healthy, safety, environment, quality, willing to buy, and economic status. It is concluded that appropriate communication to the consumer is necessary since it greatly influences the consumer decision making of purchasing the new technology-based agricultural products (Van den Heuvel et al. 2006). Shew et al. (2018) evaluated the public acceptance and valuation of genome edited crop produced food in comparison with GM and conventional food (criteria of survey was willingness-to-consume and willingness-to-pay) across five developed countries. The study revealed that genome edited based agricultural products may face the same regulations and challenges of GMOs which may hamper their contribution towards meeting future food demand.

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Wunderlich and Gatto (2015) had reviewed the consumer perception and labeling significance of GM derived food products based on up-to-date available surveys across different developed nations. These kinds of surveys are way backward for underdeveloped and developing countries. The above studies cannot be generalized since from country to country the perception and decision making changes based on their beliefs and economic constraints.

13.4

Market Performance of Technology Derived Crops

The current section discusses the market performance of two prominent examples of GM derived crops released into the market. Flavr Savr tomato (CGN-89564-2) is the first commercially grown GM of the USA introduced in 1994. However very soon after its release it is removed from the US markets (Wunderlich and Gatto 2015). The potential reason for its failure is the consumers of UK who are reluctant to buy the GM based tomato puree even though it was sold at cut rate price. Although GM golden rice was developed in 2000 (Ye et al. 2000) only recently it got approved from the US for its commercial cultivation (Andy 2018) (Fig. 13.2). In Indian scenario, the research is still in the field trails stage (Shukla et al. 2018). The challenges in cultivation of golden rice are; seed market monopoly by the seed companies; corporate control of agriculture; loss of biodiversity among the local or traditional varieties. Many environmental activists and malnutrition experts claim that it is better to promote and increase the production of alternate food supplements like carrot and leafy vegetables rather than genetic modification of staple food of majority of world population (Enserink 2008). However, the consumer if he does not find better look, better taste, better nutrition when compared to conventional food products, then they have also a choice to opt out, which questions the existence of the GM based or technology-based food products in the markets (Gautam and Kushwaha 2018). The above examples open varied discussions among the intellectuals to set up benchmarks in new food technologies so that the food security problem of the world can be answered in a more sensible and acceptable form.

13.5

Cultivation of Transgenic(S) in India: A Case Study

In India where majority of the rural population is dependent on agriculture and most of them are illiterates. This poses a challenge to the scientific community to disseminate the science to the farmers in a more simplified and understandable way. The status of GM crops cultivation in India is still a matter of debate. Although GM cotton is approved for commercial cultivation in 2002 (Raghuram 2002), Indian policy maker’s perception towards encouraging the GMOs is standby. Sixty-nine different GMOs developed across ten different crops under public or private sectors were still in the evaluation stages and not yet recommended for their release into the commercial cultivation by apex body (Shukla et al. 2018). The striking reasons for the policy making might be monopolization of the seed companies (Thomas and De

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Fig. 13.2 Timeline of crop improvement

2017), illegal (unlicensed) seeds might overtake the market because of lesser prices which would be a toughest task for the government to monitor (Jayaraman 2004). The illiteracy coupled with lack of proper management skills of cultivating new technology-based crops in emerging countries like India led to many BT cotton growing farmer suicides. The farmer’s suicides and the Indian biotech company’s monopoly in relation with political, economic marginalization of Indian farmers had been critically reviewed by Thomas and De 2017. They have concluded that biotechbased companies were given preference for their market rather than the farmer’s welfare, which is a big debatable issue need to be sorted out before promoting new technology-based cultivation in emerging economies like India with second world’s highest population. This is directly related to ensure food security for the people of India.

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A study of GM versus non-GM derived food and food products was carried out in India, for the consumer perception. The study indicated that if proper labeling is made with cost at par of non-GM derived food, then the Indian community may accept the GM derived food products (http://parisinnovationreview.com/article/lesmathematiques-du-chaos). But these kinds of surveys across India (a diverse place of different socio economic communities) are more essential in near future which can set a benchmark for the policy makers to take progressive decisions for food security. For instance, Shew et al. 2018 have surveyed randomly in one district of India about the public acceptance of GMOs and cis-genic Rice. It is noteworthy to note that the respondents could not differentiate between GMO rice and cis-genic rice, which strongly suggests that scientific knowledge is very poor among the people. A proper and well-planned strategy is essential to reach this scientific knowledge to the common public.

13.6

Environmental Issues to Be Addressed by the Scientific Community

Environment risk assessment has large knowledge gaps in understanding genomics and environment. Risk assessment of the technology driven crops on environment, food, and feed has to be done based on (a) off target effects, where the introduction of nuclease may unintentionally alter DNA at a different site in addition to the target site; (b) Unintended on target effects—where the intended change may lead to further genomic alteration; for instance, switch on/off of genomic regulations; (c) Non-target effects are unintended adverse effects on non-target organisms in the environment, a potential ecological concern (Cotter et al. 2020). In order to study the cross-species gene transfer, it is pre requisite to have the genome sequence databases. The next generation sequencing has significantly reduced the cost of sequencing and made genome sequences available for several organisms, this technique it has not been explored to carry out environmental effects on off target species and this research gap has to be filled quickly. Unanticipated health and environmental risks may result via plant breeding exploiting genome editing. The Convention on Biological Diversity which exists in each country must be very stringent in introducing such genome edited food crops into the cultivation. Environmental risk assessment of the genome edited crops in the cultivated fields requires standardization (Araki and Ishii 2015). Environmental concern is a big challenge to promote new technology driven agricultural products. There are evidences of gene flow from GM crop to their wild species and their relatives (Van de Wiel et al. 2003). In some parts of India, cotton seed oil is edible and used in cooking. This may have significant impact on health aspects of people who consume Bt cotton derived oil and it is expected that it has already entered into Indian markets. Awareness is essential for consumer before buying a GM derived product. Unfortunately, such awareness is debatable in India. Araki and Ishii (2015) have critically reviewed the necessity of the genome edited crops for social acceptance. It is suggested that “Right to know” movement should be taken in to consideration and make labeling

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the food that contain genetically engineered products or its derivatives. In contrary, Swiatkiewicz et al. 2014 have critically reviewed by then research articles which focus on the GM feed given to food producing animals like chicken, hen, goat pig, etc. Significant studies have summarized that GM based feed is nontoxic with no adverse effects on the food producing animals and is safe for the human consumption. Another possible harm with GM crop is about creation of super weed. Gene flow from GM herbicide tolerant crops to their wild relative can give rise to super weed (Science daily: https://www.sciencedaily.com/releases/2012/10/ 121002092839.htm). Other than these major issues rise of secondary pest for insect resistant GM crop is also becoming serious day by day. A recent review by Hudzik et al. 2020 has discussed the potential exchange of small RNAs between cross species with reference to host pathogen interactions. It is noteworthy to note the important point that majority of the GM or CRISPER based studies were targeted for insect or pesticide resistance. In this context, the exchange of small RNAs between species may have a role in pathogen evolution. There is a need to do extensive research in this direction by the scientific community to address the concerns which would be raised by the environmentalists in near future.

13.7

Legislation and Regulatory Issues with Respect to Technology-Based Crops

Conventional breeding, mutation breeding, new breeding techniques (NBTs), GM and genome editing technologies are based on genetic alterations of the original genotype. Difficulties in policy making of these technologies driven crops have slowed down their transition from lab to the farmer’s field. Malyska et al. 2016 have discussed the role of policy makers and scientists in shaping the public perception towards NBTs. It is opined that the policy makers would be extremely under pressure and make laws based on the public demand rather than based on the scientific reality. They discussed this issue with a real incidence happened at EU during 2015, where a group of eight NGOs were involved in making stringent regulations for the new breeding techniques (Kumar et al. 2020b). It is suggested that the scientific community must be more active in sharing the knowledge with the public. Since NBTs, GMOs, and CRISPR based products may not come under same umbrella, a well-planned and executed knowledge exchange is critical for the benefit of R&D and the public significantly. GMOs/NBT derived products are thought to be safe from scientist’s perception but for environment activists it is a matter of controversy and debate. Production of GMOs has been banned in Europe and the labeling has become mandatory and strictly enforced for the GMO derived products. Additionally, there exists a great confusion on how to label the technology-based food products; Process-based labeling (process applied during development) or Product-based labeling (the characteristics of the resulting product) (Eckerstorfer et al. 2019). Zilberman et al. (2018) have carried out a conceptual framework and discussed various aspects of labeling decisions towards food products developed using novel food technologies

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decided by the policy makers. It is concluded that the labeling decision (GMOs or any closely related products) may evolve with innovative scientific knowledge, new information technologies, and changing public attitudes. In addition, Anti GM movement of EU influences other countries in regulatory decision making since they may lose the European markets as a consequence. Hence decision making becomes more complex for new technology-based crops cultivation among exporting countries. This may indirectly affect the production of GM or technology-based crops. For instance, among the European Union countries majority of the countries are opted for “opt out strategy” for growing the GM crops. Regulatory decisions of genome edited crops may phase the same consequences as of GM. As on date genome edited are followed by product-based categorization (as per the standards set for the mutation breeding) and such policies were not uniform across nations. For instance, Genome edited mushrooms developed by CRISPER with reduced tendency to brown were considered to be non-GM by USDA (Waltz 2016a). USDA considers the plants derived from the CRISPER as non-GM (Waltz 2016b). In Canada, genome edited crops fall under the category of plants expressing novel traits and not GM. But in European Union, the genome edited crops were under pressure to consider them as GMOs by the anti GMO activists. Many scientist’s opinion that genome edited crops can be considered as equivalent to mutation breeding as they do not have any foreign DNA introduced. The advantages of genome edited crops if considered as mutation breeding is that the development and approval of such lines would be cost effective, and it is possible to overcome the monopoly of the seed production by the few Multi-National companies (MNCs) via patents (Georges and Ray 2017). Lusser et al. 2012 had reviewed the regulatory issues with regard to NBT derived food crops across different countries (Argentina, Australia, Canada, EU, Japan, South Africa, and the USA). According to the authors there exist a non-harmonized regulatory approach; asynchronous development and marketing of NBT derived food crops across different nations. These dissimilarities are due to the significant varied definitions, legislation, and regulatory approaches for NBT derived crops. Still there is debate whether to classify the NBT driven crops equivalent to GMOs or not; which might result in more challenge in making policies. This eventually might result in trade disputes in exporting the food and feed derived from biotechnologybased crops. Hence there is an urgent need to frame a structured evaluation of technology driven crops with more precision and efficiency. There is a need to establish a global policy for filling the gap between process-based and product-based regulations for genome edited food products and GMOs (Araki and Ishii 2015). Eckerstorfer et al. 2019 had recently reviewed the regulatory networks that are present among ten different nations. The basis of their study was up-to-date available literature analysis followed by interviews with regulatory experts and risk assessment members of GMOs in the respective countries. The authors proposed significant strategies which can be adopted by the policy makers to make synchronized legislation which can make the international trade of such food and feed derived from NBTs easier and more transparent. They are (1) The NBT derived food crops can be assessed similar to GMOs based on country policy [as exemplified by EU or

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case by case in case of USA]; (2) Technical revision of existing laws (definitions and legislation) according to the need and necessity as in case of Australia and New Zealand; (3) New legislation for NBT derived crops and GMOs; (4) A new framework for all biotechnology derived food crops.

13.8

Promising Approaches to Address Food Security in near Future

Continuous breeding for longer periods led to narrow genetic base and loss of diversity which eventually make the plants more prone to biotic and abiotic stresses. In a recent editorial report published in nature genetics, it is suggested to utilize the potential of genomics in crop improvement and food security by collecting wide range of germplasm, followed by sequencing and maintaining the seed or gene banks. These would be rich resource for the unexplored genetic potential (https:// doi.org/10.1038/s41588-019-0352-8). For instance, the diversity of global barley collections using gene bank genomics followed by GWAS was recently published. The preserved, well-maintained gene bank material with genome information can be the best starting material for the crop improvement programs. This approach is serving as a model and can be employed for other crops too (Milner et al. 2019). “Orphan crops” are species that have been neglected by researchers and industry because they are not economically important on the global market (Murphy 2016). The orphan crops have been used by the local communities from ages for their flexible adoptability and nutritive qualities, though they have lesser yield and moderate to low resistance for diseases and pests. It may be more appropriate to develop and promote better yielding varieties of orphan crops to the suitable ecosystems so as to ensure food security locally. The scientists and the policy makers may have to work hand in hand in this direction. The orphan crop cultivation (for instance, Cassava, Banana) is still a way backward and the best is yet to come. Much research in this direction is still necessary to address the global food security (Khan et al. 2019). Furthermore, it is essential to understand the genetic basis of crop adoptability to varied climate changes. Genomics may play a crucial role in this context (Murphy 2016). An attempt has been made in this current chapter to compare the success of the conventional breeding; Genomics assisted breeding with technology driven crop improvement. Although in the technology driven crop improvement, the estimated time of its development is less and precision of attaining the required trait is absolute, but the lack of public acceptance and the government policies coupled with environmental concerns has become a huge barrier for their performance and acceptability among public (Figs. 13.3 and 13.4). Hence it is the responsibility of the scientific community to best exploit the scientific tools and educate the policy makers, environmentalists, and the public in a more scientific and convincing way such that global food security can be addressed. Increasing the food production in a sustainable manner aiming zero hunger is the responsibility of the researchers.

Fig. 13.3 Summary of the factors affecting the NBT derived food crops

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Fig. 13.4 Relationship and role of researchers, public, and policy makers for social acceptance of technology driven food crops

Public Private Partnerships (PPPs) would be more effective strategy to address the food security challenge. (Murphy 2016).

13.9

Conclusions

The technologies must be very objective and need to be exploited to address the food security. Such novel food technology processed foods must be cheaper, healthy, and eco-friendly. They must be highly profitable for the farmers and the industry without compromising the health and environment safety. The government regulations along with proper labeling of the products coupled with appropriate education of the scientific terminology may certainly encourage the consumer to accept the genomics assisted food technologies.

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Acknowledgements SJS Rama Devi thank the DST-SERB for providing the National Post Doctoral Fellowship.

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