OMICS-Based Approaches in Plant Biotechnology

Rintu Banerjee, Ex-MNRE- Chair-Professor, Indian Institute of Technology, Kharagpur has created a niche of her own in the area of Biomass Deconstruction/Biofuel Production/Enzyme Technology. In the process of her innovative development, she was granted 8 Indian, 3 international (US, Japanese and Chinese) patents. She has published more than 180 papers in peer-reviewed national/international journals. Garlapati Vijay Kumar is an Assistant Professor at the Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, India. He has 3 patents, 33 research articles in peer-reviewed journals and 10 book chapters to his credit. His research interest areas are: Bioprocess engineering / industrial biotechnology, deployment of OMICS technologies for crop improvement, fermentation technology, biofuels, and biocatalysis. S.P. Jeevan Kumar is a scientist in ICAR-Indian Institute of Seed Science, Mau, U.P, India. His interests include OMICS technologies for plant biotechnology, crop improvement, seed deterioration mechanisms, genetic purity and bioenergy. He has published 25 papers in peer-reviewed journals and multiple book chapters.

• Introduction xiiiPart 1: Genomics 11 Exploring Genomics Research in the Context of Some Underutilized Legumes—A Review 3Patrush Lepcha, Pittala Ranjith Kumar and N. Sathyanarayana1.1 Introduction 31.2 Velvet Bean [Mucuna pruriens (L.) DC. var. utilis (Wall. ex Wight)] Baker ex Burck 41.3 Psophocarpus tetragonolobus (L.) DC. 71.4 Vigna umbellata (Thunb.) Ohwiet. Ohashi 81.5 Lablab purpureus (L.) Sweet 91.6 Avenues for Future Research 101.7 Conclusions 12Acknowledgments 12References 122 Overview of Insecticidal Genes Used in Crop Improvement Program 19Neeraj Kumar Dubey, Prashant Kumar Singh, Satyendra Kumar Yadav and Kunwar Deelip Singh2.1 Introduction 192.2 Insect-Resistant Transgenic Model Plant 212.3 Insect-Resistant Transgenic Dicot Plants 272.4 Insect-Resistant Transgenic Monocot Plants 342.5 Working Principle of Insecticidal Genes Used in Transgenic Plant Preparation 392.6 Discussion 41References 423 Advances in Crop Improvement: Use of miRNA Technologies for Crop Improvement 55Clarissa Challam, N. Nandhakumar and Hemant Balasaheb Kardile3.1 Introduction 563.2 Discovery of miRNAs 563.3 Evolution and Organization of Plant miRNAs 573.4 Identification of Plant miRNAs 583.5 miRNA vs. siRNA 593.6 Biogenesis of miRNAs and Their Regulatory Action in Plants 603.7 Application of miRNA for Crop Improvement 613.8 Concluding Remarks 62References 704 Gene Discovery by Forward Genetic Approach in the Era of High-Throughput Sequencing 75Vivek Thakur and Samart Wanchana4.1 Introduction 754.2 Mutagens Differ for Type and Density of Induced Mutations 764.3 High-Throughput Sequencing is Getting Better and Cheaper 774.4 Mapping-by-Sequencing 774.5 Different Mapping Populations for Specific Need 814.6 Effect of Mutagen Type on Mapping 834.7 Effect of Bulk Size and Sequencing Coverage on Mapping 834.8 Challenges in Variant Calling 854.9 Cases Where Genome Sequence is either Unavailable or Highly Diverged 854.10 Bioinformatics Tools for Mapping-by-Sequencing Analysis 86Acknowledgments 87References 875 Functional Genomics of Thermotolerant Plants 91Nagendra Nath Das5.1 Introduction 915.2 Functional Genomics in Plants 935.3 Thermotolerant Plants 945.4 Studies on Functional Genomics of Thermotolerant Plants 985.5 Concluding Remarks 99Abbreviations 100References 100Part 2: Metabolomics 1056 A Workflow in Single Cell-Type Metabolomics: From Data Pre-Processing and Statistical Analysis to Biological Insights 107Biswapriya B. Misra6.1 Introduction 1086.2 Methods and Data 1096.2.1 Source of Data 1096.2.2 Processing of Raw Mass Spectrometry Data 1096.2.3 Statistical Analyses 1096.2.4 Pathway Enrichment and Clustering Analysis 1106.3 Results 1106.3.1 Design of the Study and Data Analysis 1106.3.2 The Guard Cell Metabolomics Dataset 1106.3.3 Multivariate Analysis for Insights into Data Pre-Processing 1136.3.4 Effect of Data Normalization Methods 1196.4 Discussion 1226.5 Conclusion 124Conflicts of Interest 124Acknowledgment 125References 1257 Metabolite Profiling and Metabolomics of Plant Systems Using 1H NMR and GC-MS 129Manu Shree, Maneesh Lingwan and Shyam K. Masakapalli7.1 Introduction 1297.2 Materials and Methods 1317.2.1 1H NMR-Based Metabolite Profiling of Plant Samples 1327.2.1.1 Metabolite Extraction 1327.2.1.2 1H NMR Spectroscopy 1327.2.1.3 Qualitative and Quantitative Analysis of NMR Signals 1347.2.2 Gas Chromatography–Mass Spectroscopy (GC-MS) Based Metabolite Profiling 1347.2.2.1 Sample Preparation 1347.2.2.2 GC-MS Data Acquisition 1357.2.2.3 GC-MS Data Pretreatment and Metabolite Profiling 1367.2.2.4 Validation of Identified Metabolites 1367.2.3 Multivariate Data Analysis 1377.3 Selected Applications of Metabolomics and Metabolite Profiling 139Acknowledgments 140Competing Interests 140References 1408 OMICS-Based Approaches for Elucidation of Picrosides Biosynthesis in Picrorhiza kurroa 145Varun Kumar8.1 Introduction 1468.2 Cross-Talk of Picrosides Biosynthesis Among Different Tissues of P. kurroa 1488.3 Strategies Used for the Elucidation of Picrosides Biosynthetic Route in P. kurroa 1488.3.1 Retro-Biosynthetic Approach 1498.3.2 In Vitro Feeding of Different Precursors and Inhibitors 1498.3.3 Metabolomics of Natural Variant Chemotypes of P. kurroa 1508.4 Strategies Used for Shortlisting Key/Candidate Genes Involved in Picrosides Biosynthesis 1518.4.1 Comparative Genomics 1518.4.2 Differential Next-Generation Sequencing (NGS) Transcriptomes and Expression Levels of Pathway Genes Vis-à-Vis Picrosides Content 1528.5 Complete Architecture of Picrosides Biosynthetic Pathway 1538.6 Challenges and Future Perspectives 161Abbreviations 162References 1639 Relevance of Poly-Omics in System Biology Studies of Industrial Crops 167Nagendra Nath Das9.1 Introduction 1679.2 System Biology of Crops 1699.3 Industrial Crops 1719.4 Poly-Omics Application in System Biology Studies of Industrial Crops 1769.5 Concluding Remarks 177Abbreviations 177References 178Part 3: Bioinformatics 18310 Emerging Advances in Computational Omics Tools for Systems Analysis of Gramineae Family Grass Species and Their Abiotic Stress Responsive Functions 185Pandiyan Muthuramalingam, Rajendran Jeyasri, Dhamodharan Kalaiyarasi, Subramani Pandian, Subramanian Radhesh Krishnan, Lakkakula Satish, Shunmugiah Karutha Pandian and Manikandan Ramesh10.1 Introduction 18610.2 Gramineae Family Grass Species 18710.2.1 Oryza sativa 18710.2.2 Setaria italica 18710.2.3 Sorghum bicolor 18810.2.4 Zea mays 18810.3 Abiotic Stress 18810.4 Emerging Sequencing Technologies 19810.4.1 NGS-Based Genomic and RNA Sequencing 19910.4.2 Tanscriptome Analysis Based on NGS 20010.4.3 High-Throughput Omics Layers 20110.5 Omics Resource in Poaceae Species 20210.6 Role of Functional Omics in Dissecting the Stress Physiology of Gramineae Members 20310.7 Systems Analysis in Gramineae Plant Species 20410.8 Nutritional Omics of Gramineae Species 20510.9 Future Prospects 20510.10 Conclusion 206Acknowledgments 207References 20711 OMIC Technologies in Bioethanol Production: An Indian Context 217Pulkit A. Srivastava and Ragothaman M. Yennamalli11.1 Introduction 21711.2 Indian Scenario 21911.3 Cellulolytic Enzymes Producing Bacterial Strains Isolated from India 22011.3.1 Bacillus Genus of Lignocellulolytic Degrading Enzymes 22211.3.2 Bhargavaea cecembensis 22211.3.3 Streptomyces Genus for Hydrolytic Enzymes 23011.4 Biomass Sources Native to India 23011.4.1 Albizia lucida (Moj) 23011.4.2 Areca catechu (Betel Nut) 23111.4.3 Arundo donax (Giant Reed) 23111.4.4 Pennisetum purpureum (Napier Grass) 23111.4.5 Brassica Family of Biomass Crops 23111.4.6 Cajanus cajan (Pigeon Pea)/Cenchrus americanus (Pearl Millet)/Corchorus capsularis (Jute)/Lens culinaris (Lentil)/Saccharum officinarum (Sugarcane)/Triticum sp. (Wheat)/Zea mays (Maize) 23211.4.7 Medicago sativa (Alfalfa) 23211.4.8 Manihot esculenta (Cassava)/Salix viminalis (Basket Willow)/Setaria italica (Foxtail Millet)/ Setaria viridis (Green Foxtail) 23211.4.9 Vetiveria zizanioides (Vetiver or Khas) 23211.4.10 Millets and Sorghum bicolor (Sorghum) 23311.5 Omics Data and Its Application to Bioethanol Production 23311.6 Conclusion 239References 239Part 4: Advances in Crop Improvement: Emerging Technologies 24512 Genome Editing: New Breeding Technologies in Plants 247Kalyani M. Barbadikar, Supriya B. Aglawe, Satendra K. Mangrauthia, M. Sheshu Madhav and S.P. Jeevan Kumar12.1 Introduction: Genome Editing 24812.2 GE: The Basics 24912.2.1 Nonhomologous End-Joining (NHEJ) 25012.2.2 Homology Directed Repair (HR) 25112.3 Engineered Nucleases: The Key Players in GE 25112.3.1 Meganucleases 25112.3.2 Zinc-Finger Nucleases 25612.3.3 Transcription Activator-Like Effector Nucleases 25712.3.4 CRISPR/Cas System: The Forerunner 25812.4 Targeted Mutations and Practical Considerations 25912.4.1 Targeted Mutations 25912.4.2 Steps Involved 26012.4.2.1 Selection of Target Sequence 26112.4.2.2 Designing Nucleases 26212.4.2.3 Transformation 26312.4.2.4 Screening for Mutation 26412.5 New Era: CRISPR/Cas9 26412.5.1 Vector Construction 26412.5.2 Delivery Methods 26612.5.3 CRISPR/Cas Variants 26612.5.3.1 SpCas9 Nickases (nSpCas9) 26612.5.3.2 Cas9 Variant without Endonuclease Activity 26612.5.3.3 FokI Fused Catalytically Inactive Cas9 26712.5.3.4 Naturally Available and Engineered Cas9 Variants with Altered PAM 26812.5.3.5 Cas9 Variants for Increased On-Target Effect 26812.5.3.6 CRISPR/Cpf1 26812.6 GE for Improving Economic Traits 26912.6.1 Development of Next-Generation Smart Climate Resilient Crops 27112.6.2 Breaking Yield Incompatibility Barriers and Hybrid Breeding 27112.6.3 Creating New Variation through Engineered QTLs 27112.6.4 Transcriptional Regulation 27212.6.5 GE for Noncoding RNA, microRNA 27212.6.6 Epigenetic Modifications 27312.6.7 Gene Dosage Effect 27312.7 Biosafety of GE Plants 27312.8 What’s Next: Prospects 276References 27613 Regulation of Gene Expression by Global Methylation Pattern in Plants Development 287Vrijesh Kumar Yadav, Krishan Mohan Rai, Nishant Kumar and Vikash Kumar Yadav13.1 Introduction 28813.2 Nucleic Acid Methylation Targets in the Genome 28913.3 Nucleic Acid Methyl Transferase (DNMtase) 29013.4 Genomic DNA Methylation and Expression Pattern 29113.5 Pattern of DNA Methylation in Early Plant Life 29213.6 DNA Methylation Pattern in Mushroom 29313.7 Methylation Pattern in Tumor 29413.8 DNA Methylation Analysis Approaches 29413.8.1 Locus-Specific DNA Methylation 29513.8.2 Genome-Wide and Global DNA Methylation 29513.8.3 Whole Genome Sequence Analysis by Bioinformatics Analysis 296References 29714 High-Throughput Phenotyping: Potential Tool for Genomics 303Kalyani M. Barbadikar, Divya Balakrishnan, C. Gireesh, Hemant Kardile, Tejas C. Bosamia and Ankita Mishra14.1 Introduction 30414.2 Relation of Phenotype, Genotype, and Environment 30414.3 Features of HTP 30614.4 HTP Pipeline and Platforms 31014.5 Controlled Environment-Based Phenotyping 31114.6 Field-Based High-Throughput Plant Phenotyping (Fb-HTPP) 31114.7 Applications of HTP 31314.7.1 Marker-Assisted Selection and QTL Detection 31414.7.2 Forward and Reverse Genetics 31514.7.3 New Breeding Techniques 31514.7.3.1 Envirotyping 31514.8 Conclusion and Future Thrust 316References 316Index 323