Sustainable Agriculture Reviews: Animal Biotechnology for Livestock Production 4 (Sustainable Agriculture Reviews, 62) 3031543718, 9783031543715

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Sustainable Agriculture Reviews  62

Vinod Kumar Yata Ashok Kumar Mohanty Eric Lichtfouse   Editors

Sustainable Agriculture Reviews Animal Biotechnology for Livestock Production 4

Sustainable Agriculture Reviews Volume 62

Series Editor Eric Lichtfouse, State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an, China Advisory Editors Shivendu Ranjan, School of Bio Sciences and Technology, VIT University, Vellore, Tamil Nadu, India Nandita, Dasgupta, Nano-food Research Group, School of Bio Sciences and Technology, VIT University, Vellore, Tamil Nadu, India

Sustainable agriculture is a rapidly growing field aiming at producing food and energy in a sustainable way for humans and their children. Sustainable agriculture is a discipline that addresses current issues such as climate change, increasing food and fuel prices, poor-nation starvation, rich-nation obesity, water pollution, soil erosion, fertility loss, pest control, and biodiversity depletion. Novel, environmentally-friendly solutions are proposed based on integrated knowledge from sciences as diverse as agronomy, soil science, molecular biology, chemistry, toxicology, ecology, economy, and social sciences. Indeed, sustainable agriculture decipher mechanisms of processes that occur from the molecular level to the farming system to the global level at time scales ranging from seconds to centuries. For that, scientists use the system approach that involves studying components and interactions of a whole system to address scientific, economic and social issues. In that respect, sustainable agriculture is not a classical, narrow science. Instead of solving problems using the classical painkiller approach that treats only negative impacts, sustainable agriculture treats problem sources. Because most actual society issues are now intertwined, global, and fastdeveloping, sustainable agriculture will bring solutions to build a safer world. This book series gathers review articles that analyze current agricultural issues and knowledge, then propose alternative solutions. It will therefore help all scientists, decision-makers, professors, farmers and politicians who wish to build a safe agriculture, energy and food system for future generations.

Vinod Kumar Yata  •  Ashok Kumar Mohanty Eric Lichtfouse Editors

Sustainable Agriculture Reviews Animal Biotechnology for Livestock Production 4

Editors Vinod Kumar Yata Research Centre KBK Multispecialty Hospitals Hyderabad, Telangana, India

Ashok Kumar Mohanty ICAR-Central Institute for Research on Cattle (CIRC) Meerut, Uttar Pradesh, India

Eric Lichtfouse Xi’an Jiaotong University Xian, China

ISSN 2210-4410     ISSN 2210-4429 (electronic) Sustainable Agriculture Reviews ISBN 978-3-031-54371-5    ISBN 978-3-031-54372-2 (eBook) https://doi.org/10.1007/978-3-031-54372-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Preface

Recent research in biotechnology has enabled animal sciences to develop novel methods to increase livestock production and improve animal health. The new developments in animal biotechnology should also lead to improve the economy of poor and developing countries by increasing the production of animal-based meat and milk. The combination of animal computational and experimental approaches should accelerate the animal biotechnology by developing cost-effective and efficient methods. The chapters in this book review the recent research methods, challenges, and opportunities. Chapter 1 focuses on the use of omics technologies in livestock animals. This chapter also covers the challenges in genomics, transcriptomics, proteomics, and metabolomics approaches. Chapter 2 describes the use of omics technologies for the classification of cancer types. This chapter covers the topics related to canine cancer such as canine hemangiosarcoma (HSA) biomarkers, canine lymphoma blood test, clinical application of serum biomarkers for canine lymphoma, and dogs as a model system for cancer research. Chapter 3 summarizes recent advances in transgenic technologies such as protein-­guided engineered nucleases and RNA-guided nucleases. This chapter also covers genome editing strategies and livestock such as cattle swine, sheep, and goat. Chapter 4 provides the information on the inter-relationship between the type I helper T lymphocytes (Th1) and type II helper T cells (Th2) cytokine shift and early pregnancy establishment in bovines. This chapter also covers the immune tolerance mechanism by indoleamine 2, 3-dioxygenase during the implantation window in bovines. Chapter 5 emphasizes on the applications of artificial intelligence and machine learning innovations in farm animals. This chapter focuses on artificial intelligence approaches for drug discovery, methodological description for data science, and machine learning. Chapter 6 focuses on the role of probiotics and prebiotics in animal nutrition and health. This chapter also covers the information on safety issues in the use of probiotics and prebiotics for animal use. v

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Karan Swiss cross breed produced at ICAR-National Dairy Research Institute, India

Chapter 7 discusses the meta-omics role in sequencing and its investigative pipelines in how the gut microbiome impacts physiology and propensity to disease. Chapter 8 presents the synthesis and characterization of nanoparticles and their applications in veterinary medicine. We thank all authors for their kind co-operation extended during the various stages of processing of the book. We express heartfelt appreciation to all reviewers of the book for their valuable suggestions. We would like to thank Springer production team for quick and efficient publication of this book.

Hyderabad, Telangana, India  Vinod Kumar Yata   Meerut, Uttar Pradesh, India  Ashok Kumar Mohanty   Xi’an, China  Eric Lichtfouse

About the Book

This book presents advanced reviews on latest developments and future trends of animal biotechnology with focus on computational and experimental approaches. The chapters discuss the implications of recent methods in animal biotechnology to improve the livestock production. Applications of omics technologies and system biology approaches for livestock production and animal disorders are discussed in detail. The evolution of transgenic technology, genome editing, and markers for early pregnancy detection are elucidated. A couple of chapters are dedicated to understanding the role of gut microbiome, probiotics, and prebiotics on livestock health and production. The use of nanomaterials have shown beneficial effects in biological applications. This book also discusses the veterinary applications of nanomaterials. The chapters of this book provide state-of-the-art information appropriate to academicians, researchers, and students involved in the animal biotechnology research.

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Contents

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Applications of Omics Technologies in Livestock Production, Improvement and Sustainability ��������������������������������������    1 Devangkumar Maru and Anmol Kumar

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System Biology Research to Advance the Understanding of Canine Cancer��������������������������������������������������������������������������������������   55 Sonia Batan, Harpreet Kaur, Swasti Rawal, Deepti Mittal, Parul Singh, Gurjeet Kaur, and Syed Azmal Ali

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Evolution of Transgenic Technology: From Random Transgenesis to Precise Genome Editing ����������������������������������������������   85 Shrabani Saugandhika and Nishkarsh Jain

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Pregnancy Associated Cytokines for Successful Pregnancy Establishment in Bovines ����������������������������������������������������  131 Sunil Kumar Mohapatra, Bibhudatta S. K. Panda, Sameni Deepika, Dheeraj Chaudhary, Rajeev Kapila, and Ajay Kumar Dang

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Data-Driven and Artificial Intelligence Approaches for System-Wide Prediction of the Drugable Proteome to Drug Discovery in Farm Animals������������������������������������������������������  155 A. S. Ben Geoffrey, Jitender Singh Virk, Deepti Mittal, Gurjeet Kaur, and Syed Azmal Ali

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 Role of Probiotics and Prebiotics in Animal Nutrition������������������������  173 Divya Limbu, Bapi Ray Sarkar, and Manab Deb Adhikari

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Ruminant Gut Microbiota: Interplay, Implications, and Innovations for Sustainable Livestock Production������������������������  205 Swasti Rawal, Harpreet Kaur, Sonia Bhathan, Deepti Mittal, Gurjeet Kaur, and Syed Azmal Ali

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Nanoparticles and Their Prospective Solicitations in Veterinary Medicine����������������������������������������������������������������������������  229 Ananda Kumar Chettupalli, Ajmera Srivani, Peri Sarvani, and Aziz Unnisa

About the Editors

Vinod  Kumar  Yata  is currently serving as the Research Director and Principal Scientist at the Research Centre of KBK Multi-Specialty Hospitals in Hyderabad, India. He is also serving as a Visiting faculty at Centre for Biotechnology, Institute of Science and Technology, Jawaharlal Nehru Technological University, Hyderabad, Telangana, 500085, India. Prior to this role, he worked as a Research Scientist at the Center of Excellence for Aging and Brain Repair at the University of South Florida (USF) in Tampa, Florida, USA. He also worked as a Research Associate at the National Dairy Research Institute in Karnal, India. Additionally, he served as an Assistant Professor in the Department of Biotechnology at Dr. B R Ambedkar National Institute of Technology in Jalandhar, Punjab, India. He received his Ph.D. in Biotechnology from Indian Institute of Technology Guwahati. He specializes in interdisciplinary research which includes nanotechnology, microfluidics, animal biotechnology, cancer biology, and bioinformatics. He has developed a microfluidic device for the separation of live and motile spermatozoa form cattle semen samples. He opened up a new avenue to prodrug enzyme therapy by introducing nanocarriers for the delivery of non-mammalian prodrug activating enzymes. He elucidated the structural features and binding interactions of several biomolecules by in silico methods. He has published four books as an editor and one book as an author with Springer Nature. He has published several research papers in peer-reviewed international journals and presented papers at several international conferences. xi

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About the Editors

Ashok  Kumar  Mohanty  is an eminent scientist in Animal Biotechnology and is currently serving as Director, ICAR-Central Institute for Research on Cattle (CIRC), Meerut, India. Previously, he served as a Joint Director, ICAR-Indian Veterinary Research Institute, Mukteswar, Uttarakhand, India. He worked as a principal scientist at Animal Biotechnology Centre, ICARNational Dairy Research Institute, Karnal, India. His group is involved in various basic and applied research related to animal production systems. His research group has made pioneering contributions in the field of Animal Biotechnology, with emphasis on gene cloning, expression and functional characterization of animal proteins, proteomics in animal production, cell and molecular biology and structural biology of proteins. His group has developed a Buffalo Mammary Epithelial cell line for the first time, which can be used as a model system to understand lactation biology in animal as well as human. His team has also developed a pregnancy diagnostic kit for the early detection of pregnancy cattle and buffalo. His group is also extensively involved in developing low-cost technology for semen sexing in cattle. Recently, his research team developed a  Lumpy Skin Disease virus vaccine at IVRI, Mukteswar. He has organized a number of national and international workshops and international conferences. He is a recipient of several awards, including DBT Overseas Associateship by Ministry of Science & Technology, Govt. of India, Jawaharlal Nehru Award (gold medal) by Indian Council of Agricultural Research (ICAR), New Delhi, for outstanding postgraduate research in the field of Animal Biotechnology, and Young Scientist Award sponsored by International union of Crystallography (IUCr) and Dept. of Science and Technology (DST), Govt. of India, for attending IUCr congress at Geneva, Switzerland. He is a Elected fellow of National Academy of Agricultural Sciences (NAAS), India, Fellow of National Academy of Dairy Sciences, India, executive member of Proteomics Society of India, and  associate fellow of National Academy of Veterinary Science, India. He has supervised more than 50 graduate and Ph.D. students and post docs. He has published more than 200 peerreviewed research and review papers. He has  edited four Books, and also authored eleven book chapters in the areas of animal and food biotechnology published by national and international publishers.

About the Editors

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Eric  Lichtfouse  is a professor at Xi’an Jiaotong University who has opened the discipline of Single Sample Molecular Chronology, by inventing carbon-13 relative  dating, a molecular-level method allowing to study the dynamics of organic compounds in temporal pools of complex media. He is Chief Editor of the journal Environmental Chemistry Letters, and the book series Sustainable Agriculture Reviews and Environmental Chemistry for a Sustainable World. He is the author of the book Scientific Writing for Impact Factor Journals, which includes an innovative writing tool: the Micro-Article.

Chapter 1

Applications of Omics Technologies in Livestock Production, Improvement and Sustainability Devangkumar Maru and Anmol Kumar

Abstract  The livestock industry is facing a massive challenge to meet the increasing demand for livestock-based commodities. Decreasing fertility, immunity, feed efficiency, and poor production of commodities in animals adversely affect the livestock economy. As a solution, improving livestock health, performance, and overall well-being is necessary. Omics technology provides a better understanding of the genetic architecture of livestock that drives major economic traits. Being capable of describing global variation in gene, protein, and metabolite expression levels, Omics technology has evolved significantly in the last decade. In this review, we discuss omics technologies such as genomics, proteomics, transcriptomics and metabolomics, their complete workflow including data collection using various analytical techniques, data analysis using computational tools, and finally, their use in livestock animals such as marker-assisted selective breeding and candidate gene selection in genetic engineering for the production, improvement, and sustainability of livestock animals such as cattle, buffalo, sheep, goats, pigs and chicken in detail for the identification and comparative analysis of markers associated with economically important traits such as milk, egg, wool, and meat. The application of these cutting-edge omics technologies gives a personalized and highly accurate research decision that is applied to the improvement and production of livestock yielding high-quality commodities which will significantly foster the livestock economy. Keywords  Genomics · Proteomics · Metabolomics · Transcriptomics · Livestock · Production · Improvement · Breeding · Candidate marker · Pig · Cattle · Buffalo · Sheep · Goat · Chicken

D. Maru · A. Kumar (*) Department of Biotechnology, Atmiya University, Rajkot, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 V. Kumar Yata et al. (eds.), Sustainable Agriculture Reviews, Sustainable Agriculture Reviews 62, https://doi.org/10.1007/978-3-031-54372-2_1

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Abbreviations 3′UTR 3′ untranslated region BLAST Basic local alignment sequencing tool CSFV Classical swine fever virus DNA deoxyribonucleic acid GWASs Genome-wide association studies iTRAQ Isobaric tag for relative and absolute quantitation SILAC Stable Isotopic Labelling with Amino Acids TRHDE Thyrotropin-Releasing Hormone Degrading Enzyme RNA Ribonucleic Acid

1.1 Introduction The spectacular findings obtained by animal genome sequencing over the last decade have enabled the development of various analytical approaches capable of describing the global diversity of gene, protein, and metabolite expression levels associated with different qualitative and quantitative traits of livestock (Zampiga et al. 2018). The suffix -omics is used to describe something broad, and it refers to a field of research in life sciences that focuses on large-scale data or information to understand life, described in “omics” such as genomics, epigenomics, proteomics, metabolomics, transcriptomics, etc. The omics approach allows for in-depth study of any aspect of molecular biology, be it a gene (genomics), protein (proteomics), transcript (transcriptomics), or metabolites (metabolomics). Although there are several other omics, these four have long been the focus of molecular biology study. Basic interaction between nucleic acids, proteins, and metabolites has been the focus of scientific study for decades, modern formulations of big data science are now gaining attraction. Organisms have thousands of genes that are transcribed into more transcripts and that are further translated into a greater number of proteins than genes by trans-splicing and alternative splicing mechanisms those further involved in catalysis in metabolism and its regulation (Qin et al. 2019). Gene products such as Ribonucleic acids, proteins, and other metabolites vary their expression levels quickly and dynamically, controlled by a wide range of physiological and environmental conditions because genomic information remains unchanged throughout an animal’s lifetime (Rexroad et al. 2019). Analysis of genes and proteins is carried out by whole-genome and proteome sequencing, transcriptome analysis uses Ribonucleic acids sequencing, real time  – polymerase chain reation, Northern blotting, and microarray, while metabolites can be analysed using various high-resolution chromatography and spectroscopy techniques (Misra et al. 2018). Such massive data generated in a wet lab that includes thousands of genes and metabolites and thousands to millions of proteins are very difficult to manage and analyse, thus bioinformatics is required for the integration and analysis of these data.

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Data generated from the wet lab by multiple analytical techniques is analyzed by different computational tools such as machine learning, deep learning, statistical and mathematical modelling to find out candidate genes/proteins which is known as a marker associated with different traits of livestock such as fertility, immunity, quality and yield of meat, milk, egg, and wool (Biswas and Chakrabarti 2020). A study of all genes/proteins/metabolites at a time gives a comprehensive idea of all the markers related to a particular trait which can be further used in marker-assisted selection and genetic engineering for the production and improvement of livestock having traits of interest (Yang et al. 2017). For example, the ryanodine receptor-1 gene affects pork meat quality by regulating Ca+2 transport across muscle cell membranes, and mutation in Rendement Napole gene increases muscle glycogen levels leading to an increase in pork size (Vashi et al. 2018). Another example is the identification of disease-resistant markers in livestock animals using omics technologies to help in the selection of breeds that improve disease resistance potential in the new breed (Islam et  al. 2020). Overall, this flow of information in a living system is studied in omics technologies for precise, perfect and personalized outcomes in research. Rather than taking a reductionist approach and concentrating on only one or a few proteins/genes, the omics approach allows for a comprehensive investigation of all aspects of molecular biology. The application of various omics techniques and their related research and development to livestock species is imperative to bring out new solutions and innovations so that sufficient animal-sourced food and other commodities production can be achieved in a sustainable way without harming nature and especially at low cost and with existing resources. The integration of genomics and other omics tools, in conjunction with phenotype data and systems analyses, will enable continued improvements that will be necessary for sustainable livestock production in the future (Fig. 1.1).

1.2 History Francis Crick proposed the central dogma of molecular biology which elucidate the flow of genetic information in a living system. According to him genetic material of organisms is encoded by unique sequences of deoxyribonucleic acid (DNA) called genes and is transcribed into ribonucleic acid also called transcript which is finally translated into proteins that act as functional entities in cells (Cobb 2017). The emergence of this idea revolutionized the study of organisms and expanded our understanding at a molecular level. Watson and Crick discovered the nucleic acid molecular structure and its significance for information transfer in living material and were jointly awarded the Nobel Prize in 1962. Later, Har Gobind Khorana, Marshall W. Nirenberg and Robert W. Holley interpreted genetic code and its functional role in protein synthesis and were awarded a Nobel Prize jointly in 1968. The development of different analytical techniques such as genetic material isolation techniques, polymerase chain reaction, sequencing, library construction, genetic analysis and involvement of computational tools and databases in research

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Fig. 1.1  Capturing the intricate landscape of Omics technology from a bird’s-eye view, this aerial perspective reveals the expansive realm of biological data analysis. Omics, an umbrella term encompassing genomics, transcriptomics, proteomics, and metabolomics, unfolds like a vibrant mosaic as each discipline contributes to a holistic understanding of biological systems. The intricate interplay of data generated from diverse molecular levels is depicted, showcasing the comprehensive insights gained through this multidimensional approach. From unraveling the genetic code to deciphering the dynamic molecular interactions, the bird’s-eye view encapsulates the transformative power of Omics in advancing our understanding of life at the molecular level

dramatically revolutionized the research in healthcare, agriculture, veterinary and other subdomains. Hans Winkler, a German botanist, coined the term ‘genome’ in 1920 to describe the haploid chromosome set. After advancement in analytical technologies and bioinformatics tools with years, proteome, metabolome, transcriptome, etc. emerged, and its study is referred to using omics as suffix such as genomics, proteomics, metabolomics, transcriptomics etc. The use of omics technologies was started in livestock production and improvement related research in the first decade of the twenty-first century. Publication of omics related research has increased over time with the evolution of different omics technologies (Fig. 1.2).

1.3 Types of Omics Studies With increasing research, various omics have emerged that are classified based on the target biomolecule of research interest. The study of nucleic acids, ribonucleic acid transcripts, proteins and metabolites are referred to as Genomics,

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Fig. 1.2  Tracing the evolutionary journey of Omics technology, this timeline encapsulates key milestones in its rich history. From the Human Genome Project’s groundbreaking completion in 2003 to the advent of high-throughput sequencing, witness the rapid strides that have propelled genomics into the forefront of biological research. The emergence of transcriptomics, proteomics, and metabolomics is illustrated, showcasing the expansion of Omics into a multi-dimensional exploration of biological data. This chronological narrative underscores the dynamic progression that has shaped Omics, transforming it into an indispensable tool for unravelling the intricacies of life’s molecular tapestry

Transcriptomics, Proteomics and Metabolomics respectively. Based on the involvement of different domains and studies types of omics can be further classified. For ex, genomics is classified into metagenomics, structural genomics, functional genomics, comparative genomics, cognitive genomics, pharmacogenomics, nutrigenomics, neurogenomics, personal genomics, viromics etc. Also, proteomics is classified into structural proteomics, functional proteomics and protein expression proteomics. Many less studied and focused omics involve lipidomics, glycomics, foodomics, cellomics, connectomics, microbiomics, epigenomics and many more emerging. Multiomics is an approach to combining different omics for particular research.

1.3.1 Genomics Genomics is an interdisciplinary branch of biology that is concerned with the structure, function, evolution, mapping, and editing of genomes. It is the analysis of an organism’s entire genome, and it incorporates elements from genetics through sequencing, assembling, and analysing the structure and function of genomes using a combination of DNA sequencing techniques, recombinant DNA, and bioinformatics. In the broad context, genomics encompasses both functional and structural aspects. The studies of reading abundance and exon, while genome assembly and read mapping are structural. DNA is a polymer of nucleotides sequence of adenine

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(A), guanine (G), cytosine (C) and thymine (T). Every organism has a unique sequence of DNA except genome cloned organisms. A unique sequence of DNA is responsible for the diversity of phenotypes in organisms. Gene is a basic functional and physical unit of heredity that codes for a protein, however, every gene does not code for a protein many genes are involved in the regulation of other genes. Gene size varies from hundreds to millions of base pairs of nucleotides. Each gene has its unique function in the cell even if it codes to protein or only ribonucleic acid Any type of mutation in a gene lead to loss of function or malfunctioning in protein or ribonucleic acid. DNA sequencing techniques determine the order of nucleotides in nucleic acid sequences. From Maxam-Gilbert and Sanger sequencing to recent next-generation sequencing methods are used for DNA sequencing. These DNA sequences are further validated and stored in databases. A complete set of DNA including each gene in an organism is referred to as genomic data which is further analysed by different computational tools for expression study, mutation, evolutionary relationship, disease resistance, and finding of genes responsible for phenotypes related to economically important traits in livestock animals. Genomics includes diverse scope of research based on the involvement of other subdomains such as epigenomics, metagenomics, comparative genomics, phylogenomics, glycogenomics, toxicogenomics, chemogenomics, single-cell genomics, and many more emerging every year.

1.3.2 Transcriptomics Transcriptome studies include whole ribonucleic acid transcripts and their quantitative level in a single cell or population of the cell to understand gene expression. This involves both protein-coding and non-coding transcriptome studies, such as long non-coding ribonucleic acid, micro ribonucleic acid s, and small nuclear ribonucleic acid s, among others. The coding transcriptome serves as a linker between the genome and the proteome in biological processes. Double-stranded ribonucleic acid a type of non-coding ribonucleic acid is known for silencing gene expression. Non-coding ribonucleic acid s regulate not only gene expression but also regulate transcription by interacting with various transcription factors and maintaining genome integrity. The protein-coding transcriptome is used to classify pathways involved in various phenotypes related to livestock economically important phenotypes, as well as to discover new genes and their functional significance when it is combined with genomics and proteomics referred to as proteogenomics. Ribonucleic acid transcripts are a single-stranded chain of nucleotides such as adenine (A), guanine (G), uracil (U), and cytosine (C). This ribonucleic acid transcript contains information on an amino acid sequence in the protein. Thus, in the expression study of genes, transcriptomes play an important role. Complete ribonucleic acid of a biological sample is sequenced and obtained sequence of nucleic acid is submitted

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to the database, from where it is retrieved for further analysis. The analysis outcome of transcriptome data reveals the expression pattern of a particular gene in animals responsible for phenotypes related to livestock commodities quality and quantity.

1.3.3 Proteomics The study of whole peptides or proteins in biological samples, their functional roles, identity, biochemical properties, quantities, structural alteration during development and in response to external and internal stimuli as well as their post-­translational modifications and interactions, is referred to as proteomics. Since several genes can generate more than one version of the protein they express by trans-splicing, an organism’s proteome is much larger and more complex than its genome. Proteins are often frequently changed by cells after they are formed. In response to external and internal stimuli, the protein composition of an organism or tissue changes constantly as new proteins are produced, existing proteins are eliminated, and proteins are modified. An organism’s genome, on the other hand, remains largely unchanged over his or her lifetime, while the proteome can alter. Protein glycosylation, phosphorylation, nitrosylation, ubiquitination, and proteolysis are all post-translational modifications that can be examined. Protein function and transport, enzymatic activity, and intracellular signalling pathways may all be affected by post-translational modifications. Proteomics is commonly used in a variety of fields of study. Proteins are polymers of amino acids joined by a peptide bond. The sequence of amino acids is dependent on the sequence of ribonucleic acid and the sequence of ribonucleic acid is dependent on the sequence of DNA thus proteomics study requires protein sequencing to obtain a sequence of amino acids. Amino acid sequences obtained from protein sequencing are submitted to databases and retrieved for analysis using bioinformatic tools that further help in the analysis and research decisions.

1.3.4 Metabolomics Metabolomics is the investigation of intermediate small molecules and metabolic products. It applies to molecules with a molecular weight of less than 1 kD, such as amino acids, fatty acids, and carbohydrates. Metabolomics measures the intermediate as well as the final product of cellular processes, providing a snapshot of a cell’s metabolic status. The key benefit of metabolomics is that the outcomes are derived from downstream processes of transcriptomics, genomics and proteomics and its finding is more strongly associated with the final phenotype. Thus, they connect phenotype and genotype in functional genomics, allowing researchers to find

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genetic loci that regulate the concentrations of specific metabolites, affecting specific biochemical pathways. Different metabolic pathways include glycolysis, citric acid cycle, gluconeogenesis, lipid oxidation, electron transport chain in energy metabolism, biosynthesis of amino acids, nucleotides, lipoproteins and many others as anabolic pathways. Intermediates and end products of these pathways play a critical role in the expression of the phenotype.

1.3.5 Other Omics Epigenomics studies the processes regulating gene expression under certain conditions in a cell or organism. Microbiomics studies the genome or proteome of all microorganisms present in an ecosystem. Metagenomics studies the genetic material present in environmental samples. Foodomics deals with the integration of omics technologies to enhance consumer health, and trust in food and nutrition issues related to bioactivity, protection, and traceability. The analysis of viral genomes includes complete nucleotide sequences and viral genes that code for proteins known as viromics. Multiomics integrates the data of more than one omics for a highly accurate and comprehensive analysis of a particular factor. Many other omics are emerging with time and advancement and involvement with other research domains.

1.4 Workflow of Applying Omics Technologies in Livestock Animals Different omics types have been discussed that focus on high throughput analysis of a single or multiple types of omics for the improvement and production of livestock animals. For the analysis of omics data collection of data is the primary step. Single or different types of tissue samples are taken from livestock animals and various analytical techniques are applied to obtain genomics, proteomic, transcriptomic and metabolomic data. This data obtained from a wet lab is very diverse and huge in volume, thus bioinformatics tolls are required for modelling, integration, and analysis of this data. The outcome of data analysis is further used in the improvement and production of livestock animals. Thus, the application of omics technologies in livestock production requires three steps. 1 . Omics data collection 2. Omics data integration and analysis by Computational approach 3. Using Omics data analysis for livestock animals These three steps are discussed individually (Fig. 1.3).

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Fig. 1.3  Workflow of using Omics technology in livestock animals. Specimen is collected from livestock animals using biopsy or surgical method which is followed by tissue disaggregation to separate cell. Suspension cells are used to perform multiple analytical approaches to collect omics data. Obtained data is integrated and analysed using computational tools which give analysis results that further help in the selection of breed and many other uses

1.4.1 Data Collection from Livestock Samples Being multicellular organisms animals have different tissues and may have different expressions of genes thus may contain different types of proteins and metabolites based on a function of a particular tissue. Thus, a sample from single or more tissues is taken from livestock animals and followed by isolation and analysis of particular biomolecules such as DNA, ribonucleic acid, protein or metabolites based on omics type to obtain data for further analysis. It involves many analytical techniques such as DNA or ribonucleic acid isolation, protein isolation, nucleic acid sequencing, protein sequencing, liquid chromatography, mass spectrometry, Nuclear magnetic resonance spectroscopy, x-ray crystallography, etc. 1.4.1.1 Specimen Collection from Livestock Animals Collection of the tissue specimen is the first step of omics data collection that provides a source of cells. Animal health professionals should be qualified in proper post-mortem and biopsy techniques for collecting specimens from different species of animals, as well as have adequate knowledge of anatomy and histopathology to select the appropriate organs and lesions for sampling. The tools needed will vary depending on the species and size of the animal, but it should include a knife, cleaver, and saw, as well as scissors, forceps, and scalpel for opening intestines, including scissors with a rounded tip on one blade. There must be a plentiful supply

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of containers with labels that are suitable for the type of sample needed. Transporting samples from the field may involve the use of specialised media. Protective equipment, such as rubber gloves and rubber boots, should be worn by the operator. Ordinary instruments should be used to remove the dead animal’s skin, but sterile instruments should be used to open the body cavities, and a new collection of sterile instruments should be used to collect the pieces of the different organs required. A vacuum flask containing wet ice should be used to keep fresh samples if it takes 24 h to reach the laboratory. Samples are expected to take longer than 24 h to arrive the laboratory should be frozen and sent to this state. A biopsy is a safer choice for organ-specific tissue sample collection in live animals because it is less painful. To collect nasal discharge sample, soaked cotton swabs in transport media is sent to the lab at 4 C can be used. Milk samples should be taken after the teat tip has been cleansed. The first stream of milk is discarded, and the next stream is poured into a tube(s). 1.4.1.2 Isolation of Cells from the Tissue Specimen Tissue derived from animals contains cells that are tightly aggregated. Tissue must be disaggregated either mechanically or using enzymes or chemicals to obtain cell suspension. DNA isolation, protein separation, and metabolite isolation are all carried out with these isolated cells in suspension. Physical or Mechanical Disaggregation The tissue is kept on a 100 mm sieve in a balanced salt solution and buffered medium containing a sterile petri dish. Cells are alternately moved into sieves with progressively smaller pore sizes (Mesh sizes: 20 mm and 50 mm). The process can be replicated if further cell disaggregation is needed. The debris that remains on the sieves is discarded, and the medium with cells is collected. A haemocytometer is used to count the cells. Cells may also be mechanically disaggregated by pushing them through a syringe and needle, or by pipetting them repeatedly. While the physical approach is simple and inexpensive but it causes harm to a large number of living cells. Enzymatic Disaggregation Enzymatic disaggregation is a better method to obtain the maximum undamaged viable cell population. A large number of cells can be obtained by using enzymes. Furthermore, a large number of cells are disaggregated in tissues with the least extracellular matrix. Collagenase and trypsin are two essential enzymes involved in tissue disaggregation. Collagenase disaggregates tissues since the intracellular matrix includes collagen. Furthermore, it may damage epithelial cells while leaving fibrous tissues unaffected. An antibiotic-containing medium is used to keep biopsy tissues. Following that, the disaggregated tissue is dissected into parts in an antibiotic-containing basal salt solution. The chopped tissue is thoroughly washed in sterile distilled water transferred to a full medium with collagenase. After 5  days treatment mixture is pipetted to isolate the cells.

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Trypsinization is the disaggregation process of tissue using the trypsin enzyme. However, since certain types of cells can withstand it and various types of tissues are affected, the enzyme trypsin in crude form is widely used. Cold trypsinization and warm trypsinization are two forms of behaviour based on the effect of temperature on trypsin. In cold trypsinization, chopped tissue pieces are washed in distilled water and soaked for 4–6 h in cold trypsin vials placed in an ice pack. After that, trypsin is removed and the tissue is incubated for 20–30 min at 36.5 °C. In warm trypsinization, Tissue pieces are chopped and washed with distilled water before being placed in a glass vial. The pieces are placed in a 250 Ml flask with warm trypsin at 36.5  °C and after 4  h of continuous stirring, the mixture is allowed to settle. Every 30  min, the disaggregated cells are collected so that the cells are exposed to the warm trypsin at the least possible time. After 3–4 h, the trypsin is extracted by centrifugation, allowing full tissue disaggregation. Dispersed cell pellets containing glass vial is placed on ice. Samples are pipetted to collect cells after different trypsinization times. Now cells are separated from tissue and ready for further treatments and analysis. If required cells can be cultured in a complete animal tissue culture medium. In some soft tissues, a directly homogenization step can be followed to obtain a mixture of cellular fragments. Now, this mixture contains cells of sample tissue and can be used for the isolation of protein, genetic material or metabolites as per requirement. This step requires some time and may lead to many cell death during disaggregation but proper follow-up of the protocol by experienced personnel minimize this problem (Fig. 1.4). 1.4.1.3 Genomic & Transcriptomic Data Collection Isolation of nucleic acid either DNA or ribonucleic acid is a preliminary step for obtaining genomic data from livestock animal samples. Isolated DNA or ribonucleic acid is later followed by genomic sequencing to obtain the arrangement of nucleotides in the sequence. Isolation of Genomic DNA from Livestock Species DNA isolation strategies rely on DNA’s chemical properties, such as its length and negative charge, to differentiate it from other molecules in the cell. Detergents, which dissolve lipid membranes and denature proteins, can be used to treat disaggregated cells. A cation like Na  +  aids in the stabilisation of negatively charged DNA and the separation of it from proteins like histones. Mg2+ ions function as a co-factor for DNA-digesting nucleases enzymes is sequestered by chelating agents like Ethylenediaminetetraacetic acid, which protect DNA by sequestering them. Thus, free double-stranded DNA molecules are separated from chromatin and collected in the extraction buffer, which still includes proteins and other cellular components. Proteins are commonly extracted by changing the salt concentration to allow them to precipitate. The DNA and remained other small metabolites in the supernatant are then treated with ethanol, which allows the DNA to precipitate. Centrifugation can be used to extract a DNA pellet, which is further dissolved in

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Fig. 1.4  Specimen collection and tissue disaggregation. Tissue sample is collected by either surgical or non-surgical biopsy which is followed by mechanical or enzymatic disaggregation of tissue to obtain cell suspension

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water and used in other reactions after the ethanol has been evaporated. The quality of DNA can be estimated by ultra voilet visible spectrometer. Extracted DNA is used for sequencing or other analysis. Ribonucleic Acid Isolation ribonucleic acid isolation is somewhat similar to DNA isolation but being different, RNA isolation is different from DNA isolation. Cell lysis buffer or chaotropic agents like guanidium chloride, guanidium isothiocyanate and sodium dodecyl sulfate breaks the cell membrane. Proteinase K and DNase cleave proteins and DNA in the cell, respectively. Phenol and chloroform remove proteins from a mixture. Chloroform addition and centrifugation separate the mixture into the aqueous and organic phases. RNA is extracted using isopropyl alcohol from the aqueous phase. Laboratory surface and instruments contain RNase enzyme that cleaves ribonucleic acid, thus RNase contamination is removed by Diethyl Pyro carbonate treatment of glassware and working area to prevent RNA digestion and its loss. Nowadays DNA/ ribonucleic acid isolation is performed by kits and automated machines that give very quick high-quality extraction. DNA/ribonucleic Acid Sequencing DNA or ribonucleic acid sequencing is the process of determining the order of nucleotides in the sequence. The structure of DNA contains four bases: Adenine (A), Cytosine (C), Guanine (G) and Thymine (T), while thymine is replaced by Uracil (U) in ribonucleic acid structure. There are various methods of DNA/ribonucleic acid sequencing including the Maxam-Gilbert method, Sanger sequencing, and many other methods but Next-generation sequencing also known as high throughput sequencing is nowadays used to obtain a highly accurate result in lesser time and at a low cost. Some majorly used next-generation sequencing includes Illumina sequencing, Ion torrent and Roche-454 sequencing. Illumina Sequencing uses 100–150  bp reads while longer fragments can be ligated to adaptors and annealed to slide by adaptors. Polymerase chain reation amplifies every read which is placed on the slide followed by flooding with DNA polymerase and nucleotides. Nucleotides are fluorescently labelled with each colour presenting to a particular base. The reaction includes a terminator to allow the addition of a single nucleotide which is followed by imaging of the slide. Every base incorporation in every location of the slide is captured by an image of fluorescent signal detection. Once signal data is recorded slide is prepared for the next cycle. The terminators are removed in preparation, to allow the next base addition, and the fluorescent signal is cleaved to prevent fluorescent signals that contaminating the next image. The process is repeated, with each nucleotide A, G, C, or T being added one at a time and imaged in between as only a single base is added in each cycle, every sequence read will have the same length. Roche 454-Sequencing is almost similar to Illumina but it can sequence longer read based on optical signal detection during base incorporation. DNA or ribonucleic acid is cleaved into shorter reads up to 1000 bp and ligated to adaptors which are annealed to beads, one DNA fragment per bead. Fragments are amplified using and each bead is placed in a single well of the slide. Each well contains a single bead

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having multiple copies of a single read. The slide is flooded with a single type of nucleoside triphosphates Where this nucleotide is next in the sequence, it is added to the sequence read. If a single base in sequence is repeated then more nucleoside triphosphate is added. If a slide is flooded with Adenine bases, and the next base in sequence is adenine, one adenine will be added, if the next bases of sequences are AAA three As will be added. In addition of every nucleotide release a light signal which is detected. Extra nucleoside triphosphates have washed away and the slide is ready for the next cycle. Because different numbers of bases are added with each cycle, all of the sequences reads from 454 sequencings will be of different lengths. Advanced technologies such as the MinION System and Ion Torrent Technology detect electrical signals on semiconductor chips rather than optical detection. Incorporation of deoxyribonucleoside triphosphates into polymer leads to a release of H+ which decreases pH which is reflected in an electrical signal. 200 bp fragments of DNA or ribonucleic acid are linked to adaptors and placed on a bead. Each bead is placed on a single well on a slide after polymerase chain reaction amplification. The slide is flooded with a single type of deoxyribonucleoside triphosphates and polymerase enzyme. pH is monitored during base incorporation. Excess deoxyribonucleoside triphosphates are washed for next cycle use. Small pocket DNA sequencing gadget that can be inserted into a laptop’s Universal Serial Bus (USB) drive and used in the field to collect data in real-time. Automated computerized machines and chips nowadays rapidly sequence DNA or ribonucleic acid and give output in computers. Other Genome Analysis Techniques Genome-wide association studies screen the genome of unmatched and matched or parent-affected progeny trios to find whether any genetic variant is associated with any phenotype. It majorly focuses on the association between single-nucleotide polymorphisms and traits such as milk yield and quality, egg quality, wool production, fertility, etc. Gene expression profiling determines gene expression patterns at the transcription level under specific conditions that give an idea about wholistic cellular function. DNA microarrays evaluate the relative activity of previously defined target genes and sequencing techniques to completely profile all active genes. The comparative genomic hybridization is a technique for detecting regional differences in DNA copy number between a control and a test genomic sample. It locates major aberrations such as duplications, amplification and, deletions (Fig. 1.5). 1.4.1.4 Proteomics Data Collection Proteins are very different in shape, size, hydrophobicity, charge, and affinity for other biomolecules. Many approaches are being employed for the separation of complex mixtures. The separation and identification of proteins is a preliminary step in proteomics study. Traditional approaches include chromatography techniques, enzyme-linked immunosorbent assay, western blotting and Edman sequencing.

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Fig. 1.5  Analysis of Genome by sequencing or/and microarray. Delving into the genomic landscape, this figure illustrates the powerful techniques of sequencing and microarray analysis. Sequencing, symbolized by its sequential depiction, unveils the precise unraveling of DNA, providing a comprehensive view of an organism’s genetic code. Concurrently, microarray analysis, represented by an array of interconnected nodes, showcases its capacity to simultaneously scrutinize thousands of genes, offering insights into gene expression patterns. Together, these methodologies serve as cornerstone approaches in genomic analysis, enabling a nuanced understanding of the intricate genetic architectures that govern biological systems. GWAS Genome-wide association studies, SNPs Single nucleotide polymorphisms

Different chromatography such as ion exchange, size exclusion, and affinity chromatography separate protein based on charge, size and molecular interaction, respectively. Enzyme-linked immunosorbent assay is an immunoassay that involves the use of an antibody against a particular protein. Western blotting is a very useful technique in low abundant protein detection using electrophoresis followed by transfer onto nitrocellulose membrane and the enzyme-conjugated antibodies for precise detection of a target protein. Edman sequencing cleaves terminal amino acids in every cycle which is detected by spectrometry. Advanced approaches include microarray and gel-based techniques. Protein microarray or protein chip is capable of high-throughput detection of small quantity samples. Functional protein microarray involves protein to other molecule interactions to find the function of the protein. Analytical microarray uses antibody labelling to measure the expression level and binding affinity of protein to its target. Reverse-phase protein microarray uses fluorescent probes to quantify protein and determine the altered function of protein in a certain condition. Sodium dodecyl-­ sulfate polyacrylamide gel electrophoresis separates protein according to its size with high resolution, thus it estimates the molecular weight of protein. 2-Dimensional gel electrophoresis separates based on its isoelectric point as well as its molecular weight.

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Quantitative techniques include Isotope-Coded Affinity Tag, Stable Isotopic Labelling with Amino Acids in Cell Culture (SILAC), and Isobaric Tags for Relative and Absolute Quantitation. Isotope-Coded Affinity Tag is an isotopic labelling technique to quantify proteins by chemical labelling. SILAC performs metabolic labelling of complete cell proteome and quantifies proteins using mass spectrometry. SILAC studies gene regulation, post-translation modification and cellular communication. Majorly used to analyse secreted proteins and pathways. Isobaric tag for relative and absolute quantitation uses multiplex labelling of protein N-terminus and side-chain and quantification using tandem mass spectrometry. X-ray crystallography is used to study 3-Dimensional protein structures. It is widely used to study interaction with other biomolecules, pathogen proteins, enzyme mechanisms, site-directed mutagenesis, drug designing and protein-ligand interaction. Mass spectroscopy and nuclear magnetic resonance are advanced analytical techniques for protein analysis. Mass spectrometry measures the molecular weight of protein by mass to charge ratio. Mass spectrometry is also used in protein characterization and the study of post-translation modification. Nuclear magnetic resonance spectroscopy investigates protein folding, behaviour and its molecular structure which is used in research areas such as homology and functional genomics (Fig. 1.6). 1.4.1.5 Metabolome Data Collection Metabolomics studies metabolites of diverse nature thus no single technique can cover all metabolite detection and analysis thus multiple techniques are involved in data collection. The choice of technique to use depends on the quantity of sample, sample matrix, and characteristics of the metabolites from various livestock animal samples such as blood plasma, saliva, milk, urine, egg white, semen, etc. Gas chromatography, high-performance liquid chromatography and capillary electrophoresis are coupled with mass spectrometry and nuclear magnetic resonance to separate, identify and quantify metabolites from various samples. Mass spectroscopy is a widely used technique for the reliable identification of metabolites. It is a rapid and sensitive technique to perform both qualitative and quantitative analyses. Nuclear magnetic resonance is a highly reproducible and non-destructive technique to detect metabolites with minimum sample preparation.

1.4.2 Omics Data Integration and Analysis by Computational Approach The heterogeneous and high-dimensional existence of omics data poses several challenges when it comes to screening candidate markers. Genome, proteome, metabolome, and transcriptome data are all available in the age of big data.

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Fig. 1.6  Navigating the diverse landscape of proteome analysis, this figure encapsulates various analytical approaches. From gel-based methods like 2D gel electrophoresis, depicted as intricate protein patterns, to cutting-edge mass spectrometry, symbolized by molecular ions, witness the spectrum of techniques employed to unravel the intricacies of the proteomic landscape. The multifaceted diagram highlights the versatility of liquid chromatography and protein microarrays, emphasizing the array of tools researchers employ to decipher protein expression, modifications, and interactions. This visual synthesis underscores the comprehensive nature of proteome analysis and the varied strategies employed in unraveling the complexities of cellular protein dynamics. LC-MS liquid chromatography-mass spectrometry, ELISA enzyme-linked immunoassay, SELDI Surface-enhanced laser desorption/ionization, SPR Surface plasmon resonance

Aside from single omics data, integrative omics, also referred to as multi-omics data are used in predictive analysis as they approach big data. To process, normalize, incorporate, and analyse omics data, various computational tools and techniques such as machine learning, data mining, deep learning, metaheuristic techniques, and statistical methods have gained importance (Kaur et al. 2021). Omics data obtained from the wet lab using multiple analytical techniques are integrated with single or classified databases that include sequences of genes, transcripts, proteins and 2D or 3D models of metabolites and proteins. The output of integrated data sources is superior to that of single data sources (Sun and Hu 2016). Many approaches are used in an integrative analysis for simultaneous analysis.

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1.4.2.1 Data Integration Methods In the integration of omics data, genomic data reveals information about expression, mutation, and control aspects of biology. This process includes the integration of data from different omics to obtain a Multiomics analysis outcome which is more precise than single omics. Different methods are discussed below. 1. Concatenation-based integration: Multiple types of omics data are combined in this process, and the dataset’s combined matrix is then used for analysis. Current analysis methods for single omics data function well in this approach for the combined matrix. 2. Model-based integration: In this approach, the data sets are evaluated separately and the results are then combined to produce the desired results. This model is extremely adaptable since different models can be used to analyse various data types. This model-based integration is widely used in the field of bioinformatics. Depending on the models applied to different data types, this model methodology is classified as supervised or unsupervised. Different types of data are used as training sets in the supervised group to produce multiple models. The developed models are then combined using bagging or voting. Clustering findings are obtained in the unsupervised group from various data types. The results are then compiled to carry out integration based on certain optimization parameters. 3. Transformation-based Integration: In this method, the data type is transformed into a graph or kernel type matrix in the first step. To obtain integrative representation, this intermediate type of data is combined. This method is more robust than concatenation-based integration because it can integrate a wide range of data types, including categorical, continuous, and sequence data. 1.4.2.2 Omics Data Predictive Modeling In life science research, machine learning analytics is used to deal with diverse and complex omics data and its modelling. The figure depicts the working mechanism of machine learning analytics for omics info. Data pre-processing, modelling, and active learning are part of omics analytics (Kim and Tagkopoulos 2018) (Fig. 1.7). Modelling: A model is constructed from training data using unsupervised or supervised learning, and then the model’s output is assessed using a variety of parameters. Supervised learning is a machine learning methodology that infers a feature from labelled data. Several machine learning approaches are used to solve prediction problems. Most of the methods are regression-based, such as K-Nearest Neighbor, Naïve Bayes, Support Vector Machine, Ensemble method and Neural network (Dhillon et al. 2020). The ensemble approach is the most widely used of all the approaches. It comes when multiple models outperform a single model. Unsupervised learning is a form of machine learning in which inferences are made from data without the need for class labels.

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Fig. 1.7  Working mechanism of Data pre-processing, modelling, and active learning. Data pre-­ processing: To manage data efficiently for analysis, multi-omics data is first normalized. Features are selected in the second stage to pick the subset of features for modelling. For feature selection, supervised approaches of mutual information and Pearson correlation coefficient, while the unsupervised approach of Principal Component Analysis is used

Active learning: Once the model has been built and evaluated, the model’s uncertainty must be reduced. This is accomplished by the use of active learning, which directs the execution of subsequent experiments. Active learning was first used in supervised settings, but it is now also being used in unsupervised settings. Active learning is primarily used to improve the accuracy of a machine learning algorithm with a limited number of labelled training examples. These all tasks are performed by data analytics software in very few moments. 1.4.2.3 Omics Data Analysis The processed data should be handed over to the main analysis stage once the pre-­processing step is completed. The research methods differ since each omics study type is unique in the context of objectives and data structure. Omics data can be analysed individually or in multiple testing.

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Individual Evaluation Long sequential molecules, several genes, genetic variations, molecules, biomarkers, and individuals are all studied in omics studies. A common way to deal with these data is to look at each one separately and generate a large number of outputs. Genome-wide association), in which several single nucleotide variants are analysed one after another, and transcriptomic ribonucleic acid seq analysis, in which all coding genes are quantified. Many p values are obtained when these individual items are statistically checked for the independence of a particular variable. This set of p values should be interpreted as a whole. The objects are divided into two subsets by multiple testing corrections: one is positive, and the other is negative. However, such item separation is inadequate, and research on the combinatorial effects of multiple items should be conducted. Multiple Testing Correction While using the item-wise testing approach to generate a list of p values, the p values cannot be interpreted in the same way as they can when only one statistical test is conducted and only a single p-value is obtained because if 20,000 single nucleotide variants or 1% of a two million single nucleotide variants s in a genome-wide association studies, would have p values of less than 0.01, even though all single nucleotide variants are unrelated to the genome-wide association studies phenotype. The rarity of the least p-value among the million p values should be viewed in terms of the distribution of smallest values among the million random values that obey the uniform distribution from 0 to 1 rather than the uniform distribution from 0 to 1. Since the large majority of single nucleotide variants must not be correlated with a specific phenotype, this form of rarity correction is known as family-wise error rate correction, which is appropriate for genome-wide association studies. The second smallest p value’s rarity should not be interpreted in the same way as the smallest p-value, and the larger threshold value must be set as the second smallest p-value to consider this value positive. As a result of this thought process, we use a less strict threshold for judging the rarity of p values based on their ranks. Genomics Analysis Genomics includes techniques for analysing DNA sequences to understand the structure and function of genomes, mutation, gene regulation, and genetic alteration linked to complex phenotypes in farm animals. The basic local alignment sequencing tool (BLAST) algorithm compares primary DNA sequences for similar regions on a gene. Deep Learning methods have been used in recent years to answer a variety of questions in genomics data. A convolutional neural network is used for spotting single-nucleotide polymorphisms and indels that find deletion type mutation. For prediction of pathogenic effects of genetic variants in livestock animals, Deep feedforward neural networks, ResNets, or Convolutional neural network methods are used. Deep feedforward neural network can also be used to predict the influence of genetic variants on gene expression. Deep Learning algorithms have been used in the field of functional genomics to predict enhancer sequences and regulatory motifs in the genome from a variety of data sources such as histone modifications. The affinities of transcription factors to DNA can be quantified using a Convolutional

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neural network. Long short-term memory and Convolutional neural network are used to predict promoter sequences in genes. Via Convolutional neural networks, Deep Learning algorithms have also aided in the identification of splice junctions. Transcriptomics Analysis Transcriptomics raw data is generally processed to produce expression matrices, which contain an approximation of each gene or transcript’s expression level across multiple samples and conditions and are commonly used as input to Deep learning methods. Deep Learning has been applied to a broad variety of transcriptomics applications successfully. The main goal of gene expression data, for example, is to analyse alternative splicing in which the synthesis of different transcript isoforms from the same gene occurs. Prediction of non-coding ribonucleic acid and characterization of their expression are two other major areas of research in transcriptomics analysis. The capability algorithm for identifying non coding is demonstrated using a recurrent neural network to distinguish between coding and non-coding ribonucleic acids. A Deep feedforward neural network can be used on reference databases to identify long non-coding ribonucleic acids with 99% accuracy. Long intergenic non-coding ribonucleic acids, a form of non-coding ribonucleic acids that is transcribed in intergenic regions, have also been successfully predicted using an Auto-encoder algorithm with previous long non-coding ribonucleic acids information. Proteomics Output signals from the Mass spectrometry are compared to peptide/protein profiles already available on public or proprietary databases to identify them. Long Short-­ Term Memory network predicts peptide mass spectroscopy /mass spectroscopy spectra. The alignment of amino acids is examined for the study of similarity and evolutionary relationships. Deep learning tools such as recurrent neural networks, including long short-term memory or gated recurrent units, and Convolutional neural networks are used to analyse and quantify complete proteome. Knowing peptide spectra in advance facilitates assigning mass spectroscopy/mass spectroscopy spectra to peptides and comparing them to theoretical spectra much easier. De novo peptide sequencing analysis is crucial for protein characterization thus it is important in proteomics applications. Metabolomics Deep Learning methods such as Random-forest, deep neural network, ensemble deep neural network, and support vector machine methods are used to perform analysis of metabolome based on nuclear magnetic resonance spectra data produced from various tissues of livestock animals. Convolution neural networks, Artificial neural networks and Ensemble can also be used in physical property analysis, interaction analysis and functional interpretation of metabolites. Omics data analysis involves different computational tools which are based on different algorithms and logic. Any error in the program, database or coding may lead to the wrong output in analysis. For example, instead of the right candidate gene for particular trait computational tools by mistake select the wrong gene will

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eventually not express desirable trait in livestock animal after marker-assisted breeding which cost a lot of human labour, time and loss of resources. But nowadays these computational tools are broadly used in the bioinformatics domain. Advancing technology with time will vastly increase the accuracy of analytical outcomes and decrease the chances of error and misanalysis with subsequent stages of improvement.

1.4.3 Using Omics Data Analysis Results for Livestock Animals Computational tools analyse big data obtained using various analytical techniques from livestock animal samples and reveal candidate marker genes, proteins, metabolites and their regulation associated to particular phenotype or trait related to quality and yield of commodities produced by livestock, fertility, disease resistance, and other factors related to its production and improvements for satisfying future demands. These identified genes or proteins or metabolites are referred to as a marker for that particular phenotype. Marker-assisted selection is suitable for precision livestock breeding. However, almost all of the major economic traits of livestock are complex quantitative traits controlled by multiple genes and proteins, making a prediction of epistatic interactions difficult. Not all genetic variations are significantly associated with the phenotype of interest. Therefore, only a few major genes are considered in actual livestock breeding having economically important phenotypes. Marker-assisted breeding is performed for the production of offspring that have high quality and yield of milk, meat, wool, egg and other commodities, disease resistance against many pathogens, high fertility, feed efficiency and many other properties that affect the livestock economy. Male having economically important phenotype possess marker gene or protein or metabolite is used for breeding purpose by artificial insemination assistance. Proteomics and Metabolomic analysis study expression profiles in different conditions such as season, infection, infertility, different phases and cycles to reveal associated protein and metabolites with particular phenotypes to improve quality, improve fertility, improve nutrition, early diagnosis of disease and prevention of its prevalence to other animals, precision medicine, and taking all preventative measures to prevent livestock commodity loss that affect livestock economy. Omics data identify the gene of interest to be inserted in genetic engineering having high economically important commodities. It reveals candidate genes that can be ligated into vectors and recombinant to be inserted in animals to obtain desirable phenotypes. The ultimate use of omics data analysis result is fulfilled by precision marker-­ assisted breeding for different purposes like disease-resistant, high-quality commodity yielding, high fertility, high feed efficiency and eco-friendly animal emitting minimum methane that improves and sustains livestock economy, environment, and overall, the well-being of animals.

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1.5 Application of Omics Technologies in Different Livestock Animals In this section, some livestock animals which are studied widely are discussed concerning different markers obtained for various commodity traits using different omics technology.

1.5.1 Cattle Cattle are domesticated ungulates that belong to the Bovine subfamily of the Bovidae family. Cows are female, while bulls are male collectively referred to as cattle. Cattles are raised for the production of dairy (milk), meat, and leather as well as ploughing and pulling carts, etc. as livestock animals. The Indian subcontinent is home to a wide range of cattle. The majority of cattle breeds produce milk and are also used to produce castrated bulls for use in agriculture, carting, and transportation. Genomic Studies in Milk Production and Composition Lactation is characterized by milk production, which is influenced by a variety of factors including the environment, nutritional condition, breed, and genes especially (Xiang et al. 2017). In the production of milk, changes in gene expression are absolutely important. Researchers have examined for links between milk production traits and candidate genes that are linked to milk protein and fat yields, as well as their percentages. The genome-wide analysis is used to detect associated biomarkers for milk production and quality and reveals key pathways and genes involved. For example, milk production is associated with gene mutations that can be revealed such as a missense mutation in a prophet of Pit 1 gene, gene polymorphisms of signal transducer and activator of transcription 5A, epidermal growth factor, and the deletion type mutation in Ribonuclease H2 Subunit B (Valour et al. 2015). Most development traits are variable in the persistent duration after the peak, which is affected by genes, as has been widely demonstrated. Genomic analysis of 1490 German Black Pied Cattle using multiple linear regression models for 30 milk traits identified 20 suggestive and 41 significant single-nucleotide polymorphisms s that influence milk production (Korkuć et al. 2021). The main nutritional components that affect milk quality are milk protein and fat. Specific genes and milk fat composition have been identified in genomic studies. In Dutch dairy cows, an association study of single-nucleotide polymorphisms discovered 54 different regions on 29 chromosomes that are significantly associated with at least one fatty acid of milk (Bouwman et al. 2011). Genomic analysis revealed the impact of genes yielding a high percentage of protein, for example, κ-CN genotype and β-casein genotype have major impacts on percentage and protein yield, respectively. Both variants of the β-κ-CN haplotype A2B and β-lactoglobulin genotype B

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Table 1.1  Important cattle marker gene associated with lactogenic property Polymorphic gene PRL (prolactin) LEP (leptin)

DGAT1 (diacylglycerol O-acyltransferase 1) SCD1 (stearoyl-CoA desaturase) GHR (growth hormone receptor) CSN1S1 (casein α s1) FASN (fatty acid synthase) LGB (β-lactoglobulin)

Chromosome Trait association 23 Overall milk production performance 4 Better milk yield with good energy balance and fertility 14 Milk yield and composition 26 Milk fatty acid composition 20 Milk yield and composition 6 Milk protein expression 19 Milk fat 11 Milk protein concentration

References He et al. (2006) Liefers et al. (2005)

Grisart et al. (2002) Kgwatalala et al. (2009) Blott et al. (2003) Kuss et al. (2005) Morris et al. (2007) Ganai et al. (2009)

increases cheese production in Dutch Holstein-Friesian cows (Schopen et al. 2011). Some important cattle markers associated with the lactogenic property are listed in Table 1.1. Transcriptomics, Metabolomics and Proteomics Studies Improve Cattle Health Clinical mastitis is a common and costly disease caused by Streptococcus agalactiae bacteria in transition dairy cows. To resolve unsatisfactory performance such as low precision, delayed detection, time, and labour-intensiveness, Omics methods are now being used to investigate and classify predictive biomarkers (Tran et al. 2020). Plasma proteins such as haptoglobin, 1-acid glycoprotein, and serum amyloid, were significantly increased in subclinical mastitis dairy cows during the transition phase, according to proteomic profiling (Yang et al. 2012). The main difference between clinical mastitis affecting cows and stable cows, according to metabolomics profiling of serum, was elevated 3′-sialyllactose level in affected cows (Zandkarimi et al. 2018). Potential blood biomarkers for disease, such as 3′-sialyllactose in mastitis, can aid in the development of early diagnosis and early therapeutic interventions to improve dairy cow health and quality of life. Comparative proteome and transcriptome analysis of S. agalactiae infected and healthy mammary tissue identified 129 genes and 144 proteins that expressed differentially. Proteins such as cathelicidin-7 precursor, immunoglobulin M precursor, complement C4-A-like isoform X1, integrin alpha-5, and 18 differentially expressed/differentially expressed proteins were found to be linked with mastitis disease (Zhang et al. 2018b). Metabolomics profiling of blood samples identified pyroglutamic acid, ornithine, phosphoric acid, glutamic acid, and D-mannose as potential biomarkers for metritis occurrence at 4 weeks prepartum (Hailemariam et al. 2018). Dervishi et al. identified potential biomarkers such as lysine, histidine, o-phosphocholine, xylose, trans-­aconitate, threonine, 3-aminoisobutyrate, and isocitrate from a urine sample to

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predict metritis risk at 4 weeks before parturition (Dervishi et al. 2018). Metabolomic profiling of 340 different metabolites in transition cows suggested that cows suffering from metritis showed altered concentrations of glycerophospholipids, multiple serum amino acids, acylcarnitines, hexose, and sphingolipids. Furthermore, metabolic pathways such as Trp metabolism, Lys degradation, protein biosynthesis, Val-­Leu-­Ile degradation, and biotin metabolism, were significantly altered in both metric and pre-metric cows (Zhang et  al. 2017). These findings aid in the early detection and prevention of disease to improve cattle health. Transcriptomics Studies Transitional and Lactation Changes in Cattle Transcriptomics analysis of red Holstein dairy cows confirmed that transition cows show major hepatic functional changes related to gluconeogenesis, cholesterol metabolism, and fatty acid oxidation. For these metabolic adaptations, responsible genes are identified such as apolipoprotein A1, cytochrome P450 family 26 subfamily A member 1, cytochrome P450 family 7 subfamily A member 1, Farnesyl-­ diphosphate farnesyltransferase 1, and Hydroxymethylglutaryl-CoA reductase, which could be candidate genes for functional changes as the transition period starts (Ha et al. 2017). More than 33,000 single-nucleotide polymorphisms linked with lactation have been identified in the transcriptomics study, which provides the platform for genotyping for studying marker-trait links in dairy cows. In Holstein cows, a transcriptome profiling study suggested 31 differentially expressed genes are identified between extremely low and high milk fat and protein percentages (Cui et al. 2014). These genes are highly correlated with specific biologic processes such as fat and protein metabolism as well as mammary gland development. Li et  al. discovered that 884 unique micro ribonucleic acids sequences in the mammary glands and 56 sequences of micro ribonucleic acids expressed differentially between lactating and non-lactating mammary glands, which suggested that the types of micro ribonucleic acids and expression levels differ between lactation and non-­ lactation periods in dairy cows (Li et al. 2012). Proteome, Metabolome and Multi-omics Find Fertility Biomarkers Fertility is a vital factor in the production of cattle. Proteome analysis reports of bull seminal plasma identified candidate proteins and genes with sophisticated computational tools analysis by in-depth transcriptome and genome profiling. In the dairy bull’s seminal plasma, 1159 proteins identified out of 50 proteins were abundant in high fertility bulls while 29 proteins were abundant in low fertility bulls. The multivariate analysis finds the association of Tissue inhibitor of metalloproteinases 2, C-type natriuretic peptide and sulfhydryl oxidase proteins with high fertility bulls; while galectin-3-binding protein, tissue factor pathway inhibitor 2, 5′-nucleotidase and clusterin proteins were associated with low fertility bulls (Viana et al. 2018). In metabolome analysis of Holstein bulls seminal fluid, 63 different metabolites belonging to seven chemical classes were analysed; fructose was most abundantly present which is followed by citric acid, urea and phosphoric acid. Comparatively, high fertility bull’s semen had less 2-oxoglutaric acid and more fructose than low fertile bulls, which indicates fructose and 2-oxoglutaric acid as potential biomarkers in bull fertility.

26

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Using separate beef cattle populations tested for fertility traits, a multi-OMICs and multi-breed approach may be used to investigate the pleiotropic effects of key-­regulator genes. Using the Cattle Quantitative Trait Loci database, data-mining analysis identify the genes shared by breeds related to fertility phenotype. Peroxisome proliferator-activated receptor gamma, MYC proto-oncogene, thyroglobulin, iodotyrosine deiodinase, and glycogen synthase kinase 3 beta genes were identified as part of the main gene network which is composed of 38 genes shared among breeds, which is essential for biological processes associated with fertility (de Fonseca et al. 2018). Genome Analysis Identified Disease Resistant Marker The recent approach of selecting sire bull integrates genomic analysis of large-scale data between or within breeds is combined with epidemiological prognosis to obtain disease-resistant offspring of cattle. Genomic analysis of Jersey cattle using stranded conformation polymorphism study identified a significant association between clinical and subclinical mastitis and Interleukin 8 gene. β-defensin are the best known genetically encoded antimicrobials peptides also used as adjuvants in mastitis vaccine. Genomic analysis shows cattle containing the beta-defensin 5 encoding gene have innate mastitis resistance (Gurao et  al. 2017). Marker-assisted breeding to obtain mastitis and other diseases resistant offspring could be made more successful by genetically identifying candidate genes. Proteomics Identifies Feed Efficiency Associated Proteins and Pathways Understanding the current situation of rising demand for animal protein and sustainable resource usage, improving nutrient use efficiency is critical for cattle. Identifying the mechanism of feed efficiency in beef cattle enables the discovery of markers for identifying and selecting the best animals for production in this context. Fonseca et al. evaluated the feed efficiency of 98 Nellore bulls and screened out six bulls having low feed efficiency and six bulls having high feed efficiency for proteomic analysis. As an outcome, 3 protein networks and 42 differential abundant proteins (were significantly associated with feed efficiency. Fatty acids biosynthesis, microbial metabolism, gluconeogenesis/glycolysis, biosynthesis of vitamins and amino acids, antigen presentation and processing, and xenobiotic metabolism were the pathways majorly associated with bull feed efficiency (Fonseca et al. 2019). Metagenomic Identify Methane Emitting Microbes in the Rumen Ruminant livestock contributes to 29% of methane emissions which is a greenhouse gas. Metagenomics understands the function and composition of complex rumen microbiota responsible for methane emission in the environment from waste. Quantitative polymerase chain reaction analysis of 18S and 16S ribosomal ribonucleic acids genes of low and high methane-producing cattle rumen microbiota was performed using Illumina HiSeq alignment. Analysis showed that archaea such as Methanobrevibacter were abundantly 2.5-fold present which is high methane emitters, while proteobacteria such as Succinivibrionaceae were four-fold less present in rumen. KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis showed that genes associated with methane production were overexpressed 2.7-fold.

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Among identified proteins, 20 proteins were related to methane emissions and 16 shows completely identical with available protein databases. An abundance of archaeal genes in rumen microbiota is strongly linked to high methane emission. This finding can be further used for screening purposes (Wallace et al. 2015).

1.5.2 Buffalo Bubalus bubalis is the scientific name for the buffalo, which belongs to the Bovine tribe. Buffalo is an important livestock animal in many countries’ agricultural economies. Buffalo is common among farm animals as a source of milk, draught, and meat. Buffalo are categorized into two types: the river (for milk purposes) and swamp (for draught purposes). Genomics Study Identified Milk Production Trait Buffalo milk production can be improved by selecting candidate genes linked to desirable milk production traits in the breeding program. In different buffalo breeds, 517 candidate genes are found to be associated with milk performance. By candidate gene approach, 19 candidate genes showed a mutation in 47 sites. Genome-­ wide association studies using buffalo single-nucleotide polymorphisms chip and bovine single-nucleotide polymorphisms chip identified 499 candidate genes linked to milk performance. In two Genome-Wide Association Studies, apolipoprotein B, catenin delta, estrogen-related receptor gamma, and fragile histidine triad genes were identified (Du et al. 2019). A Genome-Wide Association Study of 89,069 daily milk records in 250 Egyptian buffalo using the Axiom Buffalo Genotyping Array 90 K to find genomic regions and possible causative mutations correlated with milk yield identified milk trait associations on chromosomes bovine chromosome 5, bovine chromosome 1, bovine chromosome 27, and bovine chromosome 6 after further study of several genomic regions. Bovine chromosome 27 has the strongest connection to milk yield out of all of them (El-Halawany et  al. 2017a). single-­ nucleotide polymorphisms correlations with milk protein yield in buffaloes indicated cyclin D3 as a candidate gene in another study. This gene, in collaboration with prolactin, plays a role in mammary gland alveolar growth. Variations in the biological activity of cyclin D3-coded proteins can affect the structure and/or physiology of the alveolus. The circulation of blood in the mammary alveolus affects milk production (de Camargo et al. 2015). Comparative Metabolomics Identifies Biomarkers of Milk Quality Metabolic profiling of milk of 12 Murrah, 10 the Mediterranean and 10 crossbred (local swamp × Murrah) buffaloes were performed using the UPLC-Q-Orbitrap High-resolution mass spectrometry approach to assess milk quality. The milk fatty acid content of Mediterranean buffalo was found to be substantially higher than that of crossbred and Murrah buffalo. 19 metabolites were significantly different between Murrah and Mediterranean buffalo while 18 metabolites were different between crossbred and Mediterranean buffalo. A total of 11 metabolites were different

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D. Maru and A. Kumar

among all three breeds of buffalo indicating that it can be used as a biomarker for Mediterranean buffalo milk (Shi et  al. 2021). These particular metabolites could be used as biomarkers for milk quality assessment and molecular breeding of high milk fat buffalo. Proteomic Analysis Identifies Fertility-Related Markers One of the causes of buffalo’s poor reproductive performance is long postpartum anestrus. Proteomic analysis by mass spectrometry of 23 buffaloes follicular fluids identified 34 different proteins that play important role in ovarian endocrinology such as serum amyloid A-4, cadherin-23, integrin beta-7, apolipoproteins (A-I, II, IV), alpha-fetoprotein, adenylate kinase 2, protein kinase C-binding protein, alpha-­ fetoprotein, protein kinase C-binding protein and heat shock protein beta-1, etc. (Kumar et al. 2019). Comparative sperm proteomic analysis determines buffalo bull fertility by identifying differentially expressed proteins using the mass spectrometric method. As an outcome, high fertile buffalo bulls overexpressed 10 proteins while twofold under-expressed 15 proteins. Overexpressed proteins in high fertile bulls include General Transcription Factor IIF Subunit 2, PDZ domain-containing protein 8, Zinc finger protein 397, Loss of heterozygosity LOH of 12p12–13, Kizuna, Serine protease 37, Acrosin-binding protein, and Aldosterone synthase while in low-fertile bulls Metallothionein 1A, Citrate Synthase, ATP synthase F1 subunit, Proline dehydrogenase 2, T cell receptor beta chain, Isocitrate Dehydrogenase NAD(+) 3 Catalytic Subunit Alpha, Histidyl-tRNA synthetase, Tubulin Beta 2B Class IIb, and Tubulin Polymerization promoting protein family member 2 proteins were overexpressed. Metallothionein 1A and Aldosterone synthase were found to be highly abundant in low-fertile and high-fertile bulls respectively (Ma et al. 2019). This finding helps in the screening of sire for breeding to produce high-fertility buffalo bull. Transcriptome Analysis Studies Expression Patterns in Lactation Stages The expression study of genes and their regulation during lactation in buffaloes help to understand the interplay of various pathways and genes. Milk transcriptome analysis of Murrah buffalo during three lactation stages was analysed by ribonucleic acids sequencing. During the lactation stage, Alpha-S1-casein, Casein Beta, α-Lactalbumin, Casein kappa, Tumor protein translationally-controlled 1, and Secreted Phosphoprotein 1 genes were found to be overexpressed. The 12,833 transcripts out of a total were common during all stages, while 418, 205, and 271 were unique to the late, mid, and early lactation period respectively. Genes of analysed transcripts were linked to immune response, transport, and protein metabolism. It suggests the early stage has a mild immune response while late lactation has an increased immune response. This information is helpful for the well-being of buffalo that in turn may affect milk yield (Arora et al. 2019). Genomics Identifies a Marker for Disease Resistance The occurrence of genetic variants in the buffalo genome has been linked to resistance or susceptibility to specific diseases in buffalo, such as mastitis, tuberculosis, brucellosis, or any other infectious disease. The Interferon gamma gene

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29

polymorphism g.4467G>A affects the miR-125b target sequence and obtains susceptibility to bovine tuberculosis in water buffalo, suggesting its potential relevance as a molecular marker in breeding assistance programs (Iannaccone et al. 2018). In complement system cascade, complement protein C3 has a crucial role in killing the mastitis pathogen. Buffalo’s C3 cDNA sequences are 5025 bp long, with a 4986 bp open reading frame that encodes a 1661-amino-acid putative protein. Aligning the C3 cDNA sequences 6 novel single-nucleotide polymorphisms were discovered in buffalo. The major association was located in the C>A substitution (ss: 1752816097) in exon 27, according to the association study of the observed single-nucleotide polymorphisms and milk somatic cell score predicts mastitis. C3 polymorphism contributed to mastitis resistance in buffalo (El-Halawany et al. 2017b). Genomic analysis of 65 Murrah breeds of buffalo identified polymorphism in the 3′ untranslated region (3′UTR) region of Natural resistance-associated macrophage protein 1 gene in 65 Murrah breeds of buffalo associated with macrophage function in brucellosis resistance. Natural resistance-associated macrophage protein 1 is a transmembrane protein that regulates macrophages to protect them from intracellular pathogens. Four allelic variants (GT13, GT14, GT15, GT16) were identified. The GT13 allele at the 3′ untranslated region of microsatellite locus, either heterozygous or homozygous was significantly associated with improved macrophage function in buffalo by elevated production of H2O2 and NO. Buffalo bull with this variant can be used as a sire in breeding for brucellosis-resistant buffalo production. Foot and Mouth Disease in buffalo is an infectious disease caused by a single-­ stranded ribonucleic acids virus. Digestion of 302-bp amplified fragments from exon 2 with HaeIII endonuclease to analyze genetic polymorphism of Bovine lymphocyte antigen-heterodimer beta chain. Three genes in Egyptian buffalo to find candidate genes responsible for susceptibility or resistance to foot and mouth disease revealed the existence of five Bovine lymphocyte antigen- heterodimer beta chain 3 genotypes. According to the findings, genotype Hemoglobin C trait may be linked to Foot and Mouth Disease susceptibility, while genotype AA may be linked to Foot and Mouth Disease resistance in Egyptian buffaloes. This discovery has the potential to assist in the development of Foot and Mouth Disease Virus-resistant Egyptian buffalo (Othman et  al. 2018) (Fig. 1.8). In the water buffalo, subclinical mastitis caused by Staphylococcus aureus and non-aureus staphylococci is a serious problem that affects milk yield and quality. Proteomic analysis identified a total of 1530 proteins, out of 152 were significantly changed. With 162 vs 127 differential proteins and larger abundance changes, Staphylococcus aureus had a greater impact. The 119 overexpressed proteins were associated with innate immune system functions and included cathelicidins, vimentin, S100, histones, neutrophil granule proteins, lysozyme and haptoglobin. The 33 under-expressed proteins were associated with lipid metabolism and included xanthine oxidase/dehydrogenase, butyrophilin, and lipid synthesis-related enzymes. This analysis improves the detection of mastitis disease in buffalo (Pisanu et al. 2019).

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D. Maru and A. Kumar

Fig. 1.8  This illustrative guide outlines the stepwise procedure for creating disease-resistant buffaloes through genomics data. The figure illustrates the initial genomic profiling of buffalo populations, showcasing the identification of key genetic markers associated with disease resistance. Subsequent stages involve targeted breeding using this genomic information, depicted by connecting arrows indicating strategic mating for desired traits. Finally, the figure symbolizes the emergence of a disease-resistant buffalo population, emphasizing the transformative impact of genomics data in selective breeding for enhanced livestock health

1.5.3 Goat The goat (Capra aegagrus hircus) is a domesticated subspecies of C. aegagrus that originated in Eastern Europe and Southwest Asia. The goat belongs to the Bovidae family and the Caprinae subfamily, which means it is closely related to sheep. Goats have been raised for milk, fur, meat, and skins. Genetically modified goats are also used as a living bioreactor for the production of pharmaceutically important recombinant proteins. Female goats are called does or nannies, male goats are called bucks, and both sexes of immature goats are called kids. Wethers are males who have been castrated. Genome and Transcriptome Analysis Identifies Lactation and Milk Quality Markers A Murciano-Granadina goat genome-wide analysis found 24 distinct quantitative trait loci. Lactose % in milk is linked to quantitative trait loci 1 on chromosome 2. Protein percentage is linked to quantitative trait loci 6 on chromosome 6. Both dry matter and protein percentages are linked to quantitative trait loci 17 on chromosome 17. quantitative trait loci 6 shares a positional similarity with the milk casein protein genes, which code for 80% of proteins in milk (Guan et al. 2020). A total of 51,299 genes were discovered through digital gene expression sequencing of goat mammary gland cells, with 12,763 genes annotated from 1299 genes, of which 9131 genes were differently expressed through lactation stages. Expression patterns

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were found to be 16 out of total and 13 genes were identified to have regulation in lactation: Spectrin Alpha Erythrocytic 1, Polymerase gamma, G protein-coupled receptor kinase interacting ArfGAP 2, Kinesin Light Chain, Pyruvate dehydrogenase phosphatase, Cluster of differentiation 31, COP9 Signalosome Subunit 3, Tolloid-like-1, Ubiquitin Specific Peptidase16/29/37, Abelson interactor 2, Non-­ SMC Condensin I Complex Subunit H, Mitogen-Activated Protein Kinase 8 Interacting Protein 3, and DnaJ Heat Shock Protein Family Hsp40 Member C4, Carnitine palmitoyltransferase I, Phospholipase A2, Phospholipase D, Adaptor Related Protein Complex 4 Subunit Sigma 1, and SRP Receptor Subunit Beta genes have also been suggested as new and promising candidates associated with mammary fatty acid metabolism. “Glyoxylate and dicarboxylate metabolism” and “butirosin and neomycin biosynthesis” were the most lactation impacted pathways during its progression. Impacted pathways and modifications in lipid metabolism further assist to increase milk yield and quality in dairy goats (Li et al. 2020). Transcriptomics Study Expression Pattern in Infections Goat production is affected by gastrointestinal nematodes, which can be resolved by developing gastrointestinal nematodes resistance breeding programs. Transcriptome profiling of lymph node and mucosa tissues from susceptible infected, non-infected, and resistant Creole goats by sequencing ribonucleic acids. A total of 24 goats were infected twice with 10,000 L3 Haemonchus contortus, 12 gastrointestinal nematodes resistant and 12 susceptible. Infected goats had a larger number of differentially expressed genes than non-infected goats in both the lymph nodes and abomasal mucosa, with 1726 differentially expressed genes and 792 differentially expressed genes, respectively. In resistant goats, only 342 and 450 differentially expressed genes were found in mucosa and lymph respectively. Differentially expressed genes impacted important biological functions like antigen presentation and processing via Major Histocompatibility Complex type 1 in the lymph nodes (Aboshady et al. 2019). Gene expression profile of goat Peripheral blood mononuclear cell, 6 h post-­ infection of Sungri/96 Peste des petits ruminants virus vaccine strain identified 1926 genes expressed differentially out of which 1310 genes were downregulated and 616 were upregulated. Interferon Regulatory Factor 7/1, Toll-Like Receptor 7/3, Interferon-Induced Protein with Tetratricopeptide Repeats 1/2, Interferon-­ Stimulated Gene 20, Three Prime Repair Exonuclease 1, Interleukin 27 and, Interferon-Induced Transmembrane Protein 3 were associated with antiviral immune response (Manjunath et al. 2019). Skin Transcriptomics Identify Associated Genes with Pashmina Fibre Pashmina, the world’s finest natural fibre, is made from the secondary hair follicles of Changthangi goats domesticated in Jammu and Kashmir’s Ladakh region. Skin transcriptome profiling was used to find metabolic pathways and gene networks involved in Pashmina development. The analysis revealed that 525 genes were significantly upregulated and 54 genes were downregulated two-fold. In Changthangi goats, keratin-associated proteins and keratins were overexpressed. Peptidyl Arginine Deiminase 3, G Protein-Coupled Receptor Class C Group 5 Member D,

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Forkhead Box N1, Homeobox C13, E74-Like Factor 5 and, Lymphoid Enhancer-­ Binding Factor 1 are transcriptional regulators of keratin synthesis in hair follicles showing an elevated level of transcripts in cells. Hair shaft differentiation and hair follicle development in Changthangi goats are facilitated by negative regulation of Oncostatin M signalling and positive regulation of the Wnt signalling pathway. These newly discovered candidate genes aid in marker-assisted breeding to obtain pashmina-producing goats (Ahlawat et al. 2020). Some commodity-related genes are listed with their genomic association in Table 1.2. Comparative Transcriptomics Identify Meat Quality Markers For goat producers, meat quality, muscle components, and carcass weight are important economic traits. Transcriptome profiling of the Longissimus dorsi muscle tissues of five Ziwuling black and five Liaoning cashmere goats with phenotypic variations such as meat quality traits, carcass weight, and muscle components by ribonucleic acids sequencing identified 15,582 and 15,919 genes expressed in muscle tissues of Ziwuling black and Liaoning cashmere goats, respectively. In Ziwuling black goats, 133 genes were downregulated than Liaoning cashmere goats, while 78 genes showed upregulation. Genes expressed differentially were significantly associated with the muscle development and growth and intramuscular lipid and fat deposition and metabolism, Janus kinase/signal transducers and activators of transcription signalling, and hippo signalling pathway. This result provides information about genes regulating goat meat quality and production that further assist in accurate marker-based breeding for high-quality meat production (Shen et al. 2020). Proteomics and Metabolomics Identify Sperm Motility Factors Male fertility is closely associated with sperm motility. Different proteins and metabolites linked with fertility were found by proteomic and metabolomic analysis of seminal plasma. Out of 2098 proteins detected, 449 differentially expressed proteins were found in high and low motility sperm, high motility sperm were abundant in 175 differentially expressed proteins. The main functional roles of these differentially expressed proteins in cellular-metabolic processes, biological process regulation, nitrogen metabolism, etc. In metabolic profiling, 4603 metabolites were detected in seminal plasma, with 1857 metabolites found to be differentially present between the high motility and low motility sperm, while high motility sperm upregulated 999 metabolites. Differential metabolites play a major role in metabolic and synthesis activities. This study is important for understanding mechanisms leading to poor sperm motility (Jia et al. 2021).

1.5.4 Sheep Sheep (Ovis aries) are domesticated animals that belong to the Bovidae family and the Caprinaeare subfamily. They are farmed for their wool, milk, meat, and agricultural applications. A sheep’s wool is harvested by shearing is the most widely used animal fibre for winter wear. Sheep farming is practised in almost every habitat on

33

1  Applications of Omics Technologies in Livestock Production, Improvement… Table 1.2  Genomic associations of commodity and fertility trait of Goat Trait Fibre

Gene LIM Homeobox 2 (LHX2) Fibroblast Growth Factor 9 (FGF9)

Coat colour

Wingless-Type MMTV Integration Site Family Member 2 (WNT2) Oxytocin Receptor (OXTR) Microphthalmia-­ Associated Transcription Factor (MITF) Huntingtin (HTT)

Growth

Breed Mongolia cashmere

References Wang et al. (2016)

Cashmere

Wang et al. (2016)

Mongolia cashmere

Li et al. (2022)

22

Cashmere

Jin et al. (2020) Bhat et al. (2019)

22

Fibre production Colouration of coat

6

Black coat colour determination

13

Black and white colour

Casein Beta (CSN2)

6

Acetyl-CoA Carboxylase Alpha (ACACA) Diacylglycerol O-Acyltransferase 1 (DGAT1) Ribosomal Protein L3 (RPL3) T-Box Transcription Factor 15 (TBX15) Sterol Regulatory Element-Binding Transcription Factor 1 (SREBF1) Growth Hormone (GH) Apolipoprotein L3 (APOL3)

19

Encode milk protein Fatty acid synthesis

Agouti Signaling Protein (ASIP)

Milk

Chromosome Role 11 Development of secondary hair follicles 12 Promotion of hair follicle regeneration 4 Hair follicle initiation

14

5 3 19

19 22

Milk production

Bamu wild, Boer, Australian cashmere Taihang black, European black Taihang black, Saanen, Liaoning cashmere Sudanese and Saanen Saanen

Xinong Saanen and Guanzhong Saanen

Benjelloun et al. (2015)

Zonaed Siddiki et al. (2020)

Rahmatalla et al. (2021) Pardo et al. (2022) Martin et al. (2017)

Milk production Body size Guizhou control small Regulates lipid Moroccan homeostasis

Zhang et al. (2018a) Wang et al. (2016) Benjelloun et al. (2015)

Growth Sirohi and promotion Barbari Lipid transport Leizhou and metabolism

Singh et al. (2015) Zhang et al. (2018a) (continued)

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Table 1.2 (continued) Trait Gene Chromosome Role Prolificacy SMAD Family 24 Maintain Member 2 (SMAD2) normal fertile oogenesis Adenylate Cyclase 1 4 Involved in (ADCY1) oocyte meiotic arrest Bone Morphogenetic Protein Receptor Type 1B (BMPR1B)

6

Ovulation rate and litter size

Breed Laoshan dairy

References Lai et al. (2016)

Guizhou Small, Laoshan dairy Small tail han

Wang et al. (2016)

Wen et al. (2021)

the planet. Genetically engineered sheep are employed as live bioreactors for the manufacture of pharmaceutically important recombinant proteins in the same way that goats are. Winter wear, footwear, blankets, gelatine, surgical sutures, and other sheep-related products have a higher economic value. Genomic Analysis Identifies Wool Production Associated Genes An analysis of genomic estimated breeding values found candidate genes that influence sheep wool yield. For 22 phenotypes relevant to wool quality and production, the genomic estimated breeding values of 5726 Merino and crossbreed Merino sheep were estimated using genomic best linear unbiased prediction and BayesR with real and imputed 510,174 single-nucleotide polymorphisms. The three multi-­ trait studies found 206 putative quantitative trait loci in total, 20 of which were common to all three analyses. Single-nucleotide polymorphisms showed pleiotropic effects on wool properties were found near genes with significant impacts on hair growth, such as Signal Transducer and Activator of Transcription 3,Fibroblast Growth Factor 5, ALX4 (ALX Homeobox 4), and KRT86 (Keratin 86). Detailed phenotypic information helped to identify likely candidate genes. This result help in marker-assisted breeding to obtain high wool-producing sheep (Bolormaa et al. 2017). Genomics Identifies Sheep Growth Candidate Genes Sheep raised for mutton purposes comparatively grow faster than traditional sheep breeds. Genome-wide specific selection identified production traits, Fat Mass and Obesity-Associated, and Apolipoprotein B Receptor related to body mass index. Family with Sequence Similarity 190 Member A, Serine/Threonine Kinase 32B, Aldolase A are linked to marbling in the meat. Solute Carrier Family 8 Member A3 and Cyclin B2 genes have an effect on oocyte development as a reproductive trait. German mutton merino sheep have two major genes, the Growth Hormone Receptor gene that influences meat quality and production, and the Ectodysplasin A Receptor gene that controls thickness. In genome-wide association research, four genes Ribosomal Protein L7, DNA Polymerase, Shisa Family Member 9, and

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Male-­Specific Lethal 1 were linked to pre-weaning gain. This research identifies many novel genes in specific breeds which can be used to direct hybridization breeding (Wang et al. 2015). Gene polymorphism study of Ujumqin sheep of 4  months and 6-month age group, identified 3 single-nucleotide polymorphisms in Thyrotropin-Releasing Hormone Degrading Enzyme (TRHDE) and Myocyte Enhancer Factor 2B genes. 3′untranslated regions have 1 single-nucleotide polymorphisms in MEF2B gene (rs417014745 A>G) and TRHDE gene has 2 single-nucleotide polymorphisms (rs430810656 G>A and rs426980328 T>C). T>C polymorphism is associated with chest girth and body weight at 4-month age. A>G polymorphism is associated with chest girth and body weight 4-month age and chest girth at 6-month age. This analysis revealed reveals the relation between TRHDE and Myocyte Enhancer Factor 2B genes and growth traits that can be used in the future for sheep breeding (Zhang et al. 2016). Some genomic associations of commodity-related traits of sheep are listed in Table 1.3. Transcriptomics and Proteomics Discovered Genes Associated with Wool Growth Microarray analysis of Aohan fine-wool sheep identified 1494 differentially expressed probes. Out of which 892 probes and 602 probes were less and highly expressed respectively, they were linked with the regulation of the multicellular organismal process, receptor binding, macromolecular complex, and protein binding. Proteomic analysis revealed 187 proteins associated with significant differences in expression level. Janus kinase/signal transducers and activators of transcription pathway and phosphoinositide-3-kinase–protein kinase pathway-­ related proteins are expressed most differentially. Identification of these genes and proteins aids in the production of high wool-yielding sheep by accurate marker-­ based breeding (Liu et al. 2014a). Wool growth of Aohan fine-wool sheep differs in different seasons. Thus, finding genes that control the wool growth cycle might improve the yield and quality of fine wool. A microarray study of August and December time points in Aohan fine-wool sheep revealed differentially expressed 2223 transcripts, out of 1061 transcripts were down-regulated and 1162 were up-regulated. Comparison of body side skin in August to December timepoint, genes identified from GeneChip data possibly related to wool growth and follicle development associated with regulation of protein & receptor binding, extracellular space & region. Proteomics revealed 84 protein spots linked to differences in expression levels. Of the 84 proteins found, 21 were downregulated while 63 were upregulated in August/December (Liu et  al. 2014b). In a comparison of Aohan fine-wool sheep and small tail Han sheep, cDNA microarray analysis identified 702 probes showed differential expression in the body side skin of the two sheep breeds the, out of 422 transcripts down-regulated while 280 up-regulated. The total annotated transcripts/genes number was 135, of which 68 were down-regulated while 67 were up-regulated. In Aohan versus Han

D. Maru and A. Kumar

36

sheep, 2 genes, interleukin-8(IL8) and LOC443313 were >10-fold up-regulated, while 5 genes, 1-acylglycerol-3-phosphate O-acyltransferase 1, LOC101104557, intelectin 2, ferritin family member 2, and cytochrome P450 family 1 subfamily A member 1 were 10 folds downregulated (Zhao et al. 2020). Table 1.3  Genomic associations of commodity and fertility trait of sheep Commodities Gene Wool FAT Atypical Cadherin 1 (FAT1) Follistatin (FST)

Fertility

Milk

Chromosome Trait 26 Wool quality 16

Wool quality

Melanocortin 1 Receptor (MC1R)

14

Wool colour

Keratin Associated Protein 6.1 (KAP6.1) Keratin Associated Protein 9-2 (KRTAP9-2) Prolactin Receptor (PRLR) Cyclin B2 (CCNB2)

–

Wool growth

Breed Chinese merino Chinese merino Manchega & Rasa Aragonesa Barki

–

Wool growth

Merino

Sulayman et al. (2017)

16

Reproductive performance Oocyte development

Herdwick

Bowles et al. (2014) Wang et al. (2015)

Bone Morphogenetic Protein 15 (BMP15) Bone Morphogenetic Protein Receptor Type IB (BMPRIB) Pituitary-Specific Positive Transcription Factor 1 (POU1F1) Red Fluorescent Protein 145 (RFP145) Lactalbumin Alpha (LALBA)

X

Ovulation rate

Chinese Mongolian, African white dorper Cambridge Hanrahan & Belclare et al. (2004)

7

Litter size

Han sheep

Chu et al. (2007)

1

Milk production

Sarda

Mura et al. (2012)

–

Milk yield

Italian Altamurana

Moioli et al. (2013)

3

Milk production

Spanish Churra

Growth Hormone 1 (GH1)

11

Milk yield

Serrada Estrela

García-­ Gámez et al. (2012) Vacca et al. (2013)

7

References Ma et al. (2020) Ma et al. (2017) Kijas et al. (2013) Sallam et al. (2021)

(continued)

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Table 1.3 (continued) Commodities Gene Chromosome Trait Growth & Myocyte Enhancer 5 Body weight, body weight Factor 2B (MEF2B) Chest girth Ribosomal Protein 9 Post-weaning L7 (RPL7) gain

Meat

Calpain (CAPN)

12

Methyl-CpG Binding Domain Protein 5 (MBD5) Male-Specific Lethal 1 (MSL1)

2

11

9 Diacylglycerol O-Acyltransferase 1 (DGAT1) Uncoupling Protein 17 1 (UNCP1) Calpastatin (CAST) 5

Myostatin (MSTN)

2

Leptin (LEP)

4

Breed Ujumqin sheep Sunit sheep, Dorper sheep Birth & final Barki weight &Rahmani sheep Post-weaning Sunit sheep, gain Dorper sheep Pre-weaning Chinese gain Mongolian, African white dorper Back fat Lori-­ thickness and Bakhtiari & weight Zel Total lean Romney & loin meat Suffolk Meat Quality Chall Iranian sheep Meat yield New Zealand, Romney Lean meat Zel sheep weight

References Zhang et al. (2016) Zhang et al. (2013) Mahrous et al. (2016) Zhang et al. (2013) Wang et al. (2015)

Mohammadi et al. (2013) Yang et al. (2014) Aali et al. (2017) Hickford et al. (2010) Barzehkar et al. (2009)

Proteomics Identified Fertility Marker Proteins Sperm motility is very important for sheep fertility. Sperm protein differences between low and high motility in sheep are analysed by liquid chromatography-­ mass spectrometry/mass spectrometry and tandem mass tag protein labelling. As a result, 150 proteins found in high-motility sheep sperm were found to be significantly different from those found in low-motility sheep sperm. In high motility sperm, mitochondrial activity, sperm motility, and spermatogenesis involved proteins were found to be abundant while in low fertility sperm, spliceosome, and protein processing associated proteins were abundant. Phosphatidylethanolamine binding protein 4 is a sperm motility related biomarker while heat shock proteins as a low fertility sperm biomarker (Zhu et al. 2020).

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iTRAQ-based quantitative ovarian proteomic analysis identified proteins involved in ovulation rate in sheep by comparing the FecB+FecB+ genotype small-­tail Han sheep, Dorset ewes (Dorset), and FecBBFecBB Han ewes (Han BB). The analysis found many differentially expressed proteins between the Dorset and Han groups total of 212 down-regulated and 124 up-regulated proteins involved in protein translation, Mammalian target of rapamycin pathway, and ribosome assembly. Between the Dorset and Han ++ groups, 198 proteins were down-regulated while 102 proteins were up-regulated. Between Han BB and Han ++ group, 82 proteins were downregulated while 89 proteins were upregulated. Higher levels of protein expressions were related to mitochondrial oxidation functions such as electron carrier activity, cytochrome-c oxidase activity, and oxidoreductase activity, identified between Han BB and Han ++ groups. It could play a role in Han BB sheep showing a higher ovulation rate (Miao et al. 2016). This analysis enhances marker-based screening in sheep breeding to produce high fertile offspring.

1.5.5 Pig The pig is a domesticated animal belonging to the genus Sus and the even-toed ungulate family Suidae. Pigs are habited in most countries. Pigs are raised mainly for food such as pork, bacon, gammon, leather, and hair. Breeding age male and female pigs are known as boar and sow, respectively. Stag is a castrated pig. Pigs are also known as swine. Improving pig health prevents transmission of infectious disease from pig to human and increases pork quality and size. Genomics Identifies Meat Quality Markers Meat quality includes various indicators such as fat and protein level, pH, tenderness, meat colour, and water retention potential as important traits in the swine industry. A genome-wide association study of 181 Duroc pigs found 26 potential single-nucleotide polymorphisms affecting different meat quality traits. The loci identified are located in or near 23 genes. The single-nucleotide polymorphisms associated with meat quality are in or near five genes such as Bone Morphogenetic Protein 6, Ankyrin 1, Phosphatidylinositol-5-Phosphate 4-Kinase Type 2 Alpha, Forkhead Box N2, and Sonic Hedgehog (Lee et al. 2014). In another study, 69 and 11 Single-nucleotide polymorphisms were found to be associated with meat colour and pH respectively in longissimus thoracis muscle, while 29 and 54 single-­ nucleotide polymorphisms were related to meat colour and pH in semimembranosus muscle, respectively. Many candidate genes including Ryanodine Receptor 3, CXXC Finger Protein 5, Myc Target 1, and BCL2/adenovirus E1B 19  kDa Interacting Protein 3 were related to muscle development, anaerobic respiration, and Ca2+ release regulation in the muscle, as promising candidates for high-quality meat trait selection (Wu et  al. 2020). This finding aid in the production of high-­ quality meat-producing pigs by marker-assisted selective breeding.

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Disease Resistance in Pig An infectious disease caused by viruses or bacteria such as Salmonella, Escherichia coli, Haemophilus parasuis, Influenza etc. affects pig production and major economic loss worldwide. Before the breeding program, candidate genes linked with disease resistance are identified using A genome-wide association study or quantitative trait loci or other genomic tools to understand the genetic control of resistance to various infectious agents causing disease in pigs. Making pigs disease-resistant is a very essential task because in the past pork has transmitted swine flu, cryptosporidiosis, rabies and many other diseases to humans. Actinobacillus pleuropneumoniae is one of the major respiratory pathogens that affect the global pig industry. Genomic analysis of A. pleuropneumoniae infected 163 pigs to identify genetic markers associated with resistance to pleuropneumonia performed by genotyping with next-generation sequencing of 58 pigs having the most extreme phenotypes. As a result, each single-nucleotide polymorphisms shows 20% phenotypic variance and combinedly shows 52.8% phenotypic variance. Single-nucleotide polymorphisms were identified in a gene linked to pathomechanism in pleuropneumonia. This analysis revealed the genetic basis of pleuropneumoniae resistance in pigs indicating three candidates SSC12, SSC2 and SSC15 to have a possible role in the production of resistant pigs (Nietfeld et al. 2020). E.coli F18 receptor gene expression is regulated by Alpha-(1,2)-fucosyltransferase is a candidate gene. Polymerase Chain Reaction-Restriction Fragment Length Polymorphism) analysis identified polymorphisms of FUT1 and their effects on resistance to infection in pigs of different breeds. Analysis revealed that the GG genotype of FUT1is susceptible to ECF18  in Chinese pig breeds, while the AA genotype confers resistance against ECF18 in European breeds pigs. Pig with GG and AG phenotype shows a higher risk of infection. AA genotype is favourable in breed selection to produce disease-resistant pigs (Wang et al. 2012a). Transcriptomics Identifies H. Parasuis Infection Markers Transcriptome analysis of porcine alveolar macrophages (PAMs) post 6  days of H. parasuis infection identified 575 transcripts expression levels significantly different from uninfected cells. 428 genes from these transcripts were identified as differentially expressed, 90 genes exhibited down-regulation and 338 up-regulation. In the infected cells group, 575 transcripts expression was significantly increased. These genes were involved in microtubule polymerization, inflammatory and immune response, and signal transduction. Pathways concerned with genes include the Mitogen-activated protein kinase pathway, tall-like receptor signalling, cell adhesion, and cytokine ligand-receptor signalling. Revealed gene expression profiling aid in the screening of possible hosts to reduce the prevalence of H. parasuis in pigs (Wang et al. 2012b). Genomics and Transcriptomics Predict Disease Susceptibility Pigs having mutations in functional genes such as Heg homolog 1, Integrin Subunit Beta, Mucin 4, Fucosyltransferase 1, Beta-1,3-N-Acetylglucosaminyltransferase 5, Mucin 20, and Mucin 13 are more susceptible to E.coli infection (Zhao et al. 2012). In response to Salmonella enterica serovar Typhimurium infection, a total of 2527

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Fig. 1.9  Genome wide association study identifies biomarkers associated with different traits. The graphic illustrates a genomic landscape peppered with significant markers, providing a comprehensive overview of the genetic basis for various characteristics. This study marks a pivotal stride in identifying and understanding the genetic underpinnings of traits, offering valuable insights for personalized medicine and targeted interventions. FUT1 Fucosyltransferase 1, SSC12 Signal Sequence Chromosome 12, SSC15 Signal Sequence Chromosome 15, COL4A4 Collagen Type IV Alpha 4, GPER1 G Protein-Coupled Estrogen Receptor 1, PDX1 Pancreatic and Duodenal Homeobox 1, TEX2 Testis Expressed 2, PLCL2 Phospholipase C-Like 2, RYR1 Ryanodine Receptor 1, PRKAG3 Protein Kinase AMP-Activated Non-Catalytic Subunit Gamma 3, IGF2 Insulin-Like Growth Factor 2, MYH2 Myosin Heavy Chain 2, PLCz Phospholipase C Zeta, COX-2 Cyclooxygenase-2, GDF9 Growth Differentiation Factor 9, ESR1 Estrogen Receptor 1

genes were differentially regulated in pig whole blood. Single-nucleotide polymorphisms prediction by ANEXdb alignments using expressed sequence tag and porcine cDNA database identified and selected 30 mostly non-synonymous Single-nucleotide polymorphisms for genotype analysis of association with phenotypes of faecal shedding of Salmonella or tissue colonization. Single-nucleotide polymorphisms linked with Salmonella shedding, such as phosphogluconate dehydrogenase, neutrophil cytosolic factor 2, and haptoglobin were discovered. These association findings can be useful in the identification and selection of pigs for breeding to obtain salmonella-resistant pigs (Uthe et al. 2011) (Fig. 1.9).

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Transcriptomics Identify Feed Efficiency Associated with Gene Expression To identify pathways and candidate genes linked to feeding efficiency in pigs showing low and high residual feed intake, a duodenum and liver transcriptome study which was analysed by ribonucleic acids sequencing revealed 112 and 55 differentially regulated genes in duodenum and liver tissue, respectively. Co-expression analysis shows 444 and 204 co-expressed genes in the liver and duodenum respectively which were significantly related to feed efficiency in pigs (Ramayo-Caldas et al. 2018). Proteomics Identifies Biomarkers of Classical Swine Fever Virus Infection Classical swine fever virus (CSFV) causes intravascular clotting, platelet decline and immunosuppression which affects the economy to pig industry significantly. Proteomic profiling of CSFV infected pigs identified 17 protein spots with 1.5-fold expression in 2D gel electrophoresis, out of 4 proteins were overexpressed and 6 were under-expressed associated with functions such as angiogenesis, anti-­ inflammatory activity and blood coagulation. Altered protein expression may be linked to the pathogenesis of swine fever and identified as a marker for early diagnosis of CSFV (Sun et al. 2011). Transcriptome Analysis Identifies Swine Flu Infection Markers In Swine flu virus infection, pigs suffer from respiratory and physiological problems. After 3 days of Swine flu infection, 268 genes were differentially expressed which are mainly involved in cell signalling and adhesion, inflammatory and immune response and signal transduction functions. Genes involved in inflammatory and immune responses were highly expressed. In lung cells, a total of 467 and 534 transcripts at recovery and acute phases were identified, respectively. Pathways involved post-infection include Mitogen-activated protein kinase signalling pathway, Toll-like receptor signalling, and Cytokine ligand-cytokine receptor interaction, suggesting that pigs use these pathways to prevent early-stage Swine flu infection. After 7 days of infections differentially expressed genes were linked to development, transport, metabolism, and transcription-related function, while pathways were switched to tissue damage repair, coagulation, complement pathway, anti-inflammatory signalling and Peroxisome proliferator-activated receptors signalling. Analysis of gene expression profile and pathways help to screen potential host agents to decrease swine influenza prevalence (Li et al. 2011). Transcriptomics Identifies High Prolificacy Linked Genes A litter is the live birth of multiple offspring at one time in animals from the same mother thus it is one of the most important economic traits for pig production as it is directly related to production efficiency which is affected by interactions between multiple genes and the environment. Transcriptomics analysis by ribonucleic acids sequencing identified gene expression differences between Yorkshire pig’s ovaries having extremely low and high litter sizes. A total of 1243 genes were expressed differentially out of which 346 were downregulated and 897 were upregulated in the ovary of pigs having high litter size compared to low litter size. Associated 59 genes with hormone regulation in ovaries were involved in steroid hormone synthesis. 11 differentially genes were directly linked to high litter capacity in Yorkshire pigs. This analysis discovers candidate genes for breed selection to produce high prolific pigs (Zhang et al. 2015b).

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Transcriptomics Predict Pig Fertility Transcriptome profiling of pig spermatozoa using micro-arrays shows the presence of 347 up-regulated and 174 down-regulated ribonucleic acids transcripts in high-­ fertility breeding boars. Protein expression analysis using PANTHER (Protein Analysis Through Evolutionary Relationships) revealed an 8× increase of C-C Chemokine Receptor Type 7, a 1.24× increase of C-X-C Chemokine Receptor Type 4 and XC Chemokine Receptor 1, and a 3.4× increase of Interleukin 23 Receptor proteins. In addition, mir-621 and miR-221 were up and downregulated, in low-­ fertility and high fertility pigs, respectively. These markers can be non-invasively used to predict the fertility of pigs (Alvarez-Rodriguez et al. 2020).

1.5.6 Chicken The chicken (Gallus gallus domesticus) is a subspecies of the red junglefowl, belonging to the Phasianidae family originally from South-eastern Asia. It is raised for egg, meat and feathers. A rooster or cock is an adult male bird. Cockerel term used for a younger male. Capon is a castrated male and hen is the adult female. Poultry farming is the form of animal husbandry to raise domesticated birds like chickens. Chicken eggs and meat have very high nutritional value and eggs are also considered a complete food containing a wide range of nutrients. Chicken has a significant contribution to the poultry economy. Genomics Identifies Egg Quality and Yield Markers Genome-wide association study of 1078 hens ranging from 72 to 80 weeks of age identified genomic variations linked to egg quality using 600 K high-density Single-­ nucleotide polymorphisms arrays. Analysis indicated that the genomic region of 8.95 Mb to 9.31 Mb on GGA13 is significantly linked to the haugh unit and the albumen height. Two genes, Dopamine Receptor D1 and Msh Homeobox 2 mapped on the narrow region are promisingly involved in the ovary and embryonic development and linked to egg production. The other three identified genes from three significant loci, Tumor Necrosis Factor Receptor Superfamily Member 4, Stromal Cell-Derived Factor 4, and Ras Homolog Family Member A were associated with eggshell colour. This finding supports improving overall egg quality for breeders (Liu et al. 2018). Genome-wide association analysis for egg number and age of first egg-laying identified 161 candidate Single-nucleotide polymorphisms located on GGA1, GGA2, GGA5, GGA6, GGA9, and GGA24. Thirteen Single-nucleotide polymorphism mapped on GGA6 were linked to the age of first egg-laying and the Prolactin-­ releasing Hormone Receptor gene may play a role in oxytocin secretion regulation in chickens. Sixteen significant Single-nucleotide polymorphisms mapped on GGA1 accounted for a 3.57% variance in phenotype. Genes Pyruvate Dehydrogenase Kinase 3, DNA Polymerase Alpha 1, Apolipoprotein O and Peroxiredoxin 4 genes identified from annotation can be considered as candidates linked with laying eggs

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Fig. 1.10  In this comparative protein estimation figure, the diverse protein profiles of chicken eggs and meat are illuminated across varying conditions, breeds, and seasons. Each column represents a unique combination, showcasing the nuanced protein content influenced by distinct environmental factors and genetic backgrounds. The dynamic interplay of bars and lines conveys the fluctuating protein levels in both eggs and meat, underscoring the intricate relationship between nutrition, breeding, and seasonal variations. This visual exploration provides a comprehensive insight into the multifaceted aspects of protein composition in chicken products, fostering a deeper understanding of their nutritional dynamics

from 37 to 50 weeks. These findings reveal Single-nucleotide polymorphism markers and promising genes associated with egg production and quality in marker-­ assisted breeding selection (Liu et al. 2019) (Fig. 1.10). Transcriptomics and Metabolomics in Meat Quality White Striping and Wooden Breast are abnormalities that are becoming more common in the fillets of chicken hybrids with high breast yield and growth rate. Transcriptome analysis identified 204 differentially expressed genes of which 102 genes were up-regulated and 102 were down-regulated in affected breasts. Genes were involved in polysaccharide metabolic processes, calcium signalling pathways, muscle development, inflammation, and proteoglycans synthesis. This finding might help to produce high-quality chicken meat (Zambonelli et al. 2016). Muscle glycogen storage is an important trait for chicken meat quality which is affected by meat’s ultimate pH.  Muscle metabolome analysis of two chicken lines by high-­ resolution nuclear magnetic resonance identified a total of 20 and 26 different metabolites in serum and muscle respectively, between the ultimate pH- and

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ultimate pH+ lines, which had a 17 per cent variance in muscle glycogen content. Ultimate while in pH+ line muscle catabolism, oxidative stress was considered as a marker. A total of 15 identified confident markers can be used to predict the meat quality of chicken (Beauclercq et al. 2016). Proteomics Analysis Identifies Fertility Associated Proteins Proteome profiling identified 1165 proteins from chicken semen that are mainly involved in energy processes, oxidoreduction mechanisms, protein localization and proteolysis. A comparative proteomic analysis of semen plasma shows that high fertile roosters are associated with overexpression of glutathione S-transferase, gallinacins, and angiotensin-converting enzyme precursor while infertile roosters overexpressed sperm liberated enzyme acrosin and apolipoprotein A1 (Labas et al. 2015). Transcriptomics Identify Feed Efficiency Markers ribonucleic acids sequencing and analysis of 23 breast muscles in broiler chickens having very high and low feed efficiency identified 1059 genes that expressed differentially among them. Gene function analysis showed upregulation of differentially genes linked functions such as inflammatory response muscle remodelling and free radical neutralizing in the high feed efficiency birds. Signalling pathway associated with differentially expressed genes includes growth hormone signalling and IGFs/PI3K/Akt pathway might contribute to the high breast muscle yield in high feed efficiency chickens (Zhou et al. 2015). Proteomics Identifies Protein Expression Post-Bird-Flue Disease Avian influenza infection to chicken significantly affects the poultry industry. Avian Influenza infected chicken cells expressed 208 proteins differently that are associated with transcription regulation, stress response, cellular component, transport, cytoskeleton and metabolic processes. Different cellular functions influenced by these proteins include Protein Folding, Post-Translational Modification, Recombination and Repair, and DNA replication. These findings aid in bird-flue disease understanding and prevention of its prevalence (Li et al. 2017). Genomics Identifies Disease-Resistant Related Genes Marek’s disease is a very contagious oncogenic and pathogenic disease that mostly affects chickens. 600 K genotyping array analysis of 18 chickens identified SRY-­ Box Transcription Factor 1, Salt-Inducible Kinase, Salt-Inducible Kinase 1, Tumor Necrosis Factor Ligand Superfamily Member 13B, and DNA Ligase 4 genes in the region of homozygosity of Marek’s resistant chickens which are related to survival and immunology. Genes involved in anti-apoptosis and cell death identified by population differential analysis including Apoptosis Inhibitor 5, AKT Serine/Threonine Kinase 1, Ubiquitin-Specific Peptidase 15, Craniofacial Development Protein, and Cadherin 13 could be involved in divergent selection during the inbreeding process (Xu et al. 2018). Chicken 60 k high-density single nucleotide polymorphism array identified a total of 39 Single-nucleotide polymorphisms from which 9 significant Single-­ nucleotide polymorphisms on chromosome 16, were significantly associated with

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serum total immunoglobulin Y concentration. Expression of identified candidate genes such as Cluster of Differentiation 1b, Interleukin 4 Induced 1, Zinc Finger Protein 692, Tripartite Motif Containing 27, and Guanine Nucleotide Binding Protein Subunit 2-Like 1, and changes in Interleukin 4 Induced 1, Cluster of Differentiation 1b transcripts was consistent with the immunoglobulin Y concentration, while Zinc Finger Protein 692 and Tripartite Motif Containing 27 have reciprocal changes to the concentration of immunoglobulin Y. 15 Single-nucleotide polymorphisms has a significant correction with avian influenza virus antibody titre and seven of them were located on sex chromosome Z. Seven markers containing 5 different single nucleotide polymorphism identified for lymphocytes and heterophils count and heterophil/lymphocyte ratio. A narrow region of 247 kb on chromosome 16 contains important quantitative trait loci for the concentration of serum total immunoglobulin Y.  These identified candidates can be used as a marker for disease-resistant chicken production (Zhang et al. 2015a).

1.6 Challenges in Omics Technologies Application Various applications of omics technologies are discussed that give precise outcomes; however, it has some challenges too. Omics data obtained from livestock animals includes different analytical techniques and every technique has some machine error rate that leads to inaccurate or false analysis eventually ending up with wrong selection or screening in the breeding program may not give the expected result. Comparative analysis of any omics using public databases which are not accurate or curated may also lead to false results and wrong selection. In the breeding program, omics-based marker-assisted selection uses multiple highly sophisticated instruments and expert personnel that add extra cost to breed selection. All these errors in instruments and databases, compromise the expected outcome in breeding or any other applying area of omics technologies that may affect commercially. Omics based breed selection is a selective mode of breeding that violate the animal right, cause discomfort to animals and impress that animal are only for human commercial purpose and other ethical issues. Selective breeding may pose some ecological or environmental risks which are not understood yet but, in the future, it may cause various complications possibly including loss of species variety, genetic depression, uncontrolled genetic mutations, and changes in the evolution of species. Multi-omics is a powerful technology to understand the interaction between genotype, environment, and life in a concerted way to solve several challenges. Further advancements in integrative analysis of multi-omics data must aim to improve the interoperability of multiple data sets and create a framework that can aid in the seamless analysis of multi-omics data. The advancement of highly accurate analytical techniques and curated biological databases will overcome these challenges in the future.

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1.7 Conclusion The application of omics technologies in different livestock animals we discussed here gives a holistic understanding of different genes, proteins and metabolites and their regulation associated with a particular phenotype, trait, or commodity, in different conditions and different factors. From using various techniques to collect samples, preparing samples, isolating target molecules from the sample and obtaining data using various types of multiple analytical techniques for different types of omics, further using different computational tools that analyse and find candidate markers for a particular trait and how to obtain and produce improved livestock by marker-assisted breeding are discussed in this chapter. Production of livestock based on this analysis produces offspring with characteristics and phenotypes having higher economical value. Omics application requires many resources from different expertise which add extra cost but with advancement and expansion, it will minimize both cost and labour. In advance, it improves, the health and overall wellbeing of animals. Since omics can give desired phenotype traits with marker-assisted selection and genetic engineering, omics-based production and improvement of livestock can solve increasing demand for food and commodities as an increasing population on earth, along with sustaining livestock without adverse effects on nature; thus, including Omics technology in livestock production, improvement, and sustainability is imperative.

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Wu P, Wang K, Zhou J, Chen D, Yang X, Jiang A, Shen L, Zhang S, Xiao W, Jiang Y, Zhu L, Zeng Y, Xu X, Li X, Tang G (2020) Whole-genome sequencing association analysis reveals the genetic architecture of meat quality traits in Chinese Qingyu pigs. Genome 63:503–515. https://doi.org/10.1139/gen-­2019-­0227 Xiang R, MacLeod IM, Bolormaa S, Goddard ME (2017) Genome-wide comparative analyses of correlated and uncorrelated phenotypes identify major pleiotropic variants in dairy cattle. Sci Rep 7:9248. https://doi.org/10.1038/s41598-­017-­09788-­9 Xu L, He Y, Ding Y, Liu GE, Zhang H, Cheng HH, Taylor RL, Song J (2018) Genetic assessment of inbred chicken lines indicates genomic signatures of resistance to Marek’s disease. J Anim Sci Biotechnol 9:65. https://doi.org/10.1186/s40104-­018-­0281-­x Yang YX, Wang JQ, Bu DP, Li SS, Yuan TJ, Zhou LY, Yang JH, Sun P (2012) Comparative proteomics analysis of plasma proteins during the transition period in dairy cows with or without subclinical mastitis after calving. Czech J Anim Sci 57:481–489. https://doi. org/10.17221/6348-­cjas Yang G, Forrest R, Zhou H, Hodge S, Hickford J (2014) Genetic variation in the ovine uncoupling protein 1 gene: association with carcass traits in New Zealand (NZ) Romney sheep, but no association with growth traits in either NZ Romney or NZ Suffolk sheep. J Anim Breed Genet 131:437–444. https://doi.org/10.1111/jbg.12097 Yang Y-L, Zhou R, Li K (2017) Future livestock breeding: precision breeding based on multi-­ omics information and population personalization. J Integr Agric 16:2784–2791. https://doi. org/10.1016/S2095-­3119(17)61780-­5 Zambonelli P, Zappaterra M, Soglia F, Petracci M, Sirri F, Cavani C, Davoli R (2016) Detection of differentially expressed genes in broiler pectoralis major muscle affected by White Striping – Wooden Breast myopathies. Poult Sci 95:2771–2785. https://doi.org/10.3382/ps/pew268 Zampiga M, Flees J, Meluzzi A, Dridi S, Sirri F (2018) Application of omics technologies for a deeper insight into quali-quantitative production traits in broiler chickens: a review. J Anim Sci Biotechnol 91(9):1–18. https://doi.org/10.1186/S40104-­018-­0278-­5 Zandkarimi F, Vanegas J, Fern X, Maier CS, Bobe G (2018) Metabotypes with elevated protein and lipid catabolism and inflammation precede clinical mastitis in prepartal transition dairy cows. J Dairy Sci 101:5531–5548. https://doi.org/10.3168/jds.2017-­13977 Zhang L, Liu J, Zhao F, Ren H, Xu L, Lu J, Zhang S, Zhang X, Wei C, Lu G, Zheng Y, Du L (2013) Genome-wide association studies for growth and meat production traits in sheep. PLoS One 8. https://doi.org/10.1371/journal.pone.0066569 Zhang L, Li P, Liu R, Zheng M, Sun Y, Wu D, Hu Y, Wen J, Zhao G (2015a) The identification of loci for immune traits in chickens using a genome-wide association study. PLoS One 10:e0117269. https://doi.org/10.1371/journal.pone.0117269 Zhang X, Huang L, Wu T, Feng Y, Ding Y, Ye P, Yin Z (2015b) Transcriptomic analysis of ovaries from pigs with high and low litter size. PLoS One 10:e0139514. https://doi.org/10.1371/ journal.pone.0139514 Zhang L, Ma X, Xuan J, Wang H, Yuan Z, Wu M, Liu R, Zhu C, Wei C, Zhao F, Du L (2016) Identification of MEF2B and TRHDE gene polymorphisms related to growth traits in a new Ujumqin sheep population. PLoS One 11:e0159504. https://doi.org/10.1371/journal. pone.0159504 Zhang G, Deng Q, Mandal R, Wishart DS, Ametaj BN (2017) 340 metabolomics-based profiling identifies serum signatures that predict the risk of metritis in transition dairy cows. J Anim Sci 95:168–168. https://doi.org/10.2527/asasann.2017.340 Zhang B, Chang L, Lan X, Asif N, Guan F, Fu D, Li B, Yan C, Zhang H, Zhang X, Huang Y, Chen H, Yu J, Li S (2018a) Genome-wide definition of selective sweeps reveals molecular evidence of trait-driven domestication among elite goat (Capra species) breeds for the production of dairy, cashmere, and meat. Gigascience 7:1–11. https://doi.org/10.1093/gigascience/giy105 Zhang H, Jiang H, Fan Y, Chen Z, Li M, Mao Y, Karrow NA, Loor JJ, Moore S, Yang Z (2018b) Transcriptomics and iTRAQ-proteomics analyses of bovine mammary tissue with Streptococcus agalactiae-induced mastitis. J Agric Food Chem 66:11188–11196. https://doi. org/10.1021/acs.jafc.8b02386

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Chapter 2

System Biology Research to Advance the Understanding of Canine Cancer Sonia Batan, Harpreet Kaur, Swasti Rawal, Deepti Mittal, Parul Singh, Gurjeet Kaur, and Syed Azmal Ali

Abstract  Dogs are easy to train and have far superior olfactory abilities than humans, making them suitable for a wide range of tasks. Therapy dogs are used in psychiatric hospitals to improve the socialization and interaction of disabled patients, thereby improving their quality of life. Unfortunately, dogs have been becoming more and more susceptible to diseases, the most serious of which is cancer, in recent years. Every year, over one million new cases are reported in the United States, with an estimated mortality rate of around 30%. It is our responsibility to save the life of an animal in this situation. Canine cancers, predictably, bear an uncanny resemblance to human cancers. While dog domestication is comparable to human domestication, it opens the door to studying human cancers through the lens of dog cancer. Early detection of cancer may be advantageous for therapeutic reasons, which necessitates the development of markers that can be used as diagnostic tools due to their differential expression between disease and normal state. Pets with unexpected cancers serve as a good model for human oncology translational research. There are numerous system biology techniques that can be used to detect cancer and the biomarkers that are associated with it in its early stages. Microarray, whole-genome sequencing, whole-exome sequencing, and RNA sequencing are

S. Batan · P. Singh · S. A. Ali (*) Cell Biology and Proteomics Lab, Animal Biotechnology Center, ICAR-NDRI, Karnal, Haryana, India H. Kaur · S. Rawal · D. Mittal Division of Biochemistry, ICAR-NDRI, Karnal, Haryana, India G. Kaur Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia Mark Wainwright Analytical Centre, Bioanalytical Mass Spectrometry Facility, University of New South Wales, Sydney, NSW, Australia Steno Diabetes Center Copenhagen, Herlev, Denmark © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 V. Kumar Yata et al. (eds.), Sustainable Agriculture Reviews, Sustainable Agriculture Reviews 62, https://doi.org/10.1007/978-3-031-54372-2_2

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novel, well-organized techniques that can be applied to a wide range of applications. They will allow for high-throughput analysis of variations in DNA, mRNA, protein expression, and activity in a patient’s tumor. Therefore, in this chapter, we reviewed recent system biology applications that have been evolved to better understand life-threatening diseases such as tumors. We also described several different types of canine cancer. Biomarker identification using system biology approaches, and therapeutic intervention for canine cancer biomarkers, are presented. Additionally, we also highlight the various ways in which dogs can serve as models for cancer research. Keywords  Canine cancer · Transcriptomics · Proteomics · System biology · Oncology

Abbreviations 2D-DIGE CRISPR GWAS MALDI-TOF-MS

Two-dimensional difference gel electrophoresis Clustered Regularly Interspaced Short Palindromic Repeat Genome-wide association study Matrix assisted laser desorption ionization-time of flight mass spectrometry

2.1 Introduction Pets are integral parts of our everyday lives, providing a strong bond with their owners while also relieving stress and providing company. Pets contribute to a variety of social activities. Dogs are readily trained and possess much better olfactory skills than humans, making them suitable for various jobs. Therapy dogs are used in care facilities to increase the socialization and interaction of their disabled patients, thereby improving their quality of life. They must be safeguarded against disease because of their overall importance in human life. Nowadays, dogs are increasingly prone to diseases, particularly cancer. Cancer is the leading cause of death in dogs; the United States alone reports over one million new cases each year, with an estimated mortality rate of approximately 30% (Fleming et  al. 2011; Paoloni and Khanna 2008). This is consistent with spontaneous tumor development that is quite similar to human tumors. Patients with histotype two often develop bladder cancer, different forms of brain cancer, and various tumor forms (Paoloni and Khanna 2008; LeBlanc et al. 2016). 90% of the approximately 21,000 human genes identified in the Sanger Institute’s cancer gene census are changed somatically, 20% are changed in the germline, and 10% are changed in both. However, substantial problems with identifying human germline variation associated with cancer risk remain, rendering the 20% and 10% estimates unofficial. This is why dog models of cancer genetics

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must be (1) comparative oncogenic, (2) experimental, and (3) translational. Instruments and bioinformatics pipelines are required for the prediction and assessment of the tolerability, pharmacology, pharmacodynamic effects, and potential efficacy of different anticancer therapies (Thamm et al. 2012). Naturally, canine cancers have a remarkable similarity to human malignancies. Human owners provide for their medical requirements, these canines may benefit from preclinical research (Vail and MacEwen 2000). The inclusion of cancer-­ stricken dogs in a comparative and integrated translational drug development pathway has been discovered as a possible approach to significantly speeding up the identification of cancer medications (Gordon et al. 2009; Khanna et al. 2009). While dog domestication is comparable to human domestication, this opens the door to the potential of using dog cancer as a model to study human cancers. When it comes to companion dogs, they have received sufficient vaccinations throughout their lives to boost their innate immunity and serve as superior models for cancer immunotherapies. Additionally, the canine immune system is “trained,” resulting in immunity against malignancies. Given that dogs are continuously exposed to various dietary allergens and other environmental contaminants, it’s unsurprising that they respond more like people than small animals to immunotherapeutic medications. Unconscious tumor growth implies that it may take weeks or months for the immune system to recognize and respond to a tumor in dogs before it gets large enough to be identified. This process takes years to unravel since many factors influence it. Therefore, early detection of cancer may be beneficial for therapeutic reasons, which requires markers that may be utilized as diagnostic tools due to their differential expression between disease and normal condition. Pets with unexpected cancers provide a reasonable model for translational study in human oncology (McCaw et al. 2007). Due to the accumulation of genetic and epigenetic processes over time, malignant transformation of cells into cancer progresses, and early detection of these malignant changes in cells may enhance cancer patients’ prognoses. Ultrasound, magnetic resonance imaging, magnetic resonance spectroscopy, mammography, digital mammography, positron emission tomography, and computed tomography are all techniques for cancer screening. Other diagnostic methods such as immunohistochemistry, polymerase chain reaction, real-time polymerase chain reaction, in situ hybridization, flow cytometry, microarray, and RNA seq (transcriptomics, proteomics) are also extensively employed today. Microarray, whole-­ genome sequencing, whole-exome sequencing, and RNA sequencing are novel, well-organized techniques that apply to a wide variety of applications. They will enable high-throughput and priceless insights into the differences between DNA, mRNA, protein expression, and activity in an individual’s tumor. With coverage of >99%, high-quality sequencing has clarified the dog genome. In terms of biological significance, the dog’s genome is more comparable to that of humans than rodents, although mice and humans have a common ancestry. Additionally, dogs and humans have 800 Mb of conserved DNA sequence that mice lost throughout evolution. Furthermore, since mice’s protein-coding DNA sequences diverged quicker than dogs and humans, their protein sequences are more

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comparable to one another than to mice’s (Lindblad-Toh et al. 2005). Another significant drawback of using mice as a model for basic cancer research is the availability of highly inbred lines and a genetic diversity deficiency that is more apparent in humans (von Scheidt et  al. 2017). Generally, mouse strains used in cancer research are chosen based on the penetrance and other characteristics of cancer models. However, it is unknown if a particular strain is typical of the wild mouse population or if such results apply to human cancer (Cheon and Orsulic 2011). The number of lineage-specific non-synonymous substitutions (i.e., amino acid altering) found during the study of the 13,816 protein-coding genes with 1:1:1 orthology in humans, mouse, and dogs is 0.017, 0.038, and 0.021, respectively (Lindblad-Toh et al. 2005).

2.2 Use of Omics Technologies for Classification of Cancer Types Cancer is the primary cause of death in dogs over the age of 10, but half of all the cancer types are curable if diagnosed early. It is very similar to the human cancer characteristic such as a lump or a bump, a wound that doesn’t heal, any swelling, enlarged lymph nodes, a lameness or swelling in the bone, abnormal bleeding. These are all considered classical signs, but sometimes there are no signs or difficulties to be observed. Here we are describing some of the different types of cancers that usually affect dogs and illustrated in Fig. 2.1.

2.2.1 Anal Sac Adenocarcinoma The anal sac adenocarcinoma affects the scent glands next to the anus of a dog. This gland secretes a trace amount of odiferous substances which mark the territorial behavior of a dog. It is most often seen in 5- to 12-year-old dog breeds and consists of apocrine gland epithelium. In dogs and cats, castration is linked with an increased incidence of malignant tumors originating from the anal sac apocrine secretory epithelium (Nakanishi et al. 2004). It is the most common kind of malignant perineal tumor, affecting dogs between 7 and 12  years (Ogawa et  al. 2011). Difficulty in defecating and feces from a sick dog are both indications of this kind of cancer. Apocrine adenocarcinoma has both adenocarcinoma and epithelial cells in masses around the dog’s anal sac. The differential cytological pattern is utilized to identify the disease (Javanbakht et al. 2013) quickly. Several previous books have attempted to explain the gender divide, while others have condemned it (Goldschmidt and Hendrick 2002). However, these tumors often spread to regional lymph nodes and sometimes to other abdominal organs, including the lungs. The majority of feline anal sac gland cancers penetrate capsular fibrous tissue and perirectal soft tissue,

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Fig. 2.1  A comprehensive overview of canine oncology and the associated diagnostic tools. The diagram illustrates various types of cancers in dogs, pinpointing their general locations on the canine body. Adjacent to the depiction of the dog are state-of-the-art diagnostic equipment and techniques used in contemporary cancer research, ranging from imaging modalities like Magnetic Resonance Imaging to molecular methods such as RNA/DNA sequencing and Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)/Cas9 technology

seldom invading the rectal wall or peritumoral lymphatics. Multiple therapy modalities may be necessary to achieve long-term illness management.

2.2.2 Bladder Cancer Bladder cancer is most common in canines than feline. Different types of tumors can cause bladder cancer in dogs. The most common, transitional cell carcinoma, develops in the deeper layers and muscles of the bladder wall. As the tumor grows, it invades the urethra and obstructs urine flow, causing difficulty in urinating or cause total blockage. The tumors of the bladder and urethra have serious consequences but have been reported infrequently and account for 25 kb) (Liu 2013). 3.3.2.3 Artificial Chromosomes In the past few years transgenesis research has found out that due to genome complexity in mammals’ large genomic sequences are required to design the transgenic construct to recapitulate endogenous gene expression patterns. Knowing only the coding sequence remains insufficient for the spatial or temporal expression of the transgene rather pieces of evidence suggest that a set of proximal and long-distance regulatory elements are required to have appropriate expression (Recillas-Targa 2006; Liu 2013). Therefore, to clone the transgenic construct with large genomic sequences, the artificial chromosomes have proved a suitable alternative as they not only help recapitulate endogenous gene expression patterns but also make easy manipulating extensive genomic sequence taking advantage of homologous recombination (Recillas-Targa 2006). The artificial chromosomes vectors commonly used for cloning large transgene constructs are the yeast artificial chromosomes and the bacterial artificial chromosomes or P1 artificial chromosome (Recillas-Targa 2006; Liu 2013). With these artificial chromosome vectors, genomic inserts ranging from 100 kb to more than 1 Mb size can be cloned (Recillas-Targa 2006). Insertion of such large genomic sequences includes all the regulatory sequences needed for the appropriate gene expression. Incorporation of all the regulatory DNA elements contributes to ensuring position-independent and copy number-­dependent optimal levels of transgene expression (Giraldo and Montoliu 2001; Recillas-Targa 2006; Liu 2013). Another advantage of using artificial chromosomes for transgene cloning is the unlimited capacity to generate a large number of modifications that can be introduced to such vectors, which includes target disruption of specific sequences, inversion, or even insertions (Recillas-Targa 2006; Liu 2013). Bacterial Artificial Chromosomes  Bacterial artificial chromosome vectors are engineered plasmid DNA molecules that can clone 300  kb (approx) of DNA sequences in Escherichia coli (Recillas-Targa 2006). A series of overlapping bacterial artificial chromosomes clones covering the entire human and mouse genomes were produced as a by-product of the genome sequencing projects. So, for a majority of mouse and human genes, bacterial artificial chromosomes clones are available from repositories such as the bacterial artificial chromosomes/P1 artificial chromosome Resource Center at Oakland Children’s Hospital (http://bacpac.chori.org/)

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(Liu 2013). In the context of transgenic research, these sequenced bacterial artificial chromosomes clones are of great help as they can be directly used for generating transgenic mouse lines. Usually, the bacterial artificial chromosomes clones are 100–300  kb long and contain all the genomic regulatory elements required to ­simulate endogenous gene expression and further, as they are large pieces of DNA can better shield the transgene from position effect (Giraldo and Montoliu 2001; Recillas-Targa 2006; Liu 2013). If overlapping bacterial artificial chromosomes clones containing the same gene are available, it is suggested to choose the clone that contains the entire transcribed region of the gene and also as much 5′ and 3′ flanking regions as possible (Liu 2013). The bacterial artificial chromosomes vector sequence need not be removed from the transgene for microinjection, as the influence of the prokaryote-derived vector sequence is often minimized by the large eukaryotic DNA fragment in it. Moreover, releasing and separating the large bacterial artificial chromosomes insert from its cloning vector can be technically tricky (Liu 2013). During preparation, storage, and microinjection of bacterial artificial chromosomes or other large DNA molecules, shearing remains a potential problem. Therefore, it is recommended to store bacterial artificial chromosomes DNA in or microinjection buffer supplied with salt and polyamines to make the long DNA strands more compact and hence less prone to shearing (Liu 2013). Wide-bore pipette tips need to be used for transferring bacterial artificial chromosomes DNA solutions. Besides bacterial artificial chromosomes, modified bacteriophage P1 cloning system, P1 artificial chromosome vectors (P1 artificial chromosome) have also been successfully used to generate transgenic mice that can accept DNA inserts of 100–250  kb size (Liu 2013). Bacterial artificial chromosomes and P1 artificial chromosomes, compared to yeast artificial chromosomes are much more stable and have been used more conveniently for the generation of transgenic cells and animals (Recillas-Targa 2006). But similar to Yeast artificial chromosomess, rearrangements, deletions, and insertions can also occur in bacterial artificial chromosomes and P1 artificial chromosomes, for which a detailed and careful analysis of the integrity of the inserted sequences is needed (Recillas-Targa 2006). For cloning of relatively small transgene construct (