Feedomics: Promises for food security with sustainable food animal production

Accepted Manuscript Feedomics: promises for food security with sustainable food animal production Hui-Zeng Sun, Le Luo Guan PII:

S0165-9936(18)30162-6

DOI:

10.1016/j.trac.2018.07.025

Reference:

TRAC 15210

To appear in:

Trends in Analytical Chemistry

Received Date: 16 April 2018 Revised Date:

26 June 2018

Accepted Date: 31 July 2018

Please cite this article as: H.-Z. Sun, L.L. Guan, Feedomics: promises for food security with sustainable food animal production, Trends in Analytical Chemistry (2018), doi: 10.1016/j.trac.2018.07.025. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Human4inedible4feed Alternative4feed Regular feed

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Feedomics

Function Metabolomics

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Proteomics Transcriptomics

Safety

Epigenomics Genomics

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Quantity

Metatranscriptomics

Metagenomics

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Symbiotic4 microbes

Animal4health

Quality

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MANUSCRIPT Feedomics: promises for foodACCEPTED security with sustainable food animal production

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Hui-Zeng Sun, Le Luo Guan*

3 Department of Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, AB,

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Canada, T6G 2P5

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*Corresponding author: [email protected]. Tel: 17804922480.

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Abstract

The production of adequate and nutritious animal proteins for the increasing human population

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is an urgent global task. Therefore, enhancing the efficiency and sustainability of food animal

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production requires advanced analytical techniques. We propose the concept of “feedomics”

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for food animal research, an emerging field using omics technologies, to understand and

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uncover the mechanisms involved in many biological processes that determine animal

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productivity, product quality, and health as a result of the interactions among feed,

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environment, animal genetics, physiology, and its symbiotic microbiota. In this review, we

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summarize the findings to date based on the omics approaches including (meta)genomics,

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epigenomics, (meta)transcriptomics, proteomics, and metabolomics in food animal species and

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consider how these can be used to understand the processes from the “gate” to “plate”. We also

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highlight future directions for applying feedomics in fundamental and practical studies to

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improve the quantity, quality, safety and functional properties of food animal products.

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Keywords

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Animal production and health; Feed science; Genomics; Epigenomics; Proteomics;

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Metabolomics; (Meta)genomics; (Meta)transcriptomics; microRNAs; Feedomics.

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Abbreviations

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DE, differential expressed; ESI, electrospray ionization; GC-TOF/MS, gas

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chromatography-time of flight/mass spectrometry; GIT, gastrointestinal tract; GC-Q-MS, gas

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chromatography-quadrupole-mass spectrometry; GWAS, genome-wide association study;

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HPLC, high-performance liquid chromatography; ICAT, isotope-coded affinity tag; LC-MS,

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liquid chromatography–mass spectrometry; MALDI-TOF/MS, matrix-assisted laser

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desorption ionization-time of flight/mass spectrometry; MeDIP-Seq, methylated DNA

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immunoprecipitation-sequencing; MiRNA, microRNA; NGS, next-generation sequencing;

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NMR, nuclear magnetic resonance; RFI, residual feed intake; RNA-Seq, RNA-Sequencing;

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MANUSCRIPT SILAC, stable isotope labeling ACCEPTED with amino acids in cell culture; SNP, single nucleotide

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polymorphisms; UPLC-Q-TOF-MS/MS, ultra-performance liquid

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chromatography-quadrupole-time of flight-mass spectrometry/mass spectrometry;

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UHPLC-QQQ-MS, ultra-high-performance liquid chromatography-triple quadrupole-mass

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spectrometry.

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1. Introduction

By 2050, the world human population is projected to reach 9.4 billion [1]. Animal protein (milk, egg, fish and meat) is an integral component of the human diet globally, and it has

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contributed to the development and elimination of poverty in human society. To meet the

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demand of the increasing human population, the global food animal production is expected to

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increase 2.3% annually to 2050 [2].

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Currently, food animal production consumes more than 32% of global human edible cereal

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grains (e.g., corns, barley) and occupies approximately 40% of all arable land [3]. Over the past

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several decades, many technologies have been applied to improve food animal production

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efficiency. First, the modern commercial food animal sector has adapted to a highly intensive

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management system with using the high proportion of cereal grains in the diets. Second, since

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the 1960s, as one such “innovation”, antimicrobial growth promotants have been widely applied

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to improve the production efficiency of food animals. Third, recent advanced research in

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molecular genetics has shown some promise for breeding more efficient animals [4]. However,

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the high grain-fed animals are more susceptible to metabolic dysfunction, disorders, and

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diseases [5], which directly affect the animal’s performance and welfare. In addition, food

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animal production has been considered to have a negative environmental impact due to the

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emission of greenhouse gases (methane, CO2) and the release of nitrous compounds (NO2,

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ammonia) and phosphate [6]. Moreover, the recent attention on reducing the antimicrobial

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growth promotants in food animal production has driven the need to seek alternative feeding

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strategies to maintain sustainable production [7]. Therefore, improving the sustainability of food

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animal production is urgent and vital to promote animals’ welfare, to prevent further expansion of

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occupied arable land, and to reduce the negative environmental impact.

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Even with the rapid development of feed science, vast hard-to-decipher biological

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mechanisms still limit the regulatory strategies of sustainable food animal production. The

“Foodomics” was first proposedACCEPTED as a disciplineMANUSCRIPT for studying the food and nutrition domains

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through the application and integration of advanced omics technologies to improve human

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well-being, health, and confidence by Cifuentes in 2009 [8]. Although the word feedomics can be

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found in the titles of a PhD thesis [9] and a project (https://cphpig.ku.dk/english/news/2015/haja/),

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a systematic definition and detailed introduction are still lacking. Similar to “foodomics”, we

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propose the term “feedomics” for food animal research, which devises a systematic omics

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approach to understand the process from “gate” to “plate” for animal productivity and health as

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well as the product nutritional value for the human. In this review, we review the main omics

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approaches recently used in different food animal species and their potential roles in helping

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achieve sustainable developed food animal production.

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2. The definition and analytical technologies of feedomics

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2.1 Definition and concept of feedomics

The recent advanced analytical technologies, especially with high-throughput

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characteristics, unbiased, and untargeted “omics”, have created the new era of feed science and

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animal nutrition/health and made it possible to identify complex findings at a system biological

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level. Here, we define feedomics as an emerging research field that studies feed science, animal

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nutrition and animal production/health in a global molecular and systematic scale by using high

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sensitivity and resolution omics technologies. These omics methodologies include genomics,

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epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, and

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metatranscriptomics. Recent efforts have been made to apply omics in food animal field (focused

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mainly on cattle, sheep, swine, poultry, and fish) to address research questions and industrial

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problems, but most published studies focus on only a single “omic” approach and lack a linkage

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between the feed input and the end products. Therefore, we propose a general workflow of

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feedomics that includes defining scientific questions, determining experimental design,

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MANUSCRIPT sampling collection, molecularACCEPTED (DNA, RNA, proteins, or metabolites) extraction, identification

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using high-throughput platforms, data analysis, mechanism interpretation, and validation.

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These steps contribute to a series of systematic processes and integrate the knowledge of feed

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science, animal nutrition, animal genetics and genomics, animal physiology and microbiology,

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plant science and biochemistry, analytical chemistry, bioinformatics, engineering, and so on, which push forward the development and inter-application of related disciplines.

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2.2 The omics technologies applied in feedomics

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In feedomics, genomics is the study of all genetic information (the genome) about a food animal organism mainly through genotyping and/or whole genome sequencing [10]. In

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transcriptomics, the expression patterns of all genes in the specific cell and/or tissue type under

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certain nutritional, physiological and environmental conditions are studied [11]. Although

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genomics and transcriptomics provide the functional potential and the activity of the functional

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coding genes, only ~3% of the genome encodes proteins, with the rest of the genome being

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non-coding regions, and up to 80% of the genome including non-coding regions is transcribed

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[12]. The non-coding RNAs are likely to contribute more in functional transcriptomes, which has

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led to an increasing emphasis on studying the food animal non-coding small RNAs (especially

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microRNAomes) [13]. Chemical changes on the genome including chromatin remodeling,

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histone protein modification and DNA methylation have been considered to alter the phenotypic

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outcomes in food animals, which may be passed on to the offspring and can be investigated by

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epigenomics [14]. Proteomics refers to all of the proteins derived from coding-gene expression

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and translation and post-translational modifications, which serve as the main components of the

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proteome of food animal [15]. The large set of downstream metabolites represents the final

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products of the genome, transcriptome, and proteome and reflects the external phenotype most

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directly, which can be qualitatively and quantitatively analyzed using untargeted and targeted

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metabolomics in feedomics [16]. The study of microbiomics provides information on all the

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MANUSCRIPT microbes of a given microbiotaACCEPTED community (referring mainly to epithelia and digesta of the

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gastrointestinal tract (GIT) in food animals) in response to feeding intake [17]. Metagenomics

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and metatranscriptomics are the main feedomics approaches that are currently used to explore the

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genes and transcripts of the microbiome, and they provide a comprehensive understanding of the

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microorganisms’ composition, function, and undergoing activities [18]. The powerful force and holistic perspectives of feedomics are mainly attributed to the

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advanced omics analytical systems, which can potentially capture all molecules at different

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biological levels and generate their corresponding big datasets (which can be defined as the

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feedome). The high-throughput next-generation sequencing (NGS) techniques are widely

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applied to generate feedome datasets. To be specific, the untargeted whole genome, epigenome,

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transcriptome, microbial metagenome and metatranscriptome datasets can be generated using

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NGS platforms such as Illumina NextSeq, MiSeq, HiSeq 2000, HiSeq 3000, HiSeq 4000 with

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different depths [19], and even third-generation sequencing methods such as 3-D genomic

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Hi-C [20]. Further, proteomes and metabolomes can be identified by spectrometry- and

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chromatography-based technologies, including liquid chromatography-mass spectrometry

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(LC-MS) and gas chromatography-time of flight/MS (GC-TOF/MS) [21].

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Proteomics and metabolomics are more relied-upon analytical techniques compared with

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other omics methods [22]. Proteomics is of great importance to assess food animal productivity

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(especially for meat and milk safety and quality), health, and welfare [23]. MS-based

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proteomic techniques usually consist of an ion source (nebulize the peptides into ionized

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droplets), a mass analyzer (measures the mass-to-charge ratio (m/z) of the ionized analyte) and

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a detector (records the ion numbers of each m/z value) [24]. Electrospray ionization (ESI) and

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matrix-assisted laser desorption/ionization (MALDI) are widely used in MS-based proteomics

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to produce ions; the former is normally combined with advanced separation tools such as GC

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ACCEPTED MANUSCRIPT and high-performance liquid chromatography (HPLC) to analyze complex peptides, and the

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latter is commonly harnessed to analyze relatively simple samples [25].

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With the rapid advances in analytical chemistry techniques and data analysis methods, metabolomics has become more accessible to broad research areas in food animal-related

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disciplines, such as animal health and disease, animal nutrition, human health and nutrition,

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and feed analysis [26]. MS-based analytical technologies are most commonly applied in

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metabolite identification because of the high selectivity, sensitivity and structural information

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of the detected metabolites [27]. TOF or quadrupole (Q) is usually combined with MS as a

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mass analyzer to strengthen the accuracy and resolution of mass measurement [28]. Prior to

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MS detection, a GC or LC separation tool is required based on the chemical properties of the

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metabolites in the sample. Typically, LC is more suitable for detecting non-volatile metabolites

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such as hydroxy-carboxylic acids, and GC is easier to capture volatile metabolites such as

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xylene [29]. Meanwhile, derivatization can be utilized to make the polar compounds volatile

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enough for GC detection. Untargeted metabolomics usually use one or several of various

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high-throughput technologies, especially mass spectrometry-based platforms (GC-MS,

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GC-TOF/MS, GC×GC-TOF/MS, GC-Q-MS, LC-MS, ultra-performance liquid

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chromatography (UPLC)-Q-TOF-MS/MS) to get a full scan of all metabolites (what

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metabolites are present and the relative abundance of these metabolites) that play an important

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role in capturing unknown or novel metabolites, subtle pathways and key mechanisms.

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Furthermore, the metabolites of interest can be examined using targeted metabolomics (the real

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concentration), with the commonly used techniques including GC-TOF/MS, GC-Q-MS,

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UPLC-Q-TOF-MS/MS, and UHPLC-QQQ-MS. For metabolomics studies, it is important to

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determine which samples should be used for analysis due to the different characteristic of

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samples, various compound extraction methods, high costs and lack of databases.

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These approaches vary in ACCEPTED their resolution,MANUSCRIPT sensitivity, accuracy, coverage, stability, run

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time, sample loading amount, cost per sample and so on, so it is important to choose suitable

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techniques based on different research objectives and comparable information including the

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applications, advantages, and limitations of major analytical technologies applied in feedomics

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(Table 1).

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3. The applications of different feedomics technologies for sustainable food animal

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production

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3.1 Genomics and nutrigenetics in food animal production efficiency and quality

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Genotyping aims to identify trait-associated genetic variations (such as a single nucleotide polymorphism (SNP), a variation in a single nucleotide that occurs at a specific

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position on the genome) that can be potentially be used for genomic breeding and/or targeted

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section [30]. High coverage SNP datasets through the whole genome not only help detect low

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abundance SNPs but also enable the efficient screening and tracking of genetic markers that could

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transfer from parents to offspring [31]. Released whole genome sequences of major food animal

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species such as cattle [32], chicken [33], sheep [34], pig [35] and a number of aquatic animals [36]

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have promoted the application of high-throughput genetic SNP markers and accelerated genetic

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improvement for food animal production and efficiency. For example, a genome-wide association

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study (GWAS) was successfully used to identify genetic variants, regions, and candidate genes

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associated with feed efficiency. In total, 25, 66, and 12 SNPs have been found to be associated

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with residual feed intake (RFI, one of the measures for feed efficiency) in hybrid bulls [37],

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Angus steers [38] and Yorkshire pigs [39] using the BeadChip measurement. The genetic

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variation identified by BeadChips is further identified which candidate genes are associated with

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animal feed efficiency for Nelore cattle [40], hybrid bulls [37], pig [41], and chicken [42] (all

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genes and their functions are summarized in Table 2). Similarly, Sanchez reported the 22 most

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ACCEPTED plausible candidate genes associated with milk MANUSCRIPT protein composition in dairy cattle using GWAS

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[43] (Table 2). Thus, whole genome- and genotyping-based GWAS studies will allow researchers

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to discover gene markers that can be exploited in future breeding programs to achieve high feed

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efficiency and high-quality products farm animals.

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Although genetic information can indicate the performance potential of the animals, nutrients act as an important intermediate to affect the biological and metabolic processes that

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determine the overall performance [44]. It is known that diet can alter the expression of genes,

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signaling, and metabolic pathways in the cells, which subsequently changes the physiological

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status and finally leads to different phenotypes [45]. Additionally, different genetics may drive

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cellular responses and host metabolism to the nutrients, which has been defined as “nutrigenetics”

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in human-related studies. Nutrigenetics is used to reveal the relationship between diet and the

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animal genome and aims to understand whether and how the individual genetic components drive

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the responses to dietary variation [46]. The concept of identifying the genetic markers related to

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dietary responses has been applied in swine production. Using the widely studied G

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protein-coupled receptor (GPR120) gene as an example, this gene encodes the omega-3 fatty acid

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receptor which is involved in various physiological homeostasis processes such as fat deposition,

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regulation of appetite and food preference [47]. The variants in GPR120 have been significantly

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associated with fat deposition and growth in pigs [48], and a recent study has identified 3 SNPs

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(g.114743623G>C; g.114764865G>A; and g.114765469C>T) among different breeds, including

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Italian Large White, Italian Duroc, Italian Landrace, Casertana, Pietrain, Meishan, and wild

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boars, which are associated with average daily gain [49]. Ichimura et al. demonstrated and

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verified that GPR120 had a key role in sensing dietary fat and controlling energy balance in both

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humans and rodents [50], suggesting that different breeds of pigs may have different ways to

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exact energy from diet for growth. However, no study has been performed on how the varied

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GPR120 genotypes could lead to altered dietary responses within the same breed and whether it

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MANUSCRIPT relates to performance. To date,ACCEPTED the linkage of “genetics” and “nutrition” is scarce in food animal

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production. Genome-wide nutrigenetics could be a powerful tool for defining appropriate feeding

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practices to raise more sustainable food animals based on an animal’s genetic potentials. The

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variability of marker genes (such as GPR120 gene) should be considered by using nutrigenetics

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approaches to define the appropriate feeding practices, to obtain more efficient feed in heavy pigs,

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and to evaluate host gene-feeding interactions.

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3.2 Transcriptomics and nutrigenomics in food animal production and health

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3.2.1 Transcriptomics

As mentioned above, having the greatest genetic potential does not guarantee the best

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performance of the animals since the function of genes can be affected by many factors, with the

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transcription process from DNA to mRNA being one of the first determinant factors. The

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emerging studies of RNA molecules (also defined as transcriptomics) can therefore provide

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valuable information on whether “important/key genes” are being “transcribed or not”. As one of

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the outcomes of transcriptomic studies, differential expressed (DE) gene analysis is used to

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elucidate the changes in biological functions and metabolic pathways that are related to

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production trait variation. Using feed efficiency in cattle as an example, this trait is complex, and

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its biology has been proposed but remains largely unidentified [51]. A recent study identified 70

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and 19 total genes that were differentially expressed in the liver of Holstein and Jersey dairy cows

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with different feed efficiency, respectively, using RNA-Sequencing (RNA-Seq)-based

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transcriptomics with the HiSeq 2500 machine. These genes were involved in steroid hormone

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biosynthesis, lipid metabolism, and drug metabolism cytochrome P450 [52]. A similar approach

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sequenced by the HiSeq 2000 system was used to study rumen epithelial tissues of cattle with

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varied feed efficiency and found 122 DE genes involved in the function of acetylation,

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remodeling of adherens junctions, cytoskeletal dynamics, cell migration, and cell turnover [53].

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Based on these findings, transcriptomics can determine the underlying regulating functions of

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these two digestive organs and ACCEPTED can help identifyMANUSCRIPT the biological mechanism underlying feed

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efficiency. In addition, RNA-Seq based transcriptomic studies provide a fundamental

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understanding of functional aspects of tissues. A recent study revealed transcriptomes of digestive

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tissues in sheep, showing the GIT transcriptomes are mainly driven by epidermal differentiation

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complex genes [54]. Such work can provide novel insights into fundamental issues concerning

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mammalian digestive and gastrointestinal systems, which can directly impact animal growth and

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performance through feed digestion and nutrient absorption.

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In addition to the understanding of altered molecular mechanisms related to animal

production, the transcriptomic-based approach can be applied to study the molecular mechanisms

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of animal health. For example, the whole blood transcriptome has been proven to be a potential

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molecular phenotype diagnostic tool for food animal health. A recent study using the RNA-Seq

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based whole blood transcriptomics with the HiSeq 2000 instrument identified 246 DE genes in

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sick pigs involved in a variety of systemic immune and inflammatory responses to many

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physiological processes, thus suggesting their gene marker functions are associated with health

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status in commercial pigs [55]. Similarly, blood transcripts can be applied as biomarkers for

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selecting pigs with reduced susceptibility to Salmonella shedding [56]. Increasing dietary DHA

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levels were associated with the upregulation of hepatic immune pathways, especially chemokine

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signaling, FC epsilon RI signaling and natural killer cell-mediated cytotoxicity pathways in

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Atlantic salmon, which expanded our understanding of how functional dietary nutrients elicit

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signaling molecules and a physiological impact [57]. Moreover, transcriptomic analysis of

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circulating leukocytes in blood using a microarray identified novel aspects of the host systemic

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inflammatory response to sheep scab mites, which provided new insights into the mechanisms of

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local allergen-induced inflammatory responses [58]. However, transcriptomics research in animal

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health is still in its infancy due to the high cost, lack of large phenotype data collection, and

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challenges in sample collection as well as the complex causal relationship. Broader applications

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MANUSCRIPT of animal health transcriptomicsACCEPTED may be expected in the near future with the dropping cost of

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NGS technologies and successful experience from human studies.

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3.2.2 Nutrigenomics

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As mentioned in section 3.1, diet can influence the physiological status of the host, which could be caused by the altered gene expression at the cellular, tissue and organ levels. To study

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such changes in response to diet, nutrigenomics has been widely accepted as a concept for

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investigating the effects of feed or nutrients on gene expression, which provides understanding on

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how nutrition affects metabolic pathways and diet-gene interactions [59]. Research on

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nutrigenomics provide the promise to uncover key biological responses to nutrients in food

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animal species, especially when considering the dietary supplementations. Recent research based

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on a microarray revealed how Zn supplementation altered the expression of genes in jugular

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venous blood of lactating sheep, which is related to immunity and cardiac contraction patterns

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[60]. Similarly, the dietary nutritional level can alter the genes involved in spermatogenesis or

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apoptosis in testis of sheep, thus providing a cellular and molecular understanding of the testis

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response to nutrition and potential nutrition strategies to improve reproduction [61]. Using

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nutrigenomics, the impact of dietary manipulations on the immune system in fish is becoming

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clearer [62]. Nutrigenomics make it possible to formulate individualized diet from alternative

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feed resources, to further develop precision feeding strategies in terms of productivity

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enhancement and/or to prevent diet-induced diseases. In summary, the transcriptomics and

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nutrigenomics studies can help develop the targeted diet modifications for highly efficient food

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animal production and low diet-related disease risk in the defined farm population. The food

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animal-based nutrigenomics has just started and focused on only limited number of animals, so it

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lacks specific, convincing evidence about diet-gene interactions. There is still a long way to

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make real personalized nutrition using food animal nutrigenomics even with high hope.

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3.3 MicroRNAomes in food animal production and health

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ACCEPTED MicroRNAs (miRNAs) have been identified asMANUSCRIPT important post-transcriptional regulators of gene

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expression and have been proven to affect stress, tissue development, and reproduction in food

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animal species [63]. To date, most of the studies on miRNA discovery are based on RNA-Seq,

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although miRNA microarrays can also be effective for screening the known miRNAs. The

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currently known miRNA genes in animals including human, orangutan, cattle, horse, pig, sheep,

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dog, mouse, rat, chicken, platypus, zebrafish, tetraodon, zebra finch and fruit fly were 1881, 642,

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808, 715, 383, 106, 502, 1193, 495, 740, 396, 346, 132, 247 and 256, respectively [64]. Similar to

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coding genes, the genetic variability of miRNA genes were identified, and the cattle had the most

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polymorphic miRNA genes, followed by human, fruit fly, mouse, chicken, pig, horse, and sheep

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(ranged from 3.8% in tetraodon to 91.7% in cattle) [65].

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We summarized the miRNAs reported to be associated with nitrogen efficiency, heat stress,

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early pregnancy, lipid metabolism immune response, fat depot and so on in food animals, and this

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summary also showed the predicted targets of certain miRNAs and their specificity to different

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tissues and species in Table 3. Such findings highlight the potential regulatory correlations

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between miRNAs and different phenotypes. Our lab is among one of the first to study bovine

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miRNAs and has established methods for studying bovine tissue [66] and circulating miRNAs

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[67] using the microarray and HiSeq 2000 system, respectively. We have identified miRNAs

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from many tissues and body fluids (whole blood, serum, and milk) and have discovered bovine

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miRNAs that are associated with dairy cow nitrogen utilization efficiency, gut tissue development,

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fat adipogenesis and foodborne pathogen shedding in pigs [68-70]. One of our recent efforts

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revealed the features of miRNAs in bovine sera and exosomes and found that miRNAs from sera

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may be preferable for detecting inflammation while miRNAs from exosomes should be better for

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monitoring muscle development and lipid metabolism status in cattle [67]. In addition, other

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studies have shown that the circulating miRNAs from plasma and serum can be used as

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informative biomarkers for food animal health and well-being (also summarized in Table 3), such

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ACCEPTED as miR-26a for cattle early pregnancy [71] and MANUSCRIPT miR-19a and miR-19b for cattle heat stress [72].

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The miR-3570 could target and inhibit the expression of the mitochondrial antiviral signaling

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protein, thereby inhibiting NF-κB and IRF3 signaling-mediated immunity in teleost fish [73],

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which was identified as a novel mechanism for virus evasion in fish. The current progress in the

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miRNAome area has provided influential evidence of miRNAs associated with various diseases

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and the potential biomarkers applied for disease prevention and diagnosis. These miRNAs offer

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potential roles in assisting industry with management strategies to improve cattle production,

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health, and welfare.

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In addition, many miRNAs have been identified from crops and observed in some public

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animal miRNA datasets; for example, one study verified that miR-168a in rice can bind to the

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low-density lipoprotein receptor adapter protein 1 mRNA of human/mouse, with the inhibited

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expression of this mRNA in liver and a decrease in low-density lipoprotein removal from mouse

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plasma based on in vitro and in vivo experiments [74]. Such findings significantly extend our

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understanding of the role of miRNAs from food/feed on animals. For food animals, the feed

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miRNAs may serve as “bioactive” components that can pass through the food animal GIT, enter

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the circulatory system, and regulate the expression of target genes in different various organs.

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This suggests that developing miRNA-based feed additives may be a lighting direction for the

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future biotechnology and food animal commodity to alter animal gene expression for improving

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productivity and health. In ruminants, the feed miRNAs may be degraded in the rumen, but they

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may be able to escape the destructive conditions in monogastric animals. To date, no research has

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been done to see whether feed-derived miRNAs can enter and regulate the physiological

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functions in food animal conditions, which has a great potential for developing miRNAs into a

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novel nutrient component.

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3.4 Epigenomics in food animal phenotypic variations

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MANUSCRIPT Epigenetic regulation isACCEPTED another factor that can affect gene expression that may alter the genetic potential. As part of the epigenetic machinery, DNA methylation-driven epigenetic

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changes in response to dietary variation have been largely observed in different food animal

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species. For example, the variation trend of the DNA methylation level in promoter and exon 1 of

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CYP19A1A and coding region of FOXL2 was negatively associated with their expression levels

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during ovarian development in Japanese flounder using the methylated DNA kit [75], which

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clarified the functions of these 2 genes in ovary development from an epigenetic perspective. The

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changed gene expression and methylation of non-structural maintenance of chromosomes

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subunits of condensin I in the liver and skeletal muscle of offspring was observed using Bisulfite

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sequencing (Bisulfite-Seq) with QIAGEN sequencing services when high and low feed protein

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level applied in the pregnant pig diet, which indicated an involvement of DNA methylation by

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maternal protein supply [76]. It has also been reported that DNA methylation differences had a

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highly significant effect on muscle fiber density and drip loss in a Chinese native chicken breed

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[77]. In addition, the DNA methylation of SMAD1, TSC1, and AKT1 genes showed significant

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differences across different sheep breeds, and 6 CpG islands were significantly correlated with

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RNA expression and body size using the methylated DNA immunoprecipitation-sequencing

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(MeDIP-Seq) with the HiSeq 2000 system [78]. Moreover, Genome-wide DNA methylation

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profiles have been reported in bovine muscle tissue [79] and placentas [80] using the MeDIP-Seq

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with the HiSeq 2000 system; pig neocortex, spleen, liver, femoral muscle, olfactory epithelium

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and pulmonary alveolar macrophages cell lines [81]; sheep muscle [82] using the reduced

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representation Bisulfite-Seq with the HiSeq 2000 analyzer; and chicken pectoral muscle tissues

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[77], retina, cornea and brain using the Bisulfite-Seq with the HiSeq 2500 platform [83].

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Moreover, it has been suggested that DNA methylation may play a regulatory role in silencing of

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gene expression related to milk protein in the dairy cow [84]. Although such relationships have

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not been studied during dietary changes, it is speculated that different feeds could lead to shifts in

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ACCEPTED the methylation pattern, which could be relatedMANUSCRIPT to phenotypic changes. Studies are increasingly

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starting to reveal the evidence of epigenetic-regulated gene expression under various

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environmental conditions, but most of them have focused only on DNA methylation. Studies on

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other epigenetic mechanisms such as histone modification and how to determine the heritability

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of epigenetic-driven traits are also needed. Specifically, how the feed composition could alter the

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epigenomes and whether the altered epigenetic variations can be passed to the next generation are

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largely unknown. For meat animals, the epigenetics should more contribute to the unexplained

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phenotypic variation in food animal production traits, but for milk production animals, the

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epigenetic variations that persist throughout life and be transmitted to the next generation are

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worth being studied well. Therefore, feed selection or management strategies should take the

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epigenetic marks into consideration, which is needed to achieve the targeted outcomes for growth,

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physiology and performance.

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3.5 Proteomics in food animal production, product quality, and health

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The number of proteomics and metabolomics research in food animal species has rapidly

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increased, indicating another advanced research era for furthering our understanding of animal

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biology. Based on the HPLC/ESI-MS proteomics method, cow milk adulteration in goat milk

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was quantified [85], and LC-MS/MS-based whey proteomics revealed that the milk quality

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was genetically and biologically diverse among ruminants. The milk could be grouped into

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three clusters: (1) cow, buffalo, and yak milk; (2) goat, cow, buffalo, and yak milk; and (3)

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camel milk [86]. It is known that different feeding strategies are applied for milk-producing

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ruminants, which contribute many of the components in the milk. This study provided

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information on nutritional values for human intake could also help to develop better strategies

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in the non-cow ruminants for productivity and milk quality. In addition, the proteomes have

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been reported to be associated with meat quality in beef, pork, and chicken, such as beef

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muscle color, meat tenderness, fresh, lean-to-fat ratio, intramuscular fat content and quality in

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ACCEPTED MANUSCRIPT different breeds using the iTRAQ labeling-based proteomics [87]. The dietary yeast cell wall

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extracts caused a change in the proteomic profile of Atlantic salmon skin mucus, and the

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calreticulin-like protein was identified as a putative biomarker for yeast-derived functional

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feeds using LC-MS/MS-based proteomics [88]. Meanwhile, the targeted feeding and

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management proteomics have shown positive outcomes to improve the water-holding capacity

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in pork loin meat (using feed additive) and meat quality in swine production [89], lamb meat

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tenderness studied by the HPLC-Q-TOF/MS [90], meat quality and yield, color, and oxidative

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stability, and woody broiler breast in poultry production identified by the ESI-Q-TOF/MS [91].

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These results suggest that proteomics is a powerful tool that can guide the production of more

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customer-traits products. Although many studies have accelerated proteomics research in food

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animal product traits, their linkage to feed biochemistry, genomics, and microbiomics should

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be investigate urgently, especially the mechanism of certain nutrients on production-related

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biology and nutrient pathways from feed to product at the molecular level.

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Proteomics can also be a valuable tool for evaluating food animal welfare and coping with unavoidable stress challenges. It is reported that the proteome changed in normal milk

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exosomes, milk fat globule membranes and whey proteomes from cows infected with mastitis

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using iTRAQ labeling-based proteomics [92]. Further, a combined omics study with

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proteomics, transcriptomics, and metabolomics showed that the carbohydrate metabolism and

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cytoskeleton stabilization cellular mechanisms in muscle tissue were affected by the restraint

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and transport stress in chickens [93], suggesting that omics can be applied for the potential

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diagnosis of animal health. Based on these findings, food animal proteomics has revealed the

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physiological, pathological, and putative diagnostic information through hundreds or even

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thousands of proteins under different diets and environmental and nutritional statuses. This

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information can therefore help optimize feed at both the quantitative and qualitative levels to

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MANUSCRIPT improve an animal’s metabolicACCEPTED activity to produce high quantity and desirable quality meat and

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milk and to balance animal production and animal health and welfare.

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3.6 Metabolomics to drive sustainable food animal production

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We summarized the study level, type, and topic in the application of metabolomics on food animals in Fig. 1 to help select the suitable platform when performing food animal

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metabolomics studies. The food animal untargeted and targeted metabolomics are typically

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classified into overall and partial levels based on the sample characteristics, which represent

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the comprehensive metabolism profiles of the whole organism and unique metabolism patterns

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of a certain tissue or organ, respectively (Fig. 1). Blood (as well as plasma and serum) and urine

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metabolomics in food animals are widely assessed to address well-being (beef cattle welfare,

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hormone abuse in cattle, dairy cow insulin resistance with cinnamon using GC-MS and

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UPLC-MS/MS [94], asphyxia, resuscitation and weaning stress in pig using NMR [95]), health

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(dairy cows hepatic lipidosis, ketosis, milk fever, displaced abomasum, lameness using

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LC-MS/MS [96], cold stress response in the discus fish using GC-TOF/MS [97]),

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comprehensive metabolism with GC-TOF/MS [98], and biomarker (infeed antibiotics in

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piglets using GC-MS [99], forage type induced different milk protein productions using

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GC-TOF/MS [100]) related problems. Digesta, fecal and saliva metabolomics have also been

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studied, which are assigned to the partial level (Fig. 1) and provide information about feed

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utilization, nutrient digest and interface with the microbiome (rumen microbiome in goat using

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GC-TOF/MS [101] and hindgut microbiome in pig using GC-MS [102]). The tissue (major

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about liver, muscle, adipose, mammary gland, rumen, and gut), milk and single cell

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metabolomics also belong to a partial level, with the research topic of nutrients partition,

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specific function, output analyzed by Electrophoresis-TOF/MS [103], and metabolism

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evaluation (insulin neutralization and fasting in chicken [104] and potential metabolic disorder

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of cattle using NMR, GC-MS and direct flow injection-MS/MS [105]).

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ACCEPTED Since the metabolites are located at theMANUSCRIPT end of biological pathways, transfer the

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information of DNA, RNA, and protein and reflect the real metabolic profiling under internal

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and external stimulates, metabolomics generates lots of internal phenotypes (also called

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“metabotype”), which can link well to external phenotypes [106]. Accumulating evidence

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identified by NMR suggests that metabotypes can benefit the sustainable food animal industry

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in many aspects. For instance, plasma metabolites are associated with variations in production

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traits in beef cattle and varied abundance of creatine, hippurate and carnitine accounted for 32%

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of the phenotypic variation in RFI [107]. The accuracy of prediction using these metabolites

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associated with feed efficiency was 95%, which strongly indicated plasma metabolites could

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be used as markers for selection for these traits [107]. In addition, Melzer et al. proposed that

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metabolites can be used to predict milk traits based on their research on the 190 milk

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metabolites identified from 1,305 Holstein cows over 18 commercial farms using GC-MS

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[108]. In addition, the rumen fluid metabolomics showed a high association between rumen

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metabolites and feed efficiency on crossbreed steers using UPLC-Q-TOF-MS/MS [109].

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Moreover, Sharifi et al. detected significant associations between egg yolk metabolites and

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hatchability traits in laying hens using GC-TOF/MS [110]. Using direct analysis in real

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time-TOF/MS-based metabolomics, the differentiation of common carp muscle in response to

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dietary supplementation was feasible [111]. These findings suggest that metabolomics is a

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powerful tool for evaluating diet on food animal health and product quality, which would

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promote sustainable food animal production and reduce animal welfare concerns. However, the

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metabolites for food animal species lack a known coverage database. Dr. Wishart’s lab at the

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University of Alberta has built an open-access and comprehensive livestock metabolome

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database (LMDB, http://www.lmdb.ca) with 1070 metabolites from different tissues and

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biofluids of 5 major livestock species (bovine, ovine, caprine, equine, and porcine) in 149

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peer-reviewed papers, which could serve as a hub for food animal researchers and the food

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ACCEPTED animal industry to further advance the field ofMANUSCRIPT food animal metabolomics [106]. This is

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currently the only database for the food animal metabolome, but it only compiles data from

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published papers without validation or in-depth investigation.

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3.7. Proteomics and metabolomics to understand the utilization of alternative feed source

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Proteomics and metabolomics were also successfully applied in the mechanical

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characterization of alternative feed source. Crop by-products is one type of alternative feed

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sources, especially for ruminants. Enhancing the utilization of crop by-products to produce

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high-quality food animal product is a promising direction to achieve sustainable food animal

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production. Corn stover (CS) and rice straw (RS), as the most abundant crop by-products, can

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significantly lower both the milk production and milk protein content when replacing 23% of

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alfalfa hay (AH, current practice in the modern dairy farm) in dairy cow diets [112]. The rumen

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fluid, serum, milk and urine metabolomics showed that the gluconeogenesis, pyruvate

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metabolism, tricarboxylic acid cycle, glycerolipid metabolism, and aspartate metabolism

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pathways were the most enriched in AH [98]; CS-fed cows had significantly down-regulated

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glycine, serine, and threonine metabolism, tyrosine metabolism, and phenylalanine metabolism

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compared with AH-fed cows [113]; and hippuric acid and N-methyl-glutamic in the urine were

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further identified as potential biomarkers for discriminating CS and AH diets [100]. The

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proteome results showed 231 up-regulated and 286 down-regulated proteins and inhibited

484

energy and fatty acid metabolism in the mammary gland tissue in the RS group [114]. The

485

above information provided a new understanding of and applicable candidates for the

486

mechanisms of crop by-product utilization. However, the lack of interactions with feed

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metabolomics and proteomics is the common limitation of these studies.

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3.8. Microbiomics with metagenomics and metatranscriptomics in food animal production

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Tremendous research has shown that symbiotic and commensal microbiota play vital roles in feed digestion and in shaping host functions. Food animal microbes play important roles

ACCEPTED MANUSCRIPT in extracting nutrients and energy harvesting from the feed, regulating methane emissions,

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relating inflammatory responses, affecting fat storage, and promoting gut epithelium renewal,

493

as revealed by the pyrosequencing using the HiSeq 2500 system [115]. For this review, we use

494

the rumen microbiome as an example to highlight the recent research on its role in cattle feed

495

efficiency and methane emission. Ruminants harbor a complex rumen microbiome that can

496

convert human indigestible feed materials into volatile fatty acids and other nutrients so the

497

host can produce high-quality protein meat and milk. The recent metagenomics research on

498

rumen microbiome of dairy cows revealed that microbial diversity, microbial taxa, and genes

499

are associated with different feed efficiency using HiSeq 2500 and MiSeq systems [116].

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Similarly, the rumen metatranscriptomic profiling of beef cattle rumen showed active

501

microbial taxa and functions associated with feed efficiency beef cattle using the HiSeq 2000

502

system [117]. Based on rumen metagenomics and metatranscriptomics, methanogenesis

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pathway transcription profiles were identified to be correlated with methane yields in sheep

504

[118], and Kamke et al. further revealed that low-methane-yield sheep had a Sharpea-enriched

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microbiome with a more lactic acid formation and utilization using rumen identified with

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metagenomics and metatranscriptomics using the HiSeq 2000 system [119]. These findings

507

provide a foundation for developing new nutritional and management strategies to mitigate

508

methane emission and improve feed efficiency by manipulating the microbiota composition

509

and functions. In other food animal species, metagenomics analysis showed that microbiota and

510

functional capacities in the cecum have also been reported to be associated with feed efficiency

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in finishing pigs identified by HiSeq 2500 platform [120] and in chickens using the MiSeq

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system [121], suggesting that the regulation of microorganism composition could be applied to

513

the pig production industry. The above ecological and mechanistic understanding of the GIT

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microbiome could lead to an increase in productivity and an environmentally friendly food

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MANUSCRIPT animal industry. The limitationsACCEPTED of these studies are that they do not consider the potential roles

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and interactions with feed and environment microbiomes.

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4. Conclusions and future outlooks In this paper, we proposed the concept of “feedomics” in feed science and animal nutrition area and provided a detailed review of the recent studies applying genomics, epigenomics,

521

transcriptomics, proteomics, metabolomics, metagenomics and metatranscriptomics strategies

522

to study the production and health issues of food animals. Feedomics provides a new

523

understanding of the complex biochemical mechanisms involved in the interaction of diet,

524

environment, host physiology, microbial functions and phenotypes and has the promise to be

525

well developed into a targeted method for improving the quantity, quality, safety and

526

functional properties of food animal products while reducing the use of antimicrobial growth

527

promotants and mitigating greenhouse gas emissions from the food animal sectors using

528

regular or alternative feed resource or novel feed additives (e.g., miRNAs) to meet the

529

increasing human consumption demand in the future. These omics-based strategies provide

530

important insights into animal biological information and omics-based nutrition, which are far

531

beyond but also tightly associated with the measurement results from traditional techniques.

532

The crucial power of these omics methods is revealing true causal internal phenotypes (gene,

533

transcripts, protein, and metabolites), metabolic functions, regulatory pathways leading to

534

improved feed efficiency, well animal welfare, and sustainable development in the food animal

535

industry. The outcomes of omics should be applied in nutritional regulation, the breeding

536

program (especially for the local breeds in extremely regions) and management improvement

537

practice, which would be great benefit to animals in the future.

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Except for the currently studied contents of improving food animal sectors’ efficiency

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and sustainability from input (feed) to output (product) in feedomics, several other directions

ACCEPTED MANUSCRIPT such as feed quality assessment, interactions with plant omics and biochemistry, food animal

541

impact on human health and future customer-traits evaluation, should be paid attention to in

542

future work (Fig. 2). Using plant genomics, proteomics, metabolomics and miRNAs to develop

543

a species-specific flavor feed, contamination reduction feed, and less-emission (methane, CO2)

544

feed source will be novel investigations in the feed quality assessment of feedomics (Fig. 2).

545

Feedomics could also be a powerful tool to reveal the underlying mechanism of food animal on

546

human health, such as antimicrobial resistance and zoonosis. Meanwhile, evaluating future

547

customer traits such as being rich in natural healthy molecules and live ingredients will require

548

tremendous efforts of feedomics, especially by proteomics, metabolomics, and microbiomics.

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The action of different levels of biomolecules (e.g., DNA, mRNA, miRNA, protein, lipid,

550

metabolite) will play a vital role and provide clues for feed, food animal, product and human in

551

future feedomics studies. The integrated application of multi-omics, especially for the entire

552

pool of different levels of biomolecules, to understand the information flow will be a new trend

553

in feedomics. Feedomics is still in its infancy period, so further enhancing its analytical

554

sensitivity, accuracy, coverage depth and matched databases is still a big challenge for future

555

work.

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556 Acknowledgments

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This work was supported by China Opportunity Fund (sponsored by University of Alberta,

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RES0031665) and NSERC Discovery grant.

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References

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[1] W. Lutz, W.P. Butz, K.e. Samir, World Population & Human Capital in the Twenty-First Century: An Overview, Oxford University Press2017. [2] L. Bouwman, K.K. Goldewijk, K.W. Van Der Hoek, A.H. Beusen, D.P. Van Vuuren, J. Willems, M.C. Rufino, E. Stehfest, Exploring global changes in nitrogen and phosphorus cycles in agriculture induced by livestock production over the 1900–2050 period, P Natl Acad Scd USA, 110 (2013) 20882-20887. [3] A. Mottet, C. de Haan, A. Falcucci, G. Tempio, C. Opio, P. Gerber, Livestock: On our plates or eating at our table? A new analysis of the feed/food debate, Glob Food Secur, DOI (2017).

EP

TE D

M AN U

SC

RI PT

[4] D. Tilman, K.G. Cassman, P.A.ACCEPTED Matson, R. Naylor, S. Polasky, Agricultural sustainability and intensive MANUSCRIPT production practices, Nature, 418 (2002) 671. [5] C. Phillips, Animal Welfare, Nutrition, and Economics: Past, Present, and Future, Nutrition and the Welfare of Farm Animals, Springer2016, pp. 229-240. [6] C. Li, W. Salas, R. Zhang, C. Krauter, A. Rotz, F. Mitloehner, Manure-DNDC: a biogeochemical process model for quantifying greenhouse gas and ammonia emissions from livestock manure systems, Nutr Cycl Agroecosys, 93 (2012) 163-200. [7] T.P. Van Boeckel, E.E. Glennon, D. Chen, M. Gilbert, T.P. Robinson, B.T. Grenfell, S.A. Levin, S. Bonhoeffer, R. Laxminarayan, Reducing antimicrobial use in food animals, Science, 357 (2017) 1350-1352. [8] A. Cifuentes, Food analysis and foodomics, Journal of Chromatography A, 1216 (2009) 7109. [9] S.K. Kar, FeedOmics, an approach to evaluate the functional properties of protein containing feed ingredients, Wageningen University, 2017. [10] S. Goodwin, J.D. McPherson, W.R. McCombie, Coming of age: ten years of next-generation sequencing technologies, Nat Rev Genet, 17 (2016) 333-351. [11] B. Tasic, V. Menon, T.N. Nguyen, T.K. Kim, T. Jarsky, Z. Yao, B. Levi, L.T. Gray, S.A. Sorensen, T. Dolbeare, Adult mouse cortical cell taxonomy by single cell transcriptomics, Nature neuroscience, 19 (2016) 335. [12] E.P. Consortium, An integrated encyclopedia of DNA elements in the human genome, Nature, 489 (2012) 57-74. [13] Y. Hasin, M. Seldin, A. Lusis, Multi-omics approaches to disease, Genome Biol, 18 (2017) 83. [14] E.M. Ibeagha-Awemu, X. Zhao, Epigenetic marks: regulators of livestock phenotypes and conceivable sources of missing variation in livestock improvement programs, Front Genet, 6 (2015). [15] M. Larance, A.I. Lamond, Multidimensional proteomics for cell biology, Nature reviews. Mol Cell Biol, 16 (2015) 269. [16] C.H. Johnson, J. Ivanisevic, G. Siuzdak, Metabolomics: beyond biomarkers and towards mechanisms, Nat Rev Mol Cell Biol, 17 (2016) 451-459. [17] C. Quince, A.W. Walker, J.T. Simpson, N.J. Loman, N. Segata, Shotgun metagenomics, from sampling to sequencing and analysis, Nat Biotechnol, DOI (2017). [18] L. Moitinho-Silva, C. Díez-Vives, G. Batani, A.I. Esteves, M.T. Jahn, T. Thomas, Integrated metabolism in sponge–microbe symbiosis revealed by genome-centered metatranscriptomics, ISME J, DOI (2017). [19] J.A. Reuter, D.V. Spacek, M.P. Snyder, High-throughput sequencing technologies, Mol Cell, 58 (2015) 586-597. [20] L. Andersson, A.L. Archibald, C.D. Bottema, R. Brauning, S.C. Burgess, D.W. Burt, E. Casas, H.H. Cheng, L. Clarke, C. Couldrey, Coordinated international action to accelerate genome-to-phenome with FAANG, the Functional Annotation of Animal Genomes project, Genome Biol, 16 (2015) 57. [21] M. Zampieri, K. Sekar, N. Zamboni, U. Sauer, Frontiers of high-throughput metabolomics, Curr Opin Chem Biol, 36 (2017) 15-23. [22] X. Zhan, Y. Long, M. Lu, Exploration of variations in proteome and metabolome for predictive diagnostics and personalized treatment algorithms: Innovative approach and examples for potential clinical application, J Proteomics, DOI (2017). [23] A. Almeida, A. Bassols, E. Bendixen, M. Bhide, F. Ceciliani, S. Cristobal, P. Eckersall, K. Hollung, F. Lisacek, G. Mazzucchelli, Animal board invited review: advances in proteomics for animal and food sciences, Animal, 9 (2015) 1-17. [24] R. Aebersold, M. Mann, Mass spectrometry-based proteomics, Nature, 422 (2003) 198. [25] L. Monaci, A. Visconti, Mass spectrometry-based proteomics methods for analysis of food allergens, TrAC-Trend Anal Chem, 28 (2009) 581-591. [26] S. Kim, J. Kim, E.J. Yun, K.H. Kim, Food metabolomics: from farm to human, Curr Opin Biotechnol, 37 (2016) 16-23. [27] J.F. Xiao, B. Zhou, H.W. Ressom, Metabolite identification and quantitation in LC-MS/MS-based metabolomics, TrAC-Trend Anal Chem, 32 (2012) 1-14. [28] G.P. Danezis, A.S. Tsagkaris, F. Camin, V. Brusic, C.A. Georgiou, Food authentication: Techniques, trends & emerging approaches, TrAC-Trend Anal Chem, 85 (2016) 123-132. [29] M. Vinaixa, E.L. Schymanski, S. Neumann, M. Navarro, R.M. Salek, O. Yanes, Mass spectral databases for LC/MS-and GC/MS-based metabolomics: state of the field and future prospects, TrAC-Trend Anal Chem, 78 (2016) 23-35. [30] A.-C. Syvänen, Toward genome-wide SNP genotyping, Nat Genet, 37 (2005) S5-S10. [31] G. Gorjanc, J.-F. Dumasy, S. Gonen, R.C. Gaynor, R. Antolin, J.M. Hickey, Potential of Low-Coverage Genotyping-by-Sequencing and Imputation for Cost-Effective Genomic Selection in Biparental Segregating Populations, Crop Sci, DOI (2017). [32] C.G. Elsik, R.L. Tellam, K.C. Worley, The genome sequence of taurine cattle: a window to ruminant

AC C

569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628

RI PT

biology and evolution, Science, 324 ACCEPTED (2009) 522-528. MANUSCRIPT [33] C.-J. Rubin, M.C. Zody, J. Eriksson, J.R. Meadows, E. Sherwood, M.T. Webster, L. Jiang, M. Ingman, T. Sharpe, S. Ka, Whole-genome resequencing reveals loci under selection during chicken domestication, Nature, 464 (2010) 587-591. [34] A. Archibald, N. Cockett, B. Dalrymple, T. Faraut, J. Kijas, J. Maddox, J. McEwan, V. Hutton Oddy, H. Raadsma, C. Wade, The sheep genome reference sequence: a work in progress, Anim Genet, 41 (2010) 449-453. [35] M.A. Groenen, A.L. Archibald, H. Uenishi, C.K. Tuggle, Y. Takeuchi, M.F. Rothschild, C. Rogel-Gaillard, C. Park, D. Milan, H.-J. Megens, Analyses of pig genomes provide insight into porcine demography and evolution, Nature, 491 (2012) 393-398. [36] P.M. Rodrigues, S.A. Martin, T.S. Silva, S. Boonanuntanasarn, D. Schrama, M. Moreira, C. Raposo, Proteomics in Fish and Aquaculture Research, Proteomics in Domestic Animals: from Farm to Systems Biology, Springer2018, pp. 311-338. [37] B. Karisa, J. Thomson, Z. Wang, P. Stothard, S. Moore, G. Plastow, Candidate genes and single nucleotide polymorphisms associated with variation in residual feed intake in beef cattle, J Anim Sci, 91 (2013) 3502-3513.

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M AN U

SC

[38] M. Rolf, J. Taylor, R. Schnabel, S. McKay, M. McClure, S. Northcutt, M. Kerley, R. Weaber, Genome‐ wide association analysis for feed efficiency in Angus cattle, Anim Genet, 43 (2012) 367-374. [39] D.N. Do, T. Ostersen, A.B. Strathe, T. Mark, J. Jensen, H.N. Kadarmideen, Genome-wide association and systems genetic analyses of residual feed intake, daily feed consumption, backfat and weight gain in pigs, BMC Genet, 15 (2014) 27. [40] P.S. de Oliveira, A.S. Cesar, M.L. do Nascimento, A.S. Chaves, P.C. Tizioto, R.R. Tullio, D.P. Lanna, A.N. Rosa, T.S. Sonstegard, G.B. Mourao, Identification of genomic regions associated with feed efficiency in Nelore cattle, BMC Genet, 15 (2014) 100. [41] D.N. Do, A.B. Strathe, T. Ostersen, S.D. Pant, H.N. Kadarmideen, Genome-wide association and pathway analysis of feed efficiency in pigs reveal candidate genes and pathways for residual feed intake, Front Genet, 5 (2014) 307. [42] J. Yuan, K. Wang, G. Yi, M. Ma, T. Dou, C. Sun, L.-J. Qu, M. Shen, L. Qu, N. Yang, Genome-wide association studies for feed intake and efficiency in two laying periods of chickens, Genet Sel Evol, 47 (2015) 82. [43] M.-P. Sanchez, A. Govignon-Gion, P. Croiseau, S. Fritz, C. Hozé, G. Miranda, P. Martin, A. Barbat-Leterrier, R. Letaïef, D. Rocha, Within-breed and multi-breed GWAS on imputed whole-genome sequence variants reveal candidate mutations affecting milk protein composition in dairy cattle, Genet Sel Evol, 49 (2017) 68. [44] A. Ribani, Towards the Development of Nutrigenetic Concepts in Pigs by Merging Genomics, Transcriptomics and Metabolomics, Alma, 2017. [45] R. Garg, N. Sharma, S.K. Jain, Nutrigenomics and Nutrigenetics: Concepts and Applications in Nutrition Research and Practice, DOI (2014). [46] S. Calsamiglia, A. Siurana, Nutrigenics and nutrigenomics: the potential application to animal nutrition: feed science, AFMA Matrix, 25 (2016) 34-35. [47] S. Miyauchi, A. Hirasawa, A. Ichimura, T. Hara, G. Tsujimoto, New frontiers in gut nutrient sensor research: free fatty acid sensing in the gastrointestinal tract, J Pharmacol Sci, 112 (2010) 19-24. [48] T. Song, J. Peng, J. Ren, H.-k. Wei, J. Peng, Cloning and characterization of spliced variants of the porcine G protein coupled receptor 120, BioMed Res Int, 2015 (2015). [49] L. Fontanesi, F. Bertolini, E. Scotti, G. Schiavo, M. Colombo, P. Trevisi, A. Ribani, L. Buttazzoni, V. Russo, S. Dall'Olio, Next generation semiconductor based-sequencing of a nutrigenetics target gene (GPR120) and association with growth rate in Italian Large White pigs, Anim Biotechnol, 26 (2015) 92-97. [50] A. Ichimura, A. Hirasawa, O. Poulain-Godefroy, A. Bonnefond, T. Hara, L. Yengo, I. Kimura, A. Leloire, N. Liu, K. Iida, Dysfunction of lipid sensor GPR120 leads to obesity in both mouse and human, Nature, 483 (2012) 350-354. [51] D. Berry, J. Crowley, Cell Biology Symposium: Genetics of feed efficiency in dairy and beef cattle, J Anim Sci, 91 (2013) 1594-1613. [52] M. Salleh, G. Mazzoni, J. Höglund, D. Olijhoek, P. Lund, P. Løvendahl, H. Kadarmideen, RNA-Seq transcriptomics and pathway analyses reveal potential regulatory genes and molecular mechanisms in high-and low-residual feed intake in Nordic dairy cattle, BMC Genomics, 18 (2017) 258. [53] R.S. Kong, G. Liang, Y. Chen, P. Stothard, Transcriptome profiling of the rumen epithelium of beef cattle differing in residual feed intake, BMC Genomics, 17 (2016) 592. [54] R. Xiang, V.H. Oddy, A.L. Archibald, P.E. Vercoe, B.P. Dalrymple, Epithelial, metabolic and innate immunity transcriptomic signatures differentiating the rumen from other sheep and mammalian gastrointestinal tract tissues, PeerJ, 4 (2016) e1762. [55] M. Ye, H. Bao, Y. Meng, L. Guan, P. Stothard, G. Plastow, Comparative transcriptomic analysis of porcine

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629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688

EP

TE D

M AN U

SC

RI PT

peripheral blood reveals differentially expressed genesMANUSCRIPT from the cytokine–cytokine receptor interaction pathway ACCEPTED related to health status, Genome, DOI (2017) 1-8. [56] A. Kommadath, H. Bao, A.S. Arantes, G.S. Plastow, C.K. Tuggle, S.M. Bearson, P. Stothard, Gene co-expression network analysis identifies porcine genes associated with variation in Salmonella shedding, BMC Genomics, 15 (2014) 452. [57] B.D. Glencross, C. De Santis, B. Bicskei, J.B. Taggart, J.E. Bron, M.B. Betancor, D.R. Tocher, A comparative analysis of the response of the hepatic transcriptome to dietary docosahexaenoic acid in Atlantic salmon (Salmo salar) post-smolts, BMC Genomics, 16 (2015) 684. [58] S.T. Burgess, A. Greer, D. Frew, B. Wells, E.J. Marr, A.J. Nisbet, J.F. Huntley, Transcriptomic analysis of circulating leukocytes reveals novel aspects of the host systemic inflammatory response to sheep scab mites, PLoS One, 7 (2012) e42778. [59] M. Kussmann, P.J. Stover, Nutrigenomics and Proteomics in Health and Disease: Towards a systems-level understanding of gene-diet interactions, John Wiley & Sons2017. [60] R. Elgendy, F. Palazzo, F. Castellani, M. Giantin, L. Grotta, L. Cerretani, M. Dacasto, G. Martino, Transcriptome profiling and functional analysis of sheep fed with high zinc-supplemented diet: A nutrigenomic approach, Anim Feed Sci Technol, DOI (2017). [61] Y. Guan, G. Liang, G.B. Martin, Functional changes in mRNA expression and alternative pre-mRNA splicing associated with the effects of nutrition on apoptosis and spermatogenesis in the adult testis, BMC Genomics, 18 (2017) 64. [62] S.A. Martin, E. Król, Nutrigenomics and immune function in fish: new insights from omics technologies, Dev Comp Immunol, 75 (2017) 86-98. [63] J. Ioannidis, J. Risse, F.X. Donadeu, Profiling of MicroRNAs in the Biofluids of Livestock Species, MicroRNA Protocols, Springer2018, pp. 65-77. [64] M. Zorc, D.J. Skok, I. Godnic, G.A. Calin, S. Horvat, Z. Jiang, P. Dovc, T. Kunej, Catalog of microRNA seed polymorphisms in vertebrates, PLoS One, 7 (2012) e30737. [65] M. Zorc, J. Obsteter, P. Dovc, T. Kunej, Genetic variability of microRNA genes in 15 animal species, J Genomics, 3 (2015) 51. [66] J.M. Romao, W. Jin, M. He, T. McAllister, Altered microRNA expression in bovine subcutaneous and visceral adipose tissues from cattle under different diet, PLoS One, 7 (2012) e40605. [67] K. Zhao, G. Liang, X. Sun, Comparative miRNAome analysis revealed different miRNA expression profiles in bovine sera and exosomes, BMC Genomics, 17 (2016) 630. [68] J.M. Romao, W. Jin, M. He, T. McAllister, MicroRNAs in bovine adipogenesis: genomic context, expression and function, BMC Genomics, 15 (2014) 137. [69] H. Bao, A. Kommadath, G. Liang, X. Sun, A.S. Arantes, C.K. Tuggle, S.M. Bearson, G.S. Plastow, P. Stothard, L.L. Guan, Genome-wide whole blood microRNAome and transcriptome analyses reveal miRNA-mRNA regulated host response to foodborne pathogen Salmonella infection in swine, Sci Rep, 5 (2015). [70] D. Wang, G. Liang, B. Wang, H. Sun, J. Liu, L.L. Guan, Systematic microRNAome profiling reveals the roles of microRNAs in milk protein metabolism and quality: insights on low-quality forage utilization, Sci Rep, 6 (2016). [71] J. Ioannidis, F.X. Donadeu, Circulating miRNA signatures of early pregnancy in cattle, BMC Genomics, 17 (2016) 184. [72] Y. Zheng, K.-l. Chen, X.-m. Zheng, H.-x. Li, G.-l. Wang, Identification and bioinformatics analysis of microRNAs associated with stress and immune response in serum of heat-stressed and normal Holstein cows, Cell Stress Chaperon, 19 (2014) 973-981. [73] T. Xu, Q. Chu, J. Cui, D. Bi, Inducible MicroRNA-3570 Feedback Inhibits the RIG-I-Dependent Innate Immune Response to Rhabdovirus in Teleost Fish by Targeting MAVS/IPS-1, J Virol, 92 (2018) e01594-01517. [74] L. Zhang, D. Hou, X. Chen, D. Li, L. Zhu, Y. Zhang, J. Li, Z. Bian, X. Liang, X. Cai, Exogenous plant MIR168a specifically targets mammalian LDLRAP1: evidence of cross-kingdom regulation by microRNA, Cell Res, 22 (2012) 107. [75] Y. Si, Y. Ding, F. He, H. Wen, J. Li, J. Zhao, Z. Huang, DNA methylation level of cyp19a1a and Foxl2 gene related to their expression patterns and reproduction traits during ovary development stages of Japanese flounder (Paralichthys olivaceus), Gene, 575 (2016) 321-330. [76] S. Altmann, E. Murani, M. Schwerin, C.C. Metges, K. Wimmers, S. Ponsuksili, Maternal dietary protein restriction and excess affects offspring gene expression and methylation of non-SMC subunits of condensin I in liver and skeletal muscle, Epigenetics, 7 (2012) 239-252. [77] Y. Zhang, J. Guo, Y. Gao, S. Niu, C. Yang, C. Bai, X. Yu, Z. Zhao, Genome-wide methylation changes are associated with muscle fiber density and drip loss in male three-yellow chickens, Mol Biol Rep, 41 (2014) 3509-3516. [78] J. Cao, C. Wei, D. Liu, H. Wang, M. Wu, Z. Xie, T.D. Capellini, L. Zhang, F. Zhao, L. Li, DNA

AC C

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RI PT

methylation Landscape of body size ACCEPTED variation in sheep,MANUSCRIPT Sci Rep, 5 (2015). [79] Y.-Z. Huang, J.-J. Sun, L.-Z. Zhang, C.-J. Li, J.E. Womack, Z.-J. Li, X.-Y. Lan, C.-Z. Lei, C.-L. Zhang, X. Zhao, Genome-wide DNA methylation profiles and their relationships with mRNA and the microRNA transcriptome in bovine muscle tissue (Bos taurine), Sci Rep, 4 (2014). [80] J. Su, Y. Wang, X. Xing, J. Liu, Y. Zhang, Genome-wide analysis of DNA methylation in bovine placentas, BMC Genomics, 15 (2014) 12. [81] M. Choi, J. Lee, M.T. Le, D.T. Nguyen, S. Park, N. Soundrarajan, K.M. Schachtschneider, J. Kim, J.-K. Park, J.-H. Kim, Genome-wide analysis of DNA methylation in pigs using reduced representation bisulfite sequencing, DNA Res, 22 (2015) 343-355. [82] C. Couldrey, R. Brauning, J. Bracegirdle, P. Maclean, H.V. Henderson, J.C. McEwan, Genome-wide DNA methylation patterns and transcription analysis in sheep muscle, PLoS One, 9 (2014) e101853. [83] I. Lee, B.A. Rasoul, A.S. Holub, A. Lejeune, R.A. Enke, W. Timp, Whole genome DNA methylation sequencing of the chicken retina, cornea and brain, Scientific Data, 4 (2017). [84] K. Singh, A. Molenaar, K. Swanson, B. Gudex, J. Arias, R. Erdman, K. Stelwagen, Epigenetics: a possible role in acute and transgenerational regulation of dairy cow milk production, Animal, 6 (2011) 375-381. [85] R.K. Chen, L.W. Chang, Y.Y. Chung, M.H. Lee, Y.C. Ling, Quantification of cow milk adulteration in

EP

TE D

M AN U

SC

goat milk using high‐performance liquid chromatography with electrospray ionization mass spectrometry, Rapid Commun Mass Sp, 18 (2004) 1167-1171. [86] Y. Yang, D. Bu, X. Zhao, P. Sun, J. Wang, L. Zhou, Proteomic analysis of cow, yak, buffalo, goat and camel milk whey proteins: quantitative differential expression patterns, J Proteome Res, 12 (2013) 1660-1667. [87] Y. Mao, D.L. Hopkins, Y. Zhang, P. Li, L. Zhu, P. Dong, R. Liang, J. Dai, X. Wang, X. Luo, Beef quality with different intramuscular fat content and proteomic analysis using isobaric tag for relative and absolute quantitation of differentially expressed proteins, Meat Sci, 118 (2016) 96-102. [88] G. Micallef, P. Cash, J.M. Fernandes, B. Rajan, J.W. Tinsley, R. Bickerdike, S.A. Martin, A.S. Bowman, Dietary yeast cell wall extract alters the proteome of the skin mucous barrier in Atlantic Salmon (Salmo salar): increased abundance and expression of a calreticulin-like protein, PLoS One, 12 (2017) e0169075. [89] R. Delles, A. Naylor, A. Kocher, K. Dawson, R. Samuel, 142 Diets with organic trace minerals Bioplex®) and yeast Protein (NuPro®) improved the water-holding capacity of pork loin meat, J Anim Sci, 94 (2016) 66-66. [90] A. della Malva, R. Marino, A. Santillo, G. Annicchiarico, M. Caroprese, A. Sevi, M. Albenzio, Proteomic approach to investigate the impact of different dietary supplementation on lamb meat tenderness, Meat Sci, 131 (2017) 74-81. [91] A. Zheng, J. Luo, K. Meng, J. Li, S. Zhang, K. Li, G. Liu, H. Cai, W.L. Bryden, B. Yao, Proteome changes underpin improved meat quality and yield of chickens (Gallus gallus) fed the probiotic Enterococcus faecium, BMC Genomics, 15 (2014) 1167. [92] T.A. Reinhardt, R.E. Sacco, B.J. Nonnecke, J.D. Lippolis, Bovine milk proteome: quantitative changes in normal milk exosomes, milk fat globule membranes and whey proteomes resulting from Staphylococcus aureus mastitis, J Proteomics, 82 (2013) 141-154. [93] D. Hazard, X. Fernandez, J. Pinguet, C. Chambon, F. Letisse, J.-C. Portais, Z. Wadih-Moussa, H. Rémignon, C. Molette, Functional genomics of the muscle response to restraint and transport in chickens, J Anim Sci, 89 (2011) 2717-2730. [94] J.A. Carrillo, Y. He, Y. Li, J. Liu, R.A. Erdman, T.S. Sonstegard, J. Song, Integrated metabolomic and transcriptome analyses reveal finishing forage affects metabolic pathways related to beef quality and animal welfare, Sci Rep, 6 (2016) 25948. [95] Q. He, H. Tang, P. Ren, X. Kong, G. Wu, Y. Yin, Y. Wang, Dietary supplementation with l-arginine partially counteracts serum metabonome induced by weaning stress in piglets, J Proteome Res, 10 (2011) 5214-5221. [96] B. Ametaj, G. Zhang, E. Dervishi, S. Dunn, R. Mandal, D. Wishart, 0150 Targeted metabolomics reveals multiple metabolite alterations in the urine of transition dairy cows preceding the incidence of lameness, J Anim Sci, 94 (2016) 72-73. [97] B. Wen, S.-R. Jin, Z.-Z. Chen, J.-Z. Gao, Physiological responses to cold stress in the gills of discus fish (Symphysodon aequifasciatus) revealed by conventional biochemical assays and GC-TOF-MS metabolomics, Sci Total Environ, 640 (2018) 1372-1381. [98] H.-Z. Sun, K. Shi, X.-H. Wu, M.-Y. Xue, Z.-H. Wei, J.-X. Liu, H.-Y. Liu, Lactation-related metabolic mechanism investigated based on mammary gland metabolomics and 4 biofluids’ metabolomics relationships in dairy cows, BMC Genomics, 18 (2017) 936. [99] C. Mu, Y. Yang, K. Yu, M. Yu, C. Zhang, Y. Su, W. Zhu, Alteration of metabolomic markers of amino-acid metabolism in piglets with in-feed antibiotics, Amino Acids, 49 (2017) 771-781. [100] H. Sun, B. Wang, J. Wang, H. Liu, J. Liu, Biomarker and pathway analyses of urine metabolomics in dairy cows when corn stover replaces alfalfa hay, J Anim Sci Biotechnol, 7 (2016) 49.

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[101] S.Y. Mao, W.J. Huo, W.Y. Zhu, Microbiome–metabolome analysis reveals unhealthy alterations in the ACCEPTED MANUSCRIPT composition and metabolism of ruminal microbiota with increasing dietary grain in a goat model, Environ Microbiol, 18 (2016) 525-541. [102] L. Zhou, L. Fang, Y. Sun, Y. Su, W. Zhu, Effects of the dietary protein level on the microbial composition and metabolomic profile in the hindgut of the pig, Anaerobe, 38 (2016) 61-69. [103] M. El‐Sabagh, D. Taniguchi, T. Sugino, T. Obitsu, Metabolomic profiling reveals differential effects of

TE D

M AN U

SC

RI PT

glucagon‐like peptide‐1 and insulin on nutrient partitioning in ovine liver, Anim Sci J, 87 (2016) 1480-1489. [104] B. Ji, B. Ernest, J.R. Gooding, S. Das, A.M. Saxton, J. Simon, J. Dupont, S. Métayer-Coustard, S.R. Campagna, B.H. Voy, Transcriptomic and metabolomic profiling of chicken adipose tissue in response to insulin neutralization and fasting, BMC Genomics, 13 (2012) 441. [105] F. Saleem, B. Ametaj, S. Bouatra, R. Mandal, Q. Zebeli, S. Dunn, D. Wishart, A metabolomics approach to uncover the effects of grain diets on rumen health in dairy cows, J Dairy Sci, 95 (2012) 6606-6623. [106] S.A. Goldansaz, A.C. Guo, T. Sajed, M.A. Steele, G.S. Plastow, D.S. Wishart, Livestock metabolomics and the livestock metabolome: A systematic review, PLoS One, 12 (2017) e0177675. [107] B. Karisa, J. Thomson, Z. Wang, C. Li, Y. Montanholi, S. Miller, S. Moore, G. Plastow, Plasma metabolites associated with residual feed intake and other productivity performance traits in beef cattle, Livest Sci, 165 (2014) 200-211. [108] N. Melzer, D. Wittenburg, S. Hartwig, S. Jakubowski, U. Kesting, L. Willmitzer, J. Lisec, N. Reinsch, D. Repsilber, Investigating associations between milk metabolite profiles and milk traits of Holstein cows, J Dairy Sci, 96 (2013) 1521-1534. [109] V.M. Artegoitia, A.P. Foote, R.M. Lewis, H.C. Freetly, Rumen Fluid Metabolomics Analysis Associated with Feed Efficiency on Crossbred Steers, Sci Rep, 7 (2017). [110] A. Sharifi, Y. Zhang, S. Weigend, A. Weigend, R. Preisinger, M. Schmutz, L. Römisch-Margl, R. Jonczyk, M. Gülersönmez, H. Piepho, Association between Metabolites in Egg Yolk and Hatchability Traits in Laying Hens, 10th World Congress on Genetics Applied to Livestock Production: Proceedings; Vancouver, BC, Canada; August 17-22, 2014, 2014. [111] T. Cajka, H. Danhelova, A. Vavrecka, K. Riddellova, V. Kocourek, F. Vacha, J. Hajslova, Evaluation of direct analysis in real time ionization–mass spectrometry (DART–MS) in fish metabolomics aimed to assess the response to dietary supplementation, Talanta, 115 (2013) 263-270. [112] B. Wang, S. Mao, H. Yang, Y. Wu, J. Wang, S. Li, Z. Shen, J. Liu, Effects of alfalfa and cereal straw as a forage source on nutrient digestibility and lactation performance in lactating dairy cows, J Dairy Sci, 97 (2014) 7706-7715. [113] H.-Z. Sun, D.-M. Wang, B. Wang, J.-K. Wang, H.-Y. Liu, L.L. Guan, J.-X. Liu, Metabolomics of four biofluids from dairy cows: potential biomarkers for milk production and quality, J Proteome Res, 14 (2015) 1287-1298. [114] W. Dai, Q. Chen, Q. Wang, R.R. White, J. Liu, H. Liu, Complementary transcriptomic and proteomic analyses reveal regulatory mechanisms of milk protein production in dairy cows consuming different forages, Sci Rep, 7 (2017).

EP

[115] S. Jiao, H. Cao, Y. Dai, J. Wu, J. Lv, R. Du, B. Han, Effect of high‐fat diet and growth stage on the diversity and composition of intestinal microbiota in healthy bovine livestock, J Sci Food Agr, DOI (2017). [116] S.K.B. Shabat, G. Sasson, A. Doron-Faigenboim, T. Durman, S. Yaacoby, M.E.B. Miller, B.A. White, N. Shterzer, I. Mizrahi, Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants, ISME J, 10 (2016) 2958-2972. [117] F. Li, Metatranscriptomic profiling reveals linkages between the active rumen microbiome and feed efficiency in beef cattle, Appl Environ Microbiol, 83 (2017) e00061-00017. [118] W. Shi, C.D. Moon, S.C. Leahy, D. Kang, J. Froula, S. Kittelmann, C. Fan, S. Deutsch, D. Gagic, H. Seedorf, Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome, Genome Res, 24 (2014) 1517-1525. [119] J. Kamke, S. Kittelmann, P. Soni, Y. Li, M. Tavendale, S. Ganesh, P.H. Janssen, W. Shi, J. Froula, E.M. Rubin, Rumen metagenome and metatranscriptome analyses of low methane yield sheep reveals a Sharpea-enriched microbiome characterised by lactic acid formation and utilisation, Microbiome, 4 (2016) 56. [120] Z. Tan, T. Yang, Y. Wang, K. Xing, F. Zhang, X. Zhao, H. Ao, S. Chen, J. Liu, C. Wang, Metagenomic Analysis of Cecal Microbiome Identified Microbiota and Functional Capacities Associated with Feed Efficiency in Landrace Finishing Pigs, Front Microbiol, 8 (2017) 1546. [121] W. Yan, C. Sun, J. Yuan, N. Yang, Gut metagenomic analysis reveals prominent roles of Lactobacillus and cecal microbiota in chicken feed efficiency, Sci Rep, 7 (2017) 45308. [122] S. Kahai, S.-C. Lee, D.Y. Lee, J. Yang, M. Li, C.-H. Wang, Z. Jiang, Y. Zhang, C. Peng, B.B. Yang, MicroRNA miR-378 regulates nephronectin expression modulating osteoblast differentiation by targeting GalNT-7, PLoS One, 4 (2009) e7535.

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[123] A.J. Enright, B. John, U. Gaul, T. Tuschl, C. MANUSCRIPT Sander, D.S. Marks, MicroRNA targets in Drosophila, ACCEPTED Genome Biol, 5 (2003) R1.

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Table 1. The applications, advantages, and limitations of major analytical technologies applied in feedomics Analytical technologies Applications in feedomics Advantages Limitations Whole (meta)genomics and NextSeq High output range; High coverage Costly (meta)transcriptomics; Epigenomics Targeted DNA, RNA and microbial Short run time; High output per Ion Torrent PGM Low sequencing quality; High error rate sequencing hour Hi-C Genomes conformation [20] Can detect chromatin interactions Low resolution; High noise BeadChip DNA methylation; Genotyping [38-43] Not suitable for low frequency sites Cost-effective for large samples; Genotyping; DNA methylation; Gene Low dynamic range; Need probe design Simple workflow Microarray expression [58, 60], microRNA [66, 68] sequences Metagenomics [116, 119, 120]; Transcriptomics [35, 52-55, 61]; Metatranscriptomics [18, 117, 119]; High-throughput; Broader dynamic Short reads; High cost; High computation HiSeq MicroRNAome [67, 70, 71]; range needs MeDIP-Seq based epigenomics [78, 79]; Bisulfite-Seq based epigenomics [83] MiSeq 16S metagenomics [116, 121] Long reads Low output methylated DNA Easy and quick to perform; High DNA methylation rates [75] Lack specific sites information qualification kit accuracy Methylation and genomic variation, Pyrosequencing Long reads Low accuracy; Costly microbial diversity [101, 115, 118] Lack whole genome scan; Require highly ChIP-on-chip Histone modifications in epigenomics Aim at broad binding specific antibodies High resolution; High coverage; ChIP-Seq Histone modifications in epigenomics Costly; Relative slow Low sample loading Separation and qualitative analysis in LC-MS/MS Less sample loading; High proteomics [86, 88, 90]and Easy to be affected by the MS stability coverage; High accuracy HPLC-TOF/MS metabolomics [96, 104] Low accuracy MALDI-TOF/MS Identification and quantitative analysis High throughput; High sensitivity

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NMR

Untargeted metabolomics [95, 105]

GC-MS

Identification and relative quantitative analysis of untargeted and targeted metabolomics [94, 97-102, 105, 108, 110, 113]

UPLC-Q-TOF-MS/MS

Untargeted and targeted metabolome quantitative analysis [109]

High reproducibility; Simple sample preparation

High throughput and sensitivity Wide m/z range; High sensitivity Four dimensions of analytical resolution High quantitative stability; High selection; High scan speed Good quantitative reproducibility; High accuracy; High sensitivity

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Broad compatibility

Long analysis time Costly Only used for live cell

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Proteome quantitative analysis [86, 87, 92, 114]

High throughput; Time saving High label efficiency

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HPLC/ESI-MS i-TRAQ SILAC

Only label peptides containing cysteine, and can only mark two samples

Low sensitivity; Less feature metabolites observation; Poor database Complex extraction and derivatization process Relatively low qualitative ability Low robustness of instruments Costly Low resolution; Costly

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Animals Nelore cattle [40]

Hybrid bulls [37]

Pig [41]

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Name HRH4 ALDH7A1 APOA2 LIN7C CXADR ADAM12 MAP7 GHR CAST ACAD11 UGT3A1 XIRP2 TTC29 SOGA1 MAS1 GPCR GPK5 GPR155 ZFYVE26 HTR1B RGS3 ABCG2 AGPAT6 DGAT1 DHX15 MROH1 SLC37A1

Table 2. The information of different phenotypes related genes in feedomics Full name or location Tissues Traits Histamine Receptor H4 Blood Feed efficiency Aldehyde Dehydrogenase 7 Family Member A1 Apolipoprotein A2 Lin-7 Homolog C, Crumbs Cell Polarity Complex Component CXADR, Ig-Like Cell Adhesion Molecule ADAM Metallopeptidase Domain 12 Microtubule Associated Protein 7 Growth Hormone Receptor Liver Calpastatin Acyl-CoA Dehydrogenase Family Member 11 UDP Glycosyltransferase Family 3 Member A1 Xin Actin Binding Repeat Containing 2 Backfat Tetratricopeptide Repeat Domain 29 Suppressor Of Glucose, Autophagy Associated 1 MAS1 Proto-Oncogene G Protein-Coupled Receptor G Protein-Coupled Receptor Kinase 5 G Protein-Coupled Receptor 155 Zinc Finger FYVE-Type Containing 26 5-Hydroxytryptamine Receptor 1B Blood Regulator Of G Protein Signaling 3 ATP-binding cassette sub-family G member 2 Milk Milk protein 1-acylglycerol-3-phosphate O-acyltransferase 6 diacylglycerol O-acyltransferase 1 DEAH-box helicase 15 maestro heat like repeat family member 1 solute carrier family 37 member 1

Chicken [42] Dairy cow [43]

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Fat deposition

Pig [48], [49]

Ovarian

Japanese flounder

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BOP1 CSN1S1 CSN2 RECQL4 PAEP GPR120

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ALPL AHKH SEL1L3 MGST1 CSN1S2 MEPE PICALM SEPSECS

casein kappa polycystin 2, transient receptor potential cation channel HECT and RLD domain containing E3 ubiquitin protein ligase 3 alkaline phosphatase, liver/bone/kidney ANKH inorganic pyrophosphate transport regulator SEL1L family member 3 microsomal glutathione S-transferase 1 casein alpha-S2 matrix extracellular phosphoglycoprotein phosphatidylinositol binding clathrin assembly protein Sep (O-phosphoserine) tRNA:Sec (selenocysteine) tRNA synthase block of proliferation 1 casein alpha s1 casein beta RecQ like helicase 4 progestagen-associated endometrial protein G protein-coupled receptors Adipose, skeletal muscle, ileum, jejunum, duodenum, kidney, lung, spleen, liver, and heart; blood, muscle and ear

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CSN3 PKD2 HERC3

CYP19A1A

cytochrome P450, family 19, subfamily A, polypeptide 1

Ovary

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FOXL2 SMAD1

forkhead box L2 SMAD Family Member 1

TSC1 AKT1

Tuberous Sclerosis 1 AKT Serine/Threonine Kinase 1

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Blood muscle

development and Body size

[75] Sheep [78]

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miR-30a-5p

miR-181b miR-345-3p miR-1246 miR-196

Holstein cows [72] Holstein-Friesian heifers [71] Holstein [72]

cows

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miR-27b

cows

GORASP1, KIAA1109, AKIRIN1, NOVA1, ONECUT2, FAM65B, EBI3, PLK2, NGFRAP1, HOXA5, CCNC TNXB, AC002451.1, MKRN3, EED, DLGAP1, B3GNT5, LHX8, INO80D, RP11-160N1.10, ANKRA2, KLHL20, STX2, PIP4K2A, SRSF7, DESI2 Same as miR-181a DAZ4, C4orf6, ZNF616, ZNF189, PRR26, ATF7IP2, LINC00998, HMGCS1 CDR1as, TMPRSS11A, GSG1L, FUT9, ZNF23, ORC6, AL355390.1, LCE2D HOXC8, HOXA7, HOXB8, RP1-170O19.20, NR6A1, HOXA9, Adipose tissue

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miR-19a miR-19b

Animals Holstein [70]

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miR-26a

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miR-181a

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Name miR-99b miR-2336 miR-652 miR-1

Table 3. The information of different phenotypes related microRNAs in feedomics Tissues Traits Predicted targets1 ST6GALNAC4, HS3ST2, KBTBD8 Rumen Nitrogen efficiency C6ORF50, COLCA2, CD28, DOK4, LIN52, EVA1B, MATR3 Duodenum ISL1 Jejunum GJA1, POGK, ARCN1, SH3BGRL3, PGRMC1, TAGLN2, Liver TMSB4X, SLC44A1, TWF1 ZNF781, ZNF283, ZNF788, ZNF791, ZNF136, ZNF594, Mammary ZNF788, ZNF302, ZFP30, RBAK, CTD-2228K2.5, ZNF268, gland ZFP82, ZNF527, CTD-2140B24.4, ZNF140, CDON, UBN2, Serum Heat stress ZNF470, ZNF256, ZNF23 STRADB, CHORDC1, KLHL42, SLC25A16, HMGA2, TET2, Blood Early MAB21L1, TAF13, PIH1D3, CKS2, OSBPL11, THAP2, pregnancy CREBZF, UBN2 KBTBD8, ZMYND11, EXOC6B, C11orf96, FOXD4L2, UBL3, Serum Heat stress ENPP5, FGF6, CNOT6, CHIC1, PMEPA1, SGK1, SLC26A7, MDM4, LONRF1, ADRB1, RAP2C, PIK3CB, MPPED2, PTEN, HBP1, PFN2

Fat depot

Hereford×Aberde

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Lipid metabolism

Beef cattle [122]

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Immune Pig [69] responses to Salmonella infection miR-3570 MAVS Cell line Immune Teleost fish [73] Response to Rhabdovirus miR-378 Cell line Osteoblast [122] differentiation 1 Predicted targets, only selected the target gene with cumulative weighted context++ score less than -0.7. The target genes of selected miRNAs were commonly predicted by TargetScan Relase 6.0 (http://www.targetscan.org/) [123] and miRanda (http://www.microrna.org/microrna/home.do).

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miR-214 miR-331-3p

en Angus [66]

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miR-2454 miR-33a miR-1281

HMGA2, HOXB7, HAND1 HIST2H3A, AL592284.1, C22orf46, SLC11A1, GAB2, LTB, MSANTD1, LIF, TEX22, CTD-3214H19.16, FAM98A, ADAMTS13 ANKRD65 Blood TSPAN18, ZNF513

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Topic

Blood metabolomics Urine metabolomics

Digesta metabolomics Fecal metabolomics

Tissue metabolomics

Feed utilization Nutrient digest Interface with microbiome

Nutrients partition Specific function Output Metabolism evaluation (potential risk, toxicity)

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Partial

Saliva metabolomics

Well being Health Biomarker Comprehensive metabolism

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Type

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Overall

Level

(liver, muscle, adipose, mammary gland, rumen, gut)

Milk metabolomics

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Cell metabolomics

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Fig. 1. Study level, type, and topic in the application of metabolomics in food animal.

ACCEPTED MANUSCRIPT Feed,quality,assessment, Flavor,&contamination,&less7emission,&novel&additive Plant&genomics,&proteomics,&metabolomics,µRNA,&soilµbial& metagenomics

Health,impact Antimicrobial&resistance,&zoonosis

Biomolecules

Future,customer3traits,evaluation

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Biomolecules

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Biomolecules

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Metabolomics,&metagenomics,&metatranscriptomics

Natural healthy&molecules,&live&ingredients&

Biomolecules

Proteomics,&metabolomics,µbiomics

Fig. 2. Future directions of feedomics. The four compositions of feedomics (feed, food animal,

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products, and human) were illustrated with grey color. The grey arrow represents the currently studied contents in feedomics. The blue arrow represents the future research directions in

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feedomics. The corresponding targets and omics tools are also described in the figure.

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Advanced analytical techniques create a new era of feed science and animal nutrition Feedomics consists of different molecular and cellular omics methods

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Feedomics improve the efficiency and sustainability of animal production for food

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Feedomics contribute to animal and human health

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•

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security

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•