Gene co-expression networks associated with carcass traits reveal new pathways for muscle and fat deposition in Nelore cattle View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Bárbara Silva-Vignato, Luiz L. Coutinho, Mirele D. Poleti, Aline S. M. Cesar, Cristina T. Moncau, Luciana C. A. Regitano, Júlio C. C. Balieiro

ABSTRACT

BACKGROUND: Positively correlated with carcass weight and animal growth, the ribeye area (REA) and the backfat thickness (BFT) are economic important carcass traits, which impact directly on producer's payment. The selection of these traits has not been satisfactory since they are expressed later in the animal's life and multigene regulated. So, next-generation technologies have been applied in this area to improve animal's selection and better understand the molecular mechanisms involved in the development of these traits. Correlation network analysis, performed by tools like WGCNA (Weighted Correlation Network Analysis), has been used to explore gene-gene interactions and gene-phenotype correlations. Thus, this study aimed to identify putative candidate genes and metabolic pathways that regulate REA and BFT by constructing a gene co-expression network using WGCNA and RNA sequencing data, to better understand genetic and molecular variations behind these complex traits in Nelore cattle. RESULTS: The gene co-expression network analysis, using WGCNA, were built using RNA-sequencing data normalized by transcript per million (TPM) from 43 Nelore steers. Forty-six gene clusters were constructed, between them, three were positively correlated (p-value< 0.1) to the BFT (Green Yellow, Ivory, and Light Yellow modules) and, one cluster was negatively correlated (p-value< 0.1) with REA (Salmon module). The enrichment analysis performed by DAVID and WebGestalt (FDR 5%) identified eight Gene Ontology (GO) terms and three KEGG pathways in the Green Yellow module, mostly associated with immune response and inflammatory mechanisms. The enrichment of the Salmon module demonstrated 19 GO terms and 21 KEGG pathways, related to muscle energy metabolism, lipid metabolism, muscle degradation, and oxidative stress diseases. The Ivory and Light yellow modules have not shown significant results in the enrichment analysis. CONCLUSION: With this study, we verified that inflammation and immune response pathways modulate the BFT trait. Energy and lipid metabolism pathways, highlighting fatty acid metabolism, were the central pathways associated with REA. Some genes, as RSAD2, EIF2AK2, ACAT1, and ACSL1 were considered as putative candidate related to these traits. Altogether these results allow us to a better comprehension of the molecular mechanisms that lead to muscle and fat deposition in bovine. More... »

PAGES

32

References to SciGraph publications

  • 2018-12. Gene co-expression networks in liver and muscle transcriptome reveal sex-specific gene expression in lambs fed with a mix of essential oils in BMC GENOMICS
  • 2017-12. Comparative muscle transcriptome associated with carcass traits of Nellore cattle in BMC GENOMICS
  • 2013-04. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions in GENOME BIOLOGY
  • 2018-12. Integrative analysis of microRNAs and mRNAs revealed regulation of composition and metabolism in Nelore cattle in BMC GENOMICS
  • 2008-12. WGCNA: an R package for weighted correlation network analysis in BMC BIOINFORMATICS
  • 2017-12. Global transcriptome analysis identifies differentially expressed genes related to lipid metabolism in Wagyu and Holstein cattle in SCIENTIFIC REPORTS
  • 2015-11. The Physiological Regulation of Skeletal Muscle Fatty Acid Supply and Oxidation During Moderate-Intensity Exercise in SPORTS MEDICINE
  • 2007-08-28. Weighted gene coexpression network analysis strategies applied to mouse weight in MAMMALIAN GENOME
  • 2009-01. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources in NATURE PROTOCOLS
  • 2014-12. Genome-wide association study for intramuscular fat deposition and composition in Nellore cattle in BMC GENETICS
  • 2011-03. Orchestrated downregulation of genes involved in oxidative metabolic pathways in obese vs. lean high-fat young male consumers in JOURNAL OF PHYSIOLOGY AND BIOCHEMISTRY
  • 2013-12. ATR-FTIR spectroscopy reveals genomic loci regulating the tissue response in high fat diet fed BXD recombinant inbred mouse strains in BMC GENOMICS
  • 2016-12. Mitochondrial fat oxidation is essential for lipid-induced inflammation in skeletal muscle in mice in SCIENTIFIC REPORTS
  • 2014-12. Whole genome association study identifies regions of the bovine genome and biological pathways involved in carcass trait performance in Holstein-Friesian cattle in BMC GENOMICS
  • 2016-12. Differences in the skeletal muscle transcriptome profile associated with extreme values of fatty acids content in BMC GENOMICS
  • 2016-04. Characterization of the promoter region of the bovine long-chain acyl-CoA synthetase 1 gene: Roles of E2F1, Sp1, KLF15, and E2F4 in SCIENTIFIC REPORTS
  • 2015-12. Liver transcriptomic networks reveal main biological processes associated with feed efficiency in beef cattle in BMC GENOMICS
  • 2010-12. Use of linear mixed models for genetic evaluation of gestation length and birth weight allowing for heavy-tailed residual effects in GENETICS SELECTION EVOLUTION
  • 2016-12. Transcriptome profiling of the rumen epithelium of beef cattle differing in residual feed intake in BMC GENOMICS
  • 2013-12. Correlated mRNAs and miRNAs from co-expression and regulatory networks affect porcine muscle and finally meat properties in BMC GENOMICS
  • 2018-01. De novo lipogenesis and desaturation of fatty acids during adipogenesis in bovine adipose-derived mesenchymal stem cells in IN VITRO CELLULAR & DEVELOPMENTAL BIOLOGY - ANIMAL
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s12864-018-5345-y

    DOI

    http://dx.doi.org/10.1186/s12864-018-5345-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1111316941

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30630417


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Genetics", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biological Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "University of Sao Paulo", 
              "id": "https://www.grid.ac/institutes/grid.11899.38", 
              "name": [
                "College of Agriculture \u201cLuiz de Queiroz\u201d, University of S\u00e3o Paulo, 13418-900, Piracicaba, SP, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Silva-Vignato", 
            "givenName": "B\u00e1rbara", 
            "id": "sg:person.016373277433.16", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016373277433.16"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Sao Paulo", 
              "id": "https://www.grid.ac/institutes/grid.11899.38", 
              "name": [
                "College of Agriculture \u201cLuiz de Queiroz\u201d, University of S\u00e3o Paulo, 13418-900, Piracicaba, SP, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Coutinho", 
            "givenName": "Luiz L.", 
            "id": "sg:person.01276461341.52", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01276461341.52"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Sao Paulo", 
              "id": "https://www.grid.ac/institutes/grid.11899.38", 
              "name": [
                "College of Animal Science and Food Engineering, University of S\u00e3o Paulo, 13635-900, Pirassununga, SP, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Poleti", 
            "givenName": "Mirele D.", 
            "id": "sg:person.01246031267.68", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01246031267.68"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Sao Paulo", 
              "id": "https://www.grid.ac/institutes/grid.11899.38", 
              "name": [
                "College of Agriculture \u201cLuiz de Queiroz\u201d, University of S\u00e3o Paulo, 13418-900, Piracicaba, SP, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Cesar", 
            "givenName": "Aline S. M.", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Federal University of Lavras", 
              "id": "https://www.grid.ac/institutes/grid.411269.9", 
              "name": [
                "Federal University of Lavras, 37200-000, Lavras, MG, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Moncau", 
            "givenName": "Cristina T.", 
            "id": "sg:person.01330071742.77", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01330071742.77"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Brazilian Agricultural Research Corporation", 
              "id": "https://www.grid.ac/institutes/grid.460200.0", 
              "name": [
                "Embrapa Pecu\u00e1ria Sudeste, 13560-970, S\u00e3o Carlos, SP, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Regitano", 
            "givenName": "Luciana C. A.", 
            "id": "sg:person.015714346774.34", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015714346774.34"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Sao Paulo", 
              "id": "https://www.grid.ac/institutes/grid.11899.38", 
              "name": [
                "College of Veterinary Medicine and Animal Science, University of S\u00e3o Paulo, 13635-900, Pirassununga, SP, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Balieiro", 
            "givenName": "J\u00falio C. C.", 
            "id": "sg:person.010047631174.10", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010047631174.10"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.jaci.2013.06.022", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000942456"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.meatsci.2015.10.014", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003021360"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/eji.201545502", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003349031"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-015-2292-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004674184", 
              "https://doi.org/10.1186/s12864-015-2292-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1590/s1516-35982012001100007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005507429"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s40279-015-0394-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006297338", 
              "https://doi.org/10.1007/s40279-015-0394-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s40279-015-0394-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006297338", 
              "https://doi.org/10.1007/s40279-015-0394-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.semcdb.2016.12.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006380794"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1590/s1516-35982012000400020", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006444936"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep37941", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007082881", 
              "https://doi.org/10.1038/srep37941"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1297-9686-42-26", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009300923", 
              "https://doi.org/10.1186/1297-9686-42-26"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-15-837", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009508517", 
              "https://doi.org/10.1186/1471-2164-15-837"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1152/ajpendo.90926.2008", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009680936"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.5713/ajas.2010.r.03", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011230907"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2013-14-4-r36", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015459845", 
              "https://doi.org/10.1186/gb-2013-14-4-r36"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2337/db13-1364", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016828556"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1074/mcp.m114.044222", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017062908"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1590/s1516-35982011000700023", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017067304"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.18632/oncotarget.11522", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018747775"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00335-007-9043-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019846773", 
              "https://doi.org/10.1007/s00335-007-9043-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00335-007-9043-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019846773", 
              "https://doi.org/10.1007/s00335-007-9043-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-9-559", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020312314", 
              "https://doi.org/10.1186/1471-2105-9-559"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0123678", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021180436"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1146/annurev-immunol-031210-101322", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022021239"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2156-15-39", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022386366", 
              "https://doi.org/10.1186/1471-2156-15-39"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-14-386", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022625761", 
              "https://doi.org/10.1186/1471-2164-14-386"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0026683", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023100850"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jneuroim.2016.01.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024895749"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cell.2010.01.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025759929"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1146/annurev.nutr.20.1.77", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026798846"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/srep19661", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027961259", 
              "https://doi.org/10.1038/srep19661"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s13105-010-0044-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029302416", 
              "https://doi.org/10.1007/s13105-010-0044-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.celrep.2016.12.028", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030076409"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1152/physiolgenomics.00066.2013", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031061530"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-016-2935-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034254957", 
              "https://doi.org/10.1186/s12864-016-2935-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-016-2935-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034254957", 
              "https://doi.org/10.1186/s12864-016-2935-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.bbalip.2009.09.014", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034610266"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.livsci.2013.01.004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035180658"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nprot.2008.211", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039987283", 
              "https://doi.org/10.1038/nprot.2008.211"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jprot.2014.09.014", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040231933"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-016-3306-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044752184", 
              "https://doi.org/10.1186/s12864-016-3306-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-016-3306-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044752184", 
              "https://doi.org/10.1186/s12864-016-3306-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1124/pr.109.001560", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044757347"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2152/jmi.61.270", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045255959"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.gene.2012.11.029", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045573258"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.mce.2014.06.018", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047080765"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.livsci.2014.06.015", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048820962"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1074/jbc.m604516200", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048887603"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btu638", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053282140"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0911679106", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053517325"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2164-14-533", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053641191", 
              "https://doi.org/10.1186/1471-2164-14-533"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2527/jas.2008-1028", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1070886506"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2527/jas.2012-6089", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1070888796"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2527/jas.2015-9280", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1070890240"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2527/jas.2016-0632", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1070890720"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.4238/2014.april.29.3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1072392018"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.4238/gmr.15028280", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1072395798"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.5713/ajas.14.0811", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1073082793"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3168/jds.s0022-0302(06)72064-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1077162289"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.bbrc.2017.03.078", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084061217"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.redox.2017.02.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084105501"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/nar/gkx356", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085320790"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-017-3897-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1090274818", 
              "https://doi.org/10.1186/s12864-017-3897-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-017-3897-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1090274818", 
              "https://doi.org/10.1186/s12864-017-3897-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-017-05702-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1090553753", 
              "https://doi.org/10.1038/s41598-017-05702-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0184120", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091547799"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ymgme.2017.11.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093054843"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11626-017-0205-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093097472", 
              "https://doi.org/10.1007/s11626-017-0205-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.7717/peerj.4107", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093169319"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-018-4514-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100864391", 
              "https://doi.org/10.1186/s12864-018-4514-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jprot.2018.02.028", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101321777"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-018-4632-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103128560", 
              "https://doi.org/10.1186/s12864-018-4632-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-018-4632-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103128560", 
              "https://doi.org/10.1186/s12864-018-4632-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s12864-018-4632-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103128560", 
              "https://doi.org/10.1186/s12864-018-4632-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1371/journal.pone.0193875", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103154480"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-12", 
        "datePublishedReg": "2019-12-01", 
        "description": "BACKGROUND: Positively correlated with carcass weight and animal growth, the ribeye area (REA) and the backfat thickness (BFT) are economic important carcass traits, which impact directly on producer's payment. The selection of these traits has not been satisfactory since they are expressed later in the animal's life and multigene regulated. So, next-generation technologies have been applied in this area to improve animal's selection and better understand the molecular mechanisms involved in the development of these traits. Correlation network analysis, performed by tools like WGCNA (Weighted Correlation Network Analysis), has been used to explore gene-gene interactions and gene-phenotype correlations. Thus, this study aimed to identify putative candidate genes and metabolic pathways that regulate REA and BFT by constructing a gene co-expression network using WGCNA and RNA sequencing data, to better understand genetic and molecular variations behind these complex traits in Nelore cattle.\nRESULTS: The gene co-expression network analysis, using WGCNA, were built using RNA-sequencing data normalized by transcript per million (TPM) from 43 Nelore steers. Forty-six gene clusters were constructed, between them, three were positively correlated (p-value<\u20090.1) to the BFT (Green Yellow, Ivory, and Light Yellow modules) and, one cluster was negatively correlated (p-value<\u20090.1) with REA (Salmon module). The enrichment analysis performed by DAVID and WebGestalt (FDR 5%) identified eight Gene Ontology (GO) terms and three KEGG pathways in the Green Yellow module, mostly associated with immune response and inflammatory mechanisms. The enrichment of the Salmon module demonstrated 19 GO terms and 21 KEGG pathways, related to muscle energy metabolism, lipid metabolism, muscle degradation, and oxidative stress diseases. The Ivory and Light yellow modules have not shown significant results in the enrichment analysis.\nCONCLUSION: With this study, we verified that inflammation and immune response pathways modulate the BFT trait. Energy and lipid metabolism pathways, highlighting fatty acid metabolism, were the central pathways associated with REA. Some genes, as RSAD2, EIF2AK2, ACAT1, and ACSL1 were considered as putative candidate related to these traits. Altogether these results allow us to a better comprehension of the molecular mechanisms that lead to muscle and fat deposition in bovine.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1186/s12864-018-5345-y", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.4473555", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1023790", 
            "issn": [
              "1471-2164"
            ], 
            "name": "BMC Genomics", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "20"
          }
        ], 
        "name": "Gene co-expression networks associated with carcass traits reveal new pathways for muscle and fat deposition in Nelore cattle", 
        "pagination": "32", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "2a22ab168b24f8e674e96bae36555c74662babb9ee7ce378168ff57e93a4f72d"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "30630417"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "100965258"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/s12864-018-5345-y"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1111316941"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/s12864-018-5345-y", 
          "https://app.dimensions.ai/details/publication/pub.1111316941"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T08:41", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000320_0000000320/records_101370_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1186%2Fs12864-018-5345-y"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s12864-018-5345-y'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12864-018-5345-y'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12864-018-5345-y'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12864-018-5345-y'


     

    This table displays all metadata directly associated to this object as RDF triples.

    344 TRIPLES      21 PREDICATES      97 URIs      21 LITERALS      9 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/s12864-018-5345-y schema:about anzsrc-for:06
    2 anzsrc-for:0604
    3 schema:author N843f77a92aec4b6fb04cdc112346a8ca
    4 schema:citation sg:pub.10.1007/s00335-007-9043-3
    5 sg:pub.10.1007/s11626-017-0205-7
    6 sg:pub.10.1007/s13105-010-0044-4
    7 sg:pub.10.1007/s40279-015-0394-8
    8 sg:pub.10.1038/nprot.2008.211
    9 sg:pub.10.1038/s41598-017-05702-5
    10 sg:pub.10.1038/srep19661
    11 sg:pub.10.1038/srep37941
    12 sg:pub.10.1186/1297-9686-42-26
    13 sg:pub.10.1186/1471-2105-9-559
    14 sg:pub.10.1186/1471-2156-15-39
    15 sg:pub.10.1186/1471-2164-14-386
    16 sg:pub.10.1186/1471-2164-14-533
    17 sg:pub.10.1186/1471-2164-15-837
    18 sg:pub.10.1186/gb-2013-14-4-r36
    19 sg:pub.10.1186/s12864-015-2292-8
    20 sg:pub.10.1186/s12864-016-2935-4
    21 sg:pub.10.1186/s12864-016-3306-x
    22 sg:pub.10.1186/s12864-017-3897-x
    23 sg:pub.10.1186/s12864-018-4514-3
    24 sg:pub.10.1186/s12864-018-4632-y
    25 https://doi.org/10.1002/eji.201545502
    26 https://doi.org/10.1016/j.bbalip.2009.09.014
    27 https://doi.org/10.1016/j.bbrc.2017.03.078
    28 https://doi.org/10.1016/j.cell.2010.01.001
    29 https://doi.org/10.1016/j.celrep.2016.12.028
    30 https://doi.org/10.1016/j.gene.2012.11.029
    31 https://doi.org/10.1016/j.jaci.2013.06.022
    32 https://doi.org/10.1016/j.jneuroim.2016.01.010
    33 https://doi.org/10.1016/j.jprot.2014.09.014
    34 https://doi.org/10.1016/j.jprot.2018.02.028
    35 https://doi.org/10.1016/j.livsci.2013.01.004
    36 https://doi.org/10.1016/j.livsci.2014.06.015
    37 https://doi.org/10.1016/j.mce.2014.06.018
    38 https://doi.org/10.1016/j.meatsci.2015.10.014
    39 https://doi.org/10.1016/j.redox.2017.02.011
    40 https://doi.org/10.1016/j.semcdb.2016.12.009
    41 https://doi.org/10.1016/j.ymgme.2017.11.011
    42 https://doi.org/10.1073/pnas.0911679106
    43 https://doi.org/10.1074/jbc.m604516200
    44 https://doi.org/10.1074/mcp.m114.044222
    45 https://doi.org/10.1093/bioinformatics/btu638
    46 https://doi.org/10.1093/nar/gkx356
    47 https://doi.org/10.1124/pr.109.001560
    48 https://doi.org/10.1146/annurev-immunol-031210-101322
    49 https://doi.org/10.1146/annurev.nutr.20.1.77
    50 https://doi.org/10.1152/ajpendo.90926.2008
    51 https://doi.org/10.1152/physiolgenomics.00066.2013
    52 https://doi.org/10.1371/journal.pone.0026683
    53 https://doi.org/10.1371/journal.pone.0123678
    54 https://doi.org/10.1371/journal.pone.0184120
    55 https://doi.org/10.1371/journal.pone.0193875
    56 https://doi.org/10.1590/s1516-35982011000700023
    57 https://doi.org/10.1590/s1516-35982012000400020
    58 https://doi.org/10.1590/s1516-35982012001100007
    59 https://doi.org/10.18632/oncotarget.11522
    60 https://doi.org/10.2152/jmi.61.270
    61 https://doi.org/10.2337/db13-1364
    62 https://doi.org/10.2527/jas.2008-1028
    63 https://doi.org/10.2527/jas.2012-6089
    64 https://doi.org/10.2527/jas.2015-9280
    65 https://doi.org/10.2527/jas.2016-0632
    66 https://doi.org/10.3168/jds.s0022-0302(06)72064-1
    67 https://doi.org/10.4238/2014.april.29.3
    68 https://doi.org/10.4238/gmr.15028280
    69 https://doi.org/10.5713/ajas.14.0811
    70 https://doi.org/10.5713/ajas.2010.r.03
    71 https://doi.org/10.7717/peerj.4107
    72 schema:datePublished 2019-12
    73 schema:datePublishedReg 2019-12-01
    74 schema:description BACKGROUND: Positively correlated with carcass weight and animal growth, the ribeye area (REA) and the backfat thickness (BFT) are economic important carcass traits, which impact directly on producer's payment. The selection of these traits has not been satisfactory since they are expressed later in the animal's life and multigene regulated. So, next-generation technologies have been applied in this area to improve animal's selection and better understand the molecular mechanisms involved in the development of these traits. Correlation network analysis, performed by tools like WGCNA (Weighted Correlation Network Analysis), has been used to explore gene-gene interactions and gene-phenotype correlations. Thus, this study aimed to identify putative candidate genes and metabolic pathways that regulate REA and BFT by constructing a gene co-expression network using WGCNA and RNA sequencing data, to better understand genetic and molecular variations behind these complex traits in Nelore cattle. RESULTS: The gene co-expression network analysis, using WGCNA, were built using RNA-sequencing data normalized by transcript per million (TPM) from 43 Nelore steers. Forty-six gene clusters were constructed, between them, three were positively correlated (p-value< 0.1) to the BFT (Green Yellow, Ivory, and Light Yellow modules) and, one cluster was negatively correlated (p-value< 0.1) with REA (Salmon module). The enrichment analysis performed by DAVID and WebGestalt (FDR 5%) identified eight Gene Ontology (GO) terms and three KEGG pathways in the Green Yellow module, mostly associated with immune response and inflammatory mechanisms. The enrichment of the Salmon module demonstrated 19 GO terms and 21 KEGG pathways, related to muscle energy metabolism, lipid metabolism, muscle degradation, and oxidative stress diseases. The Ivory and Light yellow modules have not shown significant results in the enrichment analysis. CONCLUSION: With this study, we verified that inflammation and immune response pathways modulate the BFT trait. Energy and lipid metabolism pathways, highlighting fatty acid metabolism, were the central pathways associated with REA. Some genes, as RSAD2, EIF2AK2, ACAT1, and ACSL1 were considered as putative candidate related to these traits. Altogether these results allow us to a better comprehension of the molecular mechanisms that lead to muscle and fat deposition in bovine.
    75 schema:genre research_article
    76 schema:inLanguage en
    77 schema:isAccessibleForFree true
    78 schema:isPartOf N6484c3ec9eaf40f7a7c0185afd44b3e5
    79 N7003f27ae00c4acaa5c56771774539b8
    80 sg:journal.1023790
    81 schema:name Gene co-expression networks associated with carcass traits reveal new pathways for muscle and fat deposition in Nelore cattle
    82 schema:pagination 32
    83 schema:productId N7a2694261ba94909b3a3f47186471799
    84 N9ffa5d455d244394bedb1257dba2020c
    85 Nc6c842203b5742a7bdc365932572a355
    86 Nd0cde1bd6bbe49eba5b15abe6d5039a0
    87 Nd6b716d085544cf4b5b4eface1065e5a
    88 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111316941
    89 https://doi.org/10.1186/s12864-018-5345-y
    90 schema:sdDatePublished 2019-04-11T08:41
    91 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    92 schema:sdPublisher Nee98b437f74649e88f87a5577a9a492f
    93 schema:url https://link.springer.com/10.1186%2Fs12864-018-5345-y
    94 sgo:license sg:explorer/license/
    95 sgo:sdDataset articles
    96 rdf:type schema:ScholarlyArticle
    97 N321cd92b9f3a4813842c89fbf26df509 schema:affiliation https://www.grid.ac/institutes/grid.11899.38
    98 schema:familyName Cesar
    99 schema:givenName Aline S. M.
    100 rdf:type schema:Person
    101 N5f96359d27a943a6a81095f1f55fef8a rdf:first sg:person.015714346774.34
    102 rdf:rest Nc4c8e03c0dd04f618b856be370614f3d
    103 N6484c3ec9eaf40f7a7c0185afd44b3e5 schema:issueNumber 1
    104 rdf:type schema:PublicationIssue
    105 N7003f27ae00c4acaa5c56771774539b8 schema:volumeNumber 20
    106 rdf:type schema:PublicationVolume
    107 N7a2694261ba94909b3a3f47186471799 schema:name readcube_id
    108 schema:value 2a22ab168b24f8e674e96bae36555c74662babb9ee7ce378168ff57e93a4f72d
    109 rdf:type schema:PropertyValue
    110 N843f77a92aec4b6fb04cdc112346a8ca rdf:first sg:person.016373277433.16
    111 rdf:rest Naca91da5c51149afafde64711880d40a
    112 N9ffa5d455d244394bedb1257dba2020c schema:name dimensions_id
    113 schema:value pub.1111316941
    114 rdf:type schema:PropertyValue
    115 Naca91da5c51149afafde64711880d40a rdf:first sg:person.01276461341.52
    116 rdf:rest Ne16079880b554105914b78836945970e
    117 Nc4c8e03c0dd04f618b856be370614f3d rdf:first sg:person.010047631174.10
    118 rdf:rest rdf:nil
    119 Nc6c842203b5742a7bdc365932572a355 schema:name pubmed_id
    120 schema:value 30630417
    121 rdf:type schema:PropertyValue
    122 Nd0cde1bd6bbe49eba5b15abe6d5039a0 schema:name doi
    123 schema:value 10.1186/s12864-018-5345-y
    124 rdf:type schema:PropertyValue
    125 Nd6b716d085544cf4b5b4eface1065e5a schema:name nlm_unique_id
    126 schema:value 100965258
    127 rdf:type schema:PropertyValue
    128 Ne16079880b554105914b78836945970e rdf:first sg:person.01246031267.68
    129 rdf:rest Nf63605ee6e6a46c2b98a7a6c4920cc60
    130 Ne28c3426c39e4458af956348a54649d3 rdf:first sg:person.01330071742.77
    131 rdf:rest N5f96359d27a943a6a81095f1f55fef8a
    132 Nee98b437f74649e88f87a5577a9a492f schema:name Springer Nature - SN SciGraph project
    133 rdf:type schema:Organization
    134 Nf63605ee6e6a46c2b98a7a6c4920cc60 rdf:first N321cd92b9f3a4813842c89fbf26df509
    135 rdf:rest Ne28c3426c39e4458af956348a54649d3
    136 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    137 schema:name Biological Sciences
    138 rdf:type schema:DefinedTerm
    139 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    140 schema:name Genetics
    141 rdf:type schema:DefinedTerm
    142 sg:grant.4473555 http://pending.schema.org/fundedItem sg:pub.10.1186/s12864-018-5345-y
    143 rdf:type schema:MonetaryGrant
    144 sg:journal.1023790 schema:issn 1471-2164
    145 schema:name BMC Genomics
    146 rdf:type schema:Periodical
    147 sg:person.010047631174.10 schema:affiliation https://www.grid.ac/institutes/grid.11899.38
    148 schema:familyName Balieiro
    149 schema:givenName Júlio C. C.
    150 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010047631174.10
    151 rdf:type schema:Person
    152 sg:person.01246031267.68 schema:affiliation https://www.grid.ac/institutes/grid.11899.38
    153 schema:familyName Poleti
    154 schema:givenName Mirele D.
    155 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01246031267.68
    156 rdf:type schema:Person
    157 sg:person.01276461341.52 schema:affiliation https://www.grid.ac/institutes/grid.11899.38
    158 schema:familyName Coutinho
    159 schema:givenName Luiz L.
    160 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01276461341.52
    161 rdf:type schema:Person
    162 sg:person.01330071742.77 schema:affiliation https://www.grid.ac/institutes/grid.411269.9
    163 schema:familyName Moncau
    164 schema:givenName Cristina T.
    165 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01330071742.77
    166 rdf:type schema:Person
    167 sg:person.015714346774.34 schema:affiliation https://www.grid.ac/institutes/grid.460200.0
    168 schema:familyName Regitano
    169 schema:givenName Luciana C. A.
    170 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015714346774.34
    171 rdf:type schema:Person
    172 sg:person.016373277433.16 schema:affiliation https://www.grid.ac/institutes/grid.11899.38
    173 schema:familyName Silva-Vignato
    174 schema:givenName Bárbara
    175 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016373277433.16
    176 rdf:type schema:Person
    177 sg:pub.10.1007/s00335-007-9043-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019846773
    178 https://doi.org/10.1007/s00335-007-9043-3
    179 rdf:type schema:CreativeWork
    180 sg:pub.10.1007/s11626-017-0205-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093097472
    181 https://doi.org/10.1007/s11626-017-0205-7
    182 rdf:type schema:CreativeWork
    183 sg:pub.10.1007/s13105-010-0044-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029302416
    184 https://doi.org/10.1007/s13105-010-0044-4
    185 rdf:type schema:CreativeWork
    186 sg:pub.10.1007/s40279-015-0394-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006297338
    187 https://doi.org/10.1007/s40279-015-0394-8
    188 rdf:type schema:CreativeWork
    189 sg:pub.10.1038/nprot.2008.211 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039987283
    190 https://doi.org/10.1038/nprot.2008.211
    191 rdf:type schema:CreativeWork
    192 sg:pub.10.1038/s41598-017-05702-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090553753
    193 https://doi.org/10.1038/s41598-017-05702-5
    194 rdf:type schema:CreativeWork
    195 sg:pub.10.1038/srep19661 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027961259
    196 https://doi.org/10.1038/srep19661
    197 rdf:type schema:CreativeWork
    198 sg:pub.10.1038/srep37941 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007082881
    199 https://doi.org/10.1038/srep37941
    200 rdf:type schema:CreativeWork
    201 sg:pub.10.1186/1297-9686-42-26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009300923
    202 https://doi.org/10.1186/1297-9686-42-26
    203 rdf:type schema:CreativeWork
    204 sg:pub.10.1186/1471-2105-9-559 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020312314
    205 https://doi.org/10.1186/1471-2105-9-559
    206 rdf:type schema:CreativeWork
    207 sg:pub.10.1186/1471-2156-15-39 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022386366
    208 https://doi.org/10.1186/1471-2156-15-39
    209 rdf:type schema:CreativeWork
    210 sg:pub.10.1186/1471-2164-14-386 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022625761
    211 https://doi.org/10.1186/1471-2164-14-386
    212 rdf:type schema:CreativeWork
    213 sg:pub.10.1186/1471-2164-14-533 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053641191
    214 https://doi.org/10.1186/1471-2164-14-533
    215 rdf:type schema:CreativeWork
    216 sg:pub.10.1186/1471-2164-15-837 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009508517
    217 https://doi.org/10.1186/1471-2164-15-837
    218 rdf:type schema:CreativeWork
    219 sg:pub.10.1186/gb-2013-14-4-r36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015459845
    220 https://doi.org/10.1186/gb-2013-14-4-r36
    221 rdf:type schema:CreativeWork
    222 sg:pub.10.1186/s12864-015-2292-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004674184
    223 https://doi.org/10.1186/s12864-015-2292-8
    224 rdf:type schema:CreativeWork
    225 sg:pub.10.1186/s12864-016-2935-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034254957
    226 https://doi.org/10.1186/s12864-016-2935-4
    227 rdf:type schema:CreativeWork
    228 sg:pub.10.1186/s12864-016-3306-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1044752184
    229 https://doi.org/10.1186/s12864-016-3306-x
    230 rdf:type schema:CreativeWork
    231 sg:pub.10.1186/s12864-017-3897-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1090274818
    232 https://doi.org/10.1186/s12864-017-3897-x
    233 rdf:type schema:CreativeWork
    234 sg:pub.10.1186/s12864-018-4514-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100864391
    235 https://doi.org/10.1186/s12864-018-4514-3
    236 rdf:type schema:CreativeWork
    237 sg:pub.10.1186/s12864-018-4632-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1103128560
    238 https://doi.org/10.1186/s12864-018-4632-y
    239 rdf:type schema:CreativeWork
    240 https://doi.org/10.1002/eji.201545502 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003349031
    241 rdf:type schema:CreativeWork
    242 https://doi.org/10.1016/j.bbalip.2009.09.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034610266
    243 rdf:type schema:CreativeWork
    244 https://doi.org/10.1016/j.bbrc.2017.03.078 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084061217
    245 rdf:type schema:CreativeWork
    246 https://doi.org/10.1016/j.cell.2010.01.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025759929
    247 rdf:type schema:CreativeWork
    248 https://doi.org/10.1016/j.celrep.2016.12.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030076409
    249 rdf:type schema:CreativeWork
    250 https://doi.org/10.1016/j.gene.2012.11.029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045573258
    251 rdf:type schema:CreativeWork
    252 https://doi.org/10.1016/j.jaci.2013.06.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000942456
    253 rdf:type schema:CreativeWork
    254 https://doi.org/10.1016/j.jneuroim.2016.01.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024895749
    255 rdf:type schema:CreativeWork
    256 https://doi.org/10.1016/j.jprot.2014.09.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040231933
    257 rdf:type schema:CreativeWork
    258 https://doi.org/10.1016/j.jprot.2018.02.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101321777
    259 rdf:type schema:CreativeWork
    260 https://doi.org/10.1016/j.livsci.2013.01.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035180658
    261 rdf:type schema:CreativeWork
    262 https://doi.org/10.1016/j.livsci.2014.06.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048820962
    263 rdf:type schema:CreativeWork
    264 https://doi.org/10.1016/j.mce.2014.06.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047080765
    265 rdf:type schema:CreativeWork
    266 https://doi.org/10.1016/j.meatsci.2015.10.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003021360
    267 rdf:type schema:CreativeWork
    268 https://doi.org/10.1016/j.redox.2017.02.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084105501
    269 rdf:type schema:CreativeWork
    270 https://doi.org/10.1016/j.semcdb.2016.12.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006380794
    271 rdf:type schema:CreativeWork
    272 https://doi.org/10.1016/j.ymgme.2017.11.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093054843
    273 rdf:type schema:CreativeWork
    274 https://doi.org/10.1073/pnas.0911679106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053517325
    275 rdf:type schema:CreativeWork
    276 https://doi.org/10.1074/jbc.m604516200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048887603
    277 rdf:type schema:CreativeWork
    278 https://doi.org/10.1074/mcp.m114.044222 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017062908
    279 rdf:type schema:CreativeWork
    280 https://doi.org/10.1093/bioinformatics/btu638 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053282140
    281 rdf:type schema:CreativeWork
    282 https://doi.org/10.1093/nar/gkx356 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085320790
    283 rdf:type schema:CreativeWork
    284 https://doi.org/10.1124/pr.109.001560 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044757347
    285 rdf:type schema:CreativeWork
    286 https://doi.org/10.1146/annurev-immunol-031210-101322 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022021239
    287 rdf:type schema:CreativeWork
    288 https://doi.org/10.1146/annurev.nutr.20.1.77 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026798846
    289 rdf:type schema:CreativeWork
    290 https://doi.org/10.1152/ajpendo.90926.2008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009680936
    291 rdf:type schema:CreativeWork
    292 https://doi.org/10.1152/physiolgenomics.00066.2013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031061530
    293 rdf:type schema:CreativeWork
    294 https://doi.org/10.1371/journal.pone.0026683 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023100850
    295 rdf:type schema:CreativeWork
    296 https://doi.org/10.1371/journal.pone.0123678 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021180436
    297 rdf:type schema:CreativeWork
    298 https://doi.org/10.1371/journal.pone.0184120 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091547799
    299 rdf:type schema:CreativeWork
    300 https://doi.org/10.1371/journal.pone.0193875 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103154480
    301 rdf:type schema:CreativeWork
    302 https://doi.org/10.1590/s1516-35982011000700023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017067304
    303 rdf:type schema:CreativeWork
    304 https://doi.org/10.1590/s1516-35982012000400020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006444936
    305 rdf:type schema:CreativeWork
    306 https://doi.org/10.1590/s1516-35982012001100007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005507429
    307 rdf:type schema:CreativeWork
    308 https://doi.org/10.18632/oncotarget.11522 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018747775
    309 rdf:type schema:CreativeWork
    310 https://doi.org/10.2152/jmi.61.270 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045255959
    311 rdf:type schema:CreativeWork
    312 https://doi.org/10.2337/db13-1364 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016828556
    313 rdf:type schema:CreativeWork
    314 https://doi.org/10.2527/jas.2008-1028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070886506
    315 rdf:type schema:CreativeWork
    316 https://doi.org/10.2527/jas.2012-6089 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070888796
    317 rdf:type schema:CreativeWork
    318 https://doi.org/10.2527/jas.2015-9280 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070890240
    319 rdf:type schema:CreativeWork
    320 https://doi.org/10.2527/jas.2016-0632 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070890720
    321 rdf:type schema:CreativeWork
    322 https://doi.org/10.3168/jds.s0022-0302(06)72064-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1077162289
    323 rdf:type schema:CreativeWork
    324 https://doi.org/10.4238/2014.april.29.3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072392018
    325 rdf:type schema:CreativeWork
    326 https://doi.org/10.4238/gmr.15028280 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072395798
    327 rdf:type schema:CreativeWork
    328 https://doi.org/10.5713/ajas.14.0811 schema:sameAs https://app.dimensions.ai/details/publication/pub.1073082793
    329 rdf:type schema:CreativeWork
    330 https://doi.org/10.5713/ajas.2010.r.03 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011230907
    331 rdf:type schema:CreativeWork
    332 https://doi.org/10.7717/peerj.4107 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093169319
    333 rdf:type schema:CreativeWork
    334 https://www.grid.ac/institutes/grid.11899.38 schema:alternateName University of Sao Paulo
    335 schema:name College of Agriculture “Luiz de Queiroz”, University of São Paulo, 13418-900, Piracicaba, SP, Brazil
    336 College of Animal Science and Food Engineering, University of São Paulo, 13635-900, Pirassununga, SP, Brazil
    337 College of Veterinary Medicine and Animal Science, University of São Paulo, 13635-900, Pirassununga, SP, Brazil
    338 rdf:type schema:Organization
    339 https://www.grid.ac/institutes/grid.411269.9 schema:alternateName Federal University of Lavras
    340 schema:name Federal University of Lavras, 37200-000, Lavras, MG, Brazil
    341 rdf:type schema:Organization
    342 https://www.grid.ac/institutes/grid.460200.0 schema:alternateName Brazilian Agricultural Research Corporation
    343 schema:name Embrapa Pecuária Sudeste, 13560-970, São Carlos, SP, Brazil
    344 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...