1. Aggregation of omic data and secretome prediction enable the discovery of candidate plasma biomarkers for beef tenderness
- Author
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Boudon, Sabrina, Henry-Berger, Joëlle, Cassar-Malek, Isabelle, Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Génétique, Reproduction et Développement (GReD), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Institut National de la Santé et de la Recherche Médicale (INSERM), regional council of Auvergne Rhone-Alpes (France) FEDER (Ressourcement S3, Europe), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), and ROSSI, Sabine
- Subjects
Proteomics ,data aggregation ,Quantitative Trait Loci ,Article ,tendrete de la viande ,lcsh:Chemistry ,secrétome ,meat tenderness ,[CHIM] Chemical Sciences ,Databases, Genetic ,[SDV.BBM] Life Sciences [q-bio]/Biochemistry, Molecular Biology ,Animals ,Data Mining ,[CHIM]Chemical Sciences ,Computer Simulation ,[SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology ,lcsh:QH301-705.5 ,plasma proteome ,biomarkers ,Blood Proteins ,agrégation ,Red Meat ,secretome ,lcsh:Biology (General) ,lcsh:QD1-999 ,Cattle ,biomarqueur - Abstract
Beef quality is a complex phenotype that can be evaluated only after animal slaughtering. Previous research has investigated the potential of genetic markers or muscle-derived proteins to assess beef tenderness. Thus, the use of low-invasive biomarkers in living animals is an issue for the beef sector. We hypothesized that publicly available data may help us discovering candidate plasma biomarkers. Thanks to a review of the literature, we built a corpus of articles on beef tenderness. Following data collection, aggregation, and computational reconstruction of the muscle secretome, the putative plasma proteins were searched by comparison with a bovine plasma proteome atlas and submitted to mining of biological information. Of the 44 publications included in the study, 469 unique gene names were extracted for aggregation. Seventy-one proteins putatively released in the plasma were revealed. Among them 13 proteins were predicted to be secreted in plasma, 44 proteins as hypothetically secreted in plasma, and 14 additional candidate proteins were detected thanks to network analysis. Among these 71 proteins, 24 were included in tenderness quantitative trait loci. The in-silico workflow enabled the discovery of candidate plasma biomarkers for beef tenderness from reconstruction of the secretome, to be examined in the cattle plasma proteome.
- Published
- 2020
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