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Molecular signatures of muscle growth and composition deciphered by the meta-analysis of age-related public transcriptomics data

Abstract : The lean-to-fat ratio is a major issue in the beef meat industry from both carcass and meat production perspectives. This industrial perspective has motivated meat physiologists to use transcriptomics technologies to decipher mechanisms behind fat deposition within muscle during the time course of muscle growth. However, synthetic biological information from this volume of data remains to be produced to identify mechanisms found in various breeds and rearing practices. We conducted a meta-analysis on 10 transcriptomic data sets stored in public databases, from the longissimus thoracis of five different bovine breeds divergent by age. We updated gene identifiers on the last version of the bovine genome (UCD1.2), and the 715 genes common to the 10 studies were subjected to the meta-analysis. Of the 238 genes differentially expressed (DEG), we identified a transcriptional signature of the dynamic regulation of glycolytic and oxidative metabolisms that agrees with a known shift between those two pathways from the animal puberty. We proposed some master genes of the myogenesis, namely MYOG and MAPK14, as probable regulators of the glycolytic and oxidative metabolisms. We also identified overexpressed genes related to lipid metabolism (APOE, LDLR, MXRA8, and HSP90AA1) that may contribute to the expected enhanced marbling as age increases. Lastly. we proposed a transcriptional signature related to the induction (YBX1) or repression (MAPK14, YWAH, ERBB2) of the commitment of myogenic progenitors into the adipogenic lineage. The relationships between the abundance of the identified mRNA and marbling values remain to be analyzed in a marbling biomarkers discovery perspectives.
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Submitted on : Monday, September 14, 2020 - 2:05:06 PM
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Jeanne Bazile, Florence Jaffrezic, Patrice Dehais, Matthieu Reichstadt, Christophe Klopp, et al.. Molecular signatures of muscle growth and composition deciphered by the meta-analysis of age-related public transcriptomics data. Physiological Genomics, American Physiological Society, 2020, 52 (8), pp.322-332. ⟨10.1152/physiolgenomics.00020.2020⟩. ⟨hal-02937790⟩



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