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Integrated data mining of transcriptomic and proteomic datasets to predict the secretome of adipose tissue and muscle in ruminants

Abstract : Adipose tissue and muscle are endocrine organs releasing signalling and mediator proteins termed adipokines and myokines, enabling functioning of the organism and its adaption to a wide range of different challenges such as starvation, overfeeding, stress and diseases. They also contribute to the "adipose-muscular'' cross-talk for an integrated control of body mass composition. This article integrates transcriptomic and proteomic data available in ruminant species (mainly in bovine, and when available in ovine and caprine) to computationally predict the large-scale secretome of adipose tissues and muscles. For this purpose predictive bioinformatics algorithms were employed to identify proteins putatively secreted by tissues thanks to a signal peptide. We predicted 1749 secreted proteins that were found from adipose tissues and muscles, more than a half of them being already declared as secreted proteins in public repositories. We also identified 188 and 357 proteins in the predictive secretome of adipose tissues and muscles respectively, only a minor part (3-11%) of them being reported in the overlap of public repositories used for comparison. Functional analysis of these proteins highlights their involvement in biological pathways known to sustain tissue growth and functioning. This strategy allowed us to identify some known and putative novel adipomyokines, adipokines and myokines. However their role and their expression signature depending on rearing practices remain largely to be explored.
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https://hal.inrae.fr/hal-02638660
Contributor : Migration Prodinra <>
Submitted on : Thursday, May 28, 2020 - 10:27:54 AM
Last modification on : Friday, February 5, 2021 - 3:59:00 AM

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Muriel Bonnet, Jérémy Tournayre, Isabelle Cassar-Malek. Integrated data mining of transcriptomic and proteomic datasets to predict the secretome of adipose tissue and muscle in ruminants. Molecular BioSystems, Royal Society of Chemistry, 2016, 12 (9), pp.2722-2734. ⟨10.1039/c6mb00224b⟩. ⟨hal-02638660⟩

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