Prediction of the Secretome and the Surfaceome: A Strategy to Decipher the Crosstalk between Adipose Tissue and Muscle during Fetal Growth.

Crosstalk between adipose and muscular tissues is hypothesized to regulate the number of muscular and adipose cells during fetal growth, with post-natal consequences on lean and fat masses. Such crosstalk largely remains, however, to be described. We hypothesized that a characterization of the proteomes of adipose and muscular tissues from bovine fetuses may enhance the understanding of the crosstalk between these tissues through the prediction of their secretomes and surfaceomes. Proteomic experiments have identified 751 and 514 proteins in fetal adipose tissue and muscle. These are mainly involved in the regulation of cell proliferation or differentiation, but also in pathways such as apoptosis, Wnt signalling, or cytokine-mediated signalling. Of the identified proteins, 51 adipokines, 11 myokines, and 37 adipomyokines were predicted, together with 26 adipose and 13 muscular cell surface proteins. Analysis of protein-protein interactions suggested 13 links between secreted and cell surface proteins that may contribute to the adipose-muscular crosstalk. Of these, an interaction between the adipokine plasminogen and the muscular cell surface alpha-enolase may regulate the fetal myogenesis. The in silico secretome and surfaceome analyzed herein exemplify a powerful strategy to enhance the elucidation of the crosstalk between cell types or tissues.

ProteINSIDE to Easily Investigate Proteomics Data from Ruminants: Application to Mine Proteome of Adipose and Muscle Tissues in Bovine Foetuses.

Genomics experiments are widely acknowledged to produce a huge amount of data to be analysed. The challenge is to extract meaningful biological context for proteins or genes which is currently difficult because of the lack of an integrative workflow that hinders the efficiency and the robustness of data mining performed by biologists working on ruminants. Thus, we designed ProteINSIDE, a free web service (www.proteinside.org) that (I) provides an overview of the biological information stored in public databases or provided by annotations according to the Gene Ontology, (II) predicts proteins that are secreted to search for proteins that mediate signalisation between cells or tissues, and (III) analyses protein-protein interactions to identify proteins contributing to a process or to visualize functional pathways. Using lists of proteins or genes as a unique input, ProteINSIDE is an original all-in-one tool that merges data from these searches to present a fast overview and integrative analysis of genomic and proteomic data from Bovine, Ovine, Caprine, Human, Rat, and Murine species. ProteINSIDE was bench tested with 1000 proteins identifiers from each species by comparison with DAVID, BioMyn, AgBase, PrediSi, and Phobius. Compared to DAVID or BioMyn, identifications and annotations provided by ProteINSIDE were similar from monogastric proteins but more numerous and relevant for ruminants proteins. ProteINSIDE, thanks to SignalP, listed less proteins potentially secreted with a signal peptide than PrediSi and Phobius, in agreement with the low false positive rate of SignalP. In addition ProteINSIDE is the only resource that predicts proteins secreted by cellular processes that do not involve a signal peptide. Lastly, we reported the usefulness of ProteINSIDE to bring new biological hypotheses of research from proteomics data: the biological meaning of the uptake of adiponectin by the foetal muscle and a role for autophagy during ontogenesis of adipose and muscle tissues.