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

Nicolas Kaspric,Muriel Bonnet,Brigitte Picard,Jérémy Tournayre,Matthieu Reichstadt,Frederique Lisacek

doi:10.1371/journal.pone.0128086

Nicolas Kaspric, Muriel Bonnet + Show 4 more

Open Access

https://doi.org/10.1371/journal.pone.0128086

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Abstract

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.

Highlights

A main challenge for scientists working on the efficiency of ruminant production and the quality of their products is to understand which genes and proteins control nutrient metabolism and partitioning between tissues or which genes and proteins control tissues growth and physiology [1,2,3]
Available bioinformatics tools implemented as web services are used to address these issues such as Amigo [11], Gorilla [12], or QuickGO [13] dedicated to Gene Ontology (GO) annotation, GeneCards [14] for an overview of the current available information about a gene or a protein for Human species, MiMi [15], IntAct [16], BioGrid [17], or PsicQuick [18] for protein-protein interactions (PPi) identification, STRING [19] or Apid2Net [20] for PPI identification and visualization as networks, and Sigcleave [21], SignalBlast [22], or SignalP [23] for the prediction of signal peptides
Results from the secretion module of ProteINSIDE were compared to two specific resources dedicated to the computational prediction of protein secretion PrediSi [44] and Phobius [45], chosen for their good scoring

Summary

Introduction

A main challenge for scientists working on the efficiency of ruminant production and the quality of their products (meat, milk. . .) is to understand which genes and proteins control nutrient metabolism and partitioning between tissues or which genes and proteins control tissues growth and physiology [1,2,3]. Most of them do not permit to address data mining from genomic and proteomic studies in ruminants, except AgBase [29], a curated genomic database providing functional annotations and information for agriculturally important species, among them bovine and ovine. None of these resources include the prediction of proteins that are secreted outside the cell as recommended for a computational analysis of secretome [30]. Secreted proteins play important roles in tissue growth and homeostasis, as well as in tissues cross-talk and nutrient partitioning [2,31]

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