Fig 1.
Flowchart of ProteINSIDE structure.
The four modules to query the available biological information, annotate according to the GO, predict secreted proteins and visualize PPi, are either all run in the basic analysis or individually selected and run with specific settings in the custom analysis. The basic analysis runs ProteINSIDE with automatic settings. The custom analysis operates with the settings selected by the user: option to include GO Inferred from IEA codes (electronic annotation that are automatically unselected in the basic analysis), option to make GOTree chart networks with Cytoscape web, option to search PPi among the 31 databases proposed by ProteINSIDE, option to search PPi in other species using orthologous proteins, option to extend the PPi network with proteins that are not in the dataset, and option to choose the sensitivity to detect signal peptides with SignalP 4.1.
Fig 2.
Dynamic GO tree network provided by ProteINSIDE.
Network was built with Cytoscape web. Settings are available to sort the network according to Ontology groups, biological function, result of GO enrichment (p-value), numbers (Nb) of GO, or network layout. Go terms are sorted and colorized depending on the ontology group and the number of annotated proteins. (A) Dynamic network view of GO terms related to Molecular Function. (B) Clicking on a GO term provides the GO number, proteins from the sample list annotated by this GO, and links with public GO databases (AmiGO and QuickGO).
Fig 3.
View of a PPi dynamic extended network made by ProteINSIDE.
Network was built with Cytoscape web. (A) The colour of the edge depends on experimental methods used to identify PPi. White nodes are proteins from the dataset and grey nodes are known interacting proteins not included in the dataset. (B) Clicking on a protein/node provides biological information as gene and proteins ID, function, and a link to UniProt database. (C) Available options to sort the network and highlight proteins of interest.
Table 1.
Available modules of analyses for ProteINSIDE, DAVID, BioMyn, and AgBase.
Table 2.
Comparison of identifier mapping results.
Table 3.
Numbers of annotated proteins and GO terms provided by ProteINSIDE, DAVID, BioMyn, and AgBase.
Table 4.
Number of PPi identified using ProteINSIDE.
Fig 4.
Venn diagrams of predicted signal peptides from amino acid sequence of 1000 proteins.
Resources used to predict signal peptides were SignalP that was included in ProteINSIDE, as well as PrediSI [44] and Phobius [45] for bench tests. To reinforce the prediction of proteins to be secreted, ProteINSIDE checked the cellular localization of proteins with TargetP and used GO terms relative to the cellular component. GO terms were also used to predict proteins that are secreted without signal peptide. Squares give numbers of predicted (SignalP) secreted proteins confirmed by GO terms and subcellular location prediction (TargetP), and confirmed by GO terms for proteins that are potentially secreted without signal peptide.
Table 5.
Potentially secreted proteins with a signal peptide in bovine foetal AT, muscle or both tissues.
Fig 5.
Interactions network between proteins of the bovine foetal AT.
We highlighted the relationships among different proteins involved in a same process: (A) mitochondrial metabolism, (B) redox activity, (C) proteasome complex, (D) cell proliferation, and (E) differentiation and metabolism of AT. Squares indicate key proteins identified by sorting the network with algorithms of betweeness (an average of 400) and closeness centralities (an average of 0.3).
Fig 6.
PPi network between proteins from the bovine foetal muscle.
We highlighted the relationships among different proteins involved in a same process: (A) muscle development, (B) energetic complexes, (C) respiratory chain, and (D) cell proliferation. Squares indicate key proteins identified by sorting the network with algorithms of betweeness (an average of 100) and closeness centralities (an average of 0.4).
Fig 7.
mRNA abundance of adiponectin (ADIPOQ) in bovine AT and muscle.
The abundance of ADIPOQ was normalized to the mRNA abundance of ribosomal protein P0 (RPLP0). Results are ΔΔ CT for foetal and adult AT or muscle samples, relatively to a control sample that is an adult AT. PCR were carried out as previously described [62].
Fig 8.
Extended PPi network that includes proteins identified both in foetal AT and muscle.
ProteINSIDE built a network between the 46 proteins identified in AT and muscle, and proteins outside of the dataset and know to interact with them in Human species. We filtered and sorted the network using high values of betweenness (an average of 10000) and closeness centralities (an average of 0,318). We have identified 6 proteins that were highly central in the dataset (linked with the maximum of proteins and pathways), 12 proteins were moderately central (engaged in the maximum of pathways but not necessary with many proteins), and 17 proteins were more weakly central (less linked with the maximum of proteins and less engaged in pathways, but central on the network). White boxes indicate proteins that are from the bovine dataset and grey boxes indicate proteins that are external to the dataset.