PINE: An Automation Tool to Extract and Visualize Protein-Centric Functional Networks

Abstract

Recent surges in mass spectrometry-based proteomics studies demand a concurrent rise in speedy and optimized data processing tools and pipelines. Although several stand-alone bioinformatics tools exist that provide protein-protein interaction (PPI) data, we developed Protein Interaction Network Extractor (PINE) as a fully automated, user-friendly, graphical user interface application for visualization and exploration of global proteome and post-translational modification (PTM) based networks. PINE also supports overlaying differential expression, statistical significance thresholds, and PTM sites on functionally enriched visualization networks to gain insights into proteome-wide regulatory mechanisms and PTM-mediated networks. To illustrate the relevance of the tool, we explore the total proteome and its PTM-associated relationships in two different nonalcoholic steatohepatitis (NASH) mouse models to demonstrate different context-specific case studies. The strength of this tool relies in its ability to (1) perform accurate protein identifier mapping to resolve ambiguity, (2) retrieve interaction data from multiple publicly available PPI databases, and (3) assimilate these complex networks into functionally enriched pathways, ontology categories, and terms. Ultimately, PINE can be used as an extremely powerful tool for novel hypothesis generation to understand underlying disease mechanisms.

Keywords: automated bioinformatics tool; data visualization; enriched networks; post-translational modifications; proteomics.

Figure 1.

Overview of PINE Framework. (A) PPIs derived from public databases (e.g., STRING, GeneMANIA) and functional enrichment-based network annotations from ClueGO are processed by the Cytoscape framework to create PPIs networks. By using PINE, protein or PTM associated differential expression (e.g., fold change) or statistical significance data (e.g., p-value) can also be overlaid onto the Cytoscape networks. (B) Example PPIs network depicting both statistically significant and nonsignificant PTM sites. The rectangular yellow nodes denote proteins (represented by their primary genes), connected to circular nodes representing PTM sites. The color refers to either down-regulated (blue) or up-regulated (orange) sites with statistically significant sites outlined blue. The legend describes the various node types and colors used in visualization of PPI networks

Figure 2.

PINE graphical user interface. (A) Screenshot illustrating the settings tab for easy query upload, providing users the choice to modify parameters for PPIs and annotation analysis. (B) Screenshot illustrating the Pathway Selection tab that lists annotation terms, additionally providing the option to filter, sort, and subsequently select annotation terms of interest in order to generate ontology network.

Figure 3.

Hepatic methionine metabolism in different NASH models. Two mouse models of nonalcoholic steatohepatitis (NASH): Gnmt −/− mice (left) have increased hepatic SAMe level and methylation capacity, being biologically hypermethylated, while Mat1a −/− mice (right) are SAMe deficient with reduced methylation capacity and therefore hypomethylated.

Figure 4.

Enriched functional networks generated by PINE across different NASH models. (A) The Gnmt −/− mice model indicates five activated pathways shown as orange central nodes and the one inhibited pathway shown as blue central nodes along with fold-changes from the different protein nodes. (B) The Mat1a −/− mice model indicates two activated pathways shown as orange central nodes and the four inhibited pathway shown as blue central nodes along with fold-changes from the different protein node.

Figure 5.

Enriched cellular subnetwork for mitochondrial matrix generated by PINE across different NASH models. Altered cellular components are denoted by central gray nodes. Differential expression data from multiple comparison groups are represented as bar charts and the bar height indicates degree of fold change. (Example: Zoomed view into node CPS1 indicates degree of fold change for Mat1a −/− mice vs WT littermates shown using orange bar and Gnmt −/− mice vs WT littermates shown using blue bar.)

Figure 6.

Multi-PTM network illustrating crosstalk between methylation, acetylation and succinylation. Functionally enriched pathways are shown by central gray nodes along with gene connections shown by yellow nodes. The PTM site level quantitative differences from each mouse model are shown as a bar chart (Gnmt −/− mice indicated by blue bar and Mat1a −/− mice by orange bar). Sites are indicated with unimod accession in curly brackets (Example, K{34}179 and K{36}179 on gene BHMT would indicate lysine monomethylation and lysine dimethylation events on site 179). Ambiguous sites are indicated using double asterisk (**) symbol next to the site. (Example, K{1}110** on gene GLUD1.)

Figure 7.

Phospho-proteome network illustrating significant differentially expressed PTM sites using PINE. Inhibited pathways such as mTORC1-mediated signaling and translation factors are represented as central blue nodes whereas activated pathway such as AMPK signaling pathway are represented as central orange nodes along with gene connections represented as yellow nodes. The PTM site level quantitative differences between Gnmt −/− mice relative to WT littermates are represented as blue for downregulated nodes or orange for upregulated nodes connected to their respective gene nodes. We included both significant and nonsignificant differentially expressed sites indicated by blue node outline and no node outline, respectively.