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.

The autoimmune signature of hyperinflammatory multisystem inflammatory syndrome in children

“…Clustering analysis and network analysis. PCA was performed using the Factoextra package in R. Hierarchical clustering was performed using the pheatmap package in R. Protein network analysis was performed with PINE (22). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (68) partner repository with the data set identifier PXD025462.…”

“…Genes were considered differentially expressed with an adjusted P value of less than 0.05 and FC of greater than 2. Differentially expressed genes were analyzed using PINE (22). Cell deconvolution analysis was performed with CIBERSORT (70) (cell-type identification by estimating relative subsets of RNA transcripts) using the cell signature file LM22.…”

“…Protein Interaction Network Extractor (PINE; ref. 22) analysis of differentially expressed proteins between the groups revealed an enrichment of protein networks involved in multiple inflammatory processes and pathways, including neutrophil degranulation, platelet activation, complement and coagulation cascades, phagocytosis, angiogenesis, acute-phase responses, oxidative stress, metabolism, and cell migration and adhesion (Supplemental Figure 2D).…”

Gene and protein expression in human megakaryocytes derived from induced pluripotent stem cells

“…Using the network visualization tool PINE, 24 we also visualized a subnetwork of enriched GO categories/pathway terms to compare the effect of percent CD41+CD42a+ across the 163 significantly expressed protein/gene pairs (see Figure 3). The resulting subnetwork consists of two central nodes for GO categories/pathway terms connected to its associated gene nodes.…”

Progress Identifying and Analyzing the Human Proteome: 2021 Metrics from the HUPO Human Proteome Project

“…Moreover, Kilanczyk et al have shown that SAMe promotes antioxidant and glutathionylation processes in cholangiocytes that prevent the autoimmune response in patients with primary biliary cholangitis . Finally, several tools extract and visualize protein-centric functional networks, such as PINE, and infer biological functions of uncharacterized proteins, uPE, as defined by the C-HPP, based on gene expression correlation and the PageRank algorithm UPEFinder …”