PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

Abstract

Background Quantitative proteomics studies are often used to detect proteins that are differentially expressed across different experimental conditions. Functional enrichment analyses are then typically used to detect annotations, such as biological processes that are significantly enriched among such differentially expressed proteins to provide insights into the molecular impacts of the studied conditions. While common, this analytical pipeline often heavily relies on arbitrary thresholds of significance. However, a functional annotation may be dysregulated in a given experimental condition, while none, or very few of its proteins may be individually considered to be significantly differentially expressed. Such an annotation would therefore be missed by standard approaches. Results Herein, we propose a novel graph theory-based method, PIGNON, for the detection of differentially expressed functional annotations in different conditions. PIGNON does not assess the statistical significance of the differential expression of individual proteins, but rather maps protein differential expression levels onto a protein–protein interaction network and measures the clustering of proteins from a given functional annotation within the network. This process allows the detection of functional annotations for which the proteins are differentially expressed and grouped in the network. A Monte-Carlo sampling approach is used to assess the clustering significance of proteins in an expression-weighted network. When applied to a quantitative proteomics analysis of different molecular subtypes of breast cancer, PIGNON detects Gene Ontology terms that are both significantly clustered in a protein–protein interaction network and differentially expressed across different breast cancer subtypes. PIGNON identified functional annotations that are dysregulated and clustered within the network between the HER2+, triple negative and hormone receptor positive subtypes. We show that PIGNON’s results are complementary to those of state-of-the-art functional enrichment analyses and that it highlights functional annotations missed by standard approaches. Furthermore, PIGNON detects functional annotations that have been previously associated with specific breast cancer subtypes. Conclusion PIGNON provides an alternative to functional enrichment analyses and a more comprehensive characterization of quantitative datasets. Hence, it contributes to yielding a better understanding of dysregulated functions and processes in biological samples under different experimental conditions.

Proteomics Analysis of Tears and Saliva From Sjogren’s Syndrome Patients

“…Healthy control tears were tagged with light formaldehyde (+28 Da) and SS patients’ tears with heavy formaldehyde (+34 Da; Figure 1A ). Data were analyzed using MaxQuant ( Cox and Mann, 2008 ) ( Cox et al, 2011 ) at a 1% false discovery rate (FDR), and data integration for pathway and gene ontology (GO) enrichment was performed with Metascape ( Zhou et al, 2019 ), STRING-db ( Szklarczyk et al, 2019 ) and PIGNON ( Nadeau et al, 2021 ). For the data interpretation, we describe changes in abundance of proteins as log2 fold change (SS tears over healthy controls), which means log2 values > 0 represent proteins that were upregulated in SS tears, <0 represents downregulation.…”

“…To better understand and visualize our data, we used two additional bioinformatics tools, PIGNON ( Nadeau et al, 2021 ) and STRING-db ( Szklarczyk et al, 2019 ), to add a more comprehensive characterization of our tear washes dataset. PIGNON was used to identify enriched biological processes via GO terms, while STRING-db was used to identity reactome pathways.…”

“…Saliva lysates of 5 healthy controls and 5 SS patients were digested with trypsin and isotopically labeled with light formaldehyde (+28 Da) and heavy formaldehyde (+34 Da; Figure 4A ), respectively. Data were analyzed using MaxQuant ( Cox and Mann, 2008 ) ( Cox et al, 2011 ) at a 1% false discovery rate (FDR), and data integration for pathway enrichment was performed with STRING-db ( Szklarczyk et al, 2019 ), PIGNON ( Nadeau et al, 2021 ), and Metascape ( Zhou et al, 2019 ), which are based on reactome, GO terms (biological process), and a combination of multiple databases, respectively. For the interpretation, we describe changes in abundance of proteins as log2 fold change (SS saliva over healthy controls), which means log2 values > 0 represent proteins that were upregulated in SS tears, <0 represent downregulation.…”

“…To better understand and visualize our data, we used two additional bioinformatics tools, PIGNON ( Nadeau et al, 2021 ) and STRING-db ( Szklarczyk et al, 2019 ), to add a more comprehensive characterization of our tear datasets. Using PIGNON, we identified mRNA splicing ( via spliceosome), cell adhesion mediated by integrin and detection of chemical organization stimulus involved in sensory perception of smell ( Figure 4D , Supplementary Figure S4C ).…”

Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease

“…All this makes the DEg data difficult to integrate and interpret. The integration of differential gene expression profiles with functional enrichment analysis in protein interaction networks has been recently proposed to assist in the prioritization of disease-relevant targets [27] . In a similar argumentative line and to test the analytical procedure presented in this work, we next illustrate how the mapping of disease-related DEg profiles into BioInt libraries can improve the prioritization of potential functional targets.…”