Gene Set Enrichment Analysis of Interaction Networks Weighted by Node Centrality

Gene set enrichment analysis (GSEA) is a powerful tool to associate a disease phenotype to a group of genes/proteins. GSEA attributes a specific weight to each gene/protein in the input list that depends on a metric of choice, which is usually represented by quantitative expression data. However, expression data are not always available. Here, GSEA based on betweenness centrality of a protein-protein interaction (PPI) network is described and applied to two cases, where an expression metric is missing. First, personalized PPI networks were generated from genes displaying alterations (assessed by array comparative genomic hybridization and whole exome sequencing) in four probands bearing a 16p13.11 microdeletion in common and several other point variants. Patients showed disease phenotypes linked to neurodevelopment. All networks were assembled around a cluster of first interactors of altered genes with high betweenness centrality. All four clusters included genes known to be involved in neurodevelopmental disorders with different centrality. Moreover, the GSEA results pointed out to the evidence of cell cycle among enriched pathways. Second, a large interaction network obtained by merging proteomics studies on three neurodegenerative disorders was analyzed from the topological point of view. We observed that most central proteins are often linked to Parkinson's disease. The selection of these proteins improved the specificity of GSEA, with Metabolism of amino acids and derivatives and Cellular response to stress or external stimuli as top-ranked enriched pathways. In conclusion, betweenness centrality revealed to be a suitable metric for GSEA. Thus, centrality-based GSEA represents an opportunity for precision medicine and network medicine.

Automated summary

Gene set enrichment analysis (GSEA) is a powerful tool to associate disease phenotypes with a group of genes/proteins. Utilizing betweenness centrality of a protein-protein interaction (PPI) network for GSEA can overcome the limitation of missing expression data, leading to new insights in neurodevelopmental disorders and neurodegenerative diseases research.