Abstract
Understanding the connectivity patterns of genes in a localized disease neighborhood or disease module in a molecular interaction network (interactome) is a key step toward advancing the knowledge about molecular mechanisms underlying a complex disease. In this work, we introduce a framework that detects peripheral and core regions of a disease in the human interactome. We leverage gene expression data on 104 diseases and analyze the connectivity of differentially expressed genes (quantified by a p-value < 0.05) and their topological membership in the network to distinguish between peripheral and core genes. Per definition, peripheral and core genes are topologically different and we show that they also differ biologically. Core genes are more enriched for Genome-wide association study (GWAS) and Online Mendelian Inheritance in Man (OMIM) data, whereas peripheral genes are more shared across different disease states and their overlap helps predict disease proximity in the human interactome. Based on this observation, we propose a flower model to explain the organization of genes in the human interactome, with core genes of different diseases as the petals and the peripheral genes as the (shared) stem. We show that this network model is an important step towards finding novel drug targets and improving disease classification. Overall, we were able to demonstrate how perturbations percolate through the human interactome and contribute to peripheral and core regions, an important novel feature of the omnigenic model.
