Multiple sclerosis drug target discovery and assessment using PPI networks

Mechanisms behind complex diseases, such as multiple sclerosis (MS), cannot be attributed to individual genes/proteins. Omics data provide insights at gene/protein level, but the phenotypic effect is elucidated at the system level (“genes do not work alone”). There are multiple ways to affect biological systems behind a disease and single/few variants can rarely explain a phenotype. Furthermore, a disease signal spread over multiple genes/proteins can be difficult to identify without large cohorts. Here, we used a PPI interactome (protein–protein interactions) based on a strong experimental foundation and extracted a high-confidence subset of interactions to find biological systems enriched in MS disease signal. First, gene-level P-values for association to MS (derived from UK biobank) were mapped to the interactome and our own System Significance algorithm was applied to identify sub-networks enriched in the disease signal. Performance was evaluated based on rediscovering effective MS drug targets. In total, we identified 10 networks (219 unique proteins) enriched in disease signals and observed six times more MS drug targets compared to random expectancy and gene-level data alone. The retrieved biological functions aligned with known MS biology (e.g., neuronal degeneration and muscular dystrophy), suggesting this method as a valid approach for the discovery and assessment of novel drug target candidates.