CD4+ T cells, which provide adaptive immunity against pathogens and abnormal cells, are also associated with various immune related diseases. CD4+ T cells metabolism is dysregulated in these pathologies and represents an opportunity for drug discovery and development. However, we currently lack clear view of the target space in this area. Genome-scale metabolic modeling offers an opportunity to accelerate drug discovery by providing high-quality information about possible target space in the context of a modeled disease. Here, we develop genome-scale models of naive, Th1, Th2 and Th17 CD4+ T cell subtypes to map metabolic perturbations in three autoimmune disease, rheumatoid arthritis, multiple sclerosis, and primary biliary cholangitis. We subjected these models to in silico simulations for drug response analysis of existing FDA-approved drugs, and compounds. Integration of disease-specific differentially expressed genes with altered reactions in response to metabolic perturbations identified 68 drug targets for the three autoimmune diseases. Modulation of forty percent of these targets has been observed to lead to suppression of CD4+ T cells, further increasing their potential impact as therapeutic interventions.

Integrative computational approach identifies new targets in CD4+ T cell-mediated immune disorders