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

Authors

Bhanwar L. Puniya, University of Nebraska-LincolnFollow
Rada Amin, University of Nebraska-LincolnFollow
Bailee Lichter, University of Nebraska-LincolnFollow
Robert Moore, University of Nebraska-Lincoln
Alex Ciurej, University of Nebraska-Lincoln
Sydney J. Bennett, University of Nebraska - LincolnFollow
Ab Rauf Shah, University of Nebraska-LincolnFollow
Matteo Barberis, University of SurreyFollow
Tomáš Helikar, University of Nebraska-LincolnFollow

ORCID IDs

Bhanwar Lal Puniya http://orcid.org/0000-0001-7528-3568

Matteo Barberis http://orcid.org/0000-0001-5640-7422

Tomáš Helikar http://orcid.org/0000-0003-3653-1906

Document Type

Article

Citation

npj Systems Biology and Applications (2021) 7:4 ; https://doi.org/10.1038/s41540-020-00165-3

Comments

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,

Abstract

CD4⁺ T cells provide adaptive immunity against pathogens and abnormal cells, and they are also associated with various immunerelated diseases. CD4⁺ T cells’ metabolism is dysregulated in these pathologies and represents an opportunity for drug discovery and development. 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 naïve, Th1, Th2, and Th17 CD4⁺ T-cell subtypes to map metabolic perturbations in 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. In vitro experimental validation, together with literature-based evidence, showed that modulation of fifty percent of identified drug targets suppressed CD4⁺ T cells, further increasing their potential impact as therapeutic interventions. Our approach can be generalized in the context of other diseases, and the metabolic models can be further used to dissect CD4⁺ T-cell metabolism