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

Abstract CD4+ T cells provide adaptive immunity against pathogens and abnormal cells, and they 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. Genome-scale metabo...

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Autores principales:	Bhanwar Lal Puniya, Rada Amin, Bailee Lichter, Robert Moore, Alex Ciurej, Sydney J. Bennett, Ab Rauf Shah, Matteo Barberis, Tomáš Helikar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/feccf126d97a4809abc489b6ac51bfe1
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spelling	oai:doaj.org-article:feccf126d97a4809abc489b6ac51bfe12021-12-02T10:49:39ZIntegrative computational approach identifies drug targets in CD4+ T-cell-mediated immune disorders10.1038/s41540-020-00165-32056-7189https://doaj.org/article/feccf126d97a4809abc489b6ac51bfe12021-01-01T00:00:00Zhttps://doi.org/10.1038/s41540-020-00165-3https://doaj.org/toc/2056-7189Abstract CD4+ T cells provide adaptive immunity against pathogens and abnormal cells, and they 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. 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.Bhanwar Lal PuniyaRada AminBailee LichterRobert MooreAlex CiurejSydney J. BennettAb Rauf ShahMatteo BarberisTomáš HelikarNature PortfolioarticleBiology (General)QH301-705.5ENnpj Systems Biology and Applications, Vol 7, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Bhanwar Lal Puniya Rada Amin Bailee Lichter Robert Moore Alex Ciurej Sydney J. Bennett Ab Rauf Shah Matteo Barberis Tomáš Helikar Integrative computational approach identifies drug targets in CD4+ T-cell-mediated immune disorders
description	Abstract CD4+ T cells provide adaptive immunity against pathogens and abnormal cells, and they 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. 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.
format	article
author	Bhanwar Lal Puniya Rada Amin Bailee Lichter Robert Moore Alex Ciurej Sydney J. Bennett Ab Rauf Shah Matteo Barberis Tomáš Helikar
author_facet	Bhanwar Lal Puniya Rada Amin Bailee Lichter Robert Moore Alex Ciurej Sydney J. Bennett Ab Rauf Shah Matteo Barberis Tomáš Helikar
author_sort	Bhanwar Lal Puniya
title	Integrative computational approach identifies drug targets in CD4+ T-cell-mediated immune disorders
title_short	Integrative computational approach identifies drug targets in CD4+ T-cell-mediated immune disorders
title_full	Integrative computational approach identifies drug targets in CD4+ T-cell-mediated immune disorders
title_fullStr	Integrative computational approach identifies drug targets in CD4+ T-cell-mediated immune disorders
title_full_unstemmed	Integrative computational approach identifies drug targets in CD4+ T-cell-mediated immune disorders
title_sort	integrative computational approach identifies drug targets in cd4+ t-cell-mediated immune disorders
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/feccf126d97a4809abc489b6ac51bfe1
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Integrative computational approach identifies drug targets in CD4+ T-cell-mediated immune disorders

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