Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets

Abstract Tremendous progress has been made to control the COVID‐19 pandemic caused by the SARS‐CoV‐2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronavirus...

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Autores principales:	Kuoyuan Cheng, Laura Martin‐Sancho, Lipika R Pal, Yuan Pu, Laura Riva, Xin Yin, Sanju Sinha, Nishanth Ulhas Nair, Sumit K Chanda, Eytan Ruppin
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	antiviral target genome‐scale metabolic modeling remdesivir RNAi screen SARS‐CoV‐2 Biology (General) QH301-705.5 Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/7f7daa898bfc42e18657f5c1309d7466
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spelling	oai:doaj.org-article:7f7daa898bfc42e18657f5c1309d74662021-11-29T08:21:36ZGenome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets1744-429210.15252/msb.202110260https://doaj.org/article/7f7daa898bfc42e18657f5c1309d74662021-11-01T00:00:00Zhttps://doi.org/10.15252/msb.202110260https://doaj.org/toc/1744-4292Abstract Tremendous progress has been made to control the COVID‐19 pandemic caused by the SARS‐CoV‐2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS‐CoV‐2 infection using genome‐scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS‐CoV‐2 infection. We next applied the GEM‐based metabolic transformation algorithm to predict anti‐SARS‐CoV‐2 targets that counteract the virus‐induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco‐2 cells. Further generating and analyzing RNA‐sequencing data of remdesivir‐treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti‐SARS‐CoV‐2 drug. Our study provides clinical data‐supported candidate anti‐SARS‐CoV‐2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy.Kuoyuan ChengLaura Martin‐SanchoLipika R PalYuan PuLaura RivaXin YinSanju SinhaNishanth Ulhas NairSumit K ChandaEytan RuppinWileyarticleantiviral targetgenome‐scale metabolic modelingremdesivirRNAi screenSARS‐CoV‐2Biology (General)QH301-705.5Medicine (General)R5-920ENMolecular Systems Biology, Vol 17, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	antiviral target genome‐scale metabolic modeling remdesivir RNAi screen SARS‐CoV‐2 Biology (General) QH301-705.5 Medicine (General) R5-920
spellingShingle	antiviral target genome‐scale metabolic modeling remdesivir RNAi screen SARS‐CoV‐2 Biology (General) QH301-705.5 Medicine (General) R5-920 Kuoyuan Cheng Laura Martin‐Sancho Lipika R Pal Yuan Pu Laura Riva Xin Yin Sanju Sinha Nishanth Ulhas Nair Sumit K Chanda Eytan Ruppin Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets
description	Abstract Tremendous progress has been made to control the COVID‐19 pandemic caused by the SARS‐CoV‐2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS‐CoV‐2 infection using genome‐scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS‐CoV‐2 infection. We next applied the GEM‐based metabolic transformation algorithm to predict anti‐SARS‐CoV‐2 targets that counteract the virus‐induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco‐2 cells. Further generating and analyzing RNA‐sequencing data of remdesivir‐treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti‐SARS‐CoV‐2 drug. Our study provides clinical data‐supported candidate anti‐SARS‐CoV‐2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy.
format	article
author	Kuoyuan Cheng Laura Martin‐Sancho Lipika R Pal Yuan Pu Laura Riva Xin Yin Sanju Sinha Nishanth Ulhas Nair Sumit K Chanda Eytan Ruppin
author_facet	Kuoyuan Cheng Laura Martin‐Sancho Lipika R Pal Yuan Pu Laura Riva Xin Yin Sanju Sinha Nishanth Ulhas Nair Sumit K Chanda Eytan Ruppin
author_sort	Kuoyuan Cheng
title	Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets
title_short	Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets
title_full	Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets
title_fullStr	Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets
title_full_unstemmed	Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets
title_sort	genome‐scale metabolic modeling reveals sars‐cov‐2‐induced metabolic changes and antiviral targets
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/7f7daa898bfc42e18657f5c1309d7466
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Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets

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