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

文献类型： 外文期刊

第一作者： Cheng, Kuoyuan

作者： Cheng, Kuoyuan;Pal, Lipika R.;Sinha, Sanju;Nair, Nishanth Ulhas;Ruppin, Eytan;Cheng, Kuoyuan;Sinha, Sanju;Martin-Sancho, Laura;Pu, Yuan;Riva, Laura;Yin, Xin;Chanda, Sumit K.;Yin, Xin;Ruppin, Eytan;Riva, Laura

作者机构：

关键词： antiviral target; genome-scale metabolic modeling; remdesivir; RNAi screen; SARS-CoV-2

期刊名称：MOLECULAR SYSTEMS BIOLOGY （ 影响因子：11.429； 五年影响因子：11.525 ）

ISSN： 1744-4292

年卷期： 2021 年 17 卷 11 期

页码：

收录情况： SCI

摘要： 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.

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