Inhibiting the reproduction of SARS-CoV-2 through perturbations in human lung cell metabolic network.

Inhibiting the reproduction of SARS-CoV-2 through perturbations in human lung cell metabolic network.
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DOI:
10.26508/lsa.202000869
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发表时间:
2021-01
影响因子:
4.4
通讯作者:
Soyer OS
Soyer OS
中科院分区:
生物学2区
文献类型:
--
作者:
Delattre H;Sasidharan K;Soyer OS

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利用SARS-CoV-2的基因组和结构信息,我们创建了一个生物量函数,该函数捕获了它的氨基酸和核酸需求,并将其整合到人类肺细胞的代谢模型中,以预测抑制病毒复制的代谢扰动。病毒依靠宿主繁殖。在这里,我们利用基因组和结构信息创建了一个生物量函数,用于捕获SARS-CoV-2的氨基酸和核酸需求。将这种生物量函数结合到人肺细胞的化学计量代谢模型中,并应用代谢流量平衡分析,我们识别出基于宿主的代谢扰动抑制SARS-CoV-2的复制。我们的结果突出了中央代谢中的反应,以及氨基酸和核苷酸的生物合成途径。通过将宿主细胞的维护纳入基于人类肺细胞可用蛋白质表达数据的模型中,我们发现只有少数这些代谢扰动能够选择性地抑制病毒的复制。这类反应的一些催化酶已经证明了与现有药物的相互作用,这些药物可以用于使用基因敲除和RNA干扰技术对所提出的预测进行实验测试。总之,开发的计算方法提供了一个平台,用于根据现有和新兴病毒的生物量需求,快速、可实验地生成针对现有和新出现病毒的药物预测。
Using genomic and structural information from SARS-CoV-2, we created a biomass function capturing its amino and nucleic acid requirements and incorporated this into a metabolic model of the human lung cell to predict metabolic perturbations that inhibit virus reproduction. Viruses rely on their host for reproduction. Here, we made use of genomic and structural information to create a biomass function capturing the amino and nucleic acid requirements of SARS-CoV-2. Incorporating this biomass function into a stoichiometric metabolic model of the human lung cell and applying metabolic flux balance analysis, we identified host-based metabolic perturbations inhibiting SARS-CoV-2 reproduction. Our results highlight reactions in the central metabolism, as well as amino acid and nucleotide biosynthesis pathways. By incorporating host cellular maintenance into the model based on available protein expression data from human lung cells, we find that only few of these metabolic perturbations are able to selectively inhibit virus reproduction. Some of the catalysing enzymes of such reactions have demonstrated interactions with existing drugs, which can be used for experimental testing of the presented predictions using gene knockouts and RNA interference techniques. In summary, the developed computational approach offers a platform for rapid, experimentally testable generation of drug predictions against existing and emerging viruses based on their biomass requirements.
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