Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
Microb Pathog. 2021 Sep;158:105114. doi: 10.1016/j.micpath.2021.105114. Epub 2021 Jul 30.
Understanding the pathogenesis of SARS-CoV-2 is essential for developing effective treatment strategies. Viruses hijack the host metabolism to redirect the resources for their replication and survival. The influence of SARS-CoV-2 on host metabolism is yet to be fully understood. In this study, we analyzed the transcriptomic data obtained from different human respiratory cell lines and patient samples (nasopharyngeal swab, peripheral blood mononuclear cells, lung biopsy, bronchoalveolar lavage fluid) to understand metabolic alterations in response to SARS-CoV-2 infection. We explored the expression pattern of metabolic genes in the comprehensive genome-scale network model of human metabolism, Recon3D, to extract key metabolic genes, pathways, and reporter metabolites under each SARS-CoV-2-infected condition. A SARS-CoV-2 core metabolic interactome was constructed for network-based drug repurposing. Our analysis revealed the host-dependent dysregulation of glycolysis, mitochondrial metabolism, amino acid metabolism, nucleotide metabolism, glutathione metabolism, polyamine synthesis, and lipid metabolism. We observed different pro- and antiviral metabolic changes and generated hypotheses on how the host metabolism can be targeted for reducing viral titers and immunomodulation. These findings warrant further exploration with more samples and in vitro studies to test predictions.
了解 SARS-CoV-2 的发病机制对于开发有效的治疗策略至关重要。病毒劫持宿主代谢，重新分配资源以进行复制和生存。SARS-CoV-2 对宿主代谢的影响尚未完全了解。在这项研究中，我们分析了从不同的人呼吸道细胞系和患者样本（鼻咽拭子、外周血单核细胞、肺活检、支气管肺泡灌洗液）中获得的转录组数据，以了解 SARS-CoV-2 感染引起的代谢变化。我们在人类代谢的综合基因组规模网络模型 Recon3D 中探索了代谢基因的表达模式，以提取每个 SARS-CoV-2 感染条件下的关键代谢基因、途径和报告代谢物。构建了 SARS-CoV-2 核心代谢相互作用网络，用于基于网络的药物再利用。我们的分析揭示了宿主依赖性糖酵解、线粒体代谢、氨基酸代谢、核苷酸代谢、谷胱甘肽代谢、多胺合成和脂质代谢的失调。我们观察到不同的促病毒和抗病毒代谢变化，并提出了关于如何针对宿主代谢来降低病毒滴度和免疫调节的假设。这些发现需要更多的样本和体外研究来进一步探索和验证预测。
