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基于网络的分析揭示了严重 COVID-19 的风险基因与宿主基因之间的显著相互作用,这些宿主基因与 SARS-CoV-2 蛋白相互作用。

Network-based analysis revealed significant interactions between risk genes of severe COVID-19 and host genes interacted with SARS-CoV-2 proteins.

机构信息

Department of Bioinformatics, School of Life Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an 271099, Shandong, China.

出版信息

Brief Bioinform. 2022 Jan 17;23(1). doi: 10.1093/bib/bbab372.

Abstract

Whether risk genes of severe coronavirus disease 2019 (COVID-19) from genome-wide association study could play their regulatory roles by interacting with host genes that were interacted with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins was worthy of exploration. In this study, we implemented a network-based approach by developing a user-friendly software Network Calculator (https://github.com/Haoxiang-Qi/Network-Calculator.git). By using Network Calculator, we identified a network composed of 13 risk genes and 28 SARS-CoV-2 interacted host genes that had the highest network proximity with each other, with a hub gene HNRNPK identified. Among these genes, 14 of them were identified to be differentially expressed in RNA-seq data from severe COVID-19 cases. Besides, by expression enrichment analysis in single-cell RNA-seq data, compared with mild COVID-19, these genes were significantly enriched in macrophage, T cell and epithelial cell for severe COVID-19. Meanwhile, 74 pathways were significantly enriched. Our analysis provided insights for the underlying genetic etiology of severe COVID-19 from the perspective of network biology.

摘要

从全基因组关联研究中寻找严重 2019 年冠状病毒病(COVID-19)的风险基因是否可以通过与严重急性呼吸综合征冠状病毒 2(SARS-CoV-2)蛋白相互作用的宿主基因相互作用来发挥其调节作用,这是值得探索的。在这项研究中,我们通过开发一个用户友好的软件 Network Calculator(https://github.com/Haoxiang-Qi/Network-Calculator.git),实施了一种基于网络的方法。通过使用 Network Calculator,我们确定了一个由 13 个风险基因和 28 个与 SARS-CoV-2 相互作用的宿主基因组成的网络,它们彼此之间具有最高的网络接近度,并确定了一个枢纽基因 HNRNPK。在这些基因中,有 14 个在严重 COVID-19 病例的 RNA-seq 数据中被鉴定为差异表达。此外,通过单细胞 RNA-seq 数据的表达富集分析,与轻度 COVID-19 相比,这些基因在严重 COVID-19 中在巨噬细胞、T 细胞和上皮细胞中显著富集。同时,有 74 条途径显著富集。我们的分析从网络生物学的角度为严重 COVID-19 的潜在遗传病因提供了见解。

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