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.

中文翻译：

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基于网络的分析揭示了严重 COVID-19 的风险基因与与 SARS-CoV-2 蛋白相互作用的宿主基因之间的显着相互作用

来自全基因组关联研究的 2019 年严重冠状病毒病 (COVID-19) 的风险基因是否可以通过与与严重急性呼吸系统综合症冠状病毒 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是值得的勘探。在这项研究中，我们通过开发用户友好的软件网络计算器（https://github.com/Haoxiang-Qi/Network-Calculator.git）来实现基于网络的方法。通过使用网络计算器，我们确定了一个由 13 个风险基因和 28 个 SARS-CoV-2 相互作用的宿主基因组成的网络，这些基因彼此之间的网络接近度最高，并确定了一个枢纽基因 HNRNPK。在这些基因中，其中 14 个被确定在来自严重 COVID-19 病例的 RNA-seq 数据中差异表达。此外，通过单细胞 RNA-seq 数据中的表达富集分析，与轻度 COVID-19 相比，这些基因在重症 COVID-19 的巨噬细胞、T 细胞和上皮细胞中显着富集。同时，显着富集了74条通路。我们的分析从网络生物学的角度为重症 COVID-19 的潜在遗传病因学提供了见解。