Prashanth G, Vastrad Basavaraj, Vastrad Chanabasayya, Kotrashetti Shivakumar
Department of General Medicine, Basaveshwara Medical College, Chitradurga, India.
Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, India.
Bioinform Biol Insights. 2021 Dec 23;15:11779322211067365. doi: 10.1177/11779322211067365. eCollection 2021.
Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infections (COVID 19) is a progressive viral infection that has been investigated extensively. However, genetic features and molecular pathogenesis underlying remdesivir treatment for SARS-CoV-2 infection remain unclear. Here, we used bioinformatics to investigate the candidate genes associated in the molecular pathogenesis of remdesivir-treated SARS-CoV-2-infected patients.
Expression profiling by high-throughput sequencing dataset (GSE149273) was downloaded from the Gene Expression Omnibus, and the differentially expressed genes (DEGs) in remdesivir-treated SARS-CoV-2 infection samples and nontreated SARS-CoV-2 infection samples with an adjusted value of <.05 and a |log fold change| > 1.3 were first identified by limma in R software package. Next, pathway and gene ontology (GO) enrichment analysis of these DEGs was performed. Then, the hub genes were identified by the NetworkAnalyzer plugin and the other bioinformatics approaches including protein-protein interaction network analysis, module analysis, target gene-miRNA regulatory network, and target gene-TF regulatory network. Finally, a receiver-operating characteristic analysis was performed for diagnostic values associated with hub genes.
A total of 909 DEGs were identified, including 453 upregulated genes and 457 downregulated genes. As for the pathway and GO enrichment analysis, the upregulated genes were mainly linked with influenza A and defense response, whereas downregulated genes were mainly linked with drug metabolism-cytochrome P450 and reproductive process. In addition, 10 hub genes (VCAM1, IKBKE, STAT1, IL7R, ISG15, E2F1, ZBTB16, TFAP4, ATP6V1B1, and APBB1) were identified. Receiver-operating characteristic analysis showed that hub genes (CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5, and PDE1A) had good diagnostic values.
This study provided insights into the molecular mechanism of remdesivir-treated SARS-CoV-2 infection that might be useful in further investigations.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）感染（COVID-19）是一种已被广泛研究的进行性病毒感染。然而，瑞德西韦治疗SARS-CoV-2感染的遗传特征和分子发病机制仍不清楚。在此，我们使用生物信息学方法研究了瑞德西韦治疗的SARS-CoV-2感染患者分子发病机制中相关的候选基因。
从基因表达综合数据库下载高通量测序数据集（GSE149273），首先通过R软件包中的limma软件，在调整后P值<0.05且|log2倍数变化|>1.3的条件下，鉴定瑞德西韦治疗的SARS-CoV-2感染样本和未治疗的SARS-CoV-2感染样本中的差异表达基因（DEG）。接下来，对这些DEG进行通路和基因本体（GO）富集分析。然后，通过NetworkAnalyzer插件和其他生物信息学方法，包括蛋白质-蛋白质相互作用网络分析、模块分析、靶基因- miRNA调控网络和靶基因-转录因子调控网络，鉴定枢纽基因。最后，对与枢纽基因相关的诊断价值进行受试者工作特征分析。
共鉴定出909个DEG，其中包括453个上调基因和457个下调基因。在通路和GO富集分析方面，上调基因主要与甲型流感和防御反应相关，而下调基因主要与药物代谢-细胞色素P450和生殖过程相关。此外，还鉴定出10个枢纽基因（VCAM1、IKBKE、STAT1、IL7R、ISG15、E2F1、ZBTB16、TFAP4、ATP6V1B1和APBB1）。受试者工作特征分析表明，枢纽基因（CIITA、HSPA-》6、MYD88、SOCS3、TNFRSF10A、ADH1A、CACNA2D2、DUSP9、FMO5和PDE1A）具有良好的诊断价值。
本研究为瑞德西韦治疗SARS-CoV-2感染的分子机制提供了见解，可能对进一步研究有用。
