Table_3_Understanding Gene Expression and Transcriptome Profiling of COVID-19: An Initiative Towards the Mapping of Protective Immunity Genes Against SARS-CoV-2 Infection.docx

The COVID-19 pandemic has created an urgent situation throughout the globe. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability. The DEGs will help understand the disease’s potential underlying molecular mechanisms and genetic characteristics, including the regulatory genes associated with immune response elements and protective immunity. This study aimed to determine the DEGs in mild and severe COVID-19 patients versus healthy controls. The Agilent-085982 Arraystar human lncRNA V5 microarray GEO dataset (GSE164805 dataset) was used for this study. We used statistical tools to identify the DEGs. Our 15 human samples dataset was divided into three groups: mild, severe COVID-19 patients and healthy control volunteers. We compared our result with three other published gene expression studies of COVID-19 patients. Along with significant DEGs, we developed an interactome map, a protein-protein interaction (PPI) pattern, a cluster analysis of the PPI network, and pathway enrichment analysis. We also performed the same analyses with the top-ranked genes from the three other COVID-19 gene expression studies. We also identified differentially expressed lncRNA genes and constructed protein-coding DEG-lncRNA co-expression networks. We attempted to identify the regulatory genes related to immune response elements and protective immunity. We prioritized the most significant 29 protein-coding DEGs. Our analyses showed that several DEGs were involved in forming interactome maps, PPI networks, and cluster formation, similar to the results obtained using data from the protein-coding genes from other investigations. Interestingly we found six lncRNAs (TALAM1, DLEU2, and UICLM CASC18, SNHG20, and GNAS) involved in the protein-coding DEG-lncRNA network; which might be served as potential biomarkers for COVID-19 patients. We also identified three regulatory genes from our study and 44 regulatory genes from the other investigations related to immune response elements and protective immunity. We were able to map the regulatory genes associated with immune elements and identify the virogenomic responses involved in protective immunity against SARS-CoV-2 infection during COVID-19 development.

COVID-19疫情在全球范围内造成了紧急态势。因此，识别COVID-19患者中差异表达的基因（DEGs）对于理解疾病发病机制及导致个体间变异的遗传因子至关重要。这些DEGs有助于我们理解疾病的潜在分子机制和遗传特性，包括与免疫反应元件和保护性免疫相关的调控基因。本研究旨在确定轻型和重型COVID-19患者与健康对照者之间的DEGs。本研究采用了Agilent-085982 Arraystar人类长非编码RNA V5微阵列GEO数据集（GSE164805数据集）。我们运用统计学工具来识别DEGs。我们将包含15个人类样本的数据集分为三组：轻型、重型COVID-19患者和健康对照组志愿者。我们将我们的研究结果与三篇已发表的COVID-19患者基因表达研究进行了比较。除了显著的DEGs外，我们还构建了相互作用图谱、蛋白质-蛋白质相互作用（PPI）模式、PPI网络聚类分析和通路富集分析。我们还使用来自其他三篇COVID-19基因表达研究的顶级基因进行了相同分析。我们还识别了差异表达的长非编码RNA基因，并构建了蛋白质编码DEG-lncRNA共表达网络。我们试图识别与免疫反应元件和保护性免疫相关的调控基因。我们优先考虑了最显著的29个蛋白质编码DEGs。我们的分析表明，几个DEGs参与了相互作用图谱、PPI网络和聚类形成，这与使用其他研究中的蛋白质编码基因获得的结果相似。有趣的是，我们发现六个长非编码RNA（TALAM1、DLEU2、UICLM CASC18、SNHG20和GNAS）参与了蛋白质编码DEG-lncRNA网络；这些可能作为COVID-19患者的潜在生物标志物。我们还从我们的研究中确定了三个调控基因，以及来自其他研究的与免疫反应元件和保护性免疫相关的44个调控基因。我们能够映射与免疫元件相关的调控基因，并识别出在COVID-19发展过程中，参与保护SARS-CoV-2感染免疫的病毒基因组反应。