Table_2_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.

新冠疫情的全球大流行造成了紧迫的全球性局势。因此，识别 COVID-19 患者的差异表达基因（DEGs）对于理解疾病的发病机制以及导致个体间差异的遗传因子至关重要。这些 DEGs 将有助于揭示疾病潜在的分子机制和遗传特征，包括与免疫反应元件和保护性免疫相关的调控基因。本研究旨在确定轻症和重症 COVID-19 患者与健康对照者之间的 DEGs。本研究采用了 Agilent-085982 Arraystar 人类长链非编码 RNA V5 微阵列 GEO 数据集（GSE164805 数据集）。我们运用统计工具来识别 DEGs。我们的 15 人样本数据集被分为三组：轻症、重症 COVID-19 患者和健康对照志愿者。我们将我们的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，我们还开发了一个互作图、蛋白质-蛋白质相互作用（PPI）模式、PPI 网络的聚类分析和通路富集分析。我们还对来自其他三篇 COVID-19 基因表达研究的顶级基因进行了相同的分析。我们还确定了差异表达的长链非编码 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，一些 DEGs 参与了互作图的构建、PPI 网络的形成和聚类，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，我们发现六个长链非编码 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相关于免疫反应元件和保护性免疫的 44 个调控基因。我们能够绘制与免疫元素相关的调控基因图谱，并识别出在 COVID-19 发展过程中对抗 SARS-CoV-2 感染涉及的病毒基因组反应。