Table_1_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（COVID-19 pandemic）在全球范围内引发了紧急公共卫生态势，因此亟需鉴定新冠患者体内的差异表达基因（differentially expressed genes, DEGs），以解析疾病发病机制以及导致个体间差异的遗传因素。此类差异表达基因有助于理解疾病潜在的分子机制与遗传特征，包括与免疫应答元件及保护性免疫相关的调控基因。本研究旨在鉴定轻度、重度新冠患者相较于健康对照的差异表达基因。本研究使用了Agilent-085982 Arraystar人类长链非编码RNA（long non-coding RNA, lncRNA）V5微阵列的基因表达综合（Gene Expression Omnibus, GEO）数据集（GSE164805数据集）。我们采用统计工具筛选差异表达基因。本研究的15份人类样本数据集分为三组：轻度新冠患者组、重度新冠患者组以及健康对照志愿者组。我们将本次研究结果与另外三项已发表的新冠患者基因表达研究进行了比对。除筛选得到的显著差异表达基因之外，我们构建了相互作用组图谱、蛋白质-蛋白质相互作用（protein-protein interaction, PPI）模式、PPI网络聚类分析以及通路富集分析。我们还针对另外三项新冠基因表达研究中的排名靠前的基因开展了相同分析。我们同时鉴定了差异表达的长链非编码RNA基因，并构建了蛋白编码差异表达基因-长链非编码RNA共表达网络。我们试图识别与免疫应答元件及保护性免疫相关的调控基因。我们筛选得到了29个最为显著的蛋白编码差异表达基因。我们的分析显示，多个差异表达基因参与了相互作用组图谱、蛋白质相互作用网络及聚类的构建，这与其他研究中基于蛋白编码基因数据得到的结果一致。有趣的是，我们发现6个长链非编码RNA（TALAM1、DLEU2、UICLM、CASC18、SNHG20及GNAS）参与了蛋白编码差异表达基因-长链非编码RNA网络，此类RNA或可作为新冠患者的潜在生物标志物。我们从本研究中鉴定出3个调控基因，从另外三项研究中鉴定出44个与免疫应答元件及保护性免疫相关的调控基因。我们成功绘制了与免疫元件相关的调控基因图谱，并明确了在新冠疫情发展过程中，参与对抗SARS-CoV-2感染的保护性免疫的病毒基因组应答机制。