Journal Article

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

Lee, Sang-Soo, Chakraborty, Chiranjib, Sharma, Ashish Ranjan, Zayed, Hatem, Bhattacharya, Manojit

2021

Overview

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.

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: Aged / Cluster analysis / COVID-19 / COVID-19 - epidemiology / COVID-19 - genetics / COVID-19 - immunology / Datasets / differentially expressed genes / Disease transmission / DNA microarrays / Female / Females / Gene expression / Gene Expression Profiling - methods / Gene Expression Regulation / Gene mapping / Gene Ontology / Gene Regulatory Networks / Humans / Immune response / Immune system / Immunity / Immunity (Disease) / Immunity - genetics / Immunology / Infections / interactome mapping / Male / Males / Middle Aged / Molecular modelling / Non-coding RNA / Pandemics - prevention & control / Patients / protective immunity / Protein interaction / Protein Interaction Maps - genetics / Proteins / Regulatory sequences / SARS-CoV-2 - immunology / SARS-CoV-2 - physiology / Severe acute respiratory syndrome coronavirus 2 / Signal Transduction - genetics / Signal Transduction - immunology / transcriptome profiling / Transcriptomes