Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections

Abstract

The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.

In-silico discovery of common molecular signatures for which SARS-CoV-2 infections and lung diseases stimulate each other, and drug repurposing

“…We considered three methods DESeq2, edgeR and LIMMA (voom) for the identification of DEGs between SARS-CoV-2 infections and control groups separately from RNA-Seq profile datasets [ 23 , 24 ]. To identify DEGs between lung disease and control samples based on microarray gene expression profile datasets, separately we utilized KW, SAM, and LIMMA approaches [ 25 ].…”

“…The GO functional and pathway enrichment analysis of hub-sDEGs were performed by using ‘Enrichr’ and ‘DAVID’ databases [ 6 , 24 ]. Then, we extracted common GO terms and pathways from both databases.…”

“…To obtain key transcriptional and post-transcriptional regulators of hub-sDEGs, we performed network analysis of hub-sDEGs with TFs and miRNAs from TF2DNA and TarBase (v8.0) databases, respectively, and their interaction networks were constructed by using STRING (version 11.5) database and visualized in Cytoscape (version 3.10.0) software [ 24 ]. The significant key TFs and miRNAs were identified via the ‘CytoHubba’ plugin in Cytoscape based on the highest degree scores.…”

Discovery of Bacterial Key Genes from 16S rRNA-Seq Profiles That Are Associated with the Complications of SARS-CoV-2 Infections and Provide Therapeutic Indications

“…In order to inhibit microbial pathogens, it is required to identify pathogenic genes that may lead to exploring potential inhibitors. It may be mentioned here that either disease-causing microbial proteins or host-proteins are considered the receptor proteins for exploring candidate drug molecules in order to inhabit microbial infections [ 6 , 9 , 35 , 36 , 37 , 38 , 39 , 40 ], since pathogenic proteins interact with host-proteins to develop infections [ 39 , 41 , 42 ].…”

Altered DNA methylation pattern contributes to differential epigenetic immune signaling in the upper respiratory airway of COVID-19 patients

“…A large proportion of COVID-19 research has been devoted to phenotype, proteomic 8, 9 and/or transcriptomic studies 10, 11, 12 , which has given vital information on the dynamics of the immune cell landscape heretofore. In a previous study, we revealed persistent alterations within the monocytic, dendritic cell, and T cell compartments induced by SARS-CoV-2 infection, which manifested even six to seven months post-hospitalization 13, 14 .…”