Meta-Data Analysis to Explore the Hub of the Hub-Genes That Influence SARS-CoV-2 Infections Highlighting Their Pathogenetic Processes and Drugs Repurposing

Mosharaf, Md. Parvez and Kibria, Md. Kaderi and Hossen, Md. Bayazid and Islam, Md. Ariful and Reza, Md. Selim and Mahumud, Rashidul Alam ORCID: https://orcid.org/0000-0001-9788-1868 and Alam, Khorshed ORCID: https://orcid.org/0000-0003-2232-0745 and Gow, Jeffrey ORCID: https://orcid.org/0000-0002-5726-298X and Mollah, Md. Nurul Haque (2022) Meta-Data Analysis to Explore the Hub of the Hub-Genes That Influence SARS-CoV-2 Infections Highlighting Their Pathogenetic Processes and Drugs Repurposing. Vaccines, 10 (8):1248. pp. 1-22.

[img]
Preview
Text (Published Version)
15_Parvez_SARS-CoV-2_Meta_vaccines-10-01248.pdf
Available under License Creative Commons Attribution 4.0.

Download (3MB) | Preview

Abstract

The pandemic of SARS-CoV-2 infections is a severe threat to human life and the world economic condition. Although vaccination has reduced the outspread, but still the situation is not under control because of the instability of RNA sequence patterns of SARS-CoV-2, which requires effective drugs. Several studies have suggested that the SARS-CoV-2 infection causing hub differentially expressed genes (Hub-DEGs). However, we observed that there was not any common hub gene (Hub-DEGs) in our analyses. Therefore, it may be difficult to take a common treatment plan against SARS-CoV-2 infections globally. The goal of this study was to examine if more representative Hub-DEGs from published studies by means of hub of Hub-DEGs (hHub-DEGs) and associated potential candidate drugs. In this study, we reviewed 41 articles on transcriptomic data analysis of SARS-CoV-2 and found 370 unique hub genes or studied genes in total. Then, we selected 14 more representative Hub-DEGs (AKT1, APP, CXCL8, EGFR, IL6, INS, JUN, MAPK1, STAT3, TNF, TP53, UBA52, UBC, VEGFA) as hHub-DEGs by their protein-protein interaction analysis. Their associated biological functional processes, transcriptional, and post-transcriptional regulatory factors. Then we detected hHub-DEGs guided top-ranked nine candidate drug agents (Digoxin, Avermectin, Simeprevir, Nelfinavir Mesylate, Proscillaridin, Linifanib, Withaferin, Amuvatinib, Atazanavir) by molecular docking and cross-validation for treatment of SARS-CoV-2 infections. Therefore, the findings of this study could be useful in formulating a common treatment plan against SARS-CoV-2 infections globally.


Statistics for USQ ePrint 50678
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Date Deposited: 07 Aug 2022 23:49
Last Modified: 28 Sep 2022 05:18
Uncontrolled Keywords: SARS-CoV-2 infections; selection of drug targets and agents; drug repurposing; molecular docking and dynamic simulation
Fields of Research (2020): 31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310202 Biological network analysis
31 BIOLOGICAL SCIENCES > 3105 Genetics > 310505 Gene expression (incl. microarray and other genome-wide approaches)
34 CHEMICAL SCIENCES > 3404 Medicinal and biomolecular chemistry > 340407 Proteins and peptides
Identification Number or DOI: https://doi.org/10.3390/vaccines10081248
URI: http://eprints.usq.edu.au/id/eprint/50678

Actions (login required)

View Item Archive Repository Staff Only