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

Md Parvez Mosharaf; Md Kaderi Kibria; Md Bayazid Hossen; Md Ariful Islam; Md Selim Reza; Rashidul Alam Mahumud; Khorshed Alam; Jeff Gow; Md Nurul Haque Mollah

doi:10.3390/vaccines10081248

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 (Basel). 2022 Aug 3;10(8):1248. doi: 10.3390/vaccines10081248.

Authors

Affiliations

¹ Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh.
² School of Business, Faculty of Business, Education, Law and Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
³ NHMRC Clinical Trials Centre, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia.
⁴ School of Accounting, Economics and Finance, University of KwaZulu Natal, Durban 4001, South Africa.

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.

Keywords: SARS-CoV-2 infections; drug repurposing; molecular docking and dynamic simulation; selection of drug targets and agents.

Grants and funding

This work formed part of the first author’s PhD research at the University of Southern Queensland, Australia which has been supported by funding from the Australian Government Research Training Program Scholarship. Additional funding was received from the Ministry of Science and Technology (MOST) research project, Government of the People’s Republic of Bangladesh, for the fiscal year 2020-2021 (Funding Number: 489-ID).