Computational identification of host genomic biomarkers highlighting their functions, pathways and regulators that influence SARS‑CoV‑2 infections and drug repurposing

Article


Mosharaf, Md. Parvez, Reza, Md. Selim, Kibria, Md. Kaderi, Ahmed, Fee Faysal, Kabir, Md. Hadiul, Hasan, Sohel and Mollah, Md. Nurul Haque. 2022. "Computational identification of host genomic biomarkers highlighting their functions, pathways and regulators that influence SARS‑CoV‑2 infections and drug repurposing." Scientific Reports. 12 (1). https://doi.org/10.1038/s41598-022-08073-8
Article Title

Computational identification of host genomic biomarkers highlighting their functions, pathways and regulators that influence SARS‑CoV‑2 infections and drug repurposing

ERA Journal ID201487
Article CategoryArticle
AuthorsMosharaf, Md. Parvez, Reza, Md. Selim, Kibria, Md. Kaderi, Ahmed, Fee Faysal, Kabir, Md. Hadiul, Hasan, Sohel and Mollah, Md. Nurul Haque
Journal TitleScientific Reports
Journal Citation12 (1)
Article Number4279
Number of Pages22
Year2022
PublisherNature Publishing Group
Place of PublicationUnited Kingdom
ISSN2045-2322
Digital Object Identifier (DOI)https://doi.org/10.1038/s41598-022-08073-8
Web Address (URL)https://www.nature.com/articles/s41598-022-08073-8
Abstract

The pandemic threat of COVID-19 has severely destroyed human life as well as the economy around the world. Although, the vaccination has reduced the outspread, but people are still suffering due to the unstable RNA sequence patterns of SARS-CoV-2 which demands supplementary drugs. To explore novel drug target proteins, in this study, a transcriptomics RNA-Seq data generated from SARS-CoV-2 infection and control samples were analyzed. We identified 109 differentially expressed genes (DEGs) that were utilized to identify 10 hub-genes/proteins (TLR2, USP53, GUCY1A2, SNRPD2, NEDD9, IGF2, CXCL2, KLF6, PAG1 and ZFP36) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of hub-DEGs revealed some important functions and signaling pathways that are significantly associated with SARS-CoV-2 infections. The interaction network analysis identified 5 TFs proteins and 6 miRNAs as the key regulators of hub-DEGs. Considering 10 hub-proteins and 5 key TFs-proteins as drug target receptors, we performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 FDA approved drugs. We found Torin-2, Rapamycin, Radotinib, Ivermectin, Thiostrepton, Tacrolimus and Daclatasvir as the top ranked seven candidate drugs. We investigated their resistance performance against the already published COVID-19 causing top-ranked 11 independent and 8 protonated receptor proteins by molecular docking analysis and found their strong binding affinities, which indicates that the proposed drugs are effective against the state-of-the-arts alternatives independent receptor proteins also. Finally, we investigated the stability of top three drugs (Torin-2, Rapamycin and Radotinib) by using 100 ns MD-based MM-PBSA simulations with the two top-ranked proposed receptors (TLR2, USP53) and independent receptors (IRF7, STAT1), and observed their stable performance. Therefore, the proposed drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections.

KeywordsCase-Control Studies; COVID-19; Drug Repositioning; Gene Regulatory Networks; Genetic Markers; Humans; Molecular Docking Simulation; Protein Interaction Maps; SARS-CoV-2
Contains Sensitive ContentDoes not contain sensitive content
FunderMissouri University of Science and Technology
Byline AffiliationsUniversity of Rajshahi, Bangladesh
School of Business
Jashore University of Science and Technology, Bangladesh
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