Elucidating the role of PPARG inhibition in enhancing MERS virus immune response: A network pharmacology and computational drug discovery

• Published in: Journal of infection and public health 2024-11, Vol.17 (11), p.102561, Article 102561
• Main Authors: Hassan, Ahmed M., Bajrai, Leena H., Alharbi, Azzah S., Alhamdan, Meshari M., Dwivedi, Vivek Dhar, Azhar, Esam I.
• Format: Article
• Language: English
• Subjects: Antiviral Agents - pharmacology; Computational Biology; Coronavirus Infections - drug therapy; Coronavirus Infections - immunology; Coronavirus Infections - virology; Drug Discovery; Gene Expression Profiling; Gene Ontology; Humans; MD simulation; MERS; MERS-CoV; Middle East Respiratory Syndrome Coronavirus - drug effects; Middle East Respiratory Syndrome Coronavirus - genetics; Middle East Respiratory Syndrome Coronavirus - immunology; Molecular Docking Simulation; Molecular Dynamics Simulation; Network Pharmacology; PPAR gamma - genetics; PPAR gamma - metabolism; PPARG; Protein Interaction Maps - drug effects; STAT1
• ISSN: 1876-0341; 1876-035X; 1876-035X
• DOI: 10.1016/j.jiph.2024.102561

Middle East Respiratory Syndrome (MERS) has become a severe zoonotic disease, posing significant public health concerns due to the lack of specific medications. This urgently demands the development of novel therapeutic molecules. Understanding MERS's genetic underpinnings and potential therapeutic targets is crucial for developing effective treatments. Two gene expression datasets (GSE81909 and GSE100504) were analyzed to identify differentially expressed genes (DEGs) using GEO2R. Furthermore, gene ontology (GO), pathway enrichment analysis, and protein-protein interaction (PPI) network were performed to understand the gene’s functions. A possible drug target was identified, and an FDA-approved drug library was screened against the selected target using molecular docking and validated the findings through molecular dynamics simulation, principal component analysis, free energy landscape, and MM/GBSA calculations. The study on GSE81909 and GSE100504 datasets with icMERS and MOCK samples at 24 and 48 h revealed an upregulation in 73 and 267 DEGs, respectively. In the network pharmacology, STAT1, MX1, DDX58, EIF2AK2, ISG15, IFIT1, IFIH1, OAS1, IRF9, and OASL were identified as the top 10 hub genes. STAT1 was identified as the most connected hub gene among these top 10 hub genes, which plays a crucial role in the immune response to the MERS virus. Further study on STAT1 showed that PPARG helps reduce STAT1, which could modulate the immune response. Therefore, by inhibiting PPARG, the immunological response can be successfully enhanced. The known inhibitor of PPARG, 570 (Farglitazar), was used as a control. Further, screening using Tanimoto and K-mean clustering was performed, from which three compounds were identified: 2267, 3478, and 40326. Compound 3478 showed characteristics similar to the control, indicating robust binding to PPARG. 3478 showed the highest negative binding free energy with −41.20 kcal/mol, indicating strong binding with PPARG. These findings suggest that 3478 promises to be a potential inhibitor of PPARG, and further experimental investigations can explore its potential as a MERS inhibitor.