Gene Interaction Network Analysis Reveals IFI44L as a Drug Target in Rheumatoid Arthritis and Periodontitis.

Abstract

Simple SummaryIn spite of substantial investigation, the biological link between periodontitis and rheumatoid arthritis remains unexplained. This study intended to correlate periodontitis and rheumatoid arthritis gene expression patterns to find shared targets for both the disease. We identified the differentially expressed genes (DEGs) in periodontitis and rheumatoid arthritis. The network was built by integrating DEGs and ranking the genes using GeneMANIA. FINDSITEcomb2.0 was used to find a possible inhibitor for the top-ranked gene. Further, the binding effectiveness and protein-ligand complex stability were then determined by molecular docking and molecular dynamics. The network analysis showed IFI44L as a highly ranking gene implicated in most immunological pathways. A virtual screening of 6507 compounds revealed vemurafenib as the best candidate for the IFI44L target. Molecular docking and molecular dynamics modelling revealed the stability of the IFI44L-vemurafenib complex, which suggest IFI44L is potential target and vemurafenib could be the better candidate to treat both diseases.Objective: Despite extensive research on periodontitis and rheumatoid arthritis, the underlying molecular connectivity between these condition remains largely unknown. This research aimed to integrate periodontitis and rheumatoid arthritis gene expression profiles to identify interconnecting genes and focus to develop a common lead molecule against these inflammatory conditions. Materials and Methods: Differentially expressed genes (DEGs) of periodontitis and rheumatoid arthritis were identified from the datasets retrieved from the Gene Expression Omnibus database. The network was constructed by merging DEGs, and the interconnecting genes were identified and ranked using GeneMANIA. For the selected top ranked gene, the potential inhibitor was searched using FINDSITEcomb2.0. Subsequently, the molecular docking and molecular dynamics were performed to determine the binding efficiency and protein-ligand complex stability, respectively. Results: From the network analysis, IFN-induced protein 44-like (IFI44L) was identified as a top ranked gene involved in most of the immunological pathway. With further virtual screening of 6507 molecules, vemurafenib was identified to be the best fit against the IFI44L target. The binding energy and stability of IFI44L with vemurafenib were investigated using molecular docking and molecular dynamics simulation. Docking results show binding energy of −7.7 Kcal/mol, and the simulation results show stability till 100 ns. Conclusions: The identified IFI44L may represent a common drug target for periodontitis and rheumatoid arthritis. Vemurafenib could be a potent anti-inflammatory drug for both diseases.