Mechanistic insights into iguratimod action in asthma-COPD overlap through multi-omics and in-silico modelling

Asthma-COPD Overlap (ACO) is a multifaceted condition that combines features of both asthma and COPD. This coexistence complicates clinical management and is associated with increased disease severity, more frequent exacerbations, increased hospitalization, and mortality rates. In this study, an integrated bioinformatics approach was utilized to analyse gene expression datasets for bulk and single RNA sequencing from Gene Expression Omnibus (GEO). DEGs from ACO were intersected with asthma + COPD (bulk RNA-seq) using Venny to identify the shared genes. The common DEGs obtained from Venny and DEGs for single-cell RNA seq underwent PPI analysis via the STRING database. Networks were separately analyzed in Cytoscape using CytoHubba to identify hub genes. Iguratimod (IGU) is a small-molecule anti-inflammatory drug with a multifaceted targeted action. It has shown promising activity in the amelioration of acute lung injury and pulmonary fibrosis. Molecular docking with IGU with the obtained genes was done using Autodock Tools. A molecular dynamics simulation was performed to investigate the stability and dynamic behaviour of IGU with 2AZ5 and 4MBS over 200 ns. A total of 407 DEGs and 3043 marker genes were identified from bulk and single-cell transcriptomics, respectively. The PPI network yielded hub genes such as TNF, CCL3, 4, and 5, CCR1, CXCL1, 2, & 8, ITGAM, ITGAX, NFκB, CD68, TLR4, FCGR3, ICAM1, and PTGS2. Molecular docking with IGU showed the highest binding affinity with TNF (2AZ5, -7.43 kcal/mol) and CCL groups (4MBS, -6.34 kcal/mol). MD simulation of IGU with 2AZ5 and 4MBS showed stable binding, key interactions, and structural flexibility.