An <i>In Silico</i> Analysis Integrating Bulk and Single-Cells RNA Sequencing to Study the Mechanistic Effects of Umbelliferone in COPD

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major respiratory disorder that is characterized by persistent airflow limitation and an abnormal inflammation response to noxious particles, such as cigarette smoke and environmental pollution. COPD is a leading cause of mortality, causing a significant economic and social burden in many countries worldwide. Umbelliferone, which is a coumarin derivative, has been shown to possess anti-inflammatory and antioxidant properties, which may be useful in alleviating COPD. METHODS: An integrative bioinformatics approach was adopted to analyze gene expression datasets from gene expression omnibus (GSE275503 for bulk RNA sequencing and GSE183974 for single-cell RNA sequencing). Differentially expressed genes (DEGs) were identified and overlapped with COPD-associated genes from GeneCards. Common genes were analyzed using the STRING database to construct a protein-protein interactions (PPI) network, and further processed in Cytoscape using the “cytoHubba” plug-in to identify the top 10 hub genes. Molecular docking of these genes, along with three key genes from single-cell analysis, was performed using AutoDock vina in SAMSON software. A molecular dynamic (MD) simulation study was carried out using Desmond and Schrodinger software. The MD simulations were performed to investigate the stability and dynamic behavior of the UMB with 6E3K and 1S9V over time 200 ns. RESULTS: From bulk and single-cell RNA sequencing analyses, 50 and 1053 DEGs were extracted, respectively, with the cut-off value of LogFC &gt; 1 and [Formula: see text]-value &lt; 0.05. A total number of 562 overlapping genes were identified between the DEGs and COPD-associated genes from GeneCards. The top 10 hub genes from the PPI network were ITGAM, CCL2, IFN-[Formula: see text], CCL5, FN1, IL-1B, BCL2, CDH1, IL-1A and CXCL8. Among these, molecular docking revealed the highest binding affinities for IFN-[Formula: see text] (PDB ID: 6E3K, −7.4 kcal/mol), ITGAM (PDB ID: 1NA5, −6.6 kcal/mol), IL-1B (PDB ID: 8C3U, −6.2 kcal/mol), FN1 (PDB ID: 3M7P, −6.1 kcal/mol) and BCL2 (PDB ID: 6YLD, −5.9 kcal/mol). Additionally, single-cell RNA analysis identified HLA-DQA2 (PDB ID: 1H15, −2.7 kcal/mol), HLA-DRB5 (PDB ID: 1S9V, −4.9 kcal/mol) and S100A9 (PDB ID: 6ZDY, −5.3 kcal/mol) as the top three genes. An MD simulation study evaluated protein-ligand stability, binding dynamics and conformational changes. Root mean square deviation (RMSD) and Root mean square fluctuation (RMSF) analyses identified key interacting residues involved in hydrogen bonding, hydrophobic interactions and water bridges. The 6E3K complex exhibited protein backbone RMSD of 5−7 Å, ligand RMSD of 2−5 Å and residue fluctuations &lt;3 Å. The 1S9V complex showed protein RMSD of 2.85 Å, ligand RMSD of 1.95−2.30 Å and residue fluctuations around 1.25−3.25 Å. CONCLUSION: The top 10 Genes from the PPI network and the top 3 genes from the single-cell RNA analysis were associated with COPD-related pathways. Molecular docking with the UMB showed strong binding affinities, indicating its potential to inhibit these pathways and serve as a therapeutic option for COPD. The MD simulation confirmed stable binding, key interactions and structural flexibility.