Comprehensive Analysis of Gene Expression and Biomarkers in Sepsis Using Bioinformatics, Network Pharmacology and Molecular Modeling Approaches.

Abstract

Background: Differentially expressed genes (DEGs) have been known to provide important information on disease mechanisms and potential therapeutic targets. The traditional Chinese medicine (TCM) offers a large reservoir of bioactive compounds that could modulate at these targets. This study is an attempt to investigate the biomarkers in Sepsis and COVID-19 using gene expression analysis and molecular modeling validation of TCM-derived candidate compounds targeting key DEGs associated with sepsis.

Methods: Gene expression data were obtained from NCBI, and limma package in R Studio was used to identify DEGs. Functional annotation was followed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Protein-protein interaction (PPI) networks were created using STRING, and key hub proteins identified utilizing Cytoscape. Molecular docking was conducted using 216 bioactive compounds obtained from TCM databases against target proteins. To study binding stability, molecular dynamics (MD) simulations of 100 ns were performed using GROMACS on top ranked protein-ligand complexes.

Results: A total of 432 key DEGs were functionally enriched in disease related pathways. Bioinformatics analysis identified the RRM2, AURKB, and CDK1 as hub proteins that could serve as promising therapeutic agents. Salvianolic Acid C, Hesperidin, and Gallocatechin Gallate were lead TCM compounds which showed strong binding affinity to these targets on the basis of molecular docking. Selected protein-ligand complexes were stable according to MD simulations.

Conclusion: The current study indicates the possibility of TCM compounds to target DEGs crucial in sepsis pathology. The integrated bioinformatics approach establishes an approach to identify novel drug candidates, which need further experimental validation.