Using integrated bioinformatics, network pharmacology and molecular docking to explore the mechanisms underlying the antidepressant effect of celastrol.

Abstract

Celastrol, a natural compound derived from the root of Tripterygium wilfordii Hook. F., has shown potential efficacy in alleviating depression in animal models, yet its specific target remains unelucidated. The present investigation aimed to identify the principal targets and possible signaling pathways of celastrol in major depressive disorder (MDD). Using a combination of GEO datasets, network pharmacology, molecular docking and molecular dynamics simulation techniques, we conducted an analysis to uncover the underlying mechanism through which celastrol exerts its antidepressant effects. Our analysis identified a total of 1064 drug targets and 3386 disease-related targets, resulting in 209 shared targets. A topological examination of the protein-protein interaction (PPI) network revealed 10 core targets, including STAT3, IL6, ALB, HSP90AA1, HIF1A, CASP3, EGFR, BCL2L1, INS and IGF1. GO and KEGG pathway enrichment analyses demonstrated that celastrol exerted antidepressant effects through regulating genes related to inflammation, apoptosis, oxidative stress and the PI3K/Akt, MAPK, and HIF1 signaling pathways. Furthermore, the results of molecular docking and molecular dynamics simulations revealed the strong binding affinity between celastrol and HIF1A. In conclusion, this study effectively predicted the possible molecular targets and signaling pathways of celastrol in the treatment of depression, providing a promising approach for future investigations into its mechanisms against MDD.