Evidences for action of icariin on depressive behavior in prenatal stress offspring rats and mechanism by integrating metabolomics and network pharmacology.

Depression, as a prevalent and persistent mental disorder, poses a significant load for people and the community. Given the limitations of conventional antidepressant drugs, including their adverse side effects and suboptimal efficacy, traditional Chinese medicine has garnered considerable attention as a complementary and alternative therapeutic approach. Icariin (ICA), a natural compound, has demonstrated promising antidepressant properties; however, its precise mechanisms of action remain to be fully elucidated. Our research aims to investigate the underlying mechanisms for ICA treating depression, providing new insights into its potential therapeutic applications. The antidepressant mechanisms of ICA were investigated utilizing a prenatal stress (PS)-induced offspring depression model, employing an integrated approach combining metabolomic profiling and network pharmacology analysis. The depression model was established in PS offspring, followed by comprehensive behavioral assessments. Untargeted metabolomic profiling of hippocampus (HIP) and prefrontal cortex (PFC) tissues was performed to identify differentially expressed metabolites, with subsequent metabolic pathway analysis conducted with MetaboAnalyst tool. Concurrently, network pharmacology analysis was implemented to identify potential molecular targets underlying ICA's antidepressant effects. An integrated compound-reaction-enzyme-gene network was constructed and visualized through Cytoscape. Finally, molecular docking simulations were performed to validate the binding interactions between ICA and the identified core targets. ICA demonstrated significant antidepressant effects by ameliorating depressive-like behaviors in PS offspring rats. Metabolomic analysis revealed 246 significantly differentially expressed metabolites associated with ICA's therapeutic efficacy against depression. Through integrated bioinformatics analysis, we identified 13 key targets (ALDH2, AOC3, CHKB, CYP19A1, CYP1A1, CYP1A2, CYP1B1, DPYS, NOS2, PDE5A, PNLIP, PTGS2, XDH) along with their associated metabolic pathways and key metabolites that potentially mediate ICA's antidepressant actions. Molecular docking confirmed stable binding conformations between ICA and these core targets, with favorable binding energies. Furthermore, the study elucidated distinct metabolic alterations in both HIP and PFC regions, providing insights into the region-specific neurochemical modifications induced by ICA treatment. The study is the first to explore mechanism by which ICA alleviates depressive behaviors in PS offspring through an integrated approach combining HIP and PFC tissue metabolomics, network pharmacology, and experimental validation. The molecular mechanisms responsible for the antidepressant effects of ICA was elucidated. The established approach provides a novel methodological framework for investigating the pharmacological properties and mechanistic pathways of natural compounds.