Abstract
BACKGROUND: Depression is a prevalent mental disorder, with its incidence rising alongside the increasing pressures of modern social life. Although medications remain a cornerstone of treatment, first-line antidepressants are often associated with significant side effects. Pueraria, a plant rich in isoflavonoid active ingredients, has demonstrated neuroprotective effects; however, the specific mechanisms behind its antidepressant components have not been fully elucidated. METHODS: This study employed an integrated approach combining network pharmacology, transcriptomics, and machine learning to explore the mechanisms of Puerari's antidepressant active ingredients. Multiple transcriptomic datasets were analyzed, and active ingredients, depression-related genes, and key targets were identified through the GEO, HERB, TCMSP, GWAS, and PDB databases. Molecular docking simulations were used to assess the binding affinity between the key active ingredients (daidzein and methyl p-coumarate) and the primary targets of Pueraria extracts. In vivo validation was conducted using a chronic mild stress (CMS) mouse model to evaluate the antidepressant effects of daidzein and methyl p-coumarate. RESULTS: We identified eight signature genes related to both Pueraria and depression, with MMP9, MGAM, and CDK5R1 being of particular importance. Molecular docking revealed that daidzein and methyl p-coumarate strongly bind to these three key genes, supporting their neuroprotective efficacy. In vivo experiments confirmed that both daidzein and methyl p-coumarate reversed depressive-like behaviors in CMS mice, with daidzein demonstrating a particularly significant antidepressant effect. CONCLUSION: Pueraria, as a traditional medicinal herb with both food and medicinal uses, shows promising antidepressant potential through its active ingredient daidzein. This not only offers a novel approach for the prevention and treatment of depression but also provides new theoretical perspectives and research pathways for understanding antidepressant mechanisms.