Abstract
Depression is a chronic mental disorder with high disability and mortality rates, affecting approximately 95 million individuals in China alone. The limitations of current antidepressant therapies, including restricted efficacy, delayed onset, and significant adverse effects, have motivated the search for alternative treatments from traditional Chinese medicine. Psychotria rubra (PR), a widely distributed medicinal plant in southern China with reported antidepressant properties, represents a promising candidate; however, the molecular mechanisms underlying its antidepressant effects remain largely unexplored. This study aims to elucidate the mechanistic basis of PR in depression treatment by integrating network pharmacology, molecular docking, molecular dynamics simulation, and in vivo experimental validation. We integrated two depression-related transcriptome datasets [GSE76826 (training set: 10 depressed vs. 12 healthy blood samples) and GSE98793 (validation set: 64 depressed vs. 64 healthy blood samples)] to identify differentially expressed genes in depression. Ferroptosis-related genes were retrieved from GeneCards (score > 8) and FerrDb databases, and eight machine learning models with five-fold cross-validation were employed to screen key diagnostic genes. Molecular docking and dynamics simulations validated interactions between PR's active ingredients and target genes. In vivo, eighty male C57BL/6 mice (6-week-old) were randomly allocated into eight groups (n = 10 per group): control, chronic unpredictable mild stress (CUMS) model, deacetyl asperulosidic acid (10 mg·kg⁻1·d⁻1, intraperitoneal), deacetyl asperulosidic acid methyl ester (10 mg·kg⁻1·d⁻1, intraperitoneal), Procyanidin B1 (20 mg·kg⁻1·d⁻1, intraperitoneal), Procyanidin B2 (20 mg·kg⁻1·d⁻1, intraperitoneal), Procyanidin C1 (20 mg·kg⁻1·d⁻1, intraperitoneal), and fluoxetine (10 mg·kg⁻1·d⁻1, oral gavage, positive control). Following 21 days of CUMS induction, drug treatment was administered for 14 consecutive days. Behavioral assessments (sucrose preference test, open field test, tail suspension test, and forced swimming test), inflammatory factors (serotonin, interleukin-1β, interleukin-6, tumor necrosis factor-α), oxidative stress markers (superoxide dismutase, malondialdehyde), and ferroptosis indicators (reactive oxygen species, glutathione, Fe2⁺ levels) were measured. Machine learning analysis identified EPAS1, MAPK14, and VEGF-A as candidate diagnostic genes associated with depression and ferroptosis. Molecular docking revealed favorable binding affinities between multiple PR active ingredients (including asperuloside, asperulosidic acid, deacetyl asperulosidic acid, and deacetyl asperulosidic acid methyl ester) and EPAS1, with deacetyl asperulosidic acid showing the most favorable docking score (- 8.737 kcal/mol) and asperuloside and deacetyl asperulosidic acid methyl ester exhibiting comparable binding free energies (- 35.22 and - 35.26 kcal/mol, respectively) in molecular dynamics simulations. In vivo, PR's main active ingredients significantly improved depressive-like behaviors and reduced neuroinflammation, oxidative stress, and ferroptosis-related markers in CUMS-induced mice, with Procyanidin B1 demonstrating the most pronounced antidepressant-like effect. PR alleviated depression potentially through modulating EPAS1 expression. Procyanidin B1 might be the key active ingredients in PR to alleviate the progression of depression and ferroptosis. This study offers a promising direction for developing new antidepressants from traditional Chinese medicine with multi-component, multi-target effects.