A Network Pharmacology Approach to Investigate the Anti-Depressive Mechanism of Gardeniae fructus

Zhijun Xiao, Cuicui Liu, Jingjing Duan, Ting Zhou, Xiufeng Liu, Saihua Lu and Feng Xu

Background and Objective: Depression is the most frequent psychiatric disorder all over the world. Gardeniae fructus (GF) is a major composition herb in much Traditional Chinese Medicine (TCM) antidepressant prescriptions. This study aimed to investigate the multi-target pharmacological mechanism of GF acting on depression by combined network pharmacology and molecular docking technology. Materials and Methods: Chemical compounds derived from GF were collected from three TCM databases. The active components were screened based on in silico pharmacological properties prediction models. Targets of the active components were obtained from the PubChem and the polypharmacology browser 2 databases. Protein-protein interaction networks were constructed and the hub genes were identified by Cytoscape. The binding ability of hub genes and active components were evaluated with molecular docking technology. GO enrichment and KEGG pathway analysis was performed using the cluster Profiler package in R software. Results: A total of 22 active components and 49 antidepressive targets of GF were identified. Genes including APP, CNR1, DRD2 and OPRM1 were identified as hub targets. Most of the hub targets had a good binding ability with the active components. Regulation of neurotransmitter levels was the major biological process involved in the anti-depressive effects of GF. The main KEGG pathways, including neuroactive ligand-receptor interaction, serotonergic synapse and alcoholism were related to the antidepressive effects of GF. Conclusion: This study demonstrated that GF exerts an anti-depressive effect possibly by regulating neurotransmitter levels via neuroactive ligand-receptor interaction, serotonergic synapse and alcoholism pathways.

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