Exploring the Antidepressant Mechanism of Codonopsis pilosula through Network Pharmacology and Molecular Docking Analysis
Author:
Lin Mu1, Liao Jiangrong1, Gong Yadong1, Xiao Ran1, Liu Mubo1, Ding Huihong2, Ma Qingqing1
Affiliation:
1. Guizhou Aerospace Hospital 2. Shougang Shuigang Hospital
Abstract
Abstract
Objective
To investigate the antidepressant properties and underlying mechanisms of Codonopsis pilosula using network pharmacology and molecular docking analysis.
Methods
The principal constituents of Codonopsis pilosula were identified from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Genecards and the Online Mendelian Human Inheritance Database (OMIM) were utilized to gather genes associated with depression. Subsequently, Cytoscape software and the STRING database were employed to construct a components-targets network and protein interaction network models for Codonopsis pilosula. The DAVID database was applied for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the targets, while Autodock software was utilized for molecular docking of the primary active compounds of Codonopsis pilosula with its key targets.
Results
This investigation identified 18 primary components in Codonopsis pilosula, which have the potential to modulate numerous targets and impact 57 signaling pathways. Through integration of prior research findings and molecular docking validation, it was observed that Lobetyolin, the key constituent of Codonopsis pilosula, exhibits binding affinity with the pivotal target Tumor Protein P53 (TP53).
Conclusion
The findings suggest that Codonopsis pilosula may elicit antidepressant effects through a multi-component, multi-target, and multi-pathway approach, laying a foundation for further exploration and clinical utilization of Codonopsis pilosula in the prevention and management of depression.
Publisher
Springer Science and Business Media LLC
Reference28 articles.
1. 1. Malhi, G. S.; Mann, J. J., Depression. Lancet 2018, 392 (10161), 2299–2312. 2. 2. Schramm, E.; Klein, D. N.; Elsaesser, M.; Furukawa, T. A.; Domschke, K., Review of dysthymia and persistent depressive disorder: history, correlates, and clinical implications. Lancet Psychiatry 2020, 7 (9), 801–812. 3. 3. Parent-Lamarche, A.; Marchand, A.; Saade, S., Does Depression Mediate the Effect of Work Organization Conditions on Job Performance? J Occup Environ Med 2020, 62 (4), 296–302. 4. 4. Blodgett, J. M.; Lachance, C. C.; Stubbs, B.; Co, M.; Wu, Y. T.; Prina, M.; Tsang, V. W. L.; Cosco, T. D., A systematic review of the latent structure of the Center for Epidemiologic Studies Depression Scale (CES-D) amongst adolescents. Bmc Psychiatry 2021, 21 (1), 197. 5. 5. Yohn, C. N.; Gergues, M. M.; Samuels, B. A., The role of 5-HT receptors in depression. Mol Brain 2017, 10 (1), 28.
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