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Study on the Potential Mechanism of Fructus Tribuli in the Treatment of Hypertensive Vascular Remodeling Based on Network Pharmacology and Molecular Docking
Author(s) -
Shuyue Wang,
Fei Guo,
Xiao-Chen Sun,
Xiao Song,
Yaohui Yuan,
Chao Zhang,
Guitao Lin,
Huagang Sheng
Publication year - 2021
Publication title -
evidence-based complementary and alternative medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.552
H-Index - 90
eISSN - 1741-4288
pISSN - 1741-427X
DOI - 10.1155/2021/8862176
Subject(s) - kegg , drugbank , systems pharmacology , computational biology , biology , mechanism (biology) , omim : online mendelian inheritance in man , docking (animal) , pharmacology , gene ontology , bioinformatics , gene , medicine , genetics , drug , gene expression , philosophy , nursing , epistemology , phenotype
Background Hypertensive vascular remodeling (HVR) is the pathophysiological basis of hypertension, which is also an important cause of vascular disease and target organ damage. Treatment with Fructus Tribuli (FT), a traditional Chinese medicine, has a positive effect on HVR. However, the pharmacological mechanisms of FT are still unclear. Therefore, this study aimed to reveal the potential mechanisms involved in the effects of FT on HVR based on network pharmacology and molecular docking.Materials and Methods We selected the active compounds and targets of FT according to the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and the Swiss Target Prediction database, and the targets of HVR were collected from the Online Mendelian Inheritance in Man (OMIM), GeneCards, and DrugBank databases. The protein-protein interaction network (PPI) was established using the STRING database. Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and network analysis were performed to further explore the potential mechanisms. Finally, molecular docking methods were used to evaluate the affinity between the active compounds and the main target.Results Seventeen active compounds of FT  and 164 potential targets for the treatment of HVR were identified. Component-target and PPI networks were constructed, and 12 main active components and 33 main targets were identified by analyzing the topological parameters. Additionally, GO analysis indicated that the potential targets were enriched in 483 biological processes, 52 cellular components, and 110 molecular functions. KEGG analysis revealed that the potential targets were correlated with 122 pathways, such as the HIF-1 signaling pathway, ErbB signaling pathway, and VEGF signaling pathway. Finally, molecular docking showed that the 12 main active components had a good affinity for the top five main targets.Conclusion This study demonstrated the multiple compounds, targets, and pathway characteristics of FT in the treatment of HVR. The network pharmacology method provided a novel research approach to analyze potential mechanisms.

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