Han De-En, Yue Zhong-Sheng, Li Hong-Wei, Liu Gai-Zhi, Cai Bang-Rong, Tian Ping
School of Pharmacy, Henan University of Chinese Medicine Zhengzhou 450046, China.
Henan Academy of Chinese Medicine Zhengzhou 450004, China.
Zhongguo Zhong Yao Za Zhi. 2022 Feb;47(4):1051-1063. doi: 10.19540/j.cnki.cjcmm.20210811.404.
This study aimed to explore the anti-depressant components of Rehmanniae Radix and its action mechanism based on network pharmacology combined with molecular docking. The main components of Rehmanniae Radix were identified by ultra-high performance liquid chromatography-quadrupole/Orbitrap high resolution mass spectrometry(UPLC-Q-Orbitrap HRMS), and the related targets were predicted using SwissTargetPrediction. Following the collection of depression-related targets from GeneCards, OMIM and TTD, a protein-protein interaction(PPI) network was constructed using STRING. GO and KEGG pathway enrichment analysis was performed by Metascape. Cytoscape 3.7.2 was used to construct the networks of "components-targets-disease" and "components-targets-pathways", based on which the key targets and their corresponding components were obtained and then preliminarily verified by molecular docking. Rehmanniae Radix contained 85 components including iridoids, ionones, and phenylethanoid glycosides. The results of network analysis showed that the main anti-depressant components of Rehmanniae Radix were catalpol, melittoside, genameside C, gardoside, 6-O-p-coumaroyl ajugol, genipin-1-gentiobioside, jiocarotenoside A1, neo-rehmannioside, rehmannioside C, jionoside C, jionoside D, verbascoside, rehmannioside, cistanoside F, and leucosceptoside A, corresponding to the following 16 core anti-depression targets: AKT1, ALB, IL6, APP, MAPK1, CXCL8, VEGFA, TNF, HSP90 AA1, SIRT1, CNR1, CTNNB1, OPRM1, DRD2, ESR1, and SLC6 A4. As revealed by molecular docking, hydrogen bonding and hydrophobicity might be the main action forms. The key anti-depression targets of Rehmanniae Radix were concentrated in 24 signaling pathways, including neuroactive ligand-receptor interaction, neurodegenerative disease-multiple diseases pathway, phosphatidylinositol 3-kinase/protein kinase B pathway, serotonergic synapse, and Alzheimer's disease.
本研究旨在基于网络药理学结合分子对接，探索熟地黄的抗抑郁成分及其作用机制。采用超高效液相色谱-四极杆/轨道阱高分辨率质谱（UPLC-Q-Orbitrap HRMS）鉴定熟地黄的主要成分，并使用SwissTargetPrediction预测相关靶点。从GeneCards、OMIM和TTD收集抑郁症相关靶点后，利用STRING构建蛋白质-蛋白质相互作用（PPI）网络。通过Metascape进行GO和KEGG通路富集分析。使用Cytoscape 3.7.2构建“成分-靶点-疾病”和“成分-靶点-通路”网络，据此获得关键靶点及其相应成分，然后通过分子对接进行初步验证。熟地黄含有85种成分，包括环烯醚萜类、紫罗兰酮类和苯乙醇苷类。网络分析结果表明，熟地黄的主要抗抑郁成分有梓醇、蜜力特苷、栀子苷C、栀子苷、6-O-对香豆酰筋骨草醇、京尼平-1-龙胆双糖苷、焦地黄苷A1、新地黄苷、地黄苷C、地黄苷C、肉苁蓉苷F和白刺花苷A，对应以下16个核心抗抑郁靶点：AKT1、ALB、IL6、APP、MAPK1、CXCL8、VEGFA、TNF、HSP90 AA1、SIRT1、CNR1、CTNNB1、OPRM1、DRD2、ESR1和SLC6 A4。分子对接结果显示，氢键和疏水性可能是主要作用形式。熟地黄的关键抗抑郁靶点集中在24条信号通路中，包括神经活性配体-受体相互作用、神经退行性疾病-多种疾病通路、磷脂酰肌醇3-激酶/蛋白激酶B通路、5-羟色胺能突触和阿尔茨海默病。
