Mechanism of
Astragalus membranaceus in treatment for vascular dementia based on network pharmacology and molecular docking- Journal of Army Medical University Vol. 45, Issue 10, Pages: 1070-1079(2023)
- 作者机构：
400020 重庆, 中国人民解放军陆军第九五八医院药剂科
- 作者简介：
- 基金信息：the Joint Project of Traditional Chinese Medicine from Chongqing Science and Technology Commission and Chongqing Health Commission(2022MSXM187);the Special Project of Scientific and Technological Innovation Capability Improvement of Army Medical University(2022XQN28)
DOI：10.16016/j.2097-0927.202209162
CLC： R282.71;R319;R749.16Received：19 September 2022，
Revised：2023-01-10，
Published：30 May 2023
- 稿件说明：
移动端阅览
Rui XIONG, Man ZHAO, Hao ZHANG, et al. Mechanism of
Astragalus membranaceus in treatment for vascular dementia based on network pharmacology and molecular docking[J]. Journal of Army Medical University, 2023, 45(10): 1070-1079.Rui XIONG, Man ZHAO, Hao ZHANG, et al. Mechanism of
Astragalus membranaceus in treatment for vascular dementia based on network pharmacology and molecular docking[J]. Journal of Army Medical University, 2023, 45(10): 1070-1079. DOI： 10.16016/j.2097-0927.202209162.
摘要
目的
2
利用网络药理学和分子对接方法研究黄芪治疗血管性痴呆(vascular dementia，VD)的机制。
方法
2
利用TCMSP数据库搜集黄芪活性成分，根据各成分的SMILES式在TargetNet数据库预测各成分的靶点；在GeneCards数据库搜集VD靶点，求黄芪活性成分靶点与VD靶点的交集，得到黄芪治疗VD的潜在靶点，利用Cytoscape 3.9.1软件构建活性成分-靶点网络；将黄芪治疗VD的潜在靶点导入STRING数据库构建蛋白-蛋白交互作用(protein-protein interaction，PPI)网络，并分析获得核心靶点；将核心靶点导入DAVID数据库进行基因本体(Gene Ontology，GO)和京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes，KEGG)富集分析；利用AutoDock软件对黄芪主要活性成分与VD核心靶点进行分子对接。
结果
2
共搜集到黄芪活性成分20个，包括华良姜素、异桔梗素、异黄酮烷、山奈酚和槲皮素等，得到预测靶点102个。收集并筛选得到VD靶点3 556个，得到黄芪与VD共同靶点71个，经PPI分析获得36个核心靶点，包括SLC6A4、ESR1、PTGS2、ABCB1、AR等。36个核心靶点富集到187个GO条目和32条KEGG通路，主要集中于神经功能、激素信号和血管生成方面。分子对接显示黄芪主要活性成分与VD核心靶点具有良好亲和力。
结论
2
黄芪可能主要通过调节神经功能、激素水平和血管生成治疗VD。
Abstract
Objective
2
To investigate the mechanism of
Astragalus membranaceus
(AM) in the treatment of vascular dementia (VD) by network pharmacology and molecular docking.
Methods
2
The active components of AM
were collected from TCMSP database
and the targets of active components were predicted in TargetNet database according to the SMILES. The VD targets were collected by GeneCards database
and the common targets of active components and VD was calculated to obtain the potential targets of AM in the treatment of VD. The active components-target network was constructed by Cytoscape 3.9.1 software. The potential targets of AM in the treatment of VD were imported into STRING database to construct protein-protein interaction (PPI) network
which was analyzed to obtain core targets. The core targets were imported into DAVID database to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The molecular docking was performed to assess the affinity of main active components of AM and the corresponding VD core targets
via
AutoDock software.
Results
2
A total of 20 active components of AM were collected
including jaranol
isorhamnetin
isoflavanone
kaempferol and quercetin
and a total of 102 predicted targets were obtained. A total of 3 556 VD targets were screened
71 common targets of AM and VD were calculated
and 36 core targets were obtained by PPI analysis
including SLC6A4
ESR1
PTGS2
ABCB1 and AR. A total of 187 GO items and 32 KEGG pathways were enriched
mainly focusing on neurological function
hormone signal and angiogenesis. Molecular docking showed good affinity between the main active components of AM and VD core targets.
Conclusion
2
AM treats VD mainly by regulating neurological function
hormone signal and angiogenesis.
关键词
Keywords
references
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