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基于网络药理学的黄连治疗阿尔茨海默病药理机制研究策略

Network Pharmacology-Based Strategy to Investigate the Pharmacologic Mechanisms of Coptidis Rhizoma for the Treatment of Alzheimer's Disease.

作者信息

Ye Xian-Wen, Wang Hai-Li, Cheng Shui-Qing, Xia Liang-Jing, Xu Xin-Fang, Li Xiang-Ri

机构信息

Centre of TCM Processing Research, Beijing University of Chinese Medicine, Beijing, China.

Beijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.

出版信息

Front Aging Neurosci. 2022 Jun 21;14:890046. doi: 10.3389/fnagi.2022.890046. eCollection 2022.

Abstract

BACKGROUND

Alzheimer's disease (AD) is becoming a more prevalent public health issue in today's culture. The experimental study of Coptidis Rhizoma (CR) and its chemical components in AD treatment has been widely reported, but the principle of multi-level and multi-mechanism treatment of AD urgently needs to be clarified.

OBJECTIVE

This study focuses on network pharmacology to clarify the mechanism of CR's multi-target impact on Alzheimer's disease.

METHODS

The Phytochemical-compounds of CR have been accessed from the Traditional Chinese Medicine Database and Analysis Platform (TCMSP) and Symmap database or HPLC determination. The values of Oral Bioavailability (OB) ≥ 30% and Drug Like (DL) ≥ 0.18 or blood ingredient were used to screen the active components of CR; the interactive network of targets and compounds were constructed by STRING and Cytoscape platform, and the network was analyzed by Molecular Complex Detection (MCODE); Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG) and metabolic pathway enrichment of targets were carried out with Metascape, the Database for Annotation, Visualization and Integrated Discovery (DAVID) and MetaboAnalyst platform; Based on CytoHubba, the potential efficient targets were screened by Maximal Clique Centrality (MCC) and Degree, the correlation between potential efficient targets and amyloid β-protein (Aβ), Tau pathology was analyzed by Alzdata database, and the genes related to aging were analyzed by Aging Altas database, and finally, the core targets were obtained; the binding ability between ingredients and core targets evaluated by molecular docking, and the clinical significance of core targets was assessed with Gene Expression Omnibus (GEO) database.

RESULTS

19 active components correspond to 267 therapeutic targets for AD, of which 69 is potentially effective; in module analysis, RELA, TRAF2, STAT3, and so on are the critical targets of each module; among the six core targets, RELA, MAPK8, STAT3, and TGFB1 have clinical therapeutic significance; GO function, including 3050 biological processes (BP), 257 molecular functions (MF), 184 cellular components (CC), whose functions are mainly related to antioxidation, regulation of apoptosis and cell composition; the HIF-1 signaling pathway, glutathione metabolism is the most significant result of 134 KEGG signal pathways and four metabolic pathways, respectively; most of the active components have an excellent affinity in docking with critical targets.

CONCLUSION

The pharmacological target prediction of CR based on molecular network pharmacology paves the way for a multi-level networking strategy. The study of CR in AD treatment shows a bright prospect for curing neurodegenerative diseases.

摘要

背景

在当今文化中,阿尔茨海默病(AD)正成为一个日益普遍的公共卫生问题。黄连(CR)及其化学成分在AD治疗中的实验研究已有广泛报道,但AD多层次、多机制治疗的原理亟待阐明。

目的

本研究聚焦网络药理学,以阐明CR对阿尔茨海默病多靶点作用的机制。

方法

通过中药系统药理学数据库与分析平台(TCMSP)、中药整合药理学计算平台(Symmap)数据库或高效液相色谱法(HPLC)测定获取CR的植物化学成分。采用口服生物利用度(OB)≥30%、类药性(DL)≥0.18或血液成分值筛选CR的活性成分;利用STRING和Cytoscape平台构建靶点与化合物的相互作用网络,并通过分子复合物检测(MCODE)对网络进行分析;使用Metascape、注释、可视化与整合发现数据库(DAVID)和代谢组分析平台(MetaboAnalyst)对靶点进行基因本体(GO)功能、京都基因与基因组百科全书通路(KEGG)和代谢通路富集分析;基于CytoHubba,通过最大团中心性(MCC)和度筛选潜在有效靶点,利用Alzdata数据库分析潜在有效靶点与淀粉样β蛋白(Aβ)、Tau病理的相关性,通过衰老图谱数据库分析与衰老相关的基因,最终获得核心靶点;通过分子对接评估成分与核心靶点之间的结合能力,并使用基因表达综合数据库(GEO)评估核心靶点临床意义。

结果

19种活性成分对应267个AD治疗靶点,其中69个具有潜在疗效;在模块分析中,RELA、TRAF2、STAT3等是各模块的关键靶点;在六个核心靶点中,RELA、丝裂原活化蛋白激酶8(MAPK8)、STAT3和转化生长因子β1(TGFB1)具有临床治疗意义;GO功能包括3050个生物过程(BP)、257个分子功能(MF)、184个细胞成分(CC),其功能主要与抗氧化、凋亡调控和细胞组成有关;在134条KEGG信号通路和四条代谢通路中,缺氧诱导因子-1(HIF-1)信号通路、谷胱甘肽代谢分别是最显著的结果;大多数活性成分与关键靶点对接时具有良好的亲和力。

结论

基于分子网络药理学的CR药理靶点预测为多层次网络策略铺平了道路。CR在AD治疗中的研究为治愈神经退行性疾病展现出光明前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9ad/9252849/b339317d6a05/fnagi-14-890046-g0001.jpg

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