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中国临床药理学与治疗学 ›› 2019, Vol. 24 ›› Issue (10): 1107-1119.doi: 10.12092/j.issn.1009-2501.2019.10.005

• 基础研究 • 上一篇    下一篇

基于网络药理学的虎杖抗高血脂作用及信号通路研究

郑 丽,莫娟芬,吴加元,郭 丽,鲍 轶   

  1. 嘉兴市第二医院 中心实验室,嘉兴 314000,浙江
  • 收稿日期:2019-07-18 修回日期:2019-08-20 出版日期:2019-10-26 发布日期:2019-10-28
  • 通讯作者: 鲍轶,女,博士,主任医师,研究方向:中西医结合内分泌学。 Tel:0573-82073185 E-mail:ybao2011@gmail.com
  • 作者简介:郑丽,女,博士,助理研究员,研究方向:网络药理学、中药药效物质基础。 Tel:0573-82082936 E-mail:zhengli0420@163.com
  • 基金资助:

    浙江省医药卫生科技计划项目(2018RC073);嘉兴市科技计划项目(2016BY28022)

Study on the anti-hyperlipidemia mechanisms and signaling pathways of traditional Chinese medicine Polygonum cuspidatum based on network pharmacology

ZHENG Li, MO Juanfen, WU Jiayuan, GUO Li, BAO Yi   

  1. Key Laboratory, the Second Hospital of Jiaxing, Jiaxing 314000, Zhejiang, China
  • Received:2019-07-18 Revised:2019-08-20 Online:2019-10-26 Published:2019-10-28

摘要:

目的: 利用数据挖掘结果,采用网络药理学和生物信息学方法,分析传统中药虎杖抗高血脂的作用及信号通路。方法:采用TCMSP数据库从虎杖中筛选出主要活性成分,利用Drug-CPI服务器反向药效团匹配方法,预测并获得虎杖主要活性成分靶点。通过OMIM、TTD、Genecards等疾病靶点数据库筛选出高血脂相关疾病靶点,获得活性成分靶点和疾病靶点交集。采用Cytoscape 3.7.1软件构建虎杖可视化网络,应用STRING在线平台对交集靶点进行蛋白互作网络分析,同时对交集靶点进行GO功能和KEGG通路富集分析。根据KEGG通路分析结果,应用Autodock 4.2.6分子对接软件研究虎杖中相关活性成分与胰岛素抵抗和PPAR信号通路中的关键靶点蛋白,即TNFRSF1A和PPARG的结合作用强弱与结合机制。结果:采用TCMSP数据库从虎杖中筛选出12个主要活性成分,并预测获得288个靶点。从疾病靶点数据库筛选出高血脂相关疾病靶点571个,并获得虎杖潜在抗高血脂作用靶点共48个。构建了“药物-活性成分-靶点-疾病”网络,该网络中化合物的平均度值为16.67,其中有8个化合物能与15个及以上靶点发生相互作用。KEGG通路富集分析的结果发现48个作用靶点主要参与调控胰岛素抵抗(insulin resistance)、PPAR信号通路(PPAR signaling pathway)、非酒精性脂肪性肝病(non-alcoholic fatty liver disease,NAFLD)、HIF-1信号通路(HIF-1 signaling pathway)、调节脂肪细胞中的脂肪分解(regulation of lipolysis in adipocytes)等信号通路。分子对接结果表明,4个活性成分均能与TNFRSF1A结合,其中rhein(大黄酸)与TNFRSF1A结合能最低,为-10.08 kcal/mol。在虎杖的7个活性成分中,physovenine(囊毒碱)与PPARG的结合能最低,为-13.45 kcal/mol。结合方式主要以氢键、静电力及疏水作用为主。结论:虎杖活性成分可能通过参与调控胰岛素抵抗、PPAR信号通路以及调节脂肪细胞中的脂肪分解等信号通路调节脂质代谢,从而实现抗高血脂的作用。

关键词: 高血脂, 虎杖, 靶点预测, PPAR信号通路, 胰岛素抵抗

Abstract:

AIM: To analyze the anti-hyperlipidemia effects and signaling pathways of traditional Chinese medicine Polygonum cuspidatum based on network pharmacology and bioinformatics methods. METHODS: The main active ingredients were screened from Polygonum cuspidatum using TCMSP database, and the targets were predicted by Drug-CPI server with reverse pharmacophore matching method. Meanwhile, target databases such as OMIM, TTD and Genecards disease were used to obtain the disease targets of hyperlipidemia, and the intersections of active ingredient targets and disease targets were obtained for further analysis. Cytoscape 3.7.1 software was used to construct the visualization network of "drug-active ingredient-target-disease" of Polygonum cuspidatum.The protein interaction network of the intersecting targets was analyzed by STRING online platform, and the GO function and KEGG pathway enrichment analysis were performed. Furthermore, according to the results of KEGG pathway analysis, the binding energy and and binding mechanism between the active compounds and the key target protein of insulin-resistant pathway and PPAR signaling pathway, namely TNFRSF1A and PPARG were analyzed by using molecular docking software Autodock 4.2.6. RESULTS:Twelve main active ingredients were obtained from Polygonum cuspidatum by TCMSP database, and 288 targets were predicted by Drug-CPI server, meanwhile 571 hyperlipidemia-related disease targets were obtained. Finally, 48 intersections of active ingredient targets and disease targets were obtained. The network of "drug-active ingredient-target-disease" of Polygonum cuspidatum showed that the average degree of compounds in the network was 16.67, and 8 of 12 compounds could interact with 15 or more disease targets. Through the analysis of GO function and KEGG pathway enrichment, the 48 intersecting targets were mainly involved in regulating insulin resistance, PPAR signaling pathway, non-alcoholic fatty liver disease (NAFLD), HIF-1 signaling pathway and Regulation of lipolysis in adipocytes. Molecular docking results showed that all four active components could be spontaneously bind with TNFRSF1A, and rhein had the lowest binding energy with TNFRSF1A, which was -10.08 kcal/mol. Among the seven active components of Polygonum cuspidatum, physovenine has the lowest binding energy with PPARG, which was -13.45 kcal/mol. The binding mode was mainly based on hydrogen bonding, electrostatic force and hydrophobic interaction. CONCLUSION: The active ingredients of Polygonum cuspidatum may regulate lipid metabolism by regulating insulin resistance, PPAR signaling pathway and lipid decomposition, thus realizing the anti-hyperlipidemia effects.

Key words: hyperlipidemia, Polygonum cuspidatum, target prediction, PPAR signaling pathway, insulin resistance

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