基于数据挖掘和网络拓扑学对藏药红景天调控脑微循环作用靶点和信号通路的筛选  

Screening target and signal pathway regulating cerebral microcirculation of the Tibetan medicine Rhodiola based on data mining and network topology

作　　者：马四清[1] 陈强[1] 徐颖[1] 闫秀娟 刘娟丽[1] Ma Siqing;Chen Qiang;Xu Ying;Yan Xiujuan;Liu Juanli(Department of Critical Care Medicine,Qinghai Provincial People's Hospital,Xining 810007,China;Department of Neurology,the Fifth People's Hospital of Qinghai Province,Xining 810007,China)

机构地区：[1]青海省人民医院重症医学科,西宁810007 [2]青海省第五人民医院神经内科,西宁810007

出　　处：《中华重症医学电子杂志》2022年第4期353-359,共7页Chinese Journal Of Critical Care & Intensive Care Medicine(Electronic Edition)

基　　金：青海省科技计划项目(2020-ZJ-754)。

摘　　要：目的基于化学相似性靶点预测与网络拓扑学分析方法,对藏药红景天改善脑微循环潜在作用靶点进行虚拟筛选。方法通过化学成分数据库及相关文献信息提取收集红景天化学成分,利用药代动力学参数(如吸收、分布、代谢、排泄,即ADME)筛选出活性成分;利用swiss在线靶点预测平台采用化学相似性方法预测红景天活性成分的潜在靶点;通过GeneCards数据库获取脑微循环相关靶点,并获取红景天活性成分与脑微循环共有靶点;利用STRING数据库获取共有靶点蛋白间相互作用关系;利用Cytoscape软件构建蛋白质间相互作用(PPI)网络模型,基于网络拓扑学算法获取核心靶点(core target);最后,对共有靶点进行KEGG分析,明确藏药红景天改善脑微循环的主要信号通路。结果从藏药红景天中筛选出76个活性成分和660个靶点,获取脑微循环相关靶点526个,交集靶点141个,核心靶点AKT1、肿瘤坏死因子(TNF)、血管内皮生长因子A(VEGFA)、甘油醛-3-磷酸脱氢酶(GAPDH)、丝裂原活化蛋白激酶3(MAPK3)、表皮生长因子受体(EGFR)、非受体酪氨酸激酶(SRC)、胱天蛋白酶(Casp 3),反向查找核心靶点相关成分,获取25个关键活性成分。《京都议定书》的基因与基因组百科全书(KEGG)通路富集分析得到150条通路(P<0.01),其中最主要的信号通路包括:缺氧诱导因子(HIF)-1信号通路、Rap1信号通路、黏着斑、松弛素信号通路、Ras信号通路、磷酸肌醇3激酶-蛋白激酶B(PI3K-Akt)信号通路、TNF信号通路、ErbB信号通路。结论藏药红景天可通过多成分、多靶点、多通路协同发挥改善脑微循环作用,该研究初步筛选出了藏药红景天调控脑微循环潜在作用靶点及作用的信号通路,为进一步拓展应用藏药红景天的脑保护(药理作用)提供了较为丰富的理论依据。Objective To virtually screen out the potential targets improving brain microcirculation of the Tibetan medicine Rhodiola,based on chemical similarity target prediction and network topology analysis methods.Methods The active ingredients of Rhodiola based on chemical composition database and related literatures,were selected by pharmacokinetic parameters(ADME).The swiss online target prediction platform was used to predict potential targets of active components of Rhodioal sachalinensis by chemical similarity method.Obtaining brain microcirculation-related targets through the GeneCards database,and obtaining the Rhodiola active components and brain microcirculation common target.Common target proteins were linked to the STRING database.Construction of a protein-protein interaction(PPI)network model using Cytoscape software.Obtaining core targets(core target)based on network topology algorithm.For KEGG analysis of the common targets,to clarify which signaling pathways are potential targets of Rhodiola to improve brain microcirculation.Results Seventy-six active components and 660 targets were selected from Tibetan medicine Rhodiola to obtain 526 brain microcirculation related targets,141 intersection targets,core targets AKT1,TNF,VEGFA,GAPDH,MAPK3,EGFR,SRC,Casp 3,and reverse find core target related components to obtain 25 key active components.The KEGG analysis selected 150 pathways(P<0.01),and the main signaling pathways include:HIF-1 signaling pathway,Rap1 signaling pathway,focal adhesion plaque,relaxin signaling pathway,Ras signaling pathway,and PI3K-Akt signaling pathway,TNF signaling pathway,and ErbB signaling pathway.Conclusion Tibetan medicine Rhodiola can improve the role of brain microcirculation through multi-component,multi-target and multi-pathway coordination.This study has preliminarily screened out the potential targets and signal pathway of regulating cerebral microcirculation,which provides a rich theoretical basis for further expanding the application of Tibetan medicine Rhodiola brain protectio

关 键 词：脑微循环 红景天 化学相似性预测 网络拓扑学分析 信号通路 

分 类 号：R651.15[医药卫生—外科学]