基于熵值的心律失常基因网络模块划分与中药筛选  

Entropy-Based Module Division of Arrhythmia-Related Gene Network and Screening of Chinese Herbal Medicines

作　　者：魏鹏路 尚菊菊[1] 刘红旭[1] 邢文龙[1] 龙德怀 谭玉培 WEI Peng-lu;SHANG Ju-ju;LIU Hong-xu;XING Wen-long;LONG De-huai;TAN Yu-pei(Beijing Hospital of Traditional Chinese Medicine,Capital Medical Univesity,Beijing 100010;Beijing University of Chinese Medicine,Beijing 100010)

机构地区：[1]首都医科大学附属北京中医医院,北京100010 [2]北京中医药大学,北京100010

出　　处：《世界中西医结合杂志》2023年第12期2405-2412,共8页World Journal of Integrated Traditional and Western Medicine

基　　金：国家自然科学基金项目(82174315);中国博士后科学基金(2021M702311);国家中医药管理局中药循证能力建设项目(2019XZZX-XXG001);北京市医院管理局重点医学专业发展计划(ZYLX201817)。

摘　　要：目的筛选网络模块划分的最佳方法,并进行生物功能分析及化学成分和中药的筛选。方法首先,采用四种常用的模块划分方法,包含Community Cluster、(GLay)、MCL Cluster、MCODE Cluster、Connected Components Cluster,对心律失常疾病基因网络进行模块划分,依据四种方法的网络熵值筛选出最佳的模块划分方法;其次,依据模块的平均连接度筛选主要模块,并对疾病网络和网络模块富集的通路进行比较和分析;最后,通过TCMSP(Traditional Chinese Medicine Database and Analysis Platform)和ETCM(The Encyclopedia of Traditional Chinese Medicine)数据库查找和筛选主要模块对应的成分,并用AutoDock Vina 1.1.2软件进行分子对接,通过成分查找相关的中药。结果MCODE Cluster方法共划分出5个模块,网络熵值最小,为3.55969,最适用于心律失常疾病基因网络模块的识别;通路分析结果显示,module 1能够覆盖原疾病网络56.1%的通路,主要与心肌细胞的肾上腺素能信号(Adrenergic signaling in cardiomyocytes)、肾素分泌(Renin secretion)、钙信号通路(Calcium signaling pathway)、环磷酸腺苷信号通路(cAMP signaling pathway),昼夜节律(Circadian entrainment)等多条信号通路相关;通过中药数据库筛选出三个化学成分,分别为acacetin,meletin,hesperetin,三个成分对应的中药有27味,包括菊花、野菊花、鱼腥草、虎杖等。结论网络结构熵作为模块划分方法具有可行性;划分后的模块可代表疾病基因网络的大部分功能,主要体现于module1,模块内基因所对应的化学成分与心律失常关系密切,但中医药治疗心律失常有待进一步研发。Objective To select the best method for dividing network modules,analyze the biological functions of the modules,and screen out the related chemical components and Chinese herbal medicines.Methods First,four commonly used module division methods,including Community Cluster(GLay),MCL Cluster,MCODE Cluster,and Connected Components Cluster,were used to divide the arrhythmia gene network into modules,and the optimal method was selected based on the network entropy value.Second,the primary module was selected according to the average connectivity of modules,and the pathways were compared and analyzed.Finally,the chemical components corresponding to the main modules were searched against the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP)and the Encyclopedia of Traditional Chinese Medicine(ETCM),and AutoDock Vina 1.1.2 was used for molecular docking.The relevant Chinese herbal medicines were obtained based on the chemical components.Results The division with MCODE Cluster yielded 5 modules,with the minimum network entropy value of 3.55969,which indicated that this method was most suitable for the identification of arrhythmia gene network modules.The results of pathway analysis showed that module 1 covered 56.1%of the pathways of the original disease network,and this module was mainly related to adrenergic signaling in cardiomyocytes,renin secretion,calcium signaling pathway,cAMP signaling pathway,and circadian entrainment.Three chemical components,i.e.,acacetin,meletin,and hesperetin,were retrieved from TCMSP and ETCM,which corresponded to 27 Chinese herbal medicines including Chrysanthemi Flos,Chrysanthemi Indici Flos,Houttuyniae Herba,and Polygoni Cuspidati Rhizoma et Radix.Conclusion The network entropy is feasible as a module division method.The divided modules,represented by module 1,can represent most of the functions of the disease gene network.The chemical components are closely related to arrhythmia.However,the Chinese herbal medicines for treating arrhythmia needs furth

关 键 词：心律失常 网络分析 模块划分 熵值 生物功能分析 化学成分 

分 类 号：R285.6[医药卫生—中药学]