基于生物信息学和分子动力学的中药治疗原发性骨髓纤维化作用机制研究  

Mechanism of Action of Chinese Medicinal Herbs in the Treatment of Primary Myelofibrosis based on Bioinformatics and Molecular Dynamics

作　　者：郭家缘 新吉乐 张曼 刘洺欣 刘景文 苏雅静 石慧慧 郭珏 刘文卿 魏凯路 宋亚琳 马秋玲[1,3,4] GUO Jiayuan;XIN Jile;ZHANG Man;LIU Mingxin;LIU Jingwen;SU Yajing;SHI Huihui;GUO Jue;LIU Wenqing;WEI Kailu;SONG Yalin;MA Qiuling(The Second Clinical Medical College,Henan University of Chinese Medicine,Zhengzhou,450002;School of Pharmacy,Henan University of Chinese Medicine;Henan Province Hospital of Traditional Chinese Medicine/The Second Affiliated Hospital of Henan University of Chinese Medicine;Henan Engineering Research Center for the Prevention and Treatment of Hematological Diseases with Integrated Traditional Chinese and Western Medicine)

机构地区：[1]河南中医药大学第二临床医学院,河南省郑州市450002 [2]河南中医药大学药学院 [3]河南省中医院/河南中医药大学第二附属医院 [4]河南省中西医结合防治血液病工程研究中心

出　　处：《中医杂志》2024年第21期2250-2258,共9页Journal of Traditional Chinese Medicine

基　　金：河南省中医药科学研究专项(2024ZY1010);河南省2022年科技发展计划(222102310353)。

摘　　要：目的 利用生物信息学和分子动力学方法探讨中药治疗原发性骨髓纤维化(PMF)的分子机制。方法 通过检索中国知网数据库(CNKI)、中文科技期刊数据库(CCD)和中国学术期刊数据库(CSPD)相关文献,发掘整理1985—2024年间用于PMF治疗的高频中药,利用TCMSP、Swiss Target Prediction数据库及相关文献检索高频中药的主要活性成分及其相关靶点;GEO数据库下载PMF的基因表达谱数据集GSE44426和GSE124281,并运用R软件进行数据归一化处理和差异表达基因筛选;运用加权基因共表达网络分析(WGCNA)获取关键模块枢纽基因。取中药活性成分靶点、差异表达基因和关键模块枢纽基因的共同交集基因,运用Cytoscape3.9.2软件生成目标网络,通过拓扑分析生成核心靶点网络,通过GO和KEGG通路富集分析选取关键通路,并从String数据库获得蛋白相互作用关系,构建药物-成分-靶点网络和蛋白质互作网络(PPI)关系图。利用Discovery Studio 2020软件进行分子对接,GROMACS程序进行分子动力学模拟。结果 共搜集21首处方,涉及121种中药,用药频次≥10次的中药有9种,由高到低排序依次为丹参、黄芪、白术、当归、党参、甘草、白芍、茯苓、熟地黄。9种高频中药共获得98种活性成分和1125个潜在靶点。GSE44426和GSE124281数据集共筛选24个基因样本,其中健康对照组(14例)和PMF组(10例),共鉴定出319个PMF组与健康对照组之间的差异表达基因,包含122个上调基因,197个下调基因。WGCNA共筛选24个共表达模块基因,发现与PMF发病密切相关的5个模块分别为MEpink、MEdarkred、MEblack、MEgrey、MEturquoise,包含7112个关键模块枢纽基因。GO和KEGG通路富集分析显示,上述高频中药治疗PMF主要涉及脂质和动脉粥样硬化通路,包含热休克蛋白90家族(HSP90AA1、HSP90AB1)、酪氨酸激酶(SRC)、丝裂原活化蛋白激酶(MAPK1)、白细胞介素10 (IL-10)和白细胞介素1β (IL-1β) 6个�Objective To explore the molecular mechanism implicated in the treatment of primary myelofibrosis(PMF)using Chinese medicinal herbs(CMH)by bioinformatics and molecular dynamics.Methods Data mining was performed to find the high-frequency CMH in treating PMF between the year of 1985 and 2024 by searching CNKI,Chinese Science and Technology Journal Database(CCD),and China Academic Journal Database(CSPD).TCMSP,SwissTargetPrediction and related reports were used to collect the main active ingredients of high-frequency CMH and their targets.The PMF datasets GSE44426 and GSE124281 were downloaded from GEO database,and R software was used for data normalization and differentially expressed genes(DEGs)screening.Key module hub genes were obtained by weighted gene co-expression network analysis(WGCNA)analysis.The common intersection genes of active ingredient targets,DEGs and key module hub genes of CMH were selected,and the target network was generated using Cytoscape 3.9.2 software.The core target network was generated by topological analysis,while key pathways were selected by GO and KEGG pathway enrichment analysis,and protein interaction relationships were obtained from the String database,so as to construct drug-ingredient-target network and protein interaction network(PPI)relationship diagrams.Discovery Studio 2020 software was used to perform molecular docking,and the GROMACS program was used to perform molecular dynamics simulation.Results A total of 21 prescriptions were collected involving 121 herbs.There were 9 herbs with a frequency≥10 times,which were Danshen(Radix et Rhizoma Salviae Miltiorrhizae),Huangqi(Radix Astragali),Baizhu(Rhizoma Atractylodis Macrocephalae),Danggui(Radix Angelicae Sinensis),Dangshen(Radix Codonopsis),Gancao(Radix et Rhizoma Glycyrrhizae),Baishao(Radix Paeoniae Alba),Fuling(Poria)and Shudihuang(Radix Rehmanniae Praeparata)from high-to low-frequency.A total of 98 active ingredients and 1125 potential targets were obtained from 9 high-frequency CMH.GSE44426 and GSE124281 data sets scre

关 键 词：原发性骨髓纤维化 生物信息学 分子动力学 分子对接 中药 

分 类 号：R285[医药卫生—中药学]