1 武汉市第三医院(武汉大学同仁医院)急重症医学科，武汉 430000
目的：基于系统药理学、分子对接以及GEO数据库差异基因表达分析的方法，解析抗炎合剂治疗脓毒症的分子调控机制。方法：通过TCMSP(Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform)和DrugBank数据库平台获得抗炎合剂的活性成分和对应靶标。基于GEO数据库分析获得脓毒症的差异表达基因(differentially expressed gene，DEG)，并通过David在线分析平台对药物与疾病基因进行了GO(gene ontology)功能和KEGG(Kyoto Encyclopedia of Genes and Genomes)富集分析。同时，利用BisoGenet和CytoNCA插件对交集靶点进行网络拓扑分析，并将获得的Hub基因与关键活性成分进行分子对接，以证实抗炎合剂发挥治疗效用的分子机制。结果：从抗炎合剂474个化学成分中筛选出73个活性成分，其中45个活性成分被预测为针对54个与脓毒症相关的基因。GO功能、KEGG通路及蛋白质-蛋白质相互作用(protein-protein interaction，PPI)网络拓扑分析显示：交集靶点主要参与酶结合、蛋白质均聚活性调控、转录因子结合、细胞凋亡调控、细胞缺氧反应、转录调控、癌症相关途径、细胞周期、TNF信号通路、HTLV-I感染、细胞凋亡和NF-κB信号通路等，并筛选出4个Hub基因。最后，分子对接验证了分子靶标预测网络的可靠性。结论：研究预测了抗炎合剂治疗脓毒症的重要功能活性成分及其作用靶点，揭示了该中药复方的分子调控机制，为抗炎合剂的临床应用及实验研究提供了可靠的证据。
Molecular regulation mechanism of anti-inflammatory mixtures in the treatment of sepsis based on systematic pharmacology and molecular docking
CorrespondingAuthor: FU Shouzhi
This work was supported by the Scientific Research Project of Wuhan Health and Family Planning Commission, China (WX18C19).
Objective: To analyze the molecular regulatory mechanism of anti-inflammatory mixtures in the treatment of sepsis based on the methods of systematic pharmacology, molecular docking and the analysis of gene expression omnibus (GEO) used in differentially expressed gene (DEG). Methods: The active ingredients and corresponding targets of anti-inflammatory mixtures were obtained through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and DrugBank database platform. Based on the analysis of GEO database, the DEGs of sepsis were obtained, and the enrichment analysis of gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed through the David online analysis platform. At the same time, BisoGenet and CytoNCA plug-ins was conducted by a network topology analysis of intersection targets. The obtained Hub gene was molecularly docked with key active ingredients to confirm the molecular mechanism of the therapeutic effect of anti-inflammotary mixtures. Results: A total of 73 active ingredients were screened out of 474 chemical components of the anti-inflammotary mixtures, and 45 active ingredients of them were predicted to target 54 genes related to sepsis. The GO function, KEGG pathway, and the network topology analysis of protein-protein interaction (PPI) showed that the intersection target was mainly involved in enzyme binding, the regulation of protein homopolymerization activity, transcription factor binding, apoptosis regulation, cell hypoxia response, transcription regulation, cancer-related pathways, cell cycle, TNF signaling pathway, HTLV-I infection, apoptosis and NF-κB signaling pathway, and 4 Hub genes were selected from them. Finally, the molecular docking verified the reliability of the molecular target prediction network. Conclusion: The research predicts the important functional active ingredients and their targets of anti-inflammationary mixtures in treating sepsis, reveals the molecular regulation mechanism of the Chinese herbal compound, and provides reliable evidence for the clinical application and experimental research of the anti-inflammatory mixtures.
anti-inflammatory mixtures; sepsis; systematic pharmacology; molecular docking; Hub gene