宋红新, 马旭冉, 王敦方, 王彦礼, 邹迪新, 苗金雪, 王汉, 杨伟鹏. 基于网络药理学的黄芩汤治疗溃疡性结肠炎的潜在机制研究J. 药学学报, 2020,55(2): 247-255. doi: 10.16438/j.0513-4870.2019-0455
引用本文: 宋红新, 马旭冉, 王敦方, 王彦礼, 邹迪新, 苗金雪, 王汉, 杨伟鹏. 基于网络药理学的黄芩汤治疗溃疡性结肠炎的潜在机制研究J. 药学学报, 2020,55(2): 247-255. doi: 10.16438/j.0513-4870.2019-0455
SONG Hong-xin, MA Xu-ran, WANG Dun-fang, WANG Yan-li, ZOU Di-xin, MIAO Jin-xue, WANG Han, YANG Wei-peng. Potential mechanism of Huangqin decoction for the treatment of ulcerative colitis based on network pharmacologyJ. Acta Pharmaceutica Sinica, 2020,55(2): 247-255. doi: 10.16438/j.0513-4870.2019-0455
Citation: SONG Hong-xin, MA Xu-ran, WANG Dun-fang, WANG Yan-li, ZOU Di-xin, MIAO Jin-xue, WANG Han, YANG Wei-peng. Potential mechanism of Huangqin decoction for the treatment of ulcerative colitis based on network pharmacologyJ. Acta Pharmaceutica Sinica, 2020,55(2): 247-255. doi: 10.16438/j.0513-4870.2019-0455

基于网络药理学的黄芩汤治疗溃疡性结肠炎的潜在机制研究

Potential mechanism of Huangqin decoction for the treatment of ulcerative colitis based on network pharmacology

  • 摘要: 利用网络药理学和系统生物学方法探究黄芩汤治疗溃疡性结肠炎(ulcerative colitis,UC)分子机制。从中药系统药理学分析平台TCMSP挖掘黄芩汤中4味中药相关的化学成分和作用靶点;通过OMIM数据库、TTD数据库以及GeneCard数据库筛选UC的预测靶点。利用Cytoseape_v3.7.1软件构建化合物-靶点网络;基于STRING数据库,构建黄芩汤治疗UC靶点互作网络,根据拓扑学参数筛选黄芩汤治疗UC的核心靶点。利用Bioconductor中的R包clusterprofileversion 3.12.0对疾病与药物交集靶点进行GO(gene ontology)生物学过程富集分析和KEGG(KEGG pathway analysis)通路注释分析。结果发现,黄芩汤化合物-UC靶点网络包含128个化合物和相应靶点141个,核心靶点涉及AKTI、IL6、PTGS2、IL10、IL1β等。GO功能富集分析得到151个GO条目,KEGG富集筛选得到33条和UC有关通路,主要涉及PI3K-AKT信号通路、NF-κB信号通路、TNF信号通路、Toll样受体信号通路等。本文预测了黄芩汤治疗UC疾病的可能作用机制,为进一步寻找其有效成分和作用机制奠定基础。

     

    Abstract: To study the mechanism of Huangqin decoction (HQT) in the treatment of ulcerative colitis (UC) by using network pharmacology, chemical components and targets related to the four herbs of Chinese meteria medical in HQT were searched through the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) to construct the interaction network diagram of the target point of the compounds. The UC-related targets were screened through OMIM, TTD, and GeneCard databases. The compound-target network was constructed using Cytoseape_v3.7.1 software; based on the STRING database, a target interaction network for HQT for UC was constructed, and the core target of HQT for UC was selected based on topological parameters. GO (gene ontology) biological process enrichment analysis and KEGG (KEGG pathway analysis) pathway annotation analysis were performed on the disease and drug intersection targets using the R package clusterprofile version 3.12.0 in Bioconductor. The HQT compound-UC target network contains 128 compounds and corresponding targets 141. The core targets are AKTI, IL6, PTGS2, IL10, IL1β and so on. GO functional enrichment analysis yielded 151 GO terms, and KEGG pathway enrichment screening resulted in 33 associations with UC, mainly involving PI3K-AKT signaling pathway, NF-kappa B signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway and so on. The synergetic effect of HQT with multi-components and multi-pathway was confirmed by network pharmacology, and the main possible mechanism of HQT in treating UC was predicted, which lay a foundation for the identification of effective components, the mechanism of action, and clinical application.

     

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