中国药物警戒 ›› 2023, Vol. 20 ›› Issue (2): 177-180.
DOI: 10.19803/j.1672-8629.20210700

• 基础与临床研究 • 上一篇    下一篇

基于网络药理学和转录组学探讨京尼平致肝毒性机制

王晓慧1,2, 张帆1Δ, 夏文彬1, 魏玉辉1*   

  1. 1兰州大学第一医院药剂科,甘肃 兰州 730020;
    2德州市产品质量标准计量研究院,山东 德州 253000
  • 收稿日期:2021-07-19 出版日期:2023-02-15 发布日期:2023-02-17
  • 通讯作者: *魏玉辉,男,博士,副教授·硕导,中药药理与毒理。E-mail：yhwei@lzu.edu.cn
  • 作者简介:王晓慧,女,硕士,药学。Δ为并列第一作者。
  • 基金资助:
    国家自然科学基金资助项目（81960646、82004080）

Mechanisms of genipin-induced hepatotoxicity by using network pharmacology and transcriptomics

WANG Xiaohui1,2, ZHANG Fan1Δ, XIA Wenbin1, WEI Yuhui1*   

  1. 1Department of Pharmacy, The First Hospital of Lanzhou University, Lanzhou Gansu 730020, China;
    2Dezhou Institute of Product Quality Standards and Metrology, Dezhou Shandong 253000, China
  • Received:2021-07-19 Online:2023-02-15 Published:2023-02-17

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摘要： 目的 运用网络药理学和转录组学的方法阐释京尼平所致肝毒性的机制。方法 网络药理学：通过检索TCMSP、Pharmmapper、STITCH和CTD数据库筛选及预测京尼平潜在作用靶点,同时以“chemical and drug induced liver injury”为关键词在DisGeNET、CTD、PharmGkb、GeneCards和NCBI Gene数据库进行疾病相关靶点提取。STRING进行靶点交互分析,Cytoscape软件构建京尼平-肝毒性靶点-疾病网络模型,最后采用DAVID进行京尼平肝毒性靶点的GO功能富集和KEGG通路分析。转录组学：分别用空白及含京尼平的培养基干预人源性HepaRG肝细胞24 h,利用Illumina高通量测序平台进行转录组测序与生物信息学分析。结果 网络药理学筛选京尼平可作用于142个靶点,其中112个与肝毒性相关。根据蛋白质相互作用（PPI）网络图可知,共有92个节点,382条边。富集分析显示京尼平致肝毒性的靶点主要集中于PI3K/Akt信号通路、TNF信号通路、FoxO信号通路及NF-κB信号通路等。转录组学与基因数据库对比分析京尼平组与对照组的差异表达基因,错误发现率（FDR）<0.01、差异倍数（FC）≥4为筛选条件,发现京尼平组共有1 160个差异表达基因。KEGG富集分析表明,差异基因主要参与TNF信号通路、转录失调、核糖体代谢等。结合网络药理学结果可知TNF信号通路在京尼平致肝毒性过程中发挥关键作用。结论 京尼平致肝毒性可能与其作用于TNF信号通路,诱导炎症反应、氧化应激、细胞凋亡等过程有关。

关键词: 京尼平, 肝毒性, 网络药理学, 转录组学

Abstract: Objective To explore the mechanism of genipin-induced hepatotoxicity by network pharmacology and transcriptomics. Methods Network pharmacology: Potential targets of genipin were screened by searching TCMSP, Pharmapper, Stitch and CTD databases. Meanwhile, disease-related targets were retrieved from Disgenet, CTD, PharmGKB, GeneCards and NCBI Gene databases using “Chemical and drug-induced liver injury” as the keywords. STRING was used for target interaction analysis, while Cytoscape software was used to construct a genipin-hepatotoxic target-disease network model. Finally, DAVID was used to perform GO enrichment and KEGG pathway analysis of targets of genipin-induced hepatotoxicity. Transcriptomics: Human HepaRG cells were subjected to interventions by blank and genipin medium respectively for 24 h. The Illumina high-throughput sequencing platform was used for transcriptome sequencing and bioinformatics analysis. Results Network pharmacological screening showed that genipin could act on 142 targets, 112 of which were related to hepatotoxicity. According to the protein-protein interaction (PPI) network map, there were 92 nodes and 382 edges. Enriched analysis showed that the targets of genipin-induced hepatotoxicity were mainly concentrated in PI3K/Akt signaling pathway, TNF signaling pathway, FOXO signaling pathway and NF-κB signaling pathway. Differential genes of the genipin group and control group were compared with those of the gene database, and a false discovery rate (FDR) <0.01 and Fc (fold change) ≥4 were selected as screening conditions. A total of 1 160 differential gene were found. KEGG enrichment analysis showed that the differential genes were mainly involved in TNF signaling pathway, transcription disorders, and ribosome metabolism. TNF signaling pathway played a key role in genipin-induced hepatotoxicity as was evidenced by network pharmacological results. Conclusion Genipin-induced hepatotoxicity may be related to the TNF signaling pathway, which induces inflammation, oxidative stress and cell apoptosis.

Key words: genipin, hepatotoxicity, network pharmacology, transcriptomics

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