He Cai-Jing, Wang Jing, Liang Shuai, Li Jun, Huang Xian-Ju
School of Pharmacy, South-Central University for Nationalities Wuhan 430074, China.
Zhongguo Zhong Yao Za Zhi. 2020 Apr;45(8):1789-1799. doi: 10.19540/j.cnki.cjcmm.20200115.402.
The purpose of this study was to predict the active components, targets and signaling pathways of Veratrilla baillonii for the prevention and treatment of non-alcoholic liver diseases, preliminarily verify the active components and related targets through cell experiments, and elucidate the mechanism of V. baillonii on liver protection. The candidate active components of V. baillonii were screened by searching Chinese medicine ingredients and Chinese medicine pharmacology database and analysis platform, combined with the pharmacokinetic parameters(oral availability and drug-like principle); the target of candidate active ingredients were predicted by protein database, and the target of disease related to non-alcoholic liver disease was predicted. Cytoscape software was used to construct the network of "active component-target-disease", and the protein interaction network was constructed through the STRING database to infer the core target. GO annotation analysis, KEGG pathway analysis and enrichment analysis were conducted through DAVID bioinformatics annotation database. Finally, the core target and pathway of V. baillonii were preliminarily verified by the experimental model of H_2O_2-induced liver cell damage intervened by V. baillonii water extract(WVBF). The cell viability was detected by MTT assay and real-time unlabeled assay, and the expression of related genes was analyzed by Real-time quantitative polymerase chain reaction(PCR). Firstly, 14 active components were obtained from V. baillonii through network pharmacology. There were 287 potential targets corresponding to the components, 587 targets related to non-alcoholic liver disease, and 13 core targets after the interaction between active ingredient targets and disease targets. Secondly, GO enrichment analysis showed that these genes mainly affected 26 biological processes such as nuclear receptor activity, transcription factor activity, steroid hormone receptor activity, ubi-quitin-like protein ligase binding, protein heterodimerization activity, and transcription cofactor binding. KEGG enrichment analysis showed that PI3 K-AKT signaling pathway, HIF-1 signaling pathway, MAPK signaling pathway, insulin signaling pathway, TNF signaling pathway and some cancer-related pathways were more enriched. Finally, TNF-α and MAPK8 were successfully verified as important targets by hepatocytes in vitro, which suggested that V. baillonii could significantly improve liver damage. TNF-α and MAPK8 were one of the targets. Based on the above results, we systematically predicted the material basis and biological mechanism of V. baillonii through multi-component, multi-target and multi-pathway regulation of nonalcoholic liver disease, and the core targets were successfully verified by cells, providing data basis and scientific basis for the in-depth development of V. baillonii.
本研究旨在预测滇重楼用于预防和治疗非酒精性肝病的活性成分、靶点及信号通路,通过细胞实验初步验证活性成分及相关靶点,阐明滇重楼的肝脏保护机制。通过检索中药成分数据库及中药药理学数据库与分析平台,并结合药代动力学参数(口服生物利用度和类药原则)筛选滇重楼的候选活性成分;通过蛋白质数据库预测候选活性成分的靶点,并预测与非酒精性肝病相关的疾病靶点。利用Cytoscape软件构建“活性成分-靶点-疾病”网络,通过STRING数据库构建蛋白质相互作用网络以推断核心靶点。通过DAVID生物信息学注释数据库进行GO注释分析、KEGG通路分析和富集分析。最后,采用滇重楼水提取物(WVBF)干预H₂O₂诱导的肝细胞损伤实验模型,对滇重楼的核心靶点和通路进行初步验证。采用MTT法和实时无标记分析法检测细胞活力,通过实时定量聚合酶链反应(PCR)分析相关基因的表达。首先,通过网络药理学从滇重楼中获得14个活性成分。这些成分对应287个潜在靶点,与非酒精性肝病相关的靶点有587个,活性成分靶点与疾病靶点相互作用后有13个核心靶点。其次,GO富集分析表明这些基因主要影响核受体活性、转录因子活性、类固醇激素受体活性、泛素样蛋白连接酶结合、蛋白质异二聚体化活性和转录辅因子结合等26个生物学过程。KEGG富集分析表明PI3K-AKT信号通路、HIF-1信号通路、MAPK信号通路、胰岛素信号通路、TNF信号通路及一些癌症相关通路富集程度较高。最后,通过体外肝细胞实验成功验证TNF-α和MAPK8为重要靶点,提示滇重楼可显著改善肝损伤。TNF-α和MAPK8是靶点之一。基于以上结果,我们通过多成分、多靶点和多通路调控非酒精性肝病,系统地预测了滇重楼的物质基础和生物学机制,并通过细胞成功验证了核心靶点,为滇重楼的深入开发提供了数据基础和科学依据。