Wang Yin-Yin, Li Jie, Wu Zeng-Rui, Zhang Bo, Yang Hong-Bin, Wang Qin, Cai Ying-Chun, Liu Gui-Xia, Li Wei-Hua, Tang Yun
Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
Key Laboratory of Xinjiang Endemic Phytomedicine Resources, Ministry of Education, Pharmacology Department, School of Pharmacy, Shihezi University, Shihezi 832002, China.
Acta Pharmacol Sin. 2017 May;38(5):719-732. doi: 10.1038/aps.2016.147. Epub 2017 Feb 27.
An increasing number of cases of herb-induced liver injury (HILI) have been reported, presenting new clinical challenges. In this study, taking Polygonum multiflorum Thunb (PmT) as an example, we proposed a computational systems toxicology approach to explore the molecular mechanisms of HILI. First, the chemical components of PmT were extracted from 3 main TCM databases as well as the literature related to natural products. Then, the known targets were collected through data integration, and the potential compound-target interactions (CTIs) were predicted using our substructure-drug-target network-based inference (SDTNBI) method. After screening for hepatotoxicity-related genes by assessing the symptoms of HILI, a compound-target interaction network was constructed. A scoring function, namely, Ascore, was developed to estimate the toxicity of chemicals in the liver. We conducted network analysis to determine the possible mechanisms of the biphasic effects using the analysis tools, including BiNGO, pathway enrichment, organ distribution analysis and predictions of interactions with CYP450 enzymes. Among the chemical components of PmT, 54 components with good intestinal absorption were used for analysis, and 2939 CTIs were obtained. After analyzing the mRNA expression data in the BioGPS database, 1599 CTIs and 125 targets related to liver diseases were identified. In the top 15 compounds, seven with Ascore values >3000 (emodin, quercetin, apigenin, resveratrol, gallic acid, kaempferol and luteolin) were obviously associated with hepatotoxicity. The results from the pathway enrichment analysis suggest that multiple interactions between apoptosis and metabolism may underlie PmT-induced liver injury. Many of the pathways have been verified in specific compounds, such as glutathione metabolism, cytochrome P450 metabolism, and the p53 pathway, among others. Hepatitis symptoms, the perturbation of nine bile acids and yellow or tawny urine also had corresponding pathways, justifying our method. In conclusion, this computational systems toxicology method reveals possible toxic components and could be very helpful for understanding the mechanisms of HILI. In this way, the method might also facilitate the identification of novel hepatotoxic herbs.
已报道的草药性肝损伤（HILI）病例越来越多，带来了新的临床挑战。在本研究中，以何首乌（PmT）为例，我们提出了一种计算系统毒理学方法来探究HILI的分子机制。首先，从3个主要的中药数据库以及与天然产物相关的文献中提取PmT的化学成分。然后，通过数据整合收集已知靶点，并使用我们基于子结构-药物-靶点网络的推理（SDTNBI）方法预测潜在的化合物-靶点相互作用（CTIs）。通过评估HILI的症状筛选出肝毒性相关基因后，构建了化合物-靶点相互作用网络。开发了一种评分函数，即Ascore，来估计化学物质在肝脏中的毒性。我们使用包括BiNGO、通路富集、器官分布分析以及与CYP450酶相互作用预测等分析工具进行网络分析，以确定双相效应的可能机制。在PmT的化学成分中，选取54种肠道吸收良好的成分进行分析，获得了2939个CTIs。分析BioGPS数据库中的mRNA表达数据后，鉴定出1599个CTIs和125个与肝脏疾病相关的靶点。在前15种化合物中，7种Ascore值>3000的化合物（大黄素、槲皮素、芹菜素、白藜芦醇、没食子酸、山奈酚和木犀草素）明显与肝毒性相关。通路富集分析结果表明，细胞凋亡与代谢之间的多种相互作用可能是PmT诱导肝损伤的基础。许多通路已在特定化合物中得到验证，如谷胱甘肽代谢、细胞色素P450代谢和p53通路等。肝炎症状、9种胆汁酸的紊乱以及黄色或黄褐色尿液也有相应的通路，证明了我们方法的合理性。总之，这种计算系统毒理学方法揭示了可能的有毒成分，对理解HILI的机制非常有帮助。通过这种方式，该方法也可能有助于鉴定新的肝毒性草药。
