基于生物信息学探讨大黄–栀子药对治疗非酒精性脂肪肝的作用机制
Exploring the Mechanism of Action of the Rhubarb-Gardenia Couplet in the Treatment of Non-Alcoholic Fatty Liver Disease Based on Bioinformatics

目的：基于生物信息学探讨大黄–栀子药对治疗非酒精性脂肪肝的作用机制。方法：通过中药系统药理数据库(TCMSP)收集大黄和栀子的主要活性成分，并按照口服生物利用度(OB) ≥ 30%，类药性(DL) ≥ 0.18进行选择，通过中药系统药理数据库在线靶标预测平台数据库预测并且进行筛选大黄栀子两味药的相关化合物靶点基因，疾病靶点选用GeneCards数据库以及OMIM数据库进行筛选，获取NAFLD相关疾病的潜在作用靶点。通过构建化合物–疾病靶点进行去重之后，在韦恩图中进行筛选大黄和栀子治疗NAFLD关键靶点。通过String数据库构建蛋白质–蛋白质相互作用利用Cytoscape建立药物–活性成分–靶点–疾病的网络图，使用DAVID数据库将韦恩图中所筛选出来的交集靶点进行基因本体(GO)功能富集分析和基因组百科全书(KEGG)通路富集分析。结果：总共得到相关药效成分31个，药物与疾病的交集靶点69个，GO功能富集分析得到了627条信息，KEGG通路分析总共发现144条通路，以疾病相关程度和P值进行筛选前十的数据，结果发现主要通过PI3K-Akt信号通路(PI3K-Akt signaling pathway)、化学致癌(Chemical carcinogemesis)和乙型肝炎(Hepatitis B)等通路发挥作用，得出“大黄–栀子”可能通过AKT1、TNF、IL6、IL1B等重要靶点，主要参与细胞因子介导的信号通路、RNA聚合酶Ⅱ启动转录的阳性调节等生物学过程，从而调节NAFLD等通路达到治疗作用。结论：基于生物信息学，初步探讨出“大黄–栀子”治疗非酒精性脂肪肝的作用机制，为接下来实验论证给予一定的基础。
Objective: To explore the mechanism of action of the Rhubarb-Gardenia herb pair in the treatment of non-alcoholic fatty liver disease based on bioinformatics. Methods: The main active ingredients of rhubarb and gardenia were collected from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), selecting those with an oral bioavailability (OB) of at least 30% and drug-likeness (DL) of at least 0.18. The TCMSP online target prediction platform was used to predict and screen the target genes related to these two herbs. For disease targets, selections were made from the GeneCards and OMIM databases to identify potential targets for non-alcoholic fatty liver disease (NAFLD). After constructing a compound-disease target network and removing duplicates, a Venn diagram was used to identify key targets for treating NAFLD with rhubarb and gardenia. A protein-protein interaction network was built using the String database and visualized with Cytoscape to create a drug-active ingredient-target-disease network. Finally, gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the intersecting targets identified in the Venn diagram using the DAVID database. Results: A total of 31 active ingredients were identified, and 69 intersecting targets between the drugs and the disease were found. The GO function enrichment analysis yielded 627 pieces of information, and the KEGG pathway analysis discovered 144 pathways. After filtering based on disease relevance and P-values, the top ten pathways were selected. The results indicated that the main