基于网络药理学探讨川芎嗪与阿魏酸抗动脉粥样硬化的作用机制

目的 采用网络药理学方法预测川芎嗪与阿魏酸抗动脉粥样硬化（AS）的药理作用机制。方法 通过TCMSP、BATMAN-TCM、Swisstargettion和STITCH数据库筛选川芎嗪与阿魏酸的靶标基因，利用OMIM、Genecards、Drugbank数据库查找AS的相关靶标蛋白，运用STRING数据库构建蛋白相互作用网络，并通过DAVID数据库对核心靶点基因本体（GO）和京都基因和基因组百科全书（KEGG）通路富集分析，最后使用Autoduck分子对接软件对网络药理学结果进行初步的验证。结果 最终筛选出川芎嗪与阿魏酸作用于AS的靶点47个。GO分析得到生物过程（BP）条目170个，细胞组分（CC）条目29个，分子功能（MF）条目47个。KEGG富集分析结果显示有50条通路与AS相关，主要包括低氧诱导因子信号通路、花生四烯酸新陈代谢、脂肪细胞因子信号通路等。VEGFA、TNF、PTGS2、EGFR、MAPK8、TLR4编码的蛋白作为靶点分别与川芎嗪和阿魏酸进行对接，对接结果良好。结论 川芎嗪和阿魏酸主要通过多靶点协作调控多种信号通路共同参与到抑制炎症反应的过程，从而达到治疗AS的目的。

Study on anti-arteriosclerosis mechanism of ligustrazine and ferulic acid based on network pharmacology

Objective To investigate and predict the pharmacological mechanism of tetramethylpyrazine (TMP) and ferulic acid (FA) against arteriosclerosis by the method of network pharmacology. Methods The target genes of tetramethylpyrazine and ferulic acid were screened by TCMSP, BATMAN-TCM, Swisstargettion and STITCH databases, and the related target proteins of tetramethylpyrazine and ferulic acid were found by Omim, Genecards and arteriosclerosis, the protein-protein interaction network was constructed by using STRING database, and the GO and Kegg pathways were enriched by David database. Finally, the results of network pharmacology were validated by using Autoduck software. Results Totally 47 target sites for the interaction of tetramethylpyrazine with ferulic acid were selected. Go analysis resulted in 170 BP entries, 29 CC entries and 47 MF entries. Kegg enrichment analysis revealed that 50 pathways were associated with AS, including Hypoxia inducible factor signaling, arachidonic acid metabolism, and APLN signaling. VEGFA, TNF, PTGS2, EGFR, Mapk8 and TLR4 were used as targets for docking with tetramethylpyrazine and ferulic acid respectively. Conclusion Ligustrazine and ferulic acid can inhibit the inflammatory reaction by regulating multiple signal pathways together through multi-target cooperation, thus achieving the aim of treating AS.