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引用本文:李叶,祁克明,卢伟,魏艺聪.基于代谢组学和网络药理学分析福建金线莲叶和台湾银线兰叶治疗肝纤维化作用差异[J].中国现代应用药学,2022,39(23):3092-3102.
LI Ye,QI Keming,LU Wei,WEI Yicong.Exploring the Difference in Mechanism of Action of Anoectochilus Roxburghii Leaves and Anoectochilus Formosanus Leaves in the Treatment of Liver Fibrosis Based on Integrative Metabolomics and Network Pharmacology[J].Chin J Mod Appl Pharm(中国现代应用药学),2022,39(23):3092-3102.
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基于代谢组学和网络药理学分析福建金线莲叶和台湾银线兰叶治疗肝纤维化作用差异
李叶, 祁克明, 卢伟, 魏艺聪
福建中医药大学, 福州 350122
摘要:
目的 初步分析福建金线莲(Anoectochilus roxburghii)和台湾银线兰(Anoectochilus formosanus)的叶片在治疗肝纤维化疾病的作用机制差异,为分析两者的临床疗效差异及其资源的精准利用提供参考信息。方法 利用代谢组学技术分析福建金线莲叶(the leaves of A.roxburghii,Arl)和台湾银线兰叶(the leaves of A.formosanus,Afl)的差异代谢物;并结合网络药理学方法分析其差异代谢物治疗肝纤维化疾病的作用差异。结果 利用代谢组学的方法分析初步得到Arl和Afl的46个差异代谢物,主要为黄酮类、糖苷类和甘油磷脂类等,其中有23种差异化合物具有潜在药用活性。基于网络药理学的方法,预测了23种差异化合物的759个相关靶点,与肝纤维化疾病的相关靶点进行映射,得到交集靶点249个,其中AKT1、STAT3、VEGFA等48个关键靶点被预测为治疗肝纤维化的主要差异靶点;作用靶点较多的关键差异化合物为Arl相比Afl含量较高的化合物,预示Arl中富含更多可以用于治疗肝纤维化的有效化合物。GO富集功能和KEGG富集分析表明,福建金线莲与台湾银线兰的差异成分参与治疗肝纤维化疾病的信号通路有所不同,VEGF信号通路为Arl相比Afl含量较高的差异化合物的主要差异信号通路,磷脂酰肌酶3-激酶-蛋白激酶B(PI3K-Akt)信号通路为Arl相比Afl含量较低的差异化合物治疗肝纤维化疾病的主要差异信号通路。分子对接结果表明各差异成分与其对应的关键靶点均有较好的结合活性。结论 本研究表明福建金线莲与台湾银线兰叶片中存在一些差异代谢物,预测了其治疗肝纤维化疾病作用机制差异,并初步验证了其作用靶点,为金线莲资源的精准利用具有指导性意义。
关键词:  福建金线莲  台湾银线兰  肝纤维化  代谢组学  网络药理学
DOI:10.13748/j.cnki.issn1007-7693.2022.23.007
分类号:R285.5
基金项目:福建省自然科学基金项目(2020J01732)
Exploring the Difference in Mechanism of Action of Anoectochilus Roxburghii Leaves and Anoectochilus Formosanus Leaves in the Treatment of Liver Fibrosis Based on Integrative Metabolomics and Network Pharmacology
LI Ye, QI Keming, LU Wei, WEI Yicong
Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
Abstract:
OBJECTIVE To preliminarily analyze the differences in the mechanism of action between the leaves of Anoectochilus roxburghii(Arl) and Anoectochilus formosanus(Afl) in the treatment of liver fibrosis, so as to provide reference information for the difference in clinical efficacy, and to conduct a more precise utilization of the plant resources. METHODS Metabolomics technology was used to analyze differential metabolites between Arl and Afl, and network pharmacology methods were applied to explore the differences in the effects of their differential metabolites in the treatment of liver fibrosis. RESULTS In metabolomics analysis, 46 differential metabolites between Arl and Afl were obtained, which mainly belong to flavonoids, glycosides and glycerophospholipids. Among them, 23 differential metabolites had potential medicinal activities. Based on network pharmacology analysis, the 759 related targets of 23 differential metabolites were predicted and 249 intersection targets were obtained by mapping with related targets of liver fibrosis diseases, 48 key targets including AKT1, STAT3 and VEGFA, etc were predicted as the main differential targets for the treatment of liver fibrosis. The key differential compounds with more targets were the compounds with higher content of Arl than Afl, indicating that Arl was richer in effective compounds that could be used to treat liver fibrosis. GO enrichment function and KEGG enrichment analysis showed that the A. roxburghii and A. formosanus were involved in the treatment of liver fibrosis with differential pathways, the VEGF signaling pathway was the main differential signaling pathway of the differential compounds with higher content of Arl than Afl, and the PI3K-Akt signaling pathway was the main differential signaling pathway of the differential compounds with lower content of Arl than Afl in the treatment of liver fibrosis. Molecular docking results showed that the differential components had good binding activity to their corresponding key targets. CONCLUSION This study indicates that the differential metabolites are present in Arl and Afl, which predicts the difference in the mechanism in the treatment of liver fibrosis disease and preliminarily verifies the action target. It provides guidance for the accurate utilization of A. roxburghii.
Key words:  Anoectochilus roxburghii  Anoectochilus formosanus  liver fibrosis  metabolomics  network pharmacology
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