基于GC-MS指纹图谱和网络药理学的水黄皮抗氧化活性成分及作用机制探讨

Analysis of Antioxidant Components in Pongamia pinnata and the Mechanism of Action Based on GC-MS Fingerprint and Network Pharmacology

关键词:  水黄皮  GC-MS  指纹图谱  网络药理学  抗氧化  活性成分  作用机制

Key words:  Pongamia pinnata  GC-MS  fingerprint  network pharmacology  antioxidant  active components  mechanism of action

摘要:

目的建立水黄皮的GC-MS指纹图谱,并结合化学计量学和网络药理学方法，探讨水黄皮抗氧化活性成分及其作用机制。方法采用GC-MS技术对水黄皮挥发油进行分离和鉴定,建立指纹图谱确定共有峰并进行相似度评价,运用主成分分析和聚类分析进行化学模式识别,整合网络药理学构建“成分-靶点-通路”网络图,预测分析水黄皮抗氧化活性成分及作用机制。结果水黄皮挥发油中鉴定出61种成分,指纹图谱确定20个共有峰,18批样品与对照图谱的相似度在0.89～1.00；聚类分析可将18批水黄皮分为3类,主成分分析提取的4个主成分累计方差贡献率为87.71%,主成分分析与聚类分析结果基本一致；水黄皮的质量与采收地点有一定相关性,文昌和临高地区的水黄皮质量较优。通过网络药理学筛选出香叶基丙酮、3-蒈烯、α-杜松醇、β-石竹烯、β-紫罗兰酮环氧化物、雌二醇-17-醇、氧化石竹烯和紫罗兰酮等成分可能是水黄皮的潜在抗氧化活性成分；“成分-靶点-通路”网络图显示活性成分主要通过调控AR、MAPK8、MAPK9、MAPK10、RELA、AKT1和MAPK14等核心靶点,作用于化学致癌-活性氧基团、脂质和动脉粥样硬化通路等信号通路发挥抗氧化活性。结论本研究通过GC-MS分析水黄皮挥发油的成分,建立快速可靠的指纹图谱,采用网络药理学分析水黄皮抗氧化活性成分及其作用机制,为水黄皮的质量控制和评价,及合理开发利用热带药用植物资源提供科学依据。

Abstract:

OBJECTIVE To establish Gas Chromatography-Mass Spectrometry (GC-MS) fingerprint of Pongamia pinnata and analysis the antioxidant components and their mechanism of action by chemometrics and network pharmacology. METHODS The volatile oil of P. pinnata was analyzed and identified by GC-MS. The fingerprints of 18 batches of samples were established, the common peaks were defined and the similarities were calculated. The differences of samples were compared by principal component analysis and cluster analysis. The “component-target-pathway” network diagram was constructed to analyze the antioxidant components in P. pinnata and their mechanism of action by network pharmacology. RESULTS Sixty-one components were respectively identified from the volatile oil of P. pinnata and twenty common peaks were identified. The similarities between 18 batches of samples and standardized characteristics fingerprint were between 0.89 and 1.00. The samples can be divided into three categories by cluster analysis. Four main components were extracted by principal component analysis and the cumulative variance contribution rate was 87.71%. The result of principal component analysis and cluster analysis were basically consistent. The quality of P. pinnata has a certain correlation with the harvesting location, and the samples collected in Wenchang and Lingao areas were better. The predictive antioxidant components in P. pinnata obtained from the “component-target-pathway” network diagram included 6,10-dimethyl-5,9-undecadien-2-one, 3-carene, α-cadinol, β-caryophyllene, β-ionone epoxide, estra-1,3,5(10)-trien-17-ol, β-caryophyllene oxide and alpha-ionone. There were seven key targets involved such as AR, MAPK8, MAPK9, MAPK10, RELA, AKT1 and MAPK14. The main signaling pathways were chemical carcinogenesis-reactive oxygen species pathway and lipid and atherosclerosis pathway. CONCLUSION In this study the components in the volatile oil of P. pinnata were identified by GC-MS, and a rapid and reliable fingerprint analysis method was established. The antioxidant components in P. pinnata and its mechanism of action were predictive analyzed by network pharmacology, which provides a scientific basis for the quality control and evaluation of P. pinnata and further development of tropical medicinal plant resources.