Application of UHPLC-Q-TOF MS based untargeted metabolomics reveals variation and correlation amongst different tissues of Eucommia ulmoides Oliver
Application of UHPLC-Q-TOF MS based untargeted metabolomics reveals variation and correlation amongst different tissues of Eucommia ulmoides Oliver
复制标题
DOI：
10.1016/j.microc.2021.106919
复制
发表时间：
2021-10-20
影响因子：
4.8
通讯作者：
Liu, Li
中科院分区：
文献类型：
--
作者：
Chen, Jiangyan;Wang, Weitao;Liu, Li
杜仲叶的生物学功能与其代谢产物有关。然而，由于代谢物在不同组织中分布的复杂性，目前对Eu代谢物的综合信息分析还很有限。本研究利用超高效液相色谱-串联飞行时间质谱仪(UHPLC-QTOF MS)非靶向代谢组学技术，对欧洲菊叶片、种子和树皮的化学成分进行了分析，共鉴定出2373种代谢产物。通过主成分分析、偏最小二乘判别分析等多元统计分析方法，发现116种代谢物在所有样品中表达差异。结果表明，叶片的代谢组成与树皮相似，但在HCA分析中，不同组织间的代谢组成仍存在显著差异。此外，差异代谢物的热图还显示，种子中的有机酸及其衍生物、类脂和类脂分子的浓度高于树叶和树皮。此外，我们检测了13,456个代谢物-代谢物之间的相关性，并通过皮尔逊相关性分析确定了1098个代谢物对，这些代谢物对之间存在显著的相关性。最后，所有检测到的代谢物都被标注在KEGG中，其中966个代谢物具有KEGG ID。经过浓缩分析，在168条途径中定位了311种代谢物，其中26条途径对欧盟不同地区之间的代谢差异有明显影响。本工作首次提供了Eu的全面代谢组，为Eu药理活性的分离和鉴定提供了理论依据，并有望为Eu代谢工程的进一步研究提供帮助。
The biological function of Eucommia ulmoides Oliver (EU) is related to its metabolites. However, due to the complexity of distribution of metabolites in different tissues, currently, the comprehensive information analysis on metabolome of EU has been limited. In this study, we analyzed the components of leaves, seeds and barks of EU by using ultra high-performance liquid chromatography-tandem time-of-flight mass spectrometer (UHPLC-QTOF MS) untargeted metabolomics before 2373 metabolites were identified in total. By using Principal Component Analysis (PCA), Partial Least Square Discriminant Analysis (PLS-DA) and other multivariate statistical analysis methods, there were 116 metabolites expressing differently in all samples. The result showed that the metabolic composition of leaves was similar to that of barks and there still existed significant differences amongst different tissues in HCA analysis. Besides, the heatmap of differential metabolites also showed the higher concentrations of organic acids and derivatives, lipids and lipid-like molecules in seeds compared to leaves and barks. Furthermore, we detected 13,456 metabolites-metabolites correlations and determined 1098 metabolic pairs which resulted in significant correlation by Pearson's correlation analysis. At last, all detected metabolites were annotated in KEGG and 966 of them had KEGG ID. After enrichment analysis, 311 metabolites were mapped in 168 pathways and 26 of these pathways had apparent influence in the metabolic differences amongst different parts of EU. This work provides the first comprehensive metabolomic of EU, which will provide theoretical basis for the separation and identification of medicinal activities of EU and potentially help advance studies in EU metabolic engineering.