Metabolome analysis of Ephedra plants with different contents of ephedrine alkaloids by using UPLC-Q-TOF-MS

Abstract

Metabolome analysis of four varieties of Ephedra plants, which contain different amounts of ephedrine alkaloids, was demonstrated in this study. The metabolites were comprehensively analyzed by using ultra performance liquid chromatography (UPLC) coupled with quadrupole time-of-flight mass spectrometry (Q-TOF-MS) and the ephedrine alkaloids were also profiled. Subsequently, multivariate analyses of principal component analysis (PCA) and batch-learning self-organizing mapping (BL-SOM) analysis were applied to the raw data of the total ion chromatogram (TIC). PCA was performed to visualize the fingerprints characteristic for each Ephedra variant and the independent metabolome clusters were formed. The metabolite fingerprints were also visualized by BL-SOM analysis and were displayed as a lattice of colored individual cells which was characteristic for each Ephedra variant. BL-SOM analysis was also used for identification of chemical marker peaks because the information assigned to a cell represented either increases or decreases in peak intensities. Using this analysis, ephedrine alkaloids were successfully selected from the TICs as chemical markers for each Ephedra variant and this result suggested that BL-SOM analysis was an effective method for the selection of marker metabolites. We report our study here as a practical case of metabolomic study on medicinal resources.