Integration of non-targeted metabolomics and automated determination of elemental compositions for comprehensive alkaloid profiling in plants. (October 2018)

• Record Type: Journal Article
• Authors: Vanderplanck, Maryse; Glauser, Gaétan
• Is Part Of: Phytochemistry. Volume 154(2018)
• Page Start: 1
• Page End: 9
• Publication Date: 2018-10
• Subjects: Aconitum lycoctonum -- Aconitum napellus -- Ranunculaceae -- Alkaloids -- Metabolite profiling -- UHPLC-HRMS -- Elemental composition -- Chemotaxonomy; Botanical chemistry -- Periodicals; Biochemistry -- Periodicals; Botany -- Periodicals; Chimie végétale -- Périodiques; 572.2
• DOI: 10.1016/j.phytochem.2018.06.011
• Languages: English
• ISSNs: 0031-9422

Abstract

Abstract: Plants produce a large array of specialized metabolites to protect themselves. Among these allelochemicals, alkaloids display highly diverse and complex structures that are directly related to their biological activities. Plant alkaloid profiling traditionally requires extensive and time-consuming sample preparation and analysis. Herein, we developed a rapid and efficient approach for the comprehensive profiling of alkaloids in plants using ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS)-based metabolomics. Using automated compound extraction and elemental composition assignment, our method achieved >83% correct alkaloid identification and even >90% for medium to high intensity peaks. This represented a significant improvement in identification rate compared to generic methods used for EC determination with no a priori, such as in untargeted metabolomics studies. The developed approach was then applied to identify specific alkaloids of Aconitum lycoctonum L. and A. napellus L. (Ranunculaceae) using different parts of the plant (leaf, perianth and pollen). Significant differences in alkaloid profiles between the two species were highlighted and discussed under taxonomic and evolutionary perspectives. Taken together, the presented approach constitutes a valuable chemotaxonomic tool in the search for known and unknown alkaloids from plants. Graphical abstract: Highlights: A method for the comprehensive profiling of alkaloids in plants is presented. Non-targeted UHPLC-HRMS is performed on crude plant extracts. Automated computation of alkaloid-like elemental compositions is achieved. Optimized method outperforms generic methods for alkaloid identification. The approach is employed as a chemotaxonomic tool using Aconitum as model plant.