Strategy for exhaustive plant metabolomes characterization from a qualitative and quantitative perspective

Plant extract metabolome characterization is of growing interest for the academic research community and regulatory affairs, quality control and research & development at the industry level. In the case of industry, reliable analyses are crucial for quality assessment of a final product. Current analytical methods mainly focus on the quantitation of specific and often non-bioactive markers of plant extracts. Therefore, efficient procedures are needed to provide an exhaustive analysis of the metabolome composition of a natural extract, addressing qualitative and quantitative aspects. We argue that combining semi-quantitative data with the fingerprint of minor metabolites could improve quality, authenticity, origin or toxicity assessments. In the context of such original classification, we used untargeted UHPLC-HRMS/MS-based metabolomics, which is a qualitative and highly sensitive approach together with semi-quantitative detection based on ELSD. Four known medicinal plants of industrial interest containing bitter principles (Quassia amara L., Swertia chirayta (Roxb.) Karst., Gentiana lutea L. and Aloe ferox Mill.) were selected as model. After a generic untargeted workflow, we mined the data to extract relevant information, using taxonomy and chemometric approaches to query in silico annotated Molecular Networks (MN). [1],[2] This allowed the rapid and confident annotation of 23% of 16ʹ624 MS2 spectra from diverse specialized metabolites. Mapping semi-quantitative information on the networks also provided a preliminary global evaluation of their relative abundance within the extract. With both qualitative and quantitative data combined, enriched molecular networks represent relevant and detailed compositional information on natural extracts from a generic perspective.

• References

• 1 Dounoue-Kubo M, Rutz A, Bisson J, Saesong T, Bagheri M, Ebrahimi SN. et al. Taxonomically informed scoring enhances confidence in natural products annotation. Front Plant Sci. 2019 (article in preparation).
• 2 Allard P-M, Péresse T, Bisson J, Gindro K, Marcourt L, Pham VC. et al. Integration of molecular networking and In-Silico MS/MS fragmentation for natural products dereplication. Anal Chem 2016; 88: 3317-3323.