Introduction: Comparative analysis of metabolic features of plants has a high potential for determination of quality control of active ingredients, ecological or chemotaxonomic purposes. Specifically, the development of efficient and rapid analytical tools that allow the differentiation among species, subspecies and varieties of plants is a relevant issue. Here we describe a multivariate model based on LC-MS/MS fingerprinting capable of discriminating between subspecies and varieties of the medicinal plant Chamaecrista nictitans, a rare distributed species in Costa Rica.
Methods: Determination of the chemical fingerprint was carried out on a LC-MS (ESI-QTOF) in negative ionization mode, main detected and putatively identified compounds included proanthocyanidin oligomers, several flavonoid C- and O-glycosides, and flavonoid acetates. Principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA) and cluster analysis of chemical profiles were performed.
Results: Our method showed a clear discrimination between the subspecies and varieties of Chamaecrista nictitans, separating the samples into four fair differentiated groups: M1 = C. nictitans ssp. patellaria; M2 = C. nictitans ssp. disadena; M3 = C. nictitans ssp. nictitans var. jaliscensis and M4 = C. nictitans ssp. disadena var. pilosa. LC-MS/MS fingerprint data was validated using both morphological characters and DNA barcoding with ITS2 region. The comparison of the morphological characters against the chemical profiles and DNA barcoding shows a 63% coincidence, evidencing the morphological similarity in C. nictitans. On the other hand, genetic data and chemical profiles grouped all samples in a similar pattern, validating the functionality of our metabolomic approach.
Conclusion: The metabolomic method described in this study allows a reliably differentiation between subspecies and varieties of C. nictitans using a straightforward protocol that lacks extensive purification steps.
Keywords: Chamaecrista nictitans; Chemical fingerprinting; Chemotaxonomy; LC–MS/MS; Metabolite fingerprinting.