Luis Quirós-Guerrero1,2, Federico Albertazzi3,4, Emanuel Araya-Valverde5,6, Rosaura M Romero1,2, Heidy Villalobos2,3, Luis Poveda7, Max Chavarría1,2,5, Giselle Tamayo-Castillo8,9. 1. Centro de Investigaciones en Productos Naturales (CIPRONA), Universidad de Costa Rica, San Jose, 11501-2060, Costa Rica. 2. Escuela de Química, Universidad de Costa Rica, Sede Central, San Pedro de Montes de Oca, San Jose, 11501-2060, Costa Rica. 3. Centro de Investigación en Biología Celular y Molecular (CIBCM), Universidad de Costa Rica, San Jose, 11501-2060, Costa Rica. 4. Escuela de Biología, Universidad de Costa Rica, San Jose, 11501-2060, Costa Rica. 5. Centro Nacional de Innovaciones Biotecnológicas (CENiBiot), CeNAT-CONARE, San Jose, 1174-1200, Costa Rica. 6. Escuela de Biología, Instituto Tecnológico de Costa Rica, Cartago, 159-7050, Costa Rica. 7. Herbario Juvenal Valerio Rodríguez, Universidad Nacional, Heredia, Costa Rica. 8. Centro de Investigaciones en Productos Naturales (CIPRONA), Universidad de Costa Rica, San Jose, 11501-2060, Costa Rica. giselle.tamayo@ucr.ac.cr. 9. Escuela de Química, Universidad de Costa Rica, Sede Central, San Pedro de Montes de Oca, San Jose, 11501-2060, Costa Rica. giselle.tamayo@ucr.ac.cr.
Abstract
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.
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.
Entities:
Keywords:
Chamaecrista nictitans; Chemical fingerprinting; Chemotaxonomy; LC–MS/MS; Metabolite fingerprinting
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