Interspecific and Environmental Influence on the Foliar Metabolomes of Mitragyna Species Through Recursive OPLSDA Modeling

Guardado en:
Detalles Bibliográficos
Publicado en:Plants vol. 14, no. 17 (2025), p. 2721-2737
Autor principal: Andriyas Tushar
Otros Autores: Leksungnoen Nisa, Uthairatsamee Suwimon, Ngernsaengsaruay Chatchai, Sanyogita, Andriyas
Publicado:
MDPI AG
Materias:
Acceso en línea:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!

MARC

LEADER 00000nab a2200000uu 4500
001 3249701059
003 UK-CbPIL
022 |a 2223-7747 
024 7 |a 10.3390/plants14172721  |2 doi 
035 |a 3249701059 
045 2 |b d20250101  |b d20251231 
084 |a 231551  |2 nlm 
100 1 |a Andriyas Tushar  |u Department of Forest Biology, Faculty of Forestry, Kasetsart University, Bangkok 10900, Thailand; thugnomics28@gmail.com (T.A.); fforsmu@ku.ac.th (S.U.) 
245 1 |a Interspecific and Environmental Influence on the Foliar Metabolomes of <i>Mitragyna</i> Species Through Recursive OPLSDA Modeling 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a Understanding interspecific and environmental influences on secondary metabolite profiles can be critical in plant metabolomics. This study used a hierarchical orthogonal projections to latent structure discriminant analysis (OPLS-DA) to classify the foliar metabolomes of four naturally growing Mitragyna species in Thailand, M. speciosa, M. diversifolia, M. hirsuta, and M. rotundifolia. Using a recursive binary classification, interspecific and environmental influences were determined in multiple class separations, while identifying key metabolites driving these distinctions. Gas chromatography–mass spectrometry (GC-MS) annotated 409 metabolites, and through a progressive class differentiation using hierarchical OPLS-DA, M. speciosa exhibited a metabolome distinct from the other three species. However, the metabolomes of M. hirsuta and M. rotundifolia had a lot of overlap, while M. diversifolia displayed regional metabolic variation, emphasizing the role of environmental factors in shaping its chemical composition. Key metabolites, such as mitragynine, isorhynchophylline, squalene, and vanillic acid, among others, were identified as major discriminators across the hierarchical splits. Unlike conventional OPLS-DA, which struggles with multiclass datasets, the recursive approach identified class structures that were biologically relevant, without the need for manual pairwise modeling. The results aligned with prior morphological and genetic studies, validating the method’s robustness in capturing interspecific and environmental differences, which can be used in high-dimensional multiclass plant metabolomics. 
651 4 |a Thailand 
651 4 |a Africa 
653 |a Mass spectrometry 
653 |a Interspecific 
653 |a Gas chromatography 
653 |a Datasets 
653 |a Modelling 
653 |a Nuclear magnetic resonance--NMR 
653 |a Metabolomics 
653 |a Scientific imaging 
653 |a Metabolites 
653 |a Metabolism 
653 |a Discriminant analysis 
653 |a Chromatography 
653 |a Vanillic acid 
653 |a Chemical composition 
653 |a Recursive methods 
653 |a Squalene 
653 |a Classification 
653 |a Mass spectroscopy 
653 |a Environmental factors 
653 |a Mitella diversifolia 
700 1 |a Leksungnoen Nisa  |u Department of Forest Biology, Faculty of Forestry, Kasetsart University, Bangkok 10900, Thailand; thugnomics28@gmail.com (T.A.); fforsmu@ku.ac.th (S.U.) 
700 1 |a Uthairatsamee Suwimon  |u Department of Forest Biology, Faculty of Forestry, Kasetsart University, Bangkok 10900, Thailand; thugnomics28@gmail.com (T.A.); fforsmu@ku.ac.th (S.U.) 
700 1 |a Ngernsaengsaruay Chatchai  |u Department of Botany, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand; fsciccn@ku.ac.th 
700 1 |a Sanyogita, Andriyas  |u Department of Irrigation and Drainage Engineering, Vaugh Institute of Agriculture Engineering and Technology, Sam Higginbottom University of Agriculture, Technology, and Sciences, Prayagraj 211007, India; sandriyas@gmail.com 
773 0 |t Plants  |g vol. 14, no. 17 (2025), p. 2721-2737 
786 0 |d ProQuest  |t Agriculture Science Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3249701059/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3249701059/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3249701059/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch