Interspecific and Environmental Influence on the Foliar Metabolomes of Mitragyna Species Through Recursive OPLSDA Modeling
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| Publicado en: | Plants vol. 14, no. 17 (2025), p. 2721-2737 |
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| Otros Autores: | , , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 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 |