Combined NMR and LC−DAD-MS Analysis Reveals Comprehensive Metabonomic Variations for Three Phenotypic Cultivars of Salvia Miltiorrhiza Bunge

Metabonomic analysis is an important molecular phenotyping method for understanding plant ecotypic variations and gene functions. Here, we systematically characterized the metabonomic variations associated with three Salvia miltiorrhiza Bunge (SMB) cultivars using the combined NMR and LC−DAD-MS dete...

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Veröffentlicht in:Journal of proteome research 2010-03, Vol.9 (3), p.1565-1578
Hauptverfasser: Dai, Hui, Xiao, Chaoni, Liu, Hongbing, Hao, Fuhua, Tang, Huiru
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Sprache:eng
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Zusammenfassung:Metabonomic analysis is an important molecular phenotyping method for understanding plant ecotypic variations and gene functions. Here, we systematically characterized the metabonomic variations associated with three Salvia miltiorrhiza Bunge (SMB) cultivars using the combined NMR and LC−DAD-MS detections in conjunction with multivariate data analysis. Our results indicated that NMR methods were effective to quantitatively detect the abundant plant metabolites including both the primary and secondary metabolites whereas the LC−DAD-MS methods were excellent for selectively detecting the secondary metabolites. We found that the SMB metabonome was dominated by 28 primary metabolites including sugars, amino acids, and carboxylic acids and 4 polyphenolic secondary metabolites, among which N-acetylglutamate, asparate, fumurate, and yunnaneic acid D were reported for the first time in this plant. We also found that three SMB cultivars growing at the same location had significant metabonomic differences in terms of metabolisms of carbohydrates, amino acids, and choline, TCA cycle, and the shikimate-mediated secondary metabolisms. We further found that the same SMB cultivar growing at different locations differed in their metabonome. These results provided important information on the ecotypic dependence of SMB metabonome on the growing environment and demonstrated that the combination of NMR and LC−MS methods was effective for plant metabonomic phenotype analysis.
ISSN:1535-3893
1535-3907
DOI:10.1021/pr901045c