OBJECTIVE: We aimed to compare the phytochemical composition of 7 varieties growing in different conditions at various geographical locations. We also aimed to establish the quality control markers for the authentication of these varieties.
METHODS: We applied untargeted UHPLC-TOFMS metabolomics to discriminate 100 leaf samples of F. deltoidea collected from 6 locations in Malaysia. A genetic analysis on 21 leaf samples was also performed to validate the chemotaxonomy differentiation.
RESULTS: The PCA and HCA analysis revealed the existence of 3 chemotypes based on the differentiation in the flavonoid content. The PLS-DA analysis identified 15 glycosylated flavone markers together with 1 furanocoumarin. These markers were always consistent for the respective varieties, regardless of the geographical locations and growing conditions. The chemotaxonomy differentiation was in agreement with the DNA sequencing. In particular, var. bilobata accession which showed divergent morphology was also differentiated by the chemical fingerprints and genotype.
CONCLUSION: Chemotype differentiation based on the flavonoid fingerprints along with the proposed markers provide a powerful identification tool to complement morphology and genetic analyses for the quality control of raw materials and products from F. deltoidea.
OBJECTIVES: Here, we explored the phytochemical diversity of the seven varieties from Peninsular Malaysia using Nuclear Magnetic Resonance (NMR) and Liquid Chromatography-Mass Spectrometry (LC-MS) analyses and correlated it with the α-glucosidase inhibitory activity.
METHODOLOGY: The Nuclear Overhauser Effect Spectroscopy (NOESY) One-Dimensional (1D)-NMR and LC-MS data were processed, annotated, and correlated with in vitro α-glucosidase inhibitory using multivariate data analysis.
RESULTS: The α-glucosidase results demonstrated that different varieties have varying inhibitory effects, with the highest inhibition rate being F. deltoidea var. trengganuensis and var. kunstleri. Furthermore, diverse habitats and plant ages could also influence the inhibitory rate. The heat map from NMR and LC-MS profiles showed unique patterns according to varying levels of α-glucosidase inhibition rate. The Partial Least Squares (PLS) model constructed from both NMR and LC-MS further confirmed the correlation between the α-glucosidase inhibition rate of F. deltoidea varieties and its metabolite profiles. The Variable Influence on Projection (VIP) and correlation coefficient (p(corr)) values values were used to determine the highly relevant metabolites for explaining the anticipated inhibitory action.
CONCLUSION: NMR and LC-MS annotations allow the identification of flavan-3-ols and proanthocyanidins as the key bioactive factors. Our current results demonstrated the value of multivariate data analysis to predict the quality of herbal materials from both biological and chemical aspects.
RESULTS: A clear separation was only observed between non-organic G and organic Z, which were then selected for further investigation in the fermentation of soybeans (GF and ZF). All four groups (G, Z, GF, ZF) were analyzed using nuclear magnetic resonance (NMR) spectroscopy along with liquid chromatography-tandem mass spectrometry (LC-MS/MS). In this way a total of 41 and 47 metabolites were identified respectively, with 12 in common. A clear variation (|log1.5 FC| > 2 and P
OBJECTIVES: To identify novel biomarkers able to discriminate between alcohol-dependent, non-AD alcohol drinkers and controls using metabolomics.
METHOD: Urine samples were collected from 30 alcohol-dependent persons who did not yet start AD treatment, 54 social drinkers and 60 controls, who were then analysed using NMR. Data analysis was done using multivariate analysis including principal component analysis (PCA) and orthogonal partial least square-discriminate analysis (OPLS-DA), followed by univariate and multivariate logistic regression to develop the discriminatory model. The reproducibility was done using intraclass correlation coefficient (ICC).
RESULTS: The OPLS-DA revealed significant discrimination between AD and other groups with sensitivity 86.21%, specificity 97.25% and accuracy 94.93%. Six biomarkers were significantly associated with AD in the multivariate logistic regression model. These biomarkers were cis-aconitic acid, citric acid, alanine, lactic acid, 1,2-propanediol and 2-hydroxyisovaleric acid. The reproducibility of all biomarkers was excellent (0.81-1.0).
CONCLUSION: This study revealed that metabolomics analysis of urine using NMR identified AD novel biomarkers which can discriminate AD from social drinkers and controls with high accuracy.