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.
METHODS: The N. oleracea fractions were obtained using solid phase extraction (SPE). A metabolomics approach that coupled the use of proton nuclear magnetic resonance (1H NMR) with multivariate data analysis (MVDA) was applied to distinguish the metabolite variations among the N. oleracea fractions, as well as to assess the correlation between metabolite variation and the studied bioactivities (DPPH free radical scavenging and α-glucosidase inhibitory activities). The bioactive fractions were then subjected to ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) analysis to profile and identify the potential bioactive constituents.
RESULTS: The principal component analysis (PCA) discriminated EF and MF from the other fractions with the higher distributions of phenolics. Partial least squares (PLS) analysis revealed a strong correlation between the phenolics and the studied bioactivities in the EF and the MF. The UHPLC-MS/MS profiling of EF and MF had tentatively identified the phenolics present. Together with some non-phenolic metabolites, a total of 37 metabolites were tentatively assigned.
CONCLUSIONS: The findings of this work supported that N. oleracea is a rich source of phenolics that can be potential antioxidants and α-glucosidase inhibitors for the management of diabetes. To our knowledge, this study is the first report on the metabolite-bioactivity correlation and UHPLC-MS/MS analysis of N. oleracea fractions.
RESULTS: Compounds 2, 4, 8, 12 and 20 exhibited the highest activity (IC50 = 69.20, 59.60, 49.40, 50.20 and 83.20 μM, respectively) compared with the standard acarbose (IC50 = 143.54 μM).
CONCLUSION: A new class of potent α-glucosidase inhibitors was identified, and the molecular docking predicted plausible binding interaction of the targets in the binding pocket of α-glucosidase and rationalized the structure-activity relationship (SARs) of the target compounds.