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.
OBJECTIVE: Evaluate the metabolite variations and antioxidant activity among M. calabura leaves subjected to different drying methods and extracted with different ethanol ratios using proton nuclear magnetic resonance (1 H-NMR)-based metabolomics. Methodology The antioxidant activity of M. calabura leaves dried with three different drying methods and extracted with three different ethanol ratios was determined by using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and nitric oxide (NO) scavenging assays. The metabolites variation among the extracts and correlation with antioxidant activity were analysed by 1 H-NMR-based metabolomics.
RESULTS: Muntingia calabura leaves extracted with 50% and 100% ethanol from air-drying and freeze-drying methods had the highest total phenolic content and the lowest IC50 value for the DPPH scavenging activity. Meanwhile, oven-dried leaves extracted with 100% ethanol had the lowest IC50 value for the NO scavenging activity. A total of 43 metabolites, including sugars, organic acids, amino acids, phytosterols, phenolics and terpene glycoside were tentatively identified. A noticeable discrimination was observed in the different ethanol ratios by the principal component analysis. The partial least-squares analysis suggested that 32 compounds out of 43 compounds identified were the contributors to the bioactivities.
CONCLUSION: The results established set the preliminary steps towards developing this plant into a high value product for phytomedicinal preparations.
OBJECTIVE: This study investigated the metabolite variations in A. elliptica leaves and the correlation with antioxidant activities.
METHODOLOGY: Total phenolic content (TPC), 2,2-diphenyl-1-picrylhydrazyl (DPPH) and nitric oxide (NO) radicals scavenging assays were performed on A. elliptica leaves extracted with four different ethanol ratios (0%, 50%, 70% and absolute ethanol). The correlation of metabolites with antioxidant activities was evaluated using a nuclear magnetic resonance (NMR)-based metabolomics approach.
RESULTS: The results showed that the 50% and 70% ethanolic extracts retained the highest TPC, and the 70% ethanolic extract was the most active, exhibiting half maximal inhibitory concentration (IC50 ) values of 10.18 ± 0.83 and 43.05 ± 1.69 μg/mL, respectively, in both radical scavenging assays. A total of 46 metabolites were tentatively identified, including flavonoids, benzoquinones, triterpenes and phenolic derivatives. The 50% and 70% ethanolic extracts showed similarities in metabolites content and were well discriminated from water and absolute ethanol extracts in a principal component analysis (PCA) model. Moreover, 31 metabolites were found to contribute significantly to the differentiation and antioxidant activity.
CONCLUSION: This study provides information on bioactive compounds in A. elliptica leaves, which is promising as a functional ingredient for food production or for the development of phytomedicinal products.