OBJECTIVE: Evaluate the relationship between the chemical composition of C. nutans and its anti-inflammatory properties using nuclear magnetic resonance (NMR) metabolomics approach.
METHODOLOGY: The anti-inflammatory effect of C. nutans air-dried leaves extracted using five different binary extraction solvent ratio and two extraction methods was determined based on their nitric oxide (NO) inhibition effect in lipopolysaccharide-interferon-gamma (LPS-IFN-γ) activated RAW 264.7 macrophages. The relationship between extract bioactivity and metabolite profiles and quantifications were established using 1 H-NMR metabolomics and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The possible metabolite biosynthesis pathway was constructed to further strengthen the findings.
RESULTS: Water and sonication prepared air-dried leaves possessed the highest NO inhibition activity (IC50 = 190.43 ± 12.26 μg/mL, P
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: 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.
METHODS: We analysed plasma and urine samples of 50 stable CAD patients and 50 healthy controls using 1H NMR. Orthogonal partial least square discriminant analysis (OPLS-DA) followed by multivariate logistic regression (MVLR) models were developed to indicate the discriminating metabotypes. Metabolic pathway analysis was performed to identify the implicated pathways.
RESULTS: Both plasma and urine OPLS-DA models had specificity, sensitivity and accuracy of 100%, 96% and 98%, respectively. Plasma MVLR model had specificity, sensitivity, accuracy and AUROC of 92%, 86%, 89% and 0.96, respectively. The MVLR model of urine had specificity, sensitivity, accuracy and AUROC of 90%, 80%, 85% and 0.92, respectively. 35 and 12 metabolites were identified in plasma and urine metabotypes, respectively. Metabolic pathway analysis revealed that urea cycle, aminoacyl-tRNA biosynthesis and synthesis and degradation of ketone bodies pathways were significantly disturbed in plasma, while methylhistidine metabolism and galactose metabolism pathways were significantly disturbed in urine. The enrichment over representation analysis against SNPs-associated-metabolite sets library revealed that 85 SNPs were significantly enriched in plasma metabotype.
CONCLUSIONS: Cardiometabolic diseases, dysbiotic gut-microbiota and genetic variabilities are largely implicated in the pathogenesis of CAD.
Methods: Proton nuclear magnetic resonance spectroscopy (1H NMR)-based metabolomics approach was used to investigate fecal and serum metabolome of rat model of IBS-D with and without HPM treatment.
Results: The current results showed that IBS-induced metabolic alterations in fecal and serum sample include higher level of threonine and UDP-glucose together with lower levels of aspartate, ornithine, leucine, isoleucine, proline, 2-hydroxy butyrate, valine, lactate, ethanol, arginine, 2-oxoisovalerate and bile acids. These altered metabolites potentially involve in impaired gut secretory immune system and intestinal inflammation, malabsorption of nutrients, and disordered metabolism of bile acids. Notably, the HPM treatment was found able to normalize the Bristol stool forms scale scores, fecal water content, plasma endotoxin level, and a number of IBS-induced metabolic changes.
Conclusions: These findings may provide useful insight into the molecular basis of IBS and mechanism of the HPM intervention.