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.

RESULTS: Here, we investigated the microbial dynamics by next-generation sequencing, and outlined a differential non-targeted metabolite profiling in the process of serofluid dish fermentation using the method of hydrophilic interaction liquid chromatography column with ultra-high-performance liquid chromatography-quadruple time-of-flight mass spectrometry. Lactobacillus was the leading genus of bacteria, while Pichia and Issatchenkia were the dominant fungi. They all accumulated during fermentation. In total, 218 differential metabolites were identified, of which organic acids, amino acids, sugar and sugar alcohols, fatty acids, and esters comprised the majority. The constructed metabolic network showed that tricarboxylic acid cycle, urea cycle, sugar metabolism, amino acids metabolism, choline metabolism, and flavonoid metabolism were regulated by the fermentation. Furthermore, correlation analysis revealed that the leading fungi, Pichia and Issatchenkia, were linked to organic acids, amino acid and sugar metabolism, flavonoids, and several other flavor and functional components. Antibacterial tests indicated the antibacterial effect of serofluid soup against Salmonella and Staphylococcus.

RESULTS: A total of 31 constituents comprising primary and secondary metabolites belonging to the chemical classes of fatty acids, amino acids, sugars, terpenoids and phenolic compounds were identified. Shade-dried leaves were identified to possess the highest concentrations of bioactive secondary metabolites such as chlorogenic acid, caffeic acid, luteolin, orthosiphol and apigenin, followed by microwave-dried samples. Freeze-dried leaves had higher concentrations of choline, amino acids leucine, alanine and glutamine and sugars such as fructose and α-glucose, but contained the lowest levels of secondary metabolites.

RESULTS: Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities.

METHODS: A cross sectional study was conducted on three groups: individuals with alcohol use disorders (n=30), social drinkers (n=54) and alcohol-naive controls (n=60). 1H NMR-based metabolomics was used to obtain the metabolic profiles of plasma samples. Data were processed by multivariate principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) followed by univariate and multivariate logistic regressions to produce the best fit-model for discrimination between groups.

RESULTS: The OPLS-DA model was able to distinguish between the AUD group and the other groups with high sensitivity, specificity and accuracy of 64.29%, 98.17% and 91.24% respectively. The logistic regression model identified two biomarkers in plasma (propionic acid and acetic acid) as being significantly associated with alcohol use disorders. The reproducibility of all biomarkers was excellent (0.81-1.0).