No abstract

(1)H NMR spectroscopy of aqueous fecal extracts has been used to investigate differences in metabolic activity of gut microbiota in patients with ulcerative colitis (UC) (n = 13), irritable bowel syndrome (IBS) (n = 10), and healthy controls (C) (n = 22). Up to four samples per individual were collected over 2 years giving a total of 124 samples. Multivariate discriminant analysis, based on NMR data from all three groups, was able to predict UC and C group membership with good sensitivity and specificity; classification of IBS samples was less successful and could not be used for diagnosis. Trends were detected toward increased taurine and cadaverine levels in UC with increased bile acid and decreased branched chain fatty acids in IBS relative to controls; changes in short chain fatty acids and amino acids were not significant. Previous PCR-denaturing gradient gel electrophoresis (PCR-DGGE) analysis of the same fecal material had shown alterations of the gut microbiota when comparing UC and IBS groups with controls. Hierarchical cluster analysis showed that DGGE profiles from the same individual were stable over time, but NMR spectra were more variable; canonical correlation analysis of NMR and DGGE data partly separated the three groups and revealed a correlation between the gut microbiota profile and metabolite composition.

Variations in early fruit development and composition may have major impacts on the taste and the overall quality of ripe tomato (Solanum lycopersicum) fruit. To get insights into the networks involved in these coordinated processes and to identify key regulatory genes, we explored the transcriptional and metabolic changes in expanding tomato fruit tissues using multivariate analysis and gene-metabolite correlation networks. To this end, we demonstrated and took advantage of the existence of clear structural and compositional differences between expanding mesocarp and locular tissue during fruit development (12-35 d postanthesis). Transcriptome and metabolome analyses were carried out with tomato microarrays and analytical methods including proton nuclear magnetic resonance and liquid chromatography-mass spectrometry, respectively. Pairwise comparisons of metabolite contents and gene expression profiles detected up to 37 direct gene-metabolite correlations involving regulatory genes (e.g. the correlations between glutamine, bZIP, and MYB transcription factors). Correlation network analyses revealed the existence of major hub genes correlated with 10 or more regulatory transcripts and embedded in a large regulatory network. This approach proved to be a valuable strategy for identifying specific subsets of genes implicated in key processes of fruit development and metabolism, which are therefore potential targets for genetic improvement of tomato fruit quality.