Saccharomyces boulardii, a yeast that was isolated from fruit in Indochina, has been used as a remedy for diarrhea since 1950 and is now a commercially available treatment throughout Europe, Africa, and South America. Though initially classified as a separate species of Saccharomyces, recent publications have shown that the genome of S. boulardii is so similar to Saccharomyces cerevisiae that the two should be classified as conspecific. This raises the question of the distinguishing molecular and phenotypic characteristics present in S. boulardii that make it perform more effectively as a probiotic organism compared to other strains of S. cerevisiae. This investigation reports some of these distinguishing characteristics including enhanced ability for pseudohyphal switching upon nitrogen limitation and increased resistance to acidic pH. However, these differences did not correlate with increased adherence to epithelial cells or transit through mouse gut. Pertinent characteristics of the S. boulardii genome such as trisomy of chromosome IX, altered copy number of a number of individual genes, and sporulation deficiency have been revealed by comparative genome hybridization using oligonucleotide-based microarrays coupled with a rigorous statistical analysis. The contributions of the different genomic and phenotypic features of S. boulardii to its probiotic nature are discussed.

Genome-scale metabolic models promise important insights into cell function. However, the definition of pathways and functional network modules within these models, and in the biochemical literature in general, is often based on intuitive reasoning. Although mathematical methods have been proposed to identify modules, which are defined as groups of reactions with correlated fluxes, there is a need for experimental verification. We show here that multivariate statistical analysis of the NMR-derived intra-and extracellular metabolite profiles of single-gene deletion mutants in specific metabolic pathways in the yeast Saccharomyces cerevisiae identified outliers whose profiles were markedly different from those of the other mutants in their respective pathways. Application of flux coupling analysis to a metabolic model of this yeast showed that the deleted gene in an outlying mutant encoded an enzyme that was not part of the same functional network module as the other enzymes in the pathway. We suggest that metabolomic methods such as this, which do not require any knowledge of how a gene deletion might perturb the metabolic network, provide an empirical method for validating and ultimately refining the predicted network structure.

The importance of metabolomic data in functional genomic investigations is increasingly becoming evident, as is its utility in novel biomarker discovery. We demonstrate a simple approach to the screening of metabolic information that we believe will be valuable in generating metabolomic data. Laser desorption ionisation mass spectrometry on porous silicon was effective in detecting 22 of 30 metabolites in a mixture in the negative-ion mode and 19 of 30 metabolites in the positive-ion mode, without the employment of any prior analyte separation steps. Overall, 26 of the 30 metabolites could be covered between the positive and negative-ion modes. Although the response for the metabolites at a given concentration differed, it was possible to generate direct quantitative information for a given analyte in the mixture. This technique was subsequently used to generate metabolic footprints from cell-free supernatants and, when combined with chemometric analysis, enabled us to discriminate haploid yeast single-gene deletants (mutants). In particular, the metabolic footprint of a deletion mutant in a gene encoding a transcriptional activator (Gln3p) showed increased levels of peaks, including one corresponding to glutamate, compared to the other mutants and the wildtype strain tested, enabling its discrimination based on metabolic information.