Ethanol-induced water stress in yeast

This review considers the effect of ethanol-induced water stress on yeast metabolism and integrity. Ethanol causes water stress by lowering water activity (a(w)) and thereby interferes with hydrogen bonding within and between hydrated cell components, ultimately disrupting enzyme and membrane strut and function. The impact of ethanol on the energetic status of water is considered in relation to cell metabolism. Even moderate ethanol concentrations (5 to 10%, w/v) cause a sufficient reduction of a(w) to have metabolic consequences. When exposed to ethanol, cells synthesize compatible solutes such as glycerol and trehalose that protect against water stress and hydrogen-bond disruption. Ethanol affects the control of gene expression by the mechanism that is normally associated with (so-called) osmotic control. Furthermore, ethanol-induced water stress has ecological implications.

Carboxypeptidase-mediated metabolism of calcitonin gene-related peptide in human gingival crevicular fluid - a role in periodontal inflammation?

Background: Metabolism by peptidases plays an important role in modulating the levels of biologically-active neuropeptides. The metabolism of the anti-inflammatory neuropeptide calcitonin gene-related peptide (GCRP), but not the pro-inflammatory neuropeptides substance P (SP) and neurokinin A (NKA) by components of the gingival crevicular fluid (GCF), could potentiate the inflammatory process in periodontitis.

Evaluation of predicted network modules in yeast metabolism using NMR-based metabolite profiling

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.