Bacteria adapt to environmental stimuli by adjusting their transcriptomes in a complex manner, the full potential of which has yet to be established for any individual bacterial species. Here, we report the transcriptomes of Bacillus subtilis exposed to a wide range of environmental and nutritional conditions that the organism might encounter in nature. We comprehensively mapped transcription units (TUs) and grouped 2935 promoters into regulons controlled by various RNA polymerase sigma factors, accounting for ~66% of the observed variance in transcriptional activity. This global classification of promoters and detailed description of TUs revealed that a large proportion of the detected antisense RNAs arose from potentially spurious transcription initiation by alternative sigma factors and from imperfect control of transcription termination.

BackgroundEukaryotic cells have evolved various response mechanisms to counteract the deleterious consequences of oxidative stress. Among these processes, metabolic alterations seem to play an important role.ResultsWe recently discovered that yeast cells with reduced activity of the key glycolytic enzyme triosephosphate isomerase exhibit an increased resistance to the thiol-oxidizing reagent diamide. Here we show that this phenotype is conserved in Caenorhabditis elegans and that the underlying mechanism is based on a redirection of the metabolic flux from glycolysis to the pentose phosphate pathway, altering the redox equilibrium of the cytoplasmic NADP(H) pool. Remarkably, another key glycolytic enzyme, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), is known to be inactivated in response to various oxidant treatments, and we show that this provokes a similar redirection of the metabolic flux.ConclusionThe naturally occurring inactivation of GAPDH functions as a metabolic switch for rerouting the carbohydrate flux to counteract oxidative stress. As a consequence, altering the homoeostasis of cytoplasmic metabolites is a fundamental mechanism for balancing the redox state of eukaryotic cells under stress conditions.

Genomic data now allow the large-scale manual or semi-automated reconstruction of metabolic networks. A network reconstruction represents a highly curated organism-specific knowledge base. A few genome-scale network reconstructions have appeared for metabolism in the baker’s yeast Saccharomyces cerevisiae. These alternative network reconstructions differ in scope and content, and further have used different terminologies to describe the same chemical entities, thus making comparisons between them difficult. The formulation of a ‘community consensus’ network that collects and formalizes the ‘community knowledge’ of yeast metabolism is thus highly desirable. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. Special emphasis is laid on referencing molecules to persistent databases or using database-independent forms such as SMILES or InChI strings, since this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language, and we describe the manner in which it can be maintained as a community resource. It should serve as a common denominator for system biology studies of yeast. Similar strategies will be of benefit to communities studying genome-scale metabolic networks of other organisms.

Integration of experimental studies with mathematical modeling allows insight into systems properties, prediction of perturbation effects and generation of hypotheses for further research. We present a comprehensive mathematical description of the cellular response of yeast to hyperosmotic shock. The model integrates a biochemical reaction network comprising receptor stimulation, mitogen-activated protein kinase cascade dynamics, activation of gene expression and adaptation of cellular metabolism with a thermodynamic description of volume regulation and osmotic pressure. Simulations agree well with experimental results obtained under different stress conditions or with specific mutants. The model is predictive since it suggests previously unrecognized features of the system with respect to osmolyte accumulation and feedback control, as confirmed with experiments. The mathematical description presented is a valuable tool for future studies on osmoregulation in yeast and-with appropriate modifications-other organisms. It also serves as a starting point for a comprehensive description of cellular signaling.Osmoregulation encompasses active processes with which cells monitor and adjust osmotic pressure and control shape, turgor and relative water content. Even individual cells in multicellular organisms respond to osmotic changes, and strategies of cellular adaptation are conserved from bacteria to human 1 . The yeast Saccharomyces cerevisiae is a suitable model system to study osmoregulation and a substantial amount of information is available on osmotic shockinduced signal transduction, control of gene expression and accumulation of osmolytes 2 .Osmoregulation is a homeostatic process, though commonly studied as a response to osmotic shock. Central to yeast osmotic adaptation is the high osmolarity glycerol (HOG) signaling system 2,3 (Fig. 1). S. cerevisiae monitors osmotic changes through the plasma membrane-localized sensor histidine kinase Sln1. Under ambient conditions, Sln1 is active and inhibits signaling. Upon loss of turgor pressure, Sln1 is inactivated 4 resulting in activation of a mitogen-activated protein (MAP) kinase cascade and phosphorylation of the MAP kinase Hog1. Active Hog1 accumulates in the nucleus where it affects gene expression. Two HOG target genes encode enzymes in glycerol production. Hence, activation of Hog1 stimulates the production of glycerol, which serves as an osmolyte to increase intracellular osmotic pressure. Glycerol accumulation is also controlled by rapid closing of the aquaglyceroporin Fps1, which is an osmolarity-regulated glycerol channel. Hog1 activation and Hog1-dependent transcriptional stimulation are transient processes, indicating rigorous feedback control. Several protein phosphatases are known as negative regulators of the pathway. Although the overall organization of the systems is well characterized, open questions concern the mechanisms underlying activation and deactivation of the system, feedback control and the causal relationship between different events in...

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