Each

of these steps leads to concomitant changes in protein complexes, starting from the phosphotransfer http://www.selleckchem.com/products/CHIR-258.html system to carbohydrate metabolizing enzyme complexes. However, as these two examples already show, the sequence of changes depends on the succession of concentration changes, the last example would refer well to a situation where there are high concentrations of glucose and, in the end, there is some lactose available to profit from the switch. The prokaryotic response to changing metabolic conditions is thus condition dependent (see e.g., Jozefcuk [6] for data on E. coli). However, our overall current Inhibitors,research,lifescience,medical understanding of the involved, fine-tuned regulation and feedback, as well as feedforward, loops is limited. More studies to elucidate the details of such physiological changes in protein complexes and bacterial responses to metabolic changes are clearly needed. In fact, system switching states occur often fast in bacteria. Whole cascades or even larger networks are rapidly reorganized as the whole network is controlled often

by one master regulator. A good example is the Inhibitors,research,lifescience,medical pathogenicity switch by the PrfA protein Inhibitors,research,lifescience,medical of Listeria which simultaneously accomplishes (i) adaptation of a number of virulence pathways, and (ii) reorganization of nutrient utilization, thus facilitating adaptation of L. monocytogenes from a more saprophytic to an intracellular lifestyle. Also in Staphylococci (and many other bacterial species), such major system changes in metabolism (stress response or growth behavior) are mediated with tight control just by the activation of transcription factors (including repressors such as the Rex family). Other switching states include diauxic shift, glucose limitation under aerobic or anaerobic conditions, Inhibitors,research,lifescience,medical differentiation (e.g., biofilm formation) or amino acid limitation. In a full “on” state for sellectchem pathways and networks (e.g., growth on full medium and central carbohydrate metabolism)

correlation between gene expression and metabolite flux is high. For not-so-central pathways, Inhibitors,research,lifescience,medical gene expression data may provide a lower limit as the metabolite flux can still become higher when enzymes are regulated Drug_discovery to be more active. However, for such a system-switching state the correlation in activity for the pathways changed simultaneously is high, as seen both for S. aureus [41], as well as in other organisms (e.g., Jozefcuk [6] for E. coli). Besides the high correlation between the concerned pathways, there are structural changes in complexes such as pyruvate dehydrogenase complex, central for carbohydrate metabolism to accompany such system changes (see examples above). However, the involvement for transcription and regulatory factors, changes in the respective protein complexes, correlated pathway changes and correlation between different data sets also apply to other major system changes such as bacterial differentiation (sporulation, apoptosis) and adaptation in general.