Distinct transcriptional regulation of the two Escherichia coli transhydrogenases PntAB and UdhA.

Transhydrogenases catalyse interconversion of the redox cofactors NADH and NADPH, thereby conveying metabolic flexibility to balance catabolic NADPH formation with anabolic or stress-based consumption of NADPH. Escherichia coli is one of the very few microbes that possesses two isoforms: the membrane-bound, proton-translocating transhydrogenase PntAB and the cytosolic, energy-independent transhydrogenase UdhA. Despite their physiological relevance, we have only fragmented information on their regulation and the signals coordinating their counteracting activities. Here we investigated PntAB and UdhA regulation by studying transcriptional responses to environmental and genetic perturbations. By testing pntAB and udhA GFP reporter constructs in the background of WT E. coli and 62 transcription factor mutants during growth on different carbon sources, we show distinct transcriptional regulation of the two transhydrogenase promoters. Surprisingly, transhydrogenase regulation was independent of the actual catabolic overproduction or underproduction of NADPH but responded to nutrient levels and growth rate in a fashion that matches the cellular need for the redox cofactors NADPH and/or NADH. Specifically, the identified transcription factors Lrp, ArgP and Crp link transhydrogenase expression to particular amino acids and intracellular concentrations of cAMP. The overall identified set of regulators establishes a primarily biosynthetic role for PntAB and link UdhA to respiration.

Functioning of a metabolic flux sensor in Escherichia coli.

Regulation of metabolic operation in response to extracellular cues is crucial for cells' survival. Next to the canonical nutrient sensors, which measure the concentration of nutrients, recently intracellular "metabolic flux" was proposed as a novel impetus for metabolic regulation. According to this concept, cells would have molecular systems ("flux sensors") in place that regulate metabolism as a function of the actually occurring metabolic fluxes. Although this resembles an appealing concept, we have not had any experimental evidence for the existence of flux sensors and also we have not known how these flux sensors would work in detail. Here, we show experimental evidence that supports the hypothesis that Escherichia coli is indeed able to measure its glycolytic flux and uses this signal for metabolic regulation. Combining experiment and theory, we show how this flux-sensing function could emerge from an aggregate of several molecular mechanisms: First, the system of reactions of lower glycolysis and the feedforward activation of fructose-1,6-bisphosphate on pyruvate kinase translate flux information into the concentration of the metabolite fructose-1,6-bisphosphate. The interaction of this "flux-signaling metabolite" with the transcription factor Cra then leads to flux-dependent regulation. By responding to glycolytic flux, rather than to the concentration of individual carbon sources, the cell may minimize sensing and regulatory expenses.

A role for EIIA(Ntr) in controlling fluxes in the central metabolism of E. coli K12.

To investigate a possible role of the nitrogen-PTS (PTS(Ntr)) in controlling carbon metabolism, we determined the growth of Escherichia coli LJ110 and of isogenic derivatives, mutated in components of the PTS(Ntr), on different carbon sources. The PTS(Ntr) is a set of proteins homologous to the PEP-dependent phosphotransferase system (C-PTS) that transfers a phosphate group from PEP over EI(Ntr) (encoded by ptsP) and NPr (encoded by ptsO) to EIIA(Ntr) (encoded by ptsN). Strains deleted in ptsN were characterized by a high acetate production coupled to slow growth on glycolytic substrates. The ΔptsP and the ΔptsO strain showed the same behavior as the parent strain. As the phosphorylation level of EIIA(Ntr) in these mutants differed significantly from that of the parent strain, phosphorylation of EIIA(Ntr) obviously is not important for its function. During growth in minimal medium with defined carbon sources, EIIA(Ntr) was always completely phosphorylated in LJ110. Significant amounts of dephosphorylated EIIA(Ntr) were only visible in strains lacking EI(Ntr) or NPr. mRNA expression studies on glucose revealed a downregulation of genes encoding TCA cycle enzymes when EIIA(Ntr) was absent. (13)C-flux analyses confirmed higher fluxes towards acetate and lower fluxes in the TCA cycle in the ptsN mutants but additionally hinted to a slightly but significantly increased flux through the pyruvate dehydrogenase complex (PDH). During growth on succinate the ΔptsN strain accumulated mutations in rpoS, while no rpoS mutants were observed for the ΔptsN-O strain. This hints to an additional function of NPr during growth with succinate.

Large-scale 13C-flux analysis reveals distinct transcriptional control of respiratory and fermentative metabolism in Escherichia coli.

Despite our increasing topological knowledge on regulation networks in model bacteria, it is largely unknown which of the many co-occurring regulatory events actually control metabolic function and the distribution of intracellular fluxes. Here, we unravel condition-dependent transcriptional control of Escherichia coli metabolism by large-scale (13)C-flux analysis in 91 transcriptional regulator mutants on glucose and galactose. In contrast to the canonical respiro-fermentative glucose metabolism, fully respiratory galactose metabolism depends exclusively on the phosphoenol-pyruvate (PEP)-glyoxylate cycle. While 2/3 of the regulators directly or indirectly affected absolute flux rates, the partitioning between different pathways remained largely stable with transcriptional control focusing primarily on the acetyl-CoA branch point. Flux distribution control was achieved by nine transcription factors on glucose, including ArcA, Fur, PdhR, IHF A and IHF B, but was exclusively mediated by the cAMP-dependent Crp regulation of the PEP-glyoxylate cycle flux on galactose. Five further transcription factors affected this flux only indirectly through cAMP and Crp by increasing the galactose uptake rate. Thus, E. coli actively limits its galactose catabolism at the expense of otherwise possible faster growth.

Pseudo-transition Analysis Identifies the Key Regulators of Dynamic Metabolic Adaptations from Steady-State Data.

Hundreds of molecular-level changes within central metabolism allow a cell to adapt to the changing environment. A primary challenge in cell physiology is to identify which of these molecular-level changes are active regulatory events. Here, we introduce pseudo-transition analysis, an approach that uses multiple steady-state observations of (13)C-resolved fluxes, metabolites, and transcripts to infer which regulatory events drive metabolic adaptations following environmental transitions. Pseudo-transition analysis recapitulates known biology and identifies an unexpectedly sparse, transition-dependent regulatory landscape: typically a handful of regulatory events drive adaptation between carbon sources, with transcription mainly regulating TCA cycle flux and reactants regulating EMP pathway flux. We verify these observations using time-resolved measurements of the diauxic shift, demonstrating that some dynamic transitions can be approximated as monotonic shifts between steady-state extremes. Overall, we show that pseudo-transition analysis can explore the vast regulatory landscape of dynamic transitions using relatively few steady-state data, thereby guiding time-consuming, hypothesis-driven molecular validations.