Transcriptional regulation is insufficient to explain substrate-induced flux changes in Bacillus subtilis

Regulation of enzyme expression is one key mechanism by which cells control their metabolic programs. In this work, a quantitative analysis of metabolism in a model bacterium under different conditions shows that expression alone cannot explain the majority of the observed metabolic changes.

Most enzymes are indeed highly expressed in conditions where they are more active.

Quantitatively, however, the observed changes in expression between conditions do not match the changes in activity for most enzymes.

A good quantitative match is only observed for enzymes involved in the TCA cycle.

Metabolomics reveals that increased substrate availability explains only a few instances of changes in activity.

One of the key ways in which microbes are thought to regulate their metabolism is by modulating the availability of enzymes through transcriptional regulation. However, the limited success of efforts to manipulate metabolic fluxes by rewiring the transcriptional network has cast doubt on the idea that transcript abundance controls metabolic fluxes. In this study, we investigate control of metabolic flux in the model bacterium Bacillus subtilis by quantifying fluxes, transcripts, and metabolites in eight metabolic states enforced by different environmental conditions. We find that most enzymes whose flux switches between on and off states, such as those involved in substrate uptake, exhibit large corresponding transcriptional changes. However, for the majority of enzymes in central metabolism, enzyme concentrations were insufficient to explain the observed fluxes—only for a number of reactions in the tricarboxylic acid cycle were enzyme changes approximately proportional to flux changes. Surprisingly, substrate changes revealed by metabolomics were also insufficient to explain observed fluxes, leaving a large role for allosteric regulation and enzyme modification in the control of metabolic fluxes.

The IHF regulon of exponentially growing Pseudomonas putida cells

Integration host factor (IHF) sites are largely absent from intergenic regions of ORFs encoding central metabolic functions in Pseudomonas putida mt-2. To gain an insight into this unequal distribution of otherwise abundant IHF-binding sequences, the transcriptome of IHF-plus and IHF-minus cells growing exponentially on glucose as sole carbon source was examined. In parallel, the cognate metabolic fluxes of the wild-type P. putida strain and its ihfA derivative were determined by culturing cells to a steady-state physiological regime with (13)C-labelled glucose. While expression of many transcripts was altered by the lack of IHF, flux balance analysis revealed that the ihfA mutation did not influence central carbon metabolism. Identification of multiple IHF sites adjacent to genes responsive to the factor allowed a refinement of the consensus and the mapping of the preferred binding positions for activation or repression of associated promoters. That few (if any) of the genes affected by IHF involved core pathways suggested that the central carbon metabolism tolerates the loss of the factor. Instead, IHF controlled various cell surface-related functions and downregulated genes encoding ribosomal proteins, the alpha subunit of RNA polymerase and components of the ATP synthase. These results were confirmed with lacZ fusions to a suite of promoters detected in the transcriptome as affected by IHF. Taken together, the data suggest that IHF plays a role in the physiological shift that sets P. putida for entering stationary phase.

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

The authors analyze the role transcription plays in regulating bacterial metabolic flux. Of 91 transcriptional regulators studied, 2/3 affect absolute fluxes, but only a small number of regulators control the partitioning of flux between different metabolic pathways.

In contrast to the canonical respiro-fermentative glucose metabolism, fully respiratory galactose metabolism depends exclusively on the PEP-glyoxylate cycle.

Of 91 transcription factors, 2/3 affect absolute fluxes, but only one controls the distribution of fluxes on galactose and nine on glucose.

Transcriptional control of hexose flux distributions is confined to the acetyl-CoA branch point.

The PEP-glyoxylate cycle is controlled by cAMP-Crp in a hexose uptake rate-dependent manner.

Focusing on central carbon metabolism of Escherichia coli, we aim here to systematically identify transcriptional regulators that control the distribution of metabolic fluxes during aerobic growth on hexoses. To assess the condition dependence of transcriptional control of flux, we selected glucose and galactose as two substrates that are highly similar, yet lead to distinct growth rates (Soupene et al, 2003), overall metabolic rates (De Anda et al, 2006; Samir El et al, 2009) and levels of catabolite repression (Hogema et al, 1998; Bettenbrock et al, 2007).

Experimentally determined fluxes (Fischer and Sauer, 2003a) during growth on glucose and galactose reveal two distinct metabolic states. On glucose, high metabolic rates lead to high overflow metabolism and respiratory fluxes through the TCA cycle. On galactose, in contrast, metabolism was much slower without overflow metabolism and respiratory fluxes exclusively through the PEP-glyoxylate cycle (Fischer and Sauer, 2003b).

To determine which transcriptional events controlled these two distinct metabolic states, we determined intracellular fluxes in 91 transcription factor mutants. These genetic perturbations primarily affected absolute fluxes but not the distribution of fluxes. The distribution of flux between glycolysis and pentose–phosphate pathway in upper metabolism, e.g., remained constant in all mutants under all conditions. Transcriptional control of the flux distribution was exclusively seen at the acetyl-CoA branch point. On glucose, nine transcription factors controlled the distribution of fluxes at this branch point, five of which (ArcA, IHFA, IHFB, PdhR, Fur) did so presumably directly through their known targets in TCA cycle and/or respiration. Without known targets in the relevant pathways, the remaining four transcription factors (GlpR, QseB, HdfR, GlcC) may act either indirectly or directly through unknown targets. On galactose, transcriptional control focused exclusively on the PEP-glyoxylate cycle. While deletion of six transcription factors (Cra, Crp, IHFA, IHFB, Mlc, NagC) abolished or reduced the PEP-glyoxylate cycle flux, we demonstrate by substrate-limited chemostat experiments, derepression of galactose uptake and show by metabolomics that five of these transcription factors act indirectly through increased cAMP concentrations that allosterically activate Crp, the only direct transcription factors that controls the PEP-glyoxylate cycle (Nanchen et al, 2008).

Overall, our absolute flux data demonstrate that control of flux splitting during growth on hexoses was confined to the acetyl-CoA branch point in E. coli. Of the 36 transcription factors known to target genes in pathways that diverge from the acetyl-CoA branch point, only one transcription factor on galactose and five plus potentially four others on glucose showed altered flux splitting. The primary focus of steady state transcriptional control on the acetyl-CoA branch point, and thus the metabolic decision between the energetically efficient respiration and the less efficient but more rapid fermentation, was recently also demonstrated with only relative flux data for Saccharomyces cerevisiae (Fendt et al, 2010).

In contrast to glucose-grown Bacillus subtilis (Fischer et al, 2005), for batch glucose-grown E. coli, none of the investigated transcription factor mutants exhibited improved biomass productivity. However, the mutants Cra, IHF A, IHF B and NagC with increased uptake rates grew much faster at almost unaltered biomass yields. As the removal of the glucose PTS-based repression with a Crr mutant also resulted in increased galactose uptake, we provide evidence that E. coli actively represses its galactose uptake at the expense of otherwise possible rapid growth.

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 ¹³C-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.

Metabolic fluxes during strong carbon catabolite repression by malate in Bacillus subtilis

Commonly glucose is considered to be the only preferred substrate in Bacillus subtilis whose presence represses utilization of other alternative substrates. Because recent data indicate that malate might be an exception, we quantify here the carbon source utilization hierarchy. Based on physiology and transcriptional data during co-utilization experiments with eight carbon substrates, we demonstrate that malate is a second preferred carbon source for B. subtilis, which is rapidly co-utilized with glucose and strongly represses the uptake of alternative substrates. From the different hierarchy and degree of catabolite repression exerted by glucose and malate, we conclude that both substrates might act through different molecular mechanisms. To obtain a quantitative and functional network view of how malate is (co)metabolized, we developed a novel approach to metabolic flux analysis that avoids error-prone, intuitive, and ad hoc decisions on (13)C rearrangements. In particular, we developed a rigorous approach for deriving reaction reversibilities by combining in vivo intracellular metabolite concentrations with a thermodynamic feasibility analysis. The thus-obtained analytical model of metabolism was then used for network-wide isotopologue balancing to estimate the intracellular fluxes. These (13)C-flux data revealed an extraordinarily high malate influx that is primarily catabolized via the gluconeogenic reactions and toward overflow metabolism. Furthermore, a considerable NADPH-producing malic enzyme flux is required to supply the biosynthetically required NADPH in the presence of malate. Co-utilization of glucose and malate resulted in a synergistic decrease of the respiratory tricarboxylic acid cycle flux.

Metabolic flux analysis of a glycerol-overproducing Saccharomyces cerevisiae strain based on GC-MS, LC-MS and NMR-derived C-labelling data

This study focuses on unravelling the carbon and redox metabolism of a previously developed glycerol-overproducing Saccharomyces cerevisiae strain with deletions in the structural genes encoding triosephosphate isomerase (TPI1), the external mitochondrial NADH dehydrogenases (NDE1 and NDE2) and the respiratory chain-linked glycerol-3-phosphate dehydrogenase (GUT2). Two methods were used for analysis of metabolic fluxes: metabolite balancing and (13)C-labelling-based metabolic flux analysis. The isotopic enrichment of intracellular primary metabolites was measured both directly (liquid chromatography-MS) and indirectly through proteinogenic amino acids (nuclear magnetic resonance and gas chromatography-MS). Because flux sensitivity around several important metabolic nodes proved to be dependent on the applied technique, the combination of the three (13)C quantification techniques generated the most accurate overall flux pattern. When combined, the measured conversion rates and (13)C-labelling data provided evidence that a combination of assimilatory metabolism and pentose phosphate pathway activity diverted some of the carbon away from glycerol formation. Metabolite balancing indicated that this results in excess cytosolic NADH, suggesting the presence of a cytosolic NADH sink in addition to those that were deleted. The exchange flux of four-carbon dicarboxylic acids across the mitochondrial membrane, as measured by the (13)C-labelling data, supports a possible role of a malate/aspartate or malate/oxaloacetate redox shuttle in the transfer of these redox equivalents from the cytosol to the mitochondrial matrix.

13C-labeled gluconate tracing as a direct and accurate method for determining the pentose phosphate pathway split ratio in Penicillium chrysogenum

In this study we developed a new method for accurately determining the pentose phosphate pathway (PPP) split ratio, an important metabolic parameter in the primary metabolism of a cell. This method is based on simultaneous feeding of unlabeled glucose and trace amounts of [U-13C]gluconate, followed by measurement of the mass isotopomers of the intracellular metabolites surrounding the 6-phosphogluconate node. The gluconate tracer method was used with a penicillin G-producing chemostat culture of the filamentous fungus Penicillium chrysogenum. For comparison, a 13C-labeling-based metabolic flux analysis (MFA) was performed for glycolysis and the PPP of P. chrysogenum. For the first time mass isotopomer measurements of 13C-labeled primary metabolites are reported for P. chrysogenum and used for a 13C-based MFA. Estimation of the PPP split ratio of P. chrysogenum at a growth rate of 0.02 h(-1) yielded comparable values for the gluconate tracer method and the 13C-based MFA method, 51.8% and 51.1%, respectively. A sensitivity analysis of the estimated PPP split ratios showed that the 95% confidence interval was almost threefold smaller for the gluconate tracer method than for the 13C-based MFA method (40.0 to 63.5% and 46.0 to 56.5%, respectively). From these results we concluded that the gluconate tracer method permits accurate determination of the PPP split ratio but provides no information about the remaining cellular metabolism, while the 13C-based MFA method permits estimation of multiple fluxes but provides a less accurate estimate of the PPP split ratio.

Cytosolic NADPH metabolism in penicillin-G producing and non-producing chemostat cultures of Penicillium chrysogenum

This study addresses the relation between NADPH supply and penicillin synthesis, by comparing the flux through the oxidative branch of the pentose phosphate pathway (PPP; the main source of cytosolic NADPH) in penicillin-G producing and non-producing chemostat cultures of Penicillium chrysogenum. The fluxes through the oxidative part of the PPP were determined using the recently introduced gluconate-tracer method. Significantly higher oxidative PPP fluxes were observed in penicillin-G producing chemostat cultures, indicating that penicillin production puts a major burden on the supply of cytosolic NADPH. To our knowledge this is the first time direct experimental proof is presented for the causal relationship between penicillin production and NADPH supply. Additional insight in the metabolism of P. chrysogenum was obtained by comparing the PPP fluxes from the gluconate-tracer experiment to oxidative PPP fluxes derived via metabolic flux analysis, using different assumptions for the stoichiometry of NADPH consumption and production.

Measurement of intracellular metabolites of primary metabolism and adenine nucleotides in chemostat cultivated Penicillium chrysogenum

An experimental platform has been developed for rapid sampling and quenching of chemostat cultivated Penicillium chrysogenum broth for metabolome analysis in highly dynamic experiments, aimed at the elucidation of the in vivo kinetic properties of metabolism. The sampling and quenching protocol available from Saccharomyces cerevisiae had to be modified for Penicillium chrysogenum mainly because of its filamentous character. Intracellular metabolites of glycolysis, TCA cycle, and adenine nucleotides were measured with isotope dilution mass spectrometry (IDMS) using a U-(13)C-labeled metabolite mix produced from yeast cells as internal standard. By addition of the U-(13)C internal standard mix prior to the metabolite extraction procedure, partial degradation of metabolites as well as non-linearity and drift of the LC-MS/MS could be successfully compensated for. It was found that there is a serious matrix effect on metabolite extraction between different organisms, which is however completely corrected for by the IDMS approach. Intracellular metabolites could be analyzed with standard deviations of around 5%. A comparison of the metabolite levels between Saccharomyces cerevisiae and Penicillium chrysogenum showed both significant similarities and large differences, which seem to be related to the presence of the penicillin pathway.

Revisiting the 13C-label distribution of the non-oxidative branch of the pentose phosphate pathway based upon kinetic and genetic evidence

The currently applied reaction structure in stoichiometric flux balance models for the nonoxidative branch of the pentose phosphate pathway is not in accordance with the established ping-pong kinetic mechanism of the enzymes transketolase (EC 2.2.1.1) and transaldolase (EC 2.2.1.2). Based upon the ping-pong mechanism, the traditional reactions of the nonoxidative branch of the pentose phosphate pathway are replaced by metabolite specific, reversible, glycolaldehyde moiety (C(2)) and dihydroxyacetone moiety (C(3)) fragments producing and consuming half-reactions. It is shown that a stoichiometric model based upon these half-reactions is fundamentally different from the currently applied stoichiometric models with respect to the number of independent C(2) and C(3) fragment pools in the pentose phosphate pathway and can lead to different label distributions for (13)C-tracer experiments. To investigate the actual impact of the new reaction structure on the estimated flux patterns within a cell, mass isotopomer measurements from a previously published (13)C-based metabolic flux analysis of Saccharomyces cerevisiae were used. Different flux patterns were found. From a genetic point of view, it is well known that several micro-organisms, including Escherichia coli and S. cerevisiae, contain multiple genes encoding isoenzymes of transketolase and transaldolase. However, the extent to which these gene products are also actively expressed remains unknown. It is shown that the newly proposed stoichiometric model allows study of the effect of isoenzymes on the (13)C-label distribution in the nonoxidative branch of the pentose phosphate pathway by extending the half-reaction based stoichiometric model with two distinct transketolase enzymes instead of one. Results show that the inclusion of isoenzymes affects the ensuing flux estimates.

Metabolic-flux analysis of Saccharomyces cerevisiae CEN.PK113-7D based on mass isotopomer measurements of (13)C-labeled primary metabolites

Metabolic-flux analyses in microorganisms are increasingly based on (13)C-labeling data. In this paper a new approach for the measurement of (13)C-label distributions is presented: rapid sampling and quenching of microorganisms from a cultivation, followed by extraction and detection by liquid chromatography-mass spectrometry of free intracellular metabolites. This approach allows the direct assessment of mass isotopomer distributions of primary metabolites. The method is applied to the glycolytic and pentose phosphate pathways of Saccharomyces cerevisiae strain CEN.PK113-7D grown in an aerobic, glucose-limited chemostat culture. Detailed investigations of the measured mass isotopomer distributions demonstrate the accuracy and information-richness of the obtained data. The mass fractions are fitted with a cumomer model to yield the metabolic fluxes. It is estimated that 24% of the consumed glucose is catabolized via the pentose phosphate pathway. Furthermore, it is found that turnover of storage carbohydrates occurs. Inclusion of this turnover in the model leads to a large confidence interval of the estimated split ratio.