Junyoung O. Park

BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions

BackgroundGenome-scale metabolic reconstructions under the Constraint Based Reconstruction and Analysis (COBRA) framework are valuable tools for analyzing the metabolic capabilities of organisms and interpreting experimental data. As the number of such reconstructions and analysis methods increases, there is a greater need for data uniformity and ease of distribution and use.DescriptionWe describe BiGG, a knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest.ConclusionsBiGG addresses a need in the systems biology community to have access to high quality curated metabolic models and reconstructions. It is freely available for academic use at http://bigg.ucsd.edu.

Metabolite concentrations, fluxes, and free energies imply efficient enzyme usage

In metabolism, available free energy is limited and must be divided across pathway steps to maintain ΔG negative throughout. For each reaction, ΔG is log-proportional both to a concentration ratio (reaction quotient-to-equilibrium constant) and to a flux ratio (backward-to-forward flux). Here we use isotope labeling to measure absolute metabolite concentrations and fluxes in Escherichia coli, yeast, and a mammalian cell line. We then integrate this information to obtain a unified set of concentrations and ΔG for each organism. In glycolysis, we find that free energy is partitioned so as to mitigate unproductive backward fluxes associated with ΔG near zero. Across metabolism, we observe that absolute metabolite concentrations and ΔG are substantially conserved, and that most substrate (but not inhibitor) concentrations exceed the associated enzyme binding site affinity. The observed conservation of metabolite concentrations is consistent with an evolutionary drive to utilize enzymes efficiently given thermodynamic and osmotic constraints.

Systems-level analysis of mechanisms regulating yeast metabolic flux

Quantitation of metabolic pathway regulation Although metabolic biochemical pathways are well understood, less is known about precisely how reaction rates or fluxes through the various enzymes are controlled. Hackett et al. developed a method to quantitate such regulatory influence in yeast. They monitored concentrations of metabolites, enzymes, and potential regulators by LC-MS/MS (liquid chromatography–tandem mass spectrometry) and isotope ratio measurements for 56 reactions, over 100 metabolites, and 370 metabolic enzymes in yeast in 25 different steady-state conditions. Bayesian analysis was used to examine the probability of regulatory interactions. Regulation of flux through the pathways was predominantly controlled by changes in the concentration of small-molecule metabolites rather than changes in enzyme abundance. The analysis also revealed previously unrecognized regulation between pathways. Science, this issue p. 432 Metabolomics, proteomics, and flux analysis are used to dissect quantitatively metabolic regulation in living yeast. INTRODUCTION Metabolism is among the most strongly conserved processes across all domains of life and is crucial for both bioengineering and disease research, yet we still have an unclear understanding of how metabolic rates (fluxes) are determined. Qualitatively, this deficiency involves missing knowledge of enzyme regulators. Quantitatively, it involves limited understanding of the relative contributions of enzyme and metabolite concentrations in controlling flux across physiological conditions. Addressing these gaps has been challenging because in vitro biochemical approaches lack the physiological context, whereas models of cellular metabolic dynamics have limited capacity for identifying or quantitating specific regulatory events because of overall model complexity. RATIONALE Flux through individual metabolic reactions is directly determined by the concentrations of enzyme, substrates, products, and any allosteric regulators, as captured quantitatively by a Michaelis-Menten–style reaction equation. Analogous to how experimental variation of reaction species in vitro allows for the inference of regulators and reaction equation kinetic parameters, physiological changes in flux entail a change in reaction species that can be used to determine reaction equations on the basis of cellular data. This requires measurement across multiple biological conditions of flux, enzyme concentrations, and metabolite concentrations. We reasoned that chemostat cultures could be used to induce predictable and strong flux changes, with changes in enzymes and metabolites measurable by proteomics and metabolomics. By directly relating cellular flux to the reaction species that determine it, we can carry out regulatory inference at the level of single metabolic reactions by using cellular data. An important benefit is that the physiological significance of any identified regulator is implicit from its role in determining cellular flux. RESULTS Here we introduce systematic identification of meaningful metabolic enzyme regulation (SIMMER). We measured fluxes, and metabolite and enzyme concentrations, in each of 25 yeast chemostats. For each of 56 reactions for which the flux, enzyme, and substrates were measured, we determined whether variation in measured flux could be explained by simple Michaelis-Menten kinetics. We also evaluated alternative models of each reaction’s kinetics that included a suite of allosteric regulators drawn from across all organisms. For 46 reactions, we were able to identify a useful kinetic model, with 17 reactions not including any regulation and 29 reactions being regulated by one to two allosteric regulators. Three previously unrecognized cross-pathway regulatory interactions were validated biochemically. These included inhibition of pyruvate kinase by citrate and inhibition of pyruvate decarboxylase by phenypyruvate. These metabolites accumulated and thereby curtailed glycolytic outflow and ethanol production in nitrogen-limited yeast. For well-supported reaction forms, we were able to determine the extent to which nutrient-based changes in flux were determined by changes in the concentrations of individual reaction species. We find that substrates are the most important determinant of fluxes in general, with enzymes and allosteric regulators having a comparably important role in the case of physiologically irreversible reactions. CONCLUSION By connecting changes in flux to their root cause, SIMMER parallels classic in vitro approaches, but it allows simultaneous testing of numerous regulators of many reactions under physiological conditions. Its application to yeast showed that changes in flux across nutrient conditions are predominantly due to metabolite, not enzyme, levels. Thus, yeast metabolism is substantially self-regulating. Integrative analysis of fluxes and metabolite and enzyme concentrations by SIMMER. Measured flux is related, on a reaction-by-reaction basis, to enzyme and metabolite concentrations through a Michaelis-Menten equation. The extent to which variation in flux across experimental conditions can be explained by the enzyme, substrates, and products is assessed. If unregulated kinetics disagrees with the measured flux, we test a set of possible allosteric regulators to determine which, if any, regulators are supported on the basis of improvement in fit. Cellular metabolic fluxes are determined by enzyme activities and metabolite abundances. Biochemical approaches reveal the impact of specific substrates or regulators on enzyme kinetics but do not capture the extent to which metabolite and enzyme concentrations vary across physiological states and, therefore, how cellular reactions are regulated. We measured enzyme and metabolite concentrations and metabolic fluxes across 25 steady-state yeast cultures. We then assessed the extent to which flux can be explained by a Michaelis-Menten relationship between enzyme, substrate, product, and potential regulator concentrations. This revealed three previously unrecognized instances of cross-pathway regulation, which we biochemically verified. One of these involved inhibition of pyruvate kinase by citrate, which accumulated and thereby curtailed glycolytic outflow in nitrogen-limited yeast. Overall, substrate concentrations were the strongest driver of the net rates of cellular metabolic reactions, with metabolite concentrations collectively having more than double the physiological impact of enzymes.

Malic enzyme tracers reveal hypoxia-induced switch in adipocyte NADPH pathway usage

The critical cellular hydride donor NADPH is produced through various means, including the oxidative pentose phosphate pathway (oxPPP), folate metabolism and malic enzyme. In growing cells, it is efficient to produce NADPH via the oxPPP and folate metabolism, which also make nucleotide precursors. In nonproliferating adipocytes, a metabolic cycle involving malic enzyme holds the potential to make both NADPH and two-carbon units for fat synthesis. Recently developed deuterium ((2)H) tracer methods have enabled direct measurement of NADPH production by the oxPPP and folate metabolism. Here we enable tracking of NADPH production by malic enzyme with [2,2,3,3-(2)H]dimethyl-succinate and [4-(2)H]glucose. Using these tracers, we show that most NADPH in differentiating 3T3-L1 mouse adipocytes is made by malic enzyme. The associated metabolic cycle is disrupted by hypoxia, which switches the main adipocyte NADPH source to the oxPPP. Thus, (2)H-labeled tracers enable dissection of NADPH production routes across cell types and environmental conditions.

Hierarchy in Pentose Sugar Metabolism in Clostridium acetobutylicum

ABSTRACT Bacterial metabolism of polysaccharides from plant detritus into acids and solvents is an essential component of the terrestrial carbon cycle. Understanding the underlying metabolic pathways can also contribute to improved production of biofuels. Using a metabolomics approach involving liquid chromatography-mass spectrometry, we investigated the metabolism of mixtures of the cellulosic hexose sugar (glucose) and hemicellulosic pentose sugars (xylose and arabinose) in the anaerobic soil bacterium Clostridium acetobutylicum. Simultaneous feeding of stable isotope-labeled glucose and unlabeled xylose or arabinose revealed that, as expected, glucose was preferentially used as the carbon source. Assimilated pentose sugars accumulated in pentose phosphate pathway (PPP) intermediates with minimal flux into glycolysis. Simultaneous feeding of xylose and arabinose revealed an unexpected hierarchy among the pentose sugars, with arabinose utilized preferentially over xylose. The phosphoketolase pathway (PKP) provides an alternative route of pentose catabolism in C. acetobutylicum that directly converts xylulose-5-phosphate into acetyl-phosphate and glyceraldehyde-3-phosphate, bypassing most of the PPP. When feeding the mixture of pentose sugars, the labeling patterns of lower glycolytic intermediates indicated more flux through the PKP than through the PPP and upper glycolysis, and this was confirmed by quantitative flux modeling. Consistent with direct acetyl-phosphate production from the PKP, growth on the pentose mixture resulted in enhanced acetate excretion. Taken collectively, these findings reveal two hierarchies in clostridial pentose metabolism: xylose is subordinate to arabinose, and the PPP is used less than the PKP.

RNA Futile Cycling in Model Persisters Derived from MazF Accumulation

ABSTRACT Metabolism plays an important role in the persister phenotype, as evidenced by the number of strategies that perturb metabolism to sabotage this troublesome subpopulation. However, the absence of techniques to isolate high-purity populations of native persisters has precluded direct measurement of persister metabolism. To address this technical challenge, we studied Escherichia coli populations whose growth had been inhibited by the accumulation of the MazF toxin, which catalyzes RNA cleavage, as a model system for persistence. Using chromosomally integrated, orthogonally inducible promoters to express MazF and its antitoxin MazE, bacterial populations that were almost entirely tolerant to fluoroquinolone and β-lactam antibiotics were obtained upon MazF accumulation, and these were subjected to direct metabolic measurements. While MazF model persisters were nonreplicative, they maintained substantial oxygen and glucose consumption. Metabolomic analysis revealed accumulation of all four ribonucleotide monophosphates (NMPs). These results are consistent with a MazF-catalyzed RNA futile cycle, where the energy derived from catabolism is dissipated through continuous transcription and MazF-mediated RNA degradation. When transcription was inhibited, oxygen consumption and glucose uptake decreased, and nucleotide triphosphates (NTPs) and NTP/NMP ratios increased. Interestingly, the MazF-inhibited cells were sensitive to aminoglycosides, and this sensitivity was blocked by inhibition of transcription. Thus, in MazF model persisters, futile cycles of RNA synthesis and degradation result in both significant metabolic demands and aminoglycoside sensitivity. IMPORTANCE Metabolism plays a critical role in controlling each stage of bacterial persistence (shutdown, stasis, and reawakening). In this work, we generated an E. coli strain in which the MazE antitoxin and MazF toxin were artificially and independently inducible, and we used this strain to generate model persisters and study their metabolism. We found that even though growth of the model persisters was inhibited, they remained highly metabolically active. We further uncovered a futile cycle driven by continued transcription and MazF-mediated transcript degradation that dissipated the energy derived from carbon catabolism. Interestingly, the existence of this futile cycle acted as an Achilles’ heel for MazF model persisters, rendering them vulnerable to killing by aminoglycosides. Metabolism plays a critical role in controlling each stage of bacterial persistence (shutdown, stasis, and reawakening). In this work, we generated an E. coli strain in which the MazE antitoxin and MazF toxin were artificially and independently inducible, and we used this strain to generate model persisters and study their metabolism. We found that even though growth of the model persisters was inhibited, they remained highly metabolically active. We further uncovered a futile cycle driven by continued transcription and MazF-mediated transcript degradation that dissipated the energy derived from carbon catabolism. Interestingly, the existence of this futile cycle acted as an Achilles’ heel for MazF model persisters, rendering them vulnerable to killing by aminoglycosides.

Glucose becomes one of the worst carbon sources for E.coli on poor nitrogen sources due to suboptimal levels of cAMP

In most conditions, glucose is the best carbon source for E. coli: it provides faster growth than other sugars, and is consumed first in sugar mixtures. Here we identify conditions in which E. coli strains grow slower on glucose than on other sugars, namely when a single amino acid (arginine, glutamate, or proline) is the sole nitrogen source. In sugar mixtures with these nitrogen sources, E. coli still consumes glucose first, but grows faster rather than slower after exhausting glucose, generating a reversed diauxic shift. We trace this counterintuitive behavior to a metabolic imbalance: levels of TCA-cycle metabolites including α-ketoglutarate are high, and levels of the key regulatory molecule cAMP are low. Growth rates were increased by experimentally increasing cAMP levels, either by adding external cAMP, by genetically perturbing the cAMP circuit or by inhibition of glucose uptake. Thus, the cAMP control circuitry seems to have a ‘bug’ that leads to slow growth under what may be an environmentally rare condition.

Escherichia coli translation strategies differ across carbon, nitrogen and phosphorus limitation conditions

For cells to grow faster they must increase their protein production rate. Microorganisms have traditionally been thought to accomplish this increase by producing more ribosomes to enhance protein synthesis capacity, leading to the linear relationship between ribosome level and growth rate observed under most growth conditions previously examined. Past studies have suggested that this linear relationship represents an optimal resource allocation strategy for each growth rate, independent of any specific nutrient state. Here we investigate protein production strategies in continuous cultures limited for carbon, nitrogen and phosphorus, which differentially impact substrate supply for protein versus nucleic acid metabolism. Unexpectedly, we find that at slow growth rates, Escherichiacoli achieves the same protein production rate using three different strategies under the three different nutrient limitations. Under phosphorus (P) limitation, translation is slow due to a particularly low abundance of ribosomes, which are RNA-rich and thus particularly costly for phosphorous-limited cells. Under nitrogen (N) limitation, translation elongation is slowed by processes including ribosome stalling at glutamine codons. Under carbon (C) limitation, translation is slowed by accumulation of inactive ribosomes not bound to messenger RNA. These extra ribosomes enable rapid growth acceleration during nutrient upshift. Thus, bacteria tune ribosome usage across different limiting nutrients to enable balanced nutrient-limited growth while also preparing for future nutrient upshifts.Different nutrient limitations slow down Escherichia coli translation via distinct mechanisms: P limitation leads to fewer ribosomes; N limitation reduces translation elongation via ribosome stalling; and C limitation is characterized by inactive ribosomes not bound to mRNA.

Targeting hepatic glutaminase activity to ameliorate hyperglycemia

Glucagon levels increase under homeostatic, fasting conditions, promoting the release of glucose from the liver by accelerating the breakdown of glycogen (also known as glycogenolysis). Glucagon also enhances gluconeogenic flux, including from an increase in the hepatic consumption of amino acids. In type 2 diabetes, dysregulated glucagon signaling contributes to the elevated hepatic glucose output and fasting hyperglycemia that occur in this condition. Yet, the mechanism by which glucagon stimulates gluconeogenesis remains incompletely understood. Contrary to the prevailing belief that glucagon acts primarily on cytoplasmic and nuclear targets, we find glucagon-dependent stimulation of mitochondrial anaplerotic flux from glutamine that increases the contribution of this amino acid to the carbons of glucose generated during gluconeogenesis. This enhanced glucose production is dependent on protein kinase A (PKA) and is associated with glucagon-stimulated calcium release from the endoplasmic reticulum, activation of mitochondrial α-ketoglutarate dehydrogenase, and increased glutaminolysis. Mice with reduced levels of hepatic glutaminase 2 (GLS2), the enzyme that catalyzes the first step in glutamine metabolism, show lower glucagon-stimulated glutamine-to-glucose flux in vivo, and GLS2 knockout results in higher fasting plasma glucagon and glutamine levels with lower fasting blood glucose levels in insulin-resistant conditions. As found in genome-wide association studies (GWAS), human genetic variation in the region of GLS2 is associated with higher fasting plasma glucose; here we show in human cryopreserved primary hepatocytes in vitro that these natural gain-of-function missense mutations in GLS2 result in higher glutaminolysis and glucose production. These data emphasize the importance of gluconeogenesis from glutamine, particularly in pathological states of increased glucagon signaling, while suggesting a possible new therapeutic avenue to treat hyperglycemia.

Publisher Correction: Targeting hepatic glutaminase activity to ameliorate hyperglycemia

In the version of this article initially published, the “[13C2]α-ketoglutarate” label on Fig. 1g is incorrect. It should be “[13C5]α-ketoglutarate”. Additionally, in Fig. 3b, the “AAV-GFP” group is missing a notation for significance, and in Fig. 3c, the “AAV-GLS2-sh” group is missing a notation for significance. There should be a double asterisk notating significance in both panels. Finally, in the Fig. 4g legend, “[13C6]UDP-glucose” should be “[13C3]UDP-glucose”, and in the Fig. 4h legend, “[13C6]hexose” should be “[13C3]hexose”. The errors have been corrected in the HTML and PDF versions of this article.