- Research article
- Open Access
Impact of the solvent capacity constraint on E. coli metabolism
- Alexei Vazquez†1Email author,
- Qasim K Beg†2, 7,
- Marcio A deMenezes3,
- Jason Ernst4,
- Ziv Bar-Joseph4,
- Albert-László Barabási5,
- László G Boros6 and
- Zoltán N Oltvai2Email author
© Vazquez et al; licensee BioMed Central Ltd. 2008
Received: 29 August 2007
Accepted: 23 January 2008
Published: 23 January 2008
Obtaining quantitative predictions for cellular metabolic activities requires the identification and modeling of the physicochemical constraints that are relevant at physiological growth conditions. Molecular crowding in a cell's cytoplasm is one such potential constraint, as it limits the solvent capacity available to metabolic enzymes.
Using a recently introduced flux balance modeling framework (FBAwMC) here we demonstrate that this constraint determines a metabolic switch in E. coli cells when they are shifted from low to high growth rates. The switch is characterized by a change in effective optimization strategy, the excretion of acetate at high growth rates, and a global reorganization of E. coli metabolic fluxes, the latter being partially confirmed by flux measurements of central metabolic reactions.
These results implicate the solvent capacity as an important physiological constraint acting on E. coli cells operating at high metabolic rates and for the activation of a metabolic switch when they are shifted from low to high growth rates. The relevance of this constraint in the context of both the aerobic ethanol excretion seen in fast growing yeast cells (Crabtree effect) and the aerobic glycolysis observed in rapidly dividing cancer cells (Warburg effect) should be addressed in the future.
Understanding an organism's metabolism at a system level requires knowledge of the physicochemical constraints limiting its metabolic capabilities under different growth conditions, and the genetic regulatory mechanisms that ultimately allow it to adapt to a changing environment. In some cases there is an obvious connection between an environmental change and the regulatory mechanisms responding to it, an example being a switch from aerobic to anaerobic growth . However, there are constraints leading to less obvious metabolic changes, involving a complex global rearrangement of the cell's metabolism. A key aim of systems biology is to uncover the metabolic constraints determining such complex phenotypic changes, which can be understood only when the system is analyzed at a global scale [2–4].
In the absence of cell-scale kinetic models, flux balance analysis (FBA) provides experimentally testable predictions on an organism's metabolic flux state [4–8], which are based on conservation principles, particularly mass conservation, and metabolic capacity constraints. The impact of local constraints, such as uptake capacities, have been investigated [4–7], and capacity constraints over full metabolic pathways have been considered as well . Moreover, it has been hypothesized that the high concentration of macromolecules in the cell's cytoplasm imposes a global constraint on the metabolic capacity of an organism [10, 11]. More recently, we demonstrated that the key quantity is the total intracellular volume available to metabolic enzymes that result in a limited solvent capacity . The addition of the solvent capacity constraint to a FBA model allowed us to explain, within a metabolic efficiency framework, the hierarchy of substrate consumption of E. coli cells growing in a mixture of carbon sources . On the other hand, the pattern of substrate consumption can also be reproduced by superimposing regulatory information obtained e.g., from microarray data . Taking together, these results indicate that the FBA model together with the solvent capacity constraint can be used to predict the regulatory mechanisms and, equally importantly, to understand their advantage in terms of metabolic efficiency and constraints. It is not clear, however, if the limited capacity constraint play a role at other physiological growth conditions, e.g., when nutrients are scarce.
Here we study the impact of the limited solvent capacity on E. coli cell metabolism at different physiological growth conditions. We demonstrate that this constraint is relevant for fast growing cells, and predict the existence of a metabolic switch between cells growing at low and high nutrient abundance, respectively. We carry out flux measurements of several reactions in the E. coli central metabolism, observing a partial agreement with the model predictions. Moreover, to uncover the regulatory mechanisms that control the changes in flux rates, we perform gene expression and enzyme activity measurements, finding that the switch is controlled predominantly at the enzyme activity level implemented by changes in the activity of a few key enzymes in the E. coli central metabolism. Finally, we discuss the potential relevance of the limited solvent capacity constraint to experimental observations in other organisms.
Limited solvent capacity constrains the metabolic rate of fast growing E. coli cells
Since the coefficients a i (units of inverse flux) quantifies the contribution to the overall crowding by reaction i we refer to them as the 'crowding coefficients'.
FBAwMC predicts a change of effective metabolic efficiency objective
Associated with the predicted saturation of E. coli metabolic rates, FBAwMC predicts a metabolic switch characterized by a change in the effective criteria of metabolic efficiency. At low growth rates the ratio between the biomass production rate and the glucose uptake rate is at a maximum but decreases with increasing the growth rate. In contrast, the ratio between the biomass production rate and the average reaction rate increases with increasing the growth rate, reaching a maximum at high growth rates. In agreement with our expectations, at low growth rates nutrients are scarce and the best strategy for a cell is to maximize the biomass production rate per unit of limiting nutrient (in this case, glucose) uptake rate. In contrast, at high growth rates the nutrients are abundant, the predicted metabolic rate is limited by the solvent capacity constraint (Eq. 3) and, therefore, the maximum growth rate is achieved by maximizing the biomass production rate per average reaction rate (Fig. 2c). The predicted change in metabolic efficiency objective is accompanied by a redistribution of the metabolic fluxes, including those of exchange fluxes. Indeed, a characteristic example is the predicted excretion of acetate at high growth rates (Fig. 2d) that is well supported by experimental observations [9, 19, 20].
FBAwMC-predicted metabolic fluxes are within the range of experimental values
Limiting our analysis to the expected behavior, we observe a slope change for several fluxes when reaching the highest growth rates. The reactions of the glycolytic pathway, the flux towards the pentose-phosphate pathway via the reaction catalyzed by the gene product of zwf, and the acetate pathway switch at high growth rates to a faster flux increase with increasing the growth rate (Fig. 3). The experimental values corroborate this qualitative behavior, but the change is bigger for the ptsG-catalyzed reaction and even qualitatively different for the pykA-catalyzed reaction, both being part of the glycolytic pathway. A second noticeable effect is the predicted saturation of the TCA cycle flux at high growth rates. The experimentally measured values of the TCA cycle flux exhibit, however, a stronger effect characterized by a decreasing tendency at high growth rates (Fig. 3). Taken together these results indicate that while for most reactions the FBAwMC predictions are within the range of experimental measurements, a method for a more accurate estimate of the crowding coefficients on a network scale will be required to provide more precise predictions.
Identifying the regulatory mechanism(s) that control the action of the metabolic switch
In parallel with the enzyme activity measurements we also prepared mRNA from samples obtained at all five dilution rates and processed them for microarray analysis. The full microarray data set is presented in the Additional file 3 and its detailed analysis is presented in Additional file 4 (SI text 5–8). In contrast to the observed overall correlation between measured fluxes and in vitro enzyme activities we do not observe a significant correlation between the measured metabolic fluxes and the relative changes in mRNA levels of enzyme-encoding genes (SI Fig. S5 in Additional file 4), implying that the switch and corresponding enzymatic functions are not predominantly controlled at the transcriptional level. Correspondingly, no significant correlation between the in vitro enzyme activities and the relative changes in mRNA levels of enzyme-encoding genes can be seen (SI Fig. S5 in Additional file 4). Taken together these results indicate that the metabolic switch is predominantly controlled by an increase in the enzyme activities of the end products of ptsG and pfkA controlling the glycolysis flux, and pta controlling the acetate pathway flux, respectively.
Developing a modeling framework that can describe and predict in a quantitative manner the experimentally observed behavior of an organism is a significant challenge for systems biology. One prerequisite of this goal is to uncover the physicochemical constraints exerting the main influences on cellular metabolism . Our results here and in Ref.  indicate that the limited solvent capacity represents a physiologically relevant constraint for fast growing E. coli cells. The incorporation of this constraint to the FBA modeling framework leads to the FBAwMC model whose predictions indicate that the solvent capacity constraint results in a maximum glucose uptake rate and growth rate that are within the range of experimentally determined values. The flux predictions for several reactions of the E. coli metabolism are within the range of our measurements, as well.
From the perspective of quantitative modeling using flux balance approximations, the solvent capacity constraint forces us to consider reaction kinetics via the crowding coefficients, at least for fast growing cells. At low metabolic rates the solvent capacity constraint is less relevant and flux balance alone is sufficient to obtain satisfactory predictions. In contrast, at high metabolic rates a precise knowledge of the crowding coefficients is required to obtain accurate predictions. In the absence of kinetic information we can still obtain a good approximation by sampling the crowding coefficients from a list of estimated values and then focus on the resulting general trend.
More importantly, the solvent capacity constraint allows the interpretation of the metabolic switch taking place between slow and fast growing E. coli cells. A recent study of FBA models with different objectives demonstrates that under nutrient scarcity a FBA model with the maximization of the biomass yield objective achieve the highest predictive accuracy, while maximizing the ATP or biomass yield per average flux unit is the best objective for unlimited growth on glucose under aerobic conditions . In contrast, by considering the solvent capacity constraint we obtain the same results using the maximization of biomass production rate objective alone (Fig. 2c). This is more consistent with the expectation that cells achieving the fastest growth rates outgrow cells growing at a slower rate, but how the highest growth rate is achieved is determined by both the availability of substrates and internal metabolic constraints, such as the solvent capacity. Furthermore, the well-known acetate excretion [9, 19, 20] is explained by the solvent capacity constraint as well. We should note, however, that this does not exclude the possibility that under certain physiological conditions acetate excretion may result from a limited availability of oxygen in the culture medium .
From a more general perspective the results reported in this study further implicate the limited solvent capacity as an important constraint acting on the metabolism of cells operating at high metabolic rates. Thus, the relevance of this constraint will also need to be examined in the context of the observed aerobic ethanol excretion of fast growing yeast cells (Crabtree effect)  and aerobic glycolysis in fast growing mammalian cells, particularly tumor cells (Warburg effect) . The common theme of these effects is that (i) they occur in fast growing cells and (ii) that in the presence of oxygen cells partially switch to anaerobic metabolism, resulting in the excretion of metabolic byproducts. Yet, the role of other proposed effects, such as a limit on attainable mitochondrial respiration  and available oxygen , cannot be excluded and, therefore, should be subject to further studies.
Estimation of crowding coefficients
The E. coli intracellular density is C = 0.34 g/ml . The specific volume was estimated for several proteins using the molar volumes and masses reported in Ref. , resulting in average of 0.73 ml/g and standard deviation of 0.02 ml/g. The enzymes' turnover rates were obtained from the BRENDA database  for 102 E. coli enzymes.
Metabolic flux predictions
The Flux Balance analysis with Molecular Crowding  is implemented by solving the following optimization problem: maximize the biomass production rate subject to the constraints: balance in the production and consumption of each metabolite (flux balance), the maximum capacity constraint for the carbon source uptake rate and the solvent capacity constraint (Eq. 3). After expressing the reaction's stoichiometric coefficients in units of mol/dry biomass, the maximum growth rate corresponds to the biomass production rate, where biomass production is an auxiliary reaction containing as substrates the cellular components in their relative concentrations and as product the cell's biomass. The crowding coefficients were modeled as noise, assigning them randomly selected values from a list of estimated values for about hundred E. coli enzymes (Additional file 1). The predictions for all fluxes are provided in Additional file 5.
Bacterial strain and general growth conditions
The E. coli K12 strain MG1655 (F- λ- ilv G rfb 50 rph 1) was used throughout the work. In order to obtain biomass samples for flux measurements, 20-ml of the overnight grown culture (~8–10 h) of wild-type cells in LB-medium was inoculated in 980-ml M9 minimal medium (Sigma) containing 2 g/L glucose, where 90% was natural glucose and the remaining 10% was labeled glucose [1,2-13C2]-glucose (with >99% purity and 99% isotope enrichment for each position, [Cambridge Isotope Laboratories, Andover, MA]). Cells were grown in a continuous growth mode at 5 different dilution rates (0.1, 0.25, 0.4, 0.55, and 0.72 L h-1) in a Labfors bioreactor (Infors, Switzerland). The growth of the bacterial culture was regularly monitored at A600nm to document steady state, as described in Additional file 4 (SI text 3 and SI Fig. S7)
Metabolic enzyme activity assays
For determining metabolic enzyme activities from three separate experiments, the cell pellets (collected at all five dilution rates) were first resuspended and washed in 100 mM Tris-HCl (pH 7.0) sonication buffer containing 20 mM KCl, 5 mM MnSO4, 2 mM DTT and 0.1 mM EDTA, and then disrupted in a sonicator by 3 sonication cycles of 30 sec each. The cell debris was removed by centrifugation and the resulting cell extract (supernatant) was immediately used for enzyme assays or stored at -20°C. All subsequent steps were carried out on ice. Supernatants of the samples were used for quantitative assaying of endogenous enzyme activities by continuous spectrophotometric rate determination at 30°C in a thermostatically controlled spectrophotometer (Cary 500) with 1-cm light path, as described in the Additional file 4 (SI text 3). Supernatants of the samples were also used for determining total protein concentrations using standard Bradford's assay (BioRad).
Flux measurements and analyses
For flux analysis, biomass (from ~100 ml culture) and supernatant samples were collected at all five dilution rates. These samples were immediately flash frozen in liquid nitrogen and stored at -80°C until further analysis. Flux rates were determined using a tracer-substrate based GC-MS and NMR metabolome mapping platform. The analyses included determining positional 13C tracer enrichment in multiple intermediary metabolites of glycolysis, glycogen synthesis, tricarboxylic acid cycle and their intracellular products from [1,2-13C2]-D-glucose, as described in detail in Additional file 4 (SI text 4). The retention times and mass-to-charge (m/z) ion clusters of selected ions of bacterial and culture media metabolites were determined using mass isotopomer analysis (MIDA) [27, 28], and expressed as net fluxes by subtracting reverse fluxes from forward tracer incorporation patterns via reversible metabolic steps . Results were expressed as mmol/hr/g dry biomass glucose. Each experiment was carried out using triplicate cell cultures for each condition within each experiment, and the experiments were repeated once. Mass spectroscopic analyses were carried out by three independent automatic injections of 1 μl samples by the automatic sampler and accepted only if the standard sample deviation was less than 1% of the normalized peak intensity. Statistical analysis was performed using the Student's t-test for unpaired samples. Two-tailed significance at the 99% confidence interval (μ +/- 2.58σ), p < 0.01 indicated significant differences in glucose-derived fluxes. For some reversible reactions, we measured both forward and reverse fluxes and calculated net fluxes towards product synthesis.
RNA preparation for microarray analysis
At all five dilution rates, 10–20 ml of the cell culture was collected, mixed with 10% (v/v) of ice cold stop-solution (5% water-saturated phenol in absolute ethanol), and cell pellets were obtained by centrifugation at 4,500 × g for 5 min at 4°C, followed by flash-freezing of pellets with liquid nitrogen. Cell pellets were stored at -80°C until further use. RNA was isolated from the frozen cell pellets using Masterpure RNA isolation kit (Epicentre Biotechnologies, Madison, WI) and RNA samples were processed for transcriptome analysis using E. coli Affymetrix microarray chips, as described previously . Dchip method was used to analyze all microarray data as described in Additional file 4 (SI text 5–8).
We thank N. Gerry and M. Lenburg at Boston University for their help with microarray experiments and A. Kochegarov at SiDMAP, LLC for metabolic flux measurements. Research at the University of Notre Dame and at the University of Pittsburgh was supported by National Institute of Health Grant # U01 AI070499.
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