Impact of the solvent capacity constraint on E. coli metabolism

BMC Systems Biology Impact of the solvent capacity constraint on E. coli metabolism Alexei Vazquez 2 Qasim K Beg 1 3 Marcio A deMenezes 0 Jason Ernst 6 Ziv Bar-Joseph 6 Albert-Lszl Barabsi 5 Lszl G Boros 4 Zoltn N Oltvai 1 0 Instituto de Fisica, Universidade Federal Fluminense , Rio de Janeiro, 24210 , Brazil 1 Department of Pathology, University of Pittsburgh , Pittsburgh, PA, 15261 , USA 2 The Simons Center for Systems Biology, Institute for Advanced Study , Princeton, NJ 08540 , USA 3 Department of Biomedical Engineering, Boston University , Boston, MA 02215 , USA 4 SiDMAP, LLC and the UCLA School of Medicine , Los Angeles, CA 90064 , USA 5 Department of Physics and Center for Complex Networks Research, University of Notre Dame , South Bend, IN 46556 , USA 6 Machine Learning Department, Carnegie-Mellon University , Pittsburgh, PA, 15217 , USA Background: 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. Results: 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. Conclusion: 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. - Background 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 [1]. 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 [9]. 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 [12]. 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 [12]. On the other hand, the pattern of substrate consumption can also be reproduced by superimposing regulatory information obtained e.g., from microarray data [13]. 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. Results Limited solvent capacity constrains the metabolic rate of fast growing E. coli cells The cell's cytoplasm is characterized by a high concentration of macromolecules [14] resulting in a limited solvent capacity for the allocation of metabolic enzymes. More precisely, given that the enzyme molecules have a finite molar volume vi only a finite number of them fit in a given cell volume V. Indeed, if ni is the number of moles of the ith enzyme, then where the inequality sign accounts for the volume of other cell components and the free volume necessary for cellular transport as well. Dividing by cell mass M we can reformulate this inequality in terms of the enzyme concentrations Ei = ni/M (moles/unit mass), resulting in where C = M/V is the cytoplasmic density. An enzyme concentration Ei results in a flux fi = biEi over reaction i, where the parameter bi is determined by the reaction mechanism, kinetic parameters, and metabolite concentrations. Therefore, the enzyme concentration constraint (Eq. 2) is reflected in the metabolic flux constraint ai = Since the coefficients ai (units of inverse flux) quantifies the contribution to the overall crowding by reaction i we refer to them as the 'crowding coefficients'. To understand the relevance of the constraint (Eq. 3) at physiological growth conditions we first estimate the crowding coefficients (Eq. 4) using data from experimental reports. The E. coli c (...truncated)