Absolute metabolite concentrations are critical to a quantitative understanding of cellular metabolism, as concentrations impact both the free energies and rates of metabolic reactions. Here we use liquid chromatography-tandem mass spectrometry to quantify more than 100 metabolite concentrations in aerobic, exponentially growing E. coli with glucose, glycerol, or acetate as the carbon source. The total observed intracellular metabolite pool is approximately 300 mM. A small number of metabolites dominate the metabolome on a molar basis, with glutamate most abundant. Metabolite concentration exceeds Km for most substrate-enzyme pairs. An exception is lower glycolysis, where concentrations of intermediates are near the Km of their consuming enzymes and all reactions are near equilibrium. This may facilitate efficient flux reversibility given thermodynamic and osmotic constraints. The data and analyses presented here highlight the ability to identify organizing metabolic principles from systems-level absolute metabolite concentration data.

A stoichiometric model of central metabolism was developed based on new information regarding metabolism in this bacterium to evaluate the steady-state growth capabilities of the serine cycle facultative methylotroph Methylobacterium extorquens AM1 during growth on methanol, succinate, and pyruvate. The model incorporates 20 reversible and 47 irreversible reactions, 65 intracellular metabolites, and experimentally-determined biomass composition. The flux space for this underdetermined system of equations was defined by finding the elementary modes, and constraints based on experimental observations were applied to determine which of these elementary modes give a reasonable description of the flux distribution for each growth substrate. The predicted biomass yield, on a carbon atom basis, is 49.8%, which agrees well with the range of published experimental yield measurements (37-50%). The model predicts the cell to be limited by reduced pyridine nucleotide availability during methylotrophic growth, but energy-limited when growing on multicarbon substrates. Mutation and phenotypic analysis was used to explore a previously unknown region of the metabolic map and to confirm the stoichiometry of the pathways in this region used in the metabolic model. Based on genome sequence data and simulation results, three enzymes involved in C(3)-C(4) interconversion pathways were predicted to be mutually redundant: malic enzyme, phosphoenolpyruvate carboxykinase, and phosphoenolpyruvate synthase. Insertion mutations in the genes predicted to encode these enzymes were made and these mutants were capable of growing on all substrates tested, confirming the redundancy of these pathways. Likewise, pathway analysis suggests that the TCA cycle enzymes citrate synthase and succinate dehydrogenase are essential for all growth substrates. In keeping with these predictions, null mutants could not be obtained in these genes. Finally, a similar model was developed for the ribulose monophosphate pathway obligate methylotroph Methylobacillus flagellatum KT to compare the efficiency of carbon utilization in the two types of methylotrophic carbon utilization pathways. The predicted yield for this organism on methanol is 65.9%.

Genome-scale analysis of predicted metabolic pathways has revealed the common occurrence of apparent redundancy for specific functional units, or metabolic modules. In many cases, mutation analysis does not resolve function, and instead, direct experimental analysis of metabolic flux under changing conditions is necessary. In order to use genome sequences to build models of cellular function, it is important to define function for such apparently redundant systems. Here we describe direct flux measurements to determine the role of redundancy in three modules involved in formaldehyde assimilation and dissimilation in a bacterium growing on methanol. A combination of deuterium and 14C labeling was used to measure the flux through each of the branches of metabolism for growth on methanol during transitions into and out of methylotrophy. The cells were found to differentially partition formaldehyde among the three modules depending on the flux of methanol into the cell. A dynamic mathematical model demonstrated that the kinetic constants of the enzymes involved are sufficient to account for this phenomenon. We demonstrate the role of redundancy in formaldehyde metabolism and have uncovered a new paradigm for coping with toxic, high-flux metabolic intermediates: a dynamic, interconnected metabolic loop.