Absolute Metabolite Concentrations and Implied Enzyme Active Site Occupancy in Escherichia coli

By Bryson D. Bennett, Elizabeth H Kimball, Melissa Gao, R. Osterhout, S. Van Dien, Joshua D. Rabinowitz. Nature Chemical Biology, 2009

1,798 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8c27837e8e46525ae82d08236f19df61f455a3de

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Diffusion, Crowding & Protein Stability in a Dynamic Molecular Model of the Bacterial Cytoplasm (2010, background): “coli have since been reported [40,70,71], and, since this work was completed, quantitative estimates of metabolite concentrations have also become available [72].”
Advances and Trends in Omics Technology Development (2022, background): “Protein-metabolite interactions are prevalent in cells which were estimated to be at the scale of millions (240–242).”
Transformation by the R Enantiomer of 2-Hydroxyglutarate Linked to EglN Activation (2012, background): “Studies in model organisms suggest that many metabolic enzymes are saturated in vivo with a mixture of substrate and competitive inhibitors and thus are sensitive to changes in substrate concentrations that are far above their nominal K m value”
Innovation: Metabolomics: the apogee of the omics trilogy (2012, methodology): “In particular, advances have been made in using triple quadrupole (QqQ) mass spectrometry to perform selected reaction monitoring experiments such that routine methods are now available for analysing most of the metabolites in central carbon metabolism, as well as amino acids and nucleotides at th”
Itaconate Links Inhibition of Succinate Dehydrogenase with Macrophage Metabolic Remodeling and Regulation of Inflammation (2016, methodology): “Notably, itaconate dose-dependently blocked the activity of Sdh when succinate was used near physiological concentration 1 mM (Bennett et al., 2009) (Figure S2B), and itaconate treatment of resting BMDM led to increased succinate levels (Figure 2D).”
Pathway Thermodynamics Highlights Kinetic Obstacles in Central Metabolism (2014, methodology): “1 [45,50], [NADPH]/[NADP] = 10 [45, 51], [Ferredoxinreduced]/[Ferredoxinoxidized] = 1 (corresponds to a reduction potential of 2400 mV [52]), [orthophosphate] = 10 mM [53,54,55], [pyrophosphate] = 1 mM [56,57], [CoA] = 1 mM [45], [CO2(aq)] = 10 mM (ambient conditions).”
A Whole-Cell Computational Model Predicts Phenotype from Genotype (2012, neutral): “(F) Ratios of observed (Sundararaj et al., 2004; Bennett et al., 2009) and average predicted concentrations of 39 metabolites.”
Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction (2013, neutral): “While enzymespecific trends in catalytic rates depend on the limiting nutrient (Bennett et al, 2009; Boer et al, 2010), our formulation is a first step toward modeling genome-scale effects of nutrient limitation and suggests that simple principles may underlie these trends.”
Energy conservation via electron bifurcating ferredoxin reduction and proton/Na(+) translocating ferredoxin oxidation. (2013, background): “Ferredoxin (Fd, clostridial type) [4,5] Fd+e−=Fd− Fd−+e−=Fd2− NAD [6] NAD+2 e−+H+=NADH NADP [6] NADP+2 e−+H+=NADPH”
Influence of Metabolism on Epigenetics and Disease (2013, neutral): “In model systems, many metabolic enzymes display Km values in vivo that are higher than their corresponding in vitro values and certainly closer to the concentrations of their cosubstrates (Bennett et al., 2009; Yuan et al., 2009).”
Computational protein design enables a novel one-carbon assimilation pathway (2015, background): “coli cytoplasmic conditions, the ATP-dependent formation of formyl-CoA can have a ΔrG′° as low as −4 kcal/mol (12, 13).”
eQuilibrator—the biochemical thermodynamics calculator (2011, background): “1 mM to 100mM, spanning about six orders of magnitude (15).”