Charges of nicotinamide adenine nucleotides and adenylate energy charge as regulatory parameters of the metabolism in Escherichia coli.

Andersen, Klaus B.; Meyenburg, Kaspar von

doi:10.1016/s0021-9258(17)40245-6

Cited by 156 publications

(31 citation statements)

References 23 publications

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“…We also examined the relative changes in intracellular nicotinamide adenine dinucleotides under adenine or uracil supplementation (Figure S5C) and observed a modest decrease in the NADPH/NADP + ratio, but not the NADH/NAD + ratio, following exogenous adenine addition (Figure 6E). Together, these results support the model predictions that adenine supplementation decreases central carbon metabolism activity (decreased adenylate energy charge) and cell anabolism (decreased NADPH/NADP + ratio) without significantly changing cell catabolism (unchanged NADH/NAD + ratio) (Figure S5D) (Andersen and von Meyenburg, 1977;Chapman and Atkinson, 1977).…”

Section: Adenine Supplementation Reduces Atp Demand and Central Carbon Metabolism Activitysupporting

confidence: 83%

“…Adenine nucleotides are important mediators of cellular homeostasis (Andersen and von Meyenburg, 1977;Chapman and Atkinson, 1977), universally coupling cellular metabolism, DNA/RNA replication, and other physiological processes. In the context of infection, adenylate metabolites such as ATP, ADP and adenosine are important components of the damage-associated molecular patterns used by the host to activate the immune system (Cekic and Linden, 2016).…”

Section: Discussionmentioning

confidence: 99%

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Faculty Opinions recommendation of A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action.

Nielsen

2019

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature

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Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here we develop an integrated "white-box" biochemical screening, network modeling and machine learning approach for revealing causal mechanisms and apply this approach towards understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that

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Section: Adenine Supplementation Reduces Atp Demand and Central Carbon Metabolism Activitysupporting

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Faculty Opinions recommendation of A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action.

Nielsen

2019

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature

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“…With respect to overall productivity of F 420 production (expressed as µmol F 420 /L/h), glycerol was the most productive carbon source (Fig. 2 B); F 420 yield with pyruvate was 0.90 µmol/g DCW which is close to the yield of the cofactor NADPH in E. coli of 1.3 µmol/g DCW 34 (Fig. 2 A).…”

Section: Resultsmentioning

confidence: 76%

Improved production of the non-native cofactor F420 in Escherichia coli

Shah

Nazem‐Bokaee

Antoney

et al. 2021

Sci Rep

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The deazaflavin cofactor F420 is a low-potential, two-electron redox cofactor produced by some Archaea and Eubacteria that is involved in methanogenesis and methanotrophy, antibiotic biosynthesis, and xenobiotic metabolism. However, it is not produced by bacterial strains commonly used for industrial biocatalysis or recombinant protein production, such as Escherichia coli, limiting our ability to exploit it as an enzymatic cofactor and produce it in high yield. Here we have utilized a genome-scale metabolic model of E. coli and constraint-based metabolic modelling of cofactor F420 biosynthesis to optimize F420 production in E. coli. This analysis identified phospho-enol pyruvate (PEP) as a limiting precursor for F420 biosynthesis, explaining carbon source-dependent differences in productivity. PEP availability was improved by using gluconeogenic carbon sources and overexpression of PEP synthase. By improving PEP availability, we were able to achieve a ~ 40-fold increase in the space–time yield of F420 compared with the widely used recombinant Mycobacterium smegmatis expression system. This study establishes E. coli as an industrial F420-production system and will allow the recombinant in vivo use of F420-dependent enzymes for biocatalysis and protein engineering applications.

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“…Note that comparison of the [NADH] curve with data on the [NADH]/[NAD + ] ratio requires knowledge of [NAD + ]. Luckily, [NAD + ] has been shown experimentally to be roughly constant across the growth rates studied here (4 mM is used here [Vemuri et al, 2006;Andersen and Meyenburg, 1977]). This minimalist calculation in Equation 5 indicates that the rise in NADH levels is likely a direct consequence of surface limitation, which forces the onset of fermentation to avoid NADH imbalance that would deplete essential [NAD + ] (Vemuri et al, 2006;Imai, 2016).…”

Section: Predictions Of the Membrane Real Estate Hypothesis The [Nadh]/[nad + ] Ratiomentioning

confidence: 98%

Why Do Fast-Growing Bacteria Enter Overflow Metabolism? Testing the Membrane Real Estate Hypothesis

2017

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Bacteria and other cells show a puzzling behavior. At high growth rates, E. coli switch from respiration (which is ATP-efficient) to using fermentation for additional ATP (which is inefficient). This overflow metabolism results in a several-fold decrease in ATP produced per glucose molecule provided as food. By integrating diverse types of experimental data into a simple biophysical model, we give evidence that this onset is the result of the membrane real estate hypothesis: Fast growth drives cells to be bigger, reducing their surface-to-volume ratios. This decreases the membrane area available for respiratory proteins despite growing demand, causing increased crowding. Only when respiratory proteins reach their crowding limit does the cell activate fermentation, since fermentation allows faster ATP production per unit membrane area. Surface limitation thus creates a Pareto trade-off between membrane efficiency and ATP yield that links metabolic choice to the size and shape of a bacterial cell. By exploring the predictions that emerge from this trade-off, we show how consideration of molecular structures, energetics, rates, and equilibria can provide important insight into cellular behavior.

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Charges of nicotinamide adenine nucleotides and adenylate energy charge as regulatory parameters of the metabolism in Escherichia coli.

Cited by 156 publications

References 23 publications

Faculty Opinions recommendation of A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action.

Faculty Opinions recommendation of A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action.

Improved production of the non-native cofactor F420 in Escherichia coli

Why Do Fast-Growing Bacteria Enter Overflow Metabolism? Testing the Membrane Real Estate Hypothesis

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