Physiological, biomass elemental composition and proteomic analyses of Escherichia coli ammonium-limited chemostat growth, and comparison with iron- and glucose-limited chemostat growth

Folsom, James P.; Carlson, Richard A.

doi:10.1099/mic.0.000118

Cited by 46 publications

(57 citation statements)

References 59 publications

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“…Lactococcus lactis shows a similar metabolic shift from mixed-acid fermentation (3 ATP per glucose) to lactic acid fermentation (2 ATP per glucose) under anaerobic conditions [4]. Besides overflow metabolism that starts at high growth rates, Escherichia coli even produces overflow products at low growth rates when growing in ammonium-limited conditions [5].…”

Section: Introductionmentioning

confidence: 98%

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The common message of constraint-based optimization approaches: overflow metabolism is caused by two growth-limiting constraints

Groot

Lischke

Muolo

et al. 2019

Cell. Mol. Life Sci.

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Living cells can express different metabolic pathways that support growth. The criteria that determine which pathways are selected in which environment remain unclear. One recurrent selection is overflow metabolism: the simultaneous usage of an ATP-efficient and-inefficient pathway, shown for example in Escherichia coli, Saccharomyces cerevisiae and cancer cells. Many models, based on different assumptions, can reproduce this observation. Therefore, they provide no conclusive evidence which mechanism is causing overflow metabolism. We compare the mathematical structure of these models. Although ranging from flux balance analyses to self-fabricating metabolism and expression models, we can rewrite all models into one standard form. We conclude that all models predict overflow metabolism when two, model-specific, growth-limiting constraints are hit. This is consistent with recent theory. Thus, identifying these two constraints is essential for understanding overflow metabolism. We list all imposed constraints by these models, so that they can hopefully be tested in future experiments.

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Section: Introductionmentioning

confidence: 98%

“…The usage of x here is not related to its usage in our standard form, Eq. (1)5 In some modeling methods this density is modeled as an upper bound[32,33,37]. In SI7 we explain the advantages and disadvantages of doing this.…”

mentioning

confidence: 99%

The common message of constraint-based optimization approaches: overflow metabolism is caused by two growth-limiting constraints

Groot

Lischke

Muolo

et al. 2019

Cell. Mol. Life Sci.

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“…TFA required a higher glucose uptake rate than the experimental one (S1 Appendix), which provoked a difference between predicted and experimental growth rate (which is equal to the dilution rate in a continuous culture). Since the biomass elemental composition does not significantly vary due to changes in the dilution rate (44), biomass reactions remained unchanged in the model (45), and the energetic requirements were assumed to be constant for both bacteria (S1 Appendix). Using the default constraints from the metabolic networks also allowed comparing the results with previously published ones.…”

Section: Methodsmentioning

confidence: 99%

Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

Tomi-Andrino

Norman

Millat

et al. 2020

Preprint

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24Metabolic engineering in the post-genomic era is characterised by the development of new 25 methods for metabolomics and fluxomics, supported by the integration of genetic engineering 26 tools and mathematical modelling. Particularly, constraint-based stoichiometric models have 27 been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis 28 (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and 29 metabolomics data to improve the predictive capabilities of these approaches. However, an 30 in-depth comparison and evaluation of these methods is lacking. This study presents a thorough 31 analysis of four different in silico methods tested against experimental data (metabolomics and 32 13 C-MFA) for the mesophile Escherichia coli and the thermophile Thermus thermophilus. In 33 particular, a modified version of the recently published matTFA toolbox has been created, 34 providing a broader range of physicochemical parameters. In addition, a max-min driving force 35 approach (as implemented in eQuilibrator) was also performed in order to compare the 36 predictive capabilities of both methods. 37Validating against experimental data allowed the determination of the best 38 physicochemical parameters to perform the TFA for E. coli, whereas the lack of metabolomics 39 data for T. thermophilus prevented from a full analysis. Results showed that analytical 40 conditions predicting reliable flux distributions (similar to the in vivo fluxes) do not necessarily 41 provide a good depiction of the experimental metabolomics landscape, and that the original 42 matTFA toolbox can be improved. An analysis of flux pattern changes in the central carbon 43 metabolism between 13 C-MFA and TFA highlighted the limited capabilities of both approaches 44 for elucidating the anaplerotic fluxes. Finally, this study highlights the need for standardisation 45 in the fluxomics community: novel approaches are frequently released but a thorough 46 comparison with currently accepted methods is not always performed. 47 Keywords 48 Constraint-based modelling, fluxomics, metabolomics, thermodynamics. Predictive capabilities of thermodynamics-based stoichiometric approaches 3 49 Author summary 50 Biotechnology has benefitted from the development of high throughput methods characterising 51 living systems at different levels (e.g. concerning genes or proteins), allowing the industrial 52 production of chemical commodities (such as ethylene). Recently, focus has been put on 53 determining reaction rates (or metabolic fluxes) in the metabolic network of certain 54 microorganisms, in order to identify bottlenecks hindering their exploitation. Two main 55 approaches can be highlighted, termed metabolic flux analysis (MFA) and flux balance analysis 56 (FBA), based on measuring and estimating fluxes, respectively. While the influence of 57 thermodynamics in living systems was accepted several decades ago, its application to study 58 biochemical networks has been only recently enabled. In t...

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“…To benchmark the predictive capabilities of the PAM, wildtype phenotypic behavior on a glucose minimal medium was simulated and compared to extensive literature data (Perrenoud and Sauer, 2005;Nanchen et al, 2006;Vemuri et al, 2006;Valgepea et al, 2010;Folsom et al, 2014;McCloskey et al, 2014;Peebo et al, 2015;Folsom and Carlson, 2015). The maximum glucose uptake rate, a parameter for the excess enzyme sector, was set to 8.9 mmol g −1 cdw h −1 which supported a maximally observed growth rate of 0.65 h −1 (Perrenoud and Sauer, 2005).…”

Section: Prediction Of E Coli Phenotypesmentioning

confidence: 99%

Protein allocation and enzymatic constraints explainEscherichia coliwildtype and mutant phenotypes

Alter

Blank

Ebert

2020

Preprint

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Proteins have generally been recognized to constitute the key cellular component in shaping microbial phenotypes. Due to limited cellular resources and space, optimal allocation of proteins is crucial for microbes to facilitate maximum proliferation rates while allowing a flexible response to environmental changes. Regulatory patterns of protein allocation were utilized to account for the condition-dependent proteome in a genome-scale metabolic reconstruction of Escherichia coli by linearly linking mass concentrations of protein sectors and single metabolic enzymes to flux variables. The resulting protein allocation model (PAM) correctly approximates wildtype phenotypes and flux distributions for various substrates, even under data scarcity. Moreover, we showed the ability of the PAM to predict metabolic responses of single gene deletion mutants by additionally assuming growth-limiting, transcriptional restrictions. Thus, we promote the integration of protein allocation constraints into classical constraint-based models to foster their predictive capabilities and application for strain analysis and metabolic engineering purposes. K E Y W O R D S constraint-based modeling, enzyme kinetics, protein allocation, transcriptional control, Escherichia coli 1

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Physiological, biomass elemental composition and proteomic analyses of Escherichia coli ammonium-limited chemostat growth, and comparison with iron- and glucose-limited chemostat growth

Cited by 46 publications

References 59 publications

The common message of constraint-based optimization approaches: overflow metabolism is caused by two growth-limiting constraints

The common message of constraint-based optimization approaches: overflow metabolism is caused by two growth-limiting constraints

Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

Protein allocation and enzymatic constraints explainEscherichia coliwildtype and mutant phenotypes

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