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A Kinetic Platform to Determine the Fate of Hydrogen Peroxide in Escherichia coli.

Adolfsen KJ, Brynildsen MP - PLoS Comput. Biol. (2015)

Bottom Line: After using an iterative computational and experimental procedure to train the model, we leveraged it to predict how H2O2 detoxification would change in response to an environmental perturbation that pathogens encounter within host phagosomes, carbon source deprivation, which leads to translational inhibition and limited availability of NADH.We found that the model accurately predicted that NADH depletion would delay clearance at low H2O2 concentrations and that detoxification at higher concentrations would resemble that of carbon-replete conditions.These results suggest that protein synthesis during bolus H2O2 stress does not affect clearance dynamics and that access to catabolites only matters at low H2O2 concentrations.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America.

ABSTRACT
Hydrogen peroxide (H2O2) is used by phagocytic cells of the innate immune response to kill engulfed bacteria. H2O2 diffuses freely into bacteria, where it can wreak havoc on sensitive biomolecules if it is not rapidly detoxified. Accordingly, bacteria have evolved numerous systems to defend themselves against H2O2, and the importance of these systems to pathogenesis has been substantiated by the many bacteria that require them to establish or sustain infections. The kinetic competition for H2O2 within bacteria is complex, which suggests that quantitative models will improve interpretation and prediction of network behavior. To date, such models have been of limited scope, and this inspired us to construct a quantitative, systems-level model of H2O2 detoxification in Escherichia coli that includes detoxification enzymes, H2O2-dependent transcriptional regulation, enzyme degradation, the Fenton reaction and damage caused by •OH, oxidation of biomolecules by H2O2, and repair processes. After using an iterative computational and experimental procedure to train the model, we leveraged it to predict how H2O2 detoxification would change in response to an environmental perturbation that pathogens encounter within host phagosomes, carbon source deprivation, which leads to translational inhibition and limited availability of NADH. We found that the model accurately predicted that NADH depletion would delay clearance at low H2O2 concentrations and that detoxification at higher concentrations would resemble that of carbon-replete conditions. These results suggest that protein synthesis during bolus H2O2 stress does not affect clearance dynamics and that access to catabolites only matters at low H2O2 concentrations. We anticipate that this model will serve as a computational tool for the quantitative exploration and dissection of oxidative stress in bacteria, and that the model and methods used to develop it will provide important templates for the generation of comparable models for other bacterial species.

No MeSH data available.


Related in: MedlinePlus

Systematic approach to construct a kinetic model of H2O2 metabolism.Uncertain parameters in each of the ten model structures are optimized on wild-type clearance data of 10, 25, 100, and 400 μM H2O2, starting from 1,000 random initial parameter sets. Any models within an evidence ratio of 10 (ER≤10) are used to calculate cumulative H2O2 clearance by the different detoxification pathways. If the calculated H2O2 distributions between the models are inconsistent, simulations are used to suggest experiments that differentiate between the disagreeing models, experiments are executed, and the optimization is performed on all experimental data for the model structures that had at least one parameter set with an ER≤10. Once consistent H2O2 distributions are realized, we identify an ensemble of parameter sets that can all describe the data comparably well with an MCMC procedure. We then assess whether H2O2 distributions are consistent across the entire ensemble. If the calculated H2O2 distributions are consistent, we undertake forward predictions.
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pcbi.1004562.g002: Systematic approach to construct a kinetic model of H2O2 metabolism.Uncertain parameters in each of the ten model structures are optimized on wild-type clearance data of 10, 25, 100, and 400 μM H2O2, starting from 1,000 random initial parameter sets. Any models within an evidence ratio of 10 (ER≤10) are used to calculate cumulative H2O2 clearance by the different detoxification pathways. If the calculated H2O2 distributions between the models are inconsistent, simulations are used to suggest experiments that differentiate between the disagreeing models, experiments are executed, and the optimization is performed on all experimental data for the model structures that had at least one parameter set with an ER≤10. Once consistent H2O2 distributions are realized, we identify an ensemble of parameter sets that can all describe the data comparably well with an MCMC procedure. We then assess whether H2O2 distributions are consistent across the entire ensemble. If the calculated H2O2 distributions are consistent, we undertake forward predictions.

Mentions: Here, we have generated a kinetic model of H2O2 stress in E. coli whose components are depicted in Fig 1. The biochemical reaction network is compartmentalized into media and intracellular spaces, includes spontaneous and enzymatic detoxification of H2O2, transcriptional regulation and inactivation of detoxification enzymes, and reactions of H2O2 and its degradation intermediates (e.g., •OH) with biomolecules (e.g., pyruvate, glutathione, all twenty amino acids). Parameters were informed from literature or trained using an iterative and integrated computational and experimental approach (Fig 2). The design criteria we chose to use to develop the model stipulated that consistent discrimination between clearance contributions by the major detoxification systems (AHP, HPI, and HPII) needed to be achieved. Once the design criteria were met, remaining parametric uncertainty was accounted for with use of a Markov chain Monte Carlo (MCMC) procedure to explore the viable parameter space and assemble an ensemble of models that performed comparably well with the training data [26]. The ensemble was then used to quantitatively investigate the importance of carbon availability and translation to H2O2 detoxification, and its predictions were experimentally confirmed.


A Kinetic Platform to Determine the Fate of Hydrogen Peroxide in Escherichia coli.

Adolfsen KJ, Brynildsen MP - PLoS Comput. Biol. (2015)

Systematic approach to construct a kinetic model of H2O2 metabolism.Uncertain parameters in each of the ten model structures are optimized on wild-type clearance data of 10, 25, 100, and 400 μM H2O2, starting from 1,000 random initial parameter sets. Any models within an evidence ratio of 10 (ER≤10) are used to calculate cumulative H2O2 clearance by the different detoxification pathways. If the calculated H2O2 distributions between the models are inconsistent, simulations are used to suggest experiments that differentiate between the disagreeing models, experiments are executed, and the optimization is performed on all experimental data for the model structures that had at least one parameter set with an ER≤10. Once consistent H2O2 distributions are realized, we identify an ensemble of parameter sets that can all describe the data comparably well with an MCMC procedure. We then assess whether H2O2 distributions are consistent across the entire ensemble. If the calculated H2O2 distributions are consistent, we undertake forward predictions.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4636272&req=5

pcbi.1004562.g002: Systematic approach to construct a kinetic model of H2O2 metabolism.Uncertain parameters in each of the ten model structures are optimized on wild-type clearance data of 10, 25, 100, and 400 μM H2O2, starting from 1,000 random initial parameter sets. Any models within an evidence ratio of 10 (ER≤10) are used to calculate cumulative H2O2 clearance by the different detoxification pathways. If the calculated H2O2 distributions between the models are inconsistent, simulations are used to suggest experiments that differentiate between the disagreeing models, experiments are executed, and the optimization is performed on all experimental data for the model structures that had at least one parameter set with an ER≤10. Once consistent H2O2 distributions are realized, we identify an ensemble of parameter sets that can all describe the data comparably well with an MCMC procedure. We then assess whether H2O2 distributions are consistent across the entire ensemble. If the calculated H2O2 distributions are consistent, we undertake forward predictions.
Mentions: Here, we have generated a kinetic model of H2O2 stress in E. coli whose components are depicted in Fig 1. The biochemical reaction network is compartmentalized into media and intracellular spaces, includes spontaneous and enzymatic detoxification of H2O2, transcriptional regulation and inactivation of detoxification enzymes, and reactions of H2O2 and its degradation intermediates (e.g., •OH) with biomolecules (e.g., pyruvate, glutathione, all twenty amino acids). Parameters were informed from literature or trained using an iterative and integrated computational and experimental approach (Fig 2). The design criteria we chose to use to develop the model stipulated that consistent discrimination between clearance contributions by the major detoxification systems (AHP, HPI, and HPII) needed to be achieved. Once the design criteria were met, remaining parametric uncertainty was accounted for with use of a Markov chain Monte Carlo (MCMC) procedure to explore the viable parameter space and assemble an ensemble of models that performed comparably well with the training data [26]. The ensemble was then used to quantitatively investigate the importance of carbon availability and translation to H2O2 detoxification, and its predictions were experimentally confirmed.

Bottom Line: After using an iterative computational and experimental procedure to train the model, we leveraged it to predict how H2O2 detoxification would change in response to an environmental perturbation that pathogens encounter within host phagosomes, carbon source deprivation, which leads to translational inhibition and limited availability of NADH.We found that the model accurately predicted that NADH depletion would delay clearance at low H2O2 concentrations and that detoxification at higher concentrations would resemble that of carbon-replete conditions.These results suggest that protein synthesis during bolus H2O2 stress does not affect clearance dynamics and that access to catabolites only matters at low H2O2 concentrations.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America.

ABSTRACT
Hydrogen peroxide (H2O2) is used by phagocytic cells of the innate immune response to kill engulfed bacteria. H2O2 diffuses freely into bacteria, where it can wreak havoc on sensitive biomolecules if it is not rapidly detoxified. Accordingly, bacteria have evolved numerous systems to defend themselves against H2O2, and the importance of these systems to pathogenesis has been substantiated by the many bacteria that require them to establish or sustain infections. The kinetic competition for H2O2 within bacteria is complex, which suggests that quantitative models will improve interpretation and prediction of network behavior. To date, such models have been of limited scope, and this inspired us to construct a quantitative, systems-level model of H2O2 detoxification in Escherichia coli that includes detoxification enzymes, H2O2-dependent transcriptional regulation, enzyme degradation, the Fenton reaction and damage caused by •OH, oxidation of biomolecules by H2O2, and repair processes. After using an iterative computational and experimental procedure to train the model, we leveraged it to predict how H2O2 detoxification would change in response to an environmental perturbation that pathogens encounter within host phagosomes, carbon source deprivation, which leads to translational inhibition and limited availability of NADH. We found that the model accurately predicted that NADH depletion would delay clearance at low H2O2 concentrations and that detoxification at higher concentrations would resemble that of carbon-replete conditions. These results suggest that protein synthesis during bolus H2O2 stress does not affect clearance dynamics and that access to catabolites only matters at low H2O2 concentrations. We anticipate that this model will serve as a computational tool for the quantitative exploration and dissection of oxidative stress in bacteria, and that the model and methods used to develop it will provide important templates for the generation of comparable models for other bacterial species.

No MeSH data available.


Related in: MedlinePlus