(598d) A Kinetic Platform to Determine the Fate of Hydrogen Peroxide in Escherichia coli

Hydrogen peroxide (H₂O₂) is a toxic molecule utilized by the immune system in the first line of defense against infections. After being engulfed by phagocytic cells of the innate immune response, pathogens are exposed to reactive oxygen species (ROS) including superoxide (O₂⁻•) and H₂O₂.  O₂⁻• cannot cross cell membranes, but H₂O₂ freely diffuses into the bacterium, where it can damage sensitive biomolecules, react with ferrous iron to form highly reactive hydroxyl radicals (•OH), or be detoxified by specialized enzymes. Accordingly, H₂O₂ detoxification is an important aspect of bacterial virulence, and deletion of one or more detoxification systems affects the ability of a variety of pathogens to establish or sustain an infection. While the importance of these H₂O₂ detoxification systems is known, a quantitative understanding of how they interact under different levels of oxidative stress is lacking. For instance, Escherichia coli utilizes an alkyl hydroperoxidase (AHP) and two separate catalases (HPI and HPII) to clear H₂O₂. AHP requires one NADH molecule per reaction cycle, affecting the maximum rate of clearance that can be achieved, whereas H₂O₂ is the only substrate required by HPI and HPII. The simultaneous activity of multiple detoxification systems and broad reactivity of H₂O₂ necessitates the use of quantitative kinetic modeling to understand how H₂O₂ distributes throughout its reaction network.

Thus, we have constructed an H₂O₂ detoxification network for E. coli consisting of 60 metabolites and enzymes, as well as 75 rate equations describing spontaneous and enzymatic reactions, transcriptional regulation, and degradation of key enzymes. Structural and parametric uncertainty with regard to enzyme degradation and the existence of an H₂O₂ gradient across the cell membrane led us to consider ten distinct structures while building the model. Using a strategic method of iteratively gathering data, optimizing uncertain parameters, and assessing comparative model performance, we found that only one model structure was able to consistently predict how H₂O₂ distributes amongst the different pathways. This observation suggested that bimolecular degradation of detoxifying enzymes is an important aspect of the H₂O₂ clearance network, and that under our conditions diffusion of H₂O₂ across the membrane was not limiting.  Using a Monte Carlo method to explore the viable parameter space, we identified an ensemble of parameter sets that could all fit the data, and used this model ensemble to gain a better understanding of the importance of protein synthesis and reducing equivalents under oxidative stress. We have developed a platform to quantitatively explore H₂O₂ stress in E. coli which enhances understanding of the impact of mutations and environmental alterations on detoxification, and our method provides a template for translation to other organisms.