Model-driven identification of dosing regimens that maximize the antimicrobial activity of nitric oxide

Loss of Gre factors leads to phenotypic heterogeneity and cheating in Escherichia coli populations under nitric oxide stress

Nitric oxide (·NO) is one of the toxic metabolites that bacteria can be exposed to within phagosomes. Gre factors, which are also known as transcript cleavage factors or transcription elongation factors, relieve back-tracked transcription elongation complexes by cleaving nascent RNAs, which allows transcription to resume after stalling. Here we discovered that loss of both Gre factors in Escherichia coli, GreA and GreB, significantly compromised ·NO detoxification due to ·NO-induced phenotypic heterogeneity in ΔgreAΔgreB populations, which did not occur in wild-type cultures. Under normal culturing conditions, both wild-type and ΔgreAΔgreB synthesized transcripts uniformly, whereas treatment with ·NO led to bimodal transcript levels in ΔgreAΔgreB that were unimodal in wild-type. Interestingly, exposure to another toxic metabolite of phagosomes, hydrogen peroxide (H2O2), produced analogous results. Furthermore, we showed that loss of Gre factors led to cheating under ·NO stress where transcriptionally deficient cells benefited from the detoxification activities of the transcriptionally proficient subpopulation. Collectively, these results show that loss of Gre factor activities produces phenotypic heterogeneity under ·NO and H2O2 stress that can yield cheating between subpopulations.IMPORTANCEToxic metabolite stress occurs in a broad range of contexts that are important to human health, microbial ecology, and biotechnology, whereas Gre factors are highly conserved throughout the bacterial kingdom. Here we discovered that loss of Gre factors in E. coli leads to phenotypic heterogeneity under ·NO and H2O2 stress, which we further show with ·NO results in cheating between subpopulations. Collectively, these data suggest that Gre factors play a role in coping with toxic metabolite stress, and that loss of Gre factors can produce cheating between neighbors.

Phagosome-Bacteria Interactions from the Bottom Up

When attempting to propagate infections, bacterial pathogens encounter phagocytes that encase them in vacuoles called phagosomes. Within phagosomes, bacteria are bombarded with a plethora of stresses that often lead to their demise. However, pathogens have evolved numerous strategies to counter those host defenses and facilitate survival. Given the importance of phagosome-bacteria interactions to infection outcomes, they represent a collection of targets that are of interest for next-generation antibacterials. To facilitate such therapies, different approaches can be employed to increase understanding of phagosome-bacteria interactions, and these can be classified broadly as top down (starting from intact systems and breaking down the importance of different parts) or bottom up (developing a knowledge base on simplified systems and progressively increasing complexity). Here we review knowledge of phagosomal compositions and bacterial survival tactics useful for bottom-up approaches, which are particularly relevant for the application of reaction engineering to quantify and predict the time evolution of biochemical species in these death-dealing vacuoles. Further, we highlight how understanding in this area can be built up through the combination of immunology, microbiology, and engineering.

Pseudomonas aeruginosa prioritizes detoxification of hydrogen peroxide over nitric oxide

OBJECTIVE

Bacteria are exposed to multiple concurrent antimicrobial stressors within phagosomes. Among the antimicrobials produced, hydrogen peroxide and nitric oxide are two of the most deleterious products. In a previous study, we discovered that when faced with both stressors simultaneously, Escherichia coli prioritized detoxification of hydrogen peroxide over nitric oxide. In this study, we investigated whether such a process was conserved in another bacterium, Pseudomonas aeruginosa.

RESULTS

P. aeruginosa prioritized hydrogen peroxide detoxification in a dose-dependent manner. Specifically, hydrogen peroxide detoxification was unperturbed by the presence of nitric oxide, whereas larger doses of hydrogen peroxide produced longer delays in nitric oxide detoxification. Computational modelling revealed that the rate of nitric oxide consumption in co-treated cultures was biphasic, with cells entering the second phase of detoxification only after hydrogen peroxide was eliminated from the culture.

Quantitative Modeling Extends the Antibacterial Activity of Nitric Oxide

Numerous materials have been developed to try and harness the antimicrobial properties of nitric oxide (NO). However, the short half-life and reactivity of NO have made precise, tunable delivery difficult. As such, conventional methodologies have generally relied on donors that spontaneously release NO at different rates, and delivery profiles have largely been constrained to decaying dynamics. In recent years, the possibility of finely controlling NO release, for instance with light, has become achievable and this raises the question of how delivery dynamics influence therapeutic potential. Here we investigated this relationship using Escherichia coli as a model organism and an approach that incorporated both experimentation and mathematical modeling. We found that the best performing delivery mode was dependent on the NO payload, and developed a mathematical model to quantitatively dissect those observations. Those analyses suggested that the duration of respiratory inhibition was a major determinant of NO-induced growth inhibition. Inspired by this, we constructed a delivery schedule that leveraged that insight to extend the antimicrobial activity of NO far beyond what was achievable by traditional delivery dynamics. Collectively, these data and analyses suggest that the delivery dynamics of NO have a considerable impact on its ability to achieve and maintain bacteriostasis.

A study of nitric oxide dynamics in a growing biofilm using a density dependent reaction-diffusion model

One of a number of critical roles played by NO· as a chemical weapon (generated by the immune system) is to neutralize pathogens. However, the virulence of pathogens depends on the production activity of reductants to detoxify NO·. Broad reactivity of NO· makes it complicated to predict the fate of NO· inside bacteria and its effects on the treatment of any infection. Here, we present a mathematical model of biofilm response to NO·, as a stressor. The model is comprised of a PDE system of highly nonlinear reaction-diffusion equations that we study in computer simulations to determine the positive and negative effects of key parameters on bacterial defenses against NO·. From the reported results, we conjecture that the oscillatory behavior of NO· under a microaerobic regime is a temporal phenomenon and does not give rise to a spatial pattern. It is also shown computationally that decreasing the initial size of the biofilm colony negatively impacts the functionality of reducing agents that deactivate NO·. Whereas nutrient deprivation results in the development of biofilms with heterogeneous structure, its effect on the activity of NO· reductants depends on the oxygen availability, biofilm size, and the amount of NO·.

Quantifying Nitric Oxide Flux Distributions

Nitric oxide (NO) is a radical that is used as an attack molecule by immune cells. NO can interact and damage a range of biomolecules, and the biological outcome for bacteria assaulted with NO will be governed by how the radical distributes within their biochemical reaction networks. Measurement of those NO fluxes is complicated by the low abundance and transience of many of its reaction products. To overcome this challenge, we use computational modeling to translate measurements of several biochemical species (e.g., NO, O₂, NO₂^-) into NO flux distributions. In this chapter, we provide a detailed protocol, which includes experimental measurements and computational modeling, to estimate the NO flux distribution in an Escherichia coli culture. Those fluxes will have uncertainty associated with them and we also discuss how further experiments and modeling can be employed for flux refinement.

Transcriptional Regulation Contributes to Prioritized Detoxification of Hydrogen Peroxide over Nitric Oxide

Hydrogen peroxide (H2O2) and nitric oxide (NO·) are toxic metabolites that immune cells use to attack pathogens. These antimicrobials can be present at the same time in phagosomes, and it remains unclear how bacteria deal with these insults when simultaneously present. Here, using Escherichia coli, we observed that simultaneous exposure to H2O2 and NO· leads to prioritized detoxification, where enzymatic removal of NO· is impeded until H2O2 has been eliminated. This phenomenon is reminiscent of carbon catabolite repression (CCR), where preferred carbon sources are catabolized prior to less desirable substrates; however, H2O2 and NO· are toxic, growth-inhibitory compounds rather than growth-promoting nutrients. To understand how NO· detoxification is delayed by H2O2 whereas H2O2 detoxification proceeds unimpeded, we confirmed that the effect depended on Hmp, which is the main NO· detoxification enzyme, and used an approach that integrated computational modeling and experimentation to delineate and test potential mechanisms. Plausible interactions included H2O2-dependent inhibition of hmp transcription and translation, direct inhibition of Hmp catalysis, and competition for reducing equivalents between Hmp and H2O2-degrading enzymes. Experiments illustrated that Hmp catalysis and NAD(P)H supply were not impaired by H2O2, whereas hmp transcription and translation were diminished. A dependence of this phenomenon on transcriptional regulation parallels CCR, and we found it to involve the transcriptional repressor NsrR. Collectively, these data suggest that bacterial regulation of growth inhibitor detoxification has similarities to the regulation of growth substrate consumption, which could have ramifications for infectious disease, bioremediation, and biocatalysis from inhibitor-containing feedstocks.IMPORTANCE Bacteria can be exposed to H2O2 and NO· concurrently within phagosomes. In such multistress situations, bacteria could have evolved to simultaneously degrade both toxic metabolites or preferentially detoxify one over the other. Here, we found that simultaneous exposure to H2O2 and NO· leads to prioritized detoxification, where detoxification of NO· is hampered until H2O2 has been eliminated. This phenomenon resembles CCR, where bacteria consume one substrate over others in carbon source mixtures. Further experimentation revealed a central role for transcriptional regulation in the prioritization of H2O2 over NO·, which is also important to CCR. This study suggests that regulatory scenarios observed in bacterial consumption of growth-promoting compound mixtures can be conserved in bacterial detoxification of toxic metabolite mixtures.

An integrated network analysis identifies how ArcAB enables metabolic oscillations in the nitric oxide detoxification network of Escherichia coli

The virulences of many pathogens depend on their abilities to detoxify the immune antimicrobial nitric oxide (NO•). The functions of bacterial NO• detoxification machinery depend on oxygen (O₂ ), with O₂ inhibiting some enzymes, whereas others use it as a substrate. Previously, Escherichia coli NO• detoxification was found to be highly attenuated under microaerobic conditions and metabolic oscillations were observed. The oscillations in [NO•] and [O₂ ] were found to result from the inhibitory action of NO• on aerobic respiration, the catalytic inactivation of NO• by Hmp (an NO• dioxygenase), and an imbalanced competition for O₂ between Hmp and cytochrome terminal oxidase activity. Here the authors investigated the role of the ArcAB two component system (TCS) in microaerobic NO• detoxification. The authors observed that wild-type, ΔarcA, and ΔarcB had comparable initial NO• clearance times; however, the mutant cultures failed to exhibit [NO•] and [O₂ ] oscillations. Using an approach that employed experimentation and computational modeling, the authors found that the loss of oscillations in ΔarcA was due to insufficient induction of cytochrome bd-I expression. Collectively, these results establish ArcAB as a TCS that influences NO• detoxification in E. coli within the physiologically-relevant microaerobic regime.

An integrated network analysis reveals that nitric oxide reductase prevents metabolic cycling of nitric oxide by Pseudomonas aeruginosa

Nitric oxide (NO) is a chemical weapon within the arsenal of immune cells, but is also generated endogenously by different bacteria. Pseudomonas aeruginosa are pathogens that contain an NO-generating nitrite (NO₂^-) reductase (NirS), and NO has been shown to influence their virulence. Interestingly, P. aeruginosa also contain NO dioxygenase (Fhp) and nitrate (NO₃^-) reductases, which together with NirS provide the potential for NO to be metabolically cycled (NO→NO₃^-→NO₂^-→NO). Deeper understanding of NO metabolism in P. aeruginosa will increase knowledge of its pathogenesis, and computational models have proven to be useful tools for the quantitative dissection of NO biochemical networks. Here we developed such a model for P. aeruginosa and confirmed its predictive accuracy with measurements of NO, O₂, NO₂^-, and NO₃^- in mutant cultures devoid of Fhp or NorCB (NO reductase) activity. Using the model, we assessed whether NO was metabolically cycled in aerobic P. aeruginosa cultures. Calculated fluxes indicated a bottleneck at NO₃^-, which was relieved upon O₂ depletion. As cell growth depleted dissolved O₂ levels, NO₃^- was converted to NO₂^- at near-stoichiometric levels, whereas NO₂^- consumption did not coincide with NO or NO₃^- accumulation. Assimilatory NO₂^- reductase (NirBD) or NorCB activity could have prevented NO cycling, and experiments with ΔnirB, ΔnirS, and ΔnorC showed that NorCB was responsible for loss of flux from the cycle. Collectively, this work provides a computational tool to analyze NO metabolism in P. aeruginosa, and establishes that P. aeruginosa use NorCB to prevent metabolic cycling of NO.

Persister formation in Escherichia coli can be inhibited by treatment with nitric oxide

Bacterial persisters are phenotypic variants that survive extraordinary concentrations of antibiotics, and are thought to underlie the propensity of biofilm infections to relapse. Unfortunately many aspects of persister physiology remain ill-defined, which prevents progress toward eradicating the phenotype. Recently, we identified respiration within non-growing Escherichia coli populations as a potential target for the elimination type I persisters, which are those that arise from passage through stationary phase. Here we discovered that nitric oxide (NO) treatment at the onset of stationary phase significantly reduced type I persister formation through its ability to inhibit respiration. NO decreased protein and RNA degradation in stationary phase cells, and produced populations that were more fit for protein synthesis and growth resumption upon introduction into fresh media than untreated controls. Overall, this data shows that NO, which is a therapeutically-relevant compound, has the potential to decrease the incidence of recurrent infections from persisters.

Discovery and dissection of metabolic oscillations in the microaerobic nitric oxide response network of Escherichia coli

The virulence of many pathogens depends upon their ability to cope with immune-generated nitric oxide (NO·). In Escherichia coli, the major NO· detoxification systems are Hmp, an NO· dioxygenase (NOD), and NorV, an NO· reductase (NOR). It is well established that Hmp is the dominant system under aerobic conditions, whereas NorV dominates anaerobic conditions; however, the quantitative contributions of these systems under the physiologically relevant microaerobic regime remain ill defined. Here, we investigated NO· detoxification in environments ranging from 0 to 50 μM O2, and discovered a regime in which E. coli NO· defenses were severely compromised, as well as conditions that exhibited oscillations in the concentration of NO·. Using an integrated computational and experimental approach, E. coli NO· detoxification was found to be extremely impaired at low O2 due to a combination of its inhibitory effects on NorV, Hmp, and translational activities, whereas oscillations were found to result from a kinetic competition for O2 between Hmp and respiratory cytochromes. Because at least 777 different bacterial species contain the genetic requirements of this stress response oscillator, we hypothesize that such oscillatory behavior could be a widespread phenomenon. In support of this hypothesis,Pseudomonas aeruginosa, whose respiratory and NO· response networks differ considerably from those of E. coli, was found to exhibit analogous oscillations in low O2 environments. This work provides insight into how bacterial NO· defenses function under the low O2 conditions that are likely to be encountered within host environments.

Construction and Experimental Validation of a Quantitative Kinetic Model of Nitric Oxide Stress in Enterohemorrhagic Escherichia coli O157:H7

Enterohemorrhagic Escherichia coli (EHEC) are responsible for large outbreaks of hemorrhagic colitis, which can progress to life-threatening hemolytic uremic syndrome (HUS) due to the release of Shiga-like toxins (Stx). The presence of a functional nitric oxide (NO·) reductase (NorV), which protects EHEC from NO· produced by immune cells, was previously found to correlate with high HUS incidence, and it was shown that NorV activity enabled prolonged EHEC survival and increased Stx production within macrophages. To enable quantitative study of EHEC NO· defenses and facilitate the development of NO·-potentiating therapeutics, we translated an existing kinetic model of the E. coli K-12 NO· response to an EHEC O157:H7 strain. To do this, we trained uncertain model parameters on measurements of [NO·] and [O₂] in EHEC cultures, assessed parametric and prediction uncertainty with the use of a Markov chain Monte Carlo approach, and confirmed the predictive accuracy of the model with experimental data from genetic mutants lacking NorV or Hmp (NO· dioxygenase). Collectively, these results establish a methodology for the translation of quantitative models of NO· stress in model organisms to pathogenic sub-species, which is a critical step toward the application of these models for the study of infectious disease.

RNA Futile Cycling in Model Persisters Derived from MazF Accumulation

Metabolism plays an important role in the persister phenotype, as evidenced by the number of strategies that perturb metabolism to sabotage this troublesome subpopulation. However, the absence of techniques to isolate high-purity populations of native persisters has precluded direct measurement of persister metabolism. To address this technical challenge, we studied Escherichia coli populations whose growth had been inhibited by the accumulation of the MazF toxin, which catalyzes RNA cleavage, as a model system for persistence. Using chromosomally integrated, orthogonally inducible promoters to express MazF and its antitoxin MazE, bacterial populations that were almost entirely tolerant to fluoroquinolone and β-lactam antibiotics were obtained upon MazF accumulation, and these were subjected to direct metabolic measurements. While MazF model persisters were nonreplicative, they maintained substantial oxygen and glucose consumption. Metabolomic analysis revealed accumulation of all four ribonucleotide monophosphates (NMPs). These results are consistent with a MazF-catalyzed RNA futile cycle, where the energy derived from catabolism is dissipated through continuous transcription and MazF-mediated RNA degradation. When transcription was inhibited, oxygen consumption and glucose uptake decreased, and nucleotide triphosphates (NTPs) and NTP/NMP ratios increased. Interestingly, the MazF-inhibited cells were sensitive to aminoglycosides, and this sensitivity was blocked by inhibition of transcription. Thus, in MazF model persisters, futile cycles of RNA synthesis and degradation result in both significant metabolic demands and aminoglycoside sensitivity.

IMPORTANCE

Metabolism plays a critical role in controlling each stage of bacterial persistence (shutdown, stasis, and reawakening). In this work, we generated an E. coli strain in which the MazE antitoxin and MazF toxin were artificially and independently inducible, and we used this strain to generate model persisters and study their metabolism. We found that even though growth of the model persisters was inhibited, they remained highly metabolically active. We further uncovered a futile cycle driven by continued transcription and MazF-mediated transcript degradation that dissipated the energy derived from carbon catabolism. Interestingly, the existence of this futile cycle acted as an Achilles' heel for MazF model persisters, rendering them vulnerable to killing by aminoglycosides.

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

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.