Jonathan L. Robinson

A Kinetic Platform to Determine the Fate of Nitric Oxide in Escherichia coli

Nitric oxide (NO•) is generated by the innate immune response to neutralize pathogens. NO• and its autoxidation products have an extensive biochemical reaction network that includes reactions with iron-sulfur clusters, DNA, and thiols. The fate of NO• inside a pathogen depends on a kinetic competition among its many targets, and is of critical importance to infection outcomes. Due to the complexity of the NO• biochemical network, where many intermediates are short-lived and at extremely low concentrations, several species can be measured, but stable products are non-unique, and damaged biomolecules are continually repaired or regenerated, kinetic models are required to understand and predict the outcome of NO• treatment. Here, we have constructed a comprehensive kinetic model that encompasses the broad reactivity of NO• in Escherichia coli. The incorporation of spontaneous and enzymatic reactions, as well as damage and repair of biomolecules, allowed for a detailed analysis of how NO• distributes in E. coli cultures. The model was informed with experimental measurements of NO• dynamics, and used to identify control parameters of the NO• distribution. Simulations predicted that NO• dioxygenase (Hmp) functions as a dominant NO• consumption pathway at O2 concentrations as low as 35 µM (microaerobic), and interestingly, loses utility as the NO• delivery rate increases. We confirmed these predictions experimentally by measuring NO• dynamics in wild-type and mutant cultures at different NO• delivery rates and O2 concentrations. These data suggest that the kinetics of NO• metabolism must be considered when assessing the importance of cellular components to NO• tolerance, and that models such as the one described here are necessary to rigorously investigate NO• stress in microbes. This model provides a platform to identify novel strategies to potentiate the effects of NO•, and will serve as a template from which analogous models can be generated for other organisms.

Deciphering Nitric Oxide Stress in Bacteria with Quantitative Modeling

Many pathogens depend on nitric oxide (NO•) detoxification and repair to establish an infection, and inhibitors of these systems are under investigation as next-generation antibiotics. Because of the broad reactivity of NO• and its derivatives with biomolecules, a deep understanding of how pathogens sense and respond to NO•, as an integrated system, has been elusive. Quantitative kinetic modeling has been proposed as a method to enhance analysis and understanding of NO• stress at the systems-level. Here we review the motivation for, current state of, and future prospects of quantitative modeling of NO• stress in bacteria, and suggest that such mathematical approaches would prove equally useful in the study of other broadly reactive antimicrobials, such as hydrogen peroxide (H2O2).

An ensemble-guided approach identifies ClpP as a major regulator of transcript levels in nitric oxide-stressed Escherichia coli

The importance of NO(∙) to immunity is highlighted by the diversity of pathogens that require NO(∙)-defensive systems to establish infections. Proteases have been identified to aid pathogens in surviving macrophage attack, inspiring us to investigate their role during NO(∙) stress in Escherichia coli. We discovered that the elimination of ClpP largely impaired NO(∙) detoxification by E. coli. Using a quantitative model of NO(∙) stress, we employed an ensemble-guided approach to identify the underlying mechanism. Iterations of in silico analyses and corresponding experiments identified the defect to result from deficient transcript levels of hmp, which encodes NO(∙) dioxygenase. Interestingly, the defect was not confined to hmp, as ΔclpP imparted widespread perturbations to the expression of NO(∙)-responsive genes. This work identified a target for anti-infective therapies based on disabling NO(∙) defenses, and demonstrated the utility of model-based approaches for exploring the complex, systems-level stress exerted by NO(∙).

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

Significance Many bacteria use NO· dioxygenase and NO· reductase to defend themselves against immune-generated NO·. The importance and contribution of these systems under microaerobic conditions, which pathogens are likely to encounter within a host, remain poorly understood. We investigated the NO· response of Escherichia coli throughout the microaerobic regime, and discovered conditions that largely disabled the NO· defenses of E. coli, and environments where the [NO·] oscillated. Components found to comprise the oscillatory circuit are distributed broadly among bacterial species, suggesting that these dynamics could be a characteristic feature of how bacteria respond to NO· in low O2 environments. In support of this hypothesis, analogous oscillations were observed in NO·-stressed cultures of Pseudomonas aeruginosa under low O2 conditions. 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.

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.

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.

Targeting CDK2 overcomes melanoma resistance against BRAF and Hsp90 inhibitors

Novel therapies are undergoing clinical trials, for example, the Hsp90 inhibitor, XL888, in combination with BRAF inhibitors for the treatment of therapy‐resistant melanomas. Unfortunately, our data show that this combination elicits a heterogeneous response in a panel of melanoma cell lines including PDX‐derived models. We sought to understand the mechanisms underlying the differential responses and suggest a patient stratification strategy. Thermal proteome profiling (TPP) identified the protein targets of XL888 in a pair of sensitive and unresponsive cell lines. Unbiased proteomics and phosphoproteomics analyses identified CDK2 as a driver of resistance to both BRAF and Hsp90 inhibitors and its expression is regulated by the transcription factor MITF upon XL888 treatment. The CDK2 inhibitor, dinaciclib, attenuated resistance to both classes of inhibitors and combinations thereof. Notably, we found that MITF expression correlates with CDK2 upregulation in patients; thus, dinaciclib would warrant consideration for treatment of patients unresponsive to BRAF‐MEK and/or Hsp90 inhibitors and/or harboring MITF amplification/overexpression.