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

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.

Synergy Screening Identifies a Compound That Selectively Enhances the Antibacterial Activity of Nitric Oxide

Antibiotic resistance poses a serious threat to global health. To reinforce the anti-infective arsenal, many novel therapeutic strategies to fight bacterial infections are being explored. Among them, anti-virulence therapies, which target pathways important for virulence, have attracted much attention. Nitric oxide (NO) defense systems have been identified as critical for the pathogenesis of various bacteria, making them an appealing therapeutic target. In this study, we performed chemical screens to identify inhibitors of NO detoxification in Escherichia coli. We found that 2-mercaptobenzothiazole (2-MBT) can potently inhibit cellular detoxification of NO, achieving a level of inhibition that resembled the effect of genetically removing Hmp, the dominant detoxification enzyme under oxygenated conditions. Further analysis revealed that in the presence of NO, 2-MBT impaired the catalysis of Hmp and synthesis of Hmp and other proteins, whereas in its absence there were minimal perturbations to growth and protein synthesis. In addition, by studying the structure-activity relationship of 2-MBT, we found that both sulfur atoms in 2-MBT were vital for its inhibition of NO detoxification. Interestingly, when 2-mercaptothiazole (2-MT), which lacked the benzene ring, was used, differing biological activities were observed, although they too were NO dependent. Specifically, 2-MT could still prohibit NO detoxification, though it did not interfere with Hmp catalysis; rather, it was a stronger inhibitor of protein synthesis and it reduced the transcript levels of hmp, which was not observed with 2-MBT. Overall, these results provide a strong foundation for further exploration of 2-MBT and 2-MT for therapeutic applications.

Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

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.

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.

A platform of genetically engineered bacteria as vehicles for localized delivery of therapeutics: Toward applications for Crohn's disease

For therapies targeting diseases of the gastrointestinal tract, we and others envision probiotic bacteria that synthesize and excrete biotherapeutics at disease sites. Toward this goal, we have engineered commensal E. coli that selectively synthesize and secrete a model biotherapeutic in the presence of nitric oxide (NO), an intestinal biomarker for Crohn's disease (CD). This is accomplished by co‐expressing the pore forming protein TolAIII with the biologic, granulocyte macrophage‐colony stimulating factor (GM‐CSF). We have additionally engineered these bacteria to accumulate at sites of elevated NO by engineering their motility circuits and controlling pseudotaxis. Importantly, because we have focused on in vitro test beds, motility and biotherapeutics production are spatiotemporally characterized. Together, the targeted recognition, synthesis, and biomolecule delivery comprises a “smart” probiotics platform that may have utility in the treatment of CD. Further, this platform could be modified to accommodate other pursuits by swapping the promoter and therapeutic gene to reflect other disease biomarkers and treatments, respectively.

Development of Cell-Based Sentinels for Nitric Oxide: Ensuring Marker Expression and Unimodality

We generated "sentinel" bacteria that respond to the biomarker nitric oxide (NO) and produce a homogeneous and strong fluorescent response. Our dual-plasmid system consists of a signal "relay" vector that employs an NO-responsive promoter that amplifies the native signal (via expression of T7 Polymerase (T7Pol)) to a second vector responsible for GFP expression. Importantly, to achieve an optimal "sentinel" response, we developed strategies that balance the transcriptional load within cells by altering (i) translation and (ii) activity of the T7Pol. Our optimized genetic circuitry was then used to transform commensal E. coli Nissle, as a proof-of-concept toward an ingestible cell-based sensor for Crohn's disease (CD) that, in turn, is marked by elevated levels of intestinal NO. Thus, the "biosensors" demonstrated here may serve as a simple diagnostic tool, contrasting the standard of care including colonoscopies or biopsies.

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.