Repurposed dihydroorotate dehydrogenase inhibitors with efficacy against drug-resistant Acinetobacter baumannii.

New antimicrobials are needed for the treatment of extensively drug-resistant Acinetobacter baumannii. The de novo pyrimidine biosynthetic enzyme dihydroorotate dehydrogenase (DHODH) is a validated drug target for malaria and human autoimmune diseases. We provide genetic evidence that A. baumannii DHODH (AbDHODH) is essential for bacterial survival in rodent infection models. We chemically validate the target by repurposing a unique library of ~450 triazolopyrimidine/imidazopyrimidine analogs developed for our malaria DHODH program to identify 21 compounds with submicromolar activity on AbDHODH. The most potent (DSM186, DHODH IC50 28 nM) had a minimal inhibitory concentration of ≤1 µg/ml against geographically diverse A. baumannii strains, including meropenem-resistant isolates. A structurally related analog (DSM161) with a long in vivo half-life conferred significant protection in the neutropenic mouse thigh infection model. Encouragingly, the development of resistance to these compounds was not identified in vitro or in vivo. Lastly, the X-ray structure of AbDHODH bound to DSM186 was solved to 1.4 Å resolution. These data support the potential of AbDHODH as a drug target for the development of antimicrobials for the treatment of A. baumannii and potentially other high-risk bacterial infections.

Penicillin Binding Protein 7/8 Is a Potential Drug Target in Carbapenem-Resistant Acinetobacter baumannii.

Limited therapeutic options dictate the need for new classes of antimicrobials active against carbapenem-resistant Acinetobacter baumannii. Presented data confirm and extend penicillin binding protein 7/8 (PBP 7/8) as a high-value target in the CR A. baumannii strain HUMC1. PBP 7/8 was essential for optimal growth/survival of HUMC1 in ex vivo human ascites and in a rat subcutaneous abscess model; in a mouse pneumonia model, the absence of PBP 7/8 decreased lethality 11-fold. The loss of PBP 7/8 resulted in increased permeability, sensitivity to complement, and lysozyme-mediated bactericidal activity. These changes did not appear to be due to alterations in the cellular fatty acid composition or capsule production. However, a decrease in lipid A and an increase in coccoidal cells and cell aggregation were noted. The compromise of the stringent permeability barrier in the PBP 7/8 mutant was reflected by an increased susceptibility to several antimicrobials. Importantly, expression of ampC was not significantly affected by the loss of PBP 7/8 and serial passage of the mutant strain in human ascites over 7 days did not yield revertants possessing a wild-type phenotype. In summary, these data and other features support PBP 7/8 as a high-value drug target for extensively drug-resistant and CR A. baumannii. Our results guide next-stage studies; the determination that the inactivation of PBP 7/8 results in an increased sensitivity to lysozyme enables the design of a high-throughput screening assay to identify small molecule compounds that can specifically inhibit PBP 7/8 activity.

Role of the SOS Response in the Generation of Antibiotic Resistance In Vivo.

The SOS response to DNA damage is a conserved stress response in Gram-negative and Gram-positive bacteria. Although this pathway has been studied for years, its relevance is still not familiar to many working in the fields of clinical antibiotic resistance and stewardship. Under some conditions, the SOS response favors DNA repair and preserves the genetic integrity of the organism. On the other hand, the SOS response also includes induction of error-prone DNA polymerases, which can increase the rate of mutation, called the mutator phenotype or "hypermutation." As a result, mutations can occur in genes conferring antibiotic resistance, increasing the acquisition of resistance to antibiotics. Almost all of the work on the SOS response has been on bacteria exposed to stressors in vitro. In this study, we sought to quantitate the effects of SOS-inducing drugs in vivo, in comparison with the same drugs in vitro. We used a rabbit model of intestinal infection with enteropathogenic Escherichia coli strain E22. SOS-inducing drugs triggered the mutator phenotype response in vivo as well as in vitro. Exposure of E. coli strain E22 to ciprofloxacin or zidovudine, both of which induce the SOS response in vitro, resulted in increased antibiotic resistance to 3 antibiotics: rifampin, minocycline, and fosfomycin. Zinc was able to inhibit the SOS-induced emergence of antibiotic resistance in vivo, as previously observed in vitro. Our findings may have relevance in reducing the emergence of resistance to new antimicrobial drugs.

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance.

Distinguishing hypervirulent (hvKp) from classical Klebsiella pneumoniae (cKp) strains is important for clinical care, surveillance, and research. Some combinations of iucA, iroB, peg-344, rmpA, and rmpA2 are most commonly used, but it is unclear what combination of genotypic or phenotypic markers (e.g., siderophore concentration, mucoviscosity) most accurately predicts the hypervirulent phenotype. Furthermore, acquisition of antimicrobial resistance may affect virulence and confound identification. Therefore, 49 K. pneumoniae strains that possessed some combinations of iucA, iroB, peg-344, rmpA, and rmpA2 and had acquired resistance were assembled and categorized as hypervirulent hvKp (hvKp) (N = 16) or cKp (N = 33) via a murine infection model. Biomarker number, siderophore production, mucoviscosity, virulence plasmid's Mash/Jaccard distances to the canonical pLVPK, and Kleborate virulence score were measured and evaluated to accurately differentiate these pathotypes. Both stepwise logistic regression and a CART model were used to determine which variable was most predictive of the strain cohorts. The biomarker count alone was the strongest predictor for both analyses. For logistic regression, the area under the curve for biomarker count was 0.962 (P = 0.004). The CART model generated the classification rule that a biomarker count = 5 would classify the strain as hvKP, resulting in a sensitivity for predicting hvKP of 94% (15/16), a specificity of 94% (31/33), and an overall accuracy of 94% (46/49). Although a count of ≥4 was 100% (16/16) sensitive for predicting hvKP, the specificity and accuracy decreased to 76% (25/33) and 84% (41/49), respectively. These findings can be used to inform the identification of hvKp.IMPORTANCEHypervirulent Klebsiella pneumoniae (hvKp) is a concerning pathogen that can cause life-threatening infections in otherwise healthy individuals. Importantly, although strains of hvKp have been acquiring antimicrobial resistance, the effect on virulence is unclear. Therefore, it is of critical importance to determine whether a given antimicrobial resistant K. pneumoniae isolate is hypervirulent. This report determined which combination of genotypic and phenotypic markers could most accurately identify hvKp strains with acquired resistance. Both logistic regression and a machine-learning prediction model demonstrated that biomarker count alone was the strongest predictor. The presence of all five of the biomarkers iucA, iroB, peg-344, rmpA, and rmpA2 was most accurate (94%); the presence of ≥4 of these biomarkers was most sensitive (100%). Accurately identifying hvKp is vital for surveillance and research, and the availability of biomarker data could alert the clinician that hvKp is a consideration, which, in turn, would assist in optimizing patient care.

Differentiation of hypervirulent and classical Klebsiella pneumoniae with acquired drug resistance.

Distinguishing hypervirulent (hvKp) from classical Klebsiella pneumoniae (cKp) strains is important for clinical care, surveillance, and research. Some combination of iucA, iroB, peg-344, rmpA, and rmpA2 are most commonly used, but it is unclear what combination of genotypic or phenotypic markers (e.g. siderophore concentration, mucoviscosity) most accurately predicts the hypervirulent phenotype. Further, acquisition of antimicrobial resistance may affect virulence and confound identification. Therefore, 49 K. pneumoniae strains that possessed some combination of iucA, iroB, peg-344, rmpA, and rmpA2 and had acquired resistance were assembled and categorized as hypervirulent hvKp (hvKp) (N=16) or cKp (N=33) via a murine infection model. Biomarker number, siderophore production, mucoviscosity, virulence plasmid's Mash/Jaccard distances to the canonical pLVPK, and Kleborate virulence score were measured and evaluated to accurately differentiate these pathotypes. Both stepwise logistic regression and a CART model were used to determine which variable was most predictive of the strain cohorts. The biomarker count alone was the strongest predictor for both analyses. For logistic regression the area under the curve for biomarker count was 0.962 (P = 0.004). The CART model generated the classification rule that a biomarker count = 5 would classify the strain as hvKP, resulting in a sensitivity for predicting hvKP of 94% (15/16), a specificity of 94% (31/33), and an overall accuracy of 94% (46/49). Although a count of ≥ 4 was 100% (16/16) sensitive for predicting hvKP, the specificity and accuracy decreased to 76% (25/33) and 84% (41/49) respectively. These findings can be used to inform the identification of hvKp. Importance: Hypervirulent Klebsiella pneumoniae (hvKp) is a concerning pathogen that can cause life-threatening infections in otherwise healthy individuals. Importantly, although strains of hvKp have been acquiring antimicrobial resistance, the effect on virulence is unclear. Therefore, it is of critical importance to determine whether a given antimicrobial resistant K. pneumoniae isolate is hypervirulent. This report determined which combination of genotypic and phenotypic markers could most accurately identify hvKp strains with acquired resistance. Both logistic regression and a machine-learning prediction model demonstrated that biomarker count alone was the strongest predictor. The presence of all 5 of the biomarkers iucA, iroB, peg-344, rmpA, and rmpA2 was most accurate (94%); the presence of ≥ 4 of these biomarkers was most sensitive (100%). Accurately identifying hvKp is vital for surveillance and research, and the availability of biomarker data could alert the clinician that hvKp is a consideration, which in turn would assist in optimizing patient care.

Psychoactive Drugs Induce the SOS Response and Shiga Toxin Production in Escherichia coli.

Several classes of non-antibiotic drugs, including psychoactive drugs, proton-pump inhibitors (PPIs), non-steroidal anti-inflammatory drugs (NSAIDs), and others, appear to have strong antimicrobial properties. We considered whether psychoactive drugs induce the SOS response in E. coli bacteria and, consequently, induce Shiga toxins in Shiga-toxigenic E. coli (STEC). We measured the induction of an SOS response using a recA-lacZ E. coli reporter strain, as RecA is an early, reliable, and quantifiable marker for activation of the SOS stress response pathway. We also measured the production and release of Shiga toxin 2 (Stx2) from a classic E. coli O157:H7 strain, derived from a food-borne outbreak due to spinach. Some, but not all, serotonin selective reuptake inhibitors (SSRIs) and antipsychotic drugs induced an SOS response. The use of SSRIs is widespread and increasing; thus, the use of these antidepressants could account for some cases of hemolytic-uremic syndrome due to STEC and is not attributable to antibiotic administration. SSRIs could have detrimental effects on the normal intestinal microbiome in humans. In addition, as SSRIs are resistant to environmental breakdown, they could have effects on microbial communities, including aquatic ecosystems, long after they have left the human body.

Inhibition of SOS Response by Nitric Oxide Donors in Escherichia coli Blocks Toxin Production and Hypermutation.

Background: Previous reports have differed as to whether nitric oxide inhibits or stimulates the SOS response, a bacterial stress response that is often triggered by DNA damage. The SOS response is an important regulator of production of Shiga toxins (Stx) in Shiga-toxigenic E. coli (STEC). In addition, the SOS response is accompanied by hypermutation, which can lead to de novo emergence of antibiotic resistance. We studied these effects in vitro as well as in vivo. Results: Nitric oxide donors inhibited induction of the SOS response by classical inducers such as mitomycin C, ciprofloxacin, and zidovudine, as measured by assays for E. coli RecA. Nitric oxide donors also inhibited Stx toxin protein production as well as stx2 RNA in vitro and in vivo. In vivo experiments were performed with ligated ileal segments in the rabbit using a 20 h infection. The NO donor S-nitroso-acetylpenicillamine (SNAP) reduced hypermutation in vitro and in vivo, as measured by emergence of rifampin resistance. SNAP blocked the ability of the RecA protein to bind to single-stranded DNA in an electrophoretic mobility shift assay (EMSA) in vitro, an early event in the SOS response. The inhibitory effects of SNAP were additive with those of zinc acetate. Conclusions: Nitric oxide donors blocked the initiation step of the SOS response. Downstream effects of this blockade included inhibition of Stx production and of hypermutation. Infection of rabbit loops with STEC resulted in a downregulation, rather than stimulation, of nitric oxide host defenses at 20 h of infection.