Abstract Meropenem is a clinically important antibacterial reserved for treatment of multi-resistant infections. In meropenem-resistant bacteria of the family Enterobacteriales, NDM-1 is considerably more common than IMP-1, despite both metallo-β-lactamases (MBLs) hydrolysing meropenem with almost identical kinetics. We show that bla NDM-1 consistently confers meropenem resistance in wild-type Enterobacteriales, but bla IMP-1 does not. The reason is higher bla NDM-1 expression because of its stronger promoter. However, the cost of meropenem resistance is reduced fitness of bla NDM-1 positive Enterobacteriales because of amino acid starvation. In parallel, from a clinical case, we identified multiple Enterobacter spp. isolates carrying a plasmid-encoded bla NDM-1 having a modified promoter region. This modification lowered MBL production to a level associated with zero fitness cost but, consequently, the isolates were not meropenem resistant. However, we identified a Klebsiella pneumoniae isolate from this same clinical case carrying the same bla NDM-1 plasmid. This isolate was meropenem resistant despite low-level NDM-1 production because of a ramR mutation, reducing envelope permeability. Overall, therefore, we show how the resistance/fitness trade-off for MBL carriage can be resolved. The result is sporadic emergence of meropenem resistance in a clinical setting.
Abstract Amikacin and piperacillin/tazobactam are frequent antibiotic choices to treat bloodstream infection, which is commonly fatal and most often caused by bacteria from the family Enterobacterales . Here we show that two gene cassettes located side-by-side in and ancestral integron similar to In 37 have been “harvested” by insertion sequence IS 26 as a transposon that is already globally disseminated among the Enterobacterales . This transposon encodes the enzymes AAC(6’)-Ib-cr and OXA-1, reported, respectively, as amikacin and piperacillin/tazobactam resistance mechanisms. However, by studying bloodstream infection isolates from 769 patients from, three hospitals serving a population of 1.5 million people in South West England, we show that increased enzyme production due to mutation in an IS 26 /In 37 -derived hybrid promoter or, more commonly, transposon copy number amplification is required to simultaneously remove these two key therapeutic options; in many cases leaving only the last-resort antibiotic, meropenem. These findings may help improve the accuracy of predicting piperacillin/tazobactam treatment failure, allowing stratification of patients to receive meropenem or piperacillin/tazobactam, which may improve outcome and slow the emergence of meropenem resistance.
Meropenem is a clinically important antibacterial reserved for treatment of multiresistant infections. In meropenem-resistant bacteria of the family Enterobacterales, NDM-1 is considerably more common than IMP-1, despite both metallo-β-lactamases (MBLs) hydrolyzing meropenem with almost identical kinetics. We show that blaNDM-1 consistently confers meropenem resistance in wild-type Enterobacterales, but blaIMP-1 does not. The reason is higher blaNDM-1 expression because of its stronger promoter. However, the cost of meropenem resistance is reduced fitness of blaNDM-1-positive Enterobacterales. In parallel, from a clinical case, we identified multiple Enterobacter spp. isolates carrying a plasmid-encoded blaNDM-1 having a modified promoter region. This modification lowered MBL production to a level associated with zero fitness cost, but, consequently, the isolates were not meropenem resistant. However, we identified a Klebsiella pneumoniae isolate from this same clinical case carrying the same blaNDM-1 plasmid. This isolate was meropenem resistant despite low-level NDM-1 production because of a ramR mutation reducing envelope permeability. Overall, therefore, we show how the resistance/fitness trade-off for MBL carriage can be resolved. The result is sporadic emergence of meropenem resistance in a clinical setting.
Amikacin and piperacillin/tazobactam are frequent antibiotic choices to treat bloodstream infection, which is commonly fatal and most often caused by bacteria from the family Enterobacterales . Here we show that two gene cassettes located side-by-side in and ancestral integron similar to In 37 have been “harvested” by insertion sequence IS 26 as a transposon that is widely disseminated among the Enterobacterales . This transposon encodes the enzymes AAC(6’)-Ib-cr and OXA-1, reported, respectively, as amikacin and piperacillin/tazobactam resistance mechanisms. However, by studying bloodstream infection isolates from 769 patients from three hospitals serving a population of 1.2 million people in South West England, we show that increased enzyme production due to mutation in an IS 26 /In 37 -derived hybrid promoter or, more commonly, increased transposon copy number is required to simultaneously remove these two key therapeutic options; in many cases leaving only the last-resort antibiotic, meropenem. These findings may help improve the accuracy of predicting piperacillin/tazobactam treatment failure, allowing stratification of patients to receive meropenem or piperacillin/tazobactam, which may improve outcome and slow the emergence of meropenem resistance.
Our primary aim was to test whether cattle-associated fluoroquinolone-resistant (FQ-R) Escherichia coli found on dairy farms are closely phylogenetically related to those causing bacteriuria in humans living in the same 50 × 50 km geographical region suggestive of farm-human sharing. Another aim was to identify risk factors for the presence of FQ-R E. coli on dairy farms.FQ-R E. coli were isolated during 2017-18 from 42 dairy farms and from community urine samples. Forty-two cattle and 489 human urinary isolates were subjected to WGS, allowing phylogenetic comparisons. Risk factors were identified using a Bayesian regularization approach.Of 489 FQ-R human isolates, 255 were also third-generation-cephalosporin-resistant, with strong genetic linkage between aac(6')Ib-cr and blaCTX-M-15. We identified possible farm-human sharing for pairs of ST744 and ST162 isolates, but minimal core genome SNP distances were larger between farm-human pairs of ST744 and ST162 isolates (71 and 63 SNPs, respectively) than between pairs of isolates from different farms (7 and 3 SNPs, respectively). Total farm fluoroquinolone use showed a positive association with the odds of isolating FQ-R E. coli, while total dry cow therapy use showed a negative association.This work suggests that FQ-R E. coli found on dairy farms have a limited impact on community bacteriuria within the local human population. Reducing fluoroquinolone use may reduce the on-farm prevalence of FQ-R E. coli and this reduction may be greater when dry cow therapy is targeted to the ecology of resistant E. coli on the farm.
Nitrofurantoin resistance in Escherichia coli is primarily caused by mutations damaging two enzymes, NfsA and NfsB. Studies based on small isolate collections with defined nitrofurantoin MICs have found significant random genetic drift in nfsA and nfsB making it extremely difficult to predict nitrofurantoin resistance from whole genome sequence (WGS) where both genes are not obviously disrupted by nonsense or frameshift mutations or insertional inactivation. Here we report a WGS survey of 200 E. coli from community urine samples, of which 34 were nitrofurantoin resistant. We characterised individual non-synonymous mutations seen in nfsA and nfsB among this collection using complementation cloning and assays of NfsA/B enzyme activity in cell extracts. We definitively identified R203C, H11Y, W212R, A112E, A112T and A122T in NfsA and R121C, Q142H, F84S, P163H, W46R, K57E and V191G in NfsB as amino acid substitutions that reduce enzyme activity sufficiently to cause resistance. In contrast, E58D, I117T, K141E, L157F, A172S, G187D and A188V in NfsA and G66D, M75I, V93A and A174E in NfsB, are functionally silent in this context. We identified that 9/166 (5.4%) of nitrofurantoin susceptible isolates were pre-resistant, defined as having loss of function mutations in nfsA or nfsB. Finally, using NfsA/B enzyme activity assay and proteomics we demonstrated that 9/34 (26.5%) of nitrofurantoin resistant isolates carried functionally wild-type nfsB or nfsB/nfsA. In these cases, enzyme activity was reduced through downregulated gene expression. Our biological understanding of nitrofurantoin resistance is greatly improved by this analysis, but is still insufficient to allow its reliable prediction from WGS data.
Abstract There is significant interest in the possibility of predicting antibacterial drug susceptibility directly though the analysis of bacterial DNA or protein. We report the use of Klebsiella pneumoniae, Escherichia coli, Pseudomonas aeruginosa and Acinetobacter baumannii transformants to define baseline predictive rules for the β- lactam susceptibility profiles of β-lactamase positive clinical isolates. We then deployed a robust and reproducible shotgun proteomics methodology to identify β-lactamase positivity and predict β-lactam susceptibility by reference to our baseline predictive rules both in cultured bacteria and in extracts of culture-positive blood. Proteomics and whole genome sequencing then allowed us to characterise K. pneumoniae and P. aeruginosa isolates that differed from the expected β-lactam susceptibility profile, iteratively expanding our predictive rules. Proteomics added considerable value over and above the information generated by whole genome sequencing, allowing for gene expression, not just gene presence to be considered. Specifically, in K. pneumoniae , we identified key differences between acrR and ramR regulatory mutations and compared the effects of OmpK36 Aspartate-Threonine or Glycine-Aspartate dipeptide porin insertions on susceptibility to cefepime and carbapenems. In P. aeruginosa , we identified differences in the gene expression effects of mexR versus nalC mutations and related these to differences in β-lactam MICs against isolates hyper-producing AmpC β-lactamase and or producing a metallo-β-lactamase.
Abstract Introduction Clinicians commonly escalate empiric antibiotic therapy due to poor clinical progress without microbiology guidance. When escalating, they should take account of how resistance to an initial antibiotic affects the probability of resistance to subsequent options. The term "escalation antibiogram" (EA) has been coined to describe this concept. One difficulty when applying the EA concept to clinical practice is understanding the uncertainty in results and how this changes for specific patient subgroups. Methods A Bayesian model was developed to estimate antibiotic resistance rates in Gram-negative bloodstream infections based on phenotypic resistance data. The model generates a series of “credible” curves to fit the resistance data, each with the same probability of representing the true rate given the inherent uncertainty. To avoid overfitting, an integrated penalisation term adaptively smooths the curves given the level of evidence. Results Rates of resistance to empiric first-choice and potential escalation antibiotics were calculated for the whole hospitalised population based on 10,486 individual bloodstream infections, and for a range of specific patient groups, including ICU (intensive care unit), haematolo-oncology, and paediatric patients. The model generated an expected value (posterior mean) with 95% credible interval to illustrate uncertainty, based on the size of the patient subgroup. For example, the posterior means of piperacillin/tazobactam resistance rates in Gram-negative bloodstream infection are different between patients on ICU and the general hospital population: 27.3% (95% CI 18.1–37.2 vs. 13.4% 95% CI 11.0–16.1) respectively. The model can also estimate the probability of inferiority between two antibiotics for a specific patient population. Differences in optimal escalation antibiotic options between specific patient groups were noted. Conclusions EA analysis informed by our Bayesian model is a useful tool to support empiric antibiotic switches, providing an estimate of local resistance rates, and a comparison of antibiotic options with a measure of the uncertainty in the data. We demonstrate that EAs calculated for the whole hospital population cannot be assumed to apply to specific patient group.
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