Mutation-Driven β-Lactam Resistance Mechanisms among Contemporary Ceftazidime-Nonsusceptible Pseudomonas aeruginosa Isolates from U.S. Hospitals

Castanheira, Mariana; Mills, Janet C.; Farrell, David J.; Jones, Ronald N.

doi:10.1128/aac.03681-14

Cited by 124 publications

(93 citation statements)

References 19 publications

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“…The largest biological challenge is differential expression for the same gene in different contexts. This is known to be important for the spectrum and resistance level of beta-lactamases (Turton et al, 2006; Castanheira et al, 2014), and it likely affected all of our predictions of resistance. These expression level differences can arise either as a result of gene location (plasmid vs. chromosomal) or regulatory sequences.…”

Section: Discussionmentioning

confidence: 95%

Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data

et al. 2016

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The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitates initial use of empiric (frequently broad-spectrum) antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0 and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance factors and incomplete genome assembly confounded the rules-based algorithm, resulting in predictions based on gene family, rather than on knowledge of the specific variant found. Low-frequency resistance caused errors in the machine-learning algorithm because those genes were not seen or seen infrequently in the test set. We also identified an example of variability in the phenotype-based results that led to disagreement with both genotype-based methods. Genotype-based antimicrobial susceptibility testing shows great promise as a diagnostic tool, and we outline specific research goals to further refine this methodology.

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Section: Discussionmentioning

confidence: 95%

Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data

et al. 2016

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“…However, the overall prevalence of resistance to these first-line agents has continuously increased in recent years, and infections caused by multidrug-resistant P. aeruginosa strains are associated with significant morbidity and mortality (Lister et al, 2009). While the acquisition of potent β-lactamases, such as the metallo-β-lactamases, represents an important threat in some geographic regions, β-lactam resistance among P. aeruginosa in the United States is still much more commonly associated with the selection of a complex range of chromosomal mutations, including those causing hyperproduction of the chromosomal AmpC, repression or inactivation of the porin OprD, and up-regulation of one of the several efflux pumps (Caille et al, 2014;Castanheira et al, 2014b;Lister et al, 2009). Furthermore, two or more of these mechanisms are frequently combined, leading to the emergence of resistance to all currently available β-lactams (Castanheira et al, 2014b).…”

Section: Discussionmentioning

confidence: 99%

“…While the acquisition of potent β-lactamases, such as the metallo-β-lactamases, represents an important threat in some geographic regions, β-lactam resistance among P. aeruginosa in the United States is still much more commonly associated with the selection of a complex range of chromosomal mutations, including those causing hyperproduction of the chromosomal AmpC, repression or inactivation of the porin OprD, and up-regulation of one of the several efflux pumps (Caille et al, 2014;Castanheira et al, 2014b;Lister et al, 2009). Furthermore, two or more of these mechanisms are frequently combined, leading to the emergence of resistance to all currently available β-lactams (Castanheira et al, 2014b). In the present study, nonsusceptibility to the key antipseudomonal β-lactams (i.e., ceftazidime, cefepime, piperacillin-tazobactam, and meropenem) ranged from 15.5% to 20.4%.…”

Section: Discussionmentioning

confidence: 99%

Ceftazidime-avibactam activity when tested against ceftazidime-nonsusceptible Citrobacter spp., Enterobacter spp., Serratia marcescens, and Pseudomonas aeruginosa from Unites States medical centers (2011–2014)

Sader

Castanheira

Farrell

et al. 2015

Diagnostic Microbiology and Infectious Disease

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“…It was hypothesized that these mutants might have reduced accumulation of ceftazidime (Talfan et al, ). The primary non‐β‐lactamase‐mediated mechanisms of β‐lactam resistance in similar non‐fermenting bacteria such as Pseudomonas aeruginosa are increased efflux and reduced outer membrane permeability due to a reduction in the production of outer membrane porins (Castanheira, Mills, Farrell, & Jones, ). In Gram‐negative bacteria, tripartite outer membrane porins are considered the only site of entry for β‐lactams and reduced porin levels can reduce β‐lactam susceptibility in many species (Pfeifer, Cullik, & Witte, ).…”

Section: Introductionmentioning

confidence: 99%

TonB‐dependent uptake of β‐lactam antibiotics in the opportunistic human pathogen Stenotrophomonas maltophilia

Calvopiña

Dulyayangkul

Heesom

et al. 2019

Molecular Microbiology

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The β‐lactam antibiotic ceftazidime is one of the handful of drugs with proven clinical efficacy against the important opportunistic human pathogen Stenotrophomonas maltophilia. Here, we show that mutations in the energy transducer TonB, encoded by smlt0009 in S. maltophilia, confer ceftazidime resistance and smlt0009 mutants have reduced uptake of ceftazidime. This breaks the dogma that β‐lactams enter Gram‐negative bacteria only by passive diffusion through outer membrane porins. We also show that ceftazidime‐resistant TonB mutants are cross‐resistant to fluoroquinolone antimicrobials and a siderophore‐conjugated lactivicin antibiotic designed to target TonB‐dependent uptake. This implies that attempts to improve the penetration of antimicrobials into S. maltophilia by conjugating them with TonB substrates will suffer from the fact that β‐lactams and fluoroquinolones coselect resistance to these novel and otherwise promising antimicrobials. Finally, we show that smlt0009 mutants already exist among S. maltophilia clinical isolates and have reduced susceptibility to siderophore‐conjugated lactivicin, despite the in vitro growth impairment seen in smlt0009 mutants selected in the laboratory.

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Mutation-Driven β-Lactam Resistance Mechanisms among Contemporary Ceftazidime-Nonsusceptible Pseudomonas aeruginosa Isolates from U.S. Hospitals

Cited by 124 publications

References 19 publications

Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data

Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data

Ceftazidime-avibactam activity when tested against ceftazidime-nonsusceptible Citrobacter spp., Enterobacter spp., Serratia marcescens, and Pseudomonas aeruginosa from Unites States medical centers (2011–2014)

TonB‐dependent uptake of β‐lactam antibiotics in the opportunistic human pathogen Stenotrophomonas maltophilia

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