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Population-based inference of aminoglycoside resistance mechanisms in Escherichia coli.

ABSTRACT: BACKGROUND:Interpretative reading of antimicrobial susceptibility test (AST) results allows inferring biochemical resistance mechanisms from resistance phenotypes. For aminoglycosides, however, correlations between resistance pathways inferred on the basis of the European Committee on Antimicrobial Susceptibility Testing (EUCAST) clinical breakpoints and expert rules versus genotypes are generally poor. This study aimed at developing and validating a decision tree based on resistance phenotypes determined by disc diffusion and based on epidemiological cut-offs (ECOFFs) to infer the corresponding resistance mechanisms in Escherichia coli. METHODS:Phenotypic antibiotic susceptibility of thirty wild-type and 458 aminoglycoside-resistant E. coli clinical isolates was determined by disc diffusion and the genomes were sequenced. Based on well-defined cut-offs, we developed a phenotype-based algorithm (Aminoglycoside Resistance Mechanism Inference Algorithm - ARMIA) to infer the biochemical mechanisms responsible for the corresponding aminoglycoside resistance phenotypes. The mechanisms inferred from susceptibility to kanamycin, tobramycin and gentamicin were analysed using ARMIA- or EUCAST-based AST interpretation and validated by whole genome sequencing (WGS) of the host bacteria. FINDINGS:ARMIA-based inference of resistance mechanisms and WGS data were congruent in 441/458 isolates (96·3%). In contrast, there was a poor correlation between resistance mechanisms inferred using EUCAST CBPs/expert rules and WGS data (418/488, 85·6%). Based on the assumption that resistance mechanisms can result in therapeutic failure, EUCAST produced 63 (12·9%) very major errors (vME), compared to only 2 (0·4%) vME with ARMIA. When used for detection and identification of resistance mechanisms, ARMIA resolved >95% vMEs generated by EUCAST-based AST interpretation. INTERPRETATION:This study demonstrates that ECOFF-based analysis of AST data of only four aminoglycosides provides accurate information on the resistance mechanisms in E. coli. Since aminoglycoside resistance mechanisms, despite having in certain cases a minimal effect on the minimal inhibitory concentration, may compromise the bactericidal activity of aminoglycosides, prompt detection of resistance mechanisms is crucial for therapy. Using ARMIA as an interpretative rule set for editing AST results allows for better predictions of in vivo activity of this drug class.

SUBMITTER: Mancini S

PROVIDER: S-EPMC6710905 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Population-based inference of aminoglycoside resistance mechanisms in Escherichia coli.

Mancini Stefano S Marchesi Martina M Imkamp Frank F Wagner Karoline K Keller Peter M PM Quiblier Chantal C Bodendoerfer Elias E Courvalin Patrice P Böttger Erik C EC

EBioMedicine 20190712

<h4>Background</h4>Interpretative reading of antimicrobial susceptibility test (AST) results allows inferring biochemical resistance mechanisms from resistance phenotypes. For aminoglycosides, however, correlations between resistance pathways inferred on the basis of the European Committee on Antimicrobial Susceptibility Testing (EUCAST) clinical breakpoints and expert rules versus genotypes are generally poor. This study aimed at developing and validating a decision tree based on resistance phe ...[more]

PMID: 31307955

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Project description:ObjectivesQuinolone resistance in Escherichia coli occurs mainly as a result of mutations in the quinolone-resistance-determining regions of gyrA and parC, which encode the drugs' primary targets. Mutational alterations affecting drug permeability or efflux as well as plasmid-based resistance mechanisms can also contribute to resistance, albeit to a lesser extent. Simplifying and generalizing complex evolutionary trajectories, low-level resistance towards fluoroquinolones arises from a single mutation in gyrA, while clinical high-level resistance is associated with two mutations in gyrA plus one mutation in parC. Both low- and high-level resistance can be detected phenotypically using nalidixic acid and fluoroquinolones such as ciprofloxacin, respectively. The aim of this study was to develop a decision tree based on disc diffusion data and to define epidemiological cut-offs to infer resistance mechanisms and to predict clinical resistance in E. coli. This diagnostic algorithm should provide a coherent genotype/phenotype classification, which separates the wildtype from any non-wildtype and further differentiates within the non-wildtype.MethodsPhenotypic susceptibility of 553 clinical E. coli isolates towards nalidixic acid, ciprofloxacin, norfloxacin and levofloxacin was determined by disc diffusion, and the genomes were sequenced. Based on epidemiological cut-offs, we developed a QUInolone Resistance Mechanisms Inference Algorithm (QUIRMIA) to infer the underlying resistance mechanisms responsible for the corresponding phenotypes, resulting in the categorization as "susceptible" (wildtype), "low-level resistance" (non-wildtype) and "high-level resistance" (non-wildtype). The congruence of phenotypes and whole genome sequencing (WGS)-derived genotypes was then assigned using QUIRMIA- and EUCAST-based AST interpretation.ResultsQUIRMIA-based inference of resistance mechanisms and sequencing data were highly congruent (542/553, 98%). In contrast, EUCAST-based classification with its binary classification into "susceptible" and "resistant" isolates failed to recognize and properly categorize low-level resistant isolates.ConclusionsQUIRMIA provides a coherent genotype/phenotype categorization and may be integrated in the EUCAST expert rule set, thereby enabling reliable detection of low-level resistant isolates, which may help to better predict outcome and to prevent the emergence of clinical resistance.

Project description:BackgroundEscherichia coli is an important pathogen in humans and is the most common cause of bacterial bloodstream infections (BSIs). The objectives of our study were to determine factors associated with E. coli BSI incidence rate and third-generation cephalosporin resistance in a multinational population-based cohort.MethodsWe included all incident E. coli BSIs (2014-2018) from national (Finland) and regional (Australia [Canberra], Sweden [Skaraborg], and Canada [Calgary, Sherbrooke, and western interior]) surveillance. Incidence rates were directly age and sex standardized to the European Union 28-country 2018 population. Multivariable negative binomial and logistic regression models estimated factors significantly associated with E. coli BSI incidence rate and third-generation cephalosporin resistance, respectively. The explanatory variables considered for inclusion in both models were year (2014-2018), region (six areas), age (< 70-years-old and ≥ 70-years-old), and sex (female and male).ResultsWe identified 31,889 E. coli BSIs from 40.7 million person-years of surveillance. Overall and third-generation cephalosporin-resistant standardized rates were 87.1 and 6.6 cases/100,000 person-years, respectively, and increased 14.0% and 40.1% over the five-year study. Overall, 7.8% (2483/31889) of E. coli BSIs were third-generation cephalosporin-resistant. Calgary, Canberra, Sherbrooke, and western interior had significantly lower E. coli BSI rates compared to Finland. The significant association between age and E. coli BSI rate varied with sex. Calgary, Canberra, and western interior had significantly greater odds of third-generation cephalosporin-resistant E. coli BSIs compared to Finland. Compared to 2014, the odds of third-generation cephalosporin-resistant E. coli BSIs were significantly increased in 2016, 2017, and 2018. The significant association between age and the odds of having a third-generation cephalosporin-resistant E. coli BSI varied with sex.ConclusionsIncreases in overall and third-generation cephalosporin-resistant standardized E. coli BSI rates were clinically important. Overall, E. coli BSI incidence rates were 40-104% greater than previous investigations from the same study areas. Region, sex, and age are important variables when analyzing E. coli BSI rates and third-generation cephalosporin resistance in E. coli BSIs. Considering E. coli is the most common cause of BSIs, this increasing burden and evolving third-generation cephalosporin resistance will have an important impact on human health, especially in aging populations.

Dataset Information

Population-based inference of aminoglycoside resistance mechanisms in Escherichia coli.

Publications

Population-based inference of aminoglycoside resistance mechanisms in Escherichia coli.

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