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Mechanisms of Resistance to Aminoglycoside Antibiotics: Overview and Perspectives.

ABSTRACT: Aminoglycoside (AG) antibiotics are used to treat many Gram-negative and some Gram-positive infections and, importantly, multidrug-resistant tuberculosis. Among various bacterial species, resistance to AGs arises through a variety of intrinsic and acquired mechanisms. The bacterial cell wall serves as a natural barrier for small molecules such as AGs and may be further fortified via acquired mutations. Efflux pumps work to expel AGs from bacterial cells, and modifications here too may cause further resistance to AGs. Mutations in the ribosomal target of AGs, while rare, also contribute to resistance. Of growing clinical prominence is resistance caused by ribosome methyltransferases. By far the most widespread mechanism of resistance to AGs is the inactivation of these antibiotics by AG-modifying enzymes. We provide here an overview of these mechanisms by which bacteria become resistant to AGs and discuss their prevalence and potential for clinical relevance.

SUBMITTER: Garneau-Tsodikova S

PROVIDER: S-EPMC4752126 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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Mechanisms of Resistance to Aminoglycoside Antibiotics: Overview and Perspectives.

Garneau-Tsodikova Sylvie S Labby Kristin J KJ

MedChemComm 20150921 1

Aminoglycoside (AG) antibiotics are used to treat many Gram-negative and some Gram-positive infections and, importantly, multidrug-resistant tuberculosis. Among various bacterial species, resistance to AGs arises through a variety of intrinsic and acquired mechanisms. The bacterial cell wall serves as a natural barrier for small molecules such as AGs and may be further fortified <i>via</i> acquired mutations. Efflux pumps work to expel AGs from bacterial cells, and modifications here too may cau ...[more]

PMID: 26877861

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Project description:BackgroundBiocide disinfectants are essential tools in infection control, but their use can inadvertently contribute to emergence of antibiotic-resistant bacteria. In this study we systematically examine the effect of the biocide benzalkonium chloride, which is primarily used for surface disinfection but is also present as a preservative in many consumer products, on the activity of aminoglycoside antibiotics in Acinetobacter baumannii.MethodsThe effect of subinhibitory BAC on aminoglycoside treatment of A. baumannii ATCC17978 was investigated using time-to-kill assays, MIC determination, directed evolution experiments, fluctuation tests and labelled gentamicin accumulation assays. Further MIC determinations and directed evolution experiments were performed with additional Gram-negative ESKAPE pathogens.FindingsIn A. baumannii ATCC17978, BAC prevents gentamicin killing and drastically increases the frequency at which resistant mutants emerge, through reducing intracellular antibiotic accumulation. BAC also increases the MIC of multiple aminoglycoside antibiotics (kanamycin, tobramycin, streptomycin, gentamicin and amikacin). BAC promotes the emergence of mutants with reduced gentamicin susceptibility in other Gram-negative ESKAPE pathogens but does not always alter the MIC. These effects occur at BAC concentrations which are similar to residual levels in high-use environments, and just below the concentration range for BAC when used as a preservative in eye drops and ear drops.InterpretationOur results suggest that subinhibitory BAC has the potential to antagonise aminoglycoside activity and promote the emergence of bacterial mutants with reduced susceptibility. We suggest that the extremely widespread use of BAC in clinical and home settings and its long half-life mean there is potential for these interactions to occur in the environment, or in patients who use BAC-containing products while taking aminoglycosides to treat skin, eye or ear infections, although such co-exposure is likely to be rare. We suggest that biocide stewardship is needed to prevent the types of exposure that can contribute to antibiotic resistance.FundingThis work was funded by the National Health and Medical Research Council of Australia. The funders had no role in study design, interpretation or decision to publish.

Project description:BackgroundInterpretative 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.MethodsPhenotypic 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.FindingsARMIA-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.InterpretationThis 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.

Dataset Information

Mechanisms of Resistance to Aminoglycoside Antibiotics: Overview and Perspectives.

Publications

Mechanisms of Resistance to Aminoglycoside Antibiotics: Overview and Perspectives.

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