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 cutoffs (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 cutoffs , 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 N95% 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.

Antimicrobial Resistance and Whole-Genome Characterisation of High-Level Ciprofloxacin-Resistant Salmonella Enterica Serovar Kentucky ST 198 Strains Isolated from Human in Poland

“…In addition, isolate No. 3/19 harboured the aac(6 )-Iid gene reported to confer gentamicin resistance [18]. Moreover, the WGS analysis showed the aph(3")-Ib, aph(3")-Id and aadA1 genes detected in one, one, and two isolates, respectively, as only detected streptomycin resistance determinants.…”

Protective Effects of Liposomal Curcumin on Oxidative Stress/Antioxidant Imbalance, Metalloproteinases 2 and -9, Histological Changes and Renal Function in Experimental Nephrotoxicity Induced by Gentamicin

“…The use of gentamicin, in parenteral administration, has shown significant better infection outcomes involving some gram-negative bacteria such as Escherichia coli , Klebsiella pneumoniae , Pseudomonas aeruginosa , and was also efficient against some other strains such as Neisseria [ 5 , 6 , 7 ]. Gentamicin also proved to be efficient in bacterial septicemia, meningitis, peritonitis, endocarditis, pneumonia, urinary tract infections, or odontogenic infections [ 8 ].…”

Aminoglycoside resistance mechanism inference algorithm: Implication for underlying resistance mechanisms to aminoglycosides

“…Recently in EBioMedicine , Mancini and colleagues presented an Aminoglycoside Resistance Mechanism Inference Algorithm (ARMIA) for the inference of resistance mechanisms from inhibition zone diameters [7]. This algorithm uses ECOFFs for gentamicin, tobramycin and kanamycin as well as a working separator cut-off for amikacin.…”

“…The results of this study showed that ARMIA-based inference of resistance mechanisms and WGS data were congruent in 96·3%. In contrast, there was a poor correlation between resistance mechanisms inferred using EUCAST CBPs/expert rules and WGS data (85·6%) [7].…”

“…When assessing the accuracy of various susceptibility testing methods as compared to standard reference methods, the terms very major errors (vME) have been used to describe false susceptible [8]. Thus, in the comparison made by Mancini and colleagues, they reported that EUCAST produced 63 (12·9%) vME, compared to only 2 (0·4%) vME with ARMIA [7].…”