QUIRMIA—A Phenotype-Based Algorithm for the Inference of Quinolone Resistance Mechanisms in Escherichia coli

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