Validation of Selective Agars for Detection and Quantification of Escherichia coli Strains Resistant to Critically Important Antimicrobials.

Success in the global fight against antimicrobial resistance (AMR) is likely to improve if surveillance can be performed on an epidemiological scale. An approach based on agars with incorporated antimicrobials has enormous potential to achieve this. However, there is a need to identify the combinations of selective agars and key antimicrobials yielding the most accurate counts of susceptible and resistant organisms. A series of experiments involving 1,202 plates identified the best candidate combinations from six commercially available agars and five antimicrobials, using 18 Escherichia coli strains as either pure cultures or inocula-spiked feces. The effects of various design factors on colony counts were analyzed in generalized linear models. Without antimicrobials, Brilliance E. coli and CHROMagar ECC agars yielded 28.9% and 23.5% more colonies, respectively, than MacConkey agar. The order of superiority of agars remained unchanged when fecal samples with or without spiking of resistant E. coli strains were inoculated onto agars with or without specific antimicrobials. When antimicrobials were incorporated at various concentrations, it was revealed that ampicillin, tetracycline, and ciprofloxacin were suitable for incorporation into Brilliance and CHROMagar agars at all defined concentrations. Gentamicin was suitable for incorporation only at 8 and 16 μg/ml, while ceftiofur was suitable only at 1 μg/ml. CHROMagar extended-spectrum β-lactamase (ESBL) agar supported growth of a wider diversity of extended-spectrum-cephalosporin-resistant E. coli strains. The findings demonstrate the potential for agars with incorporated antimicrobials to be combined with laboratory-based robotics to deliver AMR surveillance on a vast scale with greater sensitivity of detection and strategic relevance. Established models of surveillance for AMR in livestock typically have a low sampling intensity, which creates a tremendous barrier to understanding the variation of resistance among animal and food enterprises. However, developments in laboratory robotics now make it possible to rapidly and affordably process large volumes of samples. Combined with modern selective agars incorporating antimicrobials, this forms the basis of a novel surveillance process for identifying resistant bacteria by chromogenic reactions, including accurately detecting and quantifying the presence of bacteria even when they are present at low concentrations. Because Escherichia coli is a widely preferred indicator bacterium for AMR surveillance, this study identifies the optimal selective agar for quantifying resistant E. coli strains by assessing the growth performance on agars with antimicrobials. The findings are the first step toward exploiting laboratory robotics in an up-scaled approach to AMR surveillance in livestock, with wider adaptations in food, clinical microbiology, and public health.