Validation of selective agars for detection and quantification of Escherichia coli resistant to critically important antimicrobials

Abstract

Success in the global fight against antimicrobial resistance (AMR) is likely to improve if surveillance can be performed more rapidly, affordably and on a larger scale. An approach based on robotics and agars incorporated with 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 designed 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 inoculums within faeces. The effect of various design factors on colony counts were analysed in generalised linear models. Without antimicrobials, Brilliance E. coli (Brilliance) and CHROMagar ECC (CHROMagar) agars yielded 28.9% and 23.5% more colonies than MacConkey agar. The order of superiority of agars remained unchanged when faecal samples with and without spiking of resistant E. coli were inoculated onto agars with or without specific antimicrobials. When incorporating antimicrobials at varying concentrations, it was revealed that ampicillin, tetracycline and ciprofloxacin are suitable for incorporation into Brilliance and CHROMagar agars at all defined concentrations. Gentamicin was only suitable for incorporation at 8 and 16 g/mL while ceftiofur was only suitable at 1 g/mL. CHROMagar ESBL agar supported growth of a wider diversity of extended-spectrum cephalosporin-resistant E. coli. The findings demonstrate the potential for combining robotics with agars to deliver AMR surveillance on a vast scale with greater sensitivity of detection and strategic relevance. IMPORTANCEEstablished models of surveillance for AMR in livestock typically have a low sampling intensity which creates a tremendous barrier to understanding the variation of resistance amongst animal and food enterprises. However, developments in laboratory robotics now make it possible to rapidly and affordably process high 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 reaction including accurately detecting and quantifying their presence even when present at low concentration. As Escherichia coli is a widely preferred indicator bacterium for AMR surveillance, this study identifies the optimal selective agar for quantifying resistant E. coli by assessing the growth performance on agars with antimicrobials. The findings are the first step towards exploiting laboratory robotics in an up-scaled approach to AMR surveillance in livestock with wider adaptations in food, clinical microbiology and public health.