Using big data on livestock farms could improve antimicrobial resistance surveillance

A new study suggests that using big data and machine learning in antimicrobial resistance (AMR) surveillance in livestock production methods could help inform interventions and offer protections against germs that are becoming resistant to antibiotics.

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Over two and a half years, researchers at the University of Nottingham analysed microbiomes from chickens, carcasses and environments. The resulting network of correlations between livestock, environments, microbial communities and antimicrobial resistance suggests multiple routes for improving antimicrobial resistance surveillance in livestock production.

The use of antimicrobials used to prevent and treat infections in livestock production on farms is associated with the rise of antimicrobial resistant (AMR) infections.

The study, published in Nature Food, identified several antimicrobial resistant genes (ARGs) that were shared between chickens and the farms they lived on that are potentially highly transmissible.

The findings also show that a core subset of the chicken gut microbiome, featuring clinically relevant bacteria and antibiotic resistance genes correlates with AMR profiles of E. coli, colonizing the gut.