Machine learning and metagenomics enhance surveillance of antimicrobial resistance in chicken production in China

Abstract The use of antimicrobials in livestock production is associated with the rise of antimicrobial resistance (AMR). China is the largest consumer of antimicrobials and improving AMR surveillance methods may help inform intervention. Here, we report the surveillance of ten large-scale chicken farms and four connected abattoirs from three Chinese provinces, over 2.5 years. By using a bespoke data-mining approach based on machine learning, we analysed microbiomes and resistomes from birds, carcasses and environments. We found that a core subset of the chicken gut resistome and microbiome, featuring clinically relevant bacteria and antibiotic resistance genes correlates with AMR profiles of Escherichia coli colonizing the gut. This core is itself influenced by environmental temperature and humidity, contains clinically relevant mobile ARGs shared by chickens and environments, and correlates with antimicrobial usage. Our findings indicate a viable route to optimize AMR surveillance in livestock production.