Machine learning and metagenomics reveal shared antimicrobial resistance profiles across multiple chicken farms and abattoirs in China

Michelle Baker, Xibin Zhang, Alexandre Maciel-Guerra, Yinping Dong, Wei Wang, Yujie Hu, David Renney, Yue Hu, Longhai Liu, Hui Li, Zhiqin Tong, Meimei Zhang, Yingzhi Geng, Li Zhao, Zhihui Hao, Nicola Senin, Junshi Chen, Zixin Peng, Fengqin Li, Tania Dottorini

Research output: Journal PublicationArticlepeer-review

7 Citations (Scopus)

Abstract

China is the largest global consumer of antimicrobials and improving surveillance methods could help to reduce antimicrobial resistance (AMR) spread. Here we report the surveillance of ten large-scale chicken farms and four connected abattoirs in three Chinese provinces over 2.5 years. Using a data mining approach based on machine learning, we analysed 461 microbiomes from birds, carcasses and environments, identifying 145 potentially mobile antibiotic resistance genes (ARGs) shared between chickens and environments across all farms. A core set of 233 ARGs and 186 microbial species extracted from the chicken gut microbiome correlated with the AMR profiles of Escherichia coli colonizing the same gut, including Arcobacter, Acinetobacter and Sphingobacterium, clinically relevant for humans, and 38 clinically relevant ARGs. Temperature and humidity in the barns were also correlated with ARG presence. We reveal an intricate network of correlations between environments, microbial communities and AMR, suggesting multiple routes to improving AMR surveillance in livestock production.

Original languageEnglish
Pages (from-to)707-720
Number of pages14
JournalNature Food
Volume4
Issue number8
DOIs
Publication statusPublished - Aug 2023

ASJC Scopus subject areas

  • Food Science
  • Animal Science and Zoology
  • Agronomy and Crop Science

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