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

Aug 6, 2026

Upload by Aria

China’s widespread antimicrobial use makes antimicrobial resistance (AMR) surveillance critical for limiting the spread of resistant bacteria. This study analyzes 461 microbiomes collected over 2.5 years from chickens, carcasses, and farm and abattoir environments across multiple sites. A machine-learning data mining workflow identifies 145 potentially mobile antibiotic resistance genes shared across chickens and environments, and a core set of 233 ARGs and 186 microbial species links gut microbiome signals to AMR profiles of Escherichia coli and other clinically relevant bacteria.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Background and rationale

Study design and sampling scope

Data mining and machine-learning analysis

Core ARGs, microbial species, and AMR links

Environmental drivers and network correlations

King’s Research Portal DOI: 10.1038/s43016-023-00814-w Document Version Publisher's PDF, also known as Version of record Link to publication record in King's Research Portal Citation for published version (APA): Baker, M. , Zhang, X. , Maciel-Guerra, A. , Dong, Y. , Wang, W. , Hu, Y. , Renney, D. , Hu, Y. , Liu, L. , Li, H. , Tong, Z. , Zhang, M. , Geng, Y. , Zhao, L. , Hao, Z. , Senin, N. , Chen, J. , Peng, Z. , Li, F. , & Dottorini, T. (2023) . Machine learning and metagenomics reveal shared antimicrobial resistance profiles across multiple chicken farms and abattoirs in China. Nature Food, 4(8), 707-720 . [https://doi.org/10.1038/s43016-023-00814-w](https://doi.org/10.1038/s43016-023-00814-w) Citing this paper Please note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections. General rights Copyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights. •Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research. •You may not further distribute the material or use it for any profit-making activity or commercial gain •You may freely distribute the URL identifying the publication in the Research Portal Take down policy If you believe that this document breaches copyright please contact [librarypure@kcl.ac.uk](librarypure@kcl.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim. Download date: 05. Aug. 2026 nature food Article [https://doi.org/10.1038/s43016-023-00814-w](https://doi.org/10.1038/s43016-023-00814-w) | Machine learning and metagenomics reveal shared antimicrobial resistance profiles across multiple chicken farms and abattoirs in China | | | | --- | --- | --- | | Received: 9 January 2023
Accepted: 7 July 2023
Published online: 10 August 2023 Check for updates | Michelle Baker 1,12, Xibin Zhang2,12, Alexandre Maciel-Guerra1,12, Yinping Dong3,
Wei Wang3, Yujie Hu 3, David Renney4, Yue Hu1, Longhai Liu5, Hui Li6, Zhiqin Tong6, Meimei Zhang7, Yingzhi Geng7, Li Zhao8, Zhihui Hao9, NicolaSenin 10, Junshi Chen3, Zixin Peng 3,13, Fengqin Li 3,13 & Tania Dottorini 1,11
China is the largest global consumer of antimicrobialsand improving surveillance methods could help to reduce antimicrobial resistance (AMR) spread. Here we report the surveillance often 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 chickensand environments across all farms. A core set of 233 ARGsand 186 microbial species extracted from the chicken gut microbiome correlated with the AMR profiles of Escherichia coli colonizing the same gut, including Arcobacter, Acinetobacterand Sphingobacterium, clinically relevant for humans, and 38 clinically relevant ARGs. Temperature and humidity in the barns were also correlated with ARG presence. We revealan intricate network of correlations between environments, microbial communities and AMR, suggesting multiple routes to improving AMR surveillance in livestock production. | | | Antimicrobial use in poultry production in China is five times highe

FAQs

What data did the study analyze to assess antimicrobial resistance?

It analyzed 461 microbiomes from birds, carcasses, and environmental samples collected across large-scale chicken farms and four connected abattoirs over 2.5 years.

Which antimicrobial resistance signals were identified as shared across farms and environments?

The analysis identified 145 potentially mobile antibiotic resistance genes (ARGs) shared between chickens and environments across all farms, along with a core set of 233 ARGs and 186 microbial species.

How were environmental conditions connected to antimicrobial resistance?

Temperature and humidity in the barns correlated with the presence of ARGs, and the study revealed interconnected correlations among environments, microbial communities, and AMR.

Aug 6, 2026

Upload by Aria