摘要
      在畜牧生产中使用抗微生物药物与抗微生物耐药性（AMR）的上升有关。中国是最大的抗菌药物消费国，改进AMR监测方法可能有助于为干预提供信息。在这里，我们报告了2.5年来对中国三个省的十个大型养鸡场和四个相连的屠宰场的监测。通过使用基于机器学习的定制数据挖掘方法，我们分析了鸟类、尸体的微生物组和耐药性和环境。我们发现，鸡肠道耐药组和微生物组的一个核心亚群，以临床相关的细菌和抗生素耐药基因为特征，与肠道定植的大肠杆菌的AMR谱相关。该核心本身受环境温度和湿度的影响，包含鸡和环境共享的临床相关移动ARG，并与抗菌药物的使用相关。我们的研究结果表明，在畜牧生产中优化AMR监测是一条可行的途径。
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.

https://assets.researchsquare.com/files/rs-2458989/v1/46849cd8-3388-46ea-8978-4627d10e99fa.pdf?c=1673626633