Genomics to inform antimicrobial use in livestock

将来自牲畜的基因组数据整合到国家抗菌素耐药性(AMR)和抗菌素使用(AMU)监测计划中的能力对于解决加拿大动物病原体数据的主要差距和为抗菌素管理提供信息至关重要。随着基因组技术的完善，畜牧业有能力寻求将基因组数据整合到诊断系统和国家监测项目中的方法。利用综合评估模型来了解动物AMU和其他管理干预措施如何影响动物病原体的AMR是很有潜力的。我的NSERC发现研究计划的长期目标是开发利用基因组学为AMR提供信息的方法，为家畜的AMU决策提供信息。为了实现这一目标，我在未来五年提出的研究将为家畜细菌的基因组AMR数据开发分析模型。我们将使用我们拥有基因组和表型数据的概念验证家畜生物(禽类致病性大肠杆菌和溶血性曼海默氏菌)。溶血性支原体是引起牛呼吸道疾病的重要病原体，也是最常见的致病/死亡原因，也是饲养场牛非肠道AMU的原因。首先，我们将使用耐药基因的基因组数据，并与合作者一起建立和评估临床AMR的预测模型，这些模型插入现有的和正在开发的生物信息学管道中。我们将评估AMR耐药组的各种算法的预测能力，以开发针对禽类致病性大肠杆菌和溶血分枝杆菌的工具。我们的下一阶段将开发针对家禽中的禽类致病性大肠杆菌和牛肉生产链中的溶血性支原体的AMR(iAM.AMR)情景的综合评估模型。我们正在积极与加拿大AMR监测综合计划合作。AMR抗药性从农场到餐桌传播的模式情景。我们这一阶段的研究将构建IAM.AMR模型场景，以确定包括AMU在内的动物生产链上的干预措施如何调节这些病原体的AMR。我们将制定方法，纳入家禽和肉牛生产生命周期的时间差异，以及干预措施的相对应用时间。这些部件对于iAM.AMR开发来说都是新奇的。AMR/AMU综合监测必须利用快速发展的基因组技术。开发纳入基因组数据的方法对于简化畜牧业生产系统的监测和诊断测试战略至关重要。总而言之，该计划将提供新颖、强大的分析工具，以促进我们对基因组数据如何预测牲畜病原体的临床AMR并为AMU决策提供信息的理解。这项工作最终将减少畜牧业中的AMU，向消费者展示强大的抗菌剂管理能力，并提高动物福利和畜牧业生产的可持续性。

The ability to integrate genomic data from livestock into national antimicrobial resistance (AMR) and antimicrobial use (AMU) surveillance programs is pivotal to address a major gap in animal pathogen data for Canada and inform antimicrobial stewardship. With the refinement of genomic technology, the livestock sector has the ability to pursue methods to integrate genomic data into diagnostic systems and national surveillance programs. There is great potential to utilize Integrated Assessment Models to understand how animal AMU and other management interventions affect AMR in animal pathogens. The long-term objective of my NSERC Discovery research program is to develop methods using genomics for AMR to inform AMU decisions in livestock. To work towards this objective, my proposed research in the next five years will develop analytical models for genomic AMR data for livestock bacteria. We will use proof-of-concept livestock organisms (avian pathogenic E. coli and Mannheimia haemolytica) for which we have genomic and phenotypic data. M. haemolytica is an important causative agent of bovine respiratory disease, the most common cause of morbidity/mortality and reason for parenteral AMU in feedlot cattle. First, we will use genomic data for resistance genes and to build and assess predictive models for clinical AMR that plug into existing and developing bioinformatics pipelines with collaborators. We will assess the predictive ability of various algorithms for the AMR resistome to develop these tools for avian pathogenic E. coli and M. haemolytica. Our next phase will be to develop Integrative Assessment Model for AMR (iAM.AMR) scenarios for avian pathogenic E. coli in poultry and M. haemolytica in the beef production chain. We are actively working with the Canadian Integrated Program for AMR Surveillance on iAM.AMR model scenarios for farm-to-fork transmission of resistance. This phase of our research will construct iAM.AMR model scenarios to determine how interventions along the animal production chain, including AMU, modulate AMR in these pathogens. We will develop methods to incorporate the temporal differences in production life cycle for poultry and beef cattle, as well as for the relative time of application of interventions. These pieces are novel for iAM.AMR development. Integrated AMR/AMU surveillance must harness rapidly evolving genomic technology. Developing methods to incorporate genomic data is paramount to streamlining surveillance and diagnostic testing strategies for livestock production systems. Collectively, this program will provide novel, robust analytical tools to further our understanding of how genomic data can predict clinical AMR in livestock pathogens and inform AMU decisions. This work will ultimately reduce AMU in animal agriculture, demonstrating strong antimicrobial stewardship to consumers and enhance animal welfare and the sustainability of livestock production.