Genomics to inform antimicrobial use in livestock

基因组学为牲畜抗菌药物的使用提供信息

• 批准号: RGPIN-2020-04447
• 负责人: Otto, Simon
• 金额: $ 2.04万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764386
• 关键词: Genomics; inform; antimicrobial; use; livestock

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.

将家畜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并为抗菌素管理提供信息至关重要。随着基因组技术的完善，畜牧部门有能力寻求将基因组数据整合到诊断系统和国家监测计划中的方法。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具有很大的潜力。我的NSERC发现研究计划的长期目标是开发使用AMR基因组学的方法，为牲畜的AMU决策提供信息。为了实现这一目标，我计划在未来五年内开发家畜细菌基因组AMR数据的分析模型。我们将使用概念验证的家畜生物（禽致病性大肠杆菌和溶血性曼海姆氏病），因为我们有基因组和表型数据。溶血支原体是牛呼吸道疾病的重要病原体，是饲养场牛发病/死亡的最常见原因和肠外AMU的原因。首先，我们将使用耐药基因的基因组数据，并与合作者建立和评估临床抗菌素耐药性的预测模型，这些模型将插入现有的和正在开发的生物信息学管道。我们将评估AMR抵抗组的各种算法的预测能力，以开发用于禽致病性大肠杆菌和溶血分枝杆菌的这些工具。我们的下一阶段将是为家禽中的禽致病性大肠杆菌和牛肉生产链中的溶血性分枝杆菌的AMR情景开发综合评估模型（iAM.AMR）。我们正积极与加拿大抗微生物药物耐药性监测综合项目合作。抗菌素耐药性（AMR）模拟了抗药性从农场到餐桌传播的情景。这一阶段的研究将构建iAM。抗菌素耐药性模拟情景，以确定动物生产链上的干预措施（包括抗菌素）如何调节这些病原体的抗菌素耐药性。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，以及应用干预措施的相对时间。这些作品对我来说是新奇的。AMR的发展。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。总的来说，该项目将提供新颖、强大的分析工具，以进一步了解基因组数据如何预测牲畜病原体的临床抗菌素耐药性，并为抗菌素耐药性决策提供信息。这项工作将最终减少畜牧业中的AMU，向消费者展示强有力的抗菌药物管理，并提高动物福利和畜牧业生产的可持续性。