Genomics to inform antimicrobial use in livestock

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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.