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DEVELOPING PREDICTIVE TOOLS TO RESPOND TO THE CHANGING ECOLOGICAL LANDSCAPE OF E. COLI IN POULTRY PRODUCTION

Objective

Escherichia coli is a bacterial pathogen that has been studied intensively in poultry production. Despite decades of research, diseases caused by E. coli continue to be a top concern for poultry producers. Numerous methods of vaccination have been explored and commercialized, yet comprehensive control of this pathogen remains elusive. While substantial work has focused on defining avian E. coli isolated from clinical disease, there are major gaps in understanding of how these clinical isolates relate to the overall ecology of E. coli in poultry production systems. A baseline understanding of the dynamics of the total E. coli populations within poultry production systems, and their response to mitigation strategies, is necessary to better predict the best approaches for E. coli control. The goal of this work is to define E. coli ecology in poultry production, and utilize data to inform control with an emphasis on predictive approaches for understanding when, where, and how to implement mitigation approaches. Using field-based and controlled studies, the ecological landscape of E. coli in poultry production relative to disease will be defined. Shifts in E. coli ecology and disease will be studied in the context of vaccination. From these analyses, predictive models will be developed to quantify expected changes in E. coli populations in response to mitigation, and these models will be validated using real data. Overall, this study takes innovative approaches towards assessing and responding to changes in pathogen ecology that can serve as a template for applied solutions in animal agriculture. This will be accomplished with the following Objectives:Objective 1. Define the ecological landscape of E. coli in poultry production relative to disease. While disease-associated avian E. coli from poultry have been thoroughly studied, there are major knowledge gaps surrounding the overall ecology of E. coli in poultry production systems. We will leverage ongoing longitudinal studies to comprehensively examine E. coli populations in the gastrointestinal and respiratory tracts in commercial turkey production. These populations will be compared to breeder source populations, and disease-associated E. coli at the breeder and commercial flock levels, to determine the relationships between colonizing E. coli in the bird, and those ultimately causing disease. This objective will also determine transmission dynamics from parent to progeny, relationships between E. coli load and disease, and E. coli clonal diversity in the context of APEC carriage and the existing microbiota.Objective 2. Determine shifts in E. coli ecology and disease relative to vaccination. Commercial and autogenous vaccines are commonly used in poultry production to prevent or reduce E. coli-associated disease. However, we know very little about the impact of such vaccination on colonizing E. coli populations, and whether vaccination specifically targets disease prevention or more broadly targets APEC colonization throughout the bird. Here, we will utilize a large field study to examine the implementation of a commercial E. coli vaccine into an integrated turkey production system that is naïve to such vaccination. Populations of E. coli will be examined across ten different farms within a vertically integrated turkey company, with control and vaccinated barns within each farm followed for multiple flock cycles. We will utilize a top-down approach to assess shifts in E. coli populations within several niches (barn environment, gastrointestinal tract, respiratory tract, and clinical isolates from disease). Mortality, performance, and immune response will also be measured. From these analyses, predictive models will be developed to quantify expected changes in E. coli populations in response to vaccine implementation. Follow-up controlled animal experiments will be performed to validate the model and to further predict when vaccines will lose efficacy in the field.

Investigators
Johnson, Dennis
Institution
University of Minnesota
Start date
2020
End date
2023
Project number
MINV-63-128
Accession number
1022835