Location: Poultry Microbiological Safety and Processing Research Unit
Project Number: 6040-32000-079-018-T
Project Type: Trust Fund Cooperative Agreement
Start Date: Sep 1, 2023
End Date: Oct 31, 2024
Objective:
1. Define markers (resistance genes, genetic elements, virulence genes) in antimicrobial-resistant Salmonella and Escherichia coli in poultry and poultry-associated environments.
2. Predict emerging antimicrobial-resistant strains of Salmonella and Escherichia coli using a machine learning model.
Approach:
Antibiotics have long been used to treat bacterial infections and as preventive agents in food animal production. However, the emergence of resistance, particularly to therapeutic antimicrobials in pathogenic and commensal bacteria, has become a global problem. Resistance among these bacteria has the potential to compromise therapy and threaten human health as antibiotic resistant bacteria can be transmitted to humans through contaminated food including poultry and poultry products. This research proposal builds upon a collaboration between teams from the United States and Egypt that began in 2014. Our collaborative team has not only revealed the presence of antimicrobial resistant bacteria in humans, companion animals, and food, but has elucidated antimicrobial resistance genes and the mechanisms of gene dissemination among the studied bacteria. The proposed research will further this research collaboration to focus on poultry and the poultry environment to provide solutions to combat antimicrobial resistance (AR) in foodborne pathogens and commensal bacteria from poultry. Two major approaches will be employed: 1) investigations to accurately define markers of antimicrobial resistant foodborne pathogens and commensals, and 2) development of rapid screening tests to detect these bacteria from poultry and/or the poultry environment. This research will employ cutting edge technology including genomic sequencing and bioinformatics to identify genetic markers which support survival, persistence, and dissemination of antimicrobial resistant foodborne pathogens. Development of a machine learning model will assist in predicting emerging antimicrobial resistant bacteria in poultry. The ultimate goal is to combine these research outcomes to develop rapid screening tests to use at point of contact. Data and technology from the proposed research will be used to assist the poultry and agricultural biotechnology industry in addressing AR in poultry resulting in safer products for the consumer.
