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Identification and Characterization of Colistin Resistance Determinants in Food Systems Associated Pathogens from New York

Objective

Antibiotic resistance (AR) is one of the greatest risk to global health and food security. Hence, it is imperative that we develop a robust surveillance system to detect and functionally characterize AR determinants. Indeed, the Food Safety Modernization Act surveillance-working group recognized the need to improve our current diagnostic methods, emphasizing on the use of culture independent tests, whole genome sequence (WGS) analysis tools and to increase the capacity of states and local communities to track and report AR threats.Colistin is listed as "Highest Priority Critically Important Antimicrobial" by WHO and the UN recommends an immediate stop on its use in growth promotion in agriculture. Alas, genes involved in colistin resistance, such as mcr, are often encoded on mobile genetic elements that can horizontally transfer across strains. To date, 10 mcr gene variants have been reported, providing variable levels of colistin resistance. The bases of this variation and the impact of variants' expression on colistin resistance are not known.In the US, data on the prevalence and heterogeneity of mcr variants are scarce, or non-existent. Our overall goal is to identify the reservoirs of mcr gene and define the distribution of its variants in foodborne pathogens. Additionally, we aim to identify mcr variants that present higher risk to human health by characterization of the physiological impact of each variant's expression on colistin resistance. This will help us to determine prevalence of colistin resistance and identify optimum mitigation points across the food chain.To achieve these goals we will:Perform WGS-based identification and characterization of mcr diversity in foodborne pathogens in New York State (NYS).Assess the ability of the identified genes to confer resistance to colistin.Develop a pilot bioinformatics tool to detect mcr variants. This will enhance our ability to prioritize, inform and develop control strategies for reducing the spread of AR.

Investigators
Wiedmann, Martin
Institution
Cornell University
Start date
2020
End date
2023
Project number
NYC-143945
Accession number
1023966