Recipient Organization
CORNELL UNIVERSITY
(N/A)
ITHACA,NY 14853
Performing Department
Food Science
Non Technical Summary
The rapid spread of antibiotic resistance and the slow process to discover new antibacterial drugs has made it imperative that we must rely on old antibiotics such as colistin, a last-resort antibiotic reserved for treating severe infections. Genes involved in colistin resistance, such as mcr, are often encoded on mobile genetic elements that can horizontally transfer across strains. Hence, it is imperative that we develop a robust surveillance system to detect and functionally characterize mcr variants. This will help us to determine mcr genetic diversity and identify optimum mitigation points across the food chain. This project aims to define the prevalence and the physiological impact of colistin resistance genes in foodborne pathogens. To prioritize control efforts, we aim to develop a tool to define the diversity of colistin resistance genes in whole genome sequences and the impact of gene expression on colistin resistance using molecular cloning and phenotypic analyses. This work will serve as a basis to achieve the overarching aim, which is to restrict the spread of antibiotic resistance and develop new treatment tools.
Animal Health Component
80%
Research Effort Categories
Basic
20%
Applied
80%
Developmental
(N/A)
Goals / Objectives
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.
Project Methods
Aim1: WGS-based identification and characterization of mcr diversity in foodborne pathogens in NYS. Analyses will focus on existing WGS data for Salmonella and E. coli; available WGS data for other Gram-negative isolates from NY will also be screened for mcr genes. WGS data will be obtained from different sources, including: i) the Pathogen Detection database; ii) the Food Microbe Tracker database; and iii) additional WGS data of mcr-containing genera from NCBI. Additionally, we will perform whole genome sequencing for limited numbers of these isolates (e.g., colistin resistant strains) either in-house and/or as part of the NCBI Pathogen Detection database free service.We will obtain DNA sequences of all current mcr variants from ResFinder database and the National Database of Antibiotic Resistant Organisms. The encoded amino acid sequences of mcr variants will be used to detect mcr-like genes in WGS data using Translated Nucleotide BLAST (tblastn) analyses as implemented in a modified version of Btyper, a computational tool that we have developed for virulence-based classification of Bacillus strains. Multiple sequence alignments and maximum likelihood phylogenies will be done. This approach will allow us to determine the extent of colistin resistance spread in NYS, characterize the genetic diversity of mcr, and identify possible bacterial reservoirs of mcr genes.Aim 2: Assess the ability of the identified genes to confer resistance to colistin. From aim 1, we will select mcr variants that represent the phylogenetic and phylogeographic diversity. Colistin susceptibility test will be carried out to define the levels of colistin resistance in selective isolates that represent variants diversity. Moreover, mcr coding regions will be cloned and expressed in a heterologous host such as E.coli. The ability of cloned mcr variants to confer colistin resistance will be accessed using Colistin susceptibility testing methods. We will use standard broth micro-dilution following Clinical and Laboratory Standards Institute (CLSI) and European Committee on Antimicrobial Susceptibility Testing (EUCAST) recommendations and/or killing assays.We aim to define experimentally the efficacy of mcr variants' expression on colistin resistance. This will allow us to assess and prioritize threat based on variants prevalence and their biological significance in colistin resistance, which may facilitate targeted control efforts.Aim 3: Develop a pilot bioinformatics tool to detect mcr variants. We aim to develop a bioinformatics tool to i) detect mcr variants from WGS data; ii) predict variant importance in colistin resistance based on functional analysis data from Aim 2, and iii) provide data on phylogeny and phylogeography of mcr variants. This will enable stakeholders to timely detect foodborne pathogens associated with high threat level, such as colistin resistance, in order to initiate contamination and containment procedures in health care facilities, clinical and food-safety laboratories.