Abstract
Each year, approximately one in eight Canadians are affected by foodborne illness, either through outbreaks or sporadic illness, with animals being the major reservoir for the pathogens. Whole genome sequence analyses are now routinely implemented by public and animal health laboratories to define epidemiological disease clusters and to identify potential sources of infection. Similarly, a number of bioinformatics tools can be used to identify virulence and antimicrobial resistance (AMR) determinants in the genomes of pathogenic strains. Many important clinical and phenotypic characteristics of these pathogens can now be predicted using machine learning algorithms applied to whole genome sequence data. In this overview, we compare the ability of support vector machines, gradient-boosted decision trees and artificial neural networks to predict the levels of AMR within Salmonella enterica and extended-spectrum β-lactamase (ESBL) producing Escherichia coli. We show that minimum inhibitory concentrations (MIC) for each of 13 antimicrobials for S. enterica strains can be accurately determined, and that ESBL-producing E. coli strains can be accurately classified as susceptible, intermediate or resistant for each of seven antimicrobials. In addition to AMR and bacterial populations of greatest risk to human health, artificial intelligence algorithms hold promise as tools to predict other clinically and epidemiologically important phenotypes of enteric pathogens.
Highlights
Every year, about one in eight Canadians will be affected by a foodborne illness, resulting in an average of 11,600 hospitalizations and 238 deaths nationwide [1]
We show that minimum inhibitory concentrations (MIC) for each of 13 antimicrobials for S. enterica strains can be accurately determined, and that ESBL-producing E. coli strains can be accurately classified as susceptible, intermediate or resistant for each of seven antimicrobials
We examined a set of 2,413 E. coli sequences containing ESBL producers, but no MIC data were available for these strains
Summary
About one in eight Canadians will be affected by a foodborne illness, resulting in an average of 11,600 hospitalizations and 238 deaths nationwide [1]. Animals are often the reservoir for major bacterial pathogens such as Salmonella enterica and Escherichia coli. These pathogens are associated with both sporadic cases and outbreaks of foodborne disease. Antimicrobial resistance (AMR) among these organisms is a growing concern, with treatment being more difficult and expensive. Two of the most commonly used diagnostic methods are diffusion and dilution tests Diffusion methods, such as the Kirby–Bauer method, require growing a bacterial lawn in either a disk of known concentration of antimicrobials or a strip with a gradient of concentrations of antimicrobials; the zone of growth inhibition around the antimicrobial is compared with a standard to determine the resistance of the bacteria [3]. Dilution methods involve liquid cultures in serial dilution of each antimicrobial, where growth of the organism is used to determine the minimum inhibitory concentration (MIC) [3,4]
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