Abstract
Whole-genome sequencing (WGS) will soon replace traditional phenotypic methods for routine testing of foodborne antimicrobial resistance (AMR). WGS is expected to improve AMR surveillance by providing a greater understanding of the transmission of resistant bacteria and AMR genes throughout the food chain, and therefore support risk assessment activities. At this stage, it is unclear how WGS data can be integrated into quantitative microbial risk assessment (QMRA) models and whether their integration will impact final risk estimates or the assessment of risk mitigation measures. This review explores opportunities and challenges of integrating WGS data into QMRA models that follow the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR. We describe how WGS offers an opportunity to enhance the next-generation of foodborne AMR QMRA modeling. Instead of considering all hazard strains as equally likely to cause disease, WGS data can improve hazard identification by focusing on those strains of highest public health relevance. WGS results can be used to stratify hazards into strains with similar genetic profiles that are expected to behave similarly, e.g., in terms of growth, survival, virulence or response to antimicrobial treatment. The QMRA input distributions can be tailored to each strain accordingly, making it possible to capture the variability in the strains of interest while decreasing the uncertainty in the model. WGS also allows for a more meaningful approach to explore genetic similarity among bacterial populations found at successive stages of the food chain, improving the estimation of the probability and magnitude of exposure to AMR hazards at point of consumption. WGS therefore has the potential to substantially improve the utility of foodborne AMR QMRA models. However, some degree of uncertainty remains in relation to the thresholds of genetic similarity to be used, as well as the degree of correlation between genotypic and phenotypic profiles. The latter could be improved using a functional approach based on prediction of microbial behavior from a combination of ‘omics’ techniques (e.g., transcriptomics, proteomics and metabolomics). We strongly recommend that methodologies to incorporate WGS data in risk assessment be included in any future revision of the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR.
Highlights
Antimicrobial resistance (AMR) represents a major threat to public health, with an estimated 700,000 deaths attributable to AMR every year in the world, and a projected 10 million deaths per year by 2050 in the absence of additional control measures (O’Neill, 2014)
We explore opportunities and challenges of integrating WGS data into Quantitative microbial risk assessment (QMRA) models of foodborne AMR, following the framework proposed in the Codex Alimentarius Guidelines (Codex Alimentarius, 2011)
We argue that a similar framework could be developed for QMRA of foodborne AMR, with the objective of describing the probability of a hazard being transmitted from one step of the food chain to the
Summary
Antimicrobial resistance (AMR) represents a major threat to public health, with an estimated 700,000 deaths attributable to AMR every year in the world, and a projected 10 million deaths per year by 2050 in the absence of additional control measures (O’Neill, 2014). In addition to in silico speciation and sub-species level differentiation of isolates (i.e., subtyping), as well as a description of the molecular mechanisms underlying observed resistance phenotypes, the use of WGS is expected to assist AMR surveillance by providing a greater understanding of the transmission of AMR bacteria and genes throughout the food chain, and support risk assessment of foodborne AMR (Food and Drug Administration, 2018; Public Health Agency of Canada, 2018). These Guidelines, as well as other risk assessment approaches, have been applied to a number of QMRA models of foodborne AMR in the past (McEwen, 2012; Caffrey et al, 2018) All of these models, as well as the Codex Alimentarius Guidelines, were developed prior to the WGS era, and did not include or consider WGS data.
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