Integrating Whole-Genome Sequencing data into quantitative risk assessment of foodborne antimicrobial resistance: A review of opportunities and challenges. Collineau L*, Boerlin P, Carson CA, Chapman B*, Fazil A*, Hetman B*, McEwen SA, Parmley EJ, Reid-Smith RJ, Taboada EN*, Smith BA*. Front Microbiol 2019 May 21;10:1107. doi: https://doi.org/10.3389/fmicb.2019.01107
The public health impact of antimicrobial resistance (AMR) in foodborne bacteria is poorly understood. Risk models are tools used to predict the impacts of microbial hazards and inform on effective interventions. Whole genome sequencing (WGS) is another important tool that has revolutionized many aspects of public health, from pathogen diagnostics and surveillance to outbreak investigations. Bringing these powerful tools together would improve the accuracy and success of directed strategies to reduce our risks from AMR in foodborne microbial hazards.
What was known about this area prior to your work, and why was the research done?
Antimicrobial resistance (AMR) is a complex problem driven by many interconnected factors throughout the food chain. Yet, the precise impact of foodborne AMR on public health is unknown. The World Health Organization’s Codex Alimentarius provides science-based guidance on how to assess and manage this public health threat using a process known as a quantitative microbial risk assessment (QMRA). The growing access to whole genome sequencing (WGS) provides an unprecedented volume of pathogen data that can be used to predict indicators of risk, including AMR and factors influencing disease severity. This information could inform farm-to-fork QMRAs and help estimate the public health risks of foodborne AMR. However, little practical application has occurred to date to incorporate WGS into QMRAs. This review lays the foundation to bring genomics into risk modelling and how WGS could inform foodborne AMR QMRA models.
What are your most significant findings from this work?
The widespread adoption and use of WGS offers an opportunity to enhance the next-generation of foodborne AMR QMRA modeling. WGS can assist in tracing food contamination points, subtyping species, and predicting the expression of drug resistance profiles. Analyzed sequence data can be used to classify microbial hazards by their traits (e.g., growth, survival, pathogenicity, virulence or response to antimicrobial treatment). In addition, the hazard of interest may include mobile genetic elements that can move AMR genes between bacteria. This added data gives researchers a more meaningful approach to explore the genetics of bacterial populations found from farm-to-fork resulting in more accurate estimates of the potential benefits of public health interventions through QMRA.
What are the implications or impact of the research?
WGS has the potential to substantially improve the usefulness of foodborne AMR QMRA models. However, researchers in the fields of genomics and risk modelling must work together to address sources of uncertainty that may be encountered. This uncertainty can arise from inconsistent thresholds to determine genetic similarity or degree of correlation between genotype and the traits expressed by the pathogen. The QMRA could also predict the likelihood of negative health effects resulting from an antimicrobial resistant infection such as illness severity or length of hospital stay. This evidence could then be used to inform illness prevention and treatment efforts. Given these potential public health benefits, it is strongly recommended that methodologies to incorporate WGS data in risk assessments be included in future revisions of the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR.
Additional References of Significance:
- Collineau L*, Chapman B*, Bao X*, Sivapathasundaram B*, Carson CA, Fazil A*, Reid-Smith RJ, Smith BA*. A farm-to-fork quantitative risk assessment model for Salmonella Heidelberg resistant to third-generation cephalosporins in broiler chickens in Canada. Int J Food Microbiol 2020. In press. doi: https://doi.org/10.1016/j.ijfoodmicro.2020.108559
- Caffrey N, Invik J, Waldner CL, Ramsay D, Checkley SL. Risk assessments evaluating foodborne antimicrobial resistance in humans: a scoping review. Microb Risk Anal 2019 April;11:31-46. https://www.sciencedirect.com/science/article/pii/S2352352218300057
- Bengtsson-Palme J. Antibiotic resistance in the food supply chain: where can sequencing and metagenomics aid risk assessment? Curr Opin Food Sci 2017 April;14:66-71. doi: https://doi.org/10.1016/j.cofs.2017.01.010
- Codex Alimentarius (2011). Guidelines for risk analysis of foodborne antimicrobial resistance. CAC/GL 77-2011:1-29.