Next generation risk assessment: A proof of concept for the integration of genomic data on cold tolerance into quantitative microbial risk assessment for Campylobacter jejuni in poultry meat

Abstract

Quantitative Microbiological Risk assessment (QMRA) models are essential tools for setting up mitigation strategies. Traditional QMRA modelling approaches do not account for the correlation between genetic traits and variability among pathogens, potentially leading to over- or underestimation of microbial exposure and associated risks. We aimed to integrate genomic data into QMRA to propagate bacterial strain variability and update the existing framework of QMRA, following a Next Generation Risk Assessment (NGRA) approach. We used a benchmark QMRA model describing the prevalence and concentration of Campylobacter jejuni on chicken in all stages from farm-to-fork, to model the risk of infection and illness related to consumption of chicken meat. We integrated extended the storage step, to account for genetic variability in cold inactivation by incorporating gene-level genomic data associated with cold tolerance, derived from literature and a large C. jejuni genomic dataset, into the traditional QMRA model by setting up cold inactivation curves from existing data to map the relationship between the number of cold tolerance genes and temperature-dependent inactivation. The predicted number of cases was 8822 human cases/year in the benchmark QMRA model. The contamination of meat with C. jejuni strains having lower cold tolerance genes can reduce the expected number of human campylobacteriosis cases up to 100%; on the other hand, higher number of cold tolerance genes resulted in an increase up to 335.8% on the expected number of cases. Although our results are based on simulations, we show a potential implementation of the genetic information into QMRA, linking risk estimates with whole-genome sequencing data. More research is needed to understand how genetic features shape phenotypical characteristics, which is one of the main uncertainties in the current NGRA model, and to further explore the implications for risk management.