Experimental, Modeling, and Genomics Approaches to Studying and Characterizing Risks Associated with Foodborne Pathogens
Join the Department of Food Science and Nutrition for this seminar series event featuring guest speaker Ruixi Chen, a Ph.D. candidate at the Wiedmann Lab at Cornell University.
It has been estimated that approximately 9.4 million episodes of domestically acquired foodborne illnesses due to known pathogens occur in the US each year. Among foodborne pathogens, nontyphoidal Salmonella (NTS) and Listeria monocytogenes (LM) represent great public health concern. NTS is estimated to be responsible for 11 percent of illnesses, 35 percent of hospitalizations, and 28 percent of deaths, making it the leading cause of foodborne illnesses and deaths in the US. Although LM does not have the lion's share of foodborne illnesses, it is the third leading cause of foodborne deaths due to a high death rate of 16 percent and mainly targets fetuses, the elderly, and immuno-compromised individuals. Therefore, a constant improvement of control strategies and regulatory policies for a reduced risk associated with these two pathogens in food supply is of crucial importance.
Smoked seafood is a ready-to-eat product that has shown a high incidence of LM contamination and has been associated with a number of recalls. Chen and his colleagues used cold-smoked salmon as a model to (i) investigate factors that contribute to an enhanced risk associated with LM contamination and (ii) develop a second-order Monte-Carlo modeling framework to assess enterprise risks associated with the products and to guide industry practices to reduce such risks. On the other hand, differences in human virulence have been reported across NTS serovars and associated subtypes. Current NTS control strategies may unintentionally focus on serovars and subtypes with high prevalence in source populations but rarely associated with human clinical illness, while a rational and scalable approach to identify NTS with differential ability to cause human diseases is not yet available. The researchers developed and implemented a framework leveraging WGS data and associated metadata in NCBI Pathogen Detection database to identify (i) subtypes with differential likelihoods of causing human diseases and (ii) genomic signatures that are responsible for such differences. The data generated can be ultimately used to predict and quantify virulence potential for a risk-based approach to controlling a diverse range of NTS serovars associated with major agricultural animals.