Abstract #291

# 291
Whole-genome sequencing for pathogen environmental monitoring: Focus on Listeria.
Matthew J. Stasiewicz*1, 1University of Illinois, Urbana, IL.

This talk will evaluate whole-genome sequencing as a new tool to identify persistent Listeria monocytogenes in food-associated environments. The critical assumptions are two. First—that to eliminate contamination by a persistent strain, we first need to identify that the contamination is, in fact, due to a persistent strain rather than sporadic re-contamination (to apply appropriate interventions). Second—that isolates of a persistent strain will be more closely related than isolates from a sporadic contamination event. Under this paradigm, whole-genome sequencing is emerging as the gold-standard subtyping tool for Listeria, and for good reason: analyses of these data provide nearly perfect resolution of differences at the single nucleotide level. This presents a new reality for pathogen subtyping because we can no longer apply the rule of thumb that isolates which appear indistinguishable represent distinct strains. Instead, we can use phylogenetics and other genomic analyses to identify when closely related isolates are likely to represent persistent strains. These analyses can shed light on several questions: How many SNP count as a close genetic relationship? What are the implications of mobile elements on subtyping? And how might government, academic, and industry even conduct these sophisticated analyses? This talk will address these questions through a survey of recent developments and results from a case study in a related food environment, using whole-genome sequencing of hundreds of Listeria from 30 retail delis to identify persist strains.

Key Words: Listeria

Speaker Bio
Matthew Stasiewicz is an assistant professor of applied food safety in the Department of Food Science and Human Nutrition at the University of Illinois at Urbana-Champaign. His work focuses on applying engineering and data analytic approaches to advance food safety microbiology. Most relevant to ADSA, he has worked on adapting whole-genome sequence bioinformatics and other statistical approaches to identifying persistent Listeria in food-associated environments, to enable targeted management strategies. He went to Michigan State University for undergraduate study, earning both a BS in biosystems engineering, focusing on food process engineering, and a BA in philosophy, focusing on ethics. He then studied at Cornell, receiving MS and PhD degrees in food microbiology, working on risk analysis in microbial food safety. Those projects focused on the identification and management of persistent Listeria monocytogenes in food environments using whole genome sequencing and improved data analysis techniques. He has also worked on international teams to develop appropriate technology to manage mycotoxins corn, with his specific contribution being single-kernel sorting strategies to identify and remove the most heavily contaminated kernels.