Implementation of Nationwide Real-time Whole-genome Sequencing to Enhance Listeriosis Outbreak Detection and Investigation

Autor: Brendan R Jackson, Benjamin J. Silk, Jamie L. Wasilenko, Hannes Pouseele, William Klimke, Jennifer Beal, Matthew Doyle, Matthew E. Wise, Zuzana Kucerova, Steven Stroika, Katie Roache, Marc W. Allard, Peter Gerner-Smidt, John M. Besser, Angela Fields, Amanda Conrad, Stephanie Defibaugh-Chavez, Yi Chen, Glenn E. Tillman, Kelly A. Jackson, Lee S. Katz, Rajal K. Mody, Mustafa Simmons, Kristy A. Kubota, Eija Trees, Heather A. Carleton, Eric W. Brown, Errol Strain, L. Hannah Gould, Ashley Sabol, Ruth Timme, Cheryl L. Tarr
Jazyk: angličtina
Rok vydání: 2016
Předmět:
Popis: Listeria monocytogenes (Lm) causes severe foodborne illness (listeriosis). Previous molecular subtyping methods, such as pulsed-field gel electrophoresis (PFGE), were critical in detecting outbreaks that led to food safety improvements and declining incidence, but PFGE provides limited genetic resolution. A multiagency collaboration began performing real-time, whole-genome sequencing (WGS) on all US Lm isolates from patients, food, and the environment in September 2013, posting sequencing data into a public repository. Compared with the year before the project began, WGS, combined with epidemiologic and product trace-back data, detected more listeriosis clusters and solved more outbreaks (2 outbreaks in pre-WGS year, 5 in WGS year 1, and 9 in year 2). Whole-genome multilocus sequence typing and single nucleotide polymorphism analyses provided equivalent phylogenetic relationships relevant to investigations; results were most useful when interpreted in context of epidemiological data. WGS has transformed listeriosis outbreak surveillance and is being implemented for other foodborne pathogens.
Databáze: OpenAIRE