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Title: Genomic epidemiology of Salmonella enterica serotype Enteritidis based on population structure of prevalent lineages

Author
item DENG, XIANGYU - Centers For Disease Control And Prevention (CDC) - United States
item DESAI, PRERAK - University Of California
item DEN BAKKER, HENK - Cornell University - New York
item MIKOLEIT, MATTHEW - Centers For Disease Control And Prevention (CDC) - United States
item TOLAR, BETH - Centers For Disease Control And Prevention (CDC) - United States
item TREES, EIJA - Centers For Disease Control And Prevention (CDC) - United States
item HENDRIKSEN, RENE - Technical University Of Denmark
item Frye, Jonathan
item PORWOLLIK, STEFFEN - University Of California
item WEIMER, BART - University Of California
item WIEDMANN, MARTIN - Cornell University - New York
item WEINSTOCK, GEORGE - Washington University School Of Medicine
item MCCLELLAND, MICHAEL - University Of California
item FIELDS, PATRICIA - Centers For Disease Control And Prevention (CDC) - United States

Submitted to: Emerging Infectious Diseases
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/19/2014
Publication Date: 9/14/2014
Citation: Deng, X., Desai, P.T., Den Bakker, H.C., Mikoleit, M., Tolar, B., Trees, E., Hendriksen, R.S., Frye, J.G., Porwollik, S., Weimer, B.C., Wiedmann, M., Weinstock, G.M., Mcclelland, M., Fields, P.I. 2014. Genomic epidemiology of Salmonella enterica serotype Enteritidis based on population structure of prevalent lineages. Emerging Infectious Diseases. 20(9):1481-1489.

Interpretive Summary: Salmonella enterica is a foodborne pathogen that can cause gastroenteritis in humans. Salmonella serotype Enteritidis (SE) is one of the most commonly reported serotypes causing human salmonellosis globally. Because SE has a very low level of genetic variability, conventional genetic fingerprinting methods cannot subtype SE. This has made it very difficult to identify members of SE outbreaks and has complicated attempts at epidemiological studies to identify sources of outbreaks or to understand SE evolution and population dynamics. To improve subtyping of SE, we have used whole genome sequencing to detect the very small genetic variability present within this serotype. Whole genome sequences from 125 SE were analyzed and compared to three whole genome sequences from the closest relative of SE, Salmonella enterica serotype Nitra (SN). Single nucleotide changes, also known as single nucleotide polymorphisms (SNP), were filtered to identify 4,887 reliable SNP that distinguished all isolates. Major genetic lineages of SE were identified and revealed potential patterns of geographical and epidemiological distribution. Analyses of population dynamics and evolutionary history estimated that the major lineages of SE emerged and expanded in the 17th and 18th century and also between the 1940s and 1970s. Our whole genome single nucleotide polymorphism typing (WGST) approach was also shown to be robust for SE subtyping with data from two different sequencing platforms. This new typing method will be useful in identifying outbreaks and determining their sources, which can then be addressed by interventions to prevent future outbreaks.

Technical Abstract: Salmonella enterica serotype Enteritidis (SE) is one of the most commonly reported causes of human salmonellosis. The low genetic diversity of SE measured by fingerprinting methods has made subtyping a challenge. In this study, we used whole genome sequencing to characterize a total of 125 SE and Salmonella enterica serotype Nitra (SN) strains. Single nucleotide polymorphisms were filtered to identify 4,887 reliable loci that distinguished all isolates. Five major genetic lineages were recognized, revealing potential patterns of geographical and epidemiological distribution. Analyses on the population dynamics and evolutionary history of major lineages estimated that the emergence and expansion of major lineages took place during 17th and 18th century and between the 1940s and 1970s, respectively. Our whole genome single nucleotide polymorphism typing (WGST) approach was shown to be robust for SE subtyping with data from two different sequencing platforms.