4.7 Article

Whole genome sequencing as a tool to investigate a cluster of seven cases of listeriosis in Austria and Germany, 2011-2013

Journal

CLINICAL MICROBIOLOGY AND INFECTION
Volume 20, Issue 5, Pages 431-436

Publisher

ELSEVIER SCI LTD
DOI: 10.1111/1469-0691.12638

Keywords

Foodborne infections; Listeria; listeriosis; outbreaks; typing; whole genome sequencing; zoonoses

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A cluster of seven human cases of listeriosis occurred in Austria and in Germany between April 2011 and July 2013. The Listeria monocytogenes serovar (SV) 1/2b isolates shared pulsed-field gel electrophoresis (PFGE) and fluorescent amplified fragment length polymorphism (fAFLP) patterns indistinguishable from those from five food producers. The seven human isolates, a control strain with a different PFGE/fAFLP profile and ten food isolates were subjected to whole genome sequencing (WGS) in a blinded fashion. A gene-by-gene comparison (multilocus sequence typing (MLST)+) was performed, and the resulting whole genome allelic profiles were compared using SeqSphere(+) software version 1.0. On analysis of 2298 genes, the four human outbreak isolates from 2012 to 2013 had different alleles at <= 6 genes, i.e. differed by <= 6 genes from each other; the dendrogram placed these isolates in between five Austrian unaged soft cheese isolates from producer A (<= 19-gene difference from the human cluster) and two Austrian ready-to-eat meat isolates from producer B (<= 8-gene difference from the human cluster). Both food products appeared on grocery bills prospectively collected by these outbreak cases after hospital discharge. Epidemiological results on food consumption and MLST+ clearly separated the three cases in 2011 from the four 2012-2013 outbreak cases (>= 48 different genes). We showed that WGS is capable of discriminating L. monocytogenes SV1/2b clones not distinguishable by PFGE and fAFLP. The listeriosis outbreak described clearly underlines the potential of sequence-based typing methods to offer enhanced resolution and comparability of typing systems for public health applications.

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