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

Abstract

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.

Core Genome Multilocus Sequence Typing Scheme for High-Resolution Typing of Enterococcus faecium

“…This limitation may be overcome by using a genome-wide gene-by-gene comparison approach, as in classical MLST, but with an important extension of the number of analyzed genes from seven to several hundreds or even Ͼ1,000 genes (24). This approach is generally applicable and has been used to study the epidemiology of various microbial pathogens, including Campylobacter jejuni, Campylobacter coli, Listeria monocytogenes, Neisseria meningitidis, Mycobacterium tuberculosis, methicillin-resistant Staphylococcus aureus, Francisella tularensis, and Escherichia coli and has been termed whole-genome MLST (wgMLST), core genome MLST (cgMLST), or MLST ϩ (24)(25)(26)(27)(28)(29)(30)(31).…”

Genomic Analysis of Bacterial Outbreaks

“…(Snitkin et al, 2012;Espedido et al, 2013;Onori et al, 2015) Legionella pneumophila 3.5 Illumina HiSeq 2x100 bp Illumina MiSeq 2x250 bp, 2x150bp SOLiD 5500XL SE 75bp (Reuter et al, 2013a;Reuter et al, 2013b;Sánchez-Busó et al, 2014;Bartley et al, 2016) Listeria monocytogenes 3 Roche 454 GS-FLX (Gilmour et al, 2010;Schmid et al, 2014;Kwong et al, 2016 (Holt et al, 2008;Lienau et al, 2011;Quick et al, 2015;Allard et al, 2013;Cao et al, 2013;Allard et al, 2012;Taylor et al, 2015;Bekal et al, 2016) Salmonella Typhimurium 4.7 Illumina GA II system (Okoro et al, 2012) Shigella sonnei 5.06 Illumina GAII PE 2x54 bp Illumina MiSeq Illumina HiSeq2000 (Holt et al, 2012;Holt et al, 2013;McDonnell et al, 2013) 32 (Harris et al, 2010;Eyre et al, 2012;McAdam et al, 2012;Young et al, 2012;Köser et al, 2012;Holden et al, 2013;Nübel et al, 2013;Harris et al, 2013;Price et al, 2014;Azarian et al, 2015;Paterson et al, 2015;Senn et al, 2016;Kinnevey et al, 2016;Reuter et al, 2016) Streptococcus pneumoniae 1. Hendriksen et al, 2011;Chin et al, 2011;…”

Molecular typing of bacteria for epidemiological surveillance and outbreak investigation / Molekulare Typisierung von Bakterien für die epidemiologische Überwachung und Ausbruchsabklärung

“…These shifts in infectious diseases are caused by the adaptation of microorganisms to changes in human behavior, demographics, and life style (Cascio et al, 2011); changes in economic development and land use (Suhrcke et al, 2011); loss of biodiversity (Swaddle and Calos, 2008;Ostfeld, 2009); global travel (Hufnagel et al, 2004); immigration (Schmid et al, 2008); air conditioning; crowded intensive care units in large hospitals; global environmental and climate changes (Semenza et al, 2012); evolution of susceptible populations, exotic pets, exotic foods and pathogen adaptation (Casadevall et al, 2011;Price et al, 2012); as well as advances in detection techniques Allerberger, 2012;van Doorn, 2014). With industrialization of food processing, worldwide shipment of fresh and frozen food and an increased demand for fresh bagged produce foodrelated outbreaks shifted from local, often family-based, outbreaks to multistate or multicountry outbreaks, often caused by a single source (Shane et al, 2002;Tauxe, 2002;Denny et al, 2007;Nygren et al, 2013;Schmid et al, 2014;Ruppitsch et al, 2015b;Inns et al, 2016). Disease surveillance is an inevitable cornerstone for early identification of infectious disease outbreaks and for timely implementation of accurate measures to combat transmission and morbidity (Johns et al, 2011).…”

“…The most immediate field where NGS have been introduced into daily routine diagnostics in microbiology is surveillance and outbreak investigation. Several studies on a variety of bacterial species have already shown that WGS-based typing, based either on single nucleotide variants (SNVs) (Turabelidze et al, 2013;Pightling et al, 2015) or on gene-by-gene allelic profiling of core genome genes, frequently named core genome MLST (cgMLST) or MLST + (Laing et al, 2010;Mellmann et al, 2011;Köser et al, 2012;Maiden et al, 2013;Antwerpen et al, 2015;de Been et al, 2015;Moran-Gilad et al, 2015;Chaudhari et al, 2016;Moura et al, 2016), currently represents the ultimate diagnostic typing tool that have been successfully applied for outbreak investigations (Figure 9) (den Bakker et al, 2014;Schmid et al, 2014;Ruppitsch et al, 2015b; Lepuschitz, 2015). Molecular typing of bacteria for epidemiological surveillance and outbreak investigation Burckhardt et al, 2016;Chen et al, 2016;Jackson et al, 2016).…”