Whole-genome Sequencing Used to Investigate a Nationwide Outbreak of Listeriosis Caused by Ready-to-eat Delicatessen Meat, Denmark, 2014

Jensen, Anne K.; Nielsen, Eva Møller; Björkman, Jonas T.; Jensen, Thomas Wiben; Müller, Luise; Persson, Søren; Bjerager, G. O.; Perge, Annette; Krause, Tyra Grove; Kiil, Kristoffer; Sørensen, Gitte; Andersen, Jens Kirk; Mølbak, Kåre; Ethelberg, Steen

doi:10.1093/cid/ciw192

Cited by 95 publications

(69 citation statements)

References 22 publications

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“…The SNP differences (≤48 SNPs) between outbreak-associated isolates and PFGE-matched, but epidemiologically unrelated, isolates were smaller than those between the stone fruit-associated serotype 1/2b isolates and MLST-matched outgroup (>250 SNPs) and those between the outbreak-associated isolates and MLST-matched, but epidemiologically unrelated, isolates in several previous studies (6, 7). This is consistent with the observation that PFGE possessed more discriminatory power than MLST (37).…”

Section: Discussionmentioning

confidence: 50%

“…Previous studies have identified such diversity to be 0 to 5 SNPs (6, 46), 5 to 10 SNPs (47, 48), 10 to 20 SNPs (7), and 20 to 30 SNPs (9), although we need to keep in mind that different studies might target slightly different regions of the genome and employ different bioinformatics tools or parameters to identify SNPs. The Illinois clinical isolate and the outbreak-associated isolates differed by 40 to 48 SNPs, similar to the number of SNP differences (up to 42) among outbreak-associated isolates.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, whole-genome sequencing has been employed to assist in listeriosis outbreak investigations (5–9). To do this, bioinformatics tools have been developed to target different genomic variations of L.…”

Section: Introductionmentioning

confidence: 99%

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Listeria monocytogenes in Stone Fruits Linked to a Multistate Outbreak: Enumeration of Cells and Whole-Genome Sequencing

Chen

Burall

Luo

et al. 2016

Appl Environ Microbiol

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In 2014, the identification of stone fruits contaminated with Listeria monocytogenes led to the subsequent identification of a multistate outbreak. Simultaneous detection and enumeration of L. monocytogenes were performed on 105 fruits, each weighing 127 to 145 g, collected from 7 contaminated lots. The results showed that 53.3% of the fruits yielded L. monocytogenes (lower limit of detection, 5 CFU/fruit), and the levels ranged from 5 to 2,850 CFU/fruit, with a geometric mean of 11.3 CFU/fruit (0.1 CFU/g of fruit). Two serotypes, IVb-v1 and 1/2b, were identified by a combination of PCR- and antiserum-based serotyping among isolates from fruits and their packing environment; certain fruits contained a mixture of both serotypes. Single nucleotide polymorphism (SNP)-based whole-genome sequencing (WGS) analysis clustered isolates from two case-patients with the serotype IVb-v1 isolates and distinguished outbreak-associated isolates from pulsed-field gel electrophoresis (PFGE)-matched, but epidemiologically unrelated, clinical isolates. The outbreak-associated isolates differed by up to 42 SNPs. All but one serotype 1/2b isolate formed another WGS cluster and differed by up to 17 SNPs. Fully closed genomes of isolates from the stone fruits were used as references to maximize the resolution and to increase our confidence in prophage analysis. Putative prophages were conserved among isolates of each WGS cluster. All serotype IVb-v1 isolates belonged to singleton sequence type 382 (ST382); all but one serotype 1/2b isolate belonged to clonal complex 5.IMPORTANCE WGS proved to be an excellent tool to assist in the epidemiologic investigation of listeriosis outbreaks. The comparison at the genome level contributed to our understanding of the genetic diversity and variations among isolates involved in an outbreak or isolates associated with food and environmental samples from one facility. Fully closed genomes increased our confidence in the identification and comparison of accessory genomes. The diversity among the outbreak-associated isolates and the inclusion of PFGE-matched, but epidemiologically unrelated, isolates demonstrate the high resolution of WGS. The prevalence and enumeration data could contribute to our further understanding of the risk associated with Listeria monocytogenes contamination, especially among high-risk populations.

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Section: Discussionmentioning

confidence: 50%

Section: Discussionmentioning

confidence: 99%

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Listeria monocytogenes in Stone Fruits Linked to a Multistate Outbreak: Enumeration of Cells and Whole-Genome Sequencing

Chen

Burall

Luo

et al. 2016

Appl Environ Microbiol

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“…Whole genome sequencing (WGS) is a valuable and effective tool in characterizing pathogens during an outbreak investigation as it provides superior resolution for strain discrimination than other subtyping methods currently used [11, 32, 33]. These real-time outbreak investigations and other retrospective studies [34, 35] have demonstrated the utility of WGS in linking clinical and foodborne isolates with epidemiological relevance.…”

Section: Discussionmentioning

confidence: 99%

Strain-Level Discrimination of Shiga Toxin-Producing Escherichia coli in Spinach Using Metagenomic Sequencing

et al. 2016

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Consumption of fresh bagged spinach contaminated with Shiga toxin-producing Escherichia coli (STEC) has led to severe illness and death; however current culture-based methods to detect foodborne STEC are time consuming. Since not all STEC strains are considered pathogenic to humans, it is crucial to incorporate virulence characterization of STEC in the detection method. In this study, we assess the comprehensiveness of utilizing a shotgun metagenomics approach for detection and strain-level identification by spiking spinach with a variety of genomically disparate STEC strains at a low contamination level of 0.1 CFU/g. Molecular serotyping, virulence gene characterization, microbial community analysis, and E. coli core gene single nucleotide polymorphism (SNP) analysis were performed on metagenomic sequence data from enriched samples. It was determined from bacterial community analysis that E. coli, which was classified at the phylogroup level, was a major component of the population in most samples. However, in over half the samples, molecular serotyping revealed the presence of indigenous E. coli which also contributed to the percent abundance of E. coli. Despite the presence of additional E. coli strains, the serotype and virulence genes of the spiked STEC, including correct Shiga toxin subtype, were detected in 94% of the samples with a total number of reads per sample averaging 2.4 million. Variation in STEC abundance and/or detection was observed in replicate spiked samples, indicating an effect from the indigenous microbiota during enrichment. SNP analysis of the metagenomic data correctly placed the spiked STEC in a phylogeny of related strains in cases where the indigenous E. coli did not predominate in the enriched sample. Also, for these samples, our analysis demonstrates that strain-level phylogenetic resolution is possible using shotgun metagenomic data for determining the genomic relatedness of a contaminating STEC strain to other closely related E. coli.

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“…Due to this, the technology has gained increasing importance in routine clinical microbiology and for studying and detecting outbreaks and epidemics [2–4]. Various studies have shown that inference of the phylogenetic relationship between WGS isolates is helpful for determining epidemiological relationships [5, 6], and a number of methods for inferring phylogenies directly from NGS data have been created. Methods available online which accept raw reads data include snpTree [7], NDtree [8, 9] and CSI Phylogeny [10] available from Center for Genomic Epidemiology.…”

Section: Introductionmentioning

confidence: 99%

Bacterial whole genome-based phylogeny: construction of a new benchmarking dataset and assessment of some existing methods

et al. 2017

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BackgroundWhole genome sequencing (WGS) is increasingly used in diagnostics and surveillance of infectious diseases. A major application for WGS is to use the data for identifying outbreak clusters, and there is therefore a need for methods that can accurately and efficiently infer phylogenies from sequencing reads. In the present study we describe a new dataset that we have created for the purpose of benchmarking such WGS-based methods for epidemiological data, and also present an analysis where we use the data to compare the performance of some current methods.ResultsOur aim was to create a benchmark data set that mimics sequencing data of the sort that might be collected during an outbreak of an infectious disease. This was achieved by letting an E. coli hypermutator strain grow in the lab for 8 consecutive days, each day splitting the culture in two while also collecting samples for sequencing. The result is a data set consisting of 101 whole genome sequences with known phylogenetic relationship. Among the sequenced samples 51 correspond to internal nodes in the phylogeny because they are ancestral, while the remaining 50 correspond to leaves.We also used the newly created data set to compare three different online available methods that infer phylogenies from whole-genome sequencing reads: NDtree, CSI Phylogeny and REALPHY. One complication when comparing the output of these methods with the known phylogeny is that phylogenetic methods typically build trees where all observed sequences are placed as leafs, even though some of them are in fact ancestral. We therefore devised a method for post processing the inferred trees by collapsing short branches (thus relocating some leafs to internal nodes), and also present two new measures of tree similarity that takes into account the identity of both internal and leaf nodes.ConclusionsBased on this analysis we find that, among the investigated methods, CSI Phylogeny had the best performance, correctly identifying 73% of all branches in the tree and 71% of all clades.We have made all data from this experiment (raw sequencing reads, consensus whole-genome sequences, as well as descriptions of the known phylogeny in a variety of formats) publicly available, with the hope that other groups may find this data useful for benchmarking and exploring the performance of epidemiological methods. All data is freely available at: https://cge.cbs.dtu.dk/services/evolution_data.php.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3407-6) contains supplementary material, which is available to authorized users.

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Whole-genome Sequencing Used to Investigate a Nationwide Outbreak of Listeriosis Caused by Ready-to-eat Delicatessen Meat, Denmark, 2014

Cited by 95 publications

References 22 publications

Listeria monocytogenes in Stone Fruits Linked to a Multistate Outbreak: Enumeration of Cells and Whole-Genome Sequencing

Listeria monocytogenes in Stone Fruits Linked to a Multistate Outbreak: Enumeration of Cells and Whole-Genome Sequencing

Strain-Level Discrimination of Shiga Toxin-Producing Escherichia coli in Spinach Using Metagenomic Sequencing

Bacterial whole genome-based phylogeny: construction of a new benchmarking dataset and assessment of some existing methods

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