Prospective Whole-Genome Sequencing Enhances National Surveillance of Listeria monocytogenes

Abstract

Whole-genome sequencing (WGS) has emerged as a powerful tool for comparing bacterial isolates in outbreak detection and investigation. Here we demonstrate that WGS performed prospectively for national epidemiologic surveillance of Listeria monocytogenes has the capacity to be superior to our current approaches using pulsed-field gel electrophoresis (PFGE), multilocus sequence typing (MLST), multilocus variable-number tandem-repeat analysis (MLVA), binary typing, and serotyping. Initially 423 L. monocytogenes isolates underwent WGS, and comparisons uncovered a diverse genetic population structure derived from three distinct lineages. MLST, binary typing, and serotyping results inferred in silico from the WGS data were highly concordant (>99%) with laboratory typing performed in parallel. However, WGS was able to identify distinct nested clusters within groups of isolates that were otherwise indistinguishable using our current typing methods. Routine WGS was then used for prospective epidemiologic surveillance on a further 97 L. monocytogenes isolates over a 12-month period, which provided a greater level of discrimination than that of conventional typing for inferring linkage to point source outbreaks. A risk-based alert system based on WGS similarity was used to inform epidemiologists required to act on the data. Our experience shows that WGS can be adopted for prospective L. monocytogenes surveillance and investigated for other pathogens relevant to public health.

Application of Whole-Genome Sequencing Data for O-Specific Antigen Analysis and In Silico Serotyping of Pseudomonas aeruginosa Isolates

“…Importantly, since the frequency of nontypeability of in vitro serotyped P. aeruginosa isolates may amount to Ͼ65% (10), analysis with PAst is clearly advantageous and superior compared to conventional in vitro serotyping. Importantly, the superiority of the PAst tool as a reliable and fast typing method is consistent with that of other published tools for in silico serotyping (31)(32)(33)(34)(35). Similar to SerotypeFinder (in silico serotyping of Escherichia coli) (31), LisSero (in silico serotyping of Listeria monocytogenes) (34,35), and SeqSero (in silico serotyping of Salmonella) (32), PAst resolves the OSA cluster information to the most accurate typing possible as a serogroup representing 1 to 3 serotypes.…”

Navigating Microbiological Food Safety in the Era of Whole-Genome Sequencing

“…Although several studies have been done surrounding this topic, the answers are complex and no consensus has been established. For example, epidemiologically linked L. monocytogenes isolates examined in one retrospective study differed by Ͻ10 core genome SNPs (35). In the same study, two isolates recovered a day apart from the same patient differed by 21 SNPs (35).…”

“…The resources available to these monitoring systems have, in several instances, been leveraged to conduct several retrospective and prospective surveillance studies that compare the accuracy of SNP-based subtyping to that of traditional methods. The Australian Listeria reference laboratory compared the typing results for 97 isolates obtained by WGS to those of PFGE, MLST, MLVA, and PCR serotyping and found that SNP analysis could easily differentiate between epidemiologically linked and unlinked cases with identical PFGE patterns (35). In addition, this study verified that in silico tools could be used to generate data comparable to PFGE, MLST, and PCR serotyping results from a sequence, but because of the short length of the Illumina MiSeq sequence reads and the highly repetitive nature of MLVA regions, in silico MLVA was not feasible (35).…”

“…For example, epidemiologically linked L. monocytogenes isolates examined in one retrospective study differed by Ͻ10 core genome SNPs (35). In the same study, two isolates recovered a day apart from the same patient differed by 21 SNPs (35). Conversely, a different study reported that two L. monocytogenes isolates that originated in the same processing plant but were isolated 12 years apart varied by as little as one core genome SNP, though a phage sequence included in both genomes differed by 1,274 SNPs (34).…”

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;…”