The Listeria monocytogenes Core-Genome Sequence Typer (LmCGST): a bioinformatic pipeline for molecular characterization with next-generation sequence data

Wholegenome sequencing as the gold standard approach for control of Listeria monocytogenes in the food chain

Listeria monocytogenes has been implicated in numerous outbreaks and related deaths of listeriosis. In food production, L. monocytogenes occurs in raw food material and above all, through postprocessing contamination. The use of next-generation sequencing technologies such as whole-genome sequencing (WGS) facilitates foodborne outbreak investigations, pathogen source tracking and tracing geographic distributions of different clonal complexes, routine microbiological/epidemiological surveillance of listeriosis, and quantitative microbial risk assessment. WGS can also be used to predict various genetic traits related to virulence, stress, or antimicrobial resistance, which can be of great benefit for improving food safety management as well as public health.

In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

Background

Whole genome sequencing analyzed by core genome multi-locus sequence typing (cgMLST) is widely used in surveillance of the pathogenic bacteria Listeria monocytogenes. Given the heterogeneity of available bioinformatics tools to define cgMLST alleles, our aim was to identify parameters influencing the precision of cgMLST profiles.

Methods

We used three L. monocytogenes reference genomes from different phylogenetic lineages and assessed the impact of in vitro (i.e. tested genomes, successive platings, replicates of DNA extraction and sequencing) and in silico parameters (i.e. targeted depth of coverage, depth of coverage, breadth of coverage, assembly metrics, cgMLST workflows, cgMLST completeness) on cgMLST precision made of 1748 core loci. Six cgMLST workflows were tested, comprising assembly-based (BIGSdb, INNUENDO, GENPAT, SeqSphere and BioNumerics) and assembly-free (i.e. kmer-based MentaLiST) allele callers. Principal component analyses and generalized linear models were used to identify the most impactful parameters on cgMLST precision.

Results

The isolate’s genetic background, cgMLST workflows, cgMLST completeness, as well as depth and breadth of coverage were the parameters that impacted most on cgMLST precision (i.e. identical alleles against reference circular genomes). All workflows performed well at ≥40X of depth of coverage, with high loci detection (> 99.54% for all, except for BioNumerics with 97.78%) and showed consistent cluster definitions using the reference cut-off of ≤7 allele differences.

Conclusions

This highlights that bioinformatics workflows dedicated to cgMLST allele calling are largely robust when paired-end reads are of high quality and when the sequencing depth is ≥40X.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08437-4.

Molecular subtyping for source tracking of Escherichia coli using core genome multilocus sequence typing at a food manufacturing plant

When harmful bacteria are detected in the final product at a food manufacturing plant, it is necessary to identify and eliminate the source of contamination so that it does not occur again. In the current study, the source of contamination was tracked using core genome multilocus sequence typing (cgMLST) analysis in cases where Escherichia coli was detected in the final product at a food manufacturing plant. cgMLST analysis was performed on 40 strains of E. coli collected from the environment [floor (26 strains), drainage ditch (5 strains), container (4 strains), post-heating production line (1 strain)] and products [final product (3 strains) and intermediate product (1 strain)]. In total, 40 E. coli isolates were classified into 17 genogroups by cgMLST analysis. The 4 E. coli strains isolated from the intermediate and final products were classified into two genogroups (I and II). Certain isolates collected from the environment also belonged to those genogroups, it was possible to estimate the transmission of E. coli in the manufacturing plant. Thus, the dynamics of E. coli in the food manufacturing location were clarified by using cgMLST analysis. In conclusion, our results indicate that cgMLST analysis can be effectively used for hygiene management at food manufacturing locations.

Genomic Analysis of Prophages Recovered from Listeria monocytogenes Lysogens Found in Seafood and Seafood-Related Environment

A prophage is a phage-related sequence that is integrated into a bacterial chromosome. Prophages play an important role in bacterial evolution, survival, and persistence. To understand the impact of Listeria prophages on their host genome organizations, this work sequenced two L. monocytogenes strains (134LM and 036LM), previously identified as lysogens by mitomycin C induction. Draft genomes were generated with assembly sizes of 2,953,877 bp and 3,000,399 bp. One intact prophage (39,532 bp) was inserted into the comK gene of the 134LM genome. Two intact prophages (48,684 bp and 39,488 bp) were inserted in tRNA-Lys and elongation-factor genes of the 036LM genome. The findings confirmed the presence of three corresponding induced phages previously obtained by mitomycin C induction. Comparative genomic analysis of three prophages obtained in the newly sequenced lysogens with 61 prophages found in L. monocytogenes genomes, available in public databases, identified six major clusters using whole genome-based phylogenetic analysis. The results of the comparative genomic analysis of the prophage sequences provides knowledge about the diversity of Listeria prophages and their distribution among Listeria genomes in diverse environments, including different sources or geographical regions. In addition, the prophage sequences and their insertion sites contribute to the genomic diversity of L. monocytogenes genomes. These data of prophage sequences, prophage insertion sites, and prophage sequence comparisons, together with ANIb confirmation, could be useful for L. monocytogenes classification by prophages. One potential development could be refinement of prophage typing tools for monitoring or surveillance of L. monocytogenes contamination and transmission.

Evaluating the accuracy of Listeria monocytogenes assemblies from quasimetagenomic samples using long and short reads

BACKGROUND

Whole genome sequencing of cultured pathogens is the state of the art public health response for the bioinformatic source tracking of illness outbreaks. Quasimetagenomics can substantially reduce the amount of culturing needed before a high quality genome can be recovered. Highly accurate short read data is analyzed for single nucleotide polymorphisms and multi-locus sequence types to differentiate strains but cannot span many genomic repeats, resulting in highly fragmented assemblies. Long reads can span repeats, resulting in much more contiguous assemblies, but have lower accuracy than short reads.

RESULTS

We evaluated the accuracy of Listeria monocytogenes assemblies from enrichments (quasimetagenomes) of naturally-contaminated ice cream using long read (Oxford Nanopore) and short read (Illumina) sequencing data. Accuracy of ten assembly approaches, over a range of sequencing depths, was evaluated by comparing sequence similarity of genes in assemblies to a complete reference genome. Long read assemblies reconstructed a circularized genome as well as a 71 kbp plasmid after 24 h of enrichment; however, high error rates prevented high fidelity gene assembly, even at 150X depth of coverage. Short read assemblies accurately reconstructed the core genes after 28 h of enrichment but produced highly fragmented genomes. Hybrid approaches demonstrated promising results but had biases based upon the initial assembly strategy. Short read assemblies scaffolded with long reads accurately assembled the core genes after just 24 h of enrichment, but were highly fragmented. Long read assemblies polished with short reads reconstructed a circularized genome and plasmid and assembled all the genes after 24 h enrichment but with less fidelity for the core genes than the short read assemblies.

CONCLUSION

The integration of long and short read sequencing of quasimetagenomes expedited the reconstruction of a high quality pathogen genome compared to either platform alone. A new and more complete level of information about genome structure, gene order and mobile elements can be added to the public health response by incorporating long read analyses with the standard short read WGS outbreak response.

Making Sense of the Biodiversity and Virulence of Listeria monocytogenes

Listeria monocytogenes is a foodborne pathogen responsible for listeriosis, an infection that can manifest in humans as bacteremia, meningoencephalitis in immunocompromised patients and the elderly, and fetal-placental infection in pregnant women. Reference strains from this facultative intracellular bacterium have been instrumental in the investigation of basic mechanisms in microbiology, immunology, and cell biology. The integration of bacterial population genomics with environmental, epidemiological, and clinical data allowed the uncovering of new factors involved in the virulence of L. monocytogenes and its adaptation to different environments. This review illustrates how these investigations have led to a better understanding of the bacterium's virulence and the driving forces that shaped it.

An Open-Source Program (Haplo-ST) for Whole-Genome Sequence Typing Shows Extensive Diversity among Listeria monocytogenes Isolates in Outdoor Environments and Poultry Processing Plants

A reliable and standardized classification of Listeria monocytogenes is important for accurate strain identification during outbreak investigations. Current whole-genome sequencing (WGS)-based approaches for strain characterization are either difficult to standardize, rendering them less suitable for data exchange, or are not freely available. Thus, we developed a portable and open-source tool, Haplo-ST, to improve standardization and provide maximum discriminatory potential to WGS data tied to a multilocus sequence typing (MLST) framework. Haplo-ST performs whole-genome MLST (wgMLST) for L. monocytogenes while allowing for data exchangeability worldwide. This tool takes in (i) raw WGS reads as input, (ii) cleans the raw data according to user-specified parameters, (iii) assembles genes across loci by mapping to genes from reference strains, and (iv) assigns allelic profiles to assembled genes and provides a wgMLST subtyping for each isolate. Data exchangeability relies on the tool assigning allelic profiles based on a centralized nomenclature defined by the widely used BIGSdb-Lm database. Tests of Haplo-ST's performance with simulated reads from L. monocytogenes reference strains demonstrated high sensitivity (97.5%), and coverage depths of ≥20× were found to be sufficient for wgMLST profiling. We then used Haplo-ST to characterize and differentiate between two groups of L. monocytogenes isolates derived from the natural environment and poultry processing plants. Phylogenetic reconstruction identified lineages within each group, and no lineage specificity was observed with isolate phenotypes (transient versus persistent) or origins. Genetic differentiation analyses between isolate groups identified 21 significantly differentiated loci, potentially enriched for adaptation and persistence of L. monocytogenes within poultry processing plants.IMPORTANCE We have developed an open-source tool (https://github.com/swarnalilouha/Haplo-ST) that provides allele-based subtyping of L. monocytogenes isolates at the whole-genome level. Along with allelic profiles, this tool also generates allele sequences and identifies paralogs, which is useful for phylogenetic tree reconstruction and deciphering relationships between closely related isolates. More broadly, Haplo-ST is flexible and can be adapted to characterize the genome of any haploid organism simply by installing an organism-specific gene database. Haplo-ST also allows for scalable subtyping of isolates; fewer reference genes can be used for low-resolution typing, whereas higher resolution can be achieved by increasing the number of genes used in the analysis. Our tool enabled clustering of L. monocytogenes isolates into lineages and detection of potential loci for adaptation and persistence in food processing environments. Findings from these analyses highlight the effectiveness of Haplo-ST in subtyping and evaluating relationships among isolates in studies of bacterial population genetics.

Within-species contamination of bacterial whole-genome sequence data has a greater influence on clustering analyses than between-species contamination

Although it is assumed that contamination in bacterial whole-genome sequencing causes errors, the influences of contamination on clustering analyses, such as single-nucleotide polymorphism discovery, phylogenetics, and multi-locus sequencing typing, have not been quantified. By developing and analyzing 720 Listeria monocytogenes, Salmonella enterica, and Escherichia coli short-read datasets, we demonstrate that within-species contamination causes errors that confound clustering analyses, while between-species contamination generally does not. Contaminant reads mapping to references or becoming incorporated into chimeric sequences during assembly are the sources of those errors. Contamination sufficient to influence clustering analyses is present in public sequence databases.

Phylogenomic Pipeline Validation for Foodborne Pathogen Disease Surveillance

Foodborne pathogen surveillance in the United States is transitioning from strain identification using restriction digest technology (pulsed-field gel electrophoresis [PFGE]) to shotgun sequencing of the entire genome (whole-genome sequencing [WGS]). WGS requires a new suite of analysis tools, some of which have long histories in academia but are new to the field of public health and regulatory decision making. Although the general workflow is fairly standard for collecting and analyzing WGS data for disease surveillance, there are a number of differences in how the data are collected and analyzed across public health agencies, both nationally and internationally. This impedes collaborative public health efforts, so national and international efforts are underway to enable direct comparison of these different analysis methods. Ultimately, the harmonization efforts will allow the (mutually trusted and understood) production and analysis of WGS data by labs and agencies worldwide, thus improving outbreak response capabilities globally. This review provides a historical perspective on the use of WGS for pathogen tracking and summarizes the efforts underway to ensure the major steps in phylogenomic pipelines used for pathogen disease surveillance can be readily validated. The tools for doing this will ensure that the results produced are sound, reproducible, and comparable across different analytic approaches.

Interpreting Whole-Genome Sequence Analyses of Foodborne Bacteria for Regulatory Applications and Outbreak Investigations

Whole-genome sequence (WGS) analysis has revolutionized the food safety industry by enabling high-resolution typing of foodborne bacteria. Higher resolving power allows investigators to identify origins of contamination during illness outbreaks and regulatory activities quickly and accurately. Government agencies and industry stakeholders worldwide are now analyzing WGS data routinely. Although researchers have published many studies that assess the efficacy of WGS data analysis for source attribution, guidance for interpreting WGS analyses is lacking. Here, we provide the framework for interpreting WGS analyses used by the Food and Drug Administration's Center for Food Safety and Applied Nutrition (CFSAN). We based this framework on the experiences of CFSAN investigators, collaborations and interactions with government and industry partners, and evaluation of the published literature. A fundamental question for investigators is whether two or more bacteria arose from the same source of contamination. Analysts often count the numbers of nucleotide differences [single-nucleotide polymorphisms (SNPs)] between two or more genome sequences to measure genetic distances. However, using SNP thresholds alone to assess whether bacteria originated from the same source can be misleading. Bacteria that are isolated from food, environmental, or clinical samples are representatives of bacterial populations. These populations are subject to evolutionary forces that can change genome sequences. Therefore, interpreting WGS analyses of foodborne bacteria requires a more sophisticated approach. Here, we present a framework for interpreting WGS analyses that combines SNP counts with phylogenetic tree topologies and bootstrap support. We also clarify the roles of WGS, epidemiological, traceback, and other evidence in forming the conclusions of investigations. Finally, we present examples that illustrate the application of this framework to real-world situations.

GenomeTrakr proficiency testing for foodborne pathogen surveillance: an exercise from 2015

Pathogen monitoring is becoming more precise as sequencing technologies become more affordable and accessible worldwide. This transition is especially apparent in the field of food safety, which has demonstrated how whole-genome sequencing (WGS) can be used on a global scale to protect public health. GenomeTrakr coordinates the WGS performed by public-health agencies and other partners by providing a public database with real-time cluster analysis for foodborne pathogen surveillance. Because WGS is being used to support enforcement decisions, it is essential to have confidence in the quality of the data being used and the downstream data analyses that guide these decisions. Routine proficiency tests, such as the one described here, have an important role in ensuring the validity of both data and procedures. In 2015, the GenomeTrakr proficiency test distributed eight isolates of common foodborne pathogens to participating laboratories, who were required to follow a specific protocol for performing WGS. Resulting sequence data were evaluated for several metrics, including proper labelling, sequence quality and new single nucleotide polymorphisms (SNPs). Illumina MiSeq sequence data collected for the same set of strains across 21 different laboratories exhibited high reproducibility, while revealing a narrow range of technical and biological variance. The numbers of SNPs reported for sequencing runs of the same isolates across multiple laboratories support the robustness of our cluster analysis pipeline in that each individual isolate cultured and resequenced multiple times in multiple places are all easily identifiable as originating from the same source.

LOCUST: a custom sequence locus typer for classifying microbial isolates

Summary

LOCUST is a custom sequence locus typer tool for classifying microbial genomes. It provides a fully automated opportunity to customize the classification of genome-wide nucleotide variant data most relevant to biological research.

Availability and Implementation

Source code, demo data, and detailed documentation are freely available at http://sourceforge.net/projects/locustyper .

Contact

lbrinkac@jcvi.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

MentaLiST - A fast MLST caller for large MLST schemes

MLST (multi-locus sequence typing) is a classic technique for genotyping bacteria, widely applied for pathogen outbreak surveillance. Traditionally, MLST is based on identifying sequence types from a small number of housekeeping genes. With the increasing availability of whole-genome sequencing data, MLST methods have evolved towards larger typing schemes, based on a few hundred genes [core genome MLST (cgMLST)] to a few thousand genes [whole genome MLST (wgMLST)]. Such large-scale MLST schemes have been shown to provide a finer resolution and are increasingly used in various contexts such as hospital outbreaks or foodborne pathogen outbreaks. This methodological shift raises new computational challenges, especially given the large size of the schemes involved. Very few available MLST callers are currently capable of dealing with large MLST schemes. We introduce MentaLiST, a new MLST caller, based on a k-mer voting algorithm and written in the Julia language, specifically designed and implemented to handle large typing schemes. We test it on real and simulated data to show that MentaLiST is faster than any other available MLST caller while providing the same or better accuracy, and is capable of dealing with MLST schemes with up to thousands of genes while requiring limited computational resources. MentaLiST source code and easy installation instructions using a Conda package are available at https://github.com/WGS-TB/MentaLiST.

Will the emergence of core genome MLST end the role of in silico MLST?

The technological advancement of molecular epidemiological analysis using next-generation sequencing (NGS) for foodborne pathogens has a groundbreaking impact over the past three years. In particular, the emergence of cg (core genome) multilocus sequence typing(MLST) has a significant impact. This is because this technology made it possible for many researchers to carry out molecular epidemiological analysis on foodborne pathogens in a common language, using common definitions. The resolution of core genome MLST (cgMLST) far surpasses that of MLST, which only uses seven (usually, in some cases five) housekeeping genes. Therefore, cgMLST would in no doubt terminate the role of conventional MLST as the molecular epidemiological tool. However, the role of MLST would probably not end all together. Rather, the sequence type (ST) of the conventional MLST is expected to be used as in silico MLST by a wider range of researchers than ever in the next 10 years. This is because, with the arrival of the NGS era, we have come to be able to obtain ST of conventional MLST by simply entering the NGS text file into one's own PC. In other words, acquisition of ST data is no longer limited to researchers aiming to conduct MLST for the first place. The impact of such a change is large. In silico MLST will continue to be used as a tool for understanding the broad characteristics of bacterial strains. This review aimed to summarize the main information on STs that have been accumulated for representative foodborne pathogens, in particular for potential NGS users in this new era who have been not familiar with MLST until now.

Whole genome sequencing analyses of Listeria monocytogenes that persisted in a milkshake machine for a year and caused illnesses in Washington State

Background

In 2015, in addition to a United States multistate outbreak linked to contaminated ice cream, another outbreak linked to ice cream was reported in the Pacific Northwest of the United States. It was a hospital-acquired outbreak linked to milkshakes, made from contaminated ice cream mixes and milkshake maker, served to patients. Here we performed multiple analyses on isolates associated with this outbreak: pulsed-field gel electrophoresis (PFGE), whole genome single nucleotide polymorphism (SNP) analysis, species-specific core genome multilocus sequence typing (cgMLST), lineage-specific cgMLST and whole genome-specific MLST (wgsMLST)/outbreak-specific cgMLST. We also analyzed the prophages and virulence genes.

Results

The outbreak isolates belonged to sequence type 1038, clonal complex 101, genetic lineage II. There were no pre-mature stop codons in inlA. Isolates contained Listeria Pathogenicity Island 1 and multiple internalins. PFGE and multiple whole genome sequencing (WGS) analyses all clustered together food, environmental and clinical isolates when compared to outgroup from the same clonal complex, which supported the finding that L. monocytogenes likely persisted in the soft serve ice cream/milkshake maker from November 2014 to November 2015 and caused 3 illnesses, and that the outbreak strain was transmitted between two ice cream production facilities. The whole genome SNP analysis, one of the two species-specific cgMLST, the lineage II-specific cgMLST and the wgsMLST/outbreak-specific cgMLST showed that L. monocytogenes cells persistent in the milkshake maker for a year formed a unique clade inside the outbreak cluster. This clustering was consistent with the cleaning practice after the outbreak was initially recognized in late 2014 and early 2015. Putative prophages were conserved among prophage-containing isolates. The loss of a putative prophage in two isolates resulted in the loss of the AscI restriction site in the prophage, which contributed to their AscI-PFGE banding pattern differences from other isolates.

Conclusions

The high resolution of WGS analyses allowed the differentiation of epidemiologically unrelated isolates, as well as the elucidation of the microevolution and persistence of isolates within the scope of one outbreak. We applied a wgsMLST scheme which is essentially the outbreak-specific cgMLST. This scheme can be combined with lineage-specific cgMLST and species-specific cgMLST to maximize the resolution of WGS.

Food Safety in the Age of Next Generation Sequencing, Bioinformatics, and Open Data Access

Public health labs and food regulatory agencies globally are embracing whole genome sequencing (WGS) as a revolutionary new method that is positioned to replace numerous existing diagnostic and microbial typing technologies with a single new target: the microbial draft genome. The ability to cheaply generate large amounts of microbial genome sequence data, combined with emerging policies of food regulatory and public health institutions making their microbial sequences increasingly available and public, has served to open up the field to the general scientific community. This open data access policy shift has resulted in a proliferation of data being deposited into sequence repositories and of novel bioinformatics software designed to analyze these vast datasets. There also has been a more recent drive for improved data sharing to achieve more effective global surveillance, public health and food safety. Such developments have heightened the need for enhanced analytical systems in order to process and interpret this new type of data in a timely fashion. In this review we outline the emergence of genomics, bioinformatics and open data in the context of food safety. We also survey major efforts to translate genomics and bioinformatics technologies out of the research lab and into routine use in modern food safety labs. We conclude by discussing the challenges and opportunities that remain, including those expected to play a major role in the future of food safety science.

TreeToReads - a pipeline for simulating raw reads from phylogenies

BACKGROUND

Using phylogenomic analysis tools for tracking pathogens has become standard practice in academia, public health agencies, and large industries. Using the same raw read genomic data as input, there are several different approaches being used to infer phylogenetic tree. These include many different SNP pipelines, wgMLST approaches, k-mer algorithms, whole genome alignment and others; each of these has advantages and disadvantages, some have been extensively validated, some are faster, some have higher resolution. A few of these analysis approaches are well-integrated into the regulatory process of US Federal agencies (e.g. the FDA's SNP pipeline for tracking foodborne pathogens). However, despite extensive validation on benchmark datasets and comparison with other pipelines, we lack methods for fully exploring the effects of multiple parameter values in each pipeline that can potentially have an effect on whether the correct phylogenetic tree is recovered.

RESULTS

To resolve this problem, we offer a program, TreeToReads, which can generate raw read data from mutated genomes simulated under a known phylogeny. This simulation pipeline allows direct comparisons of simulated and observed data in a controlled environment. At each step of these simulations, researchers can vary parameters of interest (e.g., input tree topology, amount of sequence divergence, rate of indels, read coverage, distance of reference genome, etc) to assess the effects of various parameter values on correctly calling SNPs and reconstructing an accurate tree.

CONCLUSIONS

Such critical assessments of the accuracy and robustness of analytical pipelines are essential to progress in both research and applied settings.

Genetic subtyping of Listeria monocytogenes via multiple-locus sequence typing using iap, sigB and actA

Pulse field gel electrophoresis (PFGE) is widely used for listeriosis surveillance. Although this technique is effective for epidemiology, the data among laboratories are inconsistent. We previously reported a method for Listeria monocytogenes subtyping combined with sequence analysis of partial iap and whole genome restriction fragment length polymorphism (RFLP) using XbaI, ClaI (BanIII) and PstI. However, distinguishing subtypes was challenging, because the output comprised complicated fragment patterns. In this study, we aimed to establish a simple genotyping method that does not depend on visual observation, rather it focuses on multi-locus sequence typing (MLST) using three genes, iap, sigB and actA. Sixty-eight strains of L. monocytogenes including EGD-e as a reference strain were investigated to ensure consistency with previous data on the genetic characterization. All strains were grouped into 29 types by both analyses. Although there are some differences in classification, major clades included the same strains. Simpson's indices of diversity (SID) by MLST and iap-RFLP-based typing were 0.967 (95% confidence interval [CI]: 0.955/0.978) and 0.967 (95% CI: 0.955/0.979), respectively. The discriminatory power of both methods can be considered almost identical. Compared with the results of 38 selected strains, the strains within the MLST clusters in this study coincided with those obtained using PFGE. Thus, the MLST strategy could help differentiate among L. monocytogenes isolates during epidemiological studies.

NetF-producing Clostridium perfringens: Clonality and plasmid pathogenicity loci analysis

Clostridium perfringens is an important cause of foal necrotizing enteritis and canine acute hemorrhagic diarrhea. A major virulence determinant of the strains associated with these diseases appears to be a beta-sheet pore-forming toxin, NetF, encoded within a pathogenicity locus (NetF locus) on a large tcp-conjugative plasmid. Strains producing NetF also produce the putative toxin NetE, encoded within the same pathogenicity locus, as well as CPE enterotoxin and CPB2 on a second plasmid, and sometimes the putative toxin NetG within a pathogenicity locus (NetG locus) on another separate large conjugative plasmid. Previous genome sequences of two netF-positive C. perfringens showed that they both shared three similar plasmids, including the NetF/NetE and CPE/CPB2 toxins-encoding plasmids mentioned above and a putative bacteriocin-encoding plasmid. The main purpose of this study was to determine whether all NetF-producing strains share this common plasmid profile and whether their distinct NetF and CPE pathogenicity loci are conserved. To answer this question, 15 equine and 15 canine netF-positive isolates of C. perfringens were sequenced using Illumina Hiseq2000 technology. In addition, the clonal relationships among the NetF-producing strains were evaluated by core genome multilocus sequence typing (cgMLST). The data obtained showed that all NetF-producing strains have a common plasmid profile and that the defined pathogenicity loci on the plasmids are conserved in all these strains. cgMLST analysis showed that the NetF-producing C. perfringens strains belong to two distinct clonal complexes. The pNetG plasmid was absent from isolates of one of the clonal complexes, and there were minor but consistent differences in the NetF/NetE and CPE/CPB2 plasmids between the two clonal complexes.

Real-Time Pathogen Detection in the Era of Whole-Genome Sequencing and Big Data: Comparison of k-mer and Site-Based Methods for Inferring the Genetic Distances among Tens of Thousands of Salmonella Samples

The adoption of whole-genome sequencing within the public health realm for molecular characterization of bacterial pathogens has been followed by an increased emphasis on real-time detection of emerging outbreaks (e.g., food-borne Salmonellosis). In turn, large databases of whole-genome sequence data are being populated. These databases currently contain tens of thousands of samples and are expected to grow to hundreds of thousands within a few years. For these databases to be of optimal use one must be able to quickly interrogate them to accurately determine the genetic distances among a set of samples. Being able to do so is challenging due to both biological (evolutionary diverse samples) and computational (petabytes of sequence data) issues. We evaluated seven measures of genetic distance, which were estimated from either k-mer profiles (Jaccard, Euclidean, Manhattan, Mash Jaccard, and Mash distances) or nucleotide sites (NUCmer and an extended multi-locus sequence typing (MLST) scheme). When analyzing empirical data (whole-genome sequence data from 18,997 Salmonella isolates) there are features (e.g., genomic, assembly, and contamination) that cause distances inferred from k-mer profiles, which treat absent data as informative, to fail to accurately capture the distance between samples when compared to distances inferred from differences in nucleotide sites. Thus, site-based distances, like NUCmer and extended MLST, are superior in performance, but accessing the computing resources necessary to perform them may be challenging when analyzing large databases.

Whole genome-based population biology and epidemiological surveillance of Listeria monocytogenes

Listeria monocytogenes (Lm) is a major human foodborne pathogen. Numerous Lm outbreaks have been reported worldwide and associated with a high case fatality rate, reinforcing the need for strongly coordinated surveillance and outbreak control. We developed a universally applicable genome-wide strain genotyping approach and investigated the population diversity of Lm using 1,696 isolates from diverse sources and geographical locations. We define, with unprecedented precision, the population structure of Lm, demonstrate the occurrence of international circulation of strains and reveal the extent of heterogeneity in virulence and stress resistance genomic features among clinical and food isolates. Using historical isolates, we show that the evolutionary rate of Lm from lineage I and lineage II is low (∼2.5 × 10^-7 substitutions per site per year, as inferred from the core genome) and that major sublineages (corresponding to so-called 'epidemic clones') are estimated to be at least 50-150 years old. This work demonstrates the urgent need to monitor Lm strains at the global level and provides the unified approach needed for global harmonization of Lm genome-based typing and population biology.

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

The epidemiological investigation of a foodborne outbreak, including identification of related cases, source attribution, and development of intervention strategies, relies heavily on the ability to subtype the etiological agent at a high enough resolution to differentiate related from nonrelated cases. Historically, several different molecular subtyping methods have been used for this purpose; however, emerging techniques, such as single nucleotide polymorphism (SNP)-based techniques, that use whole-genome sequencing (WGS) offer a resolution that was previously not possible. With WGS, unlike traditional subtyping methods that lack complete information, data can be used to elucidate phylogenetic relationships and disease-causing lineages can be tracked and monitored over time. The subtyping resolution and evolutionary context provided by WGS data allow investigators to connect related illnesses that would be missed by traditional techniques. The added advantage of data generated by WGS is that these data can also be used for secondary analyses, such as virulence gene detection, antibiotic resistance gene profiling, synteny comparisons, mobile genetic element identification, and geographic attribution. In addition, several software packages are now available to generate in silico results for traditional molecular subtyping methods from the whole-genome sequence, allowing for efficient comparison with historical databases. Metagenomic approaches using next-generation sequencing have also been successful in the detection of nonculturable foodborne pathogens. This review addresses state-of-the-art techniques in microbial WGS and analysis and then discusses how this technology can be used to help support food safety investigations. Retrospective outbreak investigations using WGS are presented to provide organism-specific examples of the benefits, and challenges, associated with WGS in comparison to traditional molecular subtyping techniques.

Genome Sequence of the Listeria monocytogenes Food Isolate HPB913, Collected in Canada in 1993

Listeria monocytogenes is a pathogenic bacterium of importance to public health and food safety agencies. We present the genome sequence of the serotype 1/2a L. monocytogenes food isolate HPB913, which was collected in Canada in 1993 as part of an investigation into a sporadic case of foodborne illness.

Genome Sequence of Listeria monocytogenes Strain HPB2088 (Serotype 1/2a), an Environmental Isolate Collected in Canada in 1994

Listeria monocytogenes is a foodborne pathogen that causes severe illness. Thus, ongoing efforts at real-time whole-genome sequencing are of utmost importance. However, it is also important that retrospective analyses that place these data into context be performed. Here, we present the genome sequence of strain HPB2088, which was collected in 1994.