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 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.

A trend towards the abandonment of obtaining pure culture isolates in frontline laboratories is at a crossroads with the ability of public health agencies to perform their basic mandate of foodborne disease surveillance and response. The implementation of culture-independent diagnostic tests (CIDTs) including nucleic acid and antigen-based assays for acute gastroenteritis is leaving public health agencies without laboratory evidence to link clinical cases to each other and to food or environmental substances. This limits the efficacy of public health epidemiology and surveillance as well as outbreak detection and investigation. Foodborne outbreaks have the potential to remain undetected or have insufficient evidence to support source attribution and may inadvertently increase the incidence of foodborne diseases. Next-generation sequencing of pure culture isolates in clinical microbiology laboratories has the potential to revolutionize the fields of food safety and public health. Metagenomics and other ‘omics’ disciplines could provide the solution to a cultureless future in clinical microbiology, food safety and public health. Data mining of information obtained from metagenomics assays can be particularly useful for the identification of clinical causative agents or foodborne contamination, detection of AMR and/or virulence factors, in addition to providing high-resolution subtyping data. Thus, metagenomics assays may provide a universal test for clinical diagnostics, foodborne pathogen detection, subtyping and investigation. This information has the potential to reform the field of enteric disease diagnostics and surveillance and also infectious diseases as a whole. The aim of this review will be to present the current state of CIDTs in diagnostic and public health laboratories as they relate to foodborne illness and food safety. Moreover, we will also discuss the diagnostic and subtyping utility and concomitant bias limitations of metagenomics and comparable detection techniques in clinical microbiology, food and public health laboratories. Early advances in the discipline of metagenomics, however, have indicated noteworthy challenges. Through forthcoming improvements in sequencing technology and analytical pipelines among others, we anticipate that within the next decade, detection and characterization of pathogens via metagenomics-based workflows will be implemented in routine usage in diagnostic and public health laboratories.

A strain from Haiti shares genetic ancestry with those from Asia and Africa.

Human listeriosis outbreaks in Canada have been predominantly caused by serotype 1/2a isolates with highly similar pulsedfield gel electrophoresis (PFGE) patterns. Multilocus sequence typing (MLST) and multi-virulence-locus sequence typing (MV-LST) each identified a diverse population of Listeria monocytogenes isolates, and within that, both methods had congruent subtypes that substantiated a predominant clone (clonal complex 8; virulence type 59; proposed epidemic clone 5 [ECV]) that has been causing human illness across Canada for more than 2 decades.

A novel genomic island (LGI1) was discovered in Listeria monocytogenes isolates responsible for the deadliest listeriosis outbreak in Canada, in 2008. To investigate the functional role of LGI1, the outbreak strain 08-5578 was exposed to food chain-relevant stresses, and the expression of 16 LGI1 genes was measured. LGI1 genes with putative efflux (L. monocytogenes emrE [emrELm]), regulatory (lmo1851), and adhesion (sel1) functions were deleted, and the mutants were exposed to acid (HCl), cold (4°C), salt (10 to 20% NaCl), and quaternary ammonium-based sanitizers (QACs). Deletion of lmo1851 had no effect on the L. monocytogenes stress response, and deletion of sel1 did not influence Caco-2 and HeLa cell adherence/invasion, whereas deletion of emrE resulted in increased susceptibility to QACs (P< 0.05) but had no effect on the MICs of gentamicin, chloramphenicol, ciprofloxacin, erythromycin, tetracycline, acriflavine, and triclosan. In the presence of the QAC benzalkonium chloride (BAC; 5 μg/ml), 14/16 LGI1 genes were induced, and lmo1861 (putative repressor gene) was constitutively expressed at 4 °C, 37 °C, and 52 °C and in the presence of UV exposure (0 to 30 min). Following 1 h of exposure to BAC (10 μg/ml), upregulation of emrE (49.6-fold), lmo1851 (2.3-fold), lmo1861 (82.4-fold), and sigB (4.1-fold) occurred. Reserpine visibly suppressed the growth of the ΔemrELm strain, indicating that QAC tolerance is due at least partially to efflux activity. These data suggest that a minimal function of LGI1 is to increase the tolerance of L. monocytogenes to QACs via emrELm. Since QACs are commonly used in the food industry, there is a concern that L. monocytogenes strains possessing emrE will have an increased ability to survive this stress and thus to persist in food processing environments.

The recent widespread application of whole-genome sequencing (WGS) for microbial disease investigations has spurred the development of new bioinformatics tools, including a notable proliferation of phylogenomics pipelines designed for infectious disease surveillance and outbreak investigation. Transitioning the use of WGS data out of the research laboratory and into the front lines of surveillance and outbreak response requires user-friendly, reproducible and scalable pipelines that have been well validated. Single Nucleotide Variant Phylogenomics (SNVPhyl) is a bioinformatics pipeline for identifying high-quality single-nucleotide variants (SNVs) and constructing a whole-genome phylogeny from a collection of WGS reads and a reference genome. Individual pipeline components are integrated into the Galaxy bioinformatics framework, enabling data analysis in a user-friendly, reproducible and scalable environment. We show that SNVPhyl can detect SNVs with high sensitivity and specificity, and identify and remove regions of high SNV density (indicative of recombination). SNVPhyl is able to correctly distinguish outbreak from non-outbreak isolates across a range of variant-calling settings, sequencing-coverage thresholds or in the presence of contamination. SNVPhyl is available as a Galaxy workflow, Docker and virtual machine images, and a Unix-based command-line application. SNVPhyl is released under the Apache 2.0 license and available at http://snvphyl.readthedocs.io/ or at https://github.com/phac-nml/snvphyl-galaxy.

Salmonella serotyping remains the gold-standard tool for the classification of Salmonella isolates and forms the basis of Canada’s national surveillance program for this priority foodborne pathogen. Public health officials have been increasingly looking toward whole genome sequencing (WGS) to provide a large set of data from which all the relevant information about an isolate can be mined. However, rigorous validation and careful consideration of potential implications in the replacement of traditional surveillance methodologies with WGS data analysis tools is needed. Two in silico tools for Salmonella serotyping have been developed, the Salmonella in silico Typing Resource (SISTR) and SeqSero, while seven gene MLST for serovar prediction can be adapted for in silico analysis. All three analysis methods were assessed and compared to traditional serotyping techniques using a set of 813 verified clinical and laboratory isolates, including 492 Canadian clinical isolates and 321 isolates of human and non-human sources. Successful results were obtained for 94.8, 88.2, and 88.3% of the isolates tested using SISTR, SeqSero, and MLST, respectively, indicating all would be suitable for maintaining historical records, surveillance systems, and communication structures currently in place and the choice of the platform used will ultimately depend on the users need. Results also pointed to the need to reframe serotyping in the genomic era as a test to understand the genes that are carried by an isolate, one which is not necessarily congruent with what is antigenically expressed. The adoption of WGS for serotyping will provide the simultaneous collection of information that can be used by multiple programs within the current surveillance paradigm; however, this does not negate the importance of the various programs or the role of serotyping going forward.

To increase understanding of drug-resistant Vibrio cholerae, we studied selected molecular mechanisms of antimicrobial drug resistance in the 2010 Haiti V. cholerae outbreak strain. Most resistance resulted from acquired genes located on an integrating conjugative element showing high homology to an integrating conjugative element identified in a V. cholerae isolate from India.