Heather A. Carleton

PulseNet International: Vision for the implementation of whole genome sequencing (WGS) for global food-borne disease surveillance

PulseNet International is a global network dedicated to laboratory-based surveillance for food-borne diseases. The network comprises the national and regional laboratory networks of Africa, Asia Pacific, Canada, Europe, Latin America and the Caribbean, the Middle East, and the United States. The PulseNet International vision is the standardised use of whole genome sequencing (WGS) to identify and subtype food-borne bacterial pathogens worldwide, replacing traditional methods to strengthen preparedness and response, reduce global social and economic disease burden, and save lives. To meet the needs of real-time surveillance, the PulseNet International network will standardise subtyping via WGS using whole genome multilocus sequence typing (wgMLST), which delivers sufficiently high resolution and epidemiological concordance, plus unambiguous nomenclature for the purposes of surveillance. Standardised protocols, validation studies, quality control programmes, database and nomenclature development, and training should support the implementation and decentralisation of WGS. Ideally, WGS data collected for surveillance purposes should be publicly available, in real time where possible, respecting data protection policies. WGS data are suitable for surveillance and outbreak purposes and for answering scientific questions pertaining to source attribution, antimicrobial resistance, transmission patterns, and virulence, which will further enable the protection and improvement of public health with respect to food-borne disease.

Implementation of Whole Genome Sequencing (WGS) for Identification and Characterization of Shiga Toxin-Producing Escherichia coli (STEC) in the United States

Shiga toxin-producing Escherichia coli (STEC) is an important foodborne pathogen capable of causing severe disease in humans. Rapid and accurate identification and characterization techniques are essential during outbreak investigations. Current methods for characterization of STEC are expensive and time-consuming. With the advent of rapid and cheap whole genome sequencing (WGS) benchtop sequencers, the potential exists to replace traditional workflows with WGS. The aim of this study was to validate tools to do reference identification and characterization from WGS for STEC in a single workflow within an easy to use commercially available software platform. Publically available serotype, virulence, and antimicrobial resistance databases were downloaded from the Center for Genomic Epidemiology (CGE) (www.genomicepidemiology.org) and integrated into a genotyping plug-in with in silico PCR tools to confirm some of the virulence genes detected from WGS data. Additionally, down sampling experiments on the WGS sequence data were performed to determine a threshold for sequence coverage needed to accurately predict serotype and virulence genes using the established workflow. The serotype database was tested on a total of 228 genomes and correctly predicted from WGS for 96.1% of O serogroups and 96.5% of H serogroups identified by conventional testing techniques. A total of 59 genomes were evaluated to determine the threshold of coverage to detect the different WGS targets, 40 were evaluated for serotype and virulence gene detection and 19 for the stx gene subtypes. For serotype, 95% of the O and 100% of the H serogroups were detected at > 40x and ≥ 30x coverage, respectively. For virulence targets and stx gene subtypes, nearly all genes were detected at > 40x, though some targets were 100% detectable from genomes with coverage ≥20x. The resistance detection tool was 97% concordant with phenotypic testing results. With isolates sequenced to > 40x coverage, the different databases accurately predicted serotype, virulence, and resistance from WGS data, providing a fast and cheaper alternative to conventional typing techniques.

A Comparative Analysis of the Lyve-SET Phylogenomics Pipeline for Genomic Epidemiology of Foodborne Pathogens

Modern epidemiology of foodborne bacterial pathogens in industrialized countries relies increasingly on whole genome sequencing (WGS) techniques. As opposed to profiling techniques such as pulsed-field gel electrophoresis, WGS requires a variety of computational methods. Since 2013, United States agencies responsible for food safety including the CDC, FDA, and USDA, have been performing whole-genome sequencing (WGS) on all Listeria monocytogenes found in clinical, food, and environmental samples. Each year, more genomes of other foodborne pathogens such as Escherichia coli, Campylobacter jejuni, and Salmonella enterica are being sequenced. Comparing thousands of genomes across an entire species requires a fast method with coarse resolution; however, capturing the fine details of highly related isolates requires a computationally heavy and sophisticated algorithm. Most L. monocytogenes investigations employing WGS depend on being able to identify an outbreak clade whose inter-genomic distances are less than an empirically determined threshold. When the difference between a few single nucleotide polymorphisms (SNPs) can help distinguish between genomes that are likely outbreak-associated and those that are less likely to be associated, we require a fine-resolution method. To achieve this level of resolution, we have developed Lyve-SET, a high-quality SNP pipeline. We evaluated Lyve-SET by retrospectively investigating 12 outbreak data sets along with four other SNP pipelines that have been used in outbreak investigation or similar scenarios. To compare these pipelines, several distance and phylogeny-based comparison methods were applied, which collectively showed that multiple pipelines were able to identify most outbreak clusters and strains. Currently in the US PulseNet system, whole genome multi-locus sequence typing (wgMLST) is the preferred primary method for foodborne WGS cluster detection and outbreak investigation due to its ability to name standardized genomic profiles, its central database, and its ability to be run in a graphical user interface. However, creating a functional wgMLST scheme requires extended up-front development and subject-matter expertise. When a scheme does not exist or when the highest resolution is needed, SNP analysis is used. Using three Listeria outbreak data sets, we demonstrated the concordance between Lyve-SET SNP typing and wgMLST. Availability: Lyve-SET can be found at https://github.com/lskatz/Lyve-SET.

Next-generation sequencing technologies and their application to the study and control of bacterial infections

BACKGROUND With the efficiency and the decreasing cost of next-generation sequencing, the technology is being rapidly introduced into clinical and public health laboratory practice. AIMS The historical background and principles of first-, second- and third-generation sequencing are described, as are the characteristics of the most commonly used sequencing instruments. SOURCES Peer-reviewed literature, white papers and meeting reports. CONTENT AND IMPLICATIONS Next-generation sequencing is a technology that could potentially replace many traditional microbiological workflows, providing clinicians and public health specialists with more actionable information than hitherto achievable. Examples of the clinical and public health uses of the technology are provided. The challenge of comparability of different sequencing platforms is discussed. Finally, the future directions of the technology integrating it with laboratory management and public health surveillance systems, and moving it towards performing sequencing directly from the clinical specimen (metagenomics), could lead to yet another fundamental transformation of clinical diagnostics and public health surveillance.

Comparison of classical multi-locus sequence typing software for next-generation sequencing data

Multi-locus sequence typing (MLST) is a widely used method for categorizing bacteria. Increasingly, MLST is being performed using next-generation sequencing (NGS) data by reference laboratories and for clinical diagnostics. Many software applications have been developed to calculate sequence types from NGS data; however, there has been no comprehensive review to date on these methods. We have compared eight of these applications against real and simulated data, and present results on: (1) the accuracy of each method against traditional typing methods, (2) the performance on real outbreak datasets, (3) the impact of contamination and varying depth of coverage, and (4) the computational resource requirements.

Benchmark datasets for phylogenomic pipeline validation, applications for foodborne pathogen surveillance

Background As next generation sequence technology has advanced, there have been parallel advances in genome-scale analysis programs for determining evolutionary relationships as proxies for epidemiological relationship in public health. Most new programs skip traditional steps of ortholog determination and multi-gene alignment, instead identifying variants across a set of genomes, then summarizing results in a matrix of single-nucleotide polymorphisms or alleles for standard phylogenetic analysis. However, public health authorities need to document the performance of these methods with appropriate and comprehensive datasets so they can be validated for specific purposes, e.g., outbreak surveillance. Here we propose a set of benchmark datasets to be used for comparison and validation of phylogenomic pipelines. Methods We identified four well-documented foodborne pathogen events in which the epidemiology was concordant with routine phylogenomic analyses (reference-based SNP and wgMLST approaches). These are ideal benchmark datasets, as the trees, WGS data, and epidemiological data for each are all in agreement. We have placed these sequence data, sample metadata, and “known” phylogenetic trees in publicly-accessible databases and developed a standard descriptive spreadsheet format describing each dataset. To facilitate easy downloading of these benchmarks, we developed an automated script that uses the standard descriptive spreadsheet format. Results Our “outbreak” benchmark datasets represent the four major foodborne bacterial pathogens (Listeria monocytogenes, Salmonella enterica, Escherichia coli, and Campylobacter jejuni) and one simulated dataset where the “known tree” can be accurately called the “true tree”. The downloading script and associated table files are available on GitHub: https://github.com/WGS-standards-and-analysis/datasets. Discussion These five benchmark datasets will help standardize comparison of current and future phylogenomic pipelines, and facilitate important cross-institutional collaborations. Our work is part of a global effort to provide collaborative infrastructure for sequence data and analytic tools—we welcome additional benchmark datasets in our recommended format, and, if relevant, we will add these on our GitHub site. Together, these datasets, dataset format, and the underlying GitHub infrastructure present a recommended path for worldwide standardization of phylogenomic pipelines.

An Assessment of Different Genomic Approaches for Inferring Phylogeny of Listeria monocytogenes

Background/objectives: Whole genome sequencing (WGS) has proven to be a powerful subtyping tool for foodborne pathogenic bacteria like L. monocytogenes. The interests of genome-scale analysis for national surveillance, outbreak detection or source tracking has been largely documented. The genomic data however can be exploited with many different bioinformatics methods like single nucleotide polymorphism (SNP), core-genome multi locus sequence typing (cgMLST), whole-genome multi locus sequence typing (wgMLST) or multi locus predicted protein sequence typing (MLPPST) on either core-genome (cgMLPPST) or pan-genome (wgMLPPST). Currently, there are little comparisons studies of these different analytical approaches. Our objective was to assess and compare different genomic methods that can be implemented in order to cluster isolates of L. monocytogenes. Methods: The clustering methods were evaluated on a collection of 207 L. monocytogenes genomes of food origin representative of the genetic diversity of the Anses collection. The trees were then compared using robust statistical analyses. Results: The backward comparability between conventional typing methods and genomic methods revealed a near-perfect concordance. The importance of selecting a proper reference when calling SNPs was highlighted, although distances between strains remained identical. The analysis also revealed that the topology of the phylogenetic trees between wgMLST and cgMLST were remarkably similar. The comparison between SNP and cgMLST or SNP and wgMLST approaches showed that the topologies of phylogenic trees were statistically similar with an almost equivalent clustering. Conclusion: Our study revealed high concordance between wgMLST, cgMLST, and SNP approaches which are all suitable for typing of L. monocytogenes. The comparable clustering is an important observation considering that the two approaches have been variously implemented among reference laboratories.

Two Listeria monocytogenes Pseudo-outbreaks Caused by Contaminated Laboratory Culture Media

ABSTRACT Listeriosis is a serious foodborne infection that disproportionately affects elderly adults, pregnant women, newborns, and immunocompromised individuals. Diagnosis is made by culturing Listeria monocytogenes from sterile body fluids or from products of conception. This report describes the investigations of two listeriosis pseudo-outbreaks caused by contaminated laboratory media made from sheep blood.

Comparison Of Multi-locus Sequence Typing Software For Next Generation Sequencing Data

Multi-locus sequence typing (MLST) is a widely used method for categorising bacteria. Increasingly MLST is being performed using next generation sequencing data by reference labs and for clinical diagnostics. Many software applications have been developed to calculate sequence types from NGS data; however, there has been no comprehensive review to date on these methods. We have compared six of these applications against real and simulated data and present results on: 1. the accuracy of each method against traditional typing methods, 2. the performance on real outbreak datasets, 3. in the impact of contamination and varying depth of coverage, and 4. the computational resource requirements. DATA SUMMARY Simulated reads for datasets testing coverage and mixed samples have been deposited in Figshare; DOI: https://doi.org/10.6084/m9.figshare.4602301.vl Outbreak databases are available from Github; url - https://github.com/WGS-standards-and-analysis/datasets Docker containers used to run each of the applications are available from Github; url – https://tinyurl.com/z7ks2ft Accession numbers for the data used in this paper are available in the Supplementary material. We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. ☒ IMPACT STATEMENT Sequence typing is rapidly transitioning from traditional sequencing methods to using whole genome sequencing. A number of in silico prediction methods have been developed on an ad hoc basis and aim to replicate Multi-locus sequence typing (MLST). This is the first study to comprehensively evaluate multiple MLST software applications on real validated datasets and on common simulated difficult cases. It will give researchers a clearer understanding of the accuracy, limitations and computational performance of the methods they use, and will assist future researchers to choose the most appropriate method for their experimental goals.

Role of Whole Genome Sequencing in the Public Health Surveillance of Foodborne Pathogens

Whole genome sequencing shows promise to transform public health microbiology of foodborne pathogens. The technology could replace almost all traditional workflows in a typical public health laboratory with a single cost-efficient whole genome sequencing workflow that includes all organisms. This workflow would include identification and reference characterization, e.g., serotyping, virulence characterization and antimicrobial resistance determination, and high discriminatory subtyping for outbreak detection and investigation. A multi-locus sequence typing (MLST) based analytical approach is the optimal primary subtyping tool for public health because it may be performed by laboratory personnel with little understanding of bioinformatics, be tiered at different level of discrimination for different public health purposes, and all subtypes are stable and may therefore be named definitively. Stable nomenclature is critical for efficient communication between public health partners to follow trends and for outbreak investigations. Unlike MLST, single nucleotide polymorphism based approaches rarely lead to stable nomenclature and should therefore be reserved for situations where MLST does not provide unequivocal answers.