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

Abstract

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.

Implementation of Nationwide Real-time Whole-genome Sequencing to Enhance Listeriosis Outbreak Detection and Investigation

“…The FDA, CDC, and USDA-FSIS also analyze WGS data using free, open-source SNP-based approaches, which create phylogenetic trees based on nucleotide differences among genomes under investigation that are mapped for comparison to a reference genome [27,28]. For identifying high-quality SNP (hqSNP) differences among genomes, the SNP positions are filtered for sequence depth and quality.…”

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

“…Since the selection of the reference genome is difficult to standardize because almost all outbreaks need their own unique reference strain and there is no consensus about which of the many applications (pipelines) for hqSNP to use this method is not well suited for outbreak investigations performed at the same time in more laboratories. However, it is excellent for centralized analysis providing similar or better resolution between strains than wgMLST (9, 32, 33). …”

The Integrated Rapid Infectious Disease Analysis (IRIDA) Platform

“…Currently, IRIDA provides validated, user-friendly, fit-for-purpose, pipelines for analyses and integration of WGS and metadata. The SNVPhyl pipeline has been validated for analysis of foodborne disease outbreaks, and correctly distinguishes outbreak-related isolates from nonoutbreak isolates across a range of parameters, sequencing data qualities, and in the presence of contaminating sequence data (28,(42)(43)(44). Since 2010, SNVPhyl has been used for analysis by hundreds of public health analysts at Canada's National Microbiology Laboratory to support research and provincial laboratory services.…”