ABSTRACT
Whole-genome sequencing (WGS) via next-generation sequencing (NGS) technologies is a powerful tool for determining the relatedness of bacterial isolates in foodborne illness detection and outbreak investigations. WGS has been applied to national outbreaks (for example, Listeria monocytogenes); however, WGS has rarely been used in smaller local outbreaks. The current study demonstrates the superior resolution of genetic and evolutionary relatedness generated by WGS data analysis, compared to pulsed-field gel electrophoresis (PFGE). The current study retrospectively applies WGS and a reference-free bioinformatic analysis to a Utah-specific outbreak of Campylobacter jejuni associated with raw milk and to a national multistate outbreak of Salmonella enterica subsp. enterica serovar Typhimurium associated with rotisserie chicken, both of which were characterized previously by PFGE. Together, these analyses demonstrate how a reference-free WGS workflow is not reliant on determination of a reference sequence, like WGS workflows that are based on single-nucleotide polymorphisms, or the need for curated allele databases, like multilocus sequence typing workflows.
Subject(s)
Campylobacter Infections/microbiology , Campylobacter jejuni/isolation & purification , Disease Outbreaks , Foodborne Diseases/microbiology , Genome, Bacterial/genetics , Salmonella Infections/microbiology , Salmonella typhimurium/isolation & purification , Animals , Campylobacter Infections/epidemiology , Campylobacter jejuni/classification , Campylobacter jejuni/genetics , Chickens , Computational Biology , Electrophoresis, Gel, Pulsed-Field , Feces/microbiology , Food Microbiology , Foodborne Diseases/epidemiology , Humans , Milk/microbiology , Phylogeny , Poultry Products/microbiology , Retrospective Studies , Salmonella Infections/epidemiology , Salmonella typhimurium/classification , Salmonella typhimurium/genetics , United States/epidemiology , Whole Genome SequencingABSTRACT
The ability to generate high-quality sequence data in a public health laboratory enables the identification of pathogenic strains, the determination of relatedness among outbreak strains, and the analysis of genetic information regarding virulence and antimicrobial-resistance genes. However, the analysis of whole-genome sequence data depends on bioinformatic analysis tools and processes. Many public health laboratories do not have the bioinformatic capabilities to analyze the data generated from sequencing and therefore are unable to take full advantage of the power of whole-genome sequencing. The goal of this perspective is to provide a guide for laboratories to understand the bioinformatic analyses that are needed to interpret whole-genome sequence data and how these in silico analyses can be implemented in a public health laboratory setting easily, affordably, and, in some cases, without the need for intensive computing resources and infrastructure.
