Whole-Genome Sequencing and Bioinformatic Analysis of Isolates from Foodborne Illness Outbreaks of Campylobacter jejuni and Salmonella enterica.
Kelly F OakesonJennifer Marie WagnerAndreas RohrwasserRobyn Atkinson-DunnPublished in: Journal of clinical microbiology (2018)
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.
Keyphrases
- listeria monocytogenes
- data analysis
- quality improvement
- antimicrobial resistance
- copy number
- escherichia coli
- biofilm formation
- gene expression
- mass spectrometry
- machine learning
- big data
- artificial intelligence
- single molecule
- deep learning
- candida albicans
- solid phase extraction
- label free
- loop mediated isothermal amplification