Interpreting Whole-Genome Sequence Analyses of Foodborne Bacteria for Regulatory Applications and Outbreak Investigations

• Published in: Frontiers in microbiology Vol. 9; p. 1482
• Main Authors: Pightling, Arthur W., Pettengill, James B., Luo, Yan, Baugher, Joseph D., Rand, Hugh, Strain, Errol
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 10.07.2018
• Subjects: genomic epidemiology; interpretation; Listeria monocytogenes; Microbiology; outbreak investigation; phylogenetics; whole-genome sequence; Salmonella enterica; genomic epidemiology; interpretation; whole-genome sequence; Escherichia coli; Listeria monocytogenes; phylogenetics; outbreak investigation
• ISSN: 1664-302X; 1664-302X
• DOI: 10.3389/fmicb.2018.01482

Whole-genome sequence (WGS) analysis has revolutionized the food safety industry by enabling high-resolution typing of foodborne bacteria. Higher resolving power allows investigators to identify origins of contamination during illness outbreaks and regulatory activities quickly and accurately. Government agencies and industry stakeholders worldwide are now analyzing WGS data routinely. Although researchers have published many studies that assess the efficacy of WGS data analysis for source attribution, guidance for interpreting WGS analyses is lacking. Here, we provide the framework for interpreting WGS analyses used by the Food and Drug Administration's Center for Food Safety and Applied Nutrition (CFSAN). We based this framework on the experiences of CFSAN investigators, collaborations and interactions with government and industry partners, and evaluation of the published literature. A fundamental question for investigators is whether two or more bacteria arose from the same source of contamination. Analysts often count the numbers of nucleotide differences [single-nucleotide polymorphisms (SNPs)] between two or more genome sequences to measure genetic distances. However, using SNP thresholds alone to assess whether bacteria originated from the same source can be misleading. Bacteria that are isolated from food, environmental, or clinical samples are representatives of bacterial populations. These populations are subject to evolutionary forces that can change genome sequences. Therefore, interpreting WGS analyses of foodborne bacteria requires a more sophisticated approach. Here, we present a framework for interpreting WGS analyses that combines SNP counts with phylogenetic tree topologies and bootstrap support. We also clarify the roles of WGS, epidemiological, traceback, and other evidence in forming the conclusions of investigations. Finally, we present examples that illustrate the application of this framework to real-world situations.