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

Arthur W Pightling,Yan Luo,Joseph D Baugher,Errol Strain,Hugh Rand,James B Pettengill

doi:10.3389/fmicb.2018.01482

Abstract

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.

Highlights

Foodborne illness is a substantial hazard to human health and a significant burden on the economy
The phylogenetic analysis supports the grouping of the food and clinical isolates to the exclusion of all other isolates in the GT database
All three types of Wholegenome sequence (WGS) evidence support the hypothesis that the flour and clinical isolates originated from the same source

Summary

INTRODUCTION

Foodborne illness is a substantial hazard to human health and a significant burden on the economy. Investigators use WGS analyses to supplement traceback and epidemiological evidence during regulatory activities and outbreak investigations that identify sources of contamination These sources are populations of bacterial pathogens that are often present on raw commodities and in the production lines of food processing facilities. Bacteria from the same source may have significant numbers of SNPs. determining whether isolates match is not as TABLE 1 | Maximum pairwise SNPs measured during investigations into foodborne illness outbreaks and contamination events. Links between isolates may exist, despite the presence of large numbers of SNPs between them This approach is intended to reduce the chances that minor variations in a category of evidence or the lack of knowledge about the evolution of reservoir populations will lead to significant changes in the interpretation of WGS results. Identifying the source of an E. coli outbreak Matching food isolates from one firm to environmental isolates from another firm Identifying a resident pathogen Populations of environmental isolates can be very diverse Analyzing paraphyletic relationships Evidence that isolates arose from the same source by WGS does not necessarily mean that they are linked

Conclusion

CONCLUSION