Background: Recent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates.

Listeria monocytogenes (Lm) causes severe foodborne illness (listeriosis). Previous molecular subtyping methods, such as pulsed-field gel electrophoresis (PFGE), were critical in detecting outbreaks that led to food safety improvements and declining incidence, but PFGE provides limited genetic resolution. A multiagency collaboration began performing real-time, whole-genome sequencing (WGS) on all US Lm isolates from patients, food, and the environment in September 2013, posting sequencing data into a public repository. Compared with the year before the project began, WGS, combined with epidemiologic and product trace-back data, detected more listeriosis clusters and solved more outbreaks (2 outbreaks in pre-WGS year, 5 in WGS year 1, and 9 in year 2). Wholegenome multilocus sequence typing and single nucleotide polymorphism analyses provided equivalent phylogenetic relationships relevant to investigations; results were most useful when interpreted in context of epidemiological data. WGS has transformed listeriosis outbreak surveillance and is being implemented for other foodborne pathogens.

The FDA has created a United States-based open-source whole-genome sequencing network of state, federal, international, and commercial partners. The GenomeTrakr network represents a first-of-its-kind distributed genomic food shield for characterizing and tracing foodborne outbreak pathogens back to their sources. The GenomeTrakr network is leading investigations of outbreaks of foodborne illnesses and compliance actions with more accurate and rapid recalls of contaminated foods as well as more effective monitoring of preventive controls for food manufacturing environments. An expanded network would serve to provide an international rapid surveillance system for pathogen traceback, which is critical to support an effective public health response to bacterial outbreaks. R ecent devastating outbreaks associated with the consumption of fresh-cut produce have reinforced the notion that foodborne disease remains a substantial global challenge to public health. In the United States alone, one in six or an estimated 48 million people fall prey to foodborne pathogens, yielding 128,000 hospitalizations and 3,000 deaths per year (http://www.cdc.gov /foodborneburden). Economic burdens are estimated cumulatively at $152 billion dollars annually, $39 billion of which is attributed directly to the contamination of fresh, canned, and processed produce (see the Produce Safety Project, http://www .pewtrusts.org/en/about/news-room/press-releases/0001/01/01 /foodborne-illness-costs-nation-$152-billion-annually-nearly -$39-billion-loss-attributed-to-produce). Mitigating foodborne illness, at times, seems to be an intractable challenge.One longstanding problem is the ability to rapidly identify the food source of the contamination. Despite the best efforts of food safety experts, the previous technology, pulsed-field gel electrophoresis (PFGE), often lacks the resolution to effectively pinpoint the source of an outbreak. The promise of whole-genome sequencing (WGS) came in 2012 when scientists with the U.S. Food and Drug Administration's Center for Food Safety and Applied Nutrition (FDA-CFSAN) performed a retrospective outbreak study on a 2012 Salmonella outbreak that was linked to spicy tuna sushi rolls by PFGE. The clinical isolates, food isolates, and historical isolates of the same PFGE pattern were all sequenced on the Illumina MiSeq. In contrast to the PFGE results, where isolates from the current outbreak looked exactly the same as unrelated historical isolates, WGS uncovered a surprising level of resolution distinguishing all of the isolates. Moreover, the isolates from the outbreak were most closely related to a 5-year-old historical isolate that was linked to a processing facility only 8 km away from the source of the outbreak (1). This isolate was collected at the port of entry from an earlier inspection of contaminated seafood and, like many others, was saved in the freezer collection of the FDA-CFSAN. The idea that the FDA's historical isolates could all be sequenced, providing investigators with geographic clues from a ...

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.