PRAWNS: compact pan-genomic features for whole-genome population genomics.

MOTIVATION: Scientists seeking to understand the genomic basis of bacterial phenotypes, such as antibiotic resistance, today have access to an unprecedented number of complete and nearly complete genomes. Making sense of these data requires computational tools able to perform multiple-genome comparisons efficiently, yet currently available tools cannot scale beyond several tens of genomes. RESULTS: We describe PRAWNS, an efficient and scalable tool for multiple-genome analysis. PRAWNS defines a concise set of genomic features (metablocks), as well as pairwise relationships between them, which can be used as a basis for large-scale genotype-phenotype association studies. We demonstrate the effectiveness of PRAWNS by identifying genomic regions associated with antibiotic resistance in Acinetobacter baumannii. AVAILABILITY AND IMPLEMENTATION: PRAWNS is implemented in C++ and Python3, licensed under the GPLv3 license, and freely downloadable from GitHub (https://github.com/KiranJavkar/PRAWNS.git). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Listeria monocytogenes Illness and Deaths Associated With Ongoing Contamination of a Multiregional Brand of Ice Cream Products, United States, 2010-2015.

BACKGROUND: Frozen foods have rarely been linked to Listeria monocytogenes illness. We describe an outbreak investigation prompted by both hospital clustering of illnesses and product testing. METHODS: We identified outbreak-associated listeriosis cases using whole-genome sequencing (WGS), product testing results, and epidemiologic linkage to cases in the same Kansas hospital. We reviewed hospital medical and dietary records, product invoices, and molecular subtyping results. Federal and state officials tested product and environmental samples for L. monocytogenes. RESULTS: Kansas officials were investigating 5 cases of listeriosis at a single hospital when, simultaneously, unrelated sampling for a study in South Carolina identified L. monocytogenes in Company A ice cream products made in Texas. Isolates from 4 patients and Company A products were closely related by WGS, and the 4 patients with known exposures had consumed milkshakes made with Company A ice cream while hospitalized. Further testing identified L. monocytogenes in ice cream produced in a second Company A production facility in Oklahoma; these isolates were closely related by WGS to those from 5 patients in 3 other states. These 10 illnesses, involving 3 deaths, occurred from 2010 through 2015. Company A ultimately recalled all products. CONCLUSIONS: In this US outbreak of listeriosis linked to a widely distributed brand of ice cream, WGS and product sampling helped link cases spanning 5 years to 2 production facilities, indicating longstanding contamination. Comprehensive sanitation controls and environmental and product testing for L. monocytogenes with regulatory oversight should be implemented for ice cream production.

Toward the Adoption of Loop-Mediated Isothermal Amplification for Salmonella Screening at the National Antimicrobial Resistance Monitoring System's Retail Meat Sites.

The National Antimicrobial Resistance Monitoring System (NARMS) is a One Health program in the United States that collects data on antimicrobial resistance in enteric bacteria from humans, animals, and the environment. Salmonella is a major pathogen tracked by the NARMS retail meat arm but currently lacks a uniform screening method. We evaluated a loop-mediated isothermal amplification (LAMP) assay for the rapid screening of Salmonella from 69 NARMS retail meat and poultry samples. All samples were processed side by side for culture isolation using two protocols, one from NARMS and the other one described in the U.S. Food and Drug Administration's Bacteriological Analytical Manual (BAM). Overall, 10 (14.5%) samples screened positive by the Salmonella LAMP assay. Of those, six were culture-confirmed by the NARMS protocol and six by the BAM method with overlap on four samples. No Salmonella isolates were recovered from samples that screened negative with LAMP. These results suggested 100% sensitivity for LAMP in reference to culture. Antimicrobial susceptibility testing and whole-genome sequencing analysis confirmed identities of these isolates. Using the BAM protocol, all Salmonella isolates were recovered from samples undergoing Rappaport-Vassiliadis medium selective enrichment and presumptive colonies (n = 130) were dominated by Hafnia alvei (44.6%), Proteus mirabilis (22.3%), and Morganella morganii (9.9%) based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. This method comparison study clearly demonstrated the benefit of a rapid, robust, and highly sensitive molecular screening method in streamlining the laboratory workflow. Fourteen NARMS retail meat sites further verified the performance of this assay using a portion of their routine samples, reporting an overall specificity of 98.8% and sensitivity of 90%. As of July 2022, the vast majority of NARMS retail meat sites have adopted the Salmonella LAMP assay for rapid screening of Salmonella in all samples.

Evaluating the accuracy of Listeria monocytogenes assemblies from quasimetagenomic samples using long and short reads.

BACKGROUND: Whole genome sequencing of cultured pathogens is the state of the art public health response for the bioinformatic source tracking of illness outbreaks. Quasimetagenomics can substantially reduce the amount of culturing needed before a high quality genome can be recovered. Highly accurate short read data is analyzed for single nucleotide polymorphisms and multi-locus sequence types to differentiate strains but cannot span many genomic repeats, resulting in highly fragmented assemblies. Long reads can span repeats, resulting in much more contiguous assemblies, but have lower accuracy than short reads. RESULTS: We evaluated the accuracy of Listeria monocytogenes assemblies from enrichments (quasimetagenomes) of naturally-contaminated ice cream using long read (Oxford Nanopore) and short read (Illumina) sequencing data. Accuracy of ten assembly approaches, over a range of sequencing depths, was evaluated by comparing sequence similarity of genes in assemblies to a complete reference genome. Long read assemblies reconstructed a circularized genome as well as a 71 kbp plasmid after 24 h of enrichment; however, high error rates prevented high fidelity gene assembly, even at 150X depth of coverage. Short read assemblies accurately reconstructed the core genes after 28 h of enrichment but produced highly fragmented genomes. Hybrid approaches demonstrated promising results but had biases based upon the initial assembly strategy. Short read assemblies scaffolded with long reads accurately assembled the core genes after just 24 h of enrichment, but were highly fragmented. Long read assemblies polished with short reads reconstructed a circularized genome and plasmid and assembled all the genes after 24 h enrichment but with less fidelity for the core genes than the short read assemblies. CONCLUSION: The integration of long and short read sequencing of quasimetagenomes expedited the reconstruction of a high quality pathogen genome compared to either platform alone. A new and more complete level of information about genome structure, gene order and mobile elements can be added to the public health response by incorporating long read analyses with the standard short read WGS outbreak response.

GalaxyTrakr: a distributed analysis tool for public health whole genome sequence data accessible to non-bioinformaticians.

BACKGROUND: Processing and analyzing whole genome sequencing (WGS) is computationally intense: a single Illumina MiSeq WGS run produces ~ 1 million 250-base-pair reads for each of 24 samples. This poses significant obstacles for smaller laboratories, or laboratories not affiliated with larger projects, which may not have dedicated bioinformatics staff or computing power to effectively use genomic data to protect public health. Building on the success of the cloud-based Galaxy bioinformatics platform ( http://galaxyproject.org ), already known for its user-friendliness and powerful WGS analytical tools, the Center for Food Safety and Applied Nutrition (CFSAN) at the U.S. Food and Drug Administration (FDA) created a customized 'instance' of the Galaxy environment, called GalaxyTrakr ( https://www.galaxytrakr.org ), for use by laboratory scientists performing food-safety regulatory research. The goal was to enable laboratories outside of the FDA internal network to (1) perform quality assessments of sequence data, (2) identify links between clinical isolates and positive food/environmental samples, including those at the National Center for Biotechnology Information sequence read archive ( https://www.ncbi.nlm.nih.gov/sra/ ), and (3) explore new methodologies such as metagenomics. GalaxyTrakr hosts a variety of free and adaptable tools and provides the data storage and computing power to run the tools. These tools support coordinated analytic methods and consistent interpretation of results across laboratories. Users can create and share tools for their specific needs and use sequence data generated locally and elsewhere. RESULTS: In its first full year (2018), GalaxyTrakr processed over 85,000 jobs and went from 25 to 250 users, representing 53 different public and state health laboratories, academic institutions, international health laboratories, and federal organizations. By mid-2020, it has grown to 600 registered users and processed over 450,000 analytical jobs. To illustrate how laboratories are making use of this resource, we describe how six institutions use GalaxyTrakr to quickly analyze and review their data. Instructions for participating in GalaxyTrakr are provided. CONCLUSIONS: GalaxyTrakr advances food safety by providing reliable and harmonized WGS analyses for public health laboratories and promoting collaboration across laboratories with differing resources. Anticipated enhancements to this resource will include workflows for additional foodborne pathogens, viruses, and parasites, as well as new tools and services.

Phylogenetic and Biogeographic Patterns of Vibrio parahaemolyticus Strains from North America Inferred from Whole-Genome Sequence Data.

Vibrio parahaemolyticus is the most common cause of seafood-borne illness reported in the United States. The draft genomes of 132 North American clinical and oyster V. parahaemolyticus isolates were sequenced to investigate their phylogenetic and biogeographic relationships. The majority of oyster isolate sequence types (STs) were from a single harvest location; however, four were identified from multiple locations. There was population structure along the Gulf and Atlantic Coasts of North America, with what seemed to be a hub of genetic variability along the Gulf Coast, with some of the same STs occurring along the Atlantic Coast and one shared between the coastal waters of the Gulf and those of Washington State. Phylogenetic analyses found nine well-supported clades. Two clades were composed of isolates from both clinical and oyster sources. Four were composed of isolates entirely from clinical sources, and three were entirely from oyster sources. Each single-source clade consisted of one ST. Some human isolates lack tdh, trh, and some type III secretion system (T3SS) genes, which are established virulence genes of V. parahaemolyticus Thus, these genes are not essential for pathogenicity. However, isolates in the monophyletic groups from clinical sources were enriched in several categories of genes compared to those from monophyletic groups of oyster isolates. These functional categories include cell signaling, transport, and metabolism. The identification of genes in these functional categories provides a basis for future in-depth pathogenicity investigations of V. parahaemolyticusIMPORTANCEVibrio parahaemolyticus is the most common cause of seafood-borne illness reported in the United States and is frequently associated with shellfish consumption. This study contributes to our knowledge of the biogeography and functional genomics of this species around North America. STs shared between the Gulf Coast and the Atlantic seaboard as well as Pacific waters suggest possible transport via oceanic currents or large shipping vessels. STs frequently isolated from humans but rarely, if ever, isolated from the environment are likely more competitive in the human gut than other STs. This could be due to additional functional capabilities in areas such as cell signaling, transport, and metabolism, which may give these isolates an advantage in novel nutrient-replete environments such as the human gut.

Using full chloroplast genomes of 'red' and 'yellow' Bixa orellana (achiote) for kmer based identification and phylogenetic inference.

BACKGROUND: Full chloroplast genomes provide high resolution taxonomic discrimination between closely related plant species and are quickly replacing single and multi-locus barcoding regions as reference materials of choice for DNA based taxonomic annotation of plants. Bixa orellana, commonly known as "achiote" and "annatto" is a plant used for both human and animal foods and was thus identified for full chloroplast sequencing for the Center for Veterinary Medicine (CVM) Complete Chloroplast Animal Feed database. This work was conducted in collaboration with the Instituto de Medicina Tradicional (IMET) in Iquitos, Peru. There is a wide range of color variation in pods of Bixa orellana for which genetic loci that distinguish phenotypes have not yet been identified. Here we apply whole chloroplast genome sequencing of "red" and "yellow" individuals of Bixa orellana to provide high quality reference genomes to support kmer database development for use identifying this plant from complex mixtures using shotgun data. Additionally, we describe chloroplast gene content, synteny and phylogeny, and identify an indel and snp that may be associated with seed pod color. RESULTS: Fully assembled chloroplast genomes were produced for both red and yellow Bixa orellana accessions (158,918 and 158,823 bp respectively). Synteny and gene content was identical to the only other previously reported full chloroplast genome of Bixa orellana (NC_041550). We observed a 17 base pair deletion at position 58,399-58,415 in both accessions, relative to NC_041550 and a 6 bp deletion at position 75,531-75,526 and a snp at position 86,493 in red Bixa orellana. CONCLUSIONS: Our data provide high quality reference genomes of individuals of red and yellow Bixa orellana to support kmer based identity markers for use with shotgun sequencing approaches for rapid, precise identification of Bixa orellana from complex mixtures. Kmer based phylogeny of full chloroplast genomes supports monophylly of Bixaceae consistent with alignment based approaches. A potentially discriminatory indel and snp were identified that may be correlated with the red phenotype.

Genomic diversity of Salmonella enterica isolated from papaya samples collected during multiple outbreaks in 2017.

In 2017, the US Food and Drug Administration investigated the sources of multiple outbreaks of salmonellosis. Epidemiologic and traceback investigations identified Maradol papayas as the suspect vehicles. During the investigations, the genomes of 55 Salmonella enterica that were isolated from papaya samples were sequenced. Serovar assignments and phylogenetic analysis placed the 55 isolates into ten distinct groups, each representing a different serovar. Within-serovar SNP differences are generally between 0 and 20 SNPs, while the median between-serovar distance is 51 812 SNPs. We observed two groups with SNP distances between 21 and 100 SNPs. These relatively large within-serovar SNP distances may indicate that the isolates represent either diverse populations or multiple, genetically distinct subpopulations. Further inspection of these cases with traceback evidence allowed us to identify an 11th population. We observed that high levels of genomic diversity from individual firms is possible, with one firm yielding five of the ten serovars. Also, high levels of diversity are possible within small geographic regions, as five of the serovars were isolated from papayas that originated from farms located in Armería and Tecomán, Colima. In addition, we identified AMR genes that are present in three of the serovars studied here (aph(3')-lb, aph(6)-ld, tet(C), fosA7, and qnrB19) and we detected the presence of the plasmid IncHI2A among S. Urbana isolates.

Quasimetagenomic source tracking of Listeria monocytogenes from naturally contaminated ice cream.

BACKGROUND: The more quickly bacterial pathogens responsible for foodborne illness outbreaks can be linked to a vehicle of transmission or a source, the more illnesses can be prevented. Whole genome sequencing (WGS) based approaches to source tracking have greatly increased the speed and resolution with which public health response can pinpoint the vehicle and source of outbreaks. Traditionally, WGS approaches have focused on the culture of an individual isolate before proceeding to DNA extraction and sequencing. For Listeria monocytogenes (Lm), generation of an individual isolate for sequencing typically takes about 6 days. Here we demonstrate that a hybrid, "quasimetagenomic" approach ie; direct sequencing of microbiological enrichments (first step in pathogen detection and recovery) can provide high resolution source tracking sequence data, 5 days earlier than response that focuses on culture and sequencing of an individual isolate. This expedited approach could save lives, prevent illnesses and potentially minimize unnecessary destruction of food. METHODS: Naturally contaminated ice cream (from a 2015 outbreak) was enriched to recover Listeria monocytogenes following protocols outlined in the Bacteriological Analytic Manual (BAM). DNA from enriching microbiota was extracted and sequenced at incremental time-points during the first 48 h of pre-enrichment using the Illumina MiSeq platform (2 by 250), to evaluate genomic coverage of target pathogen, Listeria monocytogenes. RESULTS: Quasimetagenomic sequence data acquired from hour 20 were sufficient to discern whether or not Lm strain/s were part of the ongoing outbreak or not. Genomic data from hours 24, 28, 32, 36, 40, 44, and 48 of pre-enrichments all provided identical phylogenetic source tracking utility to the WGS of individual isolates (which require an additional 5 days to culture). CONCLUSIONS: The speed of this approach (more than twice as fast as current methods) has the potential to reduce the number of illnesses associated with any given outbreak by as many as 75% percent of total cases and potentially with continued optimization of the entire chain of response, contribute to minimized food waste.

Short communication: Multistate outbreak of Listeria monocytogenes infections retrospectively linked to unpasteurized milk using whole-genome sequencing.

Unpasteurized milk can contain harmful bacteria such as Listeria monocytogenes. In 2016, the US Food and Drug Administration notified the Centers for Disease Control and Prevention (Atlanta, GA) that L. monocytogenes isolated from unpasteurized chocolate milk from a Pennsylvania dairy was closely related, by whole-genome sequencing, to L. monocytogenes isolates collected from blood specimens of 2 patients (in California and Florida) in 2014. The California and Florida patients consumed unpasteurized milk from the Pennsylvania dairy. Both were >65 yr old and were hospitalized in 2014; the Florida patient died. Isolates from the 2 patients had indistinguishable pulsed-field gel electrophoresis patterns and were closely related by whole-genome multilocus sequence typing analysis (by 2 alleles) to the isolate from unpasteurized chocolate milk produced by the Pennsylvania dairy in 2015. Together, epidemiologic and laboratory information indicated a common origin. This is the first multistate listeriosis outbreak linked to unpasteurized milk in the United States detected using whole-genome multilocus sequence analysis.

Phylogenomic Pipeline Validation for Foodborne Pathogen Disease Surveillance.

Foodborne pathogen surveillance in the United States is transitioning from strain identification using restriction digest technology (pulsed-field gel electrophoresis [PFGE]) to shotgun sequencing of the entire genome (whole-genome sequencing [WGS]). WGS requires a new suite of analysis tools, some of which have long histories in academia but are new to the field of public health and regulatory decision making. Although the general workflow is fairly standard for collecting and analyzing WGS data for disease surveillance, there are a number of differences in how the data are collected and analyzed across public health agencies, both nationally and internationally. This impedes collaborative public health efforts, so national and international efforts are underway to enable direct comparison of these different analysis methods. Ultimately, the harmonization efforts will allow the (mutually trusted and understood) production and analysis of WGS data by labs and agencies worldwide, thus improving outbreak response capabilities globally. This review provides a historical perspective on the use of WGS for pathogen tracking and summarizes the efforts underway to ensure the major steps in phylogenomic pipelines used for pathogen disease surveillance can be readily validated. The tools for doing this will ensure that the results produced are sound, reproducible, and comparable across different analytic approaches.

Metagenome tracking biogeographic agroecology: Phytobiota of tomatoes from Virginia, Maryland, North Carolina and California.

Describing baseline microbiota associated with agricultural commodities in the field is an important step towards improving our understanding of a wide range of important objectives from plant pathology and horticultural sustainability, to food safety. Environmental pressures on plants (wind, dust, drought, water, temperature) vary by geography and characterizing the impact of these variable pressures on phyllosphere microbiota will contribute to improved stewardship of fresh produce for both plant and human health. A higher resolution understanding of the incidence of human pathogens on food plants and co-occurring phytobiota using metagenomic approaches (metagenome tracking) may contribute to improved source attribution and risk assessment in cases where human pathogens become introduced to agro-ecologies. Between 1990 and 2007, as many as 1990 culture-confirmed Salmonella illnesses were linked to tomatoes from as many as 12 multistate outbreaks (Bell et al., 2012; Bell et al., 2015; Bennett et al., 2014; CDC, 2004; CDC, 2007; Greene et al., 2005a; Gruszynski et al., 2014). When possible, source attribution for these incidents revealed a biogeographic trend, most events were associated with eastern growing regions. To improve our understanding of potential biogeographically linked trends in contamination of tomatoes by Salmonella, we profiled microbiota from the surfaces of tomatoes from Virginia, Maryland, North Carolina and California. Bacterial profiles from California tomatoes were completely different than those of Maryland, Virginia and North Carolina (which were highly similar to each other). A statistically significant enrichment of Firmicutes taxa was observed in California phytobiota compared to the three eastern states. Rhizobiaceae, Sphingobacteriaceae and Xanthobacteraceae were the most abundant bacterial families associated with tomatoes grown in eastern states. These baseline metagenomic profiles of phyllosphere microbiota may contribute to improved understanding of how certain ecologies provide supportive resources for human pathogens on plants and how components of certain agro-ecologies may play a role in the introduction of human pathogens to plants.

TreeToReads - a pipeline for simulating raw reads from phylogenies.

BACKGROUND: Using phylogenomic analysis tools for tracking pathogens has become standard practice in academia, public health agencies, and large industries. Using the same raw read genomic data as input, there are several different approaches being used to infer phylogenetic tree. These include many different SNP pipelines, wgMLST approaches, k-mer algorithms, whole genome alignment and others; each of these has advantages and disadvantages, some have been extensively validated, some are faster, some have higher resolution. A few of these analysis approaches are well-integrated into the regulatory process of US Federal agencies (e.g. the FDA's SNP pipeline for tracking foodborne pathogens). However, despite extensive validation on benchmark datasets and comparison with other pipelines, we lack methods for fully exploring the effects of multiple parameter values in each pipeline that can potentially have an effect on whether the correct phylogenetic tree is recovered. RESULTS: To resolve this problem, we offer a program, TreeToReads, which can generate raw read data from mutated genomes simulated under a known phylogeny. This simulation pipeline allows direct comparisons of simulated and observed data in a controlled environment. At each step of these simulations, researchers can vary parameters of interest (e.g., input tree topology, amount of sequence divergence, rate of indels, read coverage, distance of reference genome, etc) to assess the effects of various parameter values on correctly calling SNPs and reconstructing an accurate tree. CONCLUSIONS: Such critical assessments of the accuracy and robustness of analytical pipelines are essential to progress in both research and applied settings.

Singleton Sequence Type 382, an Emerging Clonal Group of Listeria monocytogenes Associated with Three Multistate Outbreaks Linked to Contaminated Stone Fruit, Caramel Apples, and Leafy Green Salad.

Three multistate outbreaks between 2014 and 2016, involving case patients in and outside the United States, were linked to stone fruit, caramel apples, and packaged leafy green salad contaminated with Listeria monocytogenes singleton sequence type 382 (ST382), a serotype IVb-v1 clone with limited genomic divergence. Isolates from these outbreaks and other ST382 isolates not associated with these outbreaks were analyzed by whole-genome sequencing (WGS) analysis. The primary differences among ST382 strains were single nucleotide polymorphisms (SNPs). WGS analysis differentiated ST382 from a clonal complex 1 outbreak strain co-contaminating the caramel apples. WGS clustered food, environmental, and clinical isolates within each outbreak, and also differentiated among the three outbreak strains and epidemiologically unrelated ST382 isolates, which were indistinguishable by pulsed-field gel electrophoresis. ST382 appeared to be an emerging clone that began to diverge from its ancestor approximately 32 years before 2016. We estimated that there was 1.29 nucleotide substitution per genome (2.94 Mbp) per year for this clone.

Whole Genome and Core Genome Multilocus Sequence Typing and Single Nucleotide Polymorphism Analyses of Listeria monocytogenes Isolates Associated with an Outbreak Linked to Cheese, United States, 2013.

Epidemiological findings of a listeriosis outbreak in 2013 implicated Hispanic-style cheese produced by company A, and pulsed-field gel electrophoresis (PFGE) and whole genome sequencing (WGS) were performed on clinical isolates and representative isolates collected from company A cheese and environmental samples during the investigation. The results strengthened the evidence for cheese as the vehicle. Surveillance sampling and WGS 3 months later revealed that the equipment purchased by company B from company A yielded an environmental isolate highly similar to all outbreak isolates. The whole genome and core genome multilocus sequence typing and single nucleotide polymorphism (SNP) analyses results were compared to demonstrate the maximum discriminatory power obtained by using multiple analyses, which were needed to differentiate outbreak-associated isolates from a PFGE-indistinguishable isolate collected in a nonimplicated food source in 2012. This unrelated isolate differed from the outbreak isolates by only 7 to 14 SNPs, and as a result, the minimum spanning tree from the whole genome analyses and certain variant calling approach and phylogenetic algorithm for core genome-based analyses could not provide differentiation between unrelated isolates. Our data also suggest that SNP/allele counts should always be combined with WGS clustering analysis generated by phylogenetically meaningful algorithms on a sufficient number of isolates, and the SNP/allele threshold alone does not provide sufficient evidence to delineate an outbreak. The putative prophages were conserved across all the outbreak isolates. All outbreak isolates belonged to clonal complex 5 and serotype 1/2b and had an identical inlA sequence which did not have premature stop codons.IMPORTANCE In this outbreak, multiple analytical approaches were used for maximum discriminatory power. A PFGE-matched, epidemiologically unrelated isolate had high genetic similarity to the outbreak-associated isolates, with as few as 7 SNP differences. Therefore, the SNP/allele threshold should not be used as the only evidence to define the scope of an outbreak. It is critical that the SNP/allele counts be complemented by WGS clustering analysis generated by phylogenetically meaningful algorithms to distinguish outbreak-associated isolates from epidemiologically unrelated isolates. Careful selection of a variant calling approach and phylogenetic algorithm is critical for core-genome-based analyses. The whole-genome-based analyses were able to construct the highly resolved phylogeny needed to support the findings of the outbreak investigation. Ultimately, epidemiologic evidence and multiple WGS analyses should be combined to increase confidence levels during outbreak investigations.

Tracing Origins of the Salmonella Bareilly Strain Causing a Food-borne Outbreak in the United States.

BACKGROUND: Using a novel combination of whole-genome sequencing (WGS) analysis and geographic metadata, we traced the origins of Salmonella Bareilly isolates collected in 2012 during a widespread food-borne outbreak in the United States associated with scraped tuna imported from India. METHODS: Using next-generation sequencing, we sequenced the complete genome of 100 Salmonella Bareilly isolates obtained from patients who consumed contaminated product, from natural sources, and from unrelated historically and geographically disparate foods. Pathogen genomes were linked to geography by projecting the phylogeny on a virtual globe and produced a transmission network. RESULTS: Phylogenetic analysis of WGS data revealed a common origin for outbreak strains, indicating that patients in Maryland and New York were infected from sources originating at a facility in India. CONCLUSIONS: These data represent the first report fully integrating WGS analysis with geographic mapping and a novel use of transmission networks. Results showed that WGS vastly improves our ability to delimit the scope and source of bacterial food-borne contamination events. Furthermore, these findings reinforce the extraordinary utility that WGS brings to global outbreak investigation as a greatly enhanced approach to protecting the human food supply chain as well as public health in general.

Implementation of Nationwide Real-time Whole-genome Sequencing to Enhance Listeriosis Outbreak Detection and Investigation.

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). Whole-genome 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.

Infectious Dose of Listeria monocytogenes in Outbreak Linked to Ice Cream, United States, 2015.

The relationship between the number of ingested Listeria monocytogenes cells in food and the likelihood of developing listeriosis is not well understood. Data from an outbreak of listeriosis linked to milkshakes made from ice cream produced in 1 factory showed that contaminated products were distributed widely to the public without any reported cases, except for 4 cases of severe illness in persons who were highly susceptible. The ingestion of high doses of L. monocytogenes by these patients infected through milkshakes was unlikely if possible additional contamination associated with the preparation of the milkshake is ruled out. This outbreak illustrated that the vast majority of the population did not become ill after ingesting a low level of L. monocytogenes but raises the question of listeriosis cases in highly susceptible persons after distribution of low-level contaminated products that did not support the growth of this pathogen.

Practical Value of Food Pathogen Traceability through Building a Whole-Genome Sequencing Network and Database.

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.

Core Genome Multilocus Sequence Typing for Identification of Globally Distributed Clonal Groups and Differentiation of Outbreak Strains of Listeria monocytogenes.

Many listeriosis outbreaks are caused by a few globally distributed clonal groups, designated clonal complexes or epidemic clones, of Listeria monocytogenes, several of which have been defined by classic multilocus sequence typing (MLST) schemes targeting 6 to 8 housekeeping or virulence genes. We have developed and evaluated core genome MLST (cgMLST) schemes and applied them to isolates from multiple clonal groups, including those associated with 39 listeriosis outbreaks. The cgMLST clusters were congruent with MLST-defined clonal groups, which had various degrees of diversity at the whole-genome level. Notably, cgMLST could distinguish among outbreak strains and epidemiologically unrelated strains of the same clonal group, which could not be achieved using classic MLST schemes. The precise selection of cgMLST gene targets may not be critical for the general identification of clonal groups and outbreak strains. cgMLST analyses further identified outbreak strains, including those associated with recent outbreaks linked to contaminated French-style cheese, Hispanic-style cheese, stone fruit, caramel apple, ice cream, and packaged leafy green salad, as belonging to major clonal groups. We further developed lineage-specific cgMLST schemes, which can include accessory genes when core genomes do not possess sufficient diversity, and this provided additional resolution over species-specific cgMLST. Analyses of isolates from different common-source listeriosis outbreaks revealed various degrees of diversity, indicating that the numbers of allelic differences should always be combined with cgMLST clustering and epidemiological evidence to define a listeriosis outbreak. IMPORTANCE: Classic multilocus sequence typing (MLST) schemes targeting internal fragments of 6 to 8 genes that define clonal complexes or epidemic clones have been widely employed to study L. monocytogenes biodiversity and its relation to pathogenicity potential and epidemiology. We demonstrated that core genome MLST schemes can be used for the simultaneous identification of clonal groups and the differentiation of individual outbreak strains and epidemiologically unrelated strains of the same clonal group. We further developed lineage-specific cgMLST schemes that targeted more genomic regions than the species-specific cgMLST schemes. Our data revealed the genome-level diversity of clonal groups defined by classic MLST schemes. Our identification of U.S. and international outbreaks caused by major clonal groups can contribute to further understanding of the global epidemiology of L. monocytogenes.