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 ...

Summary1. We present data on the temporal dynamics of six viruses that infect lions (Panthera leo) in the Serengeti National Park and Ngorongoro Crater, Tanzania. These populations have been studied continuously for the past 30 years, and previous research has documented their seroprevalence for feline herpesvirus, feline immunode®ciency virus (FIV), feline calicivirus, feline parvovirus, feline coronavirus and canine distemper virus (CDV). A seventh virus, feline leukaemia virus (FeLV), was absent from these animals. 2. Comprehensive analysis reveals that feline herpesvirus and FIV were consistently prevalent at high levels, indicating that they were endemic in the host populations. Feline calici-, parvo-and coronavirus, and CDV repeatedly showed a pattern of seroprevalence that was indicative of discrete disease epidemics: a brief period of high exposure for each virus was followed by declining seroprevalence. 3. The timing of viral invasion suggests that dierent epidemic viruses are associated with dierent minimum threshold densities of susceptible hosts. Furthermore, the proportion of susceptibles that became infected during disease outbreaks was positively correlated with the number of susceptible hosts at the beginning of each outbreak. 4. Examination of the relationship between disease outbreaks and host ®tness suggest that these viruses do not aect birth and death rates in lions, with the exception of the 1994 outbreak of canine distemper virus. Although the endemic viruses (FHV and FIV) were too prevalent to measure precise health eects, there was no evidence that FIV infection reduced host longevity.

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.

BackgroundNext-Generation Sequencing (NGS) is increasingly being used as a molecular epidemiologic tool for discerning ancestry and traceback of the most complicated, difficult to resolve bacterial pathogens. Making a linkage between possible food sources and clinical isolates requires distinguishing the suspected pathogen from an environmental background and placing the variation observed into the wider context of variation occurring within a serovar and among other closely related foodborne pathogens. Equally important is the need to validate these high resolution molecular tools for use in molecular epidemiologic traceback. Such efforts include the examination of strain cluster stability as well as the cumulative genetic effects of sub-culturing on these clusters. Numerous isolates of S. Montevideo were shot-gun sequenced including diverse lineage representatives as well as numerous replicate clones to determine how much variability is due to bias, sequencing error, and or the culturing of isolates. All new draft genomes were compared to 34 S. Montevideo isolates previously published during an NGS-based molecular epidemiological case study.ResultsIntraserovar lineages of S. Montevideo differ by thousands of SNPs, that are only slightly less than the number of SNPs observed between S. Montevideo and other distinct serovars. Much less variability was discovered within an individual S. Montevideo clade implicated in a recent foodborne outbreak as well as among individual NGS replicates. These findings were similar to previous reports documenting homopolymeric and deletion error rates with the Roche 454 GS Titanium technology. In no case, however, did variability associated with sequencing methods or sample preparations create inconsistencies with our current phylogenetic results or the subsequent molecular epidemiological evidence gleaned from these data.ConclusionsImplementation of a validated pipeline for NGS data acquisition and analysis provides highly reproducible results that are stable and predictable for molecular epidemiological applications. When draft genomes are collected at 15×-20× coverage and passed through a quality filter as part of a data analysis pipeline, including sub-passaged replicates defined by a few SNPs, they can be accurately placed in a phylogenetic context. This reproducibility applies to all levels within and between serovars of Salmonella suggesting that investigators using these methods can have confidence in their conclusions.

The enteric pathogen Salmonella enterica is one of the leading causes of foodborne illness in the world. The species is extremely diverse, containing more than 2,500 named serovars that are designated for their unique antigen characters and pathogenicity profiles—some are known to be virulent pathogens, while others are not. Questions regarding the evolution of pathogenicity, significance of antigen characters, diversity of clustered regularly interspaced short palindromic repeat (CRISPR) loci, among others, will remain elusive until a strong evolutionary framework is established. We present the first large-scale S. enterica subsp. enterica phylogeny inferred from a new reference-free k-mer approach of gathering single nucleotide polymorphisms (SNPs) from whole genomes. The phylogeny of 156 isolates representing 78 serovars (102 were newly sequenced) reveals two major lineages, each with many strongly supported sublineages. One of these lineages is the S. Typhi group; well nested within the phylogeny. Lineage-through-time analyses suggest there have been two instances of accelerated rates of diversification within the subspecies. We also found that antigen characters and CRISPR loci reveal different evolutionary patterns than that of the phylogeny, suggesting that a horizontal gene transfer or possibly a shared environmental acquisition might have influenced the present character distribution. Our study also shows the ability to extract reference-free SNPs from a large set of genomes and then to use these SNPs for phylogenetic reconstruction. This automated, annotation-free approach is an important step forward for bacterial disease tracking and in efficiently elucidating the evolutionary history of highly clonal organisms.

Facile laboratory tools are needed to augment identification in contamination events to trace the contamination back to the source (traceback) of Salmonella enterica subsp. enterica serovar Enteritidis (S. Enteritidis). Understanding the evolution and diversity within and among outbreak strains is the first step towards this goal. To this end, we collected 106 new S. Enteriditis isolates within S. Enteriditis Pulsed-Field Gel Electrophoresis (PFGE) pattern JEGX01.0004 and close relatives, and determined their genome sequences. Sources for these isolates spanned food, clinical and environmental farm sources collected during the 2010 S. Enteritidis shell egg outbreak in the United States along with closely related serovars, S. Dublin, S. Gallinarum biovar Pullorum and S. Gallinarum. Despite the highly homogeneous structure of this population, S. Enteritidis isolates examined in this study revealed thousands of SNP differences and numerous variable genes (n = 366). Twenty-one of these genes from the lineages leading to outbreak-associated samples had nonsynonymous (causing amino acid changes) changes and five genes are putatively involved in known Salmonella virulence pathways. While chromosome synteny and genome organization appeared to be stable among these isolates, genome size differences were observed due to variation in the presence or absence of several phages and plasmids, including phage RE-2010, phage P125109, plasmid pSEEE3072_19 (similar to pSENV), plasmid pOU1114 and two newly observed mobile plasmid elements pSEEE1729_15 and pSEEE0956_35. These differences produced modifications to the assembled bases for these draft genomes in the size range of approximately 4.6 to 4.8 mbp, with S. Dublin being larger (∼4.9 mbp) and S. Gallinarum smaller (4.55 mbp) when compared to S. Enteritidis. Finally, we identified variable S. Enteritidis genes associated with virulence pathways that may be useful markers for the development of rapid surveillance and typing methods, potentially aiding in traceback efforts during future outbreaks involving S. Enteritidis PFGE pattern JEGX01.0004.

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.