Genetic Diversity in Salmonella enterica in Outbreaks of Foodborne and Zoonotic Origin in the USA in 2006-2017.

Whole genome sequencing is replacing traditional laboratory surveillance methods as the primary tool to track and characterize clusters and outbreaks of the foodborne and zoonotic pathogen Salmonella enterica (S. enterica). In this study, 438 S. enterica isolates representing 35 serovars and 13 broad vehicle categories from one hundred epidemiologically confirmed outbreaks were evaluated for genetic variation to develop epidemiologically relevant interpretation guidelines for Salmonella disease cluster detection. The Illumina sequences were analyzed by core genome multi-locus sequence typing (cgMLST) and screened for antimicrobial resistance (AR) determinants and plasmids. Ninety-three of the one hundred outbreaks exhibited a close allele range (less than 10 allele differences with a subset closer than 5). The remaining seven outbreaks showed increased variation, of which three were considered polyclonal. A total of 16 and 28 outbreaks, respectively, showed variations in the AR and plasmid profiles. The serovars Newport and I 4,[5],12:i:-, as well as the zoonotic and poultry product vehicles, were overrepresented among the outbreaks, showing increased variation. A close allele range in cgMLST profiles can be considered a reliable proxy for epidemiological relatedness for the vast majority of S. enterica outbreak investigations. Variations associated with mobile elements happen relatively frequently during outbreaks and could be reflective of changing selective pressures.

Antimicrobial resistance in multistate outbreaks of nontyphoidal Salmonella infections linked to animal contact-United States, 2015-2018.

Animal contact is an established risk factor for nontyphoidal Salmonella infections and outbreaks. During 2015-2018, the U.S. Centers for Disease Control and Prevention (CDC) and other U.S. public health laboratories began implementing whole-genome sequencing (WGS) of Salmonella isolates. WGS was used to supplement the traditional methods of pulsed-field gel electrophoresis for isolate subtyping, outbreak detection, and antimicrobial susceptibility testing (AST) for the detection of resistance. We characterized the epidemiology and antimicrobial resistance (AMR) of multistate salmonellosis outbreaks linked to animal contact during this time period. An isolate was considered resistant if AST yielded a resistant (or intermediate, for ciprofloxacin) interpretation to any antimicrobial tested by the CDC or if WGS showed a resistance determinant in its genome for one of these agents. We identified 31 outbreaks linked to contact with poultry (n = 23), reptiles (n = 6), dairy calves (n = 1), and guinea pigs (n = 1). Of the 26 outbreaks with resistance data available, we identified antimicrobial resistance in at least one isolate from 20 outbreaks (77%). Of 1,309 isolates with resistance information, 247 (19%) were resistant to ≥1 antimicrobial, and 134 (10%) were multidrug-resistant to antimicrobials from ≥3 antimicrobial classes. The use of resistance data predicted from WGS increased the number of isolates with resistance information available fivefold compared with AST, and 28 of 43 total resistance patterns were identified exclusively by WGS; concordance was high (>99%) for resistance determined by AST and WGS. The use of predicted resistance from WGS enhanced the characterization of the resistance profiles of outbreaks linked to animal contact by providing resistance information for more isolates.

Rapid identification of enteric bacteria from whole genome sequences using average nucleotide identity metrics.

Identification of enteric bacteria species by whole genome sequence (WGS) analysis requires a rapid and an easily standardized approach. We leveraged the principles of average nucleotide identity using MUMmer (ANIm) software, which calculates the percent bases aligned between two bacterial genomes and their corresponding ANI values, to set threshold values for determining species consistent with the conventional identification methods of known species. The performance of species identification was evaluated using two datasets: the Reference Genome Dataset v2 (RGDv2), consisting of 43 enteric genome assemblies representing 32 species, and the Test Genome Dataset (TGDv1), comprising 454 genome assemblies which is designed to represent all species needed to query for identification, as well as rare and closely related species. The RGDv2 contains six Campylobacter spp., three Escherichia/Shigella spp., one Grimontia hollisae, six Listeria spp., one Photobacterium damselae, two Salmonella spp., and thirteen Vibrio spp., while the TGDv1 contains 454 enteric bacterial genomes representing 42 different species. The analysis showed that, when a standard minimum of 70% genome bases alignment existed, the ANI threshold values determined for these species were ≥95 for Escherichia/Shigella and Vibrio species, ≥93% for Salmonella species, and ≥92% for Campylobacter and Listeria species. Using these metrics, the RGDv2 accurately classified all validation strains in TGDv1 at the species level, which is consistent with the classification based on previous gold standard methods.

Evaluation of whole and core genome multilocus sequence typing allele schemes for Salmonella enterica outbreak detection in a national surveillance network, PulseNet USA.

Salmonella enterica is a leading cause of bacterial foodborne and zoonotic illnesses in the United States. For this study, we applied four different whole genome sequencing (WGS)-based subtyping methods: high quality single-nucleotide polymorphism (hqSNP) analysis, whole genome multilocus sequence typing using either all loci [wgMLST (all loci)] and only chromosome-associated loci [wgMLST (chrom)], and core genome multilocus sequence typing (cgMLST) to a dataset of isolate sequences from 9 well-characterized Salmonella outbreaks. For each outbreak, we evaluated the genomic and epidemiologic concordance between hqSNP and allele-based methods. We first compared pairwise genomic differences using all four methods. We observed discrepancies in allele difference ranges when using wgMLST (all loci), likely caused by inflated genetic variation due to loci found on plasmids and/or other mobile genetic elements in the accessory genome. Therefore, we excluded wgMLST (all loci) results from any further comparisons in the study. Then, we created linear regression models and phylogenetic tanglegrams using the remaining three methods. K-means analysis using the silhouette method was applied to compare the ability of the three methods to partition outbreak and sporadic isolate sequences. Our results showed that pairwise hqSNP differences had high concordance with cgMLST and wgMLST (chrom) allele differences. The slopes of the regressions for hqSNP vs. allele pairwise differences were 0.58 (cgMLST) and 0.74 [wgMLST (chrom)], and the slope of the regression was 0.77 for cgMLST vs. wgMLST (chrom) pairwise differences. Tanglegrams showed high clustering concordance between methods using two statistical measures, the Baker's gamma index (BGI) and cophenetic correlation coefficient (CCC), where 9/9 (100%) of outbreaks yielded BGI values ≥ 0.60 and CCCs were ≥ 0.97 across all nine outbreaks and all three methods. K-means analysis showed separation of outbreak and sporadic isolate groups with average silhouette widths ≥ 0.87 for outbreak groups and ≥ 0.16 for sporadic groups. This study demonstrates that Salmonella isolates clustered in concordance with epidemiologic data using three WGS-based subtyping methods and supports using cgMLST as the primary method for national surveillance of Salmonella outbreak clusters.

Predicting Food Sources of Listeria monocytogenes Based on Genomic Profiling Using Random Forest Model.

Listeria monocytogenes can cause severe foodborne illness, including miscarriage during pregnancy or death in newborn infants. When outbreaks of L. monocytogenes illness occur, it may be possible to determine the food source of the outbreak. However, most reported L. monocytogenes illnesses do not occur as part of a recognized outbreak and most of the time the food source of sporadic L. monocytogenes illness in people cannot be determined. In the United States, L. monocytogenes isolates from patients, foods, and environments are routinely sequenced and analyzed by whole genome multilocus sequence typing (wgMLST) for outbreak detection by PulseNet, the national molecular surveillance system for foodborne illnesses. We investigated whether machine learning approaches applied to wgMLST allele call data could assist in attribution analysis of food source of L. monocytogenes isolates. We compiled isolates with a known source from five food categories (dairy, fruit, meat, seafood, and vegetable) using the metadata of L. monocytogenes isolates in PulseNet, deduplicated closely genetically related isolates, and developed random forest models to predict the food sources of isolates. Prediction accuracy of the final model varied across the food categories; it was highest for meat (65%), followed by fruit (45%), vegetable (45%), dairy (44%), and seafood (37%); overall accuracy was 49%, compared with the naive prediction accuracy of 28%. Our results show that random forest can be used to capture genetically complex features of high-resolution wgMLST for attribution of isolates to their sources.

Reoccurring Escherichia coli O157:H7 Strain Linked to Leafy Greens-Associated Outbreaks, 2016-2019.

Genomic characterization of an Escherichia coli O157:H7 strain linked to leafy greens-associated outbreaks dates its emergence to late 2015. One clade has notable accessory genomic content and a previously described mutation putatively associated with increased arsenic tolerance. This strain is a reoccurring, emerging, or persistent strain causing illness over an extended period.

Evaluation of core genome and whole genome multilocus sequence typing schemes for Campylobacter jejuni and Campylobacter coli outbreak detection in the USA.

Campylobacter is a leading causing of bacterial foodborne and zoonotic illnesses in the USA. Pulsed-field gene electrophoresis (PFGE) and 7-gene multilocus sequence typing (MLST) have been historically used to differentiate sporadic from outbreak Campylobacter isolates. Whole genome sequencing (WGS) has been shown to provide superior resolution and concordance with epidemiological data when compared with PFGE and 7-gene MLST during outbreak investigations. In this study, we evaluated epidemiological concordance for high-quality SNP (hqSNP), core genome (cg)MLST and whole genome (wg)MLST to cluster or differentiate outbreak-associated and sporadic Campylobacter jejuni and Campylobacter coli isolates. Phylogenetic hqSNP, cgMLST and wgMLST analyses were also compared using Baker's gamma index (BGI) and cophenetic correlation coefficients. Pairwise distances comparing all three analysis methods were compared using linear regression models. Our results showed that 68/73 sporadic C. jejuni and C. coli isolates were differentiated from outbreak-associated isolates using all three methods. There was a high correlation between cgMLST and wgMLST analyses of the isolates; the BGI, cophenetic correlation coefficient, linear regression model R 2 and Pearson correlation coefficients were >0.90. The correlation was sometimes lower comparing hqSNP analysis to the MLST-based methods; the linear regression model R 2 and Pearson correlation coefficients were between 0.60 and 0.86, and the BGI and cophenetic correlation coefficient were between 0.63 and 0.86 for some outbreak isolates. We demonstrated that C. jejuni and C. coli isolates clustered in concordance with epidemiological data using WGS-based analysis methods. Discrepancies between allele and SNP-based approaches may reflect the differences between how genomic variation (SNPs and indels) are captured between the two methods. Since cgMLST examines allele differences in genes that are common in most isolates being compared, it is well suited to surveillance: searching large genomic databases for similar isolates is easily and efficiently done using allelic profiles. On the other hand, use of an hqSNP approach is much more computer intensive and not scalable to large sets of genomes. If further resolution between potential outbreak isolates is needed, wgMLST or hqSNP analysis can be used.

Cronobacter sakazakii Infections in Two Infants Linked to Powdered Infant Formula and Breast Pump Equipment - United States, 2021 and 2022.

Cronobacter sakazakii, a species of gram-negative bacteria belonging to the Enterobacteriaceae family, is known to cause severe and often fatal meningitis and sepsis in young infants. C. sakazakii is ubiquitous in the environment, and most reported infant cases have been attributed to contaminated powdered infant formula (powdered formula) or breast milk that was expressed using contaminated breast pump equipment (1-3). Previous investigations of cases and outbreaks have identified C. sakazakii in opened powdered formula, breast pump parts, environmental surfaces in the home, and, rarely, in unopened powdered formula and formula manufacturing facilities (2,4-6). This report describes two infants with C. sakazakii meningitis reported to CDC in September 2021 and February 2022. CDC used whole genome sequencing (WGS) analysis to link one case to contaminated opened powdered formula from the patient's home and the other to contaminated breast pump equipment. These cases highlight the importance of expanding awareness about C. sakazakii infections in infants, safe preparation and storage of powdered formula, proper cleaning and sanitizing of breast pump equipment, and using WGS as a tool for C. sakazakii investigations.

The power, potential, benefits, and challenges of implementing high-throughput sequencing in food safety systems.

The development and application of modern sequencing technologies have led to many new improvements in food safety and public health. With unprecedented resolution and big data, high-throughput sequencing (HTS) has enabled food safety specialists to sequence marker genes, whole genomes, and transcriptomes of microorganisms almost in real-time. These data reveal not only the identity of a pathogen or an organism of interest in the food supply but its virulence potential and functional characteristics. HTS of amplicons, allow better characterization of the microbial communities associated with food and the environment. New and powerful bioinformatics tools, algorithms, and machine learning allow for development of new models to predict and tackle important events such as foodborne disease outbreaks. Despite its potential, the integration of HTS into current food safety systems is far from complete. Government agencies have embraced this new technology, and use it for disease diagnostics, food safety inspections, and outbreak investigations. However, adoption and application of HTS by the food industry have been comparatively slow, sporadic, and fragmented. Incorporation of HTS by food manufacturers in their food safety programs could reinforce the design and verification of effectiveness of control measures by providing greater insight into the characteristics, origin, relatedness, and evolution of microorganisms in our foods and environment. Here, we discuss this new technology, its power, and potential. A brief history of implementation by public health agencies is presented, as are the benefits and challenges for the food industry, and its future in the context of food safety.

Molecular characterization of circulating Salmonella Typhi strains in an urban informal settlement in Kenya.

A high burden of Salmonella enterica subspecies enterica serovar Typhi (S. Typhi) bacteremia has been reported from urban informal settlements in sub-Saharan Africa, yet little is known about the introduction of these strains to the region. Understanding regional differences in the predominant strains of S. Typhi can provide insight into the genomic epidemiology. We genetically characterized 310 S. Typhi isolates from typhoid fever surveillance conducted over a 12-year period (2007-2019) in Kibera, an urban informal settlement in Nairobi, Kenya, to assess the circulating strains, their antimicrobial resistance attributes, and how they relate to global S. Typhi isolates. Whole genome multi-locus sequence typing (wgMLST) identified 4 clades, with up to 303 pairwise allelic differences. The identified genotypes correlated with wgMLST clades. The predominant clade contained 290 (93.5%) isolates with a median of 14 allele differences (range 0-52) and consisted entirely of genotypes 4.3.1.1 and 4.3.1.2. Resistance determinants were identified exclusively in the predominant clade. Determinants associated with resistance to aminoglycosides were observed in 245 isolates (79.0%), sulphonamide in 243 isolates (78.4%), trimethoprim in 247 isolates (79.7%), tetracycline in 224 isolates (72.3%), chloramphenicol in 247 isolates (79.6%), ß-lactams in 239 isolates (77.1%) and quinolones in 62 isolates (20.0%). Multidrug resistance (MDR) determinants (defined as determinants conferring resistance to ampicillin, chloramphenicol and cotrimoxazole) were found in 235 (75.8%) isolates. The prevalence of MDR associated genes was similar throughout the study period (2007-2012: 203, 76.3% vs 2013-2019: 32, 72.7%; Fisher's Exact Test: P = 0.5478, while the proportion of isolates harboring quinolone resistance determinants increased (2007-2012: 42, 15.8% and 2013-2019: 20, 45.5%; Fisher's Exact Test: P<0.0001) following a decline in S. Typhi in Kibera. Some isolates (49, 15.8%) harbored both MDR and quinolone resistance determinants. There were no determinants associated with resistance to cephalosporins or azithromycin detected among the isolates sequenced in this study. Plasmid markers were only identified in the main clade including IncHI1A and IncHI1B(R27) in 226 (72.9%) isolates, and IncQ1 in 238 (76.8%) isolates. Molecular clock analysis of global typhoid isolates and isolates from Kibera suggests that genotype 4.3.1 has been introduced multiple times in Kibera. Several genomes from Kibera formed a clade with genomes from Kenya, Malawi, South Africa, and Tanzania. The most recent common ancestor (MRCA) for these isolates was from around 1997. Another isolate from Kibera grouped with several isolates from Uganda, sharing a common ancestor from around 2009. In summary, S. Typhi in Kibera belong to four wgMLST clades one of which is frequently associated with MDR genes and this poses a challenge in treatment and control.

Use of Whole Genome Sequencing by the Federal Interagency Collaboration for Genomics for Food and Feed Safety in the United States.

ABSTRACT: This multiagency report developed by the Interagency Collaboration for Genomics for Food and Feed Safety provides an overview of the use of and transition to whole genome sequencing (WGS) technology for detection and characterization of pathogens transmitted commonly by food and for identification of their sources. We describe foodborne pathogen analysis, investigation, and harmonization efforts among the following federal agencies: National Institutes of Health; Department of Health and Human Services, Centers for Disease Control and Prevention (CDC) and U.S. Food and Drug Administration (FDA); and the U.S. Department of Agriculture, Food Safety and Inspection Service, Agricultural Research Service, and Animal and Plant Health Inspection Service. We describe single nucleotide polymorphism, core-genome, and whole genome multilocus sequence typing data analysis methods as used in the PulseNet (CDC) and GenomeTrakr (FDA) networks, underscoring the complementary nature of the results for linking genetically related foodborne pathogens during outbreak investigations while allowing flexibility to meet the specific needs of Interagency Collaboration partners. We highlight how we apply WGS to pathogen characterization (virulence and antimicrobial resistance profiles) and source attribution efforts and increase transparency by making the sequences and other data publicly available through the National Center for Biotechnology Information. We also highlight the impact of current trends in the use of culture-independent diagnostic tests for human diagnostic testing on analytical approaches related to food safety and what is next for the use of WGS in the area of food safety.

Evaluating whole-genome sequencing quality metrics for enteric pathogen outbreaks.

BACKGROUND: Whole genome sequencing (WGS) has gained increasing importance in responses to enteric bacterial outbreaks. Common analysis procedures for WGS, single nucleotide polymorphisms (SNPs) and genome assembly, are highly dependent upon WGS data quality. METHODS: Raw, unprocessed WGS reads from Escherichia coli, Salmonella enterica, and Shigella sonnei outbreak clusters were characterized for four quality metrics: PHRED score, read length, library insert size, and ambiguous nucleotide composition. PHRED scores were strongly correlated with improved SNPs analysis results in E. coli and S. enterica clusters. RESULTS: Assembly quality showed only moderate correlations with PHRED scores and library insert size, and then only for Salmonella. To improve SNP analyses and assemblies, we compared seven read-healing pipelines to improve these four quality metrics and to see how well they improved SNP analysis and genome assembly. The most effective read healing pipelines for SNPs analysis incorporated quality-based trimming, fixed-width trimming, or both. The Lyve-SET SNPs pipeline showed a more marked improvement than the CFSAN SNP Pipeline, but the latter performed better on raw, unhealed reads. For genome assembly, SPAdes enabled significant improvements in healed E. coli reads only, while Skesa yielded no significant improvements on healed reads. CONCLUSIONS: PHRED scores will continue to be a crucial quality metric albeit not of equal impact across all types of analyses for all enteric bacteria. While trimming-based read healing performed well for SNPs analyses, different read healing approaches are likely needed for genome assembly or other, emerging WGS analysis methodologies.

Comparison of Molecular Subtyping and Antimicrobial Resistance Detection Methods Used in a Large Multistate Outbreak of Extensively Drug-Resistant Campylobacter jejuni Infections Linked to Pet Store Puppies.

Campylobacter jejuni is a leading cause of enteric bacterial illness in the United States. Traditional molecular subtyping methods, such as pulsed-field gel electrophoresis (PFGE) and 7-gene multilocus sequence typing (MLST), provided limited resolution to adequately identify C. jejuni outbreaks and separate out sporadic isolates during outbreak investigations. Whole-genome sequencing (WGS) has emerged as a powerful tool for C. jejuni outbreak detection. In this investigation, 45 human and 11 puppy isolates obtained during a 2016-2018 outbreak linked to pet store puppies were sequenced. Core genome multilocus sequence typing (cgMLST) and high-quality single nucleotide polymorphism (hqSNP) analysis of the sequence data separated the isolates into the same two clades containing minor within-clade differences; however, cgMLST analysis does not require selection of an appropriate reference genome, making the method preferable to hqSNP analysis for Campylobacter surveillance and cluster detection. The isolates were classified as sequence type 2109 (ST2109)-a rarely seen MLST sequence type. PFGE was performed on 38 human and 10 puppy isolates; PFGE patterns did not reliably predict clustering by cgMLST analysis. Genetic detection of antimicrobial resistance determinants predicted that all outbreak-associated isolates would be resistant to six drug classes. Traditional antimicrobial susceptibility testing (AST) confirmed a high correlation between genotypic and phenotypic antimicrobial resistance determinations. WGS analysis linked C. jejuni isolates in humans and pet store puppies even when canine exposure information was unknown, aiding the epidemiological investigation during the outbreak. WGS data were also used to quickly identify the highly drug-resistant profile of these outbreak-associated C. jejuni isolates.

Pathogen Genomics in Public Health.

Rapid advances in DNA sequencing technology ("next-generation sequencing") have inspired optimism about the potential of human genomics for "precision medicine." Meanwhile, pathogen genomics is already delivering "precision public health" through more effective investigations of outbreaks of foodborne illnesses, better-targeted tuberculosis control, and more timely and granular influenza surveillance to inform the selection of vaccine strains. In this article, we describe how public health agencies have been adopting pathogen genomics to improve their effectiveness in almost all domains of infectious disease. This momentum is likely to continue, given the ongoing development in sequencing and sequencing-related technologies.

PulseNet and the Changing Paradigm of Laboratory-Based Surveillance for Foodborne Diseases.

PulseNet, the National Molecular Subtyping Network for Foodborne Disease Surveillance, was established in 1996 through a collaboration with the Centers for Disease Control and Prevention; the US Department of Agriculture, Food Safety and Inspection Service; the US Food and Drug Administration; 4 state public health laboratories; and the Association of Public Health Laboratories. The network has since expanded to include 83 state, local, and food regulatory public health laboratories. In 2016, PulseNet was estimated to be helping prevent an estimated 270 000 foodborne illnesses annually. PulseNet is undergoing a transformation toward whole-genome sequencing (WGS), which provides better discriminatory power and precision than pulsed-field gel electrophoresis (PFGE). WGS improves the detection of outbreak clusters and could replace many traditional reference identification and characterization methods. This article highlights the contributions made by public health laboratories in transforming PulseNet's surveillance and describes how the transformation is changing local and national surveillance practices. Our data show that WGS is better at identifying clusters than PFGE, especially for clonal organisms such as Salmonella Enteritidis. The need to develop prioritization schemes for cluster follow-up and additional resources for both public health laboratory and epidemiology departments will be critical as PulseNet implements WGS for foodborne disease surveillance in the United States.

Implications of Mobile Genetic Elements for Salmonella enterica Single-Nucleotide Polymorphism Subtyping and Source Tracking Investigations.

Single-nucleotide polymorphisms (SNPs) are widely used for whole-genome sequencing (WGS)-based subtyping of foodborne pathogens in outbreak and source tracking investigations. Mobile genetic elements (MGEs) are commonly present in bacterial genomes and may affect SNP subtyping results if their evolutionary history and dynamics differ from that of the bacterial chromosomes. Using Salmonella enterica as a model organism, we surveyed major categories of MGEs, including plasmids, phages, insertion sequences, integrons, and integrative and conjugative elements (ICEs), in 990 genomes representing 21 major serotypes of S. enterica We evaluated whether plasmids and chromosomal MGEs affect SNP subtyping with 9 outbreak clusters of different serotypes found in the United States in 2018. The median total length of chromosomal MGEs accounted for 2.5% of a typical S. enterica chromosome. Of the 990 analyzed S. enterica isolates, 68.9% contained at least one assembled plasmid sequence. The median total length of assembled plasmids in these isolates was 93,671 bp. Plasmids that carry high densities of SNPs were found to substantially affect both SNP phylogenies and SNP distances among closely related isolates if they were present in the reference genome for SNP subtyping. In comparison, chromosomal MGEs were found to have limited impact on SNP subtyping. We recommend the identification of plasmid sequences in the reference genome and the exclusion of plasmid-borne SNPs from SNP subtyping analysis.IMPORTANCE Despite increasingly routine use of WGS and SNP subtyping in outbreak and source tracking investigations, whether and how MGEs affect SNP subtyping has not been thoroughly investigated. Besides chromosomal MGEs, plasmids are frequently entangled in draft genome assemblies and yet to be assessed for their impact on SNP subtyping. This study provides evidence-based guidance on the treatment of MGEs in SNP analysis for Salmonella to infer phylogenetic relationship and SNP distance between isolates.

Interpretation of Whole-Genome Sequencing for Enteric Disease Surveillance and Outbreak Investigation.

The routine use of whole-genome sequencing (WGS) as part of enteric disease surveillance is substantially enhancing our ability to detect and investigate outbreaks and to monitor disease trends. At the same time, it is revealing as never before the vast complexity of microbial and human interactions that contribute to outbreak ecology. Since WGS analysis is primarily used to characterize and compare microbial genomes with the goal of addressing epidemiological questions, it must be interpreted in an epidemiological context. In this article, we identify common challenges and pitfalls encountered when interpreting sequence data in an enteric disease surveillance and investigation context, and explain how to address them.

Metagenomic Approaches for Public Health Surveillance of Foodborne Infections: Opportunities and Challenges.

Foodborne disease surveillance in the United States is at a critical point. Clinical and diagnostic laboratories are using culture-independent diagnostic tests (CIDTs) to identify the pathogen causing foodborne illness from patient specimens. CIDTs are molecular tests that allow doctors to rapidly identify the bacteria causing illness within hours. CIDTs, unlike previous gold standard methods such as bacterial culture, do not produce an isolate that can be subtyped as part of the national molecular subtyping network for foodborne disease surveillance, PulseNet. Without subtype information, cases can no longer be linked using molecular data to identify potentially related cases that are part of an outbreak. In this review, we discuss the public health needs for a molecular subtyping approach directly from patient specimen and highlight different approaches, including amplicon and shotgun metagenomic sequencing.