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In 2021, the U.S. Food and Drug Administration (FDA), Centers for Disease Control and Prevention (CDC), and state partners investigated a multistate sample-initiated retrospective outbreak investigation (SIROI) consisting of a cluster of nine Salmonella Weltevreden illnesses associated with frozen, precooked shrimp imported from India. Import surveillance testing identified Salmonella Weltevreden recovered from a cooked shrimp sample from Supplier B. In total, nine patients with clinical isolates highly related via whole genome sequencing were reported in four states with illness onset dates between February 26 and July 17, 2021. Epidemiologic data were gathered by state partners for seven patients, who all reported exposure to shrimp. Five patients reported consuming shrimp cocktail from the same retailer. A traceback investigation for five of the six patients converged on Supplier B. This evidence demonstrated that the outbreak of Salmonella Weltevreden illnesses was caused by the consumption of cooked, ready-to-eat shrimp manufactured by Supplier B. At the time of the investigation, outbreak and recall information was shared with Indian competent authorities. In March 2022, a follow-up inspection of Supplier B’s facility in India was conducted, and insanitary conditions and practices were observed. This outbreak investigation highlighted the importance of multidisciplinary national and international public health partnerships. The lessons learned from this investigation should continue to inform investigational activities and food safety guidance for the industry.

Monitoring data submitted to the National Center for Biotechnology Information’s Pathogen Detection whole-genome sequence database, which includes the foodborne bacterial pathogens Listeria monocytogenes, Salmonella enterica, and Escherichia coli, has proven effective for detecting emerging outbreaks. As part of the submission process, new sequence data are typed using a whole-genome multi-locus sequence typing scheme and clustered with sequences already in the database. Publicly available text files contain the results of these analyses. However, contextualizing and interpreting this information is complex. We present the Rapid Intuitive Pathogen Surveillance (RIPS) tool, which shows the results of the NCBI Rapid Reports, along with appropriate metadata, in a graphical, interactive dashboard. RIPS makes the information in the Rapid Reports useful for real-time surveillance of genome sequence databases.

Abstract Background The Salmonella enterica serovar Newport red onion outbreak of 2020 was the largest foodborne outbreak of Salmonella in over a decade. The epidemiological investigation suggested two farms as the likely source of contamination. However, single nucleotide polymorphism (SNP) analysis of the whole genome sequencing data showed that none of the Salmonella isolates collected from the farm regions were linked to the clinical isolates—preventing the use of phylogenetics in source identification. Here, we explored an alternative method for analyzing the whole genome sequencing data driven by the hypothesis that if the outbreak strain had come from the farm regions, then the clinical isolates would disproportionately contain plasmids found in isolates from the farm regions due to horizontal transfer. Results SNP analysis confirmed that the clinical isolates formed a single, nearly-clonal clade with evidence for ancestry in California going back a decade. The clinical clade had a large core genome (4,399 genes) and a large and sparsely distributed accessory genome (2,577 genes, at least 64% on plasmids). At least 20 plasmid types occurred in the clinical clade, more than were found in the literature for Salmonella Newport. A small number of plasmids, 14 from 13 clinical isolates and 17 from 8 farm isolates, were found to be highly similar (> 95% identical)—indicating they might be related by horizontal transfer. Phylogenetic analysis was unable to determine the geographic origin, isolation source, or time of transfer of the plasmids, likely due to their promiscuous and transient nature. However, our resampling analysis suggested that observing a similar number and combination of highly similar plasmids in random samples of environmental Salmonella enterica within the NCBI Pathogen Detection database was unlikely, supporting a connection between the outbreak strain and the farms implicated by the epidemiological investigation. Conclusion Horizontally transferred plasmids provided evidence for a connection between clinical isolates and the farms implicated as the source of the outbreak. Our case study suggests that such analyses might add a new dimension to source tracking investigations, but highlights the need for detailed and accurate metadata, more extensive environmental sampling, and a better understanding of plasmid molecular evolution.

Foodborne outbreak investigations have traditionally included the detection of a cluster of illnesses first, followed by an epidemiologic investigation to identify a food of interest. The increasing use of whole genome sequencing (WGS) subtyping technology for clinical, environmental, and food isolates of foodborne pathogens, and the ability to share and compare the data on public platforms, present new opportunities to identify earlier links between illnesses and their potential sources. We describe a process called sample-initiated retrospective outbreak investigations (SIROIs) used by federal public health and regulatory partners in the United States. SIROIs begin with an evaluation of the genomic similarity between bacterial isolates recovered from food or environmental samples and clusters of clinical isolates while subsequent and parallel epidemiologic and traceback investigations are initiated to corroborate their connection. SIROIs allow for earlier hypothesis generation, followed by targeted collection of information about food exposures and the foods and manufacturer of interest, to confirm a link between the illnesses and their source. This often leads to earlier action that could reduce the breadth and burden of foodborne illness outbreaks. We describe two case studies of recent SIROIs and present the benefits and challenges. Benefits include insight into foodborne illness attribution, international collaboration, and opportunities for enhanced food safety efforts in the food industry. Challenges include resource intensiveness, variability of epidemiologic and traceback data, and an increasingly complex food supply chain. SIROIs are valuable in identifying connections among small numbers of illnesses that may span significant time periods; detecting early signals for larger outbreaks or food safety issues associated with manufacturers; improving our understanding of the scope of contamination of foods; and identifying novel pathogen/commodity pairs.

In 2021, the U.S. Food and Drug Administration (FDA), the Centers for Disease Control and Prevention (CDC), and state partners investigated a multistate outbreak of Salmonella Typhimurium illnesses linked to packaged leafy greens from a controlled environment agriculture (CEA) operation in Illinois. Thirty-one illnesses and four hospitalizations were reported in four states, with a significant epidemiologic signal for packaged leafy greens from Farm A. A traceback investigation for leafy greens included seven points of service (POS) with food exposure data from eight ill people. Each POS was supplied leafy greens by Farm A. FDA investigators observed operations at Farm A and noted that 1) the firm did not consider their indoor hydroponic pond water as agricultural water, 2) condensate dripping from the chiller water supply line inside the building, and 3) unprotected outdoor storage of packaged soilless growth media and pallets used for finished product. FDA collected 25 product, water, and environmental samples from Farm A. The outbreak strain was recovered from a water sample collected from a stormwater drainage basin located on the property adjacent to Farm A. In addition, an isolate of Salmonella Liverpool was recovered from two indoor growing ponds within the same growing house, but no illnesses were linked to the isolate. Farm A voluntarily recalled all implicated products and provided their root cause analysis (RCA) and return-to-market plan to FDA. While the source and route of the contamination were not determined by the RCA, epidemiologic and traceback evidence confirmed the packaged salads consumed by ill persons were produced by Farm A. This was the first investigation of a multistate foodborne illness outbreak associated with leafy greens grown in a CEA operation. This outbreak demonstrated the need for growers using hydroponic methods to review their practices for potential sources and routes of contamination and to reduce food safety risks when identified.

Next Generation Sequencing technologies significantly impact the field of Antimicrobial Resistance (AMR) detection and monitoring, with immediate uses in diagnosis and risk assessment. For this application and in general, considerable challenges remain in demonstrating sufficient trust to act upon the meaningful information produced from raw data, partly because of the reliance on bioinformatics pipelines, which can produce different results and therefore lead to different interpretations. With the constant evolution of the field, it is difficult to identify, harmonise and recommend specific methods for large-scale implementations over time. In this article, we propose to address this challenge through establishing a transparent, performance-based, evaluation approach to provide flexibility in the bioinformatics tools of choice, while demonstrating proficiency in meeting common performance standards. The approach is two-fold: first, a community-driven effort to establish and maintain “live” (dynamic) benchmarking platforms to provide relevant performance metrics, based on different use-cases, that would evolve together with the AMR field; second, agreed and defined datasets to allow the pipelines’ implementation, validation, and quality-control over time. Following previous discussions on the main challenges linked to this approach, we provide concrete recommendations and future steps, related to different aspects of the design of benchmarks, such as the selection and the characteristics of the datasets (quality, choice of pathogens and resistances, etc.), the evaluation criteria of the pipelines, and the way these resources should be deployed in the community.

Whole-genome sequence databases continue to grow. Collection times between samples are also growing, providing both a challenge for comparing recently collected sequence data to historical samples and an opportunity for evolutionary analyses that can be used to refine match criteria. We measured evolutionary rates for 22 Salmonella enterica serotypes. Based upon these measurements, we propose using an evolutionary rate of 1.97 single-nucleotide polymorphisms (SNPs) per year when determining whether genome sequences match.

Next Generation Sequencing technologies significantly impact the field of Antimicrobial Resistance (AMR) detection and monitoring, with immediate uses in diagnosis and risk assessment. For this application and in general, considerable challenges remain in demonstrating sufficient trust to act upon the meaningful information produced from raw data, partly because of the reliance on bioinformatics pipelines, which can produce different results and therefore lead to different interpretations. With the constant evolution of the field, it is difficult to identify, harmonise and recommend specific methods for large-scale implementations over time. In this article, we propose to address this challenge through establishing a transparent, performance-based, evaluation approach to provide flexibility in the bioinformatics tools of choice, while demonstrating proficiency in meeting common performance standards. The approach is two-fold: first, a community-driven effort to establish and maintain “live” (dynamic) benchmarking platforms to provide relevant performance metrics, based on different use-cases, that would evolve together with the AMR field; second, agreed and defined datasets to allow the pipelines’ implementation, validation, and quality-control over time. Following previous discussions on the main challenges linked to this approach, we provide concrete recommendations and future steps, related to different aspects of the design of benchmarks, such as the selection and the characteristics of the datasets (quality, choice of pathogens and resistances, etc.), the evaluation criteria of the pipelines, and the way these resources should be deployed in the community.

Food production facilities are often routinely tested over time for the presence of foodborne pathogens (e.g., Listeria monocytogenes or Salmonella enterica subsp. enterica). Strains detected in a single sampling event can be classified as transient; positive findings of the same strain across multiple sampling events can be classified as resident pathogens. We analyzed whole-genome sequence (WGS) data from 4,758 isolates (L. monocytogenes = 3,685; Salmonella = 1,073) from environmental samples taken by FDA from 536 U.S. facilities. Our primary objective was to determine the frequency of transient or resident pathogens within food production facilities. Strains were defined as isolates from the same facility that are less than 50 SNP (single-nucleotide polymorphisms) different from one another. Resident pathogens were defined as strains that had more than one isolate collected >59 days apart and from the same facility. We found 1,076 strains (median = 1 and maximum = 21 strains per facility); 180 were resident pathogens, 659 were transient, and 237 came from facilities that had only been sampled once. As a result, 21% of strains (180/ 839) from facilities with positive findings and that were sampled multiple times were found to be resident pathogens; nearly 1 in 4 (23%) of L. monocytogenes strains were found to be resident pathogens compared to 1 in 6 (16%) of Salmonella strains. Our results emphasize the critical importance of preventing the colonization of food production environments by foodborne pathogens, since when colonization does occur, there is an appreciable chance it will become a resident pathogen that presents an ongoing potential to contaminate product.

Abstract Although it is assumed that contamination in bacterial whole-genome sequencing causes errors, the influences of contamination on clustering analyses, such as single-nucleotide polymorphism discovery, phylogenetics, and multi-locus sequencing typing, have not been quantified. By developing and analyzing 720 Listeria monocytogenes, Salmonella enterica, and Escherichia coli short-read datasets, we demonstrate that within-species contamination causes errors that confound clustering analyses, while between-species contamination generally does not. Contaminant reads mapping to references or becoming incorporated into chimeric sequences during assembly are the sources of those errors. Contamination sufficient to influence clustering analyses is present in public sequence databases.

Next Generation Sequencing technologies significantly impact the field of Antimicrobial Resistance (AMR) detection and monitoring, with immediate uses in diagnosis and risk assessment. For this application and in general, considerable challenges remain in demonstrating sufficient trust to act upon the meaningful information produced from raw data, partly because of the reliance on bioinformatics pipelines, which can produce different results and therefore lead to different interpretations. With the constant evolution of the field, it is difficult to identify, harmonise and recommend specific methods for large-scale implementations over time. In this article, we propose to address this challenge through establishing a transparent, performance-based, evaluation approach to provide flexibility in the bioinformatics tools of choice, while demonstrating proficiency in meeting common performance standards. The approach is two-fold: first, a community-driven effort to establish and maintain “live” (dynamic) benchmarking platforms to provide relevant performance metrics, based on different use-cases, that would evolve together with the AMR field; second, agreed and defined datasets to allow the pipelines’ implementation, validation, and quality-control over time. Following previous discussions on the main challenges linked to this approach, we provide concrete recommendations and future steps, related to different aspects of the design of benchmarks, such as the selection and the characteristics of the datasets (quality, choice of pathogens and resistances, etc.), the evaluation criteria of the pipelines, and the way these resources should be deployed in the community.

Abstract Background Listeria monocytogenes is a widespread foodborne pathogen that can cause listeriosis, a potentially fatal infection. L. monocytogenes is subdivided into four phylogenetic lineages, with the highest incidence of listeriosis occurring within lineage I followed by lineage II. Strains of L. monocytogenes differ in their phenotypic characteristics, including virulence. However, the genetic bases for these observed differences are not well understood, and current efforts to monitor L. monocytogenes in food consider all strains to be equally virulent. We use a comparative genomics approach to identify genes and single nucleotide polymorphisms (SNPs) in 174 clinical and food isolates of L. monocytogenes that potentially contribute to virulence or the capacity to adapt to food environments. Results No SNPs are significantly associated with food or clinical isolates. No genes are significantly associated with food or clinical isolates from lineage I, but eight genes consisting of multiple homologues are associated with lineage II food isolates. These include three genes which encode hypothetical proteins, the cadmium resistance genes cadA and cadC, the multi-drug resistance gene ebrB, a quaternary ammonium compound resistance gene qac, and a regulatory gene. All eight genes are plasmid-borne, and most closed L. monocytogenes plasmids carry at least five of the genes (24/27). In addition, plasmids are more frequently associated with lineage II food isolates than with lineage II clinical isolates. Conclusions We identify eight genes that are significantly associated with food isolates in lineage II. Interestingly, the eight genes are virtually absent in lineage II outbreak isolates, are composed of homologues which show a nonrandom distribution among lineage I serotypes, and the sequences are highly conserved across 27 closed Listeria plasmids. The functions of these genes should be explored further and will contribute to our understanding of how L. monocytogenes adapts to the host and food environments. Moreover, these genes may also be useful as markers for risk assessment models of either pathogenicity or the ability to proliferate in food and the food processing environment.

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.

Blastocystis is the most prevalent eukaryotic microbe colonizing the human gut, infecting approximately 1 billion individuals worldwide. Although Blastocystis has been linked to intestinal disorders, its pathogenicity remains controversial because most carriers are asymptomatic. Here, the genome sequence of Blastocystis subtype (ST) 1 is presented and compared to previously published sequences for ST4 and ST7. Despite a conserved core of genes, there is unexpected diversity between these STs in terms of their genome sizes, guanine-cytosine (GC) content, intron numbers, and gene content. ST1 has 6,544 protein-coding genes, which is several hundred more than reported for ST4 and ST7. The percentage of proteins unique to each ST ranges from 6.2% to 20.5%, greatly exceeding the differences observed within parasite genera. Orthologous proteins also display extreme divergence in amino acid sequence identity between STs (i.e., 59%-61% median identity), on par with observations of the most distantly related species pairs of parasite genera. The STs also display substantial variation in gene family distributions and sizes, especially for protein kinase and protease gene families, which could reflect differences in virulence. It remains to be seen to what extent these inter-ST differences persist at the intra-ST level. A full 26% of genes in ST1 have stop codons that are created on the mRNA level by a novel polyadenylation mechanism found only in Blastocystis. Reconstructions of pathways and organellar systems revealed that ST1 has a relatively complete membrane-trafficking system and a near-complete meiotic toolkit, possibly indicating a sexual cycle. Unlike some intestinal protistan parasites, Blastocystis ST1 has near-complete de novo pyrimidine, purine, and thiamine biosynthesis pathways and is unique amongst studied stramenopiles in being able to metabolize α-glucans rather than β-glucans. It lacks all genes encoding heme-containing cytochrome P450 proteins. Predictions of the mitochondrion-related organelle (MRO) proteome reveal an expanded repertoire of functions, including lipid, cofactor, and vitamin biosynthesis, as well as proteins that may be involved in regulating mitochondrial morphology and MRO/endoplasmic reticulum (ER) interactions. In sharp contrast, genes for peroxisome-associated functions are absent, suggesting Blastocystis STs lack this organelle. Overall, this study provides an important window into the biology of Blastocystis, showcasing significant differences between STs that can guide future experimental investigations into differences in their virulence and clarifying the roles of these organisms in gut health and disease.

The adoption of whole-genome sequencing within the public health realm for molecular characterization of bacterial pathogens has been followed by an increased emphasis on real-time detection of emerging outbreaks (e.g., food-borne Salmonellosis). In turn, large databases of whole-genome sequence data are being populated. These databases currently contain tens of thousands of samples and are expected to grow to hundreds of thousands within a few years. For these databases to be of optimal use one must be able to quickly interrogate them to accurately determine the genetic distances among a set of samples. Being able to do so is challenging due to both biological (evolutionary diverse samples) and computational (petabytes of sequence data) issues. We evaluated seven measures of genetic distance, which were estimated from either k-mer profiles (Jaccard, Euclidean, Manhattan, Mash Jaccard, and Mash distances) or nucleotide sites (NUCmer and an extended multi-locus sequence typing (MLST) scheme). When analyzing empirical data (whole-genome sequence data from 18,997 Salmonella isolates) there are features (e.g., genomic, assembly, and contamination) that cause distances inferred from k-mer profiles, which treat absent data as informative, to fail to accurately capture the distance between samples when compared to distances inferred from differences in nucleotide sites. Thus, site-based distances, like NUCmer and extended MLST, are superior in performance, but accessing the computing resources necessary to perform them may be challenging when analyzing large databases.

The wide availability of whole-genome sequencing (WGS) and an abundance of open-source software have made detection of single-nucleotide polymorphisms (SNPs) in bacterial genomes an increasingly accessible and effective tool for comparative analyses. Thus, ensuring that real nucleotide differences between genomes (i.e., true SNPs) are detected at high rates and that the influences of errors (such as false positive SNPs, ambiguously called sites, and gaps) are mitigated is of utmost importance. The choices researchers make regarding the generation and analysis of WGS data can greatly influence the accuracy of short-read sequence alignments and, therefore, the efficacy of such experiments. We studied the effects of some of these choices, including: i) depth of sequencing coverage, ii) choice of reference-guided short-read sequence assembler, iii) choice of reference genome, and iv) whether to perform read-quality filtering and trimming, on our ability to detect true SNPs and on the frequencies of errors. We performed benchmarking experiments, during which we assembled simulated and real Listeria monocytogenes strain 08-5578 short-read sequence datasets of varying quality with four commonly used assemblers (BWA, MOSAIK, Novoalign, and SMALT), using reference genomes of varying genetic distances, and with or without read pre-processing (i.e., quality filtering and trimming). We found that assemblies of at least 50-fold coverage provided the most accurate results. In addition, MOSAIK yielded the fewest errors when reads were aligned to a nearly identical reference genome, while using SMALT to align reads against a reference sequence that is ∼0.82% distant from 08-5578 at the nucleotide level resulted in the detection of the greatest numbers of true SNPs and the fewest errors. Finally, we show that whether read pre-processing improves SNP detection depends upon the choice of reference sequence and assembler. In total, this study demonstrates that researchers should test a variety of conditions to achieve optimal results.

Meiosis is a defining feature of eukaryotes but its phylogenetic distribution has not been broadly determined, especially among eukaryotic microorganisms (i.e. protists)-which represent the majority of eukaryotic 'supergroups'. We surveyed genomes of animals, fungi, plants and protists for meiotic genes, focusing on the evolutionarily divergent parasitic protist Trichomonas vaginalis. We identified homologs of 29 components of the meiotic recombination machinery, as well as the synaptonemal and meiotic sister chromatid cohesion complexes. T. vaginalis has orthologs of 27 of 29 meiotic genes, including eight of nine genes that encode meiosis-specific proteins in model organisms. Although meiosis has not been observed in T. vaginalis, our findings suggest it is either currently sexual or a recent asexual, consistent with observed, albeit unusual, sexual cycles in their distant parabasalid relatives, the hypermastigotes. T. vaginalis may use meiotic gene homologs to mediate homologous recombination and genetic exchange. Overall, this expanded inventory of meiotic genes forms a useful "meiosis detection toolkit". Our analyses indicate that these meiotic genes arose, or were already present, early in eukaryotic evolution; thus, the eukaryotic cenancestor contained most or all components of this set and was likely capable of performing meiotic recombination using near-universal meiotic machinery.