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

RIPS (rapid intuitive pathogen surveillance): a tool for surveillance of genome sequence data from foodborne bacterial pathogens

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.

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.

DODGE: automated point source bacterial outbreak detection using cumulative long term genomic surveillance

SUMMARY

The reliable and timely recognition of outbreaks is a key component of public health surveillance for foodborne diseases. Whole genome sequencing (WGS) offers high resolution typing of foodborne bacterial pathogens and facilitates the accurate detection of outbreaks. This detection relies on grouping WGS data into clusters at an appropriate genetic threshold. However, methods and tools for selecting and adjusting such thresholds according to the required resolution of surveillance and epidemiological context are lacking. Here we present DODGE (Dynamic Outbreak Detection for Genomic Epidemiology), an algorithm to dynamically select and compare these genetic thresholds. DODGE can analyse expanding datasets over time and clusters that are predicted to correspond to outbreaks (or "investigation clusters") can be named with established genomic nomenclature systems to facilitate integrated analysis across jurisdictions. DODGE was tested in two real-world Salmonella genomic surveillance datasets of different duration, 2 months from Australia and 9 years from the United Kingdom. In both cases only a minority of isolates were identified as investigation clusters. Two known outbreaks in the United Kingdom dataset were detected by DODGE and were recognized at an earlier timepoint than the outbreaks were reported. These findings demonstrated the potential of the DODGE approach to improve the effectiveness and timeliness of genomic surveillance for foodborne diseases and the effectiveness of the algorithm developed.

AVAILABILITY AND IMPLEMENTATION

DODGE is freely available at https://github.com/LanLab/dodge and can easily be installed using Conda.

SARS-CoV-2 wastewater variant surveillance: pandemic response leveraging FDA's GenomeTrakr network

Wastewater surveillance has emerged as a crucial public health tool for population-level pathogen surveillance. Supported by funding from the American Rescue Plan Act of 2021, the FDA's genomic epidemiology program, GenomeTrakr, was leveraged to sequence SARS-CoV-2 from wastewater sites across the United States. This initiative required the evaluation, optimization, development, and publication of new methods and analytical tools spanning sample collection through variant analyses. Version-controlled protocols for each step of the process were developed and published on protocols.io. A custom data analysis tool and a publicly accessible dashboard were built to facilitate real-time visualization of the collected data, focusing on the relative abundance of SARS-CoV-2 variants and sub-lineages across different samples and sites throughout the project. From September 2021 through June 2023, a total of 3,389 wastewater samples were collected, with 2,517 undergoing sequencing and submission to NCBI under the umbrella BioProject, PRJNA757291. Sequence data were released with explicit quality control (QC) tags on all sequence records, communicating our confidence in the quality of data. Variant analysis revealed wide circulation of Delta in the fall of 2021 and captured the sweep of Omicron and subsequent diversification of this lineage through the end of the sampling period. This project successfully achieved two important goals for the FDA's GenomeTrakr program: first, contributing timely genomic data for the SARS-CoV-2 pandemic response, and second, establishing both capacity and best practices for culture-independent, population-level environmental surveillance for other pathogens of interest to the FDA.

IMPORTANCE

This paper serves two primary objectives. First, it summarizes the genomic and contextual data collected during a Covid-19 pandemic response project, which utilized the FDA's laboratory network, traditionally employed for sequencing foodborne pathogens, for sequencing SARS-CoV-2 from wastewater samples. Second, it outlines best practices for gathering and organizing population-level next generation sequencing (NGS) data collected for culture-free, surveillance of pathogens sourced from environmental samples.

Closing the gap: Oxford Nanopore Technologies R10 sequencing allows comparable results to Illumina sequencing for SNP-based outbreak investigation of bacterial pathogens

Whole-genome sequencing has become the method of choice for bacterial outbreak investigation, with most clinical and public health laboratories currently routinely using short-read Illumina sequencing. Recently, long-read Oxford Nanopore Technologies (ONT) sequencing has gained prominence and may offer advantages over short-read sequencing, particularly with the recent introduction of the R10 chemistry, which promises much lower error rates than the R9 chemistry. However, limited information is available on its performance for bacterial single-nucleotide polymorphism (SNP)-based outbreak investigation. We present an open-source workflow, Prokaryotic Awesome variant Calling Utility (PACU) (https://github.com/BioinformaticsPlatformWIV-ISP/PACU), for constructing SNP phylogenies using Illumina and/or ONT R9/R10 sequencing data. The workflow was evaluated using outbreak data sets of Shiga toxin-producing Escherichia coli and Listeria monocytogenes by comparing ONT R9 and R10 with Illumina data. The performance of each sequencing technology was evaluated not only separately but also by integrating samples sequenced by different technologies/chemistries into the same phylogenomic analysis. Additionally, the minimum sequencing time required to obtain accurate phylogenetic results using nanopore sequencing was evaluated. PACU allowed accurate identification of outbreak clusters for both species using all technologies/chemistries, but ONT R9 results deviated slightly more from the Illumina results. ONT R10 results showed trends very similar to Illumina, and we found that integrating data sets sequenced by either Illumina or ONT R10 for different isolates into the same analysis produced stable and highly accurate phylogenomic results. The resulting phylogenies for these two outbreaks stabilized after ~20 hours of sequencing for ONT R9 and ~8 hours for ONT R10. This study provides a proof of concept for using ONT R10, either in isolation or in combination with Illumina, for rapid and accurate bacterial SNP-based outbreak investigation.

Paired metagenomic and chemical evaluation of aflatoxin-contaminated dog kibble

Introduction

Identification of chemical toxins from complex or highly processed foods can present 'needle in the haystack' challenges for chemists. Metagenomic data can be used to guide chemical toxicity evaluations by providing DNA-based description of the wholistic composition (eukaryotic, bacterial, protozoal, viral, and antimicrobial resistance) of foods suspected to harbor toxins, allergens, or pathogens. This type of information can focus chemistry-based diagnostics, improve hazard characterization and risk assessment, and address data gaps. Additionally, there is increasing recognition that simultaneously co-occurring mycotoxins, either from single or multiple species, can impact dietary toxicity exposure. Metagenomic data provides a way to address data gaps related to co-occurrence of multiple fungal species.

Methods

Paired metagenomic and chemical data were used to evaluate aflatoxin-contaminated kibble with known levels of specific mycotoxins. Kibble was ground to a fine powder for both chemical and molecular analyses. Chemical analyses were performed with Liquid Chromatography Mass Spectrometry (LCMS) and according to the AOAC Official method 2005.08: Aflatoxins in Corn, Raw Peanuts, and Peanut Butter using Liquid Chromatography with Post-Column Photochemical Derivatization. Metagenomes were created from DNA extracted from ground kibble and sequenced on an Illumina NextSeq 2000 with an average sequence depth of 180 million reads per replicate.

Results and discussion

Metagenomic data demonstrated that the abundance of DNA from putative aflatoxigenic Aspergillus spp. correlated with the levels of aflatoxin quantified by LCMS. Metagenomic data also identified an expansive range of co-occurring fungal taxa which may produce additional mycotoxins. DNA data paired with chemical data provides a novel modality to address current data gaps surrounding dietary mycotoxin exposure, toxigenic fungal taxonomy, and mycotoxins of emerging concern.

Advance typing of Vibrio parahaemolyticus through the mtlA and aer gene: A high-resolution, cost-effective approach

Vibrio parahaemolyticus is a significant cause of foodborne illness, and its incidence worldwide is on the rise. It is thus imperative to develop a straightforward and efficient method for typing strains of this pathogen. In this study, we conducted a pangenome analysis of 75 complete genomes of V. parahaemolyticus and identified the core gene mtlA with the highest degree of variation, which distinguished 44 strains and outperformed traditional seven-gene-based MLST when combined with aer, another core gene with high degree of variation. The mtlA gene had higher resolution to type strains with a close relationship compared to the traditional MLST genes in the phylogenetic tree built by core genomes. Strong positive selection was also detected in the gene mtlA (ω > 1), representing adaptive and evolution in response to the environment. Therefore, the panel of gene mtlA and aer may serve as a tool for the typing of V. parahaemolyticus, potentially contributing to the prevention and control of this foodborne disease.

Genomic analysis of Listeria monocytogenes CC7 associated with clinical infections and persistence in the food industry

Listeria monocytogenes clonal complex 7 (CC7), belonging to lineage II, is the most common subtype among clinical listeriosis isolates in Norway, and is also commonly found in Norwegian food industry and outdoor environments. In the present study, the relative prevalence of CCs among clinical isolates of L. monocytogenes in European countries during 2010-2015 was determined. Then, phylogenomic and comparative genomic analyses was performed for 115 Norwegian and 255 international reference genomes from various sources, to examine potential explanations underlying the high prevalence of CC7 among Norwegian listeriosis cases. Selected isolates were also compared using in vitro virulence assays. The results showed a high relative prevalence of CC7 in clinical isolates from Norway and the neighboring Nordic countries Sweden and Finland. In contrast to in most other European countries, lineage II dominated among clinical isolates in these countries. Phylogenetic analysis of the 370 CC7 isolates separated the genomes into four clades, with the majority of Norwegian isolates (69 %) clustered in one of these clades, estimated to have diverged from the other clades around year 1830. The Norwegian isolates within this clade were widely distributed in different habitats; several (poultry) meat processing factories, a salmon processing plant, in nature, farms, and slugs, and among human clinical isolates. In particular, one pervasive CC7 clone was found across three poultry processing plants and one salmon processing plant, and also included three clinical isolates. All analysed CC7 isolates harbored the same set of 72 genes involved in both general and specific stress responses. Divergence was observed for plasmid-encoded genes including genes conferring resistance against arsenic (Tn554-arsCBADR), cadmium (cadA1C1 and cadA2C2), and the biocide benzalkonium chloride (bcrABC). No significant difference in prevalence of these genes was seen between isolates from different habitats or sources. Virulence attributes were highly conserved among the CC7 isolates. In vitro virulence studies of five representative CC7 isolates revealed a virulence potential that, in general, was not significantly lower than that of the control strain EGDe, with isolate-dependent differences that could not be correlated with genetic determinants. The study shows that CC7 is widespread in Norway, and that a pervasive CC7 clone was present in food processing plants. The study highlights the importance of CC7 and lineage II strains in causing listeriosis and shows that more research is needed to understand the reasons behind geographical differences in CC prevalence.

Approaching pathogenic Clostridia from a One Health perspective

Spore-forming pathogens have a unique capacity to thrive in diverse environments, and with temporal persistence afforded through their ability to sporulate. These behaviors require a One Health approach to identify critical reservoirs and outbreak-associated transmission chains, given their capacity to freely move across soils, waterways, foodstuffs, and as commensals or infecting pathogens in human and veterinary populations. Among anaerobic spore-formers, genomic resources for pathogens including C. botulinum, C. difficile, and C. perfringens enable our capacity to identify common and unique factors that support their persistence in diverse reservoirs and capacity to cause disease. Publicly available genomic resources for spore-forming pathogens at NCBI's Pathogen Detection program aid outbreak investigations and longitudinal monitoring in national and international programs in public health and food safety, as well as for local healthcare systems. These tools also enable research to derive new knowledge regarding disease pathogenesis, and to inform strategies in disease prevention and treatment. As global community resources, the continued sharing of strain genomic data and phenotypes further enhances international resources and means to develop impactful applications. We present examples showing use of these resources in surveillance, including capacity to assess linkages among clinical, environmental, and foodborne reservoirs and to further research investigations into factors promoting their persistence and virulence in different settings.

A machine learning approach to identifying Salmonella stress response genes in isolates from poultry processing

We explored the potential of machine learning to identify significant genes associated with Salmonella stress response during poultry processing using whole genome sequencing (WGS) data. The Salmonella isolates (n = 177) used in this study were obtained from various chicken sources (skin before chiller, chicken carcass before chiller, frozen chicken, and post-chill chicken carcass). Six machine learning algorithms (random forest, neural network, cost-sensitive learning, logit boost, and support vector machine linear and radial kernels) were trained on Salmonella WGS data, and model fit was assessed using standard evaluation metrics such as the area under the receiver operating characteristic (AUROC) curve and confusion matrix statistics. All models achieved high performances based on the AUROC metric, with logit boost showing the best performance with an AUROC score of 0.904, sensitivity of 0.889, and specificity of 0.920. The significant genes identified included ybtX, which encodes a Yersiniabactin-associated zinc transporter, and the transferase-encoding genes yccK and thiS. Additionally, genes coding for cold (cspA, cspD, and cspE) and heat shock (rpoH and rpoE) responses were identified. Other significant genes included those involved in lipopolysaccharide biosynthesis (irp1, waaD, rfc, and rfbX), DNA repair and replication (traI), biofilm formation (ccdA and fyuA), and cellular metabolism (irtA).

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.

Adoption of on-farm feed safety practices among livestock farmers: Evidence from Saudi Arabia

Compliance with feed safety standards and practices on the farm promotes sustainable livestock production and can positively affect the environment and human health. This study aims to examine farmers' on-farm adoption of feed safety practices and determine the differences in adoption according to the socio-economic characteristics of farmers. An online survey was conducted to collect the study data. The study participants consisted of 442 livestock farmers in Riyadh region, Saudi Arabia. The study findings revealed that farmers highly adopted categories of feed purchasing (77.9 %) and feed mixing and distribution (75.05 %), whereas they were considered as a moderate level adoption regarding feed storing (70.9 %). The cluster analysis results showed two main patterns of adoption- "high adopters" and "moderate adopters"-based on the average level of adoption for 25 feed safety practices examined. The cluster of "high adopters" accounted for 59.9 % of the sample and had higher average adoption in all practices examined. The findings revealed that there are significant differences between the two segments of farmers, corresponding to gender, membership in livestock association, extension contact, raising sheep, and feeding system. This study proposes the regular monitoring of farms to ensure strict adherence to feed safety regulations, with the implementation of awareness and capacity development initiatives pertaining to feed safety concerns.

Putting everything in its place: using the INSDC compliant Pathogen Data Object Model to better structure genomic data submitted for public health applications

Fast, efficient public health actions require well-organized and coordinated systems that can supply timely and accurate knowledge. Public databases of pathogen genomic data, such as the International Nucleotide Sequence Database Collaboration (INSDC), have become essential tools for efficient public health decisions. However, these international resources began primarily for academic purposes, rather than for surveillance or interventions. Now, queries need to access not only the whole genomes of multiple pathogens but also make connections using robust contextual metadata to identify issues of public health relevance. Databases that over time developed a patchwork of submission formats and requirements need to be consistently organized and coordinated internationally to allow effective searches.To help resolve these issues, we propose a common pathogen data structure called the Pathogen Data Object Model (DOM) that will formalize the minimum pieces of sequence data and contextual data necessary for general public health uses, while recognizing that submitters will likely withhold a wide range of non-public contextual data. Further, we propose contributors use the Pathogen DOM for all pathogen submissions (bacterial, viral, fungal, and parasites), which will simplify data submissions and provide a consistent and transparent data structure for downstream data analyses. We also highlight how improved submission tools can support the Pathogen DOM, offering users additional easy-to-use methods to ensure this structure is followed.

Identification and Characterization of ten Escherichia coli Strains Encoding Novel Shiga Toxin 2 Subtypes, Stx2n as Well as Stx2j, Stx2m, and Stx2o, in the United States

The sharing of genome sequences in online data repositories allows for large scale analyses of specific genes or gene families. This can result in the detection of novel gene subtypes as well as the development of improved detection methods. Here, we used publicly available WGS data to detect a novel Stx subtype, Stx2n in two clinical E. coli strains isolated in the USA. During this process, additional Stx2 subtypes were detected; six Stx2j, one Stx2m strain, and one Stx2o, were all analyzed for variability from the originally described subtypes. Complete genome sequences were assembled from short- or long-read sequencing and analyzed for serotype, and ST types. The WGS data from Stx2n- and Stx2o-producing STEC strains were further analyzed for virulence genes pro-phage analysis and phage insertion sites. Nucleotide and amino acid maximum parsimony trees showed expected clustering of the previously described subtypes and a clear separation of the novel Stx2n subtype. WGS data were used to design OMNI PCR primers for the detection of all known stx1 (283 bp amplicon), stx2 (400 bp amplicon), intimin encoded by eae (221 bp amplicon), and stx2f (438 bp amplicon) subtypes. These primers were tested in three different laboratories, using standard reference strains. An analysis of the complete genome sequence showed variability in serogroup, virulence genes, and ST type, and Stx2 pro-phages showed variability in size, gene composition, and phage insertion sites. The strains with Stx2j, Stx2m, Stx2n, and Stx2o showed toxicity to Vero cells. Stx2j carrying strain, 2012C-4221, was induced when grown with sub-inhibitory concentrations of ciprofloxacin, and toxicity was detected. Taken together, these data highlight the need to reinforce genomic surveillance to identify the emergence of potential new Stx2 or Stx1 variants. The importance of this surveillance has a paramount impact on public health. Per our description in this study, we suggest that 2017C-4317 be designated as the Stx2n type-strain.

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.

Global diversity and antimicrobial resistance of typhoid fever pathogens: Insights from a meta-analysis of 13,000 Salmonella Typhi genomes

Background

The Global Typhoid Genomics Consortium was established to bring together the typhoid research community to aggregate and analyse Salmonella enterica serovar Typhi (Typhi) genomic data to inform public health action. This analysis, which marks 22 years since the publication of the first Typhi genome, represents the largest Typhi genome sequence collection to date (n=13,000).

Methods

This is a meta-analysis of global genotype and antimicrobial resistance (AMR) determinants extracted from previously sequenced genome data and analysed using consistent methods implemented in open analysis platforms GenoTyphi and Pathogenwatch.

Results

Compared with previous global snapshots, the data highlight that genotype 4.3.1 (H58) has not spread beyond Asia and Eastern/Southern Africa; in other regions, distinct genotypes dominate and have independently evolved AMR. Data gaps remain in many parts of the world, and we show the potential of travel-associated sequences to provide informal 'sentinel' surveillance for such locations. The data indicate that ciprofloxacin non-susceptibility (>1 resistance determinant) is widespread across geographies and genotypes, with high-level ciprofloxacin resistance (≥3 determinants) reaching 20% prevalence in South Asia. Extensively drug-resistant (XDR) typhoid has become dominant in Pakistan (70% in 2020) but has not yet become established elsewhere. Ceftriaxone resistance has emerged in eight non-XDR genotypes, including a ciprofloxacin-resistant lineage (4.3.1.2.1) in India. Azithromycin resistance mutations were detected at low prevalence in South Asia, including in two common ciprofloxacin-resistant genotypes.

Conclusions

The consortium's aim is to encourage continued data sharing and collaboration to monitor the emergence and global spread of AMR Typhi, and to inform decision-making around the introduction of typhoid conjugate vaccines (TCVs) and other prevention and control strategies.

Funding

No specific funding was awarded for this meta-analysis. Coordinators were supported by fellowships from the European Union (ZAD received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 845681), the Wellcome Trust (SB, Wellcome Trust Senior Fellowship), and the National Health and Medical Research Council (DJI is supported by an NHMRC Investigator Grant [GNT1195210]).

Genetic diversity of Salmonella enterica isolated over 13 years from raw California almonds and from an almond orchard

A comparative genomic analysis was conducted for 171 Salmonella isolates recovered from raw inshell almonds and raw almond kernels between 2001 and 2013 and for 30 Salmonella Enteritidis phage type (PT) 30 isolates recovered between 2001 and 2006 from a 2001 salmonellosis outbreak-associated almond orchard. Whole genome sequencing was used to measure the genetic distance among isolates by single nucleotide polymorphism (SNP) analyses and to predict the presence of plasmid DNA and of antimicrobial resistance (AMR) and virulence genes. Isolates were classified by serovars with Parsnp, a fast core-genome multi aligner, before being analyzed with the CFSAN SNP Pipeline (U.S. Food and Drug Administration Center for Food Safety and Applied Nutrition). Genetically similar (≤18 SNPs) Salmonella isolates were identified among several serovars isolated years apart. Almond isolates of Salmonella Montevideo (2001 to 2013) and Salmonella Newport (2003 to 2010) differed by ≤9 SNPs. Salmonella Enteritidis PT 30 isolated between 2001 and 2013 from survey, orchard, outbreak, and clinical samples differed by ≤18 SNPs. One to seven plasmids were found in 106 (62%) of the Salmonella isolates. Of the 27 plasmid families that were identified, IncFII and IncFIB plasmids were the most predominant. AMR genes were identified in 16 (9%) of the survey isolates and were plasmid encoded in 11 of 16 cases; 12 isolates (7%) had putative resistance to at least one antibiotic in three or more drug classes. A total of 303 virulence genes were detected among the assembled genomes; a plasmid that harbored a combination of pef, rck, and spv virulence genes was identified in 23% of the isolates. These data provide evidence of long-term survival (years) of Salmonella in agricultural environments.

Weather factors, soil microbiome, and bacteria-fungi interactions as drivers of the epiphytic phyllosphere communities of romaine lettuce

Lettuce is associated with seasonal outbreaks of Shiga toxin-producing Escherichia coli (STEC) infections. Little is known about how various biotic and abiotic factors affect the lettuce microbiome, which in turn impacts STEC colonization. We characterized the lettuce phyllosphere and surface soil bacterial, fungal, and oomycete communities at harvest in late-spring and -fall in California using metagenomics. Harvest season and field type, but not cultivar, significantly influenced the microbiome composition of leaves and surface soil near plants. Phyllosphere and soil microbiome compositions were correlated with specific weather factors. The relative abundance of Enterobacteriaceae, but not E. coli, was enriched on leaves (5.2%) compared to soil (0.4%) and correlated positively with minimum air temperature and wind speed. Co-occurrence networks revealed seasonal trends in fungi-bacteria interactions on leaves. These associations represented 39%-44% of the correlations between species. All significant E. coli co-occurrences with fungi were positive, while all negative associations were with bacteria. A large proportion of the leaf bacterial species was shared with those in soil, indicating microbiome transmission from the soil surface to the canopy. Our findings provide new insight into factors that shape lettuce microbial communities and the microbial context of foodborne pathogen immigration events in the lettuce phyllosphere.

Public health implementation of pathogen genomics: the role for accreditation and application of ISO standards

Pathogen genomics has transitioned rapidly from the research setting into a powerful tool now routinely used in public health microbiology, for surveillance, outbreak investigations and disease control. As these investigations can have significant public health, treatment and legal impacts, we must ensure the accuracy of these results through validation of testing processes. For laboratories working in this space, it is important to approach this work with a quality and accreditation framework in mind, working towards implementation of quality systems and test validation that meet international regulatory standards. Here we outline the key international standards and processes that lead toward accreditation for pathogen genomics.

16S microbiome analysis of microbial communities in distribution centers handling fresh produce

Little is known about the microbial communities found in distribution centers (DCs), especially in those storing and handling food. As many foodborne bacteria are known to establish residence in food facilities, it is reasonable to assume that DCs handling foods are also susceptible to pathogen colonization. To investigate the microbial communities within DCs, 16S amplicon sequencing was completed on 317 environmental surface sponge swabs collected in DCs (n = 18) across the United States. An additional 317 swabs were collected in parallel to determine if any viable Listeria species were also present at each sampling site. There were significant differences in median diversity measures (observed, Shannon, and Chao1) across individual DCs, and top genera across all reads were Carnobacterium_A, Psychrobacter, Pseudomonas_E, Leaf454, and Staphylococcus based on taxonomic classifications using the Genome Taxonomy Database. Of the 39 16S samples containing Listeria ASVs, four of these samples had corresponding Listeria positive microbiological samples. Data indicated a predominance of ASVs identified as cold-tolerant bacteria in environmental samples collected in DCs. Differential abundance analysis identified Carnobacterium_A, Psychrobacter, and Pseudomonas_E present at a significantly greater abundance in Listeria positive microbiological compared to those negative for Listeria. Additionally, microbiome composition varied significantly across groupings within variables (e.g., DC, season, general sampling location).

Accelerating bioinformatics implementation in public health

We have adopted an open bioinformatics ecosystem to address the challenges of bioinformatics implementation in public health laboratories (PHLs). Bioinformatics implementation for public health requires practitioners to undertake standardized bioinformatic analyses and generate reproducible, validated and auditable results. It is essential that data storage and analysis are scalable, portable and secure, and that implementation of bioinformatics fits within the operational constraints of the laboratory. We address these requirements using Terra, a web-based data analysis platform with a graphical user interface connecting users to bioinformatics analyses without the use of code. We have developed bioinformatics workflows for use with Terra that specifically meet the needs of public health practitioners. These Theiagen workflows perform genome assembly, quality control, and characterization, as well as construction of phylogeny for insights into genomic epidemiology. Additonally, these workflows use open-source containerized software and the WDL workflow language to ensure standardization and interoperability with other bioinformatics solutions, whilst being adaptable by the user. They are all open source and publicly available in Dockstore with the version-controlled code available in public GitHub repositories. They have been written to generate outputs in standardized file formats to allow for further downstream analysis and visualization with separate genomic epidemiology software. Testament to this solution meeting the requirements for bioinformatic implementation in public health, Theiagen workflows have collectively been used for over 5 million sample analyses in the last 2 years by over 90 public health laboratories in at least 40 different countries. Continued adoption of technological innovations and development of further workflows will ensure that this ecosystem continues to benefit PHLs.

Development of a targeted amplicon sequencing method for genotyping Cyclospora cayetanensis from fresh produce and clinical samples with enhanced genomic resolution and sensitivity

Outbreaks of cyclosporiasis, an enteric illness caused by the parasite Cyclospora cayetanensis, have been associated with consumption of various types of fresh produce. Although a method is in use for genotyping C. cayetanensis from clinical specimens, the very low abundance of C. cayetanensis in food and environmental samples presents a greater challenge. To complement epidemiological investigations, a molecular surveillance tool is needed for use in genetic linkage of food vehicles to cyclosporiasis illnesses, estimation of the scope of outbreaks or clusters of illness, and determination of geographical areas involved. We developed a targeted amplicon sequencing (TAS) assay that incorporates a further enrichment step to gain the requisite sensitivity for genotyping C. cayetanensis contaminating fresh produce samples. The TAS assay targets 52 loci, 49 of which are located in the nuclear genome, and encompasses 396 currently known SNP sites. The performance of the TAS assay was evaluated using lettuce, basil, cilantro, salad mix, and blackberries inoculated with C. cayetanensis oocysts. A minimum of 24 markers were haplotyped even at low contamination levels of 10 oocysts in 25 g leafy greens. The artificially contaminated fresh produce samples were included in a genetic distance analysis based on haplotype presence/absence with publicly available C. cayetanensis whole genome sequence assemblies. Oocysts from two different sources were used for inoculation, and samples receiving the same oocyst preparation clustered together, but separately from the other group, demonstrating the utility of the assay for genetically linking samples. Clinical fecal samples with low parasite loads were also successfully genotyped. This work represents a significant advance in the ability to genotype C. cayetanensis contaminating fresh produce along with greatly expanding the genomic diversity included for genetic clustering of clinical specimens.

Challenges Associated with Investigating Salmonella Enteritidis with Low Genomic Diversity in New York State: The Impact of Adjusting Analytical Methods and Correlation with Epidemiological Data

Defining investigation-worthy genomic clusters among strains of Salmonella Enteritidis is challenging because of their highly clonal nature. We investigated a cluster identified by core genome multilocus sequence typing (cgMLST) consisting of 265 isolates with isolation dates spanning two and a half years. This cluster experienced chaining, growing to a range of 14 alleles. The volume of isolates and broad allele range of this cluster made it difficult to ascertain whether it represented a common-source outbreak. We explored laboratory-based methods to subdivide and refine this cluster. These methods included using cgMLST with a narrower allele range, whole genome multilocus sequence typing (wgMLST) and high-quality single-nucleotide polymorphism (hqSNP) analysis. At each analysis level, epidemiologists retroactively reviewed exposures, geography, and temporality for potential commonalities. Lowering the threshold to 0 alleles using cgMLST proved an effective method to refine this analysis, resulting in this large cluster being subdivided into 34 smaller clusters. Additional analysis by wgMLST and hqSNP provided enhanced cluster resolution, with the majority of clusters being further refined. These analysis methods combined with more stringent allele thresholds and layering of epidemiologic data proved useful in helping to subdivide this large cluster into actionable subclusters.

An Overview of Foodborne Sample-Initiated Retrospective Outbreak Investigations and Interagency Collaboration in the United States

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 (SIROI) 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.

A Schema for Digitized Surface Swab Site Metadata in Open-Source DNA Sequence Databases

Large, open-source DNA sequence databases have been generated, in part, through the collection of microbial pathogens by swabbing surfaces in built environments. Analyzing these data in aggregate through public health surveillance requires digitization of the complex, domain-specific metadata that are associated with the swab site locations. However, the swab site location information is currently collected in a single, free-text, "isolation source", field-promoting generation of poorly detailed descriptions with various word order, granularity, and linguistic errors, making automation difficult and reducing machine-actionability. We assessed 1,498 free-text swab site descriptions that were generated during routine foodborne pathogen surveillance. The lexicon of free-text metadata was evaluated to determine the informational facets and the quantity of unique terms used by data collectors. Open Biological Ontologies (OBO) Foundry libraries were used to develop hierarchical vocabularies that are connected with logical relationships to describe swab site locations. 5 informational facets that were described by 338 unique terms were identified via content analysis. Term hierarchy facets were developed, as were statements (called axioms) about how the entities within these five domains are related. The schema developed through this study has been integrated into a publicly available pathogen metadata standard, facilitating ongoing surveillance and investigations. The One Health Enteric Package was available at NCBI BioSample, beginning in 2022. The collective use of metadata standards increases the interoperability of DNA sequence databases and enables large-scale approaches to data sharing and artificial intelligence as well as big-data solutions to food safety. IMPORTANCE The regular analysis of whole-genome sequence data in collections such as NCBI's Pathogen Detection Database is used by many public health organizations to detect outbreaks of infectious disease. However, isolate metadata in these databases are often incomplete and of poor quality. These complex, raw metadata must often be reorganized and manually formatted for use in aggregate analyses. These processes are inefficient and time-consuming, increasing the interpretative labor needed by public health groups to extract actionable information. The future use of open genomic epidemiology networks will be supported through the development of an internationally applicable vocabulary system with which swab site locations can be described.

Whole-genome sequencing for surveillance of antimicrobial resistance in Ecuador: present and future implications

Whole-genome sequencing is becoming the gold standard for pathogen characterization and offers considerable advantages for understanding the evolution and dissemination of new determinants of antimicrobial resistance. Despite the benefits of whole-genome sequencing for pathogen characterization, implementation costs and lack of expertise may limit its use by public health laboratories. This article reviews the advantages of whole-genome sequencing for pathogen characterization and the current status of the use of whole-genome sequencing for antimicrobial resistance surveillance in Ecuador. A roadmap is suggested for including whole-genome sequencing for pathogen characterization based on the needs of the health reference institutions through alliances with Ecuadorian universities. Establishing a partnership between public health institutions and academia would be valuable for clinicians, policy-makers, and epidemiologists who could then take reasonable measures in those areas and establish a basis for adapting One Health strategies to tackle antimicrobial resistance in Ecuador.

Genome characteristics of clinical Salmonella enterica population from a state public health laboratory, New Hampshire, USA, 2017–2020

Background

The implementation of whole genome sequencing (WGS) by PulseNet, the molecular subtyping network for foodborne diseases, has transformed surveillance, outbreak detection, and public health laboratory practices in the United States. In 2017, the New Hampshire Public Health Laboratories, a member of PulseNet, commenced the use of WGS in tracking foodborne pathogens across the state. We present some of the initial results of New Hampshire’s initiative to transition to WGS in tracking Salmonella enterica, a bacterial pathogen that is responsible for non-typhoidal foodborne infections and enteric fever. We characterize the population structure and evolutionary history of 394 genomes of isolates recovered from human clinical cases in New Hampshire from 2017 to 2020.

Results

The New Hampshire S. enterica population is phylogenetically diverse, consisting of 78 sequence types (ST) and 67 serotypes. Six lineages dominate the population: ST 11 serotype Enteritidis, ST 19 Typhimurium, ST 32 Infantis, ST 118 Newport, ST 22 Braenderup, and ST 26 Thompson. Each lineage is derived from long ancestral branches in the phylogeny, suggesting their extended presence in the region and recent clonal expansion. We detected 61 genes associated with resistance to 14 antimicrobial classes. Of these, unique genes of five antimicrobial classes (aminocoumarins, aminoglycosides, fluoroquinolones, nitroimidazoles, and peptides) were detected in all genomes. Rather than a single clone carrying multiple resistance genes expanding in the state, we found multiple lineages carrying different combinations of independently acquired resistance determinants. We estimate the time to the most recent common ancestor of the predominant lineage ST 11 serotype Enteritidis (126 genomes) to be 1965 (95% highest posterior density intervals: 1927–1982). Its population size expanded until 1978, followed by a population decline until 1990. This lineage has been expanding since then. Comparison with genomes from other states reveal lack of geographical clustering indicative of long-distance dissemination.

Conclusions

WGS studies of standing pathogen diversity provide critical insights into the population and evolutionary dynamics of lineages and antimicrobial resistance, which can be translated to effective public health action and decision-making. We highlight the need to strengthen efforts to implement WGS-based surveillance and genomic data analyses in state public health laboratories.

Genomic characteristics and comparative genomics analysis of Salmonella enterica subsp. enterica serovar Thompson isolated from an outbreak in South Korea

Salmonella infections represent an important public health problem. In 2018, a multistate outbreak of S. enterica subsp. enterica serovar Thompson infection associated with contaminated chocolate cakes in schools was reported in South Korea. In this study, we sequenced the 37 S. Thompson strains isolated from chocolate cakes, egg whites, preserves, and cookware associated with the outbreak. In addition, we analyze the genomic sequences of 61 S. Thompson strains (37 chocolate cake-related outbreak strains, 4 strains isolated from outbreaks in South Korea and 20 strains available in the National Center for Biotechnology Information) to assess the genomic characteristics of outbreak-related strains by comparative genomics and phylogenetic analysis. The results showed that identically classified clusters divided strains into two clusters, sub-clusters A & I (with strains from 2018 in South Korea) and sub-clusters B & II (with strains from 2014 to 2015 in South Korea). S. Thompson isolated from South Korea were accurately distinguished from publicly-available strains. Unlike other S. Thompson genomes, those of chocolate cake outbreak-related strains had three Salmonella phages (SEN8, vB SosS Oslo, and SI7) integrated into their chromosome. Comparative genomics revealed several genes responsible for the specific genomic features of chocolate cake outbreak-related strains and three bacteriophages that may contribute to the pathogenicity of other S. Thompson strains.

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.

Using Genomics to Understand the Epidemiology of Infectious Diseases in the Northern Territory of Australia

The Northern Territory (NT) is a geographically remote region of northern and central Australia. Approximately a third of the population are First Nations Australians, many of whom live in remote regions. Due to the physical environment and climate, and scale of social inequity, the rates of many infectious diseases are the highest nationally. Molecular typing and genomic sequencing in research and public health have provided considerable new knowledge on the epidemiology of infectious diseases in the NT. We review the applications of genomic sequencing technology for molecular typing, identification of transmission clusters, phylogenomics, antimicrobial resistance prediction, and pathogen detection. We provide examples where these methodologies have been applied to infectious diseases in the NT and discuss the next steps in public health implementation of this technology.

The Notable Achievements and the Prospects of Bacterial Pathogen Genomics

Throughout the entirety of human history, bacterial pathogens have played an important role and even shaped the fate of civilizations. The application of genomics within the last 27 years has radically changed the way we understand the biology and evolution of these pathogens. In this review, we discuss how the short- (Illumina) and long-read (PacBio, Oxford Nanopore) sequencing technologies have shaped the discipline of bacterial pathogen genomics, in terms of fundamental research (i.e., evolution of pathogenicity), forensics, food safety, and routine clinical microbiology. We have mined and discuss some of the most prominent data/bioinformatics resources such as NCBI pathogens, PATRIC, and Pathogenwatch. Based on this mining, we present some of the most popular sequencing technologies, hybrid approaches, assemblers, and annotation pipelines. A small number of bacterial pathogens are of very high importance, and we also present the wealth of the genomic data for these species (i.e., which ones they are, the number of antimicrobial resistance genes per genome, the number of virulence factors). Finally, we discuss how this discipline will probably be transformed in the near future, especially by transitioning into metagenome-assembled genomes (MAGs), thanks to long-read sequencing.