Use of Whole-Genome Sequencing for Food Safety and Public Health in the United States

A retrospective and regional approach assessing the genomic diversity of Salmonella Dublin

From a historically rare serotype, Salmonella enterica subsp. enterica Dublin slowly became one of the most prevalent Salmonella in cattle and raw milk cheese in some regions of France. We present a retrospective genomic analysis of 480 S. Dublin isolates to address the context, evolutionary dynamics, local diversity and the genesis processes of regional S. Dublin outbreaks events between 2015 and 2017. Samples were clustered and assessed for correlation against metadata including isolation date, isolation matrices, geographical origin and epidemiological hypotheses. Significant findings can be drawn from this work. We found that the geographical distance was a major factor explaining genetic groups in the early stages of the cheese production processes (animals, farms) while down-the-line transformation steps were more likely to host genomic diversity. This supports the hypothesis of a generalised local persistence of strains from animal to finished products, with occasional migration. We also observed that the bacterial surveillance is representative of diversity, while targeted investigations without genomics evidence often included unrelated isolates. Combining both approaches in phylogeography methods allows a better representation of the dynamics, of outbreaks.

Comparative Genomic Analysis Discloses Differential Distribution of Antibiotic Resistance Determinants between Worldwide Strains of the Emergent ST213 Genotype of Salmonella Typhimurium

Salmonella enterica constitutes a global public health concern as one of the main etiological agents of human gastroenteritis. The Typhimurium serotype is frequently isolated from human, animal, food, and environmental samples, with its sequence type 19 (ST19) being the most widely distributed around the world as well as the founder genotype. The replacement of the ST19 genotype with the ST213 genotype that has multiple antibiotic resistance (MAR) in human and food samples was first observed in Mexico. The number of available genomes of ST213 strains in public databases indicates its fast worldwide dispersion, but its public health relevance is unknown. A comparative genomic analysis conducted as part of this research identified the presence of 44 genes, 34 plasmids, and five point mutations associated with antibiotic resistance, distributed across 220 genomes of ST213 strains, indicating the MAR phenotype. In general, the grouping pattern in correspondence to the presence/absence of genes/plasmids that confer antibiotic resistance cluster the genomes according to the geographical origin where the strain was isolated. Genetic determinants of antibiotic resistance group the genomes of North America (Canada, Mexico, USA) strains, and suggest a dispersion route to reach the United Kingdom and, from there, the rest of Europe, then Asia and Oceania. The results obtained here highlight the worldwide public health relevance of the ST213 genotype, which contains a great diversity of genetic elements associated with MAR.

Genomic Sequencing of Bacillus cereus Sensu Lato Strains Isolated from Meat and Poultry Products in South Africa Enables Inter- and Intranational Surveillance and Source Tracking

Members of the Bacillus cereus sensu lato species complex, also known as the B. cereus group, vary in their ability to cause illness but are frequently isolated from foods, including meat products; however, food safety surveillance efforts that use whole-genome sequencing (WGS) often neglect these potential pathogens. Here, we evaluate the surveillance and source tracking potential of WGS as applied to B. cereus sensu lato by (i) using WGS to characterize B. cereus sensu lato strains isolated during routine surveillance of meat products across South Africa (n = 25) and (ii) comparing the genomes sequenced here to all publicly available, high-quality B. cereus sensu lato genomes (n = 2,887 total genomes). Strains sequenced here were collected from meat products obtained from (i) retail outlets, processing plants, and butcheries across six South African provinces (n = 23) and (ii) imports held at port of entry (n = 2). The 25 strains sequenced here were partitioned into 15 lineages via in silico seven-gene multilocus sequence typing (MLST). While none of the South African B. cereus sensu lato strains sequenced here were identical to publicly available genomes, six MLST lineages contained multiple strains sequenced in this study, which were identical or nearly identical at the whole-genome scale (≤3 core single nucleotide polymorphisms). Five MLST lineages contained (nearly) identical genomes collected from two or three South African provinces; one MLST lineage contained nearly identical genomes from two countries (South Africa and the Netherlands), indicating that B. cereus sensu lato can spread intra- and internationally via foodstuffs. IMPORTANCE Nationwide foodborne pathogen surveillance programs that use high-resolution genomic methods have been shown to provide vast public health and economic benefits. However, Bacillus cereus sensu lato is often overlooked during large-scale routine WGS efforts. Thus, to our knowledge, no studies to date have evaluated the potential utility of WGS for B. cereus sensu lato surveillance and source tracking in foodstuffs. In this preliminary proof-of-concept study, we applied WGS to B. cereus sensu lato strains collected via South Africa's national surveillance program of domestic and imported meat products, and we provide strong evidence that B. cereus sensu lato can be disseminated intra- and internationally via the agro-food supply chain. Our results showcase that WGS has the potential to be used for source tracking of B. cereus sensu lato in foods, although future WGS and metadata collection efforts are needed to ensure that B. cereus sensu lato surveillance initiatives are on par with those of other foodborne pathogens.

Assessing the effectiveness of performance standards for Salmonella contamination of chicken parts

The United States Department of Agriculture's Food Safety and Inspection Service implemented Salmonella performance standards for establishments producing chicken parts in 2016. The standards were chosen based on the assumption that a 30 % reduction in the occurrence of Salmonella-contaminated chicken parts samples (i.e., legs, breasts or wings) would result following implementation of the performance standard program. The derivation of the performance standards was based on data collected prior to the implementation of the standards and in the intervening years, so overall changes in the Salmonella contamination of this product can be assessed. This study presents a historical review of changes in Salmonella contamination on chicken parts as these changes relate to the performance standard. The analysis demonstrates that the reduction in Salmonella contaminated chicken parts samples was more than 75 %, so the FSIS risk assessment significantly underestimated the actual reduction in Salmonella contamination. An analysis of chicken parts samples collected at retail demonstrates reductions of a similar magnitude. Changes in the characteristics of Salmonella contamination that are potentially relevant to the occurrence or severity of human illness, such as seasonal changes in contamination, the composition of serotypes and changes in antimicrobial resistance, are also assessed. Small but significant seasonal increases in contamination were observed, with the peaks occurring in late winter rather than the more traditional late summer peak. Rapid changes in both the five most common serotypes and antimicrobial resistance patterns were also observed.

Phylogenomic Analysis of Salmonella enterica subsp. enterica Serovar Bovismorbificans from Clinical and Food Samples Using Whole Genome Wide Core Genes and kmer Binning Methods to Identify Two Distinct Polyphyletic Genome Pathotypes

Salmonella enterica subsp. enterica serovar Bovismorbificans has caused multiple outbreaks involving the consumption of produce, hummus, and processed meat products worldwide. To elucidate the intra-serovar genomic structure of S. Bovismorbificans, a core-genome analysis with 2690 loci (based on 150 complete genomes representing Salmonella enterica serovars developed as part of this study) and a k-mer-binning based strategy were carried out on 95 whole genome sequencing (WGS) assemblies from Swiss, Canadian, and USA collections of S. Bovismorbificans strains from foodborne infections. Data mining of a digital DNA tiling array of legacy SARA and SARB strains was conducted to identify near-neighbors of S. Bovismorbificans. The core genome analysis and the k-mer-binning methods identified two polyphyletic clusters, each with emerging evolutionary properties. Four STs (2640, 142, 1499, and 377), which constituted the majority of the publicly available WGS datasets from >260 strains analyzed by k-mer-binning based strategy, contained a conserved core genome backbone with a different evolutionary lineage as compared to strains comprising the other cluster (ST150). In addition, the assortment of genotypic features contributing to pathogenesis and persistence, such as antimicrobial resistance, prophage, plasmid, and virulence factor genes, were assessed to understand the emerging characteristics of this serovar that are relevant clinically and for food safety concerns. The phylogenomic profiling of polyphyletic S. Bovismorbificans in this study corresponds to intra-serovar variations observed in S. Napoli and S. Newport serovars using similar high-resolution genomic profiling approaches and contributes to the understanding of the evolution and sequence divergence of foodborne Salmonellae. These intra-serovar differences may have to be thoroughly understood for the accurate classification of foodborne Salmonella strains needed for the uniform development of future food safety mitigation strategies.

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.

HIGHLIGHTS

Metagenomics Approaches for Improving Food Safety: A Review

ABSTRACT

Advancements in next-generation sequencing technology have dramatically reduced the cost and increased the ease of microbial whole genome sequencing. This approach is revolutionizing the identification and analysis of foodborne microbial pathogens, facilitating expedited detection and mitigation of foodborne outbreaks, improving public health outcomes, and limiting costly recalls. However, next-generation sequencing is still anchored in the traditional laboratory practice of the selection and culture of a single isolate. Metagenomic-based approaches, including metabarcoding and shotgun and long-read metagenomics, are part of the next disruptive revolution in food safety diagnostics and offer the potential to directly identify entire microbial communities in a single food, ingredient, or environmental sample. In this review, metagenomic-based approaches are introduced and placed within the context of conventional detection and diagnostic techniques, and essential considerations for undertaking metagenomic assays and data analysis are described. Recent applications of the use of metagenomics for food safety are discussed alongside current limitations and knowledge gaps and new opportunities arising from the use of this technology.

HIGHLIGHTS

A review on the application of bioinformatics tools in food microbiome studies

There is currently a transformed interest toward understanding the impact of fermentation on functional food development due to growing consumer interest on modified health benefits of sustainable foods. In this review, we attempt to summarize recent findings regarding the impact of Next-generation sequencing and other bioinformatics methods in the food microbiome and use prediction software to understand the critical role of microbes in producing fermented foods. Traditionally, fermentation methods and starter culture development were considered conventional methods needing optimization to eliminate errors in technique and were influenced by technical knowledge of fermentation. Recent advances in high-output omics innovations permit the implementation of additional logical tactics for developing fermentation methods. Further, the review describes the multiple functions of the predictions based on docking studies and the correlation of genomic and metabolomic analysis to develop trends to understand the potential food microbiome interactions and associated products to become a part of a healthy diet.

WGS analysis of Listeria monocytogenes from rural, urban, and farm environments in Norway: Genetic diversity, persistence, and relation to clinical and food isolates

Listeria monocytogenes is a ubiquitous environmental bacterium associated with a wide variety of natural and human-made environments, such as soil, vegetation, livestock, food processing environments, and urban areas. It is also among the deadliest foodborne pathogens, and knowledge about its presence and diversity in potential sources is crucial to effectively track and control it in the food chain. Isolation of L. monocytogenes from various rural and urban environments showed higher prevalence in agricultural and urban developments than in forest or mountain areas, and that detection was positively associated with rainfall. Whole-genome sequencing (WGS) was performed for the collected isolates and for L. monocytogenes from Norwegian dairy farms and slugs (218 isolates in total). The data were compared to available data sets from clinical and food-associated sources in Norway collected within the last decade. Multiple examples of clusters of isolates with 0 to 8 whole-genome multilocus sequence typing (wgMLST) allelic differences were collected over time in the same location, demonstrating persistence of L. monocytogenes in natural, urban, and farm environments. Furthermore, several clusters with 6 to 20 wgMLST allelic differences containing isolates collected across different locations, times, and habitats were identified, including nine clusters harboring clinical isolates. The most ubiquitous clones found in soil and other natural and animal ecosystems (CC91, CC11, and CC37) were distinct from clones predominating among both clinical (CC7, CC121, and CC1) and food (CC9, CC121, CC7, and CC8) isolates. The analyses indicated that ST91 was more prevalent in Norway than other countries and revealed a high proportion of the hypovirulent ST121 among Norwegian clinical cases.

IMPORTANCEListeria monocytogenes is a deadly foodborne pathogen that is widespread in the environment. For effective management, both public health authorities and food producers need reliable tools for source tracking, surveillance, and risk assessment. For this, whole-genome sequencing (WGS) is regarded as the present and future gold standard. In the current study, we use WGS to show that L. monocytogenes can persist for months and years in natural, urban, and dairy farm environments. Notably, clusters of almost identical isolates, with genetic distances within the thresholds often suggested for defining an outbreak cluster, can be collected from geographically and temporally unrelated sources. The work highlights the need for a greater knowledge of the genetic relationships between clinical isolates and isolates of L. monocytogenes from a wide range of environments, including natural, urban, agricultural, livestock, food production, and food processing environments, to correctly interpret and use results from WGS analyses.

Whole-Genome Analysis of Multidrug-Resistant Salmonella Enteritidis Strains Isolated from Poultry Sources in Korea

The Salmonella Enterica subsp. Enterica serovar Enteritidis is one of main serovars isolated from human patients with food poisoning and poultry without clinical signs. Consumption of poultry products contaminated with Salmonella Enteritidis is a common source of human salmonellosis; 82 Salmonella spp. were isolated from 291 samples of retail chicken meat, 201 one-day-old chicks, 30 internal organs of chickens, 156 chicken eggs, 100 duck eggs, 38 straw bedding samples, 18 samples of retail duck meat, and 19 swab samples from slaughterhouses in 2019 and 2020. An antibiotic susceptibility test was performed for all isolates, revealing 33 multidrug-resistant (MDR) strains. The whole genome of 33 MDR strains isolated in 2019 and 2020 and 10 strains isolated in 2011, 2012, and 2017 was sequenced using the MinION sequencing protocol. Within these 43 samples, 5 serovars were identified: S. Enteritidis, S. Agona, S. Virchow, S. Albany, and S. Bareilly. The most common serovar was S. Enteritidis (26/43), which showed the highest resistance to ampicillin (100%), followed by nalidixic acid (90%) and colistin (83%). Core genome multilocus sequence typing analysis showed that the S. Enteritidis strains isolated from different sources and in different years were clustered together. In addition, the S. Enteritidis strains isolated since 2011 consistently harbored the same antibiotic resistance patterns.

SNPPar: identifying convergent evolution and other homoplasies from microbial whole-genome alignments

Homoplasic SNPs are considered important signatures of strong (positive) selective pressure, and hence of adaptive evolution for clinically relevant traits such as antibiotic resistance and virulence. Here we present a new tool, SNPPar, for efficient detection and analysis of homoplasic SNPs from large whole genome sequencing datasets (>1000 isolates and/or >100 000 SNPs). SNPPar takes as input an SNP alignment, tree and annotated reference genome, and uses a combination of simple monophyly tests and ancestral state reconstruction (ASR, via TreeTime) to assign mutation events to branches and identify homoplasies. Mutations are annotated at the level of codon and gene, to facilitate analysis of convergent evolution. Testing on simulated data (120 Mycobacterium tuberculosis alignments representing local and global samples) showed SNPPar can detect homoplasic SNPs with very high specificity (zero false-positives in all tests) and high sensitivity (zero false-negatives in 89 % of tests). SNPPar analysis of three empirically sampled datasets (Elizabethkingia anophelis, Burkholderia dolosa and M. tuberculosis) produced results that were in concordance with previous studies, in terms of both individual homoplasies and evidence of convergence at the codon and gene levels. SNPPar analysis of a simulated alignment of ~64 000 genome-wide SNPs from 2000 M. tuberculosis genomes took ~23 min and ~2.6 GB of RAM to generate complete annotated results on a laptop. This analysis required ASR be conducted for only 1.25 % of SNPs, and the ASR step took ~23 s and 0.4 GB of RAM. SNPPar automates the detection and annotation of homoplasic SNPs efficiently and accurately from large SNP alignments. As demonstrated by the examples included here, this information can be readily used to explore the role of homoplasy in parallel and/or convergent evolution at the level of nucleotide, codon and/or gene.

Genomic evidence of environmental and resident Salmonella Senftenberg and Montevideo contamination in the pistachio supply-chain

Pistachios have been implicated in two salmonellosis outbreaks and multiple recalls in the U.S. This study performed an in-depth retrospective data analysis of Salmonella associated with pistachios as well as a storage study to evaluate the survivability of Salmonella on inoculated inshell pistachios to further understand the genetics and microbiological dynamics of this commodity-pathogen pair. The retrospective data analysis on isolates associated with pistachios was performed utilizing short-read and long-read sequencing technologies. The sequence data were analyzed using two methods: the FDA's Center for Food Safety and Applied Nutrition Single Nucleotide Polymorphism (SNP) analysis and Whole Genome Multilocus Sequence Typing (wgMLST). The year-long storage study evaluated the survival of five strains of Salmonella on pistachios stored at 25 °C at 35% and 54% relative humidity (RH). Our results demonstrate: i) evidence of persistent Salmonella Senftenberg and Salmonella Montevideo strains in pistachio environments, some of which may be due to clonal resident strains and some of which may be due to preharvest contamination; ii) presence of the Copper Homeostasis and Silver Resistance Island (CHASRI) in Salmonella Senftenberg and Montevideo strains in the pistachio supply chain; and iii) the use of metagenomic analysis is a novel tool for determining the composition of serovar survival in a cocktail inoculated storage study.

Genomic Characterization of Endemic and Ecdemic Non-typhoidal Salmonella enterica Lineages Circulating Among Animals and Animal Products in South Africa

In Africa, the burden of illness caused by non-typhoidal Salmonella enterica is disproportionally high; however, whole-genome sequencing (WGS) efforts are overwhelmingly concentrated in world regions with lower burdens. While WGS is being increasingly employed in South Africa to characterize Salmonella enterica, the bulk of these efforts have centered on characterizing human clinical strains. Thus, very little is known about lineages circulating among animals in the country on a genomic scale. Here, we used WGS to characterize 63 Salmonella enterica strains isolated from livestock, companion animals, wildlife, and animal products in South Africa over a 60-year period. Genomes were assigned to serotypes Dublin, Hadar, Enteritidis, and Typhimurium (n = 18, 8, 13, and 24 strains, respectively) and sequence types (STs) ST10 (all S. Dublin), ST33 (all S. Hadar), ST11/ST366 (n = 12 and 1 S. Enteritidis, respectively), and ST19/ST34 (n = 23 and 1 S. Typhimurium, respectively; via seven-gene multi-locus sequence typing). Within-ST phylogenies were constructed using genomes sequenced in this study, plus publicly available genomes representative of each ST's (i) global (n = 2,802 and 1,569 S. Dublin and Hadar genomes, respectively) and (ii) African (n = 716 and 343 S. Enteritidis and Typhimurium genomes, respectively) population. For S. Dublin ST10, a largely antimicrobial-susceptible, endemic lineage circulating among humans, animals, and food in South Africa was identified, as well as a lineage that was likely recently introduced from the United States. For S. Hadar ST33, multiple South African lineages harboring streptomycin and tetracycline resistance-conferring genes were identified. African S. Enteritidis ST11 could be primarily partitioned into one largely antimicrobial-susceptible and one largely multidrug-resistant (MDR) clade, with South African isolates confined to the largely antimicrobial-susceptible clade. S. Typhimurium ST19/ST34 strains sequenced here were distributed across the African S. Typhimurium ST19/ST34 phylogeny, representing a diverse range of lineages, including numerous MDR lineages. Overall, this study provides critical insights into endemic and ecdemic non-typhoidal Salmonella enterica lineages circulating among animals, foods, and humans in South Africa and showcases the utility of WGS in characterizing animal-associated strains from a world region with a high salmonellosis burden.

An economic evaluation of the Whole Genome Sequencing source tracking program in the U.S

The U.S. Food and Drug Administration (FDA) created the GenomeTrakr Whole Genome Sequencing (WGS) Network in 2013, as a tool to improve food safety. This study presents an analysis of Whole Genome source tracking implementation on potential food contamination and related illnesses through theoretical, empirical, and cost benefit analyses. We conduct empirical tests using data from FDA regulated food commodity outbreaks garnering FDA response from 1999 through 2019 and examine the effect of the National Center for Biotechnology Information (NCBI) Pathogen detection program of source tracking WGS isolates collected in the U.S. on outbreak illnesses for three pilot pathogens (E. coli, Listeria, and Salmonella). Empirical results are consistent with the theoretical model and suggest that each additional 1,000 WGS isolates added to the public NCBI database is associated with a reduction of approximately 6 illnesses per WGS pathogen, per year. Empirical results are connected to existing literature for a Monte Carlo analysis to estimate benefits and costs. By 2019, annual health benefits are estimated at nearly $500 million, compared to an approximately $22 million investment by public health agencies. Even under conservative assumptions, the program likely broke even in its second year of implementation and could produce increasing public health benefits as the GenomeTrakr network matures.

Characterization and Genetic Diversity of Listeria monocytogenes Isolated from Cattle Abortions in Latvia, 2013-2018

Listeria monocytogenes can cause disease in humans and in a wide range of animal species, especially in farm ruminants. The aim of the study was to determine the prevalence and genetic diversity of L. monocytogenes related to 1185 cattle abortion cases in Latvia during 2013-2018. The prevalence of L. monocytogenes among cattle abortions was 16.1% (191/1185). The seasonality of L. monocytogenes abortions was observed with significantly higher occurrence (p < 0.01) in spring (March-May). In 61.0% of the cases, the affected cattle were under four years of age. L. monocytogenes abortions were observed during the third (64.6%) and second (33.3%) trimesters of gestation. Overall, 27 different sequence types (ST) were detected, and four of them, ST29 (clonal complex, CC29), ST37 (CC37), ST451 (CC11) and ST7 (CC7), covered more than half of the L. monocytogenes isolates. Key virulence factors like the prfA-dependent virulence cluster and inlA, inlB were observed in all the analyzed isolates, but lntA, inlF, inlJ, vip were associated with individual sequence types. Our results confirmed that L. monocytogenes is the most important causative agent of cattle abortions in Latvia and more than 20 different STs were observed in L. monocytogenes abortions in cattle.

An implementation science approach to evaluating pathogen whole genome sequencing in public health

BACKGROUND

Pathogen whole genome sequencing (WGS) is being incorporated into public health surveillance and disease control systems worldwide and has the potential to make significant contributions to infectious disease surveillance, outbreak investigation and infection prevention and control. However, to date, there are limited data regarding (i) the optimal models for integration of genomic data into epidemiological investigations and (ii) how to quantify and evaluate public health impacts resulting from genomic epidemiological investigations.

METHODS

We developed the Pathogen Genomics in Public HeAlth Surveillance Evaluation (PG-PHASE) Framework to guide examination of the use of WGS in public health surveillance and disease control. We illustrate the use of this framework with three pathogens as case studies: Listeria monocytogenes, Mycobacterium tuberculosis and SARS-CoV-2.

RESULTS

The framework utilises an adaptable whole-of-system approach towards understanding how interconnected elements in the public health application of pathogen genomics contribute to public health processes and outcomes. The three phases of the PG-PHASE Framework are designed to support understanding of WGS laboratory processes, analysis, reporting and data sharing, and how genomic data are utilised in public health practice across all stages, from the decision to send an isolate or sample for sequencing to the use of sequence data in public health surveillance, investigation and decision-making. Importantly, the phases can be used separately or in conjunction, depending on the need of the evaluator. Subsequent to conducting evaluation underpinned by the framework, avenues may be developed for strategic investment or interventions to improve utilisation of whole genome sequencing.

CONCLUSIONS

Comprehensive evaluation is critical to support health departments, public health laboratories and other stakeholders to successfully incorporate microbial genomics into public health practice. The PG-PHASE Framework aims to assist public health laboratories, health departments and authorities who are either considering transitioning to whole genome sequencing or intending to assess the integration of WGS in public health practice, including the capacity to detect and respond to outbreaks and associated costs, challenges and facilitators in the utilisation of microbial genomics and public health impacts.

Framing Bacterial Genomics for Public Health(care)

Advancements in comparative genomics have generated significant interest in defining applications for healthcare-associated pathogens. Clinical microbiology, however, relies on increasingly automated platforms to quickly identify pathogens, resistance mechanisms, and therapy options within CLIA- and FDA-approved frameworks. Additionally, and most notably, healthcare-associated pathogens, especially those that are resistant to antibiotics, represent a diverse spectrum of genera harboring complex genetic targets including antibiotic, biocide, and virulence determinants that can be highly transmissible and, at least for antibiotic resistance, serve as potential targets for containment efforts. U.S. public health investments have focused on rapidly detecting outbreaks and emerging resistance in healthcare-associated pathogens using reference, culture-based, and molecular methods that are distributed, for example, across national laboratory network infrastructures. Herein we describe the public health applications of genomic science that are built from the top-down for broad surveillance, as well as the bottom-up, starting with identification of infections and infectious clusters. For healthcare-associated, including antimicrobial-resistant, pathogens, we propose a combination of top-down and bottom-up genomic approaches leveraged across the public health spectrum, from local infection control, to regional and national containment efforts, to national surveillance for understanding emerging strain ecology and fitness of healthcare pathogens.

Updates on defining and detecting diarrheagenic Escherichia coli pathotypes

PURPOSE OF REVIEW

Several types of Escherichia coli cause acute diarrhea in humans and are responsible for a large burden of disease globally. The purpose of this review is to summarize diarrheagenic Escherichia coli (DEC) pathotype definitions and discuss existing and emerging molecular, genomic, and gut microbiome methods to detect, define, and study DEC pathotypes.

RECENT FINDINGS

DEC pathotypes are currently diagnosed by molecular detection of unique virulence genes. However, some pathotypes have defied coherent molecular definitions because of imperfect gene targets, and pathotype categories are complicated by hybrid strains and isolation of pathotypes from asymptomatic individuals. Recent progress toward more efficient, sensitive, and multiplex DEC pathotype detection has been made using emerging PCR-based technologies. Genomics and gut microbiome detection methods continue to advance rapidly and are contributing to a better understanding of DEC pathotype diversity and functional potential.

SUMMARY

DEC pathotype categorizations and detection methods are useful but imperfect. The implementation of molecular and sequence-based methods and well designed epidemiological studies will continue to advance understanding of DEC pathotypes. Additional emphasis is needed on sequencing DEC genomes from regions of the world where they cause the most disease and from the pathotypes that cause the greatest burden of disease globally.

Big Data Impacting Dynamic Food Safety Risk Management in the Food Chain

Foodborne pathogens are a major contributor to foodborne illness worldwide. The adaptation of a more quantitative risk-based approach, with metrics such as Food safety Objectives (FSO) and Performance Objectives (PO) necessitates quantitative inputs from all stages of the food value chain. The potential exists for utilization of big data, generated through digital transformational technologies, as inputs to a dynamic risk management concept for food safety microbiology. The industrial revolution in Internet of Things (IoT) will leverage data inputs from precision agriculture, connected factories/logistics, precision healthcare, and precision food safety, to improve the dynamism of microbial risk management. Furthermore, interconnectivity of public health databases, social media, and e-commerce tools as well as technologies such as blockchain will enhance traceability for retrospective and real-time management of foodborne cases. Despite the enormous potential of data volume and velocity, some challenges remain, including data ownership, interoperability, and accessibility. This paper gives insight to the prospective use of big data for dynamic risk management from a microbiological safety perspective in the context of the International Commission on Microbiological Specifications for Foods (ICMSF) conceptual equation, and describes examples of how a dynamic risk management system (DRMS) could be used in real-time to identify hazards and control Shiga toxin-producing Escherichia coli risks related to leafy greens.

Centralised or Localised Pathogen Whole Genome Sequencing: Lessons Learnt From Implementation in a Clinical Diagnostic Laboratory

Whole genome sequencing (WGS) has had widespread use in the management of microbial outbreaks in a public health setting. Current models encompass sending isolates to a central laboratory for WGS who then produce a report for various levels of government. This model, although beneficial, has multiple shortcomings especially for localised infection control interventions and patient care. One reason for the slow rollout of WGS in clinical diagnostic laboratories has been the requirement for professionally trained personal in both wet lab techniques and in the analysis and interpretation of data, otherwise known as bioinformatics. A further bottleneck has been establishment of regulations in order to certify clinical and technical validity and demonstrate WGS as a verified diagnostic test. Nevertheless, this technology is far superior providing information that would normally require several diagnostic tests to achieve. An obvious barrier to informed outbreak tracking is turnaround time and requires isolates to be sequenced in real-time to rapidly identify chains of transmission. One way this can be achieved is through onsite hospital sequencing with a cumulative analysis approach employed. Onsite, as opposed to centralised sequencing, has added benefits including the increased agility to combine with local infection control staff to iterate through the data, finding links that aide in understanding transmission chains and inform infection control strategies. Our laboratory has recently instituted a pathogen WGS service within a diagnostic laboratory, separate to a public health laboratory. We describe our experience, address the challenges faced and demonstrate the advantages of de-centralised sequencing through real-life scenarios.

Coproduction of Tet(X7) Conferring High-Level Tigecycline Resistance, Fosfomycin FosA4, and Colistin Mcr-1.1 in Escherichia coli Strains from Chickens in Egypt

The plasmid-mediated tet(X7) conferring high-level tigecycline resistance was identified in five mcr-1.1-positive Escherichia coli strains (ST10 [n = 3] and ST155 [n = 2]) isolated from chickens in Egypt. Two fosfomycin-resistant fosA4-carrying IncFII plasmids (∼79 kb in size) were detected. Transposase ISCR3 (IS91 family) is syntenic with tet(X7) in all isolates, suggesting its role in the mobilization of tet(X7). To our knowledge, this is the first global report of ST4-IncHI2 plasmids cocarrying tet(X7) and mcr-1.1 from chickens.

Perspective on Clinically-Relevant Antimicrobial Resistant Enterobacterales in Food: Closing the Gaps Using Genomics

Antimicrobial resistance is one of the most important public health concerns—it causes 700,000 deaths annually according to the World Health Organization (WHO). Enterobacterales such as E. coli and Klebsiella pneumoniae, have become resistant to many relevant antimicrobials including carbapenems and extended spectrum cephalosporins. These clinically relevant resistant Enterobacterales (CRRE) members are now globally distributed in the environment including different food types (meats, produce, dairy). Unlike known foodborne pathogens, CRRE are not usually part of most food surveillance systems. However, numerous reports of CRRE highlight the importance of these bacteria in food and have been shown to contribute to the overall crisis of antimicrobial resistance. This is especially important in the context of carriage of these pathogens by immuno-compromised individuals. CRRE infections upon consumption of contaminated food could colonize the human gastrointestinal tract and eventually be a source of systemic infections such as urinary tract infections or septicemia. While different aspects need to be considered to elucidate this, whole genome sequencing along with metadata could be used to understand genomic relationships of CRRE obtained from foods and humans, including isolates from clinical infections. Once robust scientific data is available on the role of CRRE in food, countries could move forward to better survey and control CRRE in food.

Recent Advances in the Detection of Antibiotic and Multi-Drug Resistant Salmonella: An Update

Antibiotic and multi-drug resistant (MDR) Salmonella poses a significant threat to public health due to its ability to colonize animals (cold and warm-blooded) and contaminate freshwater supplies. Monitoring antibiotic resistant Salmonella is traditionally costly, involving the application of phenotypic and genotypic tests over several days. However, with the introduction of cheaper semi-automated devices in the last decade, strain detection and identification times have significantly fallen. This, in turn, has led to efficiently regulated food production systems and further reductions in food safety hazards. This review highlights current and emerging technologies used in the detection of antibiotic resistant and MDR Salmonella.

Whole Genome Sequencing Applied to Pathogen Source Tracking in Food Industry: Key Considerations for Robust Bioinformatics Data Analysis and Reliable Results Interpretation

Whole genome sequencing (WGS) has arisen as a powerful tool to perform pathogen source tracking in the food industry thanks to several developments in recent years. However, the cost associated to this technology and the degree of expertise required to accurately process and understand the data has limited its adoption at a wider scale. Additionally, the time needed to obtain actionable information is often seen as an impairment for the application and use of the information generated via WGS. Ongoing work towards standardization of wet lab including sequencing protocols, following guidelines from the regulatory authorities and international standardization efforts make the technology more and more accessible. However, data analysis and results interpretation guidelines are still subject to initiatives coming from distinct groups and institutions. There are multiple bioinformatics software and pipelines developed to handle such information. Nevertheless, little consensus exists on a standard way to process the data and interpret the results. Here, we want to present the constraints we face in an industrial setting and the steps we consider necessary to obtain high quality data, reproducible results and a robust interpretation of the obtained information. All of this, in a time frame allowing for data-driven actions supporting factories and their needs.

Determination of Genomic Epidemiology of Historical Clostridium perfringens Outbreaks in New York State by Use of Two Web-Based Platforms: National Center for Biotechnology Information Pathogen Detection and FDA GalaxyTrakr

Clostridium perfringens is the second leading cause of bacterial foodborne illness in the United States. The Wadsworth Center (WC) at the New York State Department of Health enumerates infectious dose from primary patient and food samples and, until recently, identified C. perfringens to the species level only. We investigated whether whole-genome sequence-based subtyping could benefit epidemiological investigations of this pathogen, as it has with other enteric organisms. We retrospectively sequenced 76 patient and food samples received between May 2010 and February 2020, including 52 samples linked epidemiologically to 13 outbreaks and 24 sporadic samples not linked to other samples. Phylogenetic trees were built using two Web-based platforms: National Centers for Biotechnology Information Pathogen Detection (NCBI-PD) and GalaxyTrakr (a Galaxy instance supported by the GenomeTrakr initiative). For GalaxyTrakr analyses, single nucleotide polymorphism (SNP) matrices and maximum-likelihood (ML) trees were generated using 3 different reference genomes. Across the four separate analyses, phylogenetic clustering was generally concordant with epidemiologically identified outbreaks. SNP diversity among phylogenetically linked samples from an outbreak ranged from 0 to 20 SNPs, excepting one outbreak ranging from 4 to 62 SNPs. Importantly, four of the 13 outbreak isolates harbored one or more samples that were phylogenetic outliers, and for two outbreaks, no samples were closely related. Two specimens were found harboring two distinct genotypes. For samples below CDC enumeration dose threshold, phylogenetic clustering was robust and linked patient and/or food samples. We concluded that WGS phylogenetic clusters (i) are largely concordant with epidemiologically defined outbreaks, irrespective of analysis platform or reference genome we employed; (ii) have limited pairwise SNP diversity, allowing phylogenetic clusters to be distinguished from sporadic cases; and (iii) can aid in epidemiological investigations by identifying outlier and polyclonal samples.

Cluster analysis of resistance combinations in Escherichia coli from different human and animal populations in Germany 2014-2017

Recent findings on Antibiotic Resistance (AR) have brought renewed attention to the comparison of data on AR from human and animal sectors. This is however a major challenge since the data is not harmonized. This study performs a comparative analysis of data on resistance combinations in Escherichia coli (E. coli) from different routine surveillance and monitoring systems for human and different animal populations in Germany. Data on E. coli isolates were collected between 2014 and 2017 from human clinical isolates, non-clinical animal isolates from food-producing animals and food, and clinical animal isolates from food-producing and companion animals from national routine surveillance and monitoring for AR in Germany. Sixteen possible resistance combinations to four antibiotics—ampicillin, cefotaxime, ciprofloxacin and gentamicin–for these populations were used for hierarchical clustering (Euclidian and average distance). All analyses were performed with the software R 3.5.1 (Rstudio 1.1.442). Data of 333,496 E. coli isolates and forty-one different human and animal populations were included in the cluster analysis. Three main clusters were detected. Within these three clusters, all human populations (intensive care unit (ICU), general ward and outpatient care) showed similar relative frequencies of the resistance combinations and clustered together. They demonstrated similarities with clinical isolates from different animal populations and most isolates from pigs from both non-clinical and clinical isolates. Isolates from healthy poultry demonstrated similarities in relative frequencies of resistance combinations and clustered together. However, they clustered separately from the human isolates. All isolates from different animal populations with low relative frequencies of resistance combinations clustered together. They also clustered separately from the human populations. Cluster analysis has been able to demonstrate the linkage among human isolates and isolates from various animal populations based on the resistance combinations. Further analyses based on these findings might support a better one-health approach for AR in Germany.

Whole-Genome Sequencing Analysis of Salmonella Enterica Serotype Enteritidis Isolated from Poultry Sources in South Korea, 2010-2017

Salmonella enterica subsp. enterica serotype Enteritidis (SE) is recognized as a major cause of human salmonellosis worldwide, and most human salmonellosis is due to the consumption of contaminated poultry meats and poultry byproducts. Whole-genome sequencing (data were obtained from 96 SE isolates from poultry sources, including an integrated broiler supply chain, farms, slaughterhouses, chicken transporting trucks, and retail chicken meats in South Korea during 2010–2017. Antimicrobial resistance and virulence genes were investigated using WGS data, and the phylogenetic relationship of the isolates was analyzed using single-nucleotide polymorphism (SNP) typing and core genome multilocus sequence typing (cgMLST). All isolates carried aminoglycoside resistance genes, aac(6’)-Iaa, and 56 isolates carried multiple antimicrobial resistance genes. The most frequent virulence gene profile, pef-fim-sop-inv.-org-sip-spa-sif-fli-flg-hil-ssa-sse-prg-pag-spv, was found in 90 isolates. The SNP analysis provided a higher resolution than the cgMLST analysis, but the cgMLST analysis was highly congruent with the SNP analysis. The phylogenetic results suggested the presence of resident SE within the facility of processing plants, environments of slaughterhouses, and the integrated broiler supply chain, and the phylogenetically related isolates were found in retail meats. In addition, the SE isolates from different origins showed close genetic relationships indicating that these strains may have originated from a common source. This study could be valuable reference data for future traceback investigations in South Korea.

Multilocus Sequence Typing (MLST) and Whole Genome Sequencing (WGS) of Listeria monocytogenes and Listeria innocua

Nucleotide sequence-based methods focusing on the single-nucleotide polymorphisms (SNPs) of Listeria monocytogenes and L. innocua housekeeping genes (multilocus sequence typing) and in the core genome (core genome MLST) facilitate the rapid and interlaboratory comparison in open accessible databases as provided by Institute Pasteur ( https://bigsdb.web.pasteur.fr/listeria/listeria.html ). Strains can be compared on a global level and help to track forward and trace backward pathogen contamination events in food processing facilities and in outbreak scenarios.

Current status of food safety hazards and health risks connected with aquatic food products from Southeast Asian region

Food safety issues associated with aquatic food products become more important with the increasing consumption and followed by its ongoing challenges. The objective of this paper is to review the food safety hazards and health risks related to aquatic food products for the Southeast Asian region. These hazards can be categorized as microplastics (MPs) hazard, biological hazards (pathogenic bacteria, biogenic amines, viruses, parasites), and chemical hazards (antimicrobial, formaldehyde, heavy metal). In different Southeast Asian countries, the potential health risks of aquatic food products brought by food hazards to consumers were at different intensity and classes. Among all these hazards, pathogenic bacteria, antimicrobials, and heavy metal were a particular concern in the Southeast Asian region. With environmental changes, evolving consumption patterns, and the globalization of trade, new food safety challenges are created, which put forward higher requirements on food technologies, food safety regulations, and international cooperation.

Using genomics to understand inter- and intra- outbreak diversity of Pasteurella multocida isolates associated with fowl cholera in meat chickens

Fowl cholera, caused by Pasteurella multocida, continues to be a challenge in meat-chicken-breeder operations and has emerged as a problem for free-range meat chickens. Here, using whole-genome sequencing (WGS) and phylogenomic analysis, we investigate isolate relatedness during outbreaks of fowl cholera on a free-range meat chicken farm over a 5-year period. Our genomic analysis revealed that while all outbreak isolates were sequence type (ST) 20, they could be separated into two distinct clades (clade 1 and clade 2) consistent with difference in their lipopolysaccharide (LPS) type. The isolates from the earlier outbreaks (clade 1) were carrying LPS type L3 while those from the more recent outbreaks (clade 2) were LPS type L1. Additionally, WGS data indicated high inter- and intra-chicken genetic diversity during a single outbreak. Furthermore, we demonstrate that while a killed autogenous vaccine carrying LPS type L3 had been successful in protecting against challenge from L3 isolates it might have driven the emergence of the closely related clade 2, against which the vaccine was ineffective. The genomic results also revealed a 14 bp deletion in the galactosyltransferase gene gatG in LPS type L3 isolates, which would result in producing a semi-truncated LPS in those isolates. In conclusion, our study clearly demonstrates the advantages of genomic analysis over the conventional PCR-based approaches in providing clear insights in terms of linkage of isolate within and between outbreaks. More importantly, it provides more detailed information than the multiplex PCR on the possible structure of outer LPS, which is very important in the case of strain selection for killed autogenous vaccines.

Benchmarking Long-Read Assemblers for Genomic Analyses of Bacterial Pathogens Using Oxford Nanopore Sequencing

Oxford Nanopore sequencing can be used to achieve complete bacterial genomes. However, the error rates of Oxford Nanopore long reads are greater compared to Illumina short reads. Long-read assemblers using a variety of assembly algorithms have been developed to overcome this deficiency, which have not been benchmarked for genomic analyses of bacterial pathogens using Oxford Nanopore long reads. In this study, long-read assemblers, namely Canu, Flye, Miniasm/Racon, Raven, Redbean, and Shasta, were thus benchmarked using Oxford Nanopore long reads of bacterial pathogens. Ten species were tested for mediocre- and low-quality simulated reads, and 10 species were tested for real reads. Raven was the most robust assembler, obtaining complete and accurate genomes. All Miniasm/Racon and Raven assemblies of mediocre-quality reads provided accurate antimicrobial resistance (AMR) profiles, while the Raven assembly of Klebsiella variicola with low-quality reads was the only assembly with an accurate AMR profile among all assemblers and species. All assemblers functioned well for predicting virulence genes using mediocre-quality and real reads, whereas only the Raven assemblies of low-quality reads had accurate numbers of virulence genes. Regarding multilocus sequence typing (MLST), Miniasm/Racon was the most effective assembler for mediocre-quality reads, while only the Raven assemblies of Escherichia coli O157:H7 and K. variicola with low-quality reads showed positive MLST results. Miniasm/Racon and Raven were the best performers for MLST using real reads. The Miniasm/Racon and Raven assemblies showed accurate phylogenetic inference. For the pan-genome analyses, Raven was the strongest assembler for simulated reads, whereas Miniasm/Racon and Raven performed the best for real reads. Overall, the most robust and accurate assembler was Raven, closely followed by Miniasm/Racon.

Gen-FS coordinated proficiency test data for genomic foodborne pathogen surveillance, 2017 and 2018 exercises

The US PulseNet and GenomeTrakr laboratory networks work together within the Genomics for Food Safety (Gen-FS) consortium to collect and analyze genomic data for foodborne pathogen surveillance (species include Salmonella enterica, Listeria monocytogenes, Escherichia coli (STECs), and Campylobactor). In 2017 these two laboratory networks started harmonizing their respective proficiency test exercises, agreeing on distributing a single strain-set and following the same standard operating procedure (SOP) for genomic data collection, running a jointly coordinated annual proficiency test exercise. In this data release we are publishing the reference genomes and raw data submissions for the 2017 and 2018 proficiency test exercises.

Measurement(s)	DNA • genome • sequence_assembly
Technology Type(s)	DNA sequencing • sequence assembly process
Factor Type(s)	species of foodborne pathogen
Sample Characteristic - Organism	Salmonella enterica • Escherichia coli • Listeria monocytogenes

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13135046

Benchmarking hybrid assembly approaches for genomic analyses of bacterial pathogens using Illumina and Oxford Nanopore sequencing

Background

We benchmarked the hybrid assembly approaches of MaSuRCA, SPAdes, and Unicycler for bacterial pathogens using Illumina and Oxford Nanopore sequencing by determining genome completeness and accuracy, antimicrobial resistance (AMR), virulence potential, multilocus sequence typing (MLST), phylogeny, and pan genome. Ten bacterial species (10 strains) were tested for simulated reads of both mediocre- and low-quality, whereas 11 bacterial species (12 strains) were tested for real reads.

Results

Unicycler performed the best for achieving contiguous genomes, closely followed by MaSuRCA, while all SPAdes assemblies were incomplete. MaSuRCA was less tolerant of low-quality long reads than SPAdes and Unicycler. The hybrid assemblies of five antimicrobial-resistant strains with simulated reads provided consistent AMR genotypes with the reference genomes. The MaSuRCA assembly of Staphylococcus aureus with real reads contained msr(A) and tet(K), while the reference genome and SPAdes and Unicycler assemblies harbored blaZ. The AMR genotypes of the reference genomes and hybrid assemblies were consistent for the other five antimicrobial-resistant strains with real reads. The numbers of virulence genes in all hybrid assemblies were similar to those of the reference genomes, irrespective of simulated or real reads. Only one exception existed that the reference genome and hybrid assemblies of Pseudomonas aeruginosa with mediocre-quality long reads carried 241 virulence genes, whereas 184 virulence genes were identified in the hybrid assemblies of low-quality long reads. The MaSuRCA assemblies of Escherichia coli O157:H7 and Salmonella Typhimurium with mediocre-quality long reads contained 126 and 118 virulence genes, respectively, while 110 and 107 virulence genes were detected in their MaSuRCA assemblies of low-quality long reads, respectively. All approaches performed well in our MLST and phylogenetic analyses. The pan genomes of the hybrid assemblies of S. Typhimurium with mediocre-quality long reads were similar to that of the reference genome, while SPAdes and Unicycler were more tolerant of low-quality long reads than MaSuRCA for the pan-genome analysis. All approaches functioned well in the pan-genome analysis of Campylobacter jejuni with real reads.

Conclusions

Our research demonstrates the hybrid assembly pipeline of Unicycler as a superior approach for genomic analyses of bacterial pathogens using Illumina and Oxford Nanopore sequencing.

The Benefits of Whole Genome Sequencing for Foodborne Outbreak Investigation from the Perspective of a National Reference Laboratory in a Smaller Country

Gradually, conventional methods for foodborne pathogen typing are replaced by whole genome sequencing (WGS). Despite studies describing the overall benefits, National Reference Laboratories of smaller countries often show slower uptake of WGS, mainly because of significant investments required to generate and analyze data of a limited amount of samples. To facilitate this process and incite policy makers to support its implementation, a Shiga toxin-producing Escherichia coli (STEC) O157:H7 (stx1+, stx2+, eae+) outbreak (2012) and a STEC O157:H7 (stx2+, eae+) outbreak (2013) were retrospectively analyzed using WGS and compared with their conventional investigations. The corresponding results were obtained, with WGS delivering even more information, e.g., on virulence and antimicrobial resistance genotypes. Besides a universal, all-in-one workflow with less hands-on-time (five versus seven actual working days for WGS versus conventional), WGS-based cgMLST-typing demonstrated increased resolution. This enabled an accurate cluster definition, which remained unsolved for the 2013 outbreak, partly due to scarce epidemiological linking with the suspect source. Moreover, it allowed detecting two and one earlier circulating STEC O157:H7 (stx1+, stx2+, eae+) and STEC O157:H7 (stx2+, eae+) strains as closely related to the 2012 and 2013 outbreaks, respectively, which might have further directed epidemiological investigation initially. Although some bottlenecks concerning centralized data-sharing, sampling strategies, and perceived costs should be considered, we delivered a proof-of-concept that even in smaller countries, WGS offers benefits for outbreak investigation, if a sufficient budget is available to ensure its implementation in surveillance. Indeed, applying a database with background isolates is critical in interpreting isolate relationships to outbreaks, and leveraging the true benefit of WGS in outbreak investigation and/or prevention.

Targeted surveillance strategies for efficient detection of novel antibiotic resistance variants

Genotype-based diagnostics for antibiotic resistance represent a promising alternative to empiric therapy, reducing inappropriate antibiotic use. However, because such assays infer resistance based on known genetic markers, their utility will wane with the emergence of novel resistance. Maintenance of these diagnostics will therefore require surveillance to ensure early detection of novel resistance variants, but efficient strategies to do so remain undefined. We evaluate the efficiency of targeted sampling approaches informed by patient and pathogen characteristics in detecting antibiotic resistance and diagnostic escape variants in Neisseria gonorrhoeae, a pathogen associated with a high burden of disease and antibiotic resistance and the development of genotype-based diagnostics. We show that patient characteristic-informed sampling is not a reliable strategy for efficient variant detection. In contrast, sampling informed by pathogen characteristics, such as genomic diversity and genomic background, is significantly more efficient than random sampling in identifying genetic variants associated with resistance and diagnostic escape.

Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks

Foodborne diseases (FBDs) are infections of the gastrointestinal tract caused by foodborne pathogens (FBPs) such as bacteria [Salmonella, Listeria monocytogenes and Shiga toxin-producing E. coli (STEC)] and several viruses, but also parasites and some fungi. Artificial intelligence (AI) and its sub-discipline machine learning (ML) are re-emerging and gaining an ever increasing popularity in the scientific community and industry, and could lead to actionable knowledge in diverse ranges of sectors including epidemiological investigations of FBD outbreaks and antimicrobial resistance (AMR). As genotyping using whole-genome sequencing (WGS) is becoming more accessible and affordable, it is increasingly used as a routine tool for the detection of pathogens, and has the potential to differentiate between outbreak strains that are closely related, identify virulence/resistance genes and provide improved understanding of transmission events within hours to days. In most cases, the computational pipeline of WGS data analysis can be divided into four (though, not necessarily consecutive) major steps: de novo genome assembly, genome characterization, comparative genomics, and inference of phylogeny or phylogenomics. In each step, ML could be used to increase the speed and potentially the accuracy (provided increasing amounts of high-quality input data) of identification of the source of ongoing outbreaks, leading to more efficient treatment and prevention of additional cases. In this review, we explore whether ML or any other form of AI algorithms have already been proposed for the respective tasks and compare those with mechanistic model-based approaches.