GenomeGraphR: A user-friendly open-source web application for foodborne pathogen whole genome sequencing data integration, analysis, and visualization

Analysis of Antimicrobial Resistance in Bacterial Pathogens Recovered from Food and Human Sources: Insights from 639,087 Bacterial Whole-Genome Sequences in the NCBI Pathogen Detection Database

Understanding the role of foods in the emergence and spread of antimicrobial resistance necessitates the initial documentation of antibiotic resistance genes within bacterial species found in foods. Here, the NCBI Pathogen Detection database was used to query antimicrobial resistance gene prevalence in foodborne and human clinical bacterial isolates. Of the 1,843,630 sequence entries, 639,087 (34.7%) were assigned to foodborne or human clinical sources with 147,788 (23.14%) from food and 427,614 (76.88%) from humans. The majority of foodborne isolates were either Salmonella (47.88%), Campylobacter (23.03%), Escherichia (11.79%), or Listeria (11.3%), and the remaining 6% belonged to 20 other genera. Most foodborne isolates were from meat/poultry (95,251 or 64.45%), followed by multi-product mixed food sources (29,892 or 20.23%) and fish/seafood (6503 or 4.4%); however, the most prominent isolation source varied depending on the genus/species. Resistance gene carriage also varied depending on isolation source and genus/species. Of note, Klebsiella pneumoniae and Enterobacter spp. carried larger proportions of the quinolone resistance gene qnrS and some clinically relevant beta-lactam resistance genes in comparison to Salmonella and Escherichia coli. The prevalence of mec in S. aureus did not significantly differ between meat/poultry and multi-product sources relative to clinical sources, whereas this resistance was rare in isolates from dairy sources. The proportion of biocide resistance in Bacillus and Escherichia was significantly higher in clinical isolates compared to many foodborne sources but significantly lower in clinical Listeria compared to foodborne Listeria. This work exposes the gaps in current publicly available sequence data repositories, which are largely composed of clinical isolates and are biased towards specific highly abundant pathogenic species. We also highlight the importance of requiring and curating metadata on sequence submission to not only ensure correct information and data interpretation but also foster efficient analysis, sharing, and collaboration. To effectively monitor resistance carriage in food production, additional work on sequencing and characterizing AMR carriage in common commensal foodborne bacteria is critical.

Transforming Shiga toxin-producing Escherichia coli surveillance through whole genome sequencing in food safety practices

Introduction

Shiga toxin-producing Escherichia coli (STEC) is a gastrointestinal pathogen causing foodborne outbreaks. Whole Genome Sequencing (WGS) in STEC surveillance holds promise in outbreak prevention and confinement, in broadening STEC epidemiology and in contributing to risk assessment and source attribution. However, despite international recommendations, WGS is often restricted to assist outbreak investigation and is not yet fully implemented in food safety surveillance across all European countries, in contrast to for example in the United States.

Methods

In this study, WGS was retrospectively applied to isolates collected within the context of Belgian food safety surveillance and combined with data from clinical isolates to evaluate its benefits. A cross-sector WGS-based collection of 754 strains from 1998 to 2020 was analyzed.

Results

We confirmed that WGS in food safety surveillance allows accurate detection of genomic relationships between human cases and strains isolated from food samples, including those dispersed over time and geographical locations. Identifying these links can reveal new insights into outbreaks and direct epidemiological investigations to facilitate outbreak management. Complete WGS-based isolate characterization enabled expanding epidemiological insights related to circulating serotypes, virulence genes and antimicrobial resistance across different reservoirs. Moreover, associations between virulence genes and severe disease were determined by incorporating human metadata into the data analysis. Gaps in the surveillance system were identified and suggestions for optimization related to sample centralization, harmonizing isolation methods, and expanding sampling strategies were formulated.

Discussion

This study contributes to developing a representative WGS-based collection of circulating STEC strains and by illustrating its benefits, it aims to incite policymakers to support WGS uptake in food safety surveillance.

Human Listeriosis

Listeria monocytogenes is a Gram-positive facultative intracellular pathogen that can cause severe invasive infections upon ingestion with contaminated food. Clinically, listerial disease, or listeriosis, most often presents as bacteremia, meningitis or meningoencephalitis, and pregnancy-associated infections manifesting as miscarriage or neonatal sepsis. Invasive listeriosis is life-threatening and a main cause of foodborne illness leading to hospital admissions in Western countries. Sources of contamination can be identified through international surveillance systems for foodborne bacteria and strains' genetic data sharing. Large-scale whole genome studies have increased our knowledge on the diversity and evolution of L. monocytogenes, while recent pathophysiological investigations have improved our mechanistic understanding of listeriosis. In this article, we present an overview of human listeriosis with particular focus on relevant features of the causative bacterium, epidemiology, risk groups, pathogenesis, clinical manifestations, and treatment and prevention.

Salmonella enterica serovar Typhimurium from Wild Birds in the United States Represent Distinct Lineages Defined by Bird Type

Salmonella enterica serovar Typhimurium is typically considered a host generalist; however, certain isolates are associated with specific hosts and show genetic features of host adaptation. Here, we sequenced 131 S. Typhimurium isolates from wild birds collected in 30 U.S. states during 1978–2019. We found that isolates from broad taxonomic host groups including passerine birds, water birds (Aequornithes), and larids (gulls and terns) represented three distinct lineages and certain S. Typhimurium CRISPR types presented in individual lineages. We also showed that lineages formed by wild bird isolates differed from most isolates originating from domestic animal sources, and that genomes from these lineages substantially improved source attribution of Typhimurium genomes to wild birds by a machine learning classifier. Furthermore, virulence gene signatures that differentiated S. Typhimurium from passerines, water birds, and larids were detected. Passerine isolates tended to lack S. Typhimurium-specific virulence plasmids. Isolates from the passerine, water bird, and larid lineages had close genetic relatedness with human clinical isolates, including those from a 2021 U.S. outbreak linked to passerine birds. These observations indicate that S. Typhimurium from wild birds in the United States are likely host-adapted, and the representative genomic data set examined in this study can improve source prediction and facilitate outbreak investigation.

IMPORTANCE Within-host evolution of S. Typhimurium may lead to pathovars adapted to specific hosts. Here, we report the emergence of disparate avian S. Typhimurium lineages with distinct virulence gene signatures. The findings highlight the importance of wild birds as a reservoir for S. Typhimurium and contribute to our understanding of the genetic diversity of S. Typhimurium from wild birds. Our study indicates that S. Typhimurium may have undergone adaptive evolution within wild birds in the United States. The representative S. Typhimurium genomes from wild birds, together with the virulence gene signatures identified in these bird isolates, are valuable for S. Typhimurium source attribution and epidemiological surveillance.

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.

Accurate reconstruction of bacterial pan- and core genomes with PEPPAN

Bacterial genomes can contain traces of a complex evolutionary history, including extensive homologous recombination, gene loss, gene duplications, and horizontal gene transfer. To reconstruct the phylogenetic and population history of a set of multiple bacteria, it is necessary to examine their pangenome, the composite of all the genes in the set. Here we introduce PEPPAN, a novel pipeline that can reliably construct pangenomes from thousands of genetically diverse bacterial genomes that represent the diversity of an entire genus. PEPPAN outperforms existing pangenome methods by providing consistent gene and pseudogene annotations extended by similarity-based gene predictions, and identifying and excluding paralogs by combining tree- and synteny-based approaches. The PEPPAN package additionally includes PEPPAN_parser, which implements additional downstream analyses, including the calculation of trees based on accessory gene content or allelic differences between core genes. To test the accuracy of PEPPAN, we implemented SimPan, a novel pipeline for simulating the evolution of bacterial pangenomes. We compared the accuracy and speed of PEPPAN with four state-of-the-art pangenome pipelines using both empirical and simulated data sets. PEPPAN was more accurate and more specific than any of the other pipelines and was almost as fast as any of them. As a case study, we used PEPPAN to construct a pangenome of approximately 40,000 genes from 3052 representative genomes spanning at least 80 species of Streptococcus The resulting gene and allelic trees provide an unprecedented overview of the genomic diversity of the entire Streptococcus genus.

Optimizing open data to support one health: best practices to ensure interoperability of genomic data from bacterial pathogens

The holistic approach of One Health, which sees human, animal, plant, and environmental health as a unit, rather than discrete parts, requires not only interdisciplinary cooperation, but standardized methods for communicating and archiving data, enabling participants to easily share what they have learned and allow others to build upon their findings. Ongoing work by NCBI and the GenomeTrakr project illustrates how open data platforms can help meet the needs of federal and state regulators, public health laboratories, departments of agriculture, and universities. Here we describe how microbial pathogen surveillance can be transformed by having an open access database along with Best Practices for contributors to follow. First, we describe the open pathogen surveillance framework, hosted on the NCBI platform. We cover the current community standards for WGS quality, provide an SOP for assessing your own sequence quality and recommend QC thresholds for all submitters to follow. We then provide an overview of NCBI data submission along with step by step details. And finally, we provide curation guidance and an SOP for keeping your public data current within the database. These Best Practices can be models for other open data projects, thereby advancing the One Health goals of Findable, Accessible, Interoperable and Re-usable (FAIR) data.

Listeriosis outbreak likely due to contaminated liver pâté consumed in a tavern, Austria, December 2018

In late December 2018, an outbreak of listeriosis occurred after a group of 32 individuals celebrated in a tavern in Styria, Austria; traditional Austrian food (e.g. meat, meat products and cheese) was served. After the celebration, 11 individuals developed gastrointestinal symptoms, including one case with severe sepsis. Cases had consumed mixed platters with several meat products and pâtés originating from a local production facility (company X). Human, food and environmental samples taken from the tavern and company X were tested for L. monocytogenes. Whole genome sequence-based typing detected a novel L. monocytogenes strain of serotype IVb, sequence type 4 and CT7652 in 15 samples; 12 human, two food and one environmental sample from company X with an allelic difference of 0 to 1. Active case finding identified two further cases who had not visited the tavern but tested positive for the outbreak strain. In total, 13 cases (seven females and six males; age range: 4–84 years) were identified. Liver pâté produced by company X was identified as the likely source of the outbreak. Control measures were implemented and since the end of December 2018, no more cases were detected.

Genetic diversity and profiles of genes associated with virulence and stress resistance among isolates from the 2010-2013 interagency Listeria monocytogenes market basket survey

Whole genome sequencing (WGS) was performed on 201 Listeria monocytogenes isolates recovered from 102 of 27,389 refrigerated ready-to-eat (RTE) food samples purchased at retail in U.S. FoodNet sites as part of the 2010-2013 interagency L. monocytogenes Market Basket Survey (Lm MBS). Core genome multi-locus sequence typing (cgMLST) and in-silico analyses were conducted, and these data were analyzed with metadata for isolates from five food groups: produce, seafood, dairy, meat, and combination foods. Six of 201 isolates, from 3 samples, were subsequently confirmed as L. welshimeri. Three samples contained one isolate per sample; mmong the 96 samples that contained two isolates per sample, 3 samples each contained two different strains and 93 samples each contained duplicate isolates. After 93 duplicate isolates were removed, the remaining 102 isolates were delineated into 29 clonal complexes (CCs) or singletons based on their sequence type. The five most prevalent CCs were CC155, CC1, CC5, CC87, and CC321. The Shannon's diversity index for clones per food group ranged from 1.49 for dairy to 2.32 for produce isolates, which were not significantly different in pairwise comparisons. The most common molecular serogroup as determined by in-silico analysis was IIa (45.6%), followed by IIb (27.2%), IVb (20.4%), and IIc (4.9%). The proportions of isolates within lineages I, II, and III were 48.0%, 50.0% and 2.0%, respectively. Full-length inlA was present in 89.3% of isolates. Listeria pathogenicity island 3 (LIPI-3) and LIPI-4 were found in 51% and 30.6% of lineage I isolates, respectively. Stress survival islet 1 (SSI-1) was present in 34.7% of lineage I isolates, 80.4% of lineage II isolates and the 2 lineage III isolates; SSI-2 was present only in the CC121 isolate. Plasmids were found in 48% of isolates, including 24.5% of lineage I isolates and 72.5% of lineage II isolates. Among the plasmid-carrying isolates, 100% contained at least one cadmium resistance cassette and 89.8% contained bcrABC, involved in quaternary ammonium compound tolerance. Multiple clusters of isolates from different food samples were identified by cgMLST which, along with available metadata, could aid in the investigation of possible cross-contamination and persistence events.

The EnteroBase user's guide, with case studies on Salmonella transmissions, Yersinia pestis phylogeny, and Escherichia core genomic diversity

EnteroBase is an integrated software environment that supports the identification of global population structures within several bacterial genera that include pathogens. Here, we provide an overview of how EnteroBase works, what it can do, and its future prospects. EnteroBase has currently assembled more than 300,000 genomes from Illumina short reads from Salmonella, Escherichia, Yersinia, Clostridioides, Helicobacter, Vibrio, and Moraxella and genotyped those assemblies by core genome multilocus sequence typing (cgMLST). Hierarchical clustering of cgMLST sequence types allows mapping a new bacterial strain to predefined population structures at multiple levels of resolution within a few hours after uploading its short reads. Case Study 1 illustrates this process for local transmissions of Salmonella enterica serovar Agama between neighboring social groups of badgers and humans. EnteroBase also supports single nucleotide polymorphism (SNP) calls from both genomic assemblies and after extraction from metagenomic sequences, as illustrated by Case Study 2 which summarizes the microevolution of Yersinia pestis over the last 5000 years of pandemic plague. EnteroBase can also provide a global overview of the genomic diversity within an entire genus, as illustrated by Case Study 3, which presents a novel, global overview of the population structure of all of the species, subspecies, and clades within Escherichia.

A Simple and Robust Statistical Method to Define Genetic Relatedness of Samples Related to Outbreaks at the Genomic Scale - Application to Retrospective Salmonella Foodborne Outbreak Investigations

The investigation of foodborne outbreaks (FBOs) from genomic data typically relies on inspecting the relatedness of samples through a phylogenomic tree computed on either SNPs, genes, kmers, or alleles (i.e., cgMLST and wgMLST). The phylogenomic reconstruction is often time-consuming, computation-intensive and depends on hidden assumptions, pipelines implementation and their parameterization. In the context of FBO investigations, robust links between isolates are required in a timely manner to trigger appropriate management actions. Here, we propose a non-parametric statistical method to assert the relatedness of samples (i.e., outbreak cases) or whether to reject them (i.e., non-outbreak cases). With typical computation running within minutes on a desktop computer, we benchmarked the ability of three non-parametric statistical tests (i.e., Wilcoxon rank-sum, Kolmogorov–Smirnov and Kruskal–Wallis) on six different genomic features (i.e., SNPs, SNPs excluding recombination events, genes, kmers, cgMLST alleles, and wgMLST alleles) to discriminate outbreak cases (i.e., positive control: C+) from non-outbreak cases (i.e., negative control: C−). We leveraged four well-characterized and retrospectively investigated FBOs of Salmonella Typhimurium and its monophasic variant S. 1,4,[5],12:i:- from France, setting positive and negative controls in all the assays. We show that the approaches relying on pairwise SNP differences distinguished all four considered outbreaks in contrast to the other tested genomic features (i.e., genes, kmers, cgMLST alleles, and wgMLST alleles). The freely available non-parametric method written in R has been designed to be independent of both the phylogenomic reconstruction and the detection methods of genomic features (i.e., SNPs, genes, kmers, or alleles), making it widely and easily usable to anybody working on genomic data from suspected samples.

Whole Genome Sequencing Based Surveillance of L. monocytogenes for Early Detection and Investigations of Listeriosis Outbreaks

In Austria, all laboratories are legally obligated to forward human and food/environmental L. monocytogenes isolates to the National Reference Laboratory/Center (NRL) for Listeria. Two invasive human isolates of L. monocytogenes serotype 1/2a of the same pulsed-field gel electrophoresis (PFGE) pattern, previously unknown in Austria, were cultured for the first time in January 2016. Five further human isolates, obtained from patients with invasive listeriosis between April 2016 and September 2017, showed this PFGE pattern. In Austria the NRL started to use whole-genome sequencing (WGS) based typing in 2016, using a core genome MLST (cgMLST) scheme developed by Ruppitsch et al. 2015, which contains 1701 target genes. Sequence data are submitted to a publicly available nomenclature server (Ridom GmbH, Münster, Germany) for allocation of the core genome complex type (CT). The seven invasive human isolates differed from each other with zero to two alleles and were allocated to CT1234 (declared as outbreak strain). Among the Austrian strain collection of about 6,000 cgMLST-characterized non-human isolates (i.e., food/environmental isolates) 90 isolates shared CT1234. Out of these, 83 isolates were traced back to one meat processing-company. They differed from the outbreak strain by up to seven alleles; one isolate originated from the company's industrial slicer. The remaining seven CT1234-isolates were obtained from food products of four other companies (five fish-products, one ready-to-eat dumpling and one deer-meat) and differed from the outbreak strain by six to eleven alleles. The outbreak described shows the considerable potential of WGS to identify the source of a listeriosis outbreak. Compared to PFGE analysis, WGS-based typing has higher discriminatory power, yields better data accuracy, and allows higher laboratory through-put at lower cost. Utilization of WGS-based typing results of human and food/ environmental L. monocytogenes isolates by appropriate public health analysts and epidemiologists is indispensable to support a successful outbreak investigation.

Integrating Whole-Genome Sequencing Data Into Quantitative Risk Assessment of Foodborne Antimicrobial Resistance: A Review of Opportunities and Challenges

Whole-genome sequencing (WGS) will soon replace traditional phenotypic methods for routine testing of foodborne antimicrobial resistance (AMR). WGS is expected to improve AMR surveillance by providing a greater understanding of the transmission of resistant bacteria and AMR genes throughout the food chain, and therefore support risk assessment activities. At this stage, it is unclear how WGS data can be integrated into quantitative microbial risk assessment (QMRA) models and whether their integration will impact final risk estimates or the assessment of risk mitigation measures. This review explores opportunities and challenges of integrating WGS data into QMRA models that follow the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR. We describe how WGS offers an opportunity to enhance the next-generation of foodborne AMR QMRA modeling. Instead of considering all hazard strains as equally likely to cause disease, WGS data can improve hazard identification by focusing on those strains of highest public health relevance. WGS results can be used to stratify hazards into strains with similar genetic profiles that are expected to behave similarly, e.g., in terms of growth, survival, virulence or response to antimicrobial treatment. The QMRA input distributions can be tailored to each strain accordingly, making it possible to capture the variability in the strains of interest while decreasing the uncertainty in the model. WGS also allows for a more meaningful approach to explore genetic similarity among bacterial populations found at successive stages of the food chain, improving the estimation of the probability and magnitude of exposure to AMR hazards at point of consumption. WGS therefore has the potential to substantially improve the utility of foodborne AMR QMRA models. However, some degree of uncertainty remains in relation to the thresholds of genetic similarity to be used, as well as the degree of correlation between genotypic and phenotypic profiles. The latter could be improved using a functional approach based on prediction of microbial behavior from a combination of 'omics' techniques (e.g., transcriptomics, proteomics and metabolomics). We strongly recommend that methodologies to incorporate WGS data in risk assessment be included in any future revision of the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR.