SPREAD: Spatiotemporal Pathogen Relationships and Epidemiological Analysis Dashboard.

In the scope of public health, the rapid identification and control of infectious disease outbreaks are a paramount concern. Traditional surveillance methods often face challenges in effectively combining genetic, geographical, and temporal data, which is crucial for a comprehensive understanding of disease transmission dynamics. Addressing this critical need, the Spatiotemporal Phylogenomic Research and Epidemiological Analysis Dashboard (SPREAD) emerges as an innovative standalone web-based application. SPREAD integrates several modules for detailed genomic relationships, pinpointing genetically close pathogens, and spatial mapping, providing in-depth views of how diseases spread across populations and territories, with significant advantage to manage both bacteria and viruses based on allele and variant calling, respectively. Designed for broad accessibility, SPREAD operates seamlessly within web browsers, eliminating the need for sophisticated IT infrastructure and facilitating its use across various public health contexts. Its intuitive interface ensures that users can effortlessly navigate complex datasets, facilitating widespread access to advanced surveillance capabilities. Through its initial deployments, SPREAD has proven instrumental in quickly identifying transmission clusters, significantly aiding in the formulation of prompt and targeted public health responses. Through the integration of state-of-the-art technology with a focus on user-centered design, SPREAD offers a promising solution that highlights the potential of digital health innovations.

Harmonization of supervised machine learning practices for efficient source attribution of Listeria monocytogenes based on genomic data.

BACKGROUND: Genomic data-based machine learning tools are promising for real-time surveillance activities performing source attribution of foodborne bacteria such as Listeria monocytogenes. Given the heterogeneity of machine learning practices, our aim was to identify those influencing the source prediction performance of the usual holdout method combined with the repeated k-fold cross-validation method. METHODS: A large collection of 1 100 L. monocytogenes genomes with known sources was built according to several genomic metrics to ensure authenticity and completeness of genomic profiles. Based on these genomic profiles (i.e. 7-locus alleles, core alleles, accessory genes, core SNPs and pan kmers), we developed a versatile workflow assessing prediction performance of different combinations of training dataset splitting (i.e. 50, 60, 70, 80 and 90%), data preprocessing (i.e. with or without near-zero variance removal), and learning models (i.e. BLR, ERT, RF, SGB, SVM and XGB). The performance metrics included accuracy, Cohen's kappa, F1-score, area under the curves from receiver operating characteristic curve, precision recall curve or precision recall gain curve, and execution time. RESULTS: The testing average accuracies from accessory genes and pan kmers were significantly higher than accuracies from core alleles or SNPs. While the accuracies from 70 and 80% of training dataset splitting were not significantly different, those from 80% were significantly higher than the other tested proportions. The near-zero variance removal did not allow to produce results for 7-locus alleles, did not impact significantly the accuracy for core alleles, accessory genes and pan kmers, and decreased significantly accuracy for core SNPs. The SVM and XGB models did not present significant differences in accuracy between each other and reached significantly higher accuracies than BLR, SGB, ERT and RF, in this order of magnitude. However, the SVM model required more computing power than the XGB model, especially for high amount of descriptors such like core SNPs and pan kmers. CONCLUSIONS: In addition to recommendations about machine learning practices for L. monocytogenes source attribution based on genomic data, the present study also provides a freely available workflow to solve other balanced or unbalanced multiclass phenotypes from binary and categorical genomic profiles of other microorganisms without source code modifications.

Tell me if you prefer bovine or poultry sectors and I'll tell you who you are: Characterization of Salmonella enterica subsp. enterica serovar Mbandaka in France.

Introduction: In north-western France, Salmonella enterica susp. enterica serovar Mbandaka (S. Mbandaka) is most frequently isolated from bovine and dairy samples. While this serovar most often results in asymptomatic carriage, for a number of years it has caused episodes of abortions, which have serious economic consequences for the sector. Interestingly, this serovar is also isolated from Gallus gallus in the same geographic zone. Despite its prevalence in bovines in north-western France, S. Mbandaka has not been broadly studied at the genomic level, and its prevalence and host adaptation are still not fully understood. Methods: In this study, we analyzed the genomic diversity of 304 strains of S. Mbandaka isolated from the bovine and poultry sectors in this area over a period of 5 years. A phylogenetic analysis was carried out and two approaches were followed to identify conserved genes and mutations related to host associations. The first approach targeted the genes compiled in the MEGARESv2, Resfinder, VFDB and SPI databases. Plasmid and phage contents were also investigated. The second approach refers to an in-house algorithm developed for this study that computes sensitivity, specificity, and accuracy of accessory genes and core variants according to predefined genomes groups. Results and discussion: All the analyzed strains belong to the multi-locus sequence type profile ST413, and the phylogenomic analysis revealed main clustering by host (bovine and poultry), emphasizing the circulation of 12 different major clones, of which seven circulate in poultry and five in the bovine sector in France and a likely food production chain adaptation of these clones. All strains present resistance determinants including heavy metals and biocides that could explain the ability of this serovar to survive and persist in the environment, within herds, and in food processing plants. To explore the wild animal contribution to the spread of this serovar in north-western France, we retrieved S. Mbandaka genomes isolated from wild birds from EnteroBase and included them in the phylogenomic analysis together with our collection. Lastly, screening of accessory genes and major variants allowed us to identify conserved specific mutations characteristic of each major cluster. These mutations could be used to design useful probes for food safety surveillance.

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.

Genomic elements located in the accessory repertoire drive the adaptation to biocides in Listeria monocytogenes strains from different ecological niches.

In response to the massive use of biocides for controlling Listeria monocytogenes (hereafter Lm) contaminations along the food chain, strains showing biocide tolerance emerged. Here, accessory genomic elements were associated with biocide tolerance through pangenome-wide associations performed on 197 Lm strains from different lineages, ecological, geographical and temporal origins. Mobile elements, including prophage-related loci, the Tn6188_qacH transposon and pLMST6_emrC plasmid, were widespread across lineage I and II food strains and associated with tolerance to benzalkonium-chloride (BC), a quaternary ammonium compound (QAC) widely used in food processing. The pLMST6_emrC was also associated with tolerance to another QAC, the didecyldimethylammonium-chloride, displaying a pleiotropic effect. While no associations were detected for chemically reactive biocides (alcohols and chlorines), genes encoding for cell-surface proteins were associated with BC or polymeric biguanide tolerance. The latter was restricted to lineage I strains from animal and the environment. In conclusion, different genetic markers, with polygenic nature or not, appear to have driven the Lm adaptation to biocide, especially in food strains but also from animal and the environment. These markers could aid to monitor and predict the spread of biocide tolerant Lm genotypes across different ecological niches, finally reducing the risk of such strains in food industrial settings.

A European-wide dataset to uncover adaptive traits of Listeria monocytogenes to diverse ecological niches.

Listeria monocytogenes (Lm) is a ubiquitous bacterium that causes listeriosis, a serious foodborne illness. In the nature-to-human transmission route, Lm can prosper in various ecological niches. Soil and decaying organic matter are its primary reservoirs. Certain clonal complexes (CCs) are over-represented in food production and represent a challenge to food safety. To gain new understanding of Lm adaptation mechanisms in food, the genetic background of strains found in animals and environment should be investigated in comparison to that of food strains. Twenty-one partners, including food, environment, veterinary and public health laboratories, constructed a dataset of 1484 genomes originating from Lm strains collected in 19 European countries. This dataset encompasses a large number of CCs occurring worldwide, covers many diverse habitats and is balanced between ecological compartments and geographic regions. The dataset presented here will contribute to improve our understanding of Lm ecology and should aid in the surveillance of Lm. This dataset provides a basis for the discovery of the genetic traits underlying Lm adaptation to different ecological niches.

In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes.

BACKGROUND: Whole genome sequencing analyzed by core genome multi-locus sequence typing (cgMLST) is widely used in surveillance of the pathogenic bacteria Listeria monocytogenes. Given the heterogeneity of available bioinformatics tools to define cgMLST alleles, our aim was to identify parameters influencing the precision of cgMLST profiles. METHODS: We used three L. monocytogenes reference genomes from different phylogenetic lineages and assessed the impact of in vitro (i.e. tested genomes, successive platings, replicates of DNA extraction and sequencing) and in silico parameters (i.e. targeted depth of coverage, depth of coverage, breadth of coverage, assembly metrics, cgMLST workflows, cgMLST completeness) on cgMLST precision made of 1748 core loci. Six cgMLST workflows were tested, comprising assembly-based (BIGSdb, INNUENDO, GENPAT, SeqSphere and BioNumerics) and assembly-free (i.e. kmer-based MentaLiST) allele callers. Principal component analyses and generalized linear models were used to identify the most impactful parameters on cgMLST precision. RESULTS: The isolate's genetic background, cgMLST workflows, cgMLST completeness, as well as depth and breadth of coverage were the parameters that impacted most on cgMLST precision (i.e. identical alleles against reference circular genomes). All workflows performed well at ≥40X of depth of coverage, with high loci detection (> 99.54% for all, except for BioNumerics with 97.78%) and showed consistent cluster definitions using the reference cut-off of ≤7 allele differences. CONCLUSIONS: This highlights that bioinformatics workflows dedicated to cgMLST allele calling are largely robust when paired-end reads are of high quality and when the sequencing depth is ≥40X.

SARS-CoV-2 surveillance in Italy through phylogenomic inferences based on Hamming distances derived from pan-SNPs, -MNPs and -InDels.

BACKGROUND: Faced with the ongoing global pandemic of coronavirus disease, the 'National Reference Centre for Whole Genome Sequencing of microbial pathogens: database and bioinformatic analysis' (GENPAT) formally established at the 'Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise' (IZSAM) in Teramo (Italy) is in charge of the SARS-CoV-2 surveillance at the genomic scale. In a context of SARS-CoV-2 surveillance requiring correct and fast assessment of epidemiological clusters from substantial amount of samples, the present study proposes an analytical workflow for identifying accurately the PANGO lineages of SARS-CoV-2 samples and building of discriminant minimum spanning trees (MST) bypassing the usual time consuming phylogenomic inferences based on multiple sequence alignment (MSA) and substitution model. RESULTS: GENPAT constituted two collections of SARS-CoV-2 samples. The first collection consisted of SARS-CoV-2 positive swabs collected by IZSAM from the Abruzzo region (Italy), then sequenced by next generation sequencing (NGS) and analyzed in GENPAT (n = 1592), while the second collection included samples from several Italian provinces and retrieved from the reference Global Initiative on Sharing All Influenza Data (GISAID) (n = 17,201). The main results of the present work showed that (i) GENPAT and GISAID detected the same PANGO lineages, (ii) the PANGO lineages B.1.177 (i.e. historical in Italy) and B.1.1.7 (i.e. 'UK variant') are major concerns today in several Italian provinces, and the new MST-based method (iii) clusters most of the PANGO lineages together, (iv) with a higher dicriminatory power than PANGO lineages, (v) and faster that the usual phylogenomic methods based on MSA and substitution model. CONCLUSIONS: The genome sequencing efforts of Italian provinces, combined with a structured national system of NGS data management, provided support for surveillance SARS-CoV-2 in Italy. We propose to build phylogenomic trees of SARS-CoV-2 variants through an accurate, discriminant and fast MST-based method avoiding the typical time consuming steps related to MSA and substitution model-based phylogenomic inference.

Comparative phenotypic, genotypic and genomic analyses of Bacillus thuringiensis associated with foodborne outbreaks in France.

Bacillus thuringiensis (Bt) belongs to the Bacillus cereus (Bc) group, well known as an etiological agent of foodborne outbreaks (FBOs). Bt distinguishes itself from other Bc by its ability to synthesize insecticidal crystals. However, the search for these crystals is not routinely performed in food safety or clinical investigation, and the actual involvement of Bt in the occurrence of FBOs is not known. In the present study, we reveal that Bt was detected in the context of 49 FBOs declared in France between 2007 and 2017. In 19 of these FBOs, Bt was the only microorganism detected, making it the most likely causal agent. Searching for its putative origin of contamination, we noticed that more than 50% of Bt isolates were collected from dishes containing raw vegetables, in particular tomatoes (48%). Moreover, the genomic characterization of isolates showed that most FBO-associated Bt isolates exhibited a quantified genomic proximity to Bt strains, used as biopesticides, especially those from subspecies aizawai and kurstaki. Taken together, these results strengthen the hypothesis of an agricultural origin for the Bt contamination and call for further investigations on Bt pesticides.

Prediction of Salmonella serovars isolated from clinical and food matrices in Lebanon and genomic-based investigation focusing on Enteritidis serovar.

Salmonella enterica subsp. enterica serovars are considered major causes of food poisoning and we performed this study because Salmonella is a burden in Lebanon. The present study investigated the ability of genomic information to predict serovar using a collection of Salmonella isolates from infected humans (n = 24) and contaminated food (n = 63) in Lebanon. Further, the phylogenomic relationships of the serovar the predominated in Lebanon (i.e., S. Enteritidis; n = 25) were investigated in comparison with isolates from other countries (n = 130) based on coregenome single nucleotide polymorphisms (SNPs). Genetic elements, specifically Salmonella pathogenicity islands (SPIs), plasmid replicons, and antibiotic-resistance genes were screened in S. Enteritidis genomes (n = 155). Our results revealed that the Salmonella serovars identification by seroagglutination from the samples isolated in Lebanon (n = 87) was highly correlated with the genomic-based prediction of serovars (80.4-85.0% with SeqSero1 and 93.1-94.2% with SeqSero2). The Salmonella serovars isolated from human and food samples in Lebanon were mainly Enteritidis (28.7%) and Infantis (26%). To a rare extent, other serovars included Amager, Anatum, Bredeney, Chincol, Heidelberg, Hofit, Kentucky, Montevideo, Muenster, Newport, Schwarzengrund, Senftenberg and Typhimurium. In comparison with other countries, S. Enteritidis samples isolated in Lebanon (56 ± 27 intra-group pairwise SNP differences) presented a strong phylogenomic relativeness at the coregenome level with samples, as for example with samples isolated from Syria (65 ± 31 inter-group pairwise SNP differences). Most of the studied S. Enteritidis genomes encoded 10 SPIs involved in survival in immune cells (i.e. SPIs 1, 2, 3, 4, 5, 12, 13, 14, 16 and 17). The plasmid replicons IncFIB (S)_1 and IncFII (S)_1 encoding elements involved in virulence were identified in the majority of the S. Enteritidis genomes (94% and 96%, respectively), the majority exhibiting aminoglycosides (gene aac(6')-Iaa_1). The IncI_1_Alpha replicon responsible for ampicillin-resistance was only detected in 2 of 25 S. Enteritidis Lebanese strains. Genomic-based risk assessment of Salmonella serovars in Lebanon showed that food imported from Syria might be an origin of the S. Enteritidis human cases in Lebanon. The detection of several SPIs involved in the survival, plasmid replicons involved in virulence, and aminoglycoside-resistance genes, emphasizes that S. Enteritidis is of paramount importance for public health in Lebanon and other countries.

Analysis of COMPASS, a New Comprehensive Plasmid Database Revealed Prevalence of Multireplicon and Extensive Diversity of IncF Plasmids.

Plasmids are genetic elements that enable rapid adaptation and evolution by transferring genes conferring selective advantages to their hosts. Conjugative plasmids are predominantly responsible for the global dissemination of antimicrobial resistance, representing an important threat to global health. As the number of plasmid sequences grows exponentially, it becomes critical to depict the global diversity and decipher the distribution of circulating plasmids in the bacterial community. To this end, we created COMPASS, a novel and comprehensive database compiling 12,084 complete plasmids with associated metadata from 1571 distinct species isolated worldwide over more than 100 years. The curation of the database allowed us to identify identical plasmids across different bacteria revealing mainly intraspecies dissemination and rare cases of horizontal transmission. We outlined and analyzed all relevant features, plasmid properties, host range and characterized their replication and mobilization systems. After an exhaustive comparison of PlasmidFinder and MOB-typer, the MOB-typer-based analysis revealed that the current knowledge embedded in the current typing schemes fails to classify all the plasmid sequences collected in COMPASS. We were able to categorize 6828 and 5229 plasmids by replicon and MOB typing, respectively, mostly associated with Proteobacteria and Firmicutes. We then searched for the presence of multiple core genes involved in replication and propagation. Our results showed that 2403 plasmids carried multiple replicons that were distributed in 206 bacterial species. The co-integration of replicon types from different incompatibility (Inc) groups is an adaptive mechanism, which plays an important role in plasmid survival and dissemination by extending their host range. Our results highlight the crucial role of IncF alleles (present in 56% of all multireplicons) and revealed that IncH, IncR, and IncU replicons were also frequently carried in multireplicons. Here, we provided a comprehensive picture of the different IncF subtypes by identifying 20 different profiles in 849 IncF multireplicons, which were mostly associated with Enterobacteriaceae. These results could provide the basis for a novel IncF plasmid nomenclature based on different allelic profiles.

Dynamics of mobile genetic elements of Listeria monocytogenes persisting in ready-to-eat seafood processing plants in France.

BACKGROUND: Listeria monocytogenes Clonal Complexes (CCs) have been epidemiologically associated with foods, especially ready-to-eat (RTE) products for which the most likely source of contamination depends on the occurrence of persisting clones in food-processing environments (FPEs). As the ability of L. monocytogenes to adapt to environmental stressors met in the food chain challenges the efforts to its eradication from FPEs, the threat of persistent strains to the food industry and public health authorities continues to rise. In this study, 94 food and FPEs L. monocytogenes isolates, representing persistent subtypes contaminating three French seafood facilities over 2-6 years, were whole-genome sequenced to characterize their genetic diversity and determine the biomarkers associated with long-term survival in FPEs. RESULTS: Food and FPEs isolates belonged to five CCs, comprising long-term intra- and inter-plant persisting clones. Mobile genetic elements (MGEs) such as plasmids, prophages and transposons were highly conserved within CCs, some of which harboured genes for resistance to chemical compounds and biocides used in the processing plants. Some of these genes were found in a 90.8 kbp plasmid, predicted to be" mobilizable", identical in isolates from CC204 and CC155, and highly similar to an 81.6 kbp plasmid from isolates belonging to CC7. These similarities suggest horizontal transfer between isolates, accompanied by deletion and homologous recombination in isolates from CC7. Prophage profiles characterized persistent clonal strains and several prophage-loci were plant-associated. Notably, a persistent clone from CC101 harboured a novel 31.5 kbp genomic island that we named Listeria genomic island 3 (LGI3), composed by plant-associated loci and chromosomally integrating cadmium-resistance determinants cadA1C. CONCLUSIONS: Genome-wide analysis indicated that inter- and intra-plant persisting clones harbour conserved MGEs, likely acquired in FPEs and maintained by selective pressures. The presence of closely related plasmids in L. monocytogenes CCs supports the hypothesis of horizontal gene transfer conferring enhanced survival to FPE-associated stressors, especially in hard-to-clean harbourage sites. Investigating the MGEs evolutionary and transmission dynamics provides additional resolution to trace-back potentially persistent clones. The biomarkers herein discovered provide new tools for better designing effective strategies for the removal or reduction of resident L. monocytogenes in FPEs to prevent contamination of RTE seafood.

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.

Genetic and metabolic signatures of Salmonella enterica subsp. enterica associated with animal sources at the pangenomic scale.

BACKGROUND: Salmonella enterica subsp. enterica is a public health issue related to food safety, and its adaptation to animal sources remains poorly described at the pangenome scale. Firstly, serovars presenting potential mono- and multi-animal sources were selected from a curated and synthetized subset of Enterobase. The corresponding sequencing reads were downloaded from the European Nucleotide Archive (ENA) providing a balanced dataset of 440 Salmonella genomes in terms of serovars and sources (i). Secondly, the coregenome variants and accessory genes were detected (ii). Thirdly, single nucleotide polymorphisms and small insertions/deletions from the coregenome, as well as the accessory genes were associated to animal sources based on a microbial Genome Wide Association Study (GWAS) integrating an advanced correction of the population structure (iii). Lastly, a Gene Ontology Enrichment Analysis (GOEA) was applied to emphasize metabolic pathways mainly impacted by the pangenomic mutations associated to animal sources (iv). RESULTS: Based on a genome dataset including Salmonella serovars from mono- and multi-animal sources (i), 19,130 accessory genes and 178,351 coregenome variants were identified (ii). Among these pangenomic mutations, 52 genomic signatures (iii) and 9 over-enriched metabolic signatures (iv) were associated to avian, bovine, swine and fish sources by GWAS and GOEA, respectively. CONCLUSIONS: Our results suggest that the genetic and metabolic determinants of Salmonella adaptation to animal sources may have been driven by the natural feeding environment of the animal, distinct livestock diets modified by human, environmental stimuli, physiological properties of the animal itself, and work habits for health protection of livestock.

Large-Scale Genomic Analyses and Toxinotyping of Clostridium perfringens Implicated in Foodborne Outbreaks in France.

Clostridium perfringens is both an ubiquitous environmental bacterium and the fourth most common causative agent of foodborne outbreaks (FBOs) in France and Europe. These outbreaks are known to be caused by C. perfringens enterotoxin (CPE) encoded by the cpe gene. However, additional information on the toxin/virulence gene content of C. perfringens has become available in the last few years. Therefore, to understand the enteropathogenicity of this bacterium, we need to describe the toxin and virulence genes content of strains involved in FBOs. In this study, we used a new real-time PCR typing technique based on a comprehensive set of 17 genes encoding virulence factors. The analysis was performed on a collection of 141 strains involved in 42 FBOs in the Paris region. It was combined with whole genome sequence (WGS) phylogenomic reconstruction, based on the coregenome single nucleotide polymorphisms (SNPs) of 58 isolates, representatives of the identified virulence gene profiles. Two or three different virulence gene profiles were detected in 10 FBOs, demonstrating that C. perfringens FBOs may be associated with heterogeneous strains. cpe-positive strains were isolated in 23 outbreaks, confirming the prominent role of CPE in pathogenicity. However, while C. perfringens was the sole pathogen isolated from the incriminated food, the cpe gene was not detected in strains related to 13 outbreaks. This result indicates either that the standard method was not able to isolate cpe+ strains or that the cpe gene may not be the only determinant of the enterotoxigenic potential of C. perfringens strains. Using phylogenomic reconstruction, we identified two clades distinguishing chromosomal cpe-positive from cpe-negative and plasmid-borne cpe. Important epidemiological information was also garnered from this phylogenomic reconstruction that revealed unexpected links between different outbreaks associated with closely related strains (seven SNP differences) and having common virulence gene profiles. This study provides new insight into the characterization of foodborne C. perfringens and highlights the potential of WGS for the investigation of FBOs.

Genetic Diversity of Salmonella Derby from the Poultry Sector in Europe.

Salmonella Derby (S. Derby) is emerging in Europe as a predominant serovar in fattening turkey flocks. This serovar was recorded as being predominant in the turkey sector in 2014 in the United Kingdom (UK). Only two years later, in 2016, it was also recorded in the turkey and broiler sectors in Ireland and Spain. These S. Derby isolates were characterised as members of the multilocus sequence type (MLST) profile 71 (ST71). For the first time, we characterise by whole genome sequencing (WGS) analysis a panel of 90 S. Derby ST71 genomes to understand the routes of transmission of this emerging pathogen within the poultry/turkey food trade. Selected panel included strains isolated as early as 2010 in five leading European g countries for turkey meat production. Twenty-one of the 90 genomes were extracted from a public database-Enterobase. Five of these originated from the United States (n=3), China (n=1) and Taiwan (n=1) isolated between 1986 and 2016. A phylogenomic analysis at the core-genome level revealed the presence of three groups. The largest group contained 97.5% of the European strains and included both, turkey and human isolates that were genetically related by an average of 35 ± 15 single nucleotide polymorphism substitutions (SNPs). To illustrate the diversity, the presence of antimicrobial resistance genes and phages were characteised in 30, S. Derby ST71 genomes, including 11 belonging to this study This study revealed an emergent turkey-related S. Derby ST71 clone circulating in at least five European countries (the UK, Germany, Poland, Italy, and France) since 2010 that causes human gastroenteritis. A matter of concern is the identification of a gyrA mutation involved in resistance to quinolone, present in the Italian genomes. Interestingly, the diversity of phages seems to be related to the geographic origins. These results constitute a baseline for following the spread of this emerging pathogen and identifying appropriate monitoring and prevention measures.

Correction for Sévellec et al., "Complete Genome Sequence of Salmonella enterica subsp. enterica Serotype Derby, Associated with the Pork Sector in France".

[This corrects the article DOI: 10.1128/MRA.01027-18.].

Insights from genome-wide approaches to identify variants associated to phenotypes at pan-genome scale: Application to L. monocytogenes' ability to grow in cold conditions.

Intraspecific variability of the behavior of most foodborne pathogens is well described and taken into account in Quantitative Microbial Risk Assessment (QMRA), but factors (strain origin, serotype, …) explaining these differences are scarce or contradictory between studies. Nowadays, Whole Genome Sequencing (WGS) offers new opportunities to explain intraspecific variability of food pathogens, based on various recently published bioinformatics tools. The objective of this study is to get a better insight into different existing bioinformatics approaches to associate bacterial phenotype(s) and genotype(s). Therefore, a dataset of 51 L. monocytogenes strains, isolated from multiple sources (i.e. different food matrices and environments) and belonging to 17 clonal complexes (CC), were selected to represent large population diversity. Furthermore, the phenotypic variability of growth at low temperature was determined (i.e. qualitative phenotype), and the whole genomes of selected strains were sequenced. The almost exhaustive gene content, as well as the core genome SNPs based phylogenetic reconstruction, were derived from the whole sequenced genomes. A Bayesian inference method was applied to identify the branches on which the phenotype distribution evolves within sub-lineages. Two different Genome Wide Association Studies (i.e. gene- and SNP-based GWAS) were independently performed in order to link genetic mutations to the phenotype of interest. The genomic analyses presented in this study were successfully applied on the selected dataset. The Bayesian phylogenetic approach emphasized an association with "slow" growth ability at 2 °C of the lineage I, as well as CC9 of the lineage II. Moreover, both gene- and SNP-GWAS approaches displayed significant statistical associations with the tested phenotype. A list of 114 significantly associated genes, including genes already known to be involved in the cold adaption mechanism of L. monocytogenes and genes associated to mobile genetic elements (MGE), resulted from the gene-GWAS. On the other hand, a group of 184 highly associated SNPs were highlighted by SNP-GWAS, including SNPs detected in genes which were already likely involved in cold adaption; hypothetical proteins; and intergenic regions where for example promotors and regulators can be located. The successful application of combined bioinformatics approaches associating WGS-genotypes and specific phenotypes, could contribute to improve prediction of microbial behaviors in food. The implementation of this information in hazard identification and exposure assessment processes will open new possibilities to feed QMRA-models.

Complete Genome Sequence of Salmonella enterica subsp. enterica Serotype Derby, Associated with the Pork Sector in France.

In the European Union, Salmonella enterica subsp. enterica serovar Derby is the most abundant serotype isolated from pork. Recent studies have shown that this serotype is polyphyletic. However, one main genomic lineage, characterized by sequence type 40 (ST40), the presence of the Salmonella pathogenicity island 23, and showing resistance to streptomycin, sulphonamides, and tetracycline (STR-SSS-TET), is pork associated. Here, we describe the complete genome sequence of a strain from this lineage isolated in France.

An Assessment of Different Genomic Approaches for Inferring Phylogeny of Listeria monocytogenes.

Background/objectives: Whole genome sequencing (WGS) has proven to be a powerful subtyping tool for foodborne pathogenic bacteria like L. monocytogenes. The interests of genome-scale analysis for national surveillance, outbreak detection or source tracking has been largely documented. The genomic data however can be exploited with many different bioinformatics methods like single nucleotide polymorphism (SNP), core-genome multi locus sequence typing (cgMLST), whole-genome multi locus sequence typing (wgMLST) or multi locus predicted protein sequence typing (MLPPST) on either core-genome (cgMLPPST) or pan-genome (wgMLPPST). Currently, there are little comparisons studies of these different analytical approaches. Our objective was to assess and compare different genomic methods that can be implemented in order to cluster isolates of L. monocytogenes. Methods: The clustering methods were evaluated on a collection of 207 L. monocytogenes genomes of food origin representative of the genetic diversity of the Anses collection. The trees were then compared using robust statistical analyses. Results: The backward comparability between conventional typing methods and genomic methods revealed a near-perfect concordance. The importance of selecting a proper reference when calling SNPs was highlighted, although distances between strains remained identical. The analysis also revealed that the topology of the phylogenetic trees between wgMLST and cgMLST were remarkably similar. The comparison between SNP and cgMLST or SNP and wgMLST approaches showed that the topologies of phylogenic trees were statistically similar with an almost equivalent clustering. Conclusion: Our study revealed high concordance between wgMLST, cgMLST, and SNP approaches which are all suitable for typing of L. monocytogenes. The comparable clustering is an important observation considering that the two approaches have been variously implemented among reference laboratories.