Phylogeographic Analysis Reveals Multiple International transmission Events Have Driven the Global Emergence of Escherichia coli O157:H7.

BACKGROUND: Shiga toxin-producing Escherchia coli (STEC) O157:H7 is a zoonotic pathogen that causes numerous food and waterborne disease outbreaks. It is globally distributed, but its origin and the temporal sequence of its geographical spread are unknown. METHODS: We analyzed whole-genome sequencing data of 757 isolates from 4 continents, and performed a pan-genome analysis to identify the core genome and, from this, extracted single-nucleotide polymorphisms. A timed phylogeographic analysis was performed on a subset of the isolates to investigate its worldwide spread. RESULTS: The common ancestor of this set of isolates occurred around 1890 (1845-1925) and originated from the Netherlands. Phylogeographic analysis identified 34 major transmission events. The earliest were predominantly intercontinental, moving from Europe to Australia around 1937 (1909-1958), to the United States in 1941 (1921-1962), to Canada in 1960 (1943-1979), and from Australia to New Zealand in 1966 (1943-1982). This pre-dates the first reported human case of E. coli O157:H7, which was in 1975 from the United States. CONCLUSIONS: Inter- and intra-continental transmission events have resulted in the current international distribution of E. coli O157:H7, and it is likely that these events were facilitated by animal movements (eg, Holstein Friesian cattle). These findings will inform policy on action that is crucial to reduce the further spread of E. coli O157:H7 and other (emerging) STEC strains globally.

Phylotyper: in silico predictor of gene subtypes.

SUMMARY: Whole genome sequencing (WGS) is being adopted in public health for improved surveillance and outbreak analysis. In public health, subtyping has been used to infer phenotypes and distinguish bacterial strain groups. In silico tools that predict subtypes from sequences data are needed to transition historical data to WGS-based protocols. Phylotyper is a novel solution for in silico subtype prediction from gene sequences. Designed for incorporation into WGS pipelines, it is a general prediction tool that can be applied to different subtype schemes. Phylotyper uses phylogeny to model the evolution of the subtype and infer subtypes for unannotated sequences. The phylogenic framework in Phylotyper improves accuracy over approaches based solely on sequence similarity and provides useful contextual feedback. AVAILABILITY AND IMPLEMENTATION: Phylotyper is a python and R package. It is available from: https://github.com/superphy/insilico-subtyping. CONTACT: matthew.whiteside@phac-aspc.gc.ca. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SuperPhy: predictive genomics for the bacterial pathogen Escherichia coli.

BACKGROUND: Predictive genomics is the translation of raw genome sequence data into a phenotypic assessment of the organism. For bacterial pathogens, these phenotypes can range from environmental survivability, to the severity of human disease. Significant progress has been made in the development of generic tools for genomic analyses that are broadly applicable to all microorganisms; however, a fundamental missing component is the ability to analyze genomic data in the context of organism-specific phenotypic knowledge, which has been accumulated from decades of research and can provide a meaningful interpretation of genome sequence data. RESULTS: In this study, we present SuperPhy, an online predictive genomics platform ( http://lfz.corefacility.ca/superphy/ ) for Escherichia coli. The platform integrates the analytical tools and genome sequence data for all publicly available E. coli genomes and facilitates the upload of new genome sequences from users under public or private settings. SuperPhy provides real-time analyses of thousands of genome sequences with results that are understandable and useful to a wide community, including those in the fields of clinical medicine, epidemiology, ecology, and evolution. SuperPhy includes identification of: 1) virulence and antimicrobial resistance determinants 2) statistical associations between genotypes, biomarkers, geospatial distribution, host, source, and phylogenetic clade; 3) the identification of biomarkers for groups of genomes on the based presence/absence of specific genomic regions and single-nucleotide polymorphisms and 4) in silico Shiga-toxin subtype. CONCLUSIONS: SuperPhy is a predictive genomics platform that attempts to provide an essential link between the vast amounts of genome information currently being generated and phenotypic knowledge in an organism-specific context.

Identification of a novel prophage regulator in Escherichia coli controlling the expression of type III secretion.

This study has identified horizontally acquired genomic regions of enterohaemorrhagic Escherichia coli O157:H7 that regulate expression of the type III secretion (T3S) system encoded by the locus of enterocyte effacement (LEE). Deletion of O-island 51, a 14.93 kb cryptic prophage (CP-933C), resulted in a reduction in LEE expression and T3S. The deletion also had a reduced capacity to attach to epithelial cells and significantly reduced E. coli O157 excretion levels from sheep. Further characterization of O-island 51 identified a novel positive regulator of the LEE, encoded by ecs1581 in the E. coli O157:H7 strain Sakai genome and present but not annotated in the E. coli strain EDL933 sequence. Functionally important residues of ECs1581 were identified based on phenotypic variants present in sequenced E. coli strains and the regulator was termed RgdR based on a motif demonstrated to be important for stimulation of gene expression. While RgdR activated expression from the LEE1 promoter in the presence or absence of the LEE-encoded regulator (Ler), RgdR stimulation of T3S required ler and Ler autoregulation. RgdR also controlled the expression of other phenotypes, including motility, indicating that this new family of regulators may have a more global role in E. coli gene expression.

Commensal Escherichia coli Strains of Bovine Origin Competitively Mitigated Escherichia coli O157:H7 in a Gnotobiotic Murine Intestinal Colonization Model with or without Physiological Stress.

Cattle are a primary reservoir of enterohemorrhagic Escherichia coli (EHEC) O157:H7. Currently, there are no effective methods of eliminating this important zoonotic pathogen from cattle, and colonization resistance in relation to EHEC O157:H7 in cattle is poorly understood. We developed a gnotobiotic EHEC O157:H7 murine model to examine aspects of the cattle pathogen-microbiota interaction, and to investigate competitive suppression of EHEC O157:H7 by 18 phylogenetically distinct commensal E. coli strains of bovine origin. As stress has been suggested to influence enteric colonization by EHEC O157:H7 in cattle, corticosterone administration (±) to incite a physiological stress response was included as an experimental variable. Colonization of the intestinal tract (IT) of mice by the bovine EHEC O157:H7 strain, FRIK-2001, mimicked characteristics of bovine IT colonization. In this regard, FRIK-2001 successfully colonized the IT and temporally incited minimal impacts on the host relative to other EHEC O157:H7 strains, including on the renal metabolome. The presence of the commensal E. coli strains decreased EHEC O157:H7 densities in the cecum, proximal colon, and distal colon. Moreover, histopathologic changes and inflammation markers were reduced in the distal colon of mice inoculated with commensal E. coli strains (both propagated separately and communally). Although stress induction affected the behavior of mice, it did not influence EHEC O157:H7 densities or disease. These findings support the use of a gnotobiotic murine model of enteric bovine EHEC O157:H7 colonization to better understand pathogen-host-microbiota interactions toward the development of effective on-farm mitigations for EHEC O157:H7 in cattle, including the identification of bacteria capable of competitively colonizing the IT.

Spatial and temporal drivers of zoonotic pathogen contamination of an agricultural watershed.

In regions where animal agriculture is prominent, such as southern Alberta, higher rates of gastrointestinal illness have been reported when compared with nonagricultural regions. This difference in the rate of illness is thought to be a result of increased zoonotic pathogen exposure through environmental sources such as water. In this study, temporal and spatial factors associated with bacterial pathogen contamination of the Oldman River, which transverses this region, were analyzed using classification and regression tree analysis. Significantly higher levels of fecal indicators; more frequent isolations of Campylobacter spp., Escherichia coli O157:H7, and Salmonella enterica spp.; and higher rates of detection of pig-specific Bacteroides markers occurred at downstream sites than at upstream sites, suggesting additive stream inputs. Fecal indicator densities were also significantly higher when any one of these three bacterial pathogens was present and where there were higher total animal manure units; however, occasionally pathogens were present when fecal indicator levels were low or undetectable. Overall, Salmonella spp., Campylobacter spp., and E. coli O157:H7 presence was associated with season, animal manure units, and total rainfall on the day of sampling and 3 d in advance of sampling. Several of the environmental variables analyzed in this study appear to influence pathogen prevalence and therefore may be useful in predicting water quality and safety and in the improvement of watershed management practices in this and other agricultural regions.

Genomic Analysis of Shiga Toxin-Producing E. coli O157 Cattle and Clinical Isolates from Alberta, Canada.

Shiga toxin (stx) is the principal virulence factor of the foodborne pathogen, Shiga toxin-producing Escherichia coli (STEC) O157:H7 and is associated with various lambdoid bacterio (phages). A comparative genomic analysis was performed on STEC O157 isolates from cattle (n = 125) and clinical (n = 127) samples to characterize virulence genes, stx-phage insertion sites and antimicrobial resistance genes that may segregate strains circulating in the same geographic region. In silico analyses revealed that O157 isolates harboured the toxin subtypes stx1a and stx2a. Most cattle (76.0%) and clinical (76.4%) isolates carried the virulence gene combination of stx1, stx2, eae and hlyA. Characterization of stx1 and stx2-carrying phages in assembled contigs revealed that they were associated with mlrA and wrbA insertion sites, respectively. In cattle isolates, mlrA and wrbA insertion sites were occupied more often (77% and 79% isolates respectively) than in clinical isolates (38% and 1.6% isolates, respectively). Profiling of antimicrobial resistance genes (ARGs) in the assembled contigs revealed that 8.8% of cattle (11/125) and 8.7% of clinical (11/127) isolates harboured ARGs. Eight antimicrobial resistance genes cassettes (ARCs) were identified in 14 isolates (cattle, n = 8 and clinical, n = 6) with streptomycin (aadA1, aadA2, ant(3'')-Ia and aph(3'')-Ib) being the most prevalent gene in ARCs. The profound disparity between the cattle and clinical strains in occupancy of the wrbA locus suggests that this trait may serve to differentiate cattle from human clinical STEC O157:H7. These findings are important for stx screening and stx-phage insertion site genotyping as well as monitoring ARGs in isolates from cattle and clinical samples.

Decontamination of Bacillus anthracis Spores at Subzero Temperatures by Complete Submersion.

Introduction: Bacillus anthracis, the etiological agent of anthrax, produces long-lived spores, which are resistant to heat, cold, pH, desiccation, and chemical agents. The spores maintain their ability to produce viable bacteria even after decades, and when inhaled can cause fatal disease in over half of the clinical cases. Owing to these characteristics, anthrax has been repeatedly selected for both bioweapon and bioterrorism use. In the event of a bioterrorism attack, surfaces in the vicinity of the attack will be contaminated, and recovering from such an event requires rapid and effective decontamination. Previous decontamination method development has focused mainly on temperatures >0°C, and have shown poor efficacy at subzero temperatures. Methods: In this study, we demonstrate the use of calcium chloride (CaCl2) as a freezing point depression agent for pH-adjusted sodium hypochlorite (NaOCl) for the effective and rapid decontamination of B. anthracis Sterne strain spores at subzero temperatures. Results: We show the complete decontamination of 106 B. anthracis Sterne strain spores at temperatures as low as -20°C within 2.5 min by submersion in solution containing 25% (w/v) CaCl2, 0.50% NaOCl, and 0.40% (v/v) acetic acid. We also demonstrate significant reduction in number of spores at -28°C. Conclusions: The results show promise for rapidly decontaminating equipment and materials used in the response to bioterrorism events using readily available consumer chemicals. Future study should examine the efficacy of these results on complex surfaces.

ECTyper: in silico Escherichia coli serotype and species prediction from raw and assembled whole-genome sequence data.

Escherichia coli is a priority foodborne pathogen of public health concern and phenotypic serotyping provides critical information for surveillance and outbreak detection activities. Public health and food safety laboratories are increasingly adopting whole-genome sequencing (WGS) for characterizing pathogens, but it is imperative to maintain serotype designations in order to minimize disruptions to existing public health workflows. Multiple in silico tools have been developed for predicting serotypes from WGS data, including SRST2, SerotypeFinder and EToKi EBEis, but these tools were not designed with the specific requirements of diagnostic laboratories, which include: speciation, input data flexibility (fasta/fastq), quality control information and easily interpretable results. To address these specific requirements, we developed ECTyper (https://github.com/phac-nml/ecoli_serotyping) for performing both speciation within Escherichia and Shigella, and in silico serotype prediction. We compared the serotype prediction performance of each tool on a newly sequenced panel of 185 isolates with confirmed phenotypic serotype information. We found that all tools were highly concordant, with 92-97 % for O-antigens and 98-100 % for H-antigens, and ECTyper having the highest rate of concordance. We extended the benchmarking to a large panel of 6954 publicly available E. coli genomes to assess the performance of the tools on a more diverse dataset. On the public data, there was a considerable drop in concordance, with 75-91 % for O-antigens and 62-90 % for H-antigens, and ECTyper and SerotypeFinder being the most concordant. This study highlights that in silico predictions show high concordance with phenotypic serotyping results, but there are notable differences in tool performance. ECTyper provides highly accurate and sensitive in silico serotype predictions, in addition to speciation, and is designed to be easily incorporated into bioinformatic workflows.

Antibiotic Resistance in Shiga Toxigenic Escherichia coli Isolates from Surface Waters and Sediments in a Mixed Use Urban Agricultural Landscape.

Antibiotic resistance (AR) phenotypes and acquired resistance determinants (ARDs) detected by in silico analysis of genome sequences were examined in 55 Shiga toxin-producing Escherichia coli (STEC) isolates representing diverse serotypes recovered from surfaces waters and sediments in a mixed use urban/agricultural landscape in British Columbia, Canada. The isolates displayed decreased susceptibility to florfenicol (65.5%), chloramphenicol (7.3%), tetracycline (52.7%), ampicillin (49.1%), streptomycin (34.5%), kanamycin (20.0%), gentamycin (10.9%), amikacin (1.8%), amoxicillin/clavulanic acid (21.8%), ceftiofur (18.2%), ceftriaxone (3.6%), trimethoprim-sulfamethoxazole (12.7%), and cefoxitin (3.6%). All surface water and sediment isolates were susceptible to ciprofloxacin, nalidixic acid, ertapenem, imipenem and meropenem. Eight isolates (14.6%) were multidrug resistant. ARDs conferring resistance to phenicols (floR), trimethoprim (dfrA), sulfonamides (sul1/2), tetracyclines (tetA/B), and aminoglycosides (aadA and aph) were detected. Additionally, narrow-spectrum ß-lactamase blaTEM-1b and extended-spectrum AmpC ß-lactamase (cephalosporinase) blaCMY-2 were detected in the genomes, as were replicons from plasmid incompatibility groups IncFII, IncB/O/K/Z, IncQ1, IncX1, IncY and Col156. A comparison with surveillance data revealed that AR phenotypes and ARDs were comparable to those reported in generic E. coli from food animals. Aquatic environments in the region are potential reservoirs for the maintenance and transmission of antibiotic resistant STEC, associated ARDs and their plasmids.

Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel.

Hierarchical genotyping approaches can provide insights into the source, geography and temporal distribution of bacterial pathogens. Multiple hierarchical SNP genotyping schemes have previously been developed so that new isolates can rapidly be placed within pre-computed population structures, without the need to rebuild phylogenetic trees for the entire dataset. This classification approach has, however, seen limited uptake in routine public health settings due to analytical complexity and the lack of standardized tools that provide clear and easy ways to interpret results. The BioHansel tool was developed to provide an organism-agnostic tool for hierarchical SNP-based genotyping. The tool identifies split k-mers that distinguish predefined lineages in whole genome sequencing (WGS) data using SNP-based genotyping schemes. BioHansel uses the Aho-Corasick algorithm to type isolates from assembled genomes or raw read sequence data in a matter of seconds, with limited computational resources. This makes BioHansel ideal for use by public health agencies that rely on WGS methods for surveillance of bacterial pathogens. Genotyping results are evaluated using a quality assurance module which identifies problematic samples, such as low-quality or contaminated datasets. Using existing hierarchical SNP schemes for Mycobacterium tuberculosis and Salmonella Typhi, we compare the genotyping results obtained with the k-mer-based tools BioHansel and SKA, with those of the organism-specific tools TBProfiler and genotyphi, which use gold-standard reference-mapping approaches. We show that the genotyping results are fully concordant across these different methods, and that the k-mer-based tools are significantly faster. We also test the ability of the BioHansel quality assurance module to detect intra-lineage contamination and demonstrate that it is effective, even in populations with low genetic diversity. We demonstrate the scalability of the tool using a dataset of ~8100 S. Typhi public genomes and provide the aggregated results of geographical distributions as part of the tool's output. BioHansel is an open source Python 3 application available on PyPI and Conda repositories and as a Galaxy tool from the public Galaxy Toolshed. In a public health context, BioHansel enables rapid and high-resolution classification of bacterial pathogens with low genetic diversity.

Pan-genome sequence analysis using Panseq: an online tool for the rapid analysis of core and accessory genomic regions.

BACKGROUND: The pan-genome of a bacterial species consists of a core and an accessory gene pool. The accessory genome is thought to be an important source of genetic variability in bacterial populations and is gained through lateral gene transfer, allowing subpopulations of bacteria to better adapt to specific niches. Low-cost and high-throughput sequencing platforms have created an exponential increase in genome sequence data and an opportunity to study the pan-genomes of many bacterial species. In this study, we describe a new online pan-genome sequence analysis program, Panseq. RESULTS: Panseq was used to identify Escherichia coli O157:H7 and E. coli K-12 genomic islands. Within a population of 60 E. coli O157:H7 strains, the existence of 65 accessory genomic regions identified by Panseq analysis was confirmed by PCR. The accessory genome and binary presence/absence data, and core genome and single nucleotide polymorphisms (SNPs) of six L. monocytogenes strains were extracted with Panseq and hierarchically clustered and visualized. The nucleotide core and binary accessory data were also used to construct maximum parsimony (MP) trees, which were compared to the MP tree generated by multi-locus sequence typing (MLST). The topology of the accessory and core trees was identical but differed from the tree produced using seven MLST loci. The Loci Selector module found the most variable and discriminatory combinations of four loci within a 100 loci set among 10 strains in 1 s, compared to the 449 s required to exhaustively search for all possible combinations; it also found the most discriminatory 20 loci from a 96 loci E. coli O157:H7 SNP dataset. CONCLUSION: Panseq determines the core and accessory regions among a collection of genomic sequences based on user-defined parameters. It readily extracts regions unique to a genome or group of genomes, identifies SNPs within shared core genomic regions, constructs files for use in phylogeny programs based on both the presence/absence of accessory regions and SNPs within core regions and produces a graphical overview of the output. Panseq also includes a loci selector that calculates the most variable and discriminatory loci among sets of accessory loci or core gene SNPs. AVAILABILITY: Panseq is freely available online at http://76.70.11.198/panseq. Panseq is written in Perl.

Lineage and host source are both correlated with levels of Shiga toxin 2 production by Escherichia coli O157:H7 strains.

Escherichia coli O157:H7 strains fall into three major genetic lineages that differ in their distribution among humans and cattle. Several recent studies have reported differences in the expression of virulence factors between E. coli O157:H7 strains from these two host species. In this study, we wished to determine if important virulence-associated "mobile genetic elements" such as Shiga toxin 2 (Stx2)-encoding prophage are lineage restricted or are host source related and acquired independently of the pathogen genotype. DNA sequencing of the stx(2) flanking region from a lineage II (LII) strain, EC970520, revealed that the transcriptional activator gene Q in LI strain EDL933 (upstream of stx(2)) is replaced by a pphA (serine/threonine phosphatase) homologue and an altered Q gene in this and all other LII strains tested. In addition, nearly all LI strains carried stx(2), whereas all LII strains carried variant stx(2c) and 4 of 14 LI/II strains had copies of both stx(2) and variant stx(2c). Real-time PCR (RT-PCR) and enzyme-linked immunosorbent assay (ELISA) demonstrated that LI and LI/II strains produce significantly more stx(2) mRNA and Stx2 than LII strains. However, among LI strains significantly more Stx2 is also produced by strains from humans than from cattle. Therefore, lineage-associated differences among E. coli O157:H7 strains such as prophage content, toxin type, and toxin expression may contribute to host isolation bias. However, the level of Stx2 production alone may also play an important role in the within-lineage association of E. coli O157:H7 strains with human clinical disease.

Application of artificial intelligence to the in silico assessment of antimicrobial resistance and risks to human and animal health presented by priority enteric bacterial pathogens.

Each year, approximately one in eight Canadians are affected by foodborne illness, either through outbreaks or sporadic illness, with animals being the major reservoir for the pathogens. Whole genome sequence analyses are now routinely implemented by public and animal health laboratories to define epidemiological disease clusters and to identify potential sources of infection. Similarly, a number of bioinformatics tools can be used to identify virulence and antimicrobial resistance (AMR) determinants in the genomes of pathogenic strains. Many important clinical and phenotypic characteristics of these pathogens can now be predicted using machine learning algorithms applied to whole genome sequence data. In this overview, we compare the ability of support vector machines, gradient-boosted decision trees and artificial neural networks to predict the levels of AMR within Salmonella enterica and extended-spectrum ß-lactamase (ESBL) producing Escherichia coli. We show that minimum inhibitory concentrations (MIC) for each of 13 antimicrobials for S. enterica strains can be accurately determined, and that ESBL-producing E. coli strains can be accurately classified as susceptible, intermediate or resistant for each of seven antimicrobials. In addition to AMR and bacterial populations of greatest risk to human health, artificial intelligence algorithms hold promise as tools to predict other clinically and epidemiologically important phenotypes of enteric pathogens.

Assessing the genomic relatedness and evolutionary rates of persistent verotoxigenic Escherichia coli serotypes within a closed beef herd in Canada.

Verotoxigenic Escherichia coli (VTEC) are food- and water-borne pathogens associated with both sporadic illness and outbreaks of enteric disease. While it is known that cattle are reservoirs of VTEC, little is known about the genomic variation of VTEC in cattle, and whether the variation in genomes reported for human outbreak strains is consistent with individual animal or group/herd sources of infection. A previous study of VTEC prevalence identified serotypes carried persistently by three consecutive cohorts of heifers within a closed herd of cattle. This present study aimed to: (i) determine whether the genomic relatedness of bovine isolates is similar to that reported for human strains associated with single source outbreaks, (ii) estimate the rates of genome change among dominant serotypes over time within a cattle herd, and (iii) identify genomic features of serotypes associated with persistence in cattle. Illumina MiSeq genome sequencing and genotyping based on allelic and single nucleotide variations were completed, while genome change over time was measured using Bayesian evolutionary analysis sampling trees. The accessory genome, including the non-protein-encoding intergenic regions (IGRs), virulence factors, antimicrobial-resistance genes and plasmid gene content of representative persistent and sporadic cattle strains were compared using Fisher's exact test corrected for multiple comparisons. Herd strains from serotypes O6:H34 (n=22), O22:H8 (n=30), O108:H8 (n=39), O139:H19 (n=44) and O157:H7 (n=106) were readily distinguishable from epidemiologically unrelated strains of the same serotype using a similarity threshold of 10 or fewer allele differences between adjacent nodes. Temporal-cohort clustering within each serotype was supported by date randomization analysis. Substitutions per site per year were consistent with previously reported values for E. coli; however, there was low branch support for these values. Acquisition of the phage-encoded Shiga toxin 2 gene in serotype O22:H8 was observed. Pan-genome analyses identified accessory regions that were more prevalent in persistent serotypes (P≤0.05) than in sporadic serotypes. These results suggest that VTEC serotypes from a specific cattle population are highly clonal with a similar level of relatedness as human single-source outbreak-associated strains, but changes in the genome occur gradually over time. Additionally, elements in the accessory genomes may provide a selective advantage for persistence of VTEC within cattle herds.

In silico genomic analyses reveal three distinct lineages of Escherichia coli O157:H7, one of which is associated with hyper-virulence.

BACKGROUND: Many approaches have been used to study the evolution, population structure and genetic diversity of Escherichia coli O157:H7; however, observations made with different genotyping systems are not easily relatable to each other. Three genetic lineages of E. coli O157:H7 designated I, II and I/II have been identified using octamer-based genome scanning and microarray comparative genomic hybridization (mCGH). Each lineage contains significant phenotypic differences, with lineage I strains being the most commonly associated with human infections. Similarly, a clade of hyper-virulent O157:H7 strains implicated in the 2006 spinach and lettuce outbreaks has been defined using single-nucleotide polymorphism (SNP) typing. In this study an in silico comparison of six different genotyping approaches was performed on 19 E. coli genome sequences from 17 O157:H7 strains and single O145:NM and K12 MG1655 strains to provide an overall picture of diversity of the E. coli O157:H7 population, and to compare genotyping methods for O157:H7 strains. RESULTS: In silico determination of lineage, Shiga-toxin bacteriophage integration site, comparative genomic fingerprint, mCGH profile, novel region distribution profile, SNP type and multi-locus variable number tandem repeat analysis type was performed and a supernetwork based on the combination of these methods was produced. This supernetwork showed three distinct clusters of strains that were O157:H7 lineage-specific, with the SNP-based hyper-virulent clade 8 synonymous with O157:H7 lineage I/II. Lineage I/II/clade 8 strains clustered closest on the supernetwork to E. coli K12 and E. coli O55:H7, O145:NM and sorbitol-fermenting O157 strains. CONCLUSION: The results of this study highlight the similarities in relationships derived from multi-locus genome sampling methods and suggest a "common genotyping language" may be devised for population genetics and epidemiological studies. Future genotyping methods should provide data that can be stored centrally and accessed locally in an easily transferable, informative and extensible format based on comparative genomic analyses.

Two distinct groups of porcine enteropathogenic Escherichia coli strains of serogroup O45 are revealed by comparative genomic hybridization and virulence gene microarray.

BACKGROUND: Porcine enteropathogenic Escherichia coli (PEPEC) strains of serogroup O45 cause post-weaning diarrhea and produce characteristic attaching and effacing (A/E) lesions. Most O45 PEPEC strains possess the locus of enterocyte effacement (LEE), encoding the virulence factors required for production of A/E lesions, and often possess the paa gene, which is thought to contribute to the early stages of PEPEC pathogenicity. In this study, nine O45 PEPEC strains and a rabbit enteropathogenic (REPEC) strain, known to produce A/E lesions in vivo, were characterized using an E. coli O157-E. coli K12 whole genome microarray and a virulence gene-specific microarray, and by PCR experiments. RESULTS: Based on their virulence gene profiles, the 10 strains were considered to be atypical EPEC. The differences in their genomes pointed to the identification of two distinct evolutionary groups of O45 PEPEC, Groups I and II, and provided evidence for a contribution of these genetic differences to their virulence in pigs. Group I included the REPEC strain and four O45 PEPEC strains known to induce severe A/E lesions in challenged pigs whereas Group II was composed of the five other O45 PEPEC strains, which induced less severe or no A/E lesions in challenged pigs. Significant differences between Groups I and II were found with respect to the presence or absence of 50 O-Islands (OIs) or S-loops and 13 K-islands (KIs) or K-loops, including the virulence-associated islands OI#1 (S-loop#1), OI#47 (S-loop#71), OI#57 (S-loop#85), OI#71 (S-loop#108), OI#115, OI#122, and OI#154 (S-loop#253). CONCLUSION: We have genetically characterized a collection of O45 PEPEC strains and classified them into two distinct groups. The differences in their virulence gene and genomic island content may influence the pathogenicity of O45 PEPEC strains, and explain why Group I O45 PEPEC strains induced more severe A/E lesions in explants and challenged pigs than Group II strains.

Genomic regions conserved in lineage II Escherichia coli O157:H7 strains.

Populations of the food- and waterborne pathogen Escherichia coli O157:H7 are comprised of two major lineages. Recent studies have shown that specific genotypes within these lineages differ substantially in the frequencies with which they are associated with human clinical disease. While the nucleotide sequences of the genomes of lineage I strains E. coli O157 Sakai and EDL9333 have been determined, much less is known about the genomes of lineage II strains. In this study, suppression subtractive hybridization (SSH) was used to identify genomic features that define lineage II populations. Three SSH experiments were performed, yielding 1,085 genomic fragments consisting of 811 contigs. Bacteriophage sequences were identified in 11.3% of the contigs, 9% showed insertions and 2.3% deletions with respect to E. coli O157:H7 Sakai, and 23.2% did not have significant identity to annotated sequences in GenBank. In order to test for the presence of these novel loci in lineage I and II strains, 27 PCR primer sets were designed based on sequences from these contigs. All but two of these PCR targets were found in the majority (51.9% to 100%) of 27 lineage II strains but in no more than one (<6%) of the 17 lineage I strains. Several of these lineage II-related fragments contain insertions/deletions that may play an important role in virulence. These lineage II-related loci were also shown to be useful markers for genotyping of E. coli O157:H7 strains isolated from human and animal sources.

Eleven High-Quality Reference Genome Sequences and 360 Draft Assemblies of Shiga Toxin-Producing Escherichia coli Isolates from Human, Food, Animal, and Environmental Sources in Canada.

We report high-quality closed reference genomes for 1 bovine strain and 10 human Shiga toxin (Stx)-producing Escherichia coli (STEC) strains from serogroups O26, O45, O91, O103, O104, O111, O113, O121, O145, and O157. We also report draft assemblies, with standardized metadata, for 360 STEC strains isolated from watersheds, animals, farms, food, and human infections.