For nearly 100 years serotyping has been the gold standard for the identification of Salmonella serovars. Despite the increasing adoption of DNA-based subtyping approaches, serotype information remains a cornerstone in food safety and public health activities aimed at reducing the burden of salmonellosis. At the same time, recent advances in whole-genome sequencing (WGS) promise to revolutionize our ability to perform advanced pathogen characterization in support of improved source attribution and outbreak analysis. We present the Salmonella In Silico Typing Resource (SISTR), a bioinformatics platform for rapidly performing simultaneous in silico analyses for several leading subtyping methods on draft Salmonella genome assemblies. In addition to performing serovar prediction by genoserotyping, this resource integrates sequence-based typing analyses for: Multi-Locus Sequence Typing (MLST), ribosomal MLST (rMLST), and core genome MLST (cgMLST). We show how phylogenetic context from cgMLST analysis can supplement the genoserotyping analysis and increase the accuracy of in silico serovar prediction to over 94.6% on a dataset comprised of 4,188 finished genomes and WGS draft assemblies. In addition to allowing analysis of user-uploaded whole-genome assemblies, the SISTR platform incorporates a database comprising over 4,000 publicly available genomes, allowing users to place their isolates in a broader phylogenetic and epidemiological context. The resource incorporates several metadata driven visualizations to examine the phylogenetic, geospatial and temporal distribution of genome-sequenced isolates. As sequencing of Salmonella isolates at public health laboratories around the world becomes increasingly common, rapid in silico analysis of minimally processed draft genome assemblies provides a powerful approach for molecular epidemiology in support of public health investigations. Moreover, this type of integrated analysis using multiple sequence-based methods of sub-typing allows for continuity with historical serotyping data as we transition towards the increasing adoption of genomic analyses in epidemiology. The SISTR platform is freely available on the web at https://lfz.corefacility.ca/sistr-app/.

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 identifyEscherichia coliO157:H7 andE. coliK-12 genomic islands. Within a population of 60E. coliO157: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 sixL. monocytogenesstrains 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 lociE. coliO157: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 athttp://76.70.11.198/panseq. Panseq is written in Perl.

We have compared the lipo-oligosaccharide (LOS) biosynthesis loci from 11 Campylobacter jejunistrains expressing a total of 8 different ganglioside mimics in their LOS outer cores. Based on the organization of the genes, the 11 corresponding loci could be classified into three classes, with one of them being clearly an intermediate evolutionary step between the other two. Comparative genomics and expression of specific glycosyltransferases combined with in vitro activity assays allowed us to identify at least five distinct mechanisms that allowC. jejuni to vary the structure of the LOS outer core as follows: 1) different gene complements; 2) phase variation because of homopolymeric tracts; 3) gene inactivation by the deletion or insertion of a single base (without phase variation); 4) single mutation leading to the inactivation of a glycosyltransferase; and 5) single or multiple mutations leading to “allelic” glycosyltransferases with different acceptor specificities. The differences in the LOS outer core structures expressed by the 11 C. jejuni strains examined can be explained by one or more of the five mechanisms described in this work.

We examined two variants of the genome-sequenced strain, Campylobacter jejuni NCTC11168, which show marked differences in their virulence properties including colonization of poultry, invasion of Caco-2 cells, and motility. Transcript profiles obtained from whole genome DNA microarrays and proteome analyses demonstrated that these differences are reflected in late flagellar structural components and in virulence factors including those involved in flagellar glycosylation and cytolethal distending toxin production. We identified putative σ²⁸ and σ⁵⁴ promoters for many of the affected genes and found that greater differences in expression were observed for σ²⁸-controlled genes. Inactivation of the gene encoding σ²⁸, fliA, resulted in an unexpected increase in transcripts with σ⁵⁴ promoters, as well as decreased transcription of σ²⁸-regulated genes. This was unlike the transcription profile observed for the attenuated C. jejuni variant, suggesting that the reduced virulence of this organism was not entirely due to impaired function of σ²⁸. However, inactivation of flhA, an important component of the flagellar export apparatus, resulted in expression patterns similar to that of the attenuated variant. These findings indicate that the flagellar regulatory system plays an important role in campylobacter pathogenesis and that flhA is a key element involved in the coordinate regulation of late flagellar genes and of virulence factors in C. jejuni.

The recent widespread application of whole-genome sequencing (WGS) for microbial disease investigations has spurred the development of new bioinformatics tools, including a notable proliferation of phylogenomics pipelines designed for infectious disease surveillance and outbreak investigation. Transitioning the use of WGS data out of the research laboratory and into the front lines of surveillance and outbreak response requires user-friendly, reproducible and scalable pipelines that have been well validated. Single Nucleotide Variant Phylogenomics (SNVPhyl) is a bioinformatics pipeline for identifying high-quality single-nucleotide variants (SNVs) and constructing a whole-genome phylogeny from a collection of WGS reads and a reference genome. Individual pipeline components are integrated into the Galaxy bioinformatics framework, enabling data analysis in a user-friendly, reproducible and scalable environment. We show that SNVPhyl can detect SNVs with high sensitivity and specificity, and identify and remove regions of high SNV density (indicative of recombination). SNVPhyl is able to correctly distinguish outbreak from non-outbreak isolates across a range of variant-calling settings, sequencing-coverage thresholds or in the presence of contamination. SNVPhyl is available as a Galaxy workflow, Docker and virtual machine images, and a Unix-based command-line application. SNVPhyl is released under the Apache 2.0 license and available at http://snvphyl.readthedocs.io/ or at https://github.com/phac-nml/snvphyl-galaxy.

Background: Genetic analysis of Escherichia coli O157:H7 strains has shown divergence into two distinct lineages, lineages I and II, that appear to have distinct ecological characteristics, with lineage I strains more commonly associated with human disease. In this study, microarray-based comparative genomic hybridization (CGH) was used to identify genomic differences among 31 E. coli O157:H7 strains that belong to various phage types (PTs) and different lineage-specific polymorphism assay (LSPA) types. Results: A total of 4,084 out of 6,057 ORFs were detected in all E. coli O157:H7 strains and 1,751 were variably present or absent. Based on this data, E. coli O157:H7 strains were divided into three distinct clusters, which consisted of 15 lineage I (LSPA type 111111), four lineage I/II (designated in this study) (LSPA type 211111) and 12 lineage II strains (LSPA 222222, 222211, 222212, and 222221), respectively. Eleven different genomic regions that were dominant in lineage I strains (present in ≥80% of lineage I and absent from ≥ 92% of lineage II strains) spanned segments containing as few as two and up to 25 ORFs each. These regions were identified within E. coli Sakai S-loops # 14, 16, 69, 72, 78, 83, 85, 153 and 286, Sakai phage 10 (S-loops # 91, 92 and 93) and a genomic backbone region. All four lineage I/II strains were of PT 2 and possessed eight of these 11 lineage I-dominant loci. Several differences in virulence-associated loci were noted between lineage I and lineage II strains, including divergence within S-loop 69, which encodes Shiga toxin 2, and absence of the non-LEE encoded effector genes nleF and nleH1-2 and the perC homologue gene pchD in lineage II strains. Conclusion: CGH data suggest the existence of two dominant lineages as well as LSPA type and PT-related subgroups within E. coli O157:H7. The genomic composition of these subgroups supports the phylogeny that has been inferred from other methods and further suggests that genomic divergence from an ancestral form and lateral gene transfer have contributed to their evolution. The genomic features identified in this study may contribute to apparent differences in the epidemiology and ecology of strains of different E. coli O157:H7 lineages.