Browsing Thomas, James by Author "Laing, Chad R."
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- ItemA comparison of Shiga-toxin 2 bacteriophage from classical enterohemorrhagic Escherichia coli serotypes and the German E. coli 0104:H4 outbreak strain(Public Library of Science, 2012) Laing, Chad R.; Zhang, Yongxiang; Gilmour, Matthew W.; Allen, Vanessa; Johnson, Roger; Thomas, James E.; Gannon, Victor P. J.Escherichia coli O104:H4 was associated with a severe foodborne disease outbreak originating in Germany in May 2011. More than 4000 illnesses and 50 deaths were reported. The outbreak strain was a typical enteroaggregative E. coli (EAEC) that acquired an antibiotic resistance plasmid and a Shiga-toxin 2 (Stx2)-encoding bacteriophage. Based on whole-genome phylogenies, the O104:H4 strain was most closely related to other EAEC strains; however, Stx2-bacteriophage are mobile, and do not necessarily share an evolutionary history with their bacterial host. In this study, we analyzed Stx2-bacteriophage from the E. coli O104:H4 outbreak isolates and compared them to all available Stx2-bacteriophage sequences. We also compared Stx2 production by an E. coli O104:H4 outbreak-associated isolate (ON-2011) to that of E. coli O157:H7 strains EDL933 and Sakai. Among the E. coli Stx2-phage sequences studied, that from O111:H- strain JB1-95 was most closely related phylogenetically to the Stx2-phage from the O104:H4 outbreak isolates. The phylogeny of most other Stx2-phage was largely concordant with their bacterial host genomes. Finally, O104:H4 strain ON-2011 produced less Stx2 than E. coli O157:H7 strains EDL933 and Sakai in culture; however, when mitomycin C was added, ON-2011 produced significantly more toxin than the E. coli O157:H7 strains. The Stx2-phage from the E. coli O104:H4 outbreak strain and the Stx2-phage from O111:H- strain JB1-95 likely share a common ancestor. Incongruence between the phylogenies of the Stx2-phage and their host genomes suggest the recent Stx2-phage acquisition by E. coli O104:H4. The increase in Stx2-production by ON-2011 following mitomycin C treatment may or may not be related to the high rates of hemolytic uremic syndrome associated with the German outbreak strain. Further studies are required to determine whether the elevated Stx2-production levels are due to bacteriophage or E. coli O104:H4 host related factors.
- ItemIn silico genomic analyses reveal three distinct lineages of Escherichia coli O 157:H7, one of which is associated with hyper-virulence(BioMed Central, 2009) Laing, Chad R.; Buchanan, Cody J.; Taboada, Eduardo N.; Zhang, Yongxiang; Karmali, Mohamed A.; Thomas, James E.; Gannon, Victor P. J.
- ItemPan-genome sequence analysis using Panseq: an online tool for the rapid analysis of core and accessory genomic regions(BioMed Central, 2010) Laing, Chad R.; Buchanan, Cody J.; Taboada, Eduardo N.; Zhang, Yongxiang; Kropinski, Andrew; Villegas, Andre; Thomas, James E.; Gannon, Victor P. J.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.