Thomas, James

Permanent URI for this collection

https://opus.uleth.ca/handle/10133/4746

Browse

Browsing Thomas, James by Author "Buchanan, Cody J."

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    A genome-wide association study to identify diagnostic markers for human pathogenic Campylobacter jejuni strains
    (Frontiers Media, 2017) Buchanan, Cody J.; Webb, Andrew L.; Mutschall, Steven K.; Kruczkiewicz, Peter; Barker, Dillon Oliver Reese; Hetman, Benjamin M.; Gannon, Victor P. J.; Abbott, D. Wade; Thomas, James E.; Inglis, G. Douglas; Taboada, Eduardo N.
    Campylobacter jejuni is a leading human enteric pathogen worldwide and despite an improved understanding of its biology, ecology, and epidemiology, limited tools exist for identifying strains that are likely to cause disease. In the current study, we used subtyping data in a database representing over 24,000 isolates collected through various surveillance projects in Canada to identify 166 representative genomes from prevalent C. jejuni subtypes for whole genome sequencing. The sequence data was used in a genome-wide association study (GWAS) aimed at identifying accessory gene markers associated with clinically related C. jejuni subtypes. Prospective markers (n=28) were then validated against a large number (n=3,902) of clinically associated and non-clinically associated genomes from a variety of sources. A total of 25 genes, including six sets of genetically linked genes, were identified as robust putative diagnostic markers for clinically related C. jejuni subtypes. Although some of the genes identified in this study have been previously shown to play a role in important processes such as iron acquisition and vitamin B5 biosynthesis, others have unknown function or are unique to the current study and warrant further investigation. As few as four of these markers could be used in combination to detect up to 90% of clinically associated isolates in the validation dataset, and such markers could form the basis for a screening assay to rapidly identify strains that pose an increased risk to public health. The results of the current study are consistent with the notion that specific groups of C. jejuni strains of interest are defined by the presence of specific accessory genes.
  • Thumbnail Image
    Item
    In 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.
  • Thumbnail Image
    Item
    Pan-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.