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William Hsiao Brinkman Laboratory Simon Fraser University Burnaby, BC, Canada

Genomic island analysis: Improved web-based software and insights into an apparent gene pool associated with genomic islands. William Hsiao Brinkman Laboratory Simon Fraser University Burnaby, BC, Canada. Prokaryotic Genomic Islands (GIs).

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William Hsiao Brinkman Laboratory Simon Fraser University Burnaby, BC, Canada

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  1. Genomic island analysis: Improved web-based software and insights into an apparent gene pool associated with genomic islands William Hsiao Brinkman Laboratory Simon Fraser University Burnaby, BC, Canada

  2. Prokaryotic Genomic Islands (GIs) • Definition: Genomic DNA segments with particular characteristics that indicate horizontal origins A bacterium GI

  3. Genomic Island Characteristics • Exhibit sequence and annotation features • Often contain genes encoding adaptive functions of medical and environmental importance • Pathogenicity Islands: virulence factors (genes contribute to diseases) • Resistance Islands: antibiotic resistance • Metabolic Islands: secondary metabolism (e.g. sucrose) Direct Repeats Direct Repeats Genomic Island (e.g. PAI) (%G+C, sequence composition bias) mob VF VF VF chromosome tRNA gene mob: mobility genes

  4. IslandPath: Aiding identification of GIs TCP island Vibrio cholerae N16961 Chr1 A yellow circle: %G+C above high cutoff A green circle: % G+C between cutoffs A pink circle: %G+C below low cutoff A black bar: transfer RNA A purple bar: ribosomal RNA A deep blue bar: both tRNA and rRNA A black square: transposase A black triangle: integraseA strike-line: regions with dinucleotide bias TCP = toxin co-regulated pili (Hsiao et al 2003 Bioinformatics p418-20)

  5. IslandPath V.2

  6. Which Features Best Identify GIs Examined prevalence of features in 95 published islands • 85% of islands with >25% dinucleotide bias coverage (62% have > 50% dinucleotide bias coverage) • Mobility genes identified in >75% of the islands • tRNA genesobserved in <50% of known islands • Only 20% of the islands show atypical %G+C

  7. Properties of genes in GIs? Defined a “putative island” as • 8 or more genes in a row with dinucleotide bias • 8 or more genes in a row with dinucleotide bias + an associated mobility gene Any difference for genes in islands versus outside of islands in terms of their protein Functional categories? • 63 genomes (67 chromosomes) analyzed • COG: cluster of orthologous groups of proteins

  8. Paired-t-test P value: 1.27E-18 More novel genes inside of islands Hsiao et al. PLOS Genetics e62, Nov. 2005

  9. Control for Analysis Biases • Control for mis-prediction of genes in sequence composition biased regions • Excluded genes < 300bps • Control for bias of COG Protein Classification • Used SUPERFAMILY classification which is better at detecting distant homologs • Control for compositional bias due to other factors • Used the dinucleotide bias plus mobility gene dataset

  10. More novel genes in islands in all experiments Hsiao et al. PLOS Genetics e62, Nov. 2005

  11. Phage may be the predominant donors of GIs • Some GIs are clearly of bacteriophage origin, but more may be from phage as well • Predicted subcellular localizations of proteins encoded in our GIs similar to phage genomes (lower proportion of cytoplasmic membrane proteins) • Hsiao et al. PLOS Genetics e62, Nov. 2005 • Many GI encoded genes have sequence characteristics similar to phage genes (A+T rich and short) • Daubin et al. Genome Biol. 4(9): R57

  12. P value: < 2.2E-16 Higher proportions of genes in Islands are VFs http://zdsys.chgb.org.cn/VFs/ Fedynak, Hsiao, and Brinkman (unpublished)

  13. Certain classes of VFs over-represented in GIs Most of these are “offensive” virulence factors Fedynak, Hsiao, and Brinkman (unpublished)

  14. Conclusions • Genomic islands contain disproportionately higher number of novel genes, suggesting a large and understudied gene pool contributing to horizontal gene transfer • These novel genes appear to be drawn from a large pool of phage - metagenomics studies useful • These novel genes may contribute to microbial adaptation and may play a role in pathogenesis and in antibiotic resistance

  15. Acknowledgements • Fiona Brinkman • Amber Fedynak -VF studies • Brian Coombes, Michael Lowden, and Brett Finlay (UBC) - Microarray data • Jenny Bryan (UBC) -Stats analysis • Brinkman Laboratory http://www.pathogenomics.sfu.ca/islandpath

  16. Other categories more common in islands Several metabolism-associated categories are under-represented in islands * Novel genes not included in analysis due to potential skew of other category results

  17. P value 3.0E-16

  18. IslandPath V.2

  19. Experiment: S. typhimurium LT2 ssrB gene KO Track 1: IslandPath Track 2: Microarray expression (overexp & underexp )

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