Abstract
BackgroundThe genome of the gastric pathogen Helicobacter pylori is characterised by considerable variation of both gene sequence and content, much of which is contained within three large genomic islands comprising the cag pathogenicity island (cagPAI) and two mobile integrative and conjugative elements (ICEs) termed tfs3 and tfs4. All three islands are implicated as virulence factors, although whereas the cagPAI is well characterised, understanding of how the tfs elements influence H. pylori interactions with different human hosts is significantly confounded by limited definition of their distribution, diversity and structural representation in the global H. pylori population.ResultsTo gain a global perspective of tfs ICE population dynamics we established a bioinformatics workflow to extract and precisely define the full tfs pan-gene content contained within a global collection of 221 draft and complete H. pylori genome sequences. Complete (ca. 35-55kbp) and remnant tfs ICE clusters were reconstructed from a dataset comprising > 12,000 genes, from which orthologous gene complements and distinct alleles descriptive of different tfs ICE types were defined and classified in comparative analyses. The genetic variation within defined ICE modular segments was subsequently used to provide a complete description of tfs ICE diversity and a comprehensive assessment of their phylogeographic context. Our further examination of the apparent ICE modular types identified an ancient and complex history of ICE residence, mobility and interaction within particular H. pylori phylogeographic lineages and further, provided evidence of both contemporary inter-lineage and inter-species ICE transfer and displacement.ConclusionsOur collective results establish a clear view of tfs ICE diversity and phylogeographic representation in the global H. pylori population, and provide a robust contextual framework for elucidating the functional role of the tfs ICEs particularly as it relates to the risk of gastric disease associated with different tfs ICE genotypes.
Highlights
The genome of the gastric pathogen Helicobacter pylori is characterised by considerable variation of both gene sequence and content, much of which is contained within three large genomic islands comprising the cag pathogenicity island and two mobile integrative and conjugative elements (ICEs) termed tfs3 and tfs4
We show that tfs ICEs are modular in their organisation and identify disease-associated accessory modules that are preferentially maintained in strains independently of tfs ICE Type IV secretion system (T4SS) activity
Representation of tfs ICEs in a global H. pylori strain population Previous descriptions of the tfs ICEs of H. pylori have predominantly resulted from examination of contiguous
Summary
The genome of the gastric pathogen Helicobacter pylori is characterised by considerable variation of both gene sequence and content, much of which is contained within three large genomic islands comprising the cag pathogenicity island (cagPAI) and two mobile integrative and conjugative elements (ICEs) termed tfs and tfs. Seven major genetically and geographically distinct H. pylori populations (‘hp’) and five subpopulations (‘hsp’) have been described by multi locus sequence typing (MLST) and the STRUCTURE Bayesian population cluster method, defined as hpAfrica (subpopulations hspWAfrica, hspSAfrica), hpAfrica, hpNEAfrica, hpEurope, hpAsia, hpEAsia (subpopulations hspAmerind, hspEAsia and hspMaori) and hpSahul [13,14,15,16] More recent methods such as fineSTRUCTURE have further resolved the genetic structure of these populations, expanding the number of subdivisions and providing detailed insight of inter-population admixture that contributes to the extreme genetic diversity of H. pylori strains [17]. In the absence of discernible correlations with established virulence genotypes, these theories may account for the differing susceptibilities to gastric cancer in populations with high rates of H. pylori infection [25,26,27,28], and presents discordant host-pathogen co-ancestry as an important co-factor in the determination of disease risk [22]
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