Dataset Information

Using single-nucleotide polymorphisms to discriminate disease-associated from carried genomes of Neisseria meningitidis.

ABSTRACT: Neisseria meningitidis is one of the main agents of bacterial meningitis, causing substantial morbidity and mortality worldwide. However, most of the time N. meningitidis is carried as a commensal not associated with invasive disease. The genomic basis of the difference between disease-associated and carried isolates of N. meningitidis may provide critical insight into mechanisms of virulence, yet it has remained elusive. Here, we have taken a comparative genomics approach to interrogate the difference between disease-associated and carried isolates of N. meningitidis at the level of individual nucleotide variations (i.e., single nucleotide polymorphisms [SNPs]). We aligned complete genome sequences of 8 disease-associated and 4 carried isolates of N. meningitidis to search for SNPs that show mutually exclusive patterns of variation between the two groups. We found 63 SNPs that distinguish the 8 disease-associated genomes from the 4 carried genomes of N. meningitidis, which is far more than can be expected by chance alone given the level of nucleotide variation among the genomes. The putative list of SNPs that discriminate between disease-associated and carriage genomes may be expected to change with increased sampling or changes in the identities of the isolates being compared. Nevertheless, we show that these discriminating SNPs are more likely to reflect phenotypic differences than shared evolutionary history. Discriminating SNPs were mapped to genes, and the functions of the genes were evaluated for possible connections to virulence mechanisms. A number of overrepresented functional categories related to virulence were uncovered among SNP-associated genes, including genes related to the category "symbiosis, encompassing mutualism through parasitism."

SUBMITTER: Katz LS

PROVIDER: S-EPMC3133314 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Using single-nucleotide polymorphisms to discriminate disease-associated from carried genomes of Neisseria meningitidis.

Katz Lee S LS Sharma Nitya V NV Harcourt Brian H BH Thomas Jennifer Dolan JD Wang Xin X Mayer Leonard W LW Jordan I King IK

Journal of bacteriology 20110527 14

Neisseria meningitidis is one of the main agents of bacterial meningitis, causing substantial morbidity and mortality worldwide. However, most of the time N. meningitidis is carried as a commensal not associated with invasive disease. The genomic basis of the difference between disease-associated and carried isolates of N. meningitidis may provide critical insight into mechanisms of virulence, yet it has remained elusive. Here, we have taken a comparative genomics approach to interrogate the dif ...[more]

PMID: 21622743

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Project description:The advent of high throughput sequencing technology has enabled the 1000 Genomes Project Pilot 3 to generate complete sequence data for more than 906 genes and 8,140 exons representing 697 subjects. The 1000 Genomes database provides a critical opportunity for further interpreting disease associations with single nucleotide polymorphisms (SNPs) discovered from genetic association studies. Currently, direct sequencing of candidate genes or regions on a large number of subjects remains both cost- and time-prohibitive.To accelerate the translation from discovery to functional studies, we propose an in silico gene sequencing method (ISS), which predicts phased sequences of intragenic regions, using SNPs. The key underlying idea of our method is to infer diploid sequences (a pair of phased sequences/alleles) at every functional locus utilizing the deep sequencing data from the 1000 Genomes Project and SNP data from the HapMap Project, and to build prediction models using flanking SNPs. Using this method, we have developed a database of prediction models for 611 known genes. Sequence prediction accuracy for these genes is 96.26% on average (ranges 79%-100%). This database of prediction models can be enhanced and scaled up to include new genes as the 1000 Genomes Project sequences additional genes on additional individuals. Applying our predictive model for the KCNJ11 gene to the Wellcome Trust Case Control Consortium (WTCCC) Type 2 diabetes cohort, we demonstrate how the prediction of phased sequences inferred from GWAS SNP genotype data can be used to facilitate interpretation and identify a probable functional mechanism such as protein changes.Prior to the general availability of routine sequencing of all subjects, the ISS method proposed here provides a time- and cost-effective approach to broadening the characterization of disease associated SNPs and regions, and facilitating the prioritization of candidate genes for more detailed functional and mechanistic studies.

Dataset Information

Using single-nucleotide polymorphisms to discriminate disease-associated from carried genomes of Neisseria meningitidis.

Publications

Using single-nucleotide polymorphisms to discriminate disease-associated from carried genomes of Neisseria meningitidis.

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