Comparative supragenomic analyses among the pathogens Staphylococcus aureus, Streptococcus pneumoniae, and Haemophilus influenzae Using a modification of the finite supragenome model

Robert J Boissy, Azad Ahmed, Benjamin Janto, Josh Earl, Barry G. Hall, Justin S. Hogg, Gordon D. Pusch, Luisa N. Hiller, Evan Powell, Jay Hayes, Susan Yu, Sandeep Kathju, Paul Stoodley, J. Christopher Post, Garth D. Ehrlich, Fen Z. Hu

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

Background: Staphylococcus aureus is associated with a spectrum of symbiotic relationships with its human host from carriage to sepsis and is frequently associated with nosocomial and community-acquired infections, thus the differential gene content among strains is of interest.Results: We sequenced three clinical strains and combined these data with 13 publically available human isolates and one bovine strain for comparative genomic analyses. All genomes were annotated using RAST, and then their gene similarities and differences were delineated. Gene clustering yielded 3,155 orthologous gene clusters, of which 2,266 were core, 755 were distributed, and 134 were unique. Individual genomes contained between 2,524 and 2,648 genes. Gene-content comparisons among all possible S. aureus strain pairs (n = 136) revealed a mean difference of 296 genes and a maximum difference of 476 genes. We developed a revised version of our finite supragenome model to estimate the size of the S. aureus supragenome (3,221 genes, with 2,245 core genes), and compared it with those of Haemophilus influenzae and Streptococcus pneumoniae. There was excellent agreement between RAST's annotations and our CDS clustering procedure providing for high fidelity metabolomic subsystem analyses to extend our comparative genomic characterization of these strains.Conclusions: Using a multi-species comparative supragenomic analysis enabled by an improved version of our finite supragenome model we provide data and an interpretation explaining the relatively larger core genome of S. aureus compared to other opportunistic nasopharyngeal pathogens. In addition, we provide independent validation for the efficiency and effectiveness of our orthologous gene clustering algorithm.

Original languageEnglish (US)
Article number187
JournalBMC genomics
Volume12
DOIs
StatePublished - Apr 13 2011

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Staphylococcal Pneumonia
Haemophilus influenzae
Streptococcus pneumoniae
Genes
Staphylococcus aureus
Cluster Analysis
Genome
Community-Acquired Infections
Metabolomics
Multigene Family
Sepsis

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

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Comparative supragenomic analyses among the pathogens Staphylococcus aureus, Streptococcus pneumoniae, and Haemophilus influenzae Using a modification of the finite supragenome model. / Boissy, Robert J; Ahmed, Azad; Janto, Benjamin; Earl, Josh; Hall, Barry G.; Hogg, Justin S.; Pusch, Gordon D.; Hiller, Luisa N.; Powell, Evan; Hayes, Jay; Yu, Susan; Kathju, Sandeep; Stoodley, Paul; Post, J. Christopher; Ehrlich, Garth D.; Hu, Fen Z.

In: BMC genomics, Vol. 12, 187, 13.04.2011.

Research output: Contribution to journalArticle

Boissy, RJ, Ahmed, A, Janto, B, Earl, J, Hall, BG, Hogg, JS, Pusch, GD, Hiller, LN, Powell, E, Hayes, J, Yu, S, Kathju, S, Stoodley, P, Post, JC, Ehrlich, GD & Hu, FZ 2011, 'Comparative supragenomic analyses among the pathogens Staphylococcus aureus, Streptococcus pneumoniae, and Haemophilus influenzae Using a modification of the finite supragenome model', BMC genomics, vol. 12, 187. https://doi.org/10.1186/1471-2164-12-187
Boissy, Robert J ; Ahmed, Azad ; Janto, Benjamin ; Earl, Josh ; Hall, Barry G. ; Hogg, Justin S. ; Pusch, Gordon D. ; Hiller, Luisa N. ; Powell, Evan ; Hayes, Jay ; Yu, Susan ; Kathju, Sandeep ; Stoodley, Paul ; Post, J. Christopher ; Ehrlich, Garth D. ; Hu, Fen Z. / Comparative supragenomic analyses among the pathogens Staphylococcus aureus, Streptococcus pneumoniae, and Haemophilus influenzae Using a modification of the finite supragenome model. In: BMC genomics. 2011 ; Vol. 12.
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AU - Boissy, Robert J

AU - Ahmed, Azad

AU - Janto, Benjamin

AU - Earl, Josh

AU - Hall, Barry G.

AU - Hogg, Justin S.

AU - Pusch, Gordon D.

AU - Hiller, Luisa N.

AU - Powell, Evan

AU - Hayes, Jay

AU - Yu, Susan

AU - Kathju, Sandeep

AU - Stoodley, Paul

AU - Post, J. Christopher

AU - Ehrlich, Garth D.

AU - Hu, Fen Z.

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N2 - Background: Staphylococcus aureus is associated with a spectrum of symbiotic relationships with its human host from carriage to sepsis and is frequently associated with nosocomial and community-acquired infections, thus the differential gene content among strains is of interest.Results: We sequenced three clinical strains and combined these data with 13 publically available human isolates and one bovine strain for comparative genomic analyses. All genomes were annotated using RAST, and then their gene similarities and differences were delineated. Gene clustering yielded 3,155 orthologous gene clusters, of which 2,266 were core, 755 were distributed, and 134 were unique. Individual genomes contained between 2,524 and 2,648 genes. Gene-content comparisons among all possible S. aureus strain pairs (n = 136) revealed a mean difference of 296 genes and a maximum difference of 476 genes. We developed a revised version of our finite supragenome model to estimate the size of the S. aureus supragenome (3,221 genes, with 2,245 core genes), and compared it with those of Haemophilus influenzae and Streptococcus pneumoniae. There was excellent agreement between RAST's annotations and our CDS clustering procedure providing for high fidelity metabolomic subsystem analyses to extend our comparative genomic characterization of these strains.Conclusions: Using a multi-species comparative supragenomic analysis enabled by an improved version of our finite supragenome model we provide data and an interpretation explaining the relatively larger core genome of S. aureus compared to other opportunistic nasopharyngeal pathogens. In addition, we provide independent validation for the efficiency and effectiveness of our orthologous gene clustering algorithm.

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