Show simple item record

dc.contributor.authorMcMillan, David J
dc.contributor.authorBeiko, R G
dc.contributor.authorGeffers, R
dc.contributor.authorBuer, Jan
dc.contributor.authorSchouls, Leo M
dc.contributor.authorVlaminckx, B J M
dc.contributor.authorWannet, Wim J B
dc.contributor.authorSriprakash, K S
dc.contributor.authorChhatwal, G S
dc.date.accessioned2007-01-03T13:56:10Z
dc.date.available2007-01-03T13:56:10Z
dc.date.issued2006-10-01
dc.identifier.citationClin. Infect. Dis. 2006, 43(7):884-91en
dc.identifier.issn1537-6591
dc.identifier.pmid16941370
dc.identifier.doi10.1086/507537
dc.identifier.urihttp://hdl.handle.net/10029/6782
dc.description.abstractBACKGROUND: The factors behind the reemergence of severe, invasive group A streptococcal (GAS) diseases are unclear, but it could be caused by altered genetic endowment in these organisms. However, data from previous studies assessing the association between single genetic factors and invasive disease are often conflicting, suggesting that other, as-yet unidentified factors are necessary for the development of this class of disease. METHODS: In this study, we used a targeted GAS virulence microarray containing 226 GAS genes to determine the virulence gene repertoires of 68 GAS isolates (42 associated with invasive disease and 28 associated with noninvasive disease) collected in a defined geographic location during a contiguous time period. We then employed 3 advanced machine learning methods (genetic algorithm neural network, support vector machines, and classification trees) to identify genes with an increased association with invasive disease. RESULTS: Virulence gene profiles of individual GAS isolates varied extensively among these geographically and temporally related strains. Using genetic algorithm neural network analysis, we identified 3 genes with a marginal overrepresentation in invasive disease isolates. Significantly, 2 of these genes, ssa and mf4, encoded superantigens but were only present in a restricted set of GAS M-types. The third gene, spa, was found in variable distributions in all M-types in the study. CONCLUSIONS: Our comprehensive analysis of GAS virulence profiles provides strong evidence for the incongruent relationships among any of the 226 genes represented on the array and the overall propensity of GAS to cause invasive disease, underscoring the pathogenic complexity of these diseases, as well as the importance of multiple bacteria and/or host factors.
dc.format.extent195483 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.titleGenes for the majority of group a streptococcal virulence factors and extracellular surface proteins do not confer an increased propensity to cause invasive disease.en
dc.typeArticleen
dc.format.digYES
refterms.dateFOA2018-12-18T14:45:14Z
html.description.abstractBACKGROUND: The factors behind the reemergence of severe, invasive group A streptococcal (GAS) diseases are unclear, but it could be caused by altered genetic endowment in these organisms. However, data from previous studies assessing the association between single genetic factors and invasive disease are often conflicting, suggesting that other, as-yet unidentified factors are necessary for the development of this class of disease. METHODS: In this study, we used a targeted GAS virulence microarray containing 226 GAS genes to determine the virulence gene repertoires of 68 GAS isolates (42 associated with invasive disease and 28 associated with noninvasive disease) collected in a defined geographic location during a contiguous time period. We then employed 3 advanced machine learning methods (genetic algorithm neural network, support vector machines, and classification trees) to identify genes with an increased association with invasive disease. RESULTS: Virulence gene profiles of individual GAS isolates varied extensively among these geographically and temporally related strains. Using genetic algorithm neural network analysis, we identified 3 genes with a marginal overrepresentation in invasive disease isolates. Significantly, 2 of these genes, ssa and mf4, encoded superantigens but were only present in a restricted set of GAS M-types. The third gene, spa, was found in variable distributions in all M-types in the study. CONCLUSIONS: Our comprehensive analysis of GAS virulence profiles provides strong evidence for the incongruent relationships among any of the 226 genes represented on the array and the overall propensity of GAS to cause invasive disease, underscoring the pathogenic complexity of these diseases, as well as the importance of multiple bacteria and/or host factors.


Files in this item

Thumbnail
Name:
mcmillan.pdf
Size:
190.9Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record