ArticleBMC genomics2010
Comparative genome analysis of a large Dutch Legionella pneumophila strain collection identifies five markers highly correlated with clinical strains.
Article in BMC genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Genomic heterogeneity differentiates clinical and environmental subgroups of Legionella pneumophila sequence type 1.PloS one · 2018Article
- Multiplex polymerase chain reaction of genetic markers for detection of potentially pathogenic environmentalThe Indian journal of medical research · 2017Article
- Prediction of the origin of French Legionella pneumophila strains using a mixed-genome microarray.BMC genomics · 2013Article
- Comparative molecular analyses of Borrelia burgdorferi sensu stricto strains B31 and N40D10/E9 and determination of their pathogenicity.BMC microbiology · 2012Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDiscrimination between clinical and environmental strains within many bacterial species is currently underexplored. Genomic analyses have clearly shown the enormous variability in genome composition between different strains of a bacterial species. In this study we have used Legionella pneumophila, the causative agent of Legionnaire's disease, to search for genomic markers related to pathogenicity. During a large surveillance study in The Netherlands well-characterized patient-derived strains and environmental strains were collected. We have used a mixed-genome microarray to perform comparative-genome analysis of 257 strains from this collection.
resultsMicroarray analysis indicated that 480 DNA markers (out of in total 3360 markers) showed clear variation in presence between individual strains and these were therefore selected for further analysis. Unsupervised statistical analysis of these markers showed the enormous genomic variation within the species but did not show any correlation with a pathogenic phenotype. We therefore used supervised statistical analysis to identify discriminating markers. Genetic programming was used both to identify predictive markers and to define their interrelationships. A model consisting of five markers was developed that together correctly predicted 100% of the clinical strains and 69% of the environmental strains.
conclusionsA novel approach for identifying predictive markers enabling discrimination between clinical and environmental isolates of L. pneumophila is presented. Out of over 3000 possible markers, five were selected that together enabled correct prediction of all the clinical strains included in this study. This novel approach for identifying predictive markers can be applied to all bacterial species, allowing for better discrimination between strains well equipped to cause human disease and relatively harmless strains.
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