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Comparative genome analysis of a large Dutch Legionella pneumophila strain collection identifies five markers highly correlated with clinical strains

BACKGROUND: Discrimination 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 p...

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Autores principales: Yzerman, Ed, den Boer, Jeroen W, Caspers, Martien, Almal, Arpit, Worzel, Bill, van der Meer, Walter, Montijn, Roy, Schuren, Frank
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3091632/
https://www.ncbi.nlm.nih.gov/pubmed/20630115
http://dx.doi.org/10.1186/1471-2164-11-433
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author Yzerman, Ed
den Boer, Jeroen W
Caspers, Martien
Almal, Arpit
Worzel, Bill
van der Meer, Walter
Montijn, Roy
Schuren, Frank
author_facet Yzerman, Ed
den Boer, Jeroen W
Caspers, Martien
Almal, Arpit
Worzel, Bill
van der Meer, Walter
Montijn, Roy
Schuren, Frank
author_sort Yzerman, Ed
collection PubMed
description BACKGROUND: Discrimination 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. RESULTS: Microarray 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. CONCLUSIONS: A 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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spelling pubmed-30916322011-05-11 Comparative genome analysis of a large Dutch Legionella pneumophila strain collection identifies five markers highly correlated with clinical strains Yzerman, Ed den Boer, Jeroen W Caspers, Martien Almal, Arpit Worzel, Bill van der Meer, Walter Montijn, Roy Schuren, Frank BMC Genomics Research Article BACKGROUND: Discrimination 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. RESULTS: Microarray 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. CONCLUSIONS: A 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. BioMed Central 2010-07-15 /pmc/articles/PMC3091632/ /pubmed/20630115 http://dx.doi.org/10.1186/1471-2164-11-433 Text en Copyright ©2010 Yzerman et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yzerman, Ed
den Boer, Jeroen W
Caspers, Martien
Almal, Arpit
Worzel, Bill
van der Meer, Walter
Montijn, Roy
Schuren, Frank
Comparative genome analysis of a large Dutch Legionella pneumophila strain collection identifies five markers highly correlated with clinical strains
title Comparative genome analysis of a large Dutch Legionella pneumophila strain collection identifies five markers highly correlated with clinical strains
title_full Comparative genome analysis of a large Dutch Legionella pneumophila strain collection identifies five markers highly correlated with clinical strains
title_fullStr Comparative genome analysis of a large Dutch Legionella pneumophila strain collection identifies five markers highly correlated with clinical strains
title_full_unstemmed Comparative genome analysis of a large Dutch Legionella pneumophila strain collection identifies five markers highly correlated with clinical strains
title_short Comparative genome analysis of a large Dutch Legionella pneumophila strain collection identifies five markers highly correlated with clinical strains
title_sort comparative genome analysis of a large dutch legionella pneumophila strain collection identifies five markers highly correlated with clinical strains
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3091632/
https://www.ncbi.nlm.nih.gov/pubmed/20630115
http://dx.doi.org/10.1186/1471-2164-11-433
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