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

Yzerman, Ed P. F.; Boer, Jeroen W. Den; Caspers, Martien P. M.; Almal, Arpit A.; Worzel, Bill; Meer, Walter van der; Montijn, R.C.; Schuren, Frank

doi:10.1186/1471-2164-11-433

Cited by 13 publications

(17 citation statements)

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“…A supervised statistical analysis using Genetic Programming was used to identity DNA markers that could discriminate between clinical and environmental Legionella strains, and a model consisting of five markers was developed to predict the origin of a strain: clinical or environmental. The final model correctly predicted 100% of the clinical strains and 69% of the environmental strains [12]. Despite these promising results, there might be other methodological approaches that could lead to (at least) comparable predictive performances.…”

Section: Introductionmentioning

confidence: 96%

Genome Analysis of Legionella pneumophila Strains Using a Mixed-Genome Microarray

Euser

Nagelkerke

Schuren³

et al. 2012

PLoS ONE

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Background Legionella, the causative agent for Legionnaires’ disease, is ubiquitous in both natural and man-made aquatic environments. The distribution of Legionella genotypes within clinical strains is significantly different from that found in environmental strains. Developing novel genotypic methods that offer the ability to distinguish clinical from environmental strains could help to focus on more relevant (virulent) Legionella species in control efforts. Mixed-genome microarray data can be used to perform a comparative-genome analysis of strain collections, and advanced statistical approaches, such as the Random Forest algorithm are available to process these data.MethodsMicroarray analysis was performed on a collection of 222 Legionella pneumophila strains, which included patient-derived strains from notified cases in the Netherlands in the period 2002–2006 and the environmental strains that were collected during the source investigation for those patients within the Dutch National Legionella Outbreak Detection Programme. The Random Forest algorithm combined with a logistic regression model was used to select predictive markers and to construct a predictive model that could discriminate between strains from different origin: clinical or environmental.ResultsFour genetic markers were selected that correctly predicted 96% of the clinical strains and 66% of the environmental strains collected within the Dutch National Legionella Outbreak Detection Programme.ConclusionsThe Random Forest algorithm is well suited for the development of prediction models that use mixed-genome microarray data to discriminate between Legionella strains from different origin. The identification of these predictive genetic markers could offer the possibility to identify virulence factors within the Legionella genome, which in the future may be implemented in the daily practice of controlling Legionella in the public health environment.

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Section: Introductionmentioning

confidence: 96%

Genome Analysis of Legionella pneumophila Strains Using a Mixed-Genome Microarray

Euser

Nagelkerke

Schuren³

et al. 2012

PLoS ONE

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“…The mixed-genome microarray used in this study has been described elsewhere, and was developed using a systematic collection of Dutch Legionella strains [19]. In short, four clinical Legionella isolates and four environmental L .…”

Section: Methodsmentioning

confidence: 99%

Prediction of the origin of French Legionella pneumophila strains using a mixed-genome microarray

et al. 2013

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BackgroundLegionella is a water and soil bacterium that can infect humans, causing a pneumonia known as Legionnaires’ disease. The pneumonia is almost exclusively caused by the species L. pneumophila, of which serogroup 1 is responsible for 90% of patients. Within serogroup 1, large differences in prevalence in clinical isolates have been described. A recent study, using a Dutch Legionella strain collection, identified five virulence associated markers. In our study, we verify whether these five Dutch markers can predict the patient or environmental origin of a French Legionella strain collection. In addition, we identify new potential virulence markers and verify whether these can predict better. A total of 219 French patient isolates and environmental strains were compared using a mixed-genome micro-array. The micro-array data were analysed to identify predictive markers, using a Random Forest algorithm combined with a logistic regression model. The sequences of the identified markers were compared with eleven known Legionella genomes, using BlastN and BlastX; the functionality for each of the predictive markers was checked in the literature.ResultsThe five Dutch markers insufficiently predicted the patient or environmental origin of the French Legionella strains. Subsequent analyses identified four predictive markers for the French collection that were used for the logistic regression model. This model showed a negative predictive value of 91%. Three of the French markers differed from the Dutch markers, one showed considerable overlap and was found in one of the Legionella genomes (Lorraine strain). This marker encodes for a structural toxin protein RtxA, described for L. pneumophila as a factor involved in virulence and entry in both human cells and amoebae.ConclusionsThe combination of a mixed-genome micro-array and statistical analysis using a Random Forest algorithm has identified virulence markers in a consistent way. The Lorraine strain and related Dutch and French Legionella strains contain a marker that encodes a RtxA protein which probably is involved in the increased prevalence in clinical isolates. The current set of predictive markers is insufficient to justify its use as a reliable test in the public health field in France. Our results suggest that genetic differences in Legionella strains exist between geographically distinct entities. It may be necessary to develop region-specific mixed-genome microarrays that are constantly adapted and updated.

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“…Despite the positive PCR results, L. pneumophila could not be cultivated from the same water sample on a selective agar medium (buffered charcoal yeast extract agar) for Legionella. The discrepancy between the qPCR and cultivation results can be caused by several factors: (i) the observed mip gene of L. pneumophila came from dead L. pneumophila cells, (ii) not all L. pneumophila strains in drinking water can be cultivated on the selective agar medium or (iii) the agar plates were overgrown with other Legionella species, preventing the detection of L. pneumophila. The consequence of these findings for public health remains uncertain, also because not all L. pneumophila strains are pathogenic (Yzerman et al 2010).…”

Section: Opportunistic Pathogensmentioning

confidence: 99%

Microbial Growth in Drinking-Water Supplies: Problems, Causes, Control and Research Needs

Kooij¹,

Wielen²

2013

wio

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

Cited by 13 publications

References 24 publications

Genome Analysis of Legionella pneumophila Strains Using a Mixed-Genome Microarray

Genome Analysis of Legionella pneumophila Strains Using a Mixed-Genome Microarray

Prediction of the origin of French Legionella pneumophila strains using a mixed-genome microarray

Microbial Growth in Drinking-Water Supplies: Problems, Causes, Control and Research Needs

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