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

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Abstract

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.

Methods

Microarray 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.

Results

Four 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.

Conclusions

The 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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Most cited references 28

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Is Open Access

Gene selection and classification of microarray data using random forest

Javier Díaz-Uriarte, Sara Alvarez de Andrés (2006)

Background Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of genes that can still achieve good predictive performance (for instance, for future use with diagnostic purposes in clinical practice). Many gene selection approaches use univariate (gene-by-gene) rankings of gene relevance and arbitrary thresholds to select the number of genes, can only be applied to two-class problems, and use gene selection ranking criteria unrelated to the classification algorithm. In contrast, random forest is a classification algorithm well suited for microarray data: it shows excellent performance even when most predictive variables are noise, can be used when the number of variables is much larger than the number of observations and in problems involving more than two classes, and returns measures of variable importance. Thus, it is important to understand the performance of random forest with microarray data and its possible use for gene selection. Results We investigate the use of random forest for classification of microarray data (including multi-class problems) and propose a new method of gene selection in classification problems based on random forest. Using simulated and nine microarray data sets we show that random forest has comparable performance to other classification methods, including DLDA, KNN, and SVM, and that the new gene selection procedure yields very small sets of genes (often smaller than alternative methods) while preserving predictive accuracy. Conclusion Because of its performance and features, random forest and gene selection using random forest should probably become part of the "standard tool-box" of methods for class prediction and gene selection with microarray data.

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Legionnaires' disease: description of an epidemic of pneumonia.

David W. Fraser, Theodore R. Tsai, Walter Orenstein … (1977)

An explosive, common-source outbreak of pneumonia caused by a previously unrecognized bacterium affected primarily persons attending an American Legion convention in Philadelphia in July, 1976. Twenty-nine of 182 cases were fatal. Spread of the bacterium appeared to be air borne. The source of the bacterium was not found, but epidemiologic analysis suggested that exposure may have occurred in the lobby of the headquarters hotel or in the area immediately surrounding the hotel. Person-to-person spread seemed not to have occurred. Many hotel employees appeared to be immune, suggesting that the agent may have been present in the vicinity, perhaps intermittently, for two or more years.

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Distribution of Legionella species and serogroups isolated by culture in patients with sporadic community-acquired legionellosis: an international collaborative survey.

M Pastoris, Andreas F. Widmer, T File … (2002)

This international collaborative survey identified culture-confirmed legionellosis in 508 patients with sporadic community-acquired legionellosis. Legionella pneumophila constituted 91.5% of the isolates. Serogroup 1 was the predominant serogroup (84.2%), and serogroups 2-13 (7.4%) accounted for the remaining serogroups. The Legionella species most commonly isolated were L. longbeachae (3.9%) and L. bozemanii (2.4%), followed by L. micdadei, L. dumoffii, L. feeleii, L. wadsworthii, and L. anisa (2.2% combined). L. longbeachae constituted 30.4% of the community-acquired Legionella isolates in Australia and New Zealand.

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Author and article information

Contributors

Yousef Abu Kwaik: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, USA )

ISSN (Electronic): 1932-6203

Publication date Collection: 2012

Publication date (Electronic): 18 October 2012

Volume: 7

Issue: 10

Electronic Location Identifier: e47437

Affiliations

[1 ]Regional Public Health Laboratory Kennemerland, Haarlem, The Netherlands

[2 ]Department of Community Medicine, United Arab Emirates University, Al-Ain, United Arab Emirates

[3 ]TNO Microbiology and Systems Biology, Zeist, The Netherlands

University of Louisville, United States of America

Author notes

* E-mail: s.euser@ 123456streeklabhaarlem.nl

Competing Interests: Vitens water supply company ( www.vitens.nl) has provided financial support for this study, and this does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

Conceived and designed the experiments: SE NN FS JD. Performed the experiments: SE NN FS RJ. Analyzed the data: SE NN FS RJ JD. Contributed reagents/materials/analysis tools: RJ NN. Wrote the paper: SE NN FS RJ JD.

Article

Publisher ID: PONE-D-12-16769

DOI: 10.1371/journal.pone.0047437

PMC ID: 3475688

PubMed ID: 23094048

SO-VID: 40e88cbd-1572-4ea5-8357-db86bb9c8853

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 11 June 2012

Date accepted : 17 September 2012

Page count

Pages: 6

Funding

This study was supported by a grant from Vitens water supply company ( www.vitens.nl). This company had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

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Abstract

Background

Methods

Results

Conclusions

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Most cited references 28

Gene selection and classification of microarray data using random forest

Legionnaires' disease: description of an epidemic of pneumonia.

Distribution of Legionella species and serogroups isolated by culture in patients with sporadic community-acquired legionellosis: an international collaborative survey.

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