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Open Access Highly Accessed Research article

Clustering of Pseudomonas aeruginosa transcriptomes from planktonic cultures, developing and mature biofilms reveals distinct expression profiles

Richard D Waite1*, Alberto Paccanaro12, Anastasia Papakonstantinopoulou1, Jacob M Hurst1, Mansoor Saqi1, Eddie Littler3 and Michael A Curtis1

Author Affiliations

1 MRC Molecular Pathogenesis Research Unit, Centre for Infectious Disease, Institute of Cell and Molecular Science, Barts and the London, Queen Mary School of Medicine and Dentistry, 4 Newark Street, London, E1 2AT, UK

2 Department of Computer Science, Royal Holloway, University of London Egham, TW20 0EX, UK

3 Medivir UK Ltd, Peterhouse Technology Park, 100 Fulbourn Road, Cambridge CB1 9PT, UK

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BMC Genomics 2006, 7:162  doi:10.1186/1471-2164-7-162

Published: 26 June 2006

Abstract

Background

Pseudomonas aeruginosa is a genetically complex bacterium which can adopt and switch between a free-living or biofilm lifestyle, a versatility that enables it to thrive in many different environments and contributes to its success as a human pathogen.

Results

Transcriptomes derived from growth states relevant to the lifestyle of P. aeruginosa were clustered using three different methods (K-means, K-means spectral and hierarchical clustering). The culture conditions used for this study were; biofilms incubated for 8, 14, 24 and 48 hrs, and planktonic culture (logarithmic and stationary phase). This cluster analysis revealed the existence and provided a clear illustration of distinct expression profiles present in the dataset. Moreover, it gave an insight into which genes are up-regulated in planktonic, developing biofilm and confluent biofilm states. In addition, this analysis confirmed the contribution of quorum sensing (QS) and RpoS regulated genes to the biofilm mode of growth, and enabled the identification of a 60.69 Kbp region of the genome associated with stationary phase growth (stationary phase planktonic culture and confluent biofilms).

Conclusion

This is the first study to use clustering to separate a large P. aeruginosa microarray dataset consisting of transcriptomes obtained from diverse conditions relevant to its growth, into different expression profiles. These distinct expression profiles not only reveal novel aspects of P. aeruginosa gene expression but also provide a growth specific transcriptomic reference dataset for the research community.