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Bacterial adaptation during chronic infection revealed by independent component analysis of transcriptomic data

Lei Yang1, Martin Holm Rau1, Liang Yang1*, Niels Høiby2, Søren Molin1 and Lars Jelsbak1

Author affiliations

1 Department of Systems Biology, Technical University of Denmark, DK-2800, Lyngby, Denmark

2 Faculty Of Health Sciences, Department of International Health, Immunology and Microbiology, University of Copenhagen, DK-2200, København N, Denmark

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Citation and License

BMC Microbiology 2011, 11:184  doi:10.1186/1471-2180-11-184

Published: 18 August 2011



Bacteria employ a variety of adaptation strategies during the course of chronic infections. Understanding bacterial adaptation can facilitate the identification of novel drug targets for better treatment of infectious diseases. Transcriptome profiling is a comprehensive and high-throughput approach for characterization of bacterial clinical isolates from infections. However, exploitation of the complex, noisy and high-dimensional transcriptomic dataset is difficult and often hindered by low statistical power.


In this study, we have applied two kinds of unsupervised analysis methods, principle component analysis (PCA) and independent component analysis (ICA), to extract and characterize the most informative features from transcriptomic dataset generated from cystic fibrosis (CF) Pseudomonas aeruginosa isolates. ICA was shown to be able to efficiently extract biological meaningful features from the transcriptomic dataset and improve clustering patterns of CF isolates. Decomposition of the transcriptomic dataset by ICA also facilitates gene identification and gene ontology enrichment.


Our results show that P. aeruginosa employs multiple patient-specific adaption strategies during the early stage infections while certain essential adaptations are evolved in parallel during the chronic infections.