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

Lei Yang, Martin Holm Rau, Liang Yang, Niels Høiby, Søren Molin, Lars Jelsbak

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    Abstract

    Background: 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. Results: 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. Conclusions: 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.
    Original languageEnglish
    JournalB M C Microbiology
    Volume11
    Pages (from-to)184
    ISSN1471-2180
    DOIs
    Publication statusPublished - 2011

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