Published ahead of print on November 20, 2007, doi:10.1164/rccm.200702-333OC Am. J. Respir. Crit. Care Med., Volume 177, Number 4, February 2008, 376-387 A more recent version of this article appeared on February 15, 2008
Submitted on February 27, 2007 Disease-specific Gene Expression Profiling in Multiple Models of Lung DiseaseChristina C Lewis1*,1 Lung Biology Center, University of California, San Francisco, San Francisco, CA, USA, 2 School of Mathematics and Statistics, University of Sydney, NSW, 2006, Australia, 3 Department of Biochemistry and Molecular Biology, University of Texas-Houston Medical School, Houston, TX, USA, 4 Cardiovascular Research Institute, Comprehensive Cancer Center, and Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA, 5 Departments of Medicine, Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA, 6 MedImmune Vaccines, Mountain View, CA, USA, 7 Gladstone Institute of Cardiovascular Disease, San Francisco, CA, USA, 8 Institut fuer Immunologie, Ludwig Maximilian University, Munich, Germany * To whom correspondence should be addressed. E-mail: cclewis{at}cinci.rr.com.
Rationale: Microarray technology is widely employed for studying the molecular mechanisms underlying complex diseases. However, analyses of individual diseases or models of diseases frequently yield extensive lists of differentially expressed genes with uncertain relationships to disease pathogenesis.
Objectives: To compare gene expression changes in a heterogeneous set of lung disease models to identify common gene expression changes seen in diverse forms of lung pathology as well as relatively small subsets of genes likely to be involved in specific pathophysiological processes.
Methods: We profiled lung gene expression in 12 mouse models of infection, allergy, and lung injury. A linear model was used to estimate transcript expression changes for each model, and hierarchical clustering was used to compare expression patterns between models. Selected
expression changes were verified by quantitative PCR.
Main Results: 24 transcripts including many involved in inflammation and immune activation were differentially expressed in a substantial majority (9 or more) of the models. Expression patterns distinguished 3 groups of models: 1) bacterial infection (n=5), with changes in 89
transcripts including many related to NF- Key words: gene expression, infection, asthma, fibrosis
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B signaling, cytokines, chemokines and their receptors; 2) bleomycin-induced diseases (n=2), with changes in 53 transcripts including many related to matrix remodeling and Wnt signaling; and, 3) Th2 (allergic) inflammation (n=5) with changes in 26 transcripts including many encoding epithelial secreted molecules, ion channels,
and transporters.
Conclusions: This multi-model dataset highlights novel genes likely involved in various pathophysiological processes and will be a valuable resource for the investigation of molecular mechanisms underlying lung disease pathogenesis.
