Published ahead of print on November 20, 2007, doi:10.1164/rccm.200702-333OC
© 2008 American Thoracic Society doi: 10.1164/rccm.200702-333OC
Disease-Specific Gene Expression Profiling in Multiple Models of Lung Disease1 Lung Biology Center, University of California, San Francisco, San Francisco, California; 2 School of Mathematics and Statistics, University of Sydney, New South Wales, Australia; 3 Department of Biochemistry and Molecular Biology, University of Texas–Houston Medical School, Houston Texas; 4 Cardiovascular Research Institute, Comprehensive Cancer Center, and Department of Anatomy, University of California, San Francisco, San Francisco, California; 5 Departments of Medicine, and Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee; 6 MedImmune Vaccines, Mountain View, California; 7 Gladstone Institute of Cardiovascular Disease, San Francisco, California; and 8 Institut für Immunologie, Ludwig Maximilian University, Munich, Germany Correspondence and requests for reprints should be addressed to Christina C. Lewis, Ph.D., Cincinnati Children's Hospital Medical Center/Division of Immunobiology, 3333 Burnet Avenue, MLC 7038, Cincinnati, OH 45229. 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 in order 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 polymerase chain reaction.
Measurements and Main Results: A total of 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 three groups of models: (1) bacterial infection (n = 5), with changes in 89 transcripts, including many related to nuclear factor- Conclusions: This multimodel 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.
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) T helper cell type 2 (allergic) inflammation (n = 5), with changes in 26 transcripts, including many encoding epithelial secreted molecules, ion channels, and transporters. 
