Published ahead of print on October 5, 2007, doi:10.1165/rcmb.2007-0151OC
© 2008 American Thoracic Society DOI: 10.1165/rcmb.2007-0151OC A Functional and Regulatory Map of Asthma1 School of Computer Science and Engineering, 2 Department of Molecular Genetics and Biotechnology, Faculty of Medicine, The Hebrew University, Jerusalem; 3 Department of Computer Science and Applied Mathematics, The Weizmann Institute of Science, Rehovot, Israel; and 4 Dorothy P. and Richard P. Simmons Center for Interstitial Lung Diseases, Division of Pulmonary and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania Correspondence and requests for reprints should be addressed to Naftali Kaminski, M.D., University of Pittsburgh Medical Center, NW 628 MUH, 3459 5th Avenue, Pittsburgh, PA 15261. E-mail: kaminskin{at}upmc.edu The prevalence and morbidity of asthma, a chronic inflammatory airway disease, is increasing. Animal models provide a meaningful but limited view of the mechanisms of asthma in humans. A systems-level view of asthma that integrates multiple levels of molecular and functional information is needed. For this, we compiled a gene expression compendium from five publicly available mouse microarray datasets and a gene knowledge base of 4,305 gene annotation sets. Using this collection we generated a high-level map of the functional themes that characterize animal models of asthma, dominated by innate and adaptive immune response. We used Module Networks analysis to identify co-regulated gene modules. The resulting modules reflect four distinct responses to treatment, including early response, general induction, repression, and IL-13–dependent response. One module with a persistent induction in response to treatment is mainly composed of genes with suggested roles in asthma, suggesting a similar role for other module members. Analysis of IL-13–dependent response using protein interaction networks highlights a role for TGF-β1 as a key regulator of asthma. Our analysis demonstrates the discovery potential of systems-level approaches and provides a framework for applying such approaches to asthma.
Key Words: house dust mite • IL-13 • ovalbumin • systems biology • TGF-β
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