Published ahead of print on December 30, 2005, doi:10.1164/rccm.200509-1473OC Am. J. Respir. Crit. Care Med., Volume 173, Number 6, March 2006, 653-658 A more recent version of this article appeared on March 15, 2006
Submitted on September 20, 2005 Computational Identification of Key Biologic Modules and Transcription Factors in Acute Lung InjurySina A Gharib1*,1 Department of Medicine, University of Washington, Seattle, WA, USA, 2 Department of Medicine, University of Washington, Seattle, WA, USA; Department of Pathology, University of Washington, Seattle, WA, USA, 3 Department of Medicine, University of Washington, Seattle, WA, USA; Department of Medicine, Veteran Affairs Puget Sound Healthcare System Medical Research Service, Seattle, WA, USA, 4 Department of Medicine, University of Washington, Seattle, WA, USA; Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA * To whom correspondence should be addressed. E-mail: sagharib{at}u.washington.edu.
Rationale: Mechanical ventilation augments the acute lung injury caused by bacterial products. The molecular pathogenesis of this synergistic interaction remains incompletely understood. Objective: We sought to develop a computational framework to systematically identify gene regulatory networks activated in acute lung injury. Methods: We have developed a mouse model in which the combination of mechanical ventilation and intratracheal lipopolysaccharide produces significantly more injury to the lung than either insult alone. We used global gene ontology analysis to determine over-represented biological modules and computational transcription factor analysis to identify putative regulatory factors involved in this model of acute lung injury. Results: By integrating expression profiling with gene ontology and promoter analysis, we constructed a large-scale regulatory modular map of the important processes activated in acute lung injury. This map assigned differentially expressed genes to highly over-represented biological modules, including "defense response", "immune response", and "oxidoreductase activity". These modules were then systematically incorporated into a gene regulatory network that consisted of putative transcription factors, such as ISRE, IRF7, and Sp1, that may regulate critical processes involved in the pathogenesis of acute lung injury. Conclusions: We present a novel, unbiased, and powerful computational approach to investigate the synergistic effects of mechanical ventilation and lipopolysaccharide in promoting acute lung injury. Importantly, our methodology is applicable to any expression profiling experiment involving eukaryotic organisms. Key words: acute lung injury, microarray, transcription factor, gene network
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