Computational identification of key biological modules and transcription factors in acute lung injury

Mechanical ventilation augments the acute lung injury (ALI) caused by bacterial products. The molecular pathogenesis of this synergistic interaction remains incompletely understood.

We sought to develop a computational framework to systematically identify gene regulatory networks activated in ALI.

We have developed a mouse model in which the combination of mechanical ventilation and intratracheal LPS produces significantly more injury to the lung than either insult alone. We used global gene ontology analysis to determine overrepresented biological modules and computational transcription factor analysis to identify putative regulatory factors involved in this model of ALI.

By integrating expression profiling with gene ontology and promoter analysis, we constructed a large-scale regulatory modular map of the important processes activated in ALI. This map assigned differentially expressed genes to highly overrepresented 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 IFN-stimulated response element, IRF7, and Sp1, that may regulate critical processes involved in the pathogenesis of ALI.

We present a novel, unbiased, and powerful computational approach to investigate the synergistic effects of mechanical ventilation and LPS in promoting ALI. Our methodology is applicable to any expression profiling experiment involving eukaryotic organisms.