Abstract
Cross-species analysis of microarray data has shown improved discriminating power between healthy and diseased states. Computational approaches have proven effective in deciphering the complexity of human disease by identifying upstream regulatory elements and the transcription factors that interact with them. Here we used both methods to identify highly conserved transcriptional responses during mechanical ventilation, an important therapeutic treatment that has injurious side effects. We generated control and ventilated whole lung samples from the premature baboon model of bronchopulmonary dysplasia (BPD), processed them for microarray, and combined them with existing whole lung oligonucleotide microarray data from 85 additional control samples from mouse, rat, and human and 19 additional ventilated samples from mouse and rat. Of the 2,531 orthologs shared by all 114 samples, 60 were modulated by mechanical ventilation [false discovery rate (FDR)-adjusted q value (q(FDR)) = 0.005, ANOVA]. These included transcripts encoding the transcription factors ATF3 and FOS. Because of compelling known roles for these transcription factors, we used computational methods to predict their targets in the premature baboon model of BPD, which included elastin (ELN), gastrin-releasing polypeptide (GRP), and connective tissue growth factor (CTGF). This approach identified highly conserved transcriptional responses to mechanical ventilation and may facilitate identification of therapeutic targets to reduce the side effects of this valuable treatment.