A novel genomic signature with translational significance for human idiopathic pulmonary fibrosis.
Yasmina Bauer, John Tedrow, Simon de Bernard, Magdalena Birker-Robaczewska, Kevin F Gibson, Brenda Juan Guardela, Patrick Hess, Axel Klenk, Kathleen O Lindell, Sylvie Poirey, Bérengère Renault, Markus Rey, Edgar Weber, Oliver Nayler, Naftali Kaminski
文献索引:Am. J. Respir. Cell. Mol. Biol. 52(2) , 217-31, (2015)
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摘要
The bleomycin-induced rodent lung fibrosis model is commonly used to study mechanisms of lung fibrosis and to test potential therapeutic interventions, despite the well recognized dissimilarities to human idiopathic pulmonary fibrosis (IPF). Therefore, in this study, we sought to identify genomic commonalities between the gene expression profiles from 100 IPF lungs and 108 control lungs that were obtained from the Lung Tissue Research Consortium, and rat lungs harvested at Days 3, 7, 14, 21, 28, 42, and 56 after bleomycin instillation. Surprisingly, the highest gene expression similarity between bleomycin-treated rat and IPF lungs was observed at Day 7. At this point of maximal rat-human commonality, we identified a novel set of 12 disease-relevant translational gene markers (C6, CTHRC1, CTSE, FHL2, GAL, GREM1, LCN2, MMP7, NELL1, PCSK1, PLA2G2A, and SLC2A5) that was able to separate almost all patients with IPF from control subjects in our cohort and in two additional IPF/control cohorts (GSE10667 and GSE24206). Furthermore, in combination with diffusing capacity of carbon monoxide measurements, four members of the translational gene marker set contributed to stratify patients with IPF according to disease severity. Significantly, pirfenidone attenuated the expression change of one (CTHRC1) translational gene marker in the bleomycin-induced lung fibrosis model, in transforming growth factor-β1-treated primary human lung fibroblasts and transforming growth factor-β1-treated human epithelial A549 cells. Our results suggest that a strategy focused on rodent model-human disease commonalities may identify genes that could be used to predict the pharmacological impact of therapeutic interventions, and thus facilitate the development of novel treatments for this devastating lung disease.
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