Abstract
ObjectivesTo perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans.MethodsWe performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients.ResultsTwo injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis.ConclusionTaken together, our data suggests that microarray analysis of gene expression data allows for the detection of “injury" gene predictors that can classify lung injury samples and identify patients at risk for clinically relevant lung injury complications.
Highlights
Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are associated with significant morbidity and mortality (30–50%) [1]–[3]
We demonstrated the proof of concept by validating our approach using both animal and human external microarray data sets publicly available from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO)
We considered two main comparisons: the one-hit model which compared chips from animals exposed to ‘‘one-hit’’ models of overventilation lung injury: no ventilation (NV) or minimally injurious ventilation vs. injurious ventilation; and the two-hit model which compared chips from animals exposed to ‘‘two-hit’’ models of lung injury, lung inflammation alone (Inf) vs. mechanical ventilation (MV)+Inf
Summary
Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are associated with significant morbidity and mortality (30–50%) [1]–[3]. Despite advances in supportive care, no therapies have shown benefit in large randomized clinical trials, other than the use of lung protective mechanical ventilation (MV) strategies. One reason for the lack of positive clinical trials may relate to our incomplete understanding of the pathogenesis of this syndrome. The paucity of ALI tissues for diagnostic and pathological studies, the high rate of intra-observer variability and the discrepancies between clinical and autopsy findings make it difficult to select patients for ongoing clinical trials and/or to identify clinically relevant classifiers of subgroups of patients for therapy. There is an urgent need to translate biologically relevant information to patients with lung injury
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