Science review: searching for gene candidates in acute lung injury

Abstract

Acute lung injury (ALI) is a complex and devastating illness, often occurring within the setting of sepsis, and carries an annual mortality rate of 30-50%. Although the genetic basis of ALI has not been fully established, an increasing body of evidence suggests that genetic predisposition contributes to disease susceptibility and severity. Significant difficulty exists, however, in defining the exact nature of these genetic factors, including large phenotypic variance, incomplete penetrance, complex gene-environment interactions, and strong potential for locus heterogeneity. We utilized the candidate gene approach and an ortholog gene database to provide relevant gene ontologies and insights into the genetic basis of ALI. We employed a Medline search of selected basic and clinical studies in the English literature and studies sponsored by the HopGene National Institutes of Health sponsored Program in Genomic Applications. Extensive gene expression profiling studies in animal models of ALI (rat, murine, canine), as well as in humans, were performed to identify potential candidate genes http://www.hopkins-genomics.org/. We identified a number of candidate genes for ALI, with blood coagulation and inflammation gene ontologies being the most highly represented. The candidate gene approach coupled with extensive gene profiling and novel bioinformatics approaches is a valuable way to identify genes that are involved in ALI.

Figure 1

The approach to identify mechanical stress induced candidate genes using schematic representation of cross-species ortholog database and gene ontology processes. Total RNA from rat, mouse, and canine ventilator-injured lung tissues and human endothelial cell cultures exposed to injurious mechanical stretch was extracted, and gene expression data were generated by hybridization of corresponding total mRNAs to HG-U34A, HG-U74A, HG-U133A, and HG-U95A Affymetrix arrays, respectively. These steps were performed at the PGA-HopGene center. Gene expression profiles were analyzed by the HopGene PGA center using in silico multispecies cross-platform database applying the RESOURCERER tool designed by the TIGR PGA center. Gene ontology assignment and analysis was conducted by HopGene PGA center using the MAPPFinder tool designed by the BayGenomics PGA center. The acute lung injury (ALI)-related ontologic groups were selected using filtering conditions described in Table 1. The contribution of each bioprocess to ALI is represented as the percentage of values calculated by MAPPFinder and shown in the percentage changed genes column. The literature search was done by the HopGene PGA center using PubMatrix tool designed by PGA collaborators at the National Institute on Aging, National Institutes of Health.