Science review: Searching for gene candidates in acute lung injury

被引:0
作者
Dmitry N Grigoryev
James H Finigan
Paul Hassoun
Joe GN Garcia
机构
[1] Johns Hopkins University School of Medicine,Center for Translational Respiratory Medicine, Division of Pulmonary and Critical Care Medicine
[2] Johns Hopkins University School of Medicine,Center for Translational Respiratory Medicine, Division of Pulmonary and Critical Care Medicine
[3] Johns Hopkins University School of Medicine,Center for Translational Respiratory Medicine, Division of Pulmonary and Critical Care Medicine
来源
Critical Care | / 8卷
关键词
acute lung injury; candidate genes; gene expression; gene ontology; microarrays; polymorphisms;
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摘要
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.
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