ArticlePloS one2010
Identification and interpretation of longitudinal gene expression changes in trauma.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Genetic regulation of gene expression and splicing during a 10-year period of human aging.Genome biology · 2019Article
- Inhibition of Hif1α prevents both trauma-induced and genetic heterotopic ossification.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Time-Course Gene Set Analysis for Longitudinal Gene Expression Data.PLoS computational biology · 2015Article
- Genomics of injury: The Glue Grant experience.The journal of trauma and acute care surgery · 2015Article
- A recursively partitioned mixture model for clustering time-course gene expression data.Translational cancer research · 2014Article
- Algebraic Statistical Model for Biochemical Network Dynamics Inference.Journal of coupled systems and multiscale dynamics · 2013Article
- A genomic analysis of Clostridium difficile infections in blunt trauma patients.The journal of trauma and acute care surgery · 2013Article
- Life or death? A physiogenomic approach to understand individual variation in responses to hemorrhagic shock.Current genomics · 2011Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
rationaleThe relationship between leukocyte gene expression and recovery of respiratory function after injury may provide information on the etiology of multiple organ dysfunction.
objectivesTo find a list of genes for which expression after injury predicts respiratory recovery, and to identify which networks and pathways characterize these genes.
methodsBlood was sampled at 12 hours and at 1, 4, 7, 21 and 28 days from 147 patients who had been admitted to the hospital after blunt trauma. Leukocyte gene expression was measured using Affymetrix oligonucleotide arrays. A linear model, fit to each probe-set expression value, was used to impute the gene expression trajectory over the entire follow-up period. The proportional hazards model score test was used to calculate the statistical significance of each probe-set trajectory in predicting respiratory recovery. A list of genes was determined such that the expected proportion of false positive results was less than 10%. These genes were compared to the Gene Ontology for 'response to stimulus' and, using Ingenuity software, were mapped into networks and pathways. MEASUREMENTS AND MAIN
resultsThe median time to respiratory recovery was 6 days. There were 170 probe-sets representing 135 genes that were found to be related to respiratory recovery. These genes could be mapped to nine networks. Two known pathways that were activated were antigen processing and presentation and JAK-signaling.
conclusionsThe examination of the relationship of gene expression over time with a patient's clinical course can provide information which may be useful in determining the mechanism of recovery or lack of recovery after severe injury.
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