Digital gene expression analysis of transcriptomes in lipopolysaccharide-induced acute respiratory distress syndrome

The mortality from acute respiratory distress syndrome (ARDS) is high, and its exact pathogenesis remains unclear, which forms a major obstacle for prevention and treatment of this disease. In the present study, we used digital gene expression (DGE) to detect the differentially expressed genes of the lung at 4h after lipopolysaccharide (LPS) exposure in a mouse model.

Mice were treated with LPS or control saline by intratracheal instillation for 4h, and their lung tissues were collected for DGE analysis. We used a false discovery rate ≤0.001 and an absolute value of the log2 ratio≥1 as the thresholds for judging the significance of any difference in gene expression between the two members of each pair of mice.

We obtained 3,387,842 clean tags (i.e., after filtering to remove potentially erroneous tags) and about 84,513 corresponding distinct clean tags (i.e., types of tag). Approximately 91.20% of the clean tags could be mapped, and 82.71% could be uniquely mapped, to the reference tags, and 3.82% were unknown tags. At least 2200 differentially expressed genes were identified and analyzed for enrichment of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway. Twenty genes with the greatest difference in expression levels between the two members of every pair of mice were chosen. The majority of these genes are involved in signaling transduction, molecular adhesion, and metabolic pathways.

Using the powerful technology of DGE, we present, to our knowledge, the first in-depth transcriptomic analysis of mouse lungs after LPS exposure. We found some differentially expressed genes that might play important roles in the pathogenesis of ARDS.