Identification of new biomarkers for Acute Respiratory Distress Syndrome by expression-based genome-wide association study

Accumulated to-date gene microarray data on Acute Respiratory Distress Syndrome (ARDS) in the Gene Expression Omnibus (GEO) represent a rich source for identifying new unsuspected targets and mechanisms of ARDS. The recently developed expression-based genome-wide association study (eGWAS) for analysis of GEO data was successfully used for analysis of gene expression of comparatively noncomplex adipose tissue, 75 % of which is represented by adipocytes. Although lung tissue is more heterogenic and does not possess a prevalent cell type for driving gene expression patterns, we hypothesized that eGWAS of ARDS samples will generate biologically meaningful

Our first in the field application of eGWAS in ARDS and utilization of more than 120 publicly available microarray samples of ARDS not only justified applicability of eGWAS to complex lung tissue, but also discovered 14 new candidate genes which associated with ARDS. Detailed studies of these new candidates might lead to identification of unsuspected evolutionarily conserved mechanisms triggered by ARDS.

The eGWAS was conducted according to (Proc Natl Acad Sci U S A 109:7049-7054, 2012) and genes were ranked according to p values of chi-square test.

The search of GEO retrieved 487 ARDS related entries. These entries were filtered for multiple qualitative and quantitative conditions and 219 samples were selected: mouse n sham/ARDS = 67/92, rat n = 13/13, human cells n = 11/11, canine n = 6/6 with the following ARDS model distributions: mechanical ventilation (MV)/cyclic stretch n = 11; endotoxin (LPS) treatment n = 8; MV + LPS n = 3; distant organ injury induced ARDS n = 3; chemically induced ARDS n = 2; Staphylococcus aureus induced ARDS n = 2; and one experiment each for radiation and shock induced ARDS. The eGWAS of this dataset identified 42 significant (Bonferroni threshold P < 1.55 × 10(-6)) genes. 66.6 % of these genes, were associated previously with lung injury and include the well known ARDS genes such as IL1R2 (P = 4.42 × 10(-19)), IL1β (P = 3.38 × 10(-17)), PAI1 (P = 9.59 × 10(-14)), IL6 (P = 3.57 × 10(-12)), SOCS3 (P = 1.05 × 10(-10)), and THBS1 (P = 2.01 × 10(-9)). The remaining genes were new ARDS candidates. Expression of the most prominently upregulated genes, CLEC4E (P = 4.46 × 10(-14)) and CD300LF (P = 2.31 × 10(-16)), was confirmed by real time PCR. The former was also validated by in silico pathway analysis and the latter by Western blot analysis. Conclusions: Our first in the field application of eGWAS in ARDS and utilization of more than 120 publicly available microarray samples of ARDS not only justified applicability of eGWAS to complex lung tissue, but also discovered 14 new candidate genes which associated with ARDS. Detailed studies of these new candidates might lead to identification of unsuspected evolutionarily conserved mechanisms triggered by ARDS.