Exon skipping variations modulate stress-inducible inflammation

Psychological stress reactions can stimulate mammalian immune functions due to yet unknown mechanisms. We hypothesized that these involve massive post-stress alternative splicing modulations in peripheral blood mononuclear cells (PBMCs). RNA was extracted from PBMCs of BALB/C mice following unpredictable repeated foot shocks. Among the tested group, five mice exhibiting the maximal circulation glucocorticoids were selected for the stress group. PBMC RNA of 5 BALB/C mice served as the control group. Through linear regression analysis of all the reciprocal junction pairs represented on the microarrays and the Ensembl database, 496 alternative splicing events were detected. The stressed mice showed 65% exon skipping out of total splicing event changes compared to controls. The detected genes exhibited functional enrichment (through DAVID EASE analysis) in alternative splicing (47%), cellular response to stress (12%), lymphocyte activation (8%), stress-induced proteins (2%) and heat-shock-induced proteins (2%). Specifically, exon skipping modifications in the Hnrnph1 and CLK1 splicing-related transcripts were accompanied by stress-inducible inclusions in the immune response-related IRF-1 gene. Our findings demonstrate dependence on exon skipping and independence from glucocorticoid and innate immunity for the stress-inducible exacerbation of immunity and open new venues for preventing post-trauma inflammatory crisis. 11 samples were analyzed. Mouse Junction Array (MJAY) expression data was obtained from the RNA of PBMCs from 5 stressed and 5 control BALB/C mice, with 1 restained stressed sample. Detection of exon inclusion events was conducted through both junction- and exon-level analyses on summarized and normalized junction and exon interrogating probesets (JUC/PSR). The junction-level expression data was analyzed through a linear regression approach on all the array represented reciprocal junction pairs (through adopted AltAnalyze code), and the exon-level expression data was analyzed through a splicing-index analysis approach.The functional relevance of the detected events was deduced through comparison to all the Ensembl transcripts of each gene (e.g., alternative terminus, truncation, nonsense-mediated decay, etc). Protein/motif predictions for significant probesets and over-representation scores were calculated for each impacted microRNA-binding site.