Interleukin-5 messenger RNA expression in peripheral blood CD4 + cells in asthma

Background: IL-5 has been implicated in the pathogenesis of asthma through its regulatory role on eosinophil survival, proliferation, and effector function. Objective: The study was designed to investigate the relationships between IL-5 messenger RNA expression in circulating CD4 + cells and serum concentrations of eosinophil cationic protein (ECP), a marker of eosinophil activation and disease activity in asthma. Methods: IL-5 gene expression was assessed semiquantitatively in ex-vivo stimulate CD4 + cells by reverse transcription-polymerase chain reaction and serum ECP concentration measured from venous blood samples collected from patients with acute severe asthma before the commencement of systemic steroid therapy (day 1) and on day 7 and from patients with stable and healthy volunteers. Results IL-5 gene expression was significantly higher in patients with acute asthma before steroid treatment than in those with stable disease and healthy subjects (p<0.0001). Similar results were obtained with serum ECP levels; levels in patients with acute asthma were highest (20.30 ± 5.31 μg/L), followed by levels in patients with stable asthma (2.76 ± 0.65 2mg/L) and levels in normal control subjects (1.37 ± 0.06 μg/L; p < 0.01 for all comparisons). Significant falls in both IL-5 expression and serum ECP level were seen on day 7 (p < 0.001) and coincided with a significant improvement in peak expiratory flow (p < 0001). Significant correlations were observed between IL-5 expressions and ECP level (p = 0.39, p <0.01), IL-5 expression and peak expiratory flow (p = -0.55, p < 0.0002), and peak expiratory flow and ECP level (p = -0.32, p < 0.04). Conclusions: Our data therefore support an important regulatory role of IL-5 on eosinophil function in human asthma in vivo.link_to_subscribed_fulltex

Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments

Structure probing coupled with high-throughput sequencing could revolutionize our understanding of the role of RNA structure in regulation of gene expression. Despite recent technological advances, intrinsic noise and high sequence coverage requirements greatly limit the applicability of these techniques. Here we describe a probabilistic modeling pipeline that accounts for biological variability and biases in the data, yielding statistically interpretable scores for the probability of nucleotide modification transcriptome wide. Using two yeast data sets, we demonstrate that our method has increased sensitivity, and thus our pipeline identifies modified regions on many more transcripts than do existing pipelines. Our method also provides confident predictions at much lower sequence coverage levels than those recommended for reliable structural probing. Our results show that statistical modeling extends the scope and potential of transcriptome-wide structure probing experiments