Asthma is an inflammatory disease of the airways characterised by episodic airway obstruction resulting in cough, episodic shortness of breath. It is, and is clinically and physiologically heterogeneous. It is estimated that around 300 million people worldwide have the diseaseare diagnosed with asthma, including up to 20% of children (Asher et al, 2006), with 5–10% of these children believed to have severe or difficult-to-treat asthma. Asthma has often been classified in terms of severity and based on clinical diagnostic criteria, but it is now apparent that the heterogeneity that exists at the physiological level is also a feature of the underlying pathological mechanisms (Lotvall et al, 2011). The aim of this study was to identify blood transcriptomics profiles for children diagnosed with asthma or wheeze, and establish whether these profiles suggested endotypes or mechanisms that could underlie disease, or be related to disease severity, in these children. Importantly, given that children are currently treated with the same medicines as adults, we also aimed to compare profiles of children to those of adults with asthma to help determine whether efforts should be directed to the development of medicines targeting pathways and mechanisms that may be unique to children. To this end, we used gene transcriptome data generated from blood samples from adults and children from the U-BIOPRED consortium to ask how similar or different the differential gene expression profiles were between groups of adults and pre-school or school-aged children with severe or mild-moderate asthma (or wheeze for the pre-school aged children) using current definitions.
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Disease
View SamplesExpression Profiling of a Genetic Animal Model of Depression Reveals Novel Molecular Pathways Underlying Depressive-like Behaviours
Expression profiling of a genetic animal model of depression reveals novel molecular pathways underlying depressive-like behaviours.
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View SamplesContractile and highly synthetic myofibroblasts are the key effector cells involved in excessive extracellular matrix (ECM) deposition in multiple fibrotic conditions, including idiopathic pulmonary fibrosis (IPF). In order to define the key drivers of the fibrotic response, we used laser capture microdissection to isolate RNA from myofibroblasts within fibroblastic foci and performed microarray analysis in combination with a novel eigengene approach to identify functional clusters of genes which associate with collagen gene expression.
Transcriptome analysis of IPF fibroblastic foci identifies key pathways involved in fibrogenesis.
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