U-BIOPRED is a European Union consortium of 20 academic institutions, 11 pharmaceutical companies and six patient organisations with the objective of improving the understanding of asthma disease mechanisms using a systems biology approach.This cross-sectional assessment of adults with severe asthma, mild/moderate asthma and healthy controls from 11 European countries consisted of analyses of patient-reported outcomes, lung function, blood and airway inflammatory measurements.Patients with severe asthma (nonsmokers, n=311; smokers/ex-smokers, n=110) had more symptoms and exacerbations compared to patients with mild/moderate disease (n=88) (2.5 exacerbations versus 0.4 in the preceding 12 months; p<0.001), with worse quality of life, and higher levels of anxiety and depression. They also had a higher incidence of nasal polyps and gastro-oesophageal reflux with lower lung function. Sputum eosinophil count was higher in severe asthma compared to mild/moderate asthma (median count 2.99% versus 1.05%; p=0.004) despite treatment with higher doses of inhaled and/or oral corticosteroids.Consistent with other severe asthma cohorts, U-BIOPRED is characterised by poor symptom control, increased comorbidity and airway inflammation, despite high levels of treatment. It is well suited to identify asthma phenotypes using the array of "omic" datasets that are at the core of this systems medicine approach. @ERSpublications Severe asthma results in more airway inflammation, worse symptoms and lower lung function, despite increased therapy http://ow.ly/QznR3

Clustering based on clinicophysiologic parameters yielded 4 stable and reproducible clusters that associate with different pathobiological pathways.

@ERSpublicationsChildren with severe preschool wheeze or severe asthma are usually atopic and have impaired quality of life http://ow.ly/RrrGE

RATIONALE AND OBJECTIVES: Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms. We used transcriptomic profiling of airway tissues to help define asthma phenotypes. METHODS: The transcriptome from bronchial biopsies and epithelial brushings of 107 moderate-to-severe asthmatics were annotated by gene-set variation analysis (GSVA) using 42 gene-signatures relevant to asthma, inflammation and immune function. Topological data analysis (TDA) of clinical and histological data was used to derive clusters and the nearest shrunken centroid algorithm used for signature refinement. RESULTS: 9 GSVA signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper type 2 (Th-2) cytokines and lack of corticosteroid response (Group 1 and Group 3). Group 1 had the highest submucosal eosinophils, high exhaled nitric oxide (FeNO) levels, exacerbation rates and oral corticosteroid (OCS) use whilst Group 3 patients showed the highest levels of sputum eosinophils and had a high BMI. In contrast, Group 2 and Group 4 patients had an 86% and 64% probability of having non-eosinophilic inflammation. Using machine-learning tools, we describe an inference scheme using the currently-available inflammatory biomarkers sputum eosinophilia and exhaled nitric oxide levels along with OCS use that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity. CONCLUSION: This analysis demonstrates the usefulness of a transcriptomic-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target Th2-mediated inflammation and/or corticosteroid insensitivity

OBJECTIVE: To identify novel microRNA (miR) associations in synovial fibroblasts (SF), by performing miR expression profiling on cells isolated from the human tumour necrosis factor (TNF) transgenic mouse model (TghuTNF, Tg197) and patients biopsies.METHODS: miR expression in SF from TghuTNF and wild-type (WT) control mice were determined by miR deep sequencing (miR-seq) and the arthritic profile was established by pairwise comparisons. Quantitative PCR analysis was utilised for profile validation, miR and gene quantitation in patient SF. Dysregulated miR target genes and pathways were predicted via bioinformatic algorithms and validated using gain-of-function coupled with reporter assay experiments.RESULTS: miR-seq demonstrated that TghuTNF-SF exhibit a distinct pathogenic profile with 22 significantly upregulated and 30 significantly downregulated miR. Validation assays confirmed the dysregulation of miR-223, miR-146a and miR-155 previously associated with human rheumatoid arthritis (RA) pathology, as well as that of miR-221/222 and miR-323-3p. Notably, the latter were also found significantly upregulated in patient RA SF, suggesting for the first time their association with RA pathology. Bioinformatic analysis suggested Wnt/cadherin signalling as a putative pathway target. miR-323-3p overexpression was shown to enhance Wnt pathway activation and decrease the levels of its predicted target -transducin repeat containing, an inhibitor of -catenin.CONCLUSIONS: Using miRseq-based profiling in SF from the TghuTNF mouse model and validations in RA patient biopsies, the authors identified miR-221/222 and miR-323-3p as novel dysregulated miR in RA SF. Furthermore, the authors show that miR-323-3p is a positive regulator of WNT/cadherin signalling in RA SF suggesting its potential pathogenic involvement and future use as a therapeutic target in RA.