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BACKGROUND: Exacerbation-prone asthma is a feature of severe disease. However, the basis for its persistency remains unclear.OBJECTIVES: To determine the clinical and transcriptomic features of frequent exacerbators (FEs) and persistent FEs (PFEs) in the U-BIOPRED cohort.METHODS: We compared features of FE (≥2 exacerbations in past year) to infrequent exacerbators (IE, RESULTS: Of 317 patients, 62.4% had FE, of whom 63.6% had PFE, while 37.6% had IE, of whom 61.3% had PIE. Using multivariate analysis, FE was associated with short-acting beta-agonist use, sinusitis and daily oral corticosteroid use, while PFE was associated with eczema, short-acting beta-agonist use and asthma control index. CEA cell adhesion molecule 5 (CEACAM5) was the only differentially expressed transcript in bronchial biopsies between PE and IE. There were no differentially expressed genes in the other four compartments. There were higher expression scores for type 2, T-helper type-17 and type 1 pathway signatures together with those associated with viral infections in bronchial biopsies from FE compared to IE, while there were higher expression scores of type 2, type 1 and steroid insensitivity pathway signatures in bronchial biopsies of PFE compared to PIE.CONCLUSION: The FE group and its PFE subgroup are associated with poor asthma control while expressing higher type 1 and type 2 activation pathways compared to IE and PIE, respectively.

Analysis of induced sputum supernatant is a minimally invasive approach to study the epithelial lining fluid and, thereby, provide insight into normal lung biology and the pathobiology of lung diseases. We present here a novel proteomics approach to sputum analysis developed within the U-BIOPRED (Unbiased BIOmarkers Predictive of REspiratory Disease outcomes) international project. We present practical and analytical techniques to optimise the detection of robust biomarkers in proteomic studies. The normal sputum proteome was derived using data-independent HDMSE applied to 40 healthy non-smoking participants, which provides an essential baseline from which to compare modulation of protein expression in respiratory diseases. The “core” sputum proteome (proteins detected in ≥40 % of participants) was composed of 284 proteins and the extended proteome (proteins detected in ≥3 participants) contained 1666 proteins. Quality control procedures were developed to optimise the accuracy and consistency of measurement of sputum proteins and analyse the distribution of sputum proteins in the healthy population. The analysis showed that quantitation of proteins by HDMSE is influenced by several factors, with some proteins being measured in all participants’ samples and with low measurement variance between samples from the same patient. The measurement of some proteins is highly variable between repeat analyses, susceptible to sample processing effects, or difficult to accurately quantify by mass spectrometry. Other proteins show high inter-individual variance. We also highlight that the sputum proteome of healthy individuals is related to sputum neutrophil levels, but not gender or allergic sensitisation. We illustrate the importance of design and interpretation of disease biomarker studies considering such protein population and technical measurement variance.

A recent editorial in the European Respiratory Journal highlighted the strategic importance of the Clinical Research Collaborations (CRCs) launched in 2013 by the European Respiratory Society (ERS) [1]. These have the aim of 1) promoting the exchange of research ideas among clinicians and affiliated scientists in Europe and/or globally; 2) building an infrastructure for prospective clinical research; 3) securing additional funding through national and European Union funding streams; and 4) facilitating the planning, implementation, evaluation and publication of clinical and translational studies at pan-European level and beyond. So far, there are currently 17 ongoing CRCs that cover eight major respiratory disease domains (airway diseases, interstitial lung diseases, pulmonary vascular diseases, sleep and breathing disorders, respiratory critical care, paediatric respiratory diseases, respiratory infections and thoracic oncology), all of them linked to one or more ERS assemblies [2–12]. CADSET, an acronym that stands for “Chronic Airway Diseases Early Stratification”, is the latest addition to the list of ongoing CRCs (www.ersnet.org/research/clinical-research-collaborations). This editorial presents the rationale, goals and research strategy for CADSET.

Background Asthma is a disease of varying severity and differing disease mechanisms. To date, studies aimed at stratifying asthma into clinically useful phenotypes have produced a number of phenotypes that have yet to be assessed for stability and to be validated in independent cohorts. The aim of this study was to define and validate, for the first time ever, clinically driven asthma phenotypes using two independent, severe asthma cohorts: ADEPT and U-BIOPRED. Methods Fuzzy partition-around-medoid clustering was performed on pre-specified data from the ADEPT participants (n = 156) and independently on data from a subset of U-BIOPRED asthma participants (n = 82) for whom the same variables were available. Models for cluster classification probabilities were derived and applied to the 12-month longitudinal ADEPT data and to a larger subset of the U-BIOPRED asthma dataset (n = 397). High and low type-2 inflammation phenotypes were defined as high or low Th2 activity, indicated by endobronchial biopsies gene expression changes downstream of IL-4 or IL-13. Results Four phenotypes were identified in the ADEPT (training) cohort, with distinct clinical and biomarker profiles. Phenotype 1 was “mild, good lung function, early onset”, with a low-inflammatory, predominantly Type-2, phenotype. Phenotype 2 had a “moderate, hyper-responsive, eosinophilic” phenotype, with moderate asthma control, mild airflow obstruction and predominant Type-2 inflammation. Phenotype 3 had a “mixed severity, predominantly fixed obstructive, non-eosinophilic and neutrophilic” phenotype, with moderate asthma control and low Type-2 inflammation. Phenotype 4 had a “severe uncontrolled, severe reversible obstruction, mixed granulocytic” phenotype, with moderate Type-2 inflammation. These phenotypes had good longitudinal stability in the ADEPT cohort. They were reproduced and demonstrated high classification probability in two subsets of the U-BIOPRED asthma cohort. Conclusions Focusing on the biology of the four clinical independently-validated easy-to-assess ADEPT asthma phenotypes will help understanding the unmet need and will aid in developing tailored therapies. Trial registration NCT01274507 (ADEPT), registered October 28, 2010 and NCT01982162 (U-BIOPRED), registered October 30, 2013. Electronic supplementary material The online version of this article (doi:10.1186/s12931-016-0482-9) contains supplementary material, which is available to authorized users.

BackgroundHuman rhinoviruses (HRVs) commonly precipitate asthma exacerbations. Toll-like receptor 3, an innate pattern recognition receptor, is triggered by HRV, driving inflammation that can worsen asthma.ObjectiveWe sought to evaluate an inhibitory mAb to Toll-like receptor 3, CNTO3157, on experimental HRV-16 inoculation in healthy subjects and asthmatic patients.MethodsIn this double-blind, multicenter, randomized, parallel-group study in North America and Europe, healthy subjects and patients with mild-to-moderate stable asthma received single or multiple doses of CNTO3157 or placebo, respectively, and were then inoculated with HRV-16 within 72 hours. All subjects were monitored for respiratory symptoms, lung function, and nasal viral load. The primary end point was maximal decrease in FEV1 during 10 days after inoculation.ResultsIn asthmatic patients (n = 63) CNTO3157 provided no protection against FEV1 decrease (least squares mean: CNTO3157 [n = 30] = −7.08% [SE, 8.15%]; placebo [n = 25] = −5.98% [SE, 8.56%]) or symptoms after inoculation. In healthy subjects (n = 12) CNTO3157 versus placebo significantly attenuated upper (P = .03) and lower (P = .02) airway symptom scores, with area-under-the-curve increases of 9.1 (15.1) versus 34.9 (17.6) and 13.0 (18.4) versus 50.4 (25.9) for the CNTO3157 (n = 8) and placebo (n = 4) groups, respectively, after inoculation. All of the severe and 4 of the nonserious asthma exacerbations occurred while receiving CNTO3157.ConclusionIn summary, CNTO3157 was ineffective in attenuating the effect of HRV-16 challenge on lung function, asthma control, and symptoms in asthmatic patients but suppressed cold symptoms in healthy subjects. Other approaches, including blockade of multiple pathways or antiviral agents, need to be sought for this high unmet medical need.

BACKGROUND Transcriptomic changes in patients who respond clinically to biological therapies may identify responses in other tissues or diseases. OBJECTIVE To determine whether a disease signature identified in atopic dermatitis (AD) is seen in adults with severe asthma (SA) and whether a transcriptomic signature for AD patients who respond clinically to anti-IL-22 (Fezakinumab, FZ) is enriched in SA. METHODS An AD disease signature was obtained from analysis of differentially expressed genes (DEGs) between AD lesional and non-lesional skin biopsies. DEGs from lesional skin from therapeutic super-responders before and after 12 weeks FZ treatment defined the FZ-response signature. Gene Set Variation Analysis (GSVA) was used to produce enrichment scores (ES) of AD and FZ-response signatures in the U-BIOPRED asthma cohort. RESULTS The AD disease signature (112 up-regulated genes) encompassing inflammatory, T-cell, Th2 and Th17/Th22 pathways was enriched in the blood and sputum of asthmatics with increasing severity. Asthmatics with sputum neutrophilia and mixed granulocyte phenotypes were the most enriched (p

INTRODUCTION Asthma is a heterogeneous disease with poorly defined phenotypes. Severe asthmatics often receive multiple treatments including oral corticosteroids (OCS). Treatment may modify the observed metabotype, rendering it challenging to investigate underlying disease mechanisms. Here, we aimed to identify dysregulated metabolic processes in relation to asthma severity and medication. METHODS Baseline urine was collected prospectively from healthy participants (n=100), mild-to-moderate asthmatics (n=87) and severe asthmatics (n=418) in the cross-sectional U-BIOPRED cohort; 12-18-month longitudinal samples were collected from severe asthmatics (n=305). Metabolomics data were acquired using high-resolution mass spectrometry and analysed using univariate and multivariate methods. RESULTS Ninety metabolites were identified, with 40 significantly altered (p

Additional file 1: Figure S1. Comparison of sputum ACE2 gene expression levels (box-and-whisker plots showing median and interquartile range) by OCS use in asthmatics. P value was determined using unpaired t-test. Fig S2. Comparison of ACE2, TMPRSS2 and furin gene expression levels (box-and-whisker plots showing median and interquartile range) according to nasal polyps in nasal brushing. ns, not significant (p > 0.05). Table S1. Baseline characteristics of patients with asthma and healthy controls according to the site of mRNA collection. Table S2. Six asthma-associated gene sets analyzed in this study. Table S3. Absolute expression levels of ACE2, TMPRSS2 and Furin genes in different airway compartments

Additional file 1: Figure S1. Comparison of sputum ACE2 gene expression levels (box-and-whisker plots showing median and interquartile range) by OCS use in asthmatics. P value was determined using unpaired t-test. Fig S2. Comparison of ACE2, TMPRSS2 and furin gene expression levels (box-and-whisker plots showing median and interquartile range) according to nasal polyps in nasal brushing. ns, not significant (p > 0.05). Table S1. Baseline characteristics of patients with asthma and healthy controls according to the site of mRNA collection. Table S2. Six asthma-associated gene sets analyzed in this study. Table S3. Absolute expression levels of ACE2, TMPRSS2 and Furin genes in different airway compartments

Additional file 1: Figure S1. Comparison of sputum ACE2 gene expression levels (box-and-whisker plots showing median and interquartile range) by OCS use in asthmatics. P value was determined using unpaired t-test. Fig S2. Comparison of ACE2, TMPRSS2 and furin gene expression levels (box-and-whisker plots showing median and interquartile range) according to nasal polyps in nasal brushing. ns, not significant (p > 0.05). Table S1. Baseline characteristics of patients with asthma and healthy controls according to the site of mRNA collection. Table S2. Six asthma-associated gene sets analyzed in this study. Table S3. Absolute expression levels of ACE2, TMPRSS2 and Furin genes in different airway compartments