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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.

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

Rationale Asthma phenotyping requires novel biomarker discovery. Objectives To identify plasma biomarkers associated with asthma phenotypes by application of a new proteomic panel to samples from two well-characterised cohorts of severe (SA) and mild-to-moderate (MMA) asthmatics, COPD subjects and healthy controls (HCs). Methods An antibody-based array targeting 177 proteins predominantly involved in pathways relevant to inflammation, lipid metabolism, signal transduction and extracellular matrix was applied to plasma from 525 asthmatics and HCs in the U-BIOPRED cohort, and 142 subjects with asthma and COPD from the validation cohort BIOAIR. Effects of oral corticosteroids (OCS) were determined by a 2-week, placebo-controlled OCS trial in BIOAIR, and confirmed by relation to objective OCS measures in U-BIOPRED. Results In U-BIOPRED, 110 proteins were significantly different, mostly elevated, in SA compared to MMA and HCs. 10 proteins were elevated in SA versus MMA in both U-BIOPRED and BIOAIR (alpha-1-antichymotrypsin, apolipoprotein-E, complement component 9, complement factor I, macrophage inflammatory protein-3, interleukin-6, sphingomyelin phosphodiesterase 3, TNF receptor superfamily member 11a, transforming growth factor-β and glutathione S-transferase). OCS treatment decreased most proteins, yet differences between SA and MMA remained following correction for OCS use. Consensus clustering of U-BIOPRED protein data yielded six clusters associated with asthma control, quality of life, blood neutrophils, high-sensitivity C-reactive protein and body mass index, but not Type-2 inflammatory biomarkers. The mast cell specific enzyme carboxypeptidase A3 was one major contributor to cluster differentiation. Conclusions The plasma proteomic panel revealed previously unexplored yet potentially useful Type-2independent biomarkers and validated several proteins with established involvement in the pathophysiology of SA.

The authors regret that absolute sensor differences were not calculated “by subtracting sensor values measured after six weeks of treatment from sensor values measured at baseline for each sensor”, as mentioned in the “Statistical analysis” section, page 3 of the Supplementary appendix, but “by subtracting sensor values measured at baseline from sensor values measured after six weeks of treatment for each sensor”. The authors would like to apologise for any inconvenience caused.

Rationale: Asthma phenotyping requires novel biomarker discovery.Objectives: To identify plasma biomarkers associated with asthma phenotypes by application of a new proteomic panel to samples from two well-characterised cohorts of severe (SA) and mild-to-moderate (MMA) asthmatics, COPD subjects and healthy controls (HCs).Methods: An antibody-based array targeting 177 proteins predominantly involved in pathways relevant to inflammation, lipid metabolism, signal transduction and extracellular matrix was applied to plasma from 525 asthmatics and HCs in the U-BIOPRED cohort, and 142 subjects with asthma and COPD from the validation cohort BIOAIR. Effects of oral corticosteroids (OCS) were determined by a 2-week, placebo-controlled OCS trial in BIOAIR, and confirmed by relation to objective OCS measures in U-BIOPRED.Results: In U-BIOPRED, 110 proteins were significantly different, mostly elevated, in SA compared to MMA and HCs. 10 proteins were elevated in SA versus MMA in both U-BIOPRED and BIOAIR (alpha-1-antichymotrypsin, apolipoprotein-E, complement component 9, complement factor I, macrophage inflammatory protein-3, interleukin-6, sphingomyelin phosphodiesterase 3, TNF receptor superfamily member 11a, transforming growth factor-β and glutathione S-transferase). OCS treatment decreased most proteins, yet differences between SA and MMA remained following correction for OCS use. Consensus clustering of U-BIOPRED protein data yielded six clusters associated with asthma control, quality of life, blood neutrophils, high-sensitivity C-reactive protein and body mass index, but not Type-2 inflammatory biomarkers. The mast cell specific enzyme carboxypeptidase A3 was one major contributor to cluster differentiation.Conclusions: The plasma proteomic panel revealed previously unexplored yet potentially useful Type-2-independent biomarkers and validated several proteins with established involvement in the pathophysiology of SA.

Rationale: New approaches are needed to guide personalized treatment of asthma.Objective: To test if urinary eicosanoid metabolites can direct asthma phenotyping.Methods: Urinary metabolites of prostaglandins (PG), cysteinyl-leukotrienes (LT) and isoprostanes were quantified in the Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes (U-BIOPRED) study including 86 adults with mild-to-moderate asthma (MMA), 411 with severe asthma (SA), and 100 healthy controls (HC). Validation was performed in 302 SA subjects followed-up after 12-18 months, and externally in 95 adolescents with asthma.Measurement and Main Results: Metabolite levels in HC were unrelated to age, BMI and sex, except for the PGE2-pathway. Eicosanoid levels were generally greater in MMA relative to HC, with further elevations in SA, except for PGE2-metabolites in males, which were the same or lower in non-smoking asthmatics as in HC. Metabolite levels were unchanged in asthmatics adherent to oral corticosteroid treatment as documented by urinary prednisolone detection, whereas SA treated with omalizumab had lower levels of LTE4 and the PGD2 metabolite 2,3-dinor-11?-PGF2?. High levels of LTE4 and PGD2-metabolites were associated with lower lung-function, and increased levels of exhaled nitric oxide and eosinophil markers in blood, sputum and urine in U-BIOPRED and in adolescents with asthma. These type-2 (T2) asthma associations were reproduced in the follow-up visit of the U-BIOPRED study, and found to be as sensitive to detect T2 inflammation as the established biomarkers. Conclusions: Monitoring of urinary eicosanoids can identify T2 asthma and introduces a new non-invasive approach for molecular phenotyping of adult and adolescent asthma.

Background Asthma patients suffer from periodic acute worsening of symptoms (i.e. loss of asthma control or exacerbations), triggered by a variety of exogenous stimuli. With the growing awareness that air pollutants impact respiratory diseases, we investigated whether particulate matter (PM) derived from various livestock farms (BioPM) differentially affected innate and oxidative stress responses in asthma and health. Methods Peripheral blood mononuclear cells (PBMCs), collected from patients sequentially before and during loss of asthma control and from healthy individuals, were exposed to BioPM collected from chicken, goat and pig farms (1 and 5 μg/ml), with or without pre-treatment with antioxidants. Cytokine release and oxidative stress were assessed. Results PBMCs produced IFNγ, IL-1β, IL-10 and TNFα upon stimulation with BioPM, with that from pig farms inducing the highest cytokine levels. Overall, cytokine production was irrespective of the presence or state of disease. However, PBMCs from stable asthma patients upon exposure to the three BioPM showed more extreme TNFα responses than those from healthy subjects. Furthermore, PBMCs obtained during loss of asthma control that were exposed to BioPM from pig farms showed enhanced IFNγ release as well as decreased oxidative stress levels upon pre-treatment with N-acetylcysteine (NAC) compared to stable disease. NAC, but not superoxide dismutase and catalase, also counteracted BioPM-induced cytokine release, indicating the importance of intracellular reactive oxygen species in the production of cytokines. Conclusions BioPM triggered enhanced pro-inflammatory responses by PBMCs from both healthy subjects and asthma patients, with those from patients during loss of asthma control showing increased susceptibility to BioPM from pig farms in particular.

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.

Background: Stratification by eosinophil and neutrophil counts increases our understanding of asthma and helps target therapy, but there is room for improvement in our accuracy in prediction of treatment responses and a need for better understanding of the underlying mechanisms. Objective: We sought to identify molecular subphenotypes of asthma defined by proteomic signatures for improved stratification. Methods: Unbiased label-free quantitative mass spectrometry and topological data analysis were used to analyze the proteomes of sputum supernatants from 246 participants (206 asthmatic patients) as a novel means of asthma stratification. Microarray analysis of sputum cells provided transcriptomics data additionally to inform on underlying mechanisms. Results: Analysis of the sputum proteome resulted in 10 clusters (ie, proteotypes) based on similarity in proteomic features, representing discrete molecular subphenotypes of asthma. Overlaying granulocyte counts onto the 10 clusters as metadata further defined 3 of these as highly eosinophilic, 3 as highly neutrophilic, and 2 as highly atopic with relatively low granulocytic inflammation. For each of these 3 phenotypes, logistic regression analysis identified candidate protein biomarkers, and matched transcriptomic data pointed to differentially activated underlying mechanisms. Conclusion: This study provides further stratification of asthma currently classified based on quantification of granulocytic inflammation and provided additional insight into their underlying mechanisms, which could become targets for novel therapies. asthma proteomics biomarkers eosinophils neutrophils

© 2019 Background: Stratification by eosinophil and neutrophil counts increases our understanding of asthma and helps target therapy, but there is room for improvement in our accuracy in prediction of treatment responses and a need for better understanding of the underlying mechanisms. Objective: We sought to identify molecular subphenotypes of asthma defined by proteomic signatures for improved stratification. Methods: Unbiased label-free quantitative mass spectrometry and topological data analysis were used to analyze the proteomes of sputum supernatants from 246 participants (206 asthmatic patients) as a novel means of asthma stratification. Microarray analysis of sputum cells provided transcriptomics data additionally to inform on underlying mechanisms. Results: Analysis of the sputum proteome resulted in 10 clusters (ie, proteotypes) based on similarity in proteomic features, representing discrete molecular subphenotypes of asthma. Overlaying granulocyte counts onto the 10 clusters as metadata further defined 3 of these as highly eosinophilic, 3 as highly neutrophilic, and 2 as highly atopic with relatively low granulocytic inflammation. For each of these 3 phenotypes, logistic regression analysis identified candidate protein biomarkers, and matched transcriptomic data pointed to differentially activated underlying mechanisms. Conclusion: This study provides further stratification of asthma currently classified based on quantification of granulocytic inflammation and provided additional insight into their underlying mechanisms, which could become targets for novel therapies.

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.

Topological Data Analysis (TDA) network models can represent continuous variation in the shape of disease pathology. We generated a TDA network model of asthma using 498 gene expression profiles of peripheral blood from asthma and healthy participants. The TDA network model was characterised by a core region with increased prevalence of healthy participants and connected routes to increased prevalence of severe asthma associated with increases in circulating inflammatory cells and modulated expression of inflammatory genes. However, stratified medicine requires discretisation of disease populations for targeted treatments. Therefore, a discrete Morse theory algorithm was developed and applied, identifying nine clusters, BC1-9, representing molecular phenotypes with discrete profiles of immune cell populations and activation of Type-1, 2 & 17 cytokine inflammatory pathways. The TDA network model was also characterised by differential activity of glucocorticoid receptor signalling associated with different expression profiles of glucocorticoid receptor (GR), according to microarray probesets targeted to the start or end of the GR mRNAs 3 UTR; suggesting differential GR mRNA processing as a possible driver of asthma phenotypes including steroid insensitivity.

Gastro-oesophageal reflux disease (GORD) and obesity are associated with frequent exacerbations and poor quality of life in asthmatics. Multiple mechanisms have been proposed for the effect of obesity, including modification of inflammation affecting epithelial cell proliferation and wound repair, while the role of GORD is poorly understood and proton pump inhibitor (PPI) are of variable efficacy. GORD might exert a deleterious effect by inducing vagal reflex, neuroinflammation and directly ( via microaspiration) triggering airway inflammation. Studies of reflux in animal models and human bronchial epithelial cell culture show varying impact on inflammation and airway remodelling. Footnotes This manuscript has recently been accepted for publication in the European Respiratory Journal . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article. Conflict of interest: Dr PEROTIN has nothing to disclose. Conflict of interest: Dr Schofield has nothing to disclose. Conflict of interest: Dr Wilson has nothing to disclose. Conflict of interest: Dr Ward has nothing to disclose. Conflict of interest: Dr Brandsma has nothing to disclose. Conflict of interest: Dr Strazzeri has nothing to disclose. Conflict of interest: Dr Bansal has nothing to disclose. Conflict of interest: Dr Yang has nothing to disclose. Conflict of interest: Dr Rowe reports and a full time employee and shareholder of Janssen Pharmaceutical Companies of Johnson and Johnson. Conflict of interest: Miss Corfield has nothing to disclose. Conflict of interest: Dr Lutter has nothing to disclose. Conflict of interest: Prof. Shaw reports personal fees and non-financial support from AstraZeneca, personal fees from Boehringer Ingelheim, personal fees from Novartis, personal fees from Teva, personal fees from Circassia, and a grant from GSK, outside the submitted work. Conflict of interest: Dr Bakke reports personal fees from GSK, AZ, Novartis andTeva, outside the submitted work. Conflict of interest: MC have no conflict of interest to disclose. Conflict of interest: Dr Dahlen has nothing to disclose. Conflict of interest: Dr Fowler reports personal fees and non-financial support from AstraZeneca, grants and personal fees from Boehringer Ingelheim, personal fees from Novartis, personal fees from Teva, outside the submitted work. Conflict of interest: Dr Horvath reports personal fees from Astra Zeneca, Boehringer Ingelheim, Novartis, CSL, Chiesi, Roche, GSK, Berlin-Chemie and Sandoz, outside the submitted work. Conflict of interest: Dr Howarth reports personal fees from GSK, outside the submitted work. Conflict of interest: Dr Krug has nothing to disclose. Conflict of interest: Dr Montuschi has nothing to disclose. Conflict of interest: Dr Sanak has nothing to disclose. Conflict of interest: Dr Sandstrom reports other monetary support from Boehringer Ingelheim, outside the submitted work. Conflict of interest: Dr Sun has nothing to disclose. Conflict of interest: Dr Pandis has nothing to disclose. Conflict of interest: Dr Auffray reports grants from Innovative Medicine Initiative, during the conduct of the study. Conflict of interest: Dr De Meulder reports grants from Innovative Medicine Initiative, during the conduct of the study. Conflict of interest: Ms. Lefaudeux reports grants from Innovative Medicine Initiative, grants from Innovative Medicine Initiative, during the conduct of the study. Conflict of interest: Dr Riley reports and I have shares in and I am employed by GSK. Conflict of interest: Dr Sousa has nothing to disclose. Conflict of interest: Dr Dahlen has nothing to disclose. Conflict of interest: Dr Adcock reports grants from EU-IMI, during the conduct of the study. Conflict of interest: KFC has received honoraria for participating in Advisory Board meetings of GSK, AZ, BI, Teva, Novartis and Merck regarding treatments for asthma and chronic obstructive pulmonary disease and has also been renumerated for speaking engagements. Conflict of interest: Dr Sterk reports grants from Innovative Medicines Initiative, during the conduct of the study. Conflict of interest: Dr Skipp has nothing to disclose. Conflict of interest: Dr Collins reports a patent application for use of a genetically modified Drosophila line carrying one or more mammalian genes associated with a chronic respiratory disease and uses to screen the impact of such genes. Conflict of interest: Dr Davies has nothing to disclose. Conflict of interest: Dr Djukanovic reports receiving fees for lectures at symposia organised by Novartis, AstraZeneca and TEVA, consultation for TEVA and Novartis as member of advisory boards, and participation in a scientific discussion about asthma organised by GlaxoSmithKline. He is a co-founder and current consultant, and has shares in Synairgen, a University of Southampton spin out company.

BackgroundFew genetic studies that focus on moderate-to-severe asthma exist. We aimed to identity novel genetic variants associated with moderate-to-severe asthma, see whether previously identified genetic variants for all types of asthma contribute to moderate-to-severe asthma, and provide novel mechanistic insights using expression analyses in patients with asthma.MethodsIn this genome-wide association study, we used a two-stage case-control design. In stage 1, we genotyped patient-level data from two UK cohorts (the Genetics of Asthma Severity and Phenotypes [GASP] initiative and the Unbiased BIOmarkers in PREDiction of respiratory disease outcomes [U-BIOPRED] project) and used data from the UK Biobank to collect patient-level genomic data for cases and controls of European ancestry in a 1:5 ratio. Cases were defined as having moderate-to-severe asthma if they were taking appropriate medication or had been diagnosed by a doctor. Controls were defined as not having asthma, rhinitis, eczema, allergy, emphysema, or chronic bronchitis as diagnosed by a doctor. For stage 2, an independent cohort of cases and controls (1:5) was selected from the UK Biobank only, with no overlap with stage 1 samples. In stage 1 we undertook a genome-wide association study of moderate-to-severe asthma, and in stage 2 we followed up independent variants that reached the significance threshold of p less than 1 × 10−6 in stage 1. We set genome-wide significance at p less than 5 × 10−8. For novel signals, we investigated their effect on all types of asthma (mild, moderate, and severe). For all signals meeting genome-wide significance, we investigated their effect on gene expression in patients with asthma and controls.FindingsWe included 5135 cases and 25 675 controls for stage 1, and 5414 cases and 21 471 controls for stage 2. We identified 24 genome-wide significant signals of association with moderate-to-severe asthma, including several signals in innate or adaptive immune-response genes. Three novel signals were identified: rs10905284 in GATA3 (coded allele A, odds ratio [OR] 0·90, 95% CI 0·88–0·93; p=1·76 × 10−10), rs11603634 in the MUC5AC region (coded allele G, OR 1·09, 1·06–1·12; p=2·32 × 10−8), and rs560026225 near KIAA1109 (coded allele GATT, OR 1·12, 1·08–1·16; p=3·06 × 10−9). The MUC5AC signal was not associated with asthma when analyses included mild asthma. The rs11603634 G allele was associated with increased expression of MUC5AC mRNA in bronchial epithelial brush samples via proxy SNP rs11602802; (p=2·50 × 10−5) and MUC5AC mRNA was increased in bronchial epithelial samples from patients with severe asthma (in two independent analyses, p=0·039 and p=0·022).InterpretationWe found substantial shared genetic architecture between mild and moderate-to-severe asthma. We also report for the first time genetic variants associated with the risk of developing moderate-to-severe asthma that regulate mucin production. Finally, we identify candidate causal genes in these loci and provide increased insight into this difficult to treat population.

Methods for breath sampling and analysis require robust quality assessment to minimise the risk of false discoveries. Planning large-scale multi-site breath metabolite profiling studies also requires careful consideration of systematic and random variation as a result of sampling and analysis techniques. In this study we use breath sample data from the recent U-BIOPRED cohort to evaluate and discuss some important methodological considerations such as batch variation and correction, variation between sites, storage and transportation, as well as inter-instrument analytical differences. Based on this we provide a summary of recommended best practices for new large scale multi-site studies.

Introduction: Infections such as ventilator-associated pneumonia (VAP) can be caused by one or more pathogens. Current methods for identifying these pathogenic microbes often require invasive sampling, and can be time consuming, due to the requirement for prolonged cultural enrichment along with selective and differential plating steps. This results in delays in diagnosis which in such critically ill patients can have potentially life-threatening consequences. Therefore, a non-invasive and timely diagnostic method is required. Detection of microbial volatile organic compounds (VOCs) in exhaled breath is proposed as an alternative method for identifying these pathogens and may distinguish between mono- and poly-microbial infections. Objectives: To investigate volatile metabolites that discriminate between bacterial mono- and co-cultures. Methods: VAP-associated pathogens Enterobacter cloacae and Pseudomonas aeruginosa were cultured individually and together in artificial sputum medium for 24 h and their headspace was analysed for potential discriminatory VOCs by thermal desorption gas chromatography–mass spectrometry. Results: Of the 70 VOCs putatively identified, 23 were found to significantly increase during bacterial culture (i.e. likely to be released during metabolism) and 13 decreased (i.e. likely consumed during metabolism). The other VOCs showed no transformation (similar concentrations observed as in the medium). Bacteria-specific VOCs including 2-methyl-1-propanol, 2-phenylethanol, and 3-methyl-1-butanol were observed in the headspace of axenic cultures of E. cloacae, and methyl 2-ethylhexanoate in the headspace of P. aeruginosa cultures which is novel to this investigation. Previously reported VOCs 1-undecene and pyrrole were also detected. The metabolites 2-methylbutyl acetate and methyl 2-methylbutyrate, which are reported to exhibit antimicrobial activity, were elevated in co-culture only. Conclusion: The observed VOCs were able to differentiate axenic and co-cultures. Validation of these markers in exhaled breath specimens could prove useful for timely pathogen identification and infection type diagnosis.

Background: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. Methods: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. Results: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. Conclusions: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.

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.

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.

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

[This corrects the article DOI: 10.1371/journal.pone.0091621.]

Rationale: A relatively important proportion of patients with severe asthma end up using a daily dose of oral corticosteroids (OCS) in addition to their high dose of inhaled corticosteroids (ICS). We have determined whether oral CS-dependent severe asthma is a distinct phenotype from those only on ICS. Methods: Participants were recruited from adult pulmonary clinics across Europe and severe asthma diagnosed according to IMI-criteria (Bel et al. Thorax 2011). They had either uncontrolled asthma as defined by the GINA guidelines AND/OR two or more severe exacerbations in the past 12 months. Asthma diagnosis was confirmed by a history of typical symptoms and either reversibility in FEV1 of >=12% and 200ml after 400 T-test, Mann Whitney U test and Chi-square test were used. Results: In the 374 severe asthma participants, 167 (44%) were on oral prednisolone. The oral prednisolone group was older but had similar FEV1 (% predicted) and BMI. There was a lower rate of cigarette smoking with lower proportion of current smokers and never-smokers, but a higher percentage with nasal polyps. There was no difference in atopy as measured by skin prick tests or RAST IgE. A higher proportion of patients had 2 or more exacerbations per year (68.3% vs 56.4%; p=0.02). A greater proportion of patients were using MDI and nebulised short-acting b-agonists and theophylline. Exhaled NO was raised (31 ppb vs 22 ppb; p=3% and neutrophils>=65%, there was a greater proportion of eosinophilic (38% vs 30%) and of mixed granulocytic (23% vs 5%), at the expense of lower paucigranulocytic (14% vs 41%)(Chi-square: p

International audience; Chronic diseases are diseases of long duration and slow progression. Major NCDs (cardiovascular diseases, cancer, chronic respiratory diseases, diabetes, rheumatologic diseases and mental health) represent the predominant health problem of the Century. The prevention and control of NCDs are the priority of the World Health Organization 2008 Action Plan, the United Nations 2010 Resolution and the European Union 2010 Council. The novel trend for the management of NCDs is evolving towards integrative, holistic approaches. NCDs are intertwined with ageing. The European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) has prioritised NCDs. To tackle them in their totality in order to reduce their burden and societal impact, it is proposed that NCDs should be considered as a single expression of disease with different risk factors and entities. An innovative integrated health system built around systems medicine and strategic partnerships is proposed to combat NCDs. It includes (i) understanding the social, economic, environmental, genetic determinants, as well as the molecular and cellular mechanisms underlying NCDs; (ii) primary care and practice-based interprofessional collaboration; (iii) carefully phenotyped patients; (iv) development of unbiased and accurate biomarkers for comorbidities, severity and follow up of patients; (v) socio-economic science; (vi) development of guidelines; (vii) training; and (viii) policy decisions. The results could be applicable to all countries and adapted to local needs, economy and health systems. This paper reviews the complexity of NCDs intertwined with ageing. It gives an overview of the problem and proposes two practical examples of systems medicine (MeDALL) applied to allergy and to NCD co-morbidities (MACVIA-LR).

Mechanisms of the Development of Allergy (MeDALL), a Seventh Framework Program European Union project, aims to generate novel knowledge on the mechanisms of initiation of allergy. Precise phenotypes of IgE-mediated allergic diseases will be defined in MeDALL. As part of MeDALL, a scientific seminar was held on January 24, 2011, to review current knowledge on the IgE-related phenotypes and to explore how a multidisciplinary effort could result in a new integrative translational approach. This article provides a summary of the meeting. It develops challenges in IgE-related phenotypes and new clinical and epidemiologic approaches to the investigation of allergic phenotypes, including cluster analysis, scale-free models, candidate biomarkers, and IgE microarrays; the particular case of severe asthma was reviewed. Then novel approaches to the IgE-associated phenotypes are reviewed from the individual mechanisms to the systems, including epigenetics, human in vitro immunology, systems biology, and animal models. The last chapter deals with the understanding of the population-based IgE-associated phenotypes in children and adolescents, including age effect in terms of maturation, observed effects of early-life exposures and shift of focus from early life to pregnancy, gene-environment interactions, cohort effects, and time trends in patients with allergic diseases. This review helps to define phenotypes of allergic diseases in MeDALL. (J Allergy Clin Immunol 2012;129:943-54.)

Patients with severe refractory asthma pose a major healthcare problem. Over the last decade it has become increasingly clear that, for the development of new targeted therapies, there is an urgent need for further characterisation and classification of these patients. The Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) consortium is a pan-European public-private collaboration funded by the European Commission Innovative Medicines Initiative of the European Union. U-BIOPRED aims to subphenotype patients with severe refractory asthma by using an innovative systems biology approach. This paper presents the U-BIOPRED international consensus on the definition and diagnosis of severe asthma, aligning the latest concepts in adults as well as in children. The consensus is based on existing recommendations up to 2010 and will be used for the selection of patients for the upcoming U-BIOPRED study. It includes the differentiation between ‘problematic’, ‘difficult’ and ‘severe refractory’ asthma, and provides a systematic algorithmic approach to the evaluation of patients presenting with chronic severe asthma symptoms for use in clinical research and specialised care.

Background: Inhaled corticosteroids (ICSs) and long-acting beta(2)-agonists (LABAs) are used to treat moderate to severe chronic obstructive pulmonary disease (COPD). Objective: To determine whether long-term ICS therapy, with and without LABAs, reduces inflammation and improves pulmonary function in COPD. Design: Randomized, placebo-controlled trial. (ClinicalTrials.gov registration number: NCT00158847) Setting: 2 university medical centers in The Netherlands. Patients: 114 steroid-naive current or former smokers with moderate to severe COPD. Measurements: Cell counts in bronchial biopsies and sputum (primary outcome); methacholine responsiveness at baseline, 6, and 30 months; and clinical outcomes every 3 months. Intervention: Random assignment by minimization method to receive fluticasone propionate, 500 mu g twice daily, for 6 months (n = 31) or 30 months (n = 26); fluticasone, 500 mu g twice daily, and salmeterol, 50 mu g twice daily, for 30 months (single inhaler; n = 28); or placebo twice daily (n = 29). Results: 101 patients were greater than 70% adherent to therapy. Fluticasone therapy decreased counts of mucosal CD3(+) cells (-55% [95% CI, -74% to -22%]; P = 0.004), CD4(+) cells (-78% [CI, -88% to 60%]; P

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 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.

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

Oxidative stress is believed to be a major driver of inflammation in smoking asthmatics. The U-BIOPRED project recruited a cohort of Severe Asthma smokers/ex-smokers (SAs/ex) and non-smokers (SAn) with extensive clinical and biomarker information enabling characterization of these subjects. We investigated oxidative stress in severe asthma subjects by analysing urinary 8-iso-PGF2α and the mRNA-expression of the main pro-oxidant (NOX2; NOSs) and anti-oxidant (SODs; CAT; GPX1) enzymes in the airways of SAs/ex and SAn. All the severe asthma U-BIOPRED subjects were further divided into current smokers with severe asthma (CSA), ex-smokers with severe asthma (ESA) and non-smokers with severe asthma (NSA) to deepen the effect of active smoking. Clinical data, urine and sputum were obtained from severe asthma subjects. A bronchoscopy to obtain bronchial biopsy and brushing was performed in a subset of subjects. The main clinical data were analysed for each subset of subjects (urine-8-iso-PGF2α; IS-transcriptomics; BB-transcriptomics; BBr-transcriptomics). Urinary 8-iso-PGF2α was quantified using mass spectrometry. Sputum, bronchial biopsy and bronchial brushing were processed for mRNA expression microarray analysis. Urinary 8-iso-PGF2α was increased in SAs/ex, median (IQR) = 31.7 (24.5-44.7) ng/mmol creatinine, compared to SAn, median (IQR) = 26.6 (19.6-36.6) ng/mmol creatinine (p< 0.001), and in CSA, median (IQR) = 34.25 (24.4-47.7), vs. ESA, median (IQR) = 29.4 (22.3-40.5), and NSA, median (IQR) = 26.5 (19.6-16.6) ng/mmol creatinine (p = 0.004). Sputum mRNA expression of NOX2 was increased in SAs/ex compared to SAn (probe sets 203922_PM_s_at fold-change = 1.05 p = 0.006; 203923_PM_s_at fold-change = 1.06, p = 0.003; 233538_PM_s_at fold-change = 1.06, p = 0.014). The mRNA expression of antioxidant enzymes were similar between the two severe asthma cohorts in all airway samples. NOS2 mRNA expression was decreased in bronchial brushing of SAs/ex compared to SAn (fold-change = -1.10; p = 0.029). NOS2 mRNA expression in bronchial brushing correlated with FeNO (Kendal's Tau = 0.535; p< 0.001). From clinical and inflammatory analysis, FeNO was lower in CSA than in ESA in all the analysed subject subsets (p< 0.01) indicating an effect of active smoking. Results about FeNO suggest its clinical limitation, as inflammation biomarker, in severe asthma active smokers. These data provide evidence of greater systemic oxidative stress in severe asthma smokers as reflected by a significant changes of NOX2 mRNA expression in the airways, together with elevated urinary 8-iso-PGF2α in the smokers/ex-smokers group. Trial registration ClinicalTrials.gov-Identifier: NCT01976767.

BACKGROUND Although several studies link high levels of IL-6 and soluble IL-6 receptor (sIL-6R) to asthma severity and decreased lung function, the role of IL-6 trans-signaling (IL-6TS) in asthmatic patients is unclear. OBJECTIVE We sought to explore the association between epithelial IL-6TS pathway activation and molecular and clinical phenotypes in asthmatic patients. METHODS An IL-6TS gene signature obtained from air-liquid interface cultures of human bronchial epithelial cells stimulated with IL-6 and sIL-6R was used to stratify lung epithelial transcriptomic data (Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes [U-BIOPRED] cohorts) by means of hierarchical clustering. IL-6TS-specific protein markers were used to stratify sputum biomarker data (Wessex cohort). Molecular phenotyping was based on transcriptional profiling of epithelial brushings, pathway analysis, and immunohistochemical analysis of bronchial biopsy specimens. RESULTS Activation of IL-6TS in air-liquid interface cultures reduced epithelial integrity and induced a specific gene signature enriched in genes associated with airway remodeling. The IL-6TS signature identified a subset of patients with IL-6TS-high asthma with increased epithelial expression of IL-6TS-inducible genes in the absence of systemic inflammation. The IL-6TS-high subset had an overrepresentation of frequent exacerbators, blood eosinophilia, and submucosal infiltration of T cells and macrophages. In bronchial brushings Toll-like receptor pathway genes were upregulated, whereas expression of cell junction genes was reduced. Sputum sIL-6R and IL-6 levels correlated with sputum markers of remodeling and innate immune activation, in particular YKL-40, matrix metalloproteinase 3, macrophage inflammatory protein 1β, IL-8, and IL-1β. CONCLUSIONS Local lung epithelial IL-6TS activation in the absence of type 2 airway inflammation defines a novel subset of asthmatic patients and might drive airway inflammation and epithelial dysfunction in these patients.