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Background: The anti-IgE monoclonal antibody omalizumab is widely used for severe asthma. This study aimed to identify biomarkers that predict clinical improvement during 1 year of omalizumab treatment. Methods: One-year open-label Study of Mechanisms of action of Omalizumab in Severe Asthma (SoMOSA) involving 216 patients with severe (Global Initiative for Asthma step 4/5) uncontrolled atopic asthma (at least two severe exacerbations in the previous year) taking high-dose inhaled corticosteroids and long-acting β-agonists with or without maintenance oral corticosteroids. It had two phases: 0-16 weeks, to assess early clinical improvement by Global Evaluation of Therapeutic Effectiveness (GETE); and 16-52 weeks, to assess late responses based on ⩾50% reduction in exacerbations or mOCS dose. All participants provided samples (exhaled breath, blood, sputum, urine) before and after 16 weeks of omalizumab treatment. Measurements and Main Results: A total of 191 patients completed phase 1; 63% had early improvement. Of 173 who completed phase 2, 69% had reduced exacerbations by ⩾50% and 57% (37 of 65) taking mOCSs had reduced their dose by ⩾50%. The primary outcomes 2,3-dinor-11-β-PGF2α, GETE score, and standard clinical biomarkers (blood and sputum eosinophils, exhaled nitric oxide, serum IgE) did not predict either clinical response. Five volatile organic compounds and five plasma lipid biomarkers strongly predicted the ⩾50% reduction in exacerbations (receiver operating characteristic areas under the curve of 0.780 and 0.922, respectively) and early responses (areas under the curve of 0.835 and 0.949, respectively). In an independent cohort, gas chromatography/mass spectrometry biomarkers differentiated between severe and mild asthma. Conclusions: This is the first discovery of omics biomarkers that predict improvement in asthma with biologic agent treatment. Prospective validation and development for clinical use is justified.

Rationale: Children with preschool wheezing or school-age asthma are reported to have airway microbial imbalances. Objectives: To identify clusters in children with asthma or wheezing using oropharyngeal microbiota profiles. Methods: Oropharyngeal swabs from the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) pediatric asthma or wheezing cohort were characterized using 16S ribosomal RNA gene sequencing, and unsupervised hierarchical clustering was performed on the Bray-Curtis β-diversity. Enrichment scores of the Molecular Signatures Database hallmark gene sets were computed from the blood transcriptome using gene set variation analysis. Children with severe asthma or severe wheezing were followed up for 12-18 months, with assessment of the frequency of exacerbations. Measurements and Main Results: Oropharyngeal samples from 241 children (age range, 1-17 years; 40% female) revealed four taxa-driven clusters dominated by Streptococcus , Veillonella , Rothia , and Haemophilus . The clusters showed significant differences in atopic dermatitis, grass pollen sensitization, FEV 1 % predicted after salbutamol, and annual asthma exacerbation frequency during follow-up. The Veillonella cluster was the most allergic and included the highest percentage of children with two or more exacerbations per year during follow-up. The oropharyngeal clusters were different in the enrichment scores of TGF-β (transforming growth factor-β) (highest in the Veillonella cluster) and Wnt/β-catenin signaling (highest in the Haemophilus cluster) transcriptomic pathways in blood (all q values <0.05). Conclusions: Analysis of the oropharyngeal microbiota of children with asthma or wheezing identified four clusters with distinct clinical characteristics (phenotypes) that associate with risk for exacerbation and transcriptomic pathways involved in airway remodeling. This suggests that further exploration of the oropharyngeal microbiota may lead to novel pathophysiologic insights and potentially new treatment approaches.

Background: Asthma is a chronic respiratory disease with significant heterogeneity in its clinical presentation and pathobiology. There is need for improved understanding of respiratory lipid metabolism in asthma patients and its relation to observable clinical features., Objective: We performed a comprehensive, prospective, cross-sectional analysis of the lipid composition of induced sputum supernatant obtained from asthma patients with a range of disease severities, as well as from healthy controls., Methods: Induced sputum supernatant was collected from 211 adults with asthma and 41 healthy individuals enrolled onto the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) study. Sputum lipidomes were characterized by semiquantitative shotgun mass spectrometry and clustered using topologic data analysis to identify lipid phenotypes., Results: Shotgun lipidomics of induced sputum supernatant revealed a spectrum of 9 molecular phenotypes, highlighting not just significant differences between the sputum lipidomes of asthma patients and healthy controls, but also within the asthma patient population. Matching clinical, pathobiologic, proteomic, and transcriptomic data helped inform the underlying disease processes. Sputum lipid phenotypes with higher levels of nonendogenous, cell-derived lipids were associated with significantly worse asthma severity, worse lung function, and elevated granulocyte counts., Conclusion: We propose a novel mechanism of increased lipid loading in the epithelial lining fluid of asthma patients resulting from the secretion of extracellular vesicles by granulocytic inflammatory cells, which could reduce the ability of pulmonary surfactant to lower surface tension in asthmatic small airways, as well as compromise its role as an immune regulator., (Copyright © 2023 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Background: Growing evidence indicates high comorbid anxiety and depression in patients with asthma. However, the mechanisms underlying this comorbid condition remain unclear. The aim of this study was to investigate the role of inflammation in comorbid anxiety and depression in three asthma patient cohorts of the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) project., Methods: U-BIOPRED was conducted by a European Union consortium of 16 academic institutions in 11 European countries. A subset dataset from subjects with valid anxiety and depression measures and a large blood biomarker dataset were analysed, including 198 non-smoking patients with severe asthma (SAn), 65 smoking patients with severe asthma (SAs), 61 non-smoking patients with mild-to-moderate asthma (MMA), and 20 healthy non-smokers (HC). The Hospital Anxiety and Depression Scale was used to measure anxiety and depression and a series of inflammatory markers were analysed by the SomaScan v3 platform (SomaLogic, Boulder, Colo). ANOVA and the Kruskal-Wallis test were used for multiple-group comparisons as appropriate., Results: There were significant group effects on anxiety and depression among the four cohort groups (p < 0.05). Anxiety and depression of SAn and SAs groups were significantly higher than that of MMA and HC groups (p < 0.05. There were significant differences in serum IL6, MCP1, CCL18, CCL17, IL8, and Eotaxin among the four groups (p < 0.05). Depression was significantly associated with IL6, MCP1, CCL18 level, and CCL17; whereas anxiety was associated with CCL17 only (p < 0.05)., Conclusions: The current study suggests that severe asthma patients are associated with higher levels of anxiety and depression, and inflammatory responses may underlie this comorbid condition., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Professor Hou sits on the ECNP Scientific Advisory Panel and currently holds a grant from Asthma Allergy Inflammation Research charity. Prof. Shaw receives consulting fees from Adherium, Nuvoair, Astra Zeneca and Chiesi. He also receives honoraria from Astra Zeneca and Chiesi and travel support from Chiesi and GSK. Professor Sven-Eric Dahlén declares consulting fees from Astra Zenica, Cayman Chemicals, GSK, Novartis, Regeneron, Sanofi and Teva and honorarium from Sanofi. Dr Barbro Dahlén is in receipt of grants from GSK and Novartis and declares consulting fees from Novartis, Astra Zeneca and Sanofi. She is on the advisory board for Astra Zeneca and Sanofi. Prof. Fowler receives a grant from Boehringer Ingelheim and an honorarium from Chiesi. Prof. Sandstrom received payment for the Boehringer Ingelheim lecture (paid to his institution). Dr Auffrey and Dr De Meulder have both received support for the manuscript from the Innovative Medicines Initiative. Prof. Adcock has received grants from GSK, MRC and EPSRC. He also declares consulting fees from GSK, Sanofi, Chiesi and Kinaset. He has received honoraria from Astra Zeneca, Sanofi, Eurodrug, and Sunovion. He has also received payment for expert testimony from Chiesi and travel support from Astra Zeneca. Prof. Chung is in receipt of grants from MRC, EPSRC and GSK and honoraria from Astra Zeneca and Novartis. He is on the advisory board of Astra Zeneca, GSK, Roche and Novartis. Prof. Sterk received a grant from Innovative Medicines Initiative and has a non-substantial interest in SME Breathomix. Prof. Skipp has a grant from EU UBIOPRED IMI FP and is a shareholder in TopMD Precision Medicine Ltd. Prof. Djukanovic receives consulting fees from Synairgen and honoraria from Regeneron, GSK and is on the advisory board for Synairgen. He also holds stock in Synairgen. Dr Ye, Dr Cheng, Dr Bakke, Dr Caruso, Dr Horvárth, Prof. Howarth, Dr Krug, Dr Montuschi, Dr Sanak and Dr Schofield report no potential conflict of interest., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Rationale: There is a major unmet need for improving the care of children and adolescents with severe asthma and wheeze. Identifying factors contributing to disease severity may lead to improved diagnostics, biomarkers, or therapies. The airway microbiota may be such a key factor. Objectives: To compare the oropharyngeal airway microbiota of children and adolescents with severe and mild/moderate asthma/wheeze. Methods: Oropharyngeal swab samples from school-age and preschool children in the European U-BIOPRED (Unbiased BIOmarkers in the PREDiction of respiratory disease outcomes) multicenter study of severe asthma, all receiving severity-appropriate treatment, were examined using 16S ribosomal RNA gene sequencing. Bacterial taxa were defined as amplicon sequence variants. Results: We analyzed 241 samples from four cohorts: A) 86 school-age children with severe asthma; B) 39 school-age children with mild/moderate asthma; C) 65 preschool children with severe wheeze; and D) 51 preschool children with mild/moderate wheeze. The most common bacteria were Streptococcus (mean relative abundance, 33.5%), Veillonella (10.3%), Haemophilus (7.0%), Prevotella (5.9%), and Rothia (5.5%). Age group (school-age vs. preschool) was associated with the microbiota in β-diversity analysis ( F  = 3.32, P  = 0.011) and in a differential abundance analysis (28 significant amplicon sequence variants). Among all children, we found no significant difference in the microbiota between children with severe and mild/moderate asthma/wheeze in univariable β-diversity analysis ( F  = 1.99, P  = 0.08, N  = 241), but a significant difference in a multivariable model ( F  = 2.66, P  = 0.035), including the number of exacerbations in the previous year. Age was also significant when expressed as a microbial maturity score (Spearman Rho, 0.39; P  = 4.6 × 10 -10 ); however, this score was not associated with asthma/wheeze severity. Conclusions: There was a modest difference in the oropharyngeal airway microbiota between children with severe and mild/moderate asthma/wheeze across all children but not in individual age groups, and a strong association between the microbiota and age. This suggests the oropharyngeal airway microbiota as an interesting entity in studying asthma severity, but probably without the strength to serve as a biomarker for targeted intervention.

Asthma symptoms are often exacerbated by the common-cold-causing rhinovirus (RV). In this study, we characterized the temporal behavior of circulating exosomal microRNAs (ExoMiRNAs) in a longitudinal bi-phasic case-control study of mild asthmatics ( n = 12) and matched non-atopic healthy controls ( n = 12) inoculated with rhinovirus. We aimed to define clinical and immunologic characteristics associated with differentially expressed (DE) miRNAs. In total, 26 DE ExoMiRNAs, including hsa-let-7f-5p, hsa-let-7a-5p, hsa-miR-122-5p, hsa-miR-101-3p, and hsa-miR-126-3p, were identified between asthmatic and healthy subjects after inoculation with RV. Time series clustering identified a unique Cluster of Upregulated DE ExoMiRNAs with augmenting mean expression and a distinct Cluster of Downregulated DE ExoMiRNAs with mean expression decline in asthmatic subjects upon RV challenge. Notably, the Upregulated Cluster correlated with Th1 and interferon-induced cytokines/chemokines (IFN-γ and IFN-γ-inducible protein-10) and interleukin-10 (IL-10). Conversely, the Downregulated Cluster correlated with IL-13, a Th2 cytokine, pulmonary function measurements (FVC%, FEV1%, and PEF%), and inflammatory biomarkers (FeNO, eosinophil%, and neutrophil%). Key ExoMiRNA-target gene and anti-viral defense mechanisms of the Upregulated and Downregulated Clusters were identified by network and gene enrichment analyses. Our findings provide insight into the regulatory role of ExoMiRNAs in RV-induced asthma.

Introduction: Asthma is a heterogeneous disease with poorly defined phenotypes. Patients with severe asthma 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), patients with mild-to-moderate asthma (n=87) and patients with severe asthma (n=418) in the cross-sectional U-BIOPRED cohort; 12-18-month longitudinal samples were collected from patients with severe asthma (n=305). Metabolomics data were acquired using high-resolution mass spectrometry and analysed using univariate and multivariate methods., Results: A total of 90 metabolites were identified, with 40 significantly altered (p<0.05, false discovery rate <0.05) in severe asthma and 23 by OCS use. Multivariate modelling showed that observed metabotypes in healthy participants and patients with mild-to-moderate asthma differed significantly from those in patients with severe asthma (p=2.6×10 -20 ), OCS-treated asthmatic patients differed significantly from non-treated patients (p=9.5×10 -4 ), and longitudinal metabotypes demonstrated temporal stability. Carnitine levels evidenced the strongest OCS-independent decrease in severe asthma. Reduced carnitine levels were associated with mitochondrial dysfunction via decreases in pathway enrichment scores of fatty acid metabolism and reduced expression of the carnitine transporter SLC22A5 in sputum and bronchial brushings., Conclusions: This is the first large-scale study to delineate disease- and OCS-associated metabolic differences in asthma. The widespread associations with different therapies upon the observed metabotypes demonstrate the need to evaluate potential modulating effects on a treatment- and metabolite-specific basis. Altered carnitine metabolism is a potentially actionable therapeutic target that is independent of OCS treatment, highlighting the role of mitochondrial dysfunction in severe asthma., Competing Interests: Conflict of interest: S.N. Reinke reports grants from Canadian Institutes of Health Research, during the conduct of the study. Conflict of interest: S. Naz has nothing to disclose. Conflict of interest: R. Chaleckis has nothing to disclose. Conflict of interest: H. Gallart-Ayala has nothing to disclose. Conflict of interest: J. Kolmert reports personal fees for consultancy from Gesynta Pharma AB, outside the submitted work. Conflict of interest: N.Z. Kermani has nothing to disclose. Conflict of interest: A. Tiotiu has nothing to disclose. Conflict of interest: D.I. Broadhurst has nothing to disclose. Conflict of interest: A. Lundqvist has nothing to disclose. Conflict of interest: H. Olsson is an employee and shareholder of AstraZeneca. Conflict of interest: M. Ström has nothing to disclose. Conflict of interest: Å.M. Wheelock has nothing to disclose. Conflict of interest: C. Gómez has nothing to disclose. Conflict of interest: M. Ericsson has nothing to disclose. Conflict of interest: A.R. Sousa has nothing to disclose. Conflict of interest: J.H. Riley works for and own shares in GlaxoSmithKline. Conflict of interest: S. Bates is an employee of Johnson & Johnson and has previously worked for and holds stock in GlaxoSmithKline. Conflict of interest: J. Scholfield reports grants from Innovative Medicines Initiative, during the conduct of the study; and is director and employee of TopMD Precision Medicine Ltd. Conflict of interest: M. Loza is an employee of and owns stock in Johnson & Johnson. Conflict of interest: F. Baribaud is a shareholder of Johnson & Johnson and a current employee of Bristol Myers Squibb. Conflict of interest: P.S. Bakke reports personal fees for advisory board work and lectures from AstraZeneca, and personal fees for lectures from Novartis and Boehringer Ingelheim, outside the submitted work. Conflict of interest: M. Caruso has nothing to disclose. Conflict of interest: P. Chanez reports grants and personal fees from AstraZeneca, ALK, Boehringer Ingelheim, Chiesi, Sanofi-Aventis, Novartis and GlaxoSmithKline, outside the submitted work. Conflict of interest: S.J. Fowler reports personal fees from AstraZeneca, Novartis, TEVA and Chiesi, outside the submitted work. Conflict of interest: T. Geiser has nothing to disclose. Conflict of interest: P. Howarth has nothing to disclose. Conflict of interest: I. Horvath has nothing to disclose. Conflict of interest: N. Krug has nothing to disclose. Conflict of interest: P. Montuschi has nothing to disclose. Conflict of interest: A. Behndig has nothing to disclose. Conflict of interest: F. Singer reports personal fees from Vertex Pharmaceuticals (CH) and Novartis, outside the submitted work. Conflict of interest: J. Musial has nothing to disclose. Conflict of interest: D.E. Shaw has nothing to disclose. Conflict of interest: B. Dahlén reports personal fees for advisory board work and lectures from AstraZeneca, TEVA and Sanofi, and grants from Novartis and GlaxoSmithKline, outside the submitted work. Conflict of interest: S. Hu has nothing to disclose. Conflict of interest: J. Lasky-Su has nothing to disclose. Conflict of interest: P.J. Sterk reports a public private grant from the Innovative Medicines Initiative (IMI) covered by the EU and EFPIA, during the conduct of the study. Conflict of interest: K.F. Chung has received honoraria for participating in advisory board meetings of GlaxoSmithKline, AstraZeneca, Roche, Novartis, Merck, Nocion and Shionogi regarding treatments for asthma, COPD and chronic cough and has also been remunerated for speaking engagements. Conflict of interest: R. 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; and is a co-founder and current consultant, and has shares in Synairgen, a University of Southampton spin out company. Conflict of interest: S-E. Dahlén reports personal fees for consultancy from AstraZeneca, Cayman Chemical, GlaxoSmithKline, Novartis, Merck, Regeneron, Sanofi and TEVA, outside the submitted work. Conflict of interest: I.M. Adcock has nothing to disclose. Conflict of interest: C.E. Wheelock has nothing to disclose., (Copyright ©The authors 2022.)

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, <2 exacerbations) and of PFE with repeat ≥2 exacerbations during the following year to persistent IE (PIE). Transcriptomic data in blood, bronchial and nasal epithelial brushings, bronchial biopsies and sputum cells were analysed by gene set variation analysis for 103 gene signatures., 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., (© 2022 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.)

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., Competing Interests: Conflict of interest: M. Sparreman Mikus has nothing to disclose. Conflict of interest: J. Kolmert reports personal fees from Gesynta Pharma AB. Conflict of interest: L.I. Andersson has nothing to disclose. Conflict of interest: J. Östling has nothing to disclose. Conflict of interest: R.G. Knowles has nothing to disclose. Conflict of interest: C. Gómez has nothing to disclose. Conflict of interest: M. Ericsson has nothing to disclose. Conflict of interest: J-O. Thörngren has nothing to disclose. Conflict of interest: P. Emami Khoonsari has nothing to disclose. Conflict of interest: B. Dahlén reports personal fees from AstraZeneca, Teva, Sanofi and grants from Novartis and GlaxoSmithKline outside the submitted work. Conflict of interest: M. Kupczyk has nothing to disclose. Conflict of interest: B. De Meulder report grants from the Innovative Medicines Initiative during the conduct of the study. Conflict of interest: C. Auffray report grants from the Innovative Medicines Initiative during the conduct of the study. Conflict of interest: P.S. Bakke reports personal fees from GlaxoSmithKline, AstraZeneca, Boehringer Ingelheim and Chiesi outside the submitted work. Conflict of interest: B. Beghe reports personal fees from AstraZeneca, Boehringer Ingelheim, Menarini and GlaxoSmithKline outside the submitted work. Conflict of interest: E.H. Bel reports grants and personal fees from GlaxoSmithKline, AstraZeneca, Novartis, TEVA, Sanofi/Regeneron, Chiesi, and Sterna outside the submitted work. Conflict of interest: M. Caruso has nothing to disclose. Conflict of interest: P. Chanez has nothing to disclose. Conflict of interest: B. Chawes has nothing to disclose. Conflict of interest: S.J. Fowler has nothing to disclose. Conflict of interest: M. Gaga reports grants and personal fees from Novartis, Menarini, Merck Sharp & Dohme, BMS, Galapagos, and AstraZeneca outside the submitted work. Conflict of interest: T. Geiser has nothing to disclose. Conflict of interest: M. Gjomarkaj has nothing to disclose. Conflict of interest: I. Horváth reports grants from EFPIA during the conduct of the study, and personal fees from AstraZeneca, GlaxoSmithKline, Novartis, Boehringer-Ingelheim, Sandoz, Teva and Chiesi outside the submitted work. Conflict of interest: P.H. Howarth has nothing to disclose. Conflict of interest: S.L. Johnston reports personal fees from Virtus Respiratory Research, Myelo Therapeutics GmbH, Concert Pharmaceuticals, Bayer, Synairgen, Novartis, Boehringer Ingelheim, Chiesi, Gerson Lehrman Group, resTORbio, Bioforce, Materia Medical Holdings, PrepBio Pharma, Pulmotect, Virion Health, Lallemand Pharma and AstraZeneca outside the submitted work. In addition, Sebastian L. Johnston also has three patents (Anti-virus therapy for respiratory diseases, UK patent application No. GB 0405634.7, Interferon-Beta for Anti-Virus Therapy for Respiratory Diseases, International Patent Application No. PCT/ GB05/50031 and Interferon Lambda therapy for the treatment of respiratory disease, UK patent application No.6779645.9). Conflict of interest: G. Joos reports grants and personal fees from AstraZeneca, Bayer, Chiesi, Eureca vzw, GlaxoSmithKline and Teva outside the submitted work. Conflict of interest: N. Krug has nothing to disclose. Conflict of interest: P. Montuschi has nothing to disclose. Conflict of interest: J. Musial has nothing to disclose. Conflict of interest: E. Niżankowska-Mogilnicka has nothing to disclose. Conflict of interest: H.K. Olsson reports other support from AstraZeneca outside the submitted work. Conflict of interest: A. Papi reports grants, personal fees, non-financial support and others from GlaxoSmithKline, AstraZeneca, Boehringer Ingelheim, Chiesi, Teva, Mundipharma, Zambon, Novartis, Menarini, Sanofi/Regeneron, Roche, Fondazione Salvatore Maugeri, Chiesi and Edmond pharma outside the submitted work. Conflict of interest: K.F. Rabe reports grants and personal fees from AstraZeneca, Boehringer Ingelheim, Sanofi Aventis, MERCK SHARP & DOHME, Novartis, Orion Cooperation, Berlin Chemie, Roche, Chiesi and grants for research from the Ministry of Education and Science, Germany. Conflict of interest: T. Sandström has nothing to disclose. Conflict of interest: D.E. Shaw reports personal fees and non-financial support from GlaxoSmithKline, Novartis and AstraZeneca outside the submitted work. Conflict of interest: N.M. Siafakas has nothing to disclose. Conflict of interest: M. Uhlen has nothing to disclose. Conflict of interest: J.H. Riley is an employee and shareholder in GlaxoSmithKline. Conflict of interest: S. Bates is an employee and shareholder in GlaxoSmithKline. Conflict of interest: R.J.M. Middelveld reports grants from the Swedish Strategic Research Foundation, AstraZeneca, the Swedish Heart Lung Foundation and the Swedish Asthma and Allergy Association outside the submitted work. Conflict of interest: C.E. Wheelock has nothing to disclose. Conflict of interest: K.F. Chung reports grants and personal fees from GlaxoSmithKline, AstraZeneca, Novartis, Merck, Boehringer Ingelheim, Roche and Shionogi outside the submitted work. Conflict of interest: I.M. Adcock has nothing to disclose. Conflict of interest: P.J. Sterk reports grants to Amsterdam UMC from the public private Innovative Medicines Initiative (IMI) covered by the European Union (EU) and the European Federation of Pharmaceutical Industries and Associations (EFPIA) during the conduct of the study. Conflict of interest: R. Djukanovic reports receiving fees for lectures at symposia organised by Novartis, GlaxoSmithKline, AstraZeneca and Teva, consultation fees from Teva and Novartis; he is a co-founder and current consultant and has shares in Synairgen. Conflict of interest: P. Nilsson has nothing to disclose. Conflict of interest: S-E. Dahlén reports personal fees from AstraZeneca, Cayman Chemicals, GlaxoSmithKline, Novartis, Regeneron, Sanofi and Teva outside the submitted work. Conflict of interest: A. James has nothing to disclose., (Copyright ©The authors 2022.)

Rationale: Mast cells (MCs) play a role in inflammation and both innate and adaptive immunity, but their involvement in severe asthma (SA) remains undefined. Objectives: We investigated the phenotypic characteristics of the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes) asthma cohort by applying published MC activation signatures to the sputum cell transcriptome. Methods: Eighty-four participants with SA, 20 with mild/moderate asthma (MMA), and 16 healthy participants without asthma were studied. We calculated enrichment scores (ESs) for nine MC activation signatures by asthma severity, sputum granulocyte status, and three previously defined sputum molecular phenotypes or transcriptome-associated clusters (TACs) 1, 2, and 3 using gene set variation analysis. Measurements and Main Results: MC signatures except unstimulated, repeated FcεR1-stimulated and IFN-γ-stimulated signatures were enriched in SA. A FcεR1-IgE-stimulated and a single-cell signature from asthmatic bronchial biopsies were highly enriched in eosinophilic asthma and in the TAC1 molecular phenotype. Subjects with a high ES for these signatures had elevated sputum amounts of similar genes and pathways. IL-33- and LPS-stimulated MC signatures had greater ES in neutrophilic and mixed granulocytic asthma and in the TAC2 molecular phenotype. These subjects exhibited neutrophil, NF-κB (nuclear factor-κB), and IL-1β/TNF-α (tumor necrosis factor-α) pathway activation. The IFN-γ-stimulated signature had the greatest ES in TAC2 and TAC3 that was associated with responses to viral infection. Similar results were obtained in an independent ADEPT (Airway Disease Endotyping for Personalized Therapeutics) asthma cohort. Conclusions: Gene signatures of MC activation allow the detection of SA phenotypes and indicate that MCs can be induced to take on distinct transcriptional phenotypes associated with specific clinical phenotypes. IL-33-stimulated MC signature was associated with severe neutrophilic asthma, whereas IgE-activated MC was associated with an eosinophilic phenotype.

Background: Transcriptomic changes in patients who respond clinically to biological therapies may identify responses in other tissues or diseases., Objective: We sought to determine whether a disease signature identified in atopic dermatitis (AD) is seen in adults with severe asthma and whether a transcriptomic signature for patients with AD who respond clinically to anti-IL-22 (fezakinumab [FZ]) is enriched in severe asthma., Methods: An AD disease signature was obtained from analysis of differentially expressed genes between AD lesional and nonlesional skin biopsies. Differentially expressed genes from lesional skin from therapeutic superresponders before and after 12 weeks of FZ treatment defined the FZ-response signature. Gene set variation analysis was used to produce enrichment scores of AD and FZ-response signatures in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes asthma cohort., Results: The AD disease signature (112 upregulated genes) encompassing inflammatory, T-cell, T H 2, and T H 17/T H 22 pathways was enriched in the blood and sputum of patients with asthma with increasing severity. Patients with asthma with sputum neutrophilia and mixed granulocyte phenotypes were the most enriched (P < .05). The FZ-response signature (296 downregulated genes) was enriched in asthmatic blood (P < .05) and particularly in neutrophilic and mixed granulocytic sputum (P < .05). These data were confirmed in sputum of the Airway Disease Endotyping for Personalized Therapeutics cohort. IL-22 mRNA across tissues did not correlate with FZ-response enrichment scores, but this response signature correlated with T H 22/IL-22 pathways., Conclusions: The FZ-response signature in AD identifies severe neutrophilic asthmatic patients as potential responders to FZ therapy. This approach will help identify patients for future asthma clinical trials of drugs used successfully in other chronic diseases., (Copyright © 2021 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Background: Asthma is a heterogeneous disease with differences in onset, severity, and inflammation. Bronchial epithelial cells (BECs) contribute to asthma pathophysiology., Objective: We determined whether transcriptomes of BECs reflect heterogeneity in inflammation and severity in asthma, and whether this was affected in BECs from patients with severe asthma after their regeneration by bronchial thermoplasty., Methods: RNA sequencing was performed on BECs obtained by bronchoscopy from healthy controls (n = 16), patients with mild asthma (n = 17), patients with moderate asthma (n = 5), and patients with severe asthma (n = 17), as well as on BECs from treated and untreated airways of the latter (also 6 months after bronchial thermoplasty) (n = 23). Lipidome and metabolome analyses were performed on cultured BECs from healthy controls (n = 7); patients with severe asthma (n = 9); and, for comparison, patients with chronic obstructive pulmonary disease (n = 7)., Results: Transcriptome analysis of BECs from patients showed a reduced expression of oxidative phosphorylation (OXPHOS) genes, most profoundly in patients with severe asthma but less profoundly and more heterogeneously in patients with mild asthma. Genes related to fatty acid metabolism were significantly upregulated in asthma. Lipidomics revealed enhanced levels of lipid species (phosphatidylcholines, lysophosphatidylcholines. and bis(monoacylglycerol)phosphate), whereas levels of OXPHOS metabolites were reduced in BECs from patients with severe asthma. BECs from patients with mild asthma characterized by hyperresponsive production of mediators implicated in neutrophilic inflammation had decreased expression of OXPHOS genes compared with that in BECs from patients with mild asthma with normoresponsive production. BECs obtained after thermoplasty had significantly increased expression of OXPHOS genes and decreased expression of fatty acid metabolism genes compared with BECs obtained from untreated airways., Conclusion: BECs in patients with asthma are metabolically different from those in healthy individuals. These differences are linked with inflammation and asthma severity, and they can be reversed by bronchial thermoplasty., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: In all chronic airway diseases, the dynamics of airway function are influenced by underlying airway inflammation and bronchial hyperresponsiveness along with limitations in reversibility owing to airway and lung remodeling as well as mucous plugging. The relative contribution of each component translates into specific clinical patterns of symptoms, quality of life, exacerbation risk, and treatment success., Objective: We aimed to evaluate whether subgrouping of patients with obstructive airway diseases according to patterns of fluctuation in lung function allows identification of specific phenotypes with distinct clinical characteristics., Methods: We applied the novel method of fluctuation-based clustering (FBC) to twice-daily FEV 1 measurements recorded over a 1-year period in a mixed group of 134 adults with mild-to-moderate asthma, severe asthma, or chronic obstructive pulmonary disease from the European BIOAIR cohort., Results: Independently of clinical diagnosis, FBC divided patients into 4 fluctuation-based clusters with progressively increasing alterations in lung function that corresponded to patterns of increasing clinical severity, risk of exacerbation, and lower quality of life. Clusters of patients with airway disease with significantly elevated levels of biomarkers relating to remodeling (osteonectin) and cellular senescence (plasminogen activator inhibitor-1), accompanied by a loss of airway reversibility, pulmonary hyperinflation, and loss of diffusion capacity, were identified. The 4 clusters generated were stable over time and revealed no differences in levels of markers of type 2 inflammation (blood eosinophils and periostin)., Conclusion: FBC-based phenotyping provides another level of information that is complementary to clinical diagnosis and unrelated to eosinophilic inflammation, which could identify patients who may benefit from specific treatment strategies or closer monitoring., (Copyright © 2021. Published by Elsevier Inc.)

Background: Early detection/prediction of flare-ups in asthma, commonly triggered by viruses, would enable timely treatment. Previous studies on exhaled breath analysis by electronic nose (eNose) technology could discriminate between stable and unstable episodes of asthma, using single/few time-points. To investigate its monitoring properties during these episodes, we examined day-to-day fluctuations in exhaled breath profiles, before and after a rhinovirus-16 (RV16) challenge, in healthy and asthmatic adults., Methods: In this proof-of-concept study, 12 atopic asthmatic and 12 non-atopic healthy adults were prospectively followed thrice weekly, 60 days before, and 30 days after a RV16 challenge. Exhaled breath profiles were detected using an eNose, consisting of 7 different sensors. Per sensor, individual means were calculated using pre-challenge visits. Absolute deviations (|%|) from this baseline were derived for all visits. Within-group comparisons were tested with Mann-Whitney U tests and receiver operating characteristic (ROC) analysis. Finally, Spearman's correlations between the total change in eNose deviations and fractional exhaled nitric oxide (FeNO), cold-like symptoms, and pro-inflammatory cytokines were examined., Results: Both groups had significantly increased eNose fluctuations post-challenge, which in asthma started 1 day post-challenge, before the onset of symptoms. Discrimination between pre- and post-challenge reached an area under the ROC curve of 0.82 (95% CI = 0.65-0.99) in healthy and 0.97 (95% CI = 0.91-1.00) in asthmatic adults. The total change in eNose deviations moderately correlated with IL-8 and TNFα (ρ ≈ .50-0.60) in asthmatics., Conclusion: Electronic nose fluctuations rapidly increase after a RV16 challenge, with distinct differences between healthy and asthmatic adults, suggesting that this technology could be useful in monitoring virus-driven unstable episodes in asthma., (© 2021 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.)

Background: Although estimates of suboptimal adherence to oral corticosteroids in asthma range from 30% to 50%, no ideal method for measurement exists; the impact of poor adherence in severe asthma is likely to be particularly high., Research Questions: What is the prevalence of suboptimal adherence detected by self-reporting and direct measures? Is suboptimal adherence associated with disease activity?, Study Design and Methods: Data were included from individuals with severe asthma taking part in the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) study and prescribed daily oral corticosteroids. Participants completed the Medication Adherence Report Scale, a five-item questionnaire used to grade adherence on a scale from 1 to 5, and provided a urine sample for analysis of prednisolone and metabolites by liquid chromatography-mass spectrometry., Results: Data from 166 participants were included in this study: mean (SD) age, 54.2 (± 11.9) years; FEV 1 , 65.1% (± 20.5%) predicted; female, 58%; 37% completing the Medication Adherence Report Scale reported suboptimal adherence; and 43% with urinary corticosteroid data did not have detectable prednisolone or metabolites in their urine. Good adherence by both methods was detected in 49 of the 142 (35%) of participants in whom both methods were performed; adherence detection did not match between methods in 53%. Self-reported high adherers had better asthma control and quality of life, whereas directly measured high adherers had lower blood eosinophil levels., Interpretation: Low adherence is a common problem in severe asthma, whether measured directly or self-reported. We report poor agreement between the two methods, suggesting some disassociation between self-assessment of medication adherence and regular oral corticosteroid use, which suggests that each approach may provide complementary information in clinical practice., (Crown Copyright © 2021. Published by Elsevier Inc. All rights reserved.)

Breath analysis using eNose technology can be used to discriminate between asthma and COPD patients, but it remains unclear whether results are influenced by smoking status. We aim to study whether eNose can discriminate between ever- vs. never-smokers and smoking <24 vs. >24 h before the exhaled breath, and if smoking can be considered a confounder that influences eNose results. We performed a cross-sectional analysis in adults with asthma or chronic obstructive pulmonary disease (COPD), and healthy controls. Ever-smokers were defined as patients with current or past smoking habits. eNose measurements were performed by using the SpiroNose. The principal component (PC) described the eNose signals, and linear discriminant analysis determined if PCs classified ever-smokers vs. never-smokers and smoking <24 vs. >24 h. The area under the receiver-operator characteristic curve (AUC) assessed the accuracy of the models. We selected 593 ever-smokers (167 smoked <24 h before measurement) and 303 never-smokers and measured the exhaled breath profiles of discriminated ever- and never-smokers (AUC: 0.74; 95% CI: 0.66-0.81), and no cigarette consumption <24h (AUC 0.54, 95% CI: 0.43-0.65). In healthy controls, the eNose did not discriminate between ever or never-smokers (AUC 0.54; 95% CI: 0.49-0.60) and recent cigarette consumption (AUC 0.60; 95% CI: 0.50-0.69). The eNose could distinguish between ever and never-smokers in asthma and COPD patients, but not recent smokers. Recent smoking is not a confounding factor of eNose breath profiles.

Background: Airway remodelling, which may include goblet cell hyperplasia / hypertrophy, changes in epithelial integrity, accumulation of extracellular matrix components, smooth muscle hypertrophy and thickening of the lamina reticularis, is a feature of severe asthma and contributes to the clinical phenotype., Objective: Within the U-BIOPRED severe asthma study, we have assessed histological elements of airway remodelling and their relationship to computed tomography (CT) measures of proximal airway dimensions., Methods: Bronchial biopsies were collected from two severe asthma groups, one non-smoker (SAn, n = 28) and one current/ex-smoker (SAs/ex, n = 13), and a mild-moderate asthma group (MMA, n = 28) classified and treated according to GINA guidelines, plus a healthy control group (HC, n = 33). Movat's pentachrome technique was used to identify mucin, elastin and total collagen in these biopsies. The number of goblet cells (mucin+) was counted as a percentage of the total number of epithelial cells and the percentage mucin epithelial area measured. The percentage area of elastic fibres and total collagen within the submucosa was also measured, and the morphology of the elastic fibres classified. Participants in the asthma groups also had a CT scan to assess large airway morphometry., Results: The submucosal tissue elastin percentage was higher in both severe asthma groups (16.1% SAn, 18.9% SAs/ex) compared with the HC (9.7%) but did not differ between asthma groups. There was a positive relationship between elastin and airway wall area measured by CT (n = 18-20, rho=0.544, p = 0.024), which also related to an increase in elastic fibres with a thickened lamellar morphological appearance. Mucin epithelial area and total collagen were not different between the four groups. Due to small numbers of suitable CT scans, it was not feasible to compare airway morphometry between the asthma groups., Conclusion: These findings identify a link between extent of elastin deposition and airway wall thickening in severe asthma., (© 2020 The Authors. Clinical & Experimental Allergy published by John Wiley & Sons Ltd.)

Background: Patients with severe asthma may have a greater risk of dying from COVID-19 disease. Angiotensin converting enzyme-2 (ACE2) and the enzyme proteases, transmembrane protease serine 2 (TMPRSS2) and FURIN, are needed for viral attachment and invasion into host cells., Methods: We examined microarray mRNA expression of ACE2, TMPRSS2 and FURIN in sputum, bronchial brushing and bronchial biopsies of the European U-BIOPRED cohort. Clinical parameters and molecular phenotypes, including asthma severity, sputum inflammatory cells, lung functions, oral corticosteroid (OCS) use, and transcriptomic-associated clusters, were examined in relation to gene expression levels., Results: ACE2 levels were significantly increased in sputum of severe asthma compared to mild-moderate asthma. In multivariate analyses, sputum ACE2 levels were positively associated with OCS use and male gender. Sputum FURIN levels were significantly related to neutrophils (%) and the presence of severe asthma. In bronchial brushing samples, TMPRSS2 levels were positively associated with male gender and body mass index, whereas FURIN levels with male gender and blood neutrophils. In bronchial biopsies, TMPRSS2 levels were positively related to blood neutrophils. The neutrophilic molecular phenotype characterised by high inflammasome activation expressed significantly higher FURIN levels in sputum than the eosinophilic Type 2-high or the pauci-granulocytic oxidative phosphorylation phenotypes., Conclusion: Levels of ACE2 and FURIN may differ by clinical or molecular phenotypes of asthma. Sputum FURIN expression levels were strongly associated with neutrophilic inflammation and with inflammasome activation. This might indicate the potential for a greater morbidity and mortality outcome from SARS-CoV-2 infection in neutrophilic severe asthma.

Rationale: New approaches are needed to guide personalized treatment of asthma. Objectives: To test if urinary eicosanoid metabolites can direct asthma phenotyping. Methods: Urinary metabolites of prostaglandins (PGs), cysteinyl leukotrienes (CysLTs), and isoprostanes were quantified in the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes) study including 86 adults with mild-to-moderate asthma (MMA), 411 with severe asthma (SA), and 100 healthy control participants. Validation was performed internally in 302 participants with SA followed up after 12-18 months and externally in 95 adolescents with asthma. Measurement and Main Results: Metabolite concentrations in healthy control participants were unrelated to age, body mass index, and sex, except for the PGE 2 pathway. Eicosanoid concentrations were generally greater in participants with MMA relative to healthy control participants, with further elevations in participants with SA. However, PGE 2 metabolite concentrations were either the same or lower in male nonsmokers with asthma than in healthy control participants. Metabolite concentrations were unchanged in those with asthma who adhered to oral corticosteroid treatment as documented by urinary prednisolone detection, whereas those with SA treated with omalizumab had lower concentrations of LTE 4 and the PGD 2 metabolite 2,3-dinor-11β-PGF 2α . High concentrations of LTE 4 and PGD 2 metabolites were associated with lower lung function and increased amounts of exhaled nitric oxide and eosinophil markers in blood, sputum, and urine in U-BIOPRED participants and in adolescents with asthma. These type 2 (T2) asthma associations were reproduced in the follow-up visit of the U-BIOPRED study and were 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 noninvasive approach for molecular phenotyping of adult and adolescent asthma.Clinical trial registered with www.clinicaltrials.gov (NCT01976767).

Background: Asthma is a heterogeneous disease characterized by distinct phenotypes with associated microbial dysbiosis., Objectives: Our aim was to identify severe asthma phenotypes based on sputum microbiome profiles and assess their stability after 12 to 18 months. A further aim was to evaluate clusters' robustness after inclusion of an independent cohort of patients with mild-to-moderate asthma., Methods: In this longitudinal multicenter cohort study, sputum samples were collected for microbiome profiling from a subset of the Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes adult patient cohort at baseline and after 12 to 18 months of follow-up. Unsupervised hierarchical clustering was performed by using the Bray-Curtis β-diversity measure of microbial profiles. For internal validation, partitioning around medoids, consensus cluster distribution, bootstrapping, and topological data analysis were applied. Follow-up samples were studied to evaluate within-patient clustering stability in patients with severe asthma. Cluster robustness was evaluated by using an independent cohort of patients with mild-to-moderate asthma., Results: Data were available for 100 subjects with severe asthma (median age 55 years; 42% males). Two microbiome-driven clusters were identified; they were characterized by differences in asthma onset, smoking status, residential locations, percentage of blood and/or sputum neutrophils and macrophages, lung spirometry results, and concurrent asthma medications (all P values < .05). The cluster 2 patients displayed a commensal-deficient bacterial profile that was associated with worse asthma outcomes than those of the cluster 1 patients. Longitudinal clusters revealed high relative stability after 12 to 18 months in those with severe asthma. Further inclusion of an independent cohort of 24 patients with mild-to-moderate asthma was consistent with the clustering assignments., Conclusion: Unbiased microbiome-driven clustering revealed 2 distinct robust phenotypes of severe asthma that exhibited relative overtime stability. This suggests that the sputum microbiome may serve as a biomarker for better characterizing asthma phenotypes., (Copyright © 2020 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Background: Electronic noses (eNoses) are emerging point-of-care tools that may help in the subphenotyping of chronic respiratory diseases such as asthma., Objective: We aimed to investigate whether eNoses can classify atopy in pediatric and adult patients with asthma., Methods: Participants with asthma and/or wheezing from 4 independent cohorts were included; BreathCloud participants (n = 429), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes adults (n = 96), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes pediatric participants (n = 100), and Pharmacogenetics of Asthma Medication in Children: Medication with Anti-Inflammatory Effects 2 participants (n = 30). Atopy was defined as a positive skin prick test result (≥3 mm) and/or a positive specific IgE level (≥0.35 kU/L) for common allergens. Exhaled breath profiles were measured by using either an integrated eNose platform or the SpiroNose. Data were divided into 2 training and 2 validation sets according to the technology used. Supervised data analysis involved the use of 3 different machine learning algorithms to classify patients with atopic versus nonatopic asthma with reporting of areas under the receiver operating characteristic curves as a measure of model performance. In addition, an unsupervised approach was performed by using a bayesian network to reveal data-driven relationships between eNose volatile organic compound profiles and asthma characteristics., Results: Breath profiles of 655 participants (n = 601 adults and school-aged children with asthma and 54 preschool children with wheezing [68.2% of whom were atopic]) were included in this study. Machine learning models utilizing volatile organic compound profiles discriminated between atopic and nonatopic participants with areas under the receiver operating characteristic curves of at least 0.84 and 0.72 in the training and validation sets, respectively. The unsupervised approach revealed that breath profiles classifying atopy are not confounded by other patient characteristics., Conclusion: eNoses accurately detect atopy in individuals with asthma and wheezing in cohorts with different age groups and could be used in asthma phenotyping., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Accurate detection of human respiratory viral infections is highly topical. We investigated how strongly inflammatory biomarkers (FeNO, eosinophils, neutrophils, and cytokines in nasal lavage fluid) and lung function parameters change upon rhinovirus 16 infection, in order to explore their potential use for infection detection. To this end, within a longitudinal cohort study, healthy and mildly asthmatic volunteers were experimentally inoculated with rhinovirus 16, and time series of these parameters/biomarkers were systematically recorded and compared between the pre- and post-infection phases of the study, which lasted two months and one month, respectively. We found that the parameters'/biomarkers' ability to discriminate between the infected and the uninfected state varied over the observation time period. Consistently over time, the concentration of cytokines, in nasal lavage fluid, showed moderate to very good discrimination performance, thereby qualifying for disease progression monitoring, whereas lung function and FeNO, while quickly and non-invasively measurable using cheap portable devices (e.g., at airports), performed poorly.

Background: Allergic sensitization is associated with severe asthma, but assessment of sensitization is not recommended by most guidelines., Objective: We hypothesized that patterns of IgE responses to multiple allergenic proteins differ between sensitized participants with mild/moderate and severe asthma., Methods: IgE to 112 allergenic molecules (components, c-sIgE) was measured using multiplex array among 509 adults and 140 school-age and 131 preschool children with asthma/wheeze from the Unbiased BIOmarkers for the PREDiction of respiratory diseases outcomes cohort, of whom 595 had severe disease. We applied clustering methods to identify co-occurrence patterns of components (component clusters) and patterns of sensitization among participants (sensitization clusters). Network analysis techniques explored the connectivity structure of c-sIgE, and differential network analysis looked for differences in c-sIgE interactions between severe and mild/moderate asthma., Results: Four sensitization clusters were identified, but with no difference between disease severity groups. Similarly, component clusters were not associated with asthma severity. None of the c-sIgE were identified as associates of severe asthma. The key difference between school children and adults with mild/moderate compared with those with severe asthma was in the network of connections between c-sIgE. Participants with severe asthma had higher connectivity among components, but these connections were weaker. The mild/moderate network had fewer connections, but the connections were stronger. Connectivity between components with no structural homology tended to co-occur among participants with severe asthma. Results were independent from the different sample sizes of mild/moderate and severe groups., Conclusions: The patterns of interactions between IgE to multiple allergenic proteins are predictors of asthma severity among school children and adults with allergic asthma., (Copyright © 2020 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Introduction: Asthma is a heterogeneous condition, characterised by chronic inflammation of the airways, typically managed with inhaled bronchodilators and corticosteroids. In the case of uncontrolled asthma, oral corticosteroids (OCSs) are often prescribed. Good adherence and inhalation technique are associated with improved outcomes; however, it is difficult to monitor appropriate drug intake and effectiveness in individual patients. Exhaled breath contains thousands of volatile organic compounds (VOCs) that reflect changes in the body's chemistry and may be useful for monitoring drug pharmacokinetics/pharmacodynamics. We aimed to investigate the association of exhaled VOCs in severe asthma patients from the U-BIOPRED cohort (by gas chromatography coupled with time-of-flight mass spectrometry) with urinary levels of salbutamol and OCSs (by liquid chromatography coupled with high-resolution mass spectrometry)., Methods: Samples were collected at baseline and after 12-18 months of follow-up. Statistical analysis was based on univariate and multivariate modelling, followed by area under the receiver operating characteristic curve (AUC) calculation. Results were verified through longitudinal replication and independent validation., Results: Data were available for 78 patients (baseline n=48, replication n=30 and validation n=30). Baseline AUC values were 82.1% (95% CI 70.4-93.9%) for salbutamol and 78.8% (95% CI 65.8-91.8%) for OCS. These outcomes could be adequately replicated and validated. Additional regression analysis between qualified exhaled VOCs and urinary concentrations of salbutamol and prednisone showed statistically significant correlations (p<0.01)., Conclusion: We have linked exhaled VOCs to urinary detection of salbutamol and OCSs. This merits further development of breathomics into a point-of-care tool for therapeutic drug monitoring., Competing Interests: Conflict of interest: P. Brinkman has nothing to disclose. Conflict of interest: W.M. Ahmed has nothing to disclose. Conflict of interest: C. Gómez has nothing to disclose. Conflict of interest: H.H. Knobel has a patent “Inline drying of breath sample” pending. Conflict of interest: H. Weda has nothing to disclose. Conflict of interest: T.J. Vink has nothing to disclose. Conflict of interest: T.M. Nijsen has nothing to disclose. Conflict of interest: C.E. Wheelock has nothing to disclose. Conflict of interest: S-E. Dahlén has participated in advisory boards for AstraZeneca, GSK, Merck, Novartis, Sanofi and Teva, outside the submitted work. Conflict of interest: P. Montuschi has nothing to disclose. Conflict of interest: R.G. Knowles is director of Knowles Consulting Ltd, outside the submitted work. Conflict of interest: S.J. Vijverberg has nothing to disclose. Conflict of interest: A.H. Maitland-van der Zee reports grants from Boehringer Ingelheim, personal fees for advisory board work from AstraZeneca, and personal fees for travel to meetings from Chiesi, outside the submitted work. Conflict of interest: P.J. Sterk reports grants (paid to institution) from Innovative Medicines Initiative covered by the European Union and the European Federation of Pharmaceutical Industries and Associations, during the conduct of the study; and reports a 4% interest and is a scientific advisor for Breathomix BV, outside the submitted work. Conflict of interest: S.J. Fowler reports personal fees for lectures and nonfinancial support to attend conferences from AstraZeneca, grants and personal fees for lectures from Boehringer Ingelheim, personal fees for lectures from Novartis, Teva and Chiesi, outside the submitted work., (Copyright ©ERS 2020.)

Background: Whether the clinical or pathophysiologic significance of the "treatable trait" high blood eosinophil count in COPD is the same as for asthma remains controversial. We sought to determine the relationship between the blood eosinophil count, clinical characteristics and gene expression from bronchial brushings in COPD and asthma., Methods: Subjects were recruited into a COPD (emphysema versus airway disease [EvA]) or asthma cohort (Unbiased BIOmarkers in PREDiction of respiratory disease outcomes, U-BIOPRED). We determined gene expression using RNAseq in EvA (n = 283) and Affymetrix microarrays in U-BIOPRED (n = 85). We ran linear regression analysis of the bronchial brushings transcriptional signal versus blood eosinophil counts as well as differential expression using a blood eosinophil > 200 cells/μL as a cut-off. The false discovery rate was controlled at 1% (with continuous values) and 5% (with dichotomized values)., Results: There were no differences in age, gender, lung function, exercise capacity and quantitative computed tomography between eosinophilic versus noneosinophilic COPD cases. Total serum IgE was increased in eosinophilic asthma and COPD. In EvA, there were 12 genes with a statistically significant positive association with the linear blood eosinophil count, whereas in U-BIOPRED, 1197 genes showed significant associations (266 positive and 931 negative). The transcriptome showed little overlap between genes and pathways associated with blood eosinophil counts in asthma versus COPD. Only CST1 was common to eosinophilic asthma and COPD and was replicated in independent cohorts., Conclusion: Despite shared "treatable traits" between asthma and COPD, the molecular mechanisms underlying these clinical entities are predominately different., (© 2019 The Authors. Allergy published by John Wiley & Sons Ltd.)

Background: Asthma prevalence is 339 million globally. 'Severe asthma' (SA) comprises subjects with uncontrolled asthma despite proper management., Objectives: To compare asthma from diverse ethnicities and environments., Methods: A cross-sectional analysis of two adult cohorts, a Brazilian (ProAR) and a European (U-BIOPRED). U-BIOPRED comprised of 311 non-smoking with Severe Asthma (SAn), 110 smokers or ex-smokers with SA (SAs) and 88 mild to moderate asthmatics (MMA) while ProAR included 544 SA and 452 MMA. Although these projects were independent, there were similarities in objectives and methodology, with ProAR adopting operating procedures of U-BIOPRED., Results: Among SA subjects, age, weight, proportion of former smokers and FEV 1 pre-bronchodilator were similar. The proportion of SA with a positive skin prick tests (SPT) to aeroallergens, the scores of sino-nasal symptoms and quality of life were comparable. In addition, blood eosinophil counts (EOS) and the % of subjects with EOS > 300 cells/μl were not different. The Europeans with SA however, were more severe with a greater proportion of continuous oral corticosteroids (OCS), worse symptoms and more frequent exacerbations. FEV 1 /FVC pre- and post-bronchodilator were lower among the Europeans. The MMA cohorts were less comparable in control and treatment, but similar in the proportion of allergic rhinitis, gastroesophageal reflux disease and EOS >3%., Conclusions: ProAR and U-BIOPRED cohorts, with varying severity, ethnicity and environment have similarities, which provide the basis for global external validation of asthma phenotypes. This should stimulate collaboration between asthma consortia with the aim of understanding SA, which will lead to better management., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background: Loss of asthma control and asthma exacerbations are associated with increased sputum eosinophil counts. However, whether eosinophils, or the also present neutrophils, actively contribute to the accompanying inflammation has not been extensively investigated., Methods: Twenty-three patients with mild to moderate asthma were included in a standardized prospective inhaled corticosteroid (ICS) withdrawal study; 22 of the patients experienced loss of asthma control. The study assessed various immune, inflammatory, and oxidative stress parameters, as well as markers of eosinophil and neutrophil activity, in exhaled breath condensate, plasma, and sputum collected at three phases (baseline, during loss of control, and following recovery)., Results: Loss of asthma control was characterized by increased sputum eosinophils, whereas no differences were detected between the three phases for most inflammatory and oxidative stress responses. There were also no differences detected for markers of activated eosinophils (eosinophil cationic protein and bromotyrosine) and neutrophils (myeloperoxidase and chlorotyrosine). However, free eosinophilic granules and citrullinated histone H 3 , suggestive of eosinophil cytolysis and potentially eosinophil extracellular trap formation, were enhanced. Baseline blood eosinophils and changes in asymmetric dimethylarginine (an inhibitor of nitric oxide synthase) in plasma were found to correlate with the decrease in FEV 1 percent predicted upon ICS withdrawal (both, r s  = 0.46; P = .03)., Conclusions: The clinical effect in mild to moderate asthma upon interruption of ICS therapy is not related to the classic inflammatory activation of eosinophils and neutrophils. It may, however, reflect another pathway underlying the onset of loss of disease control and asthma exacerbations., Trial Registry: The Netherlands Trial Register; No.: NTR3316; URL: trialregister.nl/trial/3172., (Copyright © 2019 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Background: The temporal in vivo response of epithelial cells to a viral challenge and its association with viral clearance and clinical outcomes has been largely unexplored in asthma., Objective: To determine gene expression profiles over time in nasal epithelial cells (NECs) challenged in vivo with rhinovirus-16 (RV16) and compare to nasal symptoms and viral clearance., Methods: Patients with stable mild to moderate asthma (n = 20) were challenged intranasally with RV16. Nasal brush samples for RNA sequencing were taken 7 days prior to infection and 3, 6 and 14 days post-infection, and blood samples 4 days prior to infection and day 6 post-infection. Viral load was measured in nasal lavage fluid at day 3, 6 and 14., Results: Top differentially (>2.5-fold increase) expressed gene sets in NECs post-RV16 at days 3 and 6, compared with baseline, were interferon alpha and gamma response genes. Patients clearing the virus within 6 days (early resolvers) had a significantly increased interferon response at day 6, whereas those having cleared the virus by day 14 (late resolvers) had significantly increased responses at day 3, 6 and 14. Interestingly, patients not having cleared the virus by day 14 (non-resolvers) had no enhanced interferon responses at any of these days. The daily Cold Symptom Scores (CSS) peaked at days 3 to 5 and correlated positively with interferon response genes at day 3 (R = 0.48), but not at other time-points. Interferon response genes were also enhanced in blood at day 6 after RV16 challenge., Conclusion and Clinical Relevance: This study shows that viral load and clearance varies markedly over time in mild to moderate asthma patients exposed to a fixed RV16 dose. The host's nasal interferon response to RV16 at day 3 is associated with upper respiratory tract symptoms. The temporal interferon response in nasal epithelium associates with viral clearance in the nasal compartment., (© 2019 The Authors. Clinical & Experimental Allergy published by John Wiley & Sons Ltd.)

Asthma is a dynamic disease, in which lung mechanical and inflammatory processes interact in a complex manner, often resulting in exaggerated physiological, in particular, inflammatory responses to exogenous triggers. We hypothesize that this may be explained by respiratory disease-related systems instability and loss of adaptability to changing environmental conditions, manifested in highly fluctuating biomarkers and symptoms. Using time series of inflammatory (eosinophils, neutrophils, FeNO), clinical and lung function biomarkers (PEF, FVC,FEV 1 ), we estimated this loss of adaptive capacity (AC) during an experimental rhinovirus infection in 24 healthy and asthmatic human volunteers. Loss of AC was estimated by comparing similarities between pre- and post-challenge time series. Unlike healthy participants, the asthmatic's post-viral-challenge state resembled more other rhinovirus-infected asthmatics than their own pre-viral-challenge state (hypergeometric-test: p=0.029). This reveals loss of AC and supports the concept that in asthma, biological processes underlying inflammatory and physiological responses are unstable, contributing to loss of control., Competing Interests: AS, RL, BX, TD, BD, PS, UF, EE No competing interests declared, (© 2019, Sinha et al.)

More than a decade has passed since the finalization of the Human Genome Project. Omics technologies made a huge leap from trendy and very expensive to routinely executed and relatively cheap assays. Simultaneously, we understood that omics is not a panacea for every problem in the area of human health and personalized medicine. Whilst in some areas of research omics showed immediate results, in other fields, including asthma, it only allowed us to identify the incredibly complicated molecular processes. Along with their possibilities, omics technologies also bring many issues connected to sample collection, analyses and interpretation. It is often impossible to separate the intrinsic imperfection of omics from asthma heterogeneity. Still, many insights and directions from applied omics were acquired-presumable phenotypic clusters of patients, plausible biomarkers and potential pathways involved. Omics technologies develop rapidly, bringing improvements also to asthma research. These improvements, together with our growing understanding of asthma subphenotypes and underlying cellular processes, will likely play a role in asthma management strategies., (© 2019 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.)

Background: The role of IL-17 immunity is well established in patients with inflammatory diseases, such as psoriasis and inflammatory bowel disease, but not in asthmatic patients, in whom further study is required., Objective: We sought to undertake a deep phenotyping study of asthmatic patients with upregulated IL-17 immunity., Methods: Whole-genome transcriptomic analysis was performed by using epithelial brushings, bronchial biopsy specimens (91 asthmatic patients and 46 healthy control subjects), and whole blood samples (n = 498) from the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) cohort. Gene signatures induced in vitro by IL-17 and IL-13 in bronchial epithelial cells were used to identify patients with IL-17-high and IL-13-high asthma phenotypes., Results: Twenty-two of 91 patients were identified with IL-17, and 9 patients were identified with IL-13 gene signatures. The patients with IL-17-high asthma were characterized by risk of frequent exacerbations, airway (sputum and mucosal) neutrophilia, decreased lung microbiota diversity, and urinary biomarker evidence of activation of the thromboxane B2 pathway. In pathway analysis the differentially expressed genes in patients with IL-17-high asthma were shared with those reported as altered in psoriasis lesions and included genes regulating epithelial barrier function and defense mechanisms, such as IL1B, IL6, IL8, and β-defensin., Conclusion: The IL-17-high asthma phenotype, characterized by bronchial epithelial dysfunction and upregulated antimicrobial and inflammatory response, resembles the immunophenotype of psoriasis, including activation of the thromboxane B2 pathway, which should be considered a biomarker for this phenotype in further studies, including clinical trials targeting IL-17., (Copyright © 2019 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Background: Activated eosinophils cause major pathology in stable and exacerbating asthma; however, they can also display protective properties like an extracellular antiviral activity. Initial murine studies led us to further explore a potential intracellular antiviral activity by eosinophils., Methods: To follow eosinophil-virus interaction, respiratory syncytial virus (RSV) and influenza virus were labeled with a fluorescent lipophilic dye (DiD). Interactions with eosinophils were visualized by confocal microscopy, electron microscopy, and flow cytometry. Eosinophil activation was assessed by both flow cytometry and ELISA. In a separate study, eosinophils were depleted in asthma patients using anti-IL-5 (mepolizumab), followed by a challenge with rhinovirus-16 (RV16)., Results: DiD-RSV and DiD-influenza rapidly adhered to human eosinophils and were internalized and inactivated (95% in ≤ 2 hours) as reflected by a reduced replication in epithelial cells. The capacity of eosinophils to capture virus was reduced up to 75% with increasing severity of asthma. Eosinophils were activated by virus in vitro and in vivo. In vivo this correlated with virus-induced loss of asthma control., Conclusions: This previously unrecognized and in asthma attenuated antiviral property provides a new perspective to eosinophils in asthma. This is indicative of an imbalance between protective and cytotoxic properties by eosinophils that may underlie asthma exacerbations., (© 2019 The Authors. Allergy Published by John Wiley & Sons Ltd.)

Inflammation, structural, and functional abnormalities within the airways are key features of asthma. Although these processes are well documented, their expression varies across the heterogeneous spectrum of asthma. Type 2 inflammatory responses are characterized by increased levels of eosinophils, FeNO, and type 2 cytokines in blood and/or airways. Presently, type 2 asthma is the best-defined endotype, typically found in patients with allergic asthma, but surprisingly also in nonallergic patients with (severe) asthma. The etiology of asthma with non-type 2 inflammation is less clear. During the past decade, targeted therapies, including biologicals and small molecules, have been increasingly integrated into treatment strategies of severe asthma. These treatments block specific inflammatory pathways or single mediators. Single or composite biomarkers help to identify patients who will benefit from these treatments. So far, only a few inflammatory biomarkers have been validated for clinical application. The European Academy of Allergy & Clinical Immunology Task Force on Biomarkers in Asthma was initiated to review different biomarker sampling methods and to investigate clinical applicability of new and existing inflammatory biomarkers (point-of-care) to support diagnosis, targeted treatment, and monitoring of severe asthma. Subsequently, we discuss existing and novel targeted therapies for asthma as well as applicable biomarkers., (© 2019 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.)

Neutrophilic inflammation in asthma is associated with interleukin (IL)-17A, corticosteroid-insensitivity and bronchodilator-induced forced expiratory volume in 1 s (FEV 1 ) reversibility. IL-17A synergises with tumour necrosis factor (TNF)-α in the production of the neutrophil chemokine CXCL-8 by primary bronchial epithelial cells (PBECs).We hypothesised that local neutrophilic inflammation in asthma correlates with IL-17A and TNF-α-induced CXCL-8 production by PBECs from asthma patients.PBECs from most asthma patients displayed an exaggerated CXCL-8 production in response to TNF-α and IL-17A, but not to TNF-α alone, and which was also insensitive to corticosteroids. This hyperresponsiveness of PBECs strongly correlated with CXCL-8 levels and neutrophil numbers in bronchoalveolar lavage from the corresponding patients, but not with that of eosinophils. In addition, this hyperresponsiveness also correlated with bronchodilator-induced FEV 1 % reversibility. At the molecular level, epithelial hyperresponsiveness was associated with failure of the translational repressor T-cell internal antigen-1 related protein (TiAR) to translocate to the cytoplasm to halt CXCL-8 production, as confirmed by TiAR knockdown. This is in line with the finding that hyperresponsive PBECs also produced enhanced levels of other inflammatory mediators.Hyperresponsive PBECs in asthma patients may underlie neutrophilic and corticosteroid-insensitive inflammation and a reduced FEV 1 , irrespective of eosinophilic inflammation. Normalising cytoplasmic translocation of TiAR is a potential therapeutic target in neutrophilic, corticosteroid-insensitive asthma., Competing Interests: Conflict of interest: S. Chowdhury has nothing to disclose. Conflict of interest: A. Dijkhuis has nothing to disclose. Conflict of interest: P.I. Bonta has nothing to disclose. Conflict of interest: P.J. Sterk has nothing to disclose. Conflict of interest: R. Lutter has nothing to disclose. Conflict of interest: A. Ravi has nothing to disclose., (Copyright ©ERS 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., (Copyright © 2019. Published by Elsevier Inc.)

Background: Eosinophils play an important role in the pathophysiology of asthma being implicated in airway epithelial damage and airway wall remodeling. We determined the genes associated with airway remodeling and eosinophilic inflammation in patients with asthma., Methods: We analyzed the transcriptomic data from bronchial biopsies of 81 patients with moderate-to-severe asthma of the U-BIOPRED cohort. Expression profiling was performed using Affymetrix arrays on total RNA. Transcription binding site analysis used the PRIMA algorithm. Localization of proteins was by immunohistochemistry., Results: Using stringent false discovery rate analysis, MMP-10 and MET were significantly overexpressed in biopsies with high mucosal eosinophils (HE) compared to low mucosal eosinophil (LE) numbers. Immunohistochemical analysis confirmed increased expression of MMP-10 and MET in bronchial epithelial cells and in subepithelial inflammatory and resident cells in asthmatic biopsies. Using less-stringent conditions (raw P-value < 0.05, log2 fold change > 0.5), we defined a 73-gene set characteristic of the HE compared to the LE group. Thirty-three of 73 genes drove the pathway annotation that included extracellular matrix (ECM) organization, mast cell activation, CC-chemokine receptor binding, circulating immunoglobulin complex, serine protease inhibitors, and microtubule bundle formation pathways. Genes including MET and MMP10 involved in ECM organization correlated positively with submucosal thickness. Transcription factor binding site analysis identified two transcription factors, ETS-1 and SOX family proteins, that showed positive correlation with MMP10 and MET expression., Conclusion: Pathways of airway remodeling and cellular inflammation are associated with submucosal eosinophilia. MET and MMP-10 likely play an important role in these processes., (© 2019 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.)

Background: Severe asthma is a heterogeneous condition, as shown by independent cluster analyses based on demographic, clinical, and inflammatory characteristics. A next step is to identify molecularly driven phenotypes using "omics" technologies. Molecular fingerprints of exhaled breath are associated with inflammation and can qualify as noninvasive assessment of severe asthma phenotypes., Objectives: We aimed (1) to identify severe asthma phenotypes using exhaled metabolomic fingerprints obtained from a composite of electronic noses (eNoses) and (2) to assess the stability of eNose-derived phenotypes in relation to within-patient clinical and inflammatory changes., Methods: In this longitudinal multicenter study exhaled breath samples were taken from an unselected subset of adults with severe asthma from the U-BIOPRED cohort. Exhaled metabolites were analyzed centrally by using an assembly of eNoses. Unsupervised Ward clustering enhanced by similarity profile analysis together with K-means clustering was performed. For internal validation, partitioning around medoids and topological data analysis were applied. Samples at 12 to 18 months of prospective follow-up were used to assess longitudinal within-patient stability., Results: Data were available for 78 subjects (age, 55 years [interquartile range, 45-64 years]; 41% male). Three eNose-driven clusters (n = 26/33/19) were revealed, showing differences in circulating eosinophil (P = .045) and neutrophil (P = .017) percentages and ratios of patients using oral corticosteroids (P = .035). Longitudinal within-patient cluster stability was associated with changes in sputum eosinophil percentages (P = .045)., Conclusions: We have identified and followed up exhaled molecular phenotypes of severe asthma, which were associated with changing inflammatory profile and oral steroid use. This suggests that breath analysis can contribute to the management of severe asthma., (Copyright © 2018 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

The SARP, ADEPT, and U-BIOPRED programs are all significant efforts in characterizing asthma and reporting clusters that will assist in designing personalized therapies for asthma, and especially severe asthma. Key aspects of the design of these programs are summarized and major findings are reported in this review., (Copyright © 2018 Elsevier Inc. All rights reserved.)