The U-BIOPRED study is a multicentre European study aimed at a better understanding of severe asthma. It included three steroid-treated adult asthma groups (severe nonsmokers (SAn group), severe current/ex-smokers (SAs/ex group) and those with mild–moderate disease (MMA group)) and healthy controls (HC group). The aim of this cross-sectional, bronchoscopy substudy was to compare bronchial immunopathology between these groups. In 158 participants, bronchial biopsies and bronchial epithelial brushings were collected for immunopathologic and transcriptomic analysis. Immunohistochemical analysis of glycol methacrylate resin-embedded biopsies showed there were more mast cells in submucosa of the HC group (33.6 mm −2 ) compared with both severe asthma groups (SAn: 17.4 mm −2 , p<0.001; SAs/ex: 22.2 mm −2 , p=0.01) and with the MMA group (21.2 mm −2 , p=0.01). The number of CD4 + lymphocytes was decreased in the SAs/ex group (4.7 mm −2 ) compared with the SAn (11.6 mm −2 , p=0.002), MMA (10.1 mm −2 , p=0.008) and HC (10.6 mm −2 , p<0.001) groups. No other differences were observed. Affymetrix microarray analysis identified seven probe sets in the bronchial brushing samples that had a positive relationship with submucosal eosinophils. These mapped to COX-2 (cyclo-oxygenase-2), ADAM-7 (disintegrin and metalloproteinase domain-containing protein 7), SLCO1A2 (solute carrier organic anion transporter family member 1A2), TMEFF2 (transmembrane protein with epidermal growth factor like and two follistatin like domains 2) and TRPM-1 (transient receptor potential cation channel subfamily M member 1); the remaining two are unnamed. We conclude that in nonsmoking and smoking patients on currently recommended therapy, severe asthma exists despite suppressed tissue inflammation within the proximal airway wall.
Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms.Using transcriptomic profiling of airway tissues, we sought to define the molecular phenotypes of severe asthma.The transcriptome derived from bronchial biopsies and epithelial brushings of 107 subjects with moderate to severe asthma were annotated by gene set variation analysis using 42 gene signatures relevant to asthma, inflammation, and immune function. Topological data analysis of clinical and histologic data was performed to derive clusters, and the nearest shrunken centroid algorithm was used for signature refinement.Nine gene set variation analysis signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper cell type 2 cytokines and lack of corticosteroid response (group 1 and group 3). Group 1 had the highest submucosal eosinophils, as well as high fractional exhaled nitric oxide levels, exacerbation rates, and oral corticosteroid use, whereas group 3 patients showed the highest levels of sputum eosinophils and had a high body mass index. In contrast, group 2 and group 4 patients had an 86% and 64% probability, respectively, of having noneosinophilic inflammation. Using machine learning tools, we describe an inference scheme using the currently available inflammatory biomarkers sputum eosinophilia and fractional exhaled nitric oxide levels, along with oral corticosteroid use, that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity.This analysis demonstrates the usefulness of a transcriptomics-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target T-helper cell type 2-mediated inflammation and/or corticosteroid insensitivity.
Background: Molecular stratification of childhood asthma could enable targeted therapy. Aims: Unbiased analysis of gene expression in paediatric severe (SA) and moderate/mild asthma (MA) blood samples to identify sub-phenotypes. Methods: Transcriptomic profiling by microarray analysis of blood from the U-BIOPRED paediatric cohort (Fleming ERJ 2015), pre- and school-age children, (SApre, n=62; MApre, n=42; SAsc, n=75 and MAsc, n=37). Topological data analysis (TDA) was used for unbiased clustering. Results: Sub-phenotypes, P1, P2, P3 and P4 were identified and are highlighted in the TDA network in the figure and a heatmap of selected variables. P1 (38% of the cohort, median 11 yrs) was characterised by low expression of glucocorticoid receptor (GR) mRNA splice variant with a long 3' UTR (q = 2.43E-17), but no significant difference in the expression of glucocorticoid receptor (GR) mRNA splice variant with a short 3' UTR. In P1, COX2 expression was up (q = 1.89E-06) and IFN-γ was down (q = 5.61E-06), characteristics of a decreased steroid response. Conclusion: Unbiased analysis of U-BIOPRED paediatric peripheral blood gene expression identified a sub-phenotype, P1, with an inhibited steroid response. P1 is associated with low expression of a splice variant of GR with a long 3' UTR.
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 optimize the detection of robust biomarkers in proteomic studies. The normal sputum proteome was derived using data-independent HDMSE applied to 40 healthy nonsmoking 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 optimize the accuracy and consistency of measurement of sputum proteins and analyze 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 interindividual variance. We also highlight that the sputum proteome of healthy individuals is related to sputum neutrophil levels, but not gender or allergic sensitization. We illustrate the importance of design and interpretation of disease biomarker studies considering such protein population and technical measurement variance.
Rationale: Severe adult-onset asthma is clinically identified as a distinct phenotype and associated with absence of atopy and eosinophilic airway inflammation [Amelink et al JACI 2013]. The aim of this study was to investigate airway transcriptomic profiles associated with adult-onset severe asthma. Methods: Microarray analysis (Affymetrix HG-U133+PM) was performed on RNA from endobronchial biopsies (52) and brushings (65), nasal brushings (41) and sputum (83) in a cross-sectional design. Enrichment of 105 inflammation and leukocyte lineage gene signatures was evaluated by Gene Set Variation Analysis. Associations with adult-onset asthma (first diagnosis of asthma or onset of symptoms at age ≥18) was tested in General Linear Models adjusted for systemic steroid use and smoking. Results: Significantly enriched signatures (p<0.05, enrichment score ±>0.2) were found in bronchial brushings, sputum and nasal brushings. Adult-onset patients expressed Type2 and mast cell signatures in bronchial brushings and sputum and an eosinophil signature in bronchial brushings (Table 1). Conclusion: This study shows selective enrichment of Type2, mast cell, and eosinophil signatures in adult-onset severe asthma, which strengthens the concept of this being a distinct asthma phenotype. These transcriptomic profiles provide a first step towards elucidating the underlying mechanisms of this phenotype.
Background: Severe asthma is a heterogeneous condition that requires deeper clinical and biological phenotyping. This can be addressed by combining clinical and several omics data in a phenotypic handprint. Objective: Sputum data from cell transcriptomics, somalogic proteomics and eicosanoids lipidomics from 73 adult U-BIOPRED patients, have been integrated, using a systems biology approach. Patient clusters supported by multiple data types were generated, along with specific biomarkers, defining a sputum handprint. Methods: The three omics datasets were fused using the Similarity Network Fusion method (Wang et al, Nature Methods, 2014). Stable clusters were defined using spectral clustering and characterised using available clinical data. Results: Three stable clusters were defined, separated mainly by immune cell composition in sputum. Cluster 3 (C3) is clinically milder, with higher FEV1% predicted and FEV1/FVC ratio. C1 has a more pronounced Th2 phenotype than C2 and C3 as defined by the percentage of sputum eosinophils and the higher periostin levels. C2 regroups the patients with both high sputum neutrophil and eosinophil counts, see table. Conclusion: We have been able to combine different sputum omics datasets to define stable clusters of patients and characterise them with clinical and biological features. This study may help refining phenotypes of severe asthma. IMI grant n°115010 (U-BIOPRED).
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