Airway Elastin is increased in severe asthma and relates to proximal wall area: histological and computed tomography findings from the U-BIOPRED severe asthma study.

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.

A Transcriptome-driven Analysis of Epithelial Brushings and Bronchial Biopsies to Define Asthma Phenotypes in U-BIOPRED.

RATIONALE: Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms. OBJECTIVES: Using transcriptomic profiling of airway tissues, we sought to define the molecular phenotypes of severe asthma. METHODS: 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. MEASUREMENTS AND MAIN RESULTS: 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. CONCLUSIONS: 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.