"T2-high" in severe asthma related to blood eosinophil, exhaled nitric oxide and serum periostin.

Type-2 (T2) immune responses in airway epithelial cells (AECs) classifies mild-moderate asthma into a T2-high phenotype. We examined whether currently available clinical biomarkers can predict AEC-defined T2-high phenotype within the U-BIOPRED cohort.The transcriptomic profile of AECs obtained from brushings of 103 patients with asthma and 44 healthy controls was obtained and gene set variation analysis used to determine the relative expression score of T2 asthma using a signature from interleukin (IL)-13-exposed AECs.37% of asthmatics (45% nonsmoking severe asthma, n=49; 33% of smoking or ex-smoking severe asthma, n=18; and 28% mild-moderate asthma, n=36) were T2-high using AEC gene expression. They were more symptomatic with higher exhaled nitric oxide fraction (F eNO) and blood and sputum eosinophils, but not serum IgE or periostin. Sputum eosinophilia correlated best with the T2-high signature. F eNO (≥30 ppb) and blood eosinophils (≥300 cells·µL-1) gave a moderate prediction of T2-high asthma. Sputum IL-4, IL-5 and IL-13 protein levels did not correlate with gene expression.T2-high severe asthma can be predicted to some extent from raised levels of F eNO, blood and sputum eosinophil counts, but serum IgE or serum periostin were poor predictors. Better bedside biomarkers are needed to detect T2-high.

Sputum proteomics and airway cell transcripts of current and ex-smokers with severe asthma in U-BIOPRED: an exploratory analysis.

Severe asthma patients with a significant smoking history have airflow obstruction with reported neutrophilia. We hypothesise that multi-omic analysis will enable the definition of smoking and ex-smoking severe asthma molecular phenotypes.The U-BIOPRED cohort of severe asthma patients, containing current-smokers (CSA), ex-smokers (ESA), nonsmokers and healthy nonsmokers was examined. Blood and sputum cell counts, fractional exhaled nitric oxide and spirometry were obtained. Exploratory proteomic analysis of sputum supernatants and transcriptomic analysis of bronchial brushings, biopsies and sputum cells was performed.Colony-stimulating factor (CSF)2 protein levels were increased in CSA sputum supernatants, with azurocidin 1, neutrophil elastase and CXCL8 upregulated in ESA. Phagocytosis and innate immune pathways were associated with neutrophilic inflammation in ESA. Gene set variation analysis of bronchial epithelial cell transcriptome from CSA showed enrichment of xenobiotic metabolism, oxidative stress and endoplasmic reticulum stress compared to other groups. CXCL5 and matrix metallopeptidase 12 genes were upregulated in ESA and the epithelial protective genes, mucin 2 and cystatin SN, were downregulated.Despite little difference in clinical characteristics, CSA were distinguishable from ESA subjects at the sputum proteomic level, with CSA patients having increased CSF2 expression and ESA patients showing sustained loss of epithelial barrier processes.

U-BIOPRED clinical adult asthma clusters linked to a subset of sputum omics.

BACKGROUND: Asthma is a heterogeneous disease in which there is a differential response to asthma treatments. This heterogeneity needs to be evaluated so that a personalized management approach can be provided. OBJECTIVES: We stratified patients with moderate-to-severe asthma based on clinicophysiologic parameters and performed an omics analysis of sputum. METHODS: Partition-around-medoids clustering was applied to a training set of 266 asthmatic participants from the European Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes (U-BIOPRED) adult cohort using 8 prespecified clinic-physiologic variables. This was repeated in a separate validation set of 152 asthmatic patients. The clusters were compared based on sputum proteomics and transcriptomics data. RESULTS: Four reproducible and stable clusters of asthmatic patients were identified. The training set cluster T1 consists of patients with well-controlled moderate-to-severe asthma, whereas cluster T2 is a group of patients with late-onset severe asthma with a history of smoking and chronic airflow obstruction. Cluster T3 is similar to cluster T2 in terms of chronic airflow obstruction but is composed of nonsmokers. Cluster T4 is predominantly composed of obese female patients with uncontrolled severe asthma with increased exacerbations but with normal lung function. The validation set exhibited similar clusters, demonstrating reproducibility of the classification. There were significant differences in sputum proteomics and transcriptomics between the clusters. The severe asthma clusters (T2, T3, and T4) had higher sputum eosinophilia than cluster T1, with no differences in sputum neutrophil counts and exhaled nitric oxide and serum IgE levels. CONCLUSION: Clustering based on clinicophysiologic parameters yielded 4 stable and reproducible clusters that associate with different pathobiological pathways.