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: Severe asthma is more prevalent in the ageing adult population. Mitochondrial oxidative stress status has been implicated in this process. Aims & Objectives: To investigate the correlation between mitochondrial function (OXPHOS) and cellular ageing signatures in subjects from the U-BIOPRED consortium. Methods: We used gene set variation analysis (GSVA) to study OXPHOS and ageing signatures in blood (312 subjects), bronchial biopsies (108 subjects), bronchial brushings (147 subjects), sputum (120 subjects) and nasal brushings (89 subjects) from the U-BIOPRED cohort. Results: The OXPHOS signature was enriched in the blood of severe asthmatics who smoked (adj.p=0.03) with a weak positive correlation with the ageing signature (r=0.32, p<10-9). The OXPHOS signature in bronchial brushings correlated with ageing signatures (r=0.5, p<10-5). OXPHOS was enriched in severe asthma biopsies to a greater extent in the smokers v9s non-smokers despite the ageing signature being reduced in all asthmatics resulting in a negative correlation (r=-0.2, p=0.04). The OXPHOS and ageing signatures in sputum were both reduced in asthma and highly correlated (r=0.77, p<10-16). OXPHOS and ageing signatures did not correlate in nasal brushings. Conclusion: The association between mitochondrial function and ageing gene signatures in severe asthma depends upon the compartment examined and is independent of the chronological age.
Objectives: Severe asthma (SA) displays diverse local and systemic inflammatory profiles that may be variably impacted by systemic corticosteroid (SCS) therapy. The profile of dysregulated serum analyte in SA, stratified by chronic SCS use, will contribute to a systemic fingerprint of SA. Methods: 1129 analytes were measured in serum samples from U-BIOPRED subjects using Somalogic SomaScan®. Differences between SA, stratified by chronic SCS use, and healthy controls, were analyzed by General Linear Models adjusted for sex and age, independently for training (n=383) and validation (n=188) sets. Associations were confirmed if FDR<0.05 in the training set and nominal p<0.05 in the validation set, with at least 1.33-fold/healthy in both sets. Results: CRP and IgE were elevated and carbonic anhydrase 6 and osteomodulin decreased in SA, regardless of SCS. MMP-3, MMP-9, and SAA were increased only in the SA-SCS group. 13 additional analytes were decreased only in the severe asthma SCS group, including MMP-12 and IL-22BP. MMP-3 and IL-22BP were significantly different between SCS yes and no groups, with MMP-3 also higher in males regardless of severity or SCS. Th2-high (defined by IL-13 activity in endobronchial brushings) SA (n=24, ±SCS) had nominally significant (p<0.05, >1.33-fold) higher IgE, PAPPA-A, and 6-Phosphogluconate dehydrogenase compared to Th2-low (n=22) and healthy subjects (n=97). Conclusions: Dysregulation of serum analytes was observed in severe asthma, including acute phase proteins, IgE, corticosteroid-associated MMP9s, and Th2-associated PAPP-A. The role of SCS on regulation of MMP9s and the impact on asthma pathology emerge as intriguing avenues of research. Funded by IMI.
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.
Background Severe neutrophilic asthma is resistant to treatment with glucocorticoids. The immunomodulatory protein macrophage migration inhibitory factor (MIF) promotes neutrophil recruitment to the lung and antagonises responses to glucocorticoids. We hypothesised that MIF promotes glucocorticoid resistance of neutrophilic inflammation in severe asthma. Methods We examined whether sputum MIF protein correlated with clinical and molecular characteristics of severe neutrophilic asthma in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) cohort. We also investigated whether MIF regulates neutrophilic inflammation and glucocorticoid responsiveness in a murine model of severe asthma in vivo. Results MIF protein levels positively correlated with the number of exacerbations in the previous year, sputum neutrophils and oral corticosteroid use across all U-BIOPRED subjects. Further analysis of MIF protein expression according to U-BIOPRED-defined transcriptomic-associated clusters (TACs) revealed increased MIF protein and a corresponding decrease in annexin-A1 protein in TAC2, which is most closely associated with airway neutrophilia and NLRP3 inflammasome activation. In a murine model of severe asthma, treatment with the MIF antagonist ISO-1 significantly inhibited neutrophilic inflammation and increased glucocorticoid responsiveness. Coimmunoprecipitation studies using lung tissue lysates demonstrated that MIF directly interacts with and cleaves annexin-A1, potentially reducing its biological activity. Conclusion Our data suggest that MIF promotes glucocorticoid-resistance of neutrophilic inflammation by reducing the biological activity of annexin-A1, a potent glucocorticoid-regulated protein that inhibits neutrophil accumulation at sites of inflammation. This represents a previously unrecognised role for MIF in the regulation of inflammation and points to MIF as a potential therapeutic target for the management of severe neutrophilic asthma.
Serum pregnancy-associated plasma protein A (PAPPA) as a predictor of eosinophilic Type-2 high asthmaTo the Editor,Pregnancy-associated plasma protein A (PAPPA), a metalloproteinase that cleaves insulin-like growth factor (IGF)-binding proteins (IGFBPs) to increase IGF availability, is expressed systemically in pregnant women but also in other tissues (1). Higher serum PAPPA levels are reported in patients with newly-diagnosed asthma (1) and allergic rhinitis compared to healthy controls and are decreased following omalizumab treatment (2). We determined whether PAPPA could represent a novel biomarker for Type-2 (T2) asthma by exploring the relationship between asthma severity and phenotypes of severe asthma and PAPPA gene and protein expression (3).We recruited 288 severe non-smoking asthma (NSA), 102 smokers and ex-smokers with severe asthma (SSA), 86 mild/moderate non-smoking asthmatics (MMA) and 95 healthy non-smoking controls (HC) from the U-BIOPRED cohort (NCT01976767) (4) (Table S1 ). Transcriptomic and proteomic profiling of blood and sputum samples and specific serum periostin ELISA were performed (3). Gene set variation analysis (GSVA) was used to calculate the enrichment score (ES) of 34 genes that were upregulated following in vitro stimulation of primary human bronchial epithelial cells with IL-13 (T2_IL-13_IVS) (3). Eosinophilic inflammation was defined by sputum eosinophilia >1.49% (3). Local Ethics Committees of the recruiting centres approved the study and all participants gave written informed consent.Sputum cell PAPPA mRNA was elevated in NSA compared to SSA, MMA and HC subjects particularly in granulocytic asthmatics and in the transcriptomic-associated cluster (TAC)1; an eosinophilic cluster (5) (Figure 1A-C ). This was more pronounced with sputum PAPPA protein analysis according to asthma severity, in eosinophilic and mixed granulocytic asthmatics and in T2-high asthmatics identified by the T2_IL-13_IVS signature (Figure 1D-F ).PAPPA mRNA expression in blood cells was similar across asthma severities, blood granulocytes and molecular phenotypes (Supplementary Figure 1A-C ). However, serum PAPPA protein levels supported the discrimination seen in sputum with significant elevation seen in SA compared to HC, in eosinophilic and mixed granulocytic asthma and in T2-high asthma (SupplementaryFigure 1D-F ).Sputum eosinophil percentages were significantly correlated with sputum (r=0.88, p=10-6) and serum (r=0.41, p=10-6) PAPPA protein levels. Overall, sputum PAPPA protein gave a greater distinction between asthma severity, granulocyte composition and T2-high asthma than with serum although fewer samples were available.These results were validated in sputum from the Airways Disease Endotyping for Personalized Therapeutics (ADEPT) study (6) (Supplementary Figure S2 ). Elevated PAPPA protein in the serum and sputum of severe asthmatics and in eosinophilic compared to non- eosinophilic subjects was seen (SupplementaryFigure S2A-D ). In addition, sputum PAPPA mRNA levels were also elevated in eosinophilic versus non-eosinophilic asthma in the ADEPT cohort (Supplementary Figure S2E ).The ES score of the T2_IL-13_IVS gene signature in bronchial brushings was significantly, but weakly, correlated with blood eosinophil counts (r=0.329, p=10-6), serum PAPPA (r=0.356, p=10-6), but not with serum periostin levels (r=0.07, p-value=0.48). In contrast, the T2 IL-13 IVS ES score was strongly correlated with sputum PAPPA levels (r=0.72, p=10-3). Sputum PAPPA protein levels also significantly correlated with markers of remodelling such as MMP10 (r=0.646, p<10-6) and MET (r=0.429, p<10-6).Receiver-operating characteristics (ROC) curve analysis was performed for sputum eosinophilia (Supplementary Table S2 ). The area under the ROC curve (AUC) for serum indicated that there was no good predictor although blood eosinophilia was the best (0.79) being marginally better than serum PAPPA and exhaled NO (Figure 2A ). In contrast, sputum PAPPA was an excellent predictor of sputum eosinophilia (0.98), better than blood eosinophilia and exhaled nitric oxide levels (Figure 2B ).Therefore, sputum PAPPA is an excellent biomarker for sputum eosinophilia and for T2-high asthma whilst serum PAPPA is as effective as blood eosinophilia in predicting high sputum eosinophil levels and with T2-high asthma.
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.
Abstract Biomedical informatics has traditionally adopted a linear view of the informatics process (collect, store and analyse) in translational medicine (TM) studies; focusing primarily on the challenges in data integration and analysis. However, a data management challenge presents itself with the new lifecycle view of data emphasized by the recent calls for data re-use, long term data preservation, and data sharing. There is currently a lack of dedicated infrastructure focused on the ‘manageability’ of the data lifecycle in TM research between data collection and analysis. Current community efforts towards establishing a culture for open science prompt the creation of a data custodianship environment for management of TM data assets to support data reuse and reproducibility of research results. Here we present the development of a lifecycle-based methodology to create a metadata management framework based on community driven standards for standardisation, consolidation and integration of TM research data. Based on this framework, we also present the development of a new platform (PlatformTM) focused on managing the lifecycle for translational research data assets.
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