Sputum Gene Expression Reveals Dysregulation of Mast Cells and Basophils in Eosinophilic COPD.

Purpose: The clinical and inflammatory associations of mast cells (MCs) and basophils in chronic obstructive pulmonary disease (COPD) are poorly understood. We previously developed and validated a qPCR-based MC/basophil gene signature in asthma to measure these cells in sputum samples. Here, we measured this gene signature in a COPD and control population to explore the relationship of sputum MCs/basophils to inflammatory and COPD clinical characteristics. Patients and Methods: MC/basophil signature genes (TPSAB1/TPSB2, CPA3, ENO2, GATA2, KIT, GPR56, HDC, SOCS2) were measured by qPCR in sputum from a COPD (n=96) and a non-respiratory control (n=17) population. Comparative analyses of gene expression between the COPD and the control population, and between eosinophilic COPD and non-eosinophilic COPD were tested. Logistic regression analysis and Spearman correlation were used to determine relationships of sputum MC/basophil genes to inflammatory (sputum eosinophil proportions, blood eosinophils) and clinical (age, body mass index, quality of life, lung function, past year exacerbations) characteristics of COPD. Results: MC/basophil genes were increased in COPD versus control participants (CPA3, KIT, GATA2, HDC) and between eosinophilic-COPD and non-eosinophilic COPD (TPSB2, CPA3, HDC, SOCS2). We found all MC/basophil genes were positively intercorrelated. In COPD, MC/basophil genes were associated with eosinophilic airway inflammation (GATA2, TPSB2, CPA3, GPR56, HDC, SOCS2), blood eosinophilia (all genes) and decreased lung function (KIT, GATA2, GPR56, HDC). Conclusion: We demonstrate associations of MCs and basophils with eosinophilic inflammation and lower lung function in COPD. These findings are consistent with prior results in asthma and may represent a new tool for endotyping eosinophilic-COPD.

Hemopexin: A Novel Anti-inflammatory Marker for Distinguishing COPD From Asthma.

PURPOSE: Systemic inflammatory biomarkers can improve diagnosis and assessment of chronic obstructive pulmonary disease (COPD) and asthma. We aimed to validate an airway disease biomarker panel of 4 systemic inflammatory biomarkers, α2-macroglobulin, ceruloplasmin, haptoglobin and hemopexin, to establish their relationship to airway disease diagnosis and inflammatory phenotypes and to identify an optimized biomarker panel for disease differentiation. METHODS: Participants with COPD or asthma were classified by inflammatory phenotypes. Immunoassay methods were used to measure levels of validation biomarkers in the sera of participants with disease and non-respiratory disease controls. Markers were analyzed individually and in combination for disease differentiation and compared to established biomarkers (C-reactive protein, interleukin-6, and white blood cell/blood eosinophil count). RESULTS: The study population comprised of 141 COPD, 127 severe asthma, 54 mild-moderate asthma and 71 control participants. Significant differences in ceruloplasmin, haptoglobin and hemopexin levels between disease groups and between systemic inflammatory phenotypes were observed. However, no differences were found between airway inflammatory phenotypes. Hemopexin was the best performing individual biomarker and could diagnose COPD versus control participants (area under the curve [AUC], 98.3%; 95% confidence interval [CI], 96.7%-99.9%) and differentiate COPD from asthmatic participants (AUC, 97.0%; 95% CI, 95.4%-98.6%), outperforming established biomarkers. A biomarker panel, including hemopexin, haptoglobin and other established biomarkers, could diagnose asthma versus control participants (AUC, 87.5%; 95% CI, 82.8%-92.2%). CONCLUSIONS: Hemopexin can be a novel biomarker with superior diagnostic ability in differentiating COPD and asthma. We propose an anti-inflammatory axis between the airways and systemic circulation, in which hemopexin is a protective component in airway disease.

Sputum mast cell/basophil gene expression relates to inflammatory and clinical features of severe asthma.

BACKGROUND: Mast cells (MCs) and basophils are important in asthma pathophysiology, however direct measurement is difficult, and clinical and inflammatory associations in severe asthma are poorly understood. Transcriptomic hallmarks of MCs/basophils may allow their measurement in sputum using gene expression. OBJECTIVES: This study sought to develop and validate a sputum MC/basophil gene signature and investigate its relationship to inflammatory and clinical characteristics of severe asthma. METHODS: A total of 134 candidate MC/basophil genes (identified by the Immunological Genome Project Consortium) were screened in sputum microarray for differential expression among control subjects (n = 18), patients with eosinophilic (n = 29), and patients with noneosinophilic asthma (n = 30). Candidate genes were validated by confirming correlation of gene expression with flow cytometry-quantified sputum MCs and basophils in a separate asthma cohort (n = 20). The validated gene signature was measured in a severe asthma cohort (n = 81), and inflammatory and clinical associations were tested. RESULTS: Through microarray screening and subsequent validation, we found quantitative PCR gene expression of 8 targets correlated with sputum MCs/basophils: TPSAB1/TPSB2, CPA3, ENO2, GATA2, KIT, GPR56, HDC, SOCS2. In severe asthma, MC/basophil genes were associated with eosinophilic airway inflammation (GATA2, TPSB2, CPA3, GPR56, HDC, SOCS2), blood eosinophils (TPSB2, CPA3, GATA2, SOCS2, FCER1A, HDC), fractional exhaled NO (GATA2, SOCS2), decreased lung function (KIT, ENO2), and moderate exacerbation history (GATA2, SOCS2). CONCLUSIONS: Quantitative PCR-based measures reflect varying sputum MC/basophil abundance, demonstrating associations of MCs/basophils with eosinophilic inflammation, spirometry and exacerbation history in severe asthma.

Molecular markers of type 2 airway inflammation are similar between eosinophilic severe asthma and eosinophilic chronic obstructive pulmonary disease.

BACKGROUND: Airway and systemic eosinophilia are important treatable traits in both severe asthma and COPD. The molecular basis of eosinophilia in COPD is poorly understood but could involve type 2 cytokines (IL5, IL13) and prostaglandin D2 (PGD2 ). METHODS: This study included non-obstructive airways disease (OAD) controls (n = 19), a COPD cohort (n = 96) and a severe asthma cohort (n = 84). Demographics, exacerbation history, disease impact (SGRQ) and spirometry were assessed. Participants were categorized as eosinophilic using either sputum eosinophil proportion (≥3%) or blood eosinophil count (≥300/µL). Sputum type 2 inflammatory measures included PGD2 by ELISA and gene expression (qPCR) of IL5, IL13 and the haematopoietic PGD2 synthase (HPGDS). RESULTS: Type 2 markers did not differ across groups except HPGDS mRNA which was highest in non-OAD controls and lowest in COPD. IL5 and IL13 mRNA and PGD2 levels were significantly increased in eosinophilic vs non-eosinophilic severe asthma but did not differ between eosinophilic COPD and eosinophilic severe asthma or non-eosinophilic COPD. HPGDS expression was higher in eosinophilic severe asthma compared with eosinophilic COPD. Results were similar using sputum or blood eosinophil cut-offs. Sputum IL5 and IL13 were highly intercorrelated in severe asthma (r = 0.907, p < 0.001) and COPD (r = 0.824, p < 0.001), were moderately correlated with sputum eosinophils in severe asthma (IL5 r = 0.440, p < 0.001; IL13 r = 0.428, p < 0.001) and were weakly correlated in COPD (IL5 r = 0.245, p < 0.05; IL13 r = 0.317, p < 0.05). CONCLUSIONS: Molecular markers of type 2 airway inflammation do not differ between eosinophilic asthma and eosinophilic COPD; however, the relationship between eosinophilia and type 2 airway markers appears weaker in COPD than in severe asthma.

Approaches to the assessment of severe asthma: barriers and strategies.

Asthma is a chronic condition with great variability. It is characterized by intermittent episodes of wheeze, cough, chest tightness, dyspnea and backed by variable airflow limitation, airway inflammation and airway hyper-responsiveness. Asthma severity varies uniquely between individuals and may change over time. Stratification of asthma severity is an integral part of asthma management linking appropriate treatment to establish asthma control. Precision assessment of severe asthma is crucial for monitoring the health of people with this disease. The literature suggests multiple factors that impede the assessment of severe asthma, these can be grouped into health care professional, patient and organizational related barriers. These barriers do not exist in isolation but interact and influence one another. Recognition of these barriers is necessary to promote precision in the assessment and management of severe asthma in the era of targeted therapy. In this review, we discuss the current knowledge of the barriers that impede assessment in severe asthma and recommend potential strategies for overcoming these barriers. We highlight the relevance of multidimensional assessment as an ideal approach to the assessment and management of severe asthma.