Sputum mediator profiling and relationship to airway wall geometry imaging in severe asthma

Background: Severe asthma is a heterogeneous disease and the relationship between airway inflammation and airway remodelling is poorly understood. We sought to define sputum mediator profiles in severe asthmatics categorised by CT-determined airway geometry and sputum differential cell counts. Methods: In a single centre cross-sectional observational study we recruited 59 subjects with severe asthma that underwent sputum induction and thoracic CT. Quantitative CT analysis of the apical segment of the right upper lobe (RB1) was performed. Forty-one mediators in sputum samples were measured of which 21 mediators that were assessable in >50% of samples were included in the analyses. Results: Independent of airway geometry, sputum MMP9 and IL-1β were elevated in those groups with a high sputum neutrophil count while sputum ICAM was elevated in those subjects with a low sputum neutrophil count. In contrast, sputum CCL11, IL-1α and fibrinogen were different in groups stratified by both sputum neutrophil count and airway geometry. Sputum CCL11 concentration was elevated in subjects with a low sputum neutrophil count and high luminal and total RB1 area, whereas sputum IL1α was increased in subjects with a high sputum neutrophil count and low total RB1 area. Sputum fibrinogen was elevated in those subjects with RB1 luminal narrowing and in those subjects with neutrophilic inflammation without luminal narrowing. Conclusions: We have demonstrated that sputum mediator profiling reveals a number of associations with airway geometry. Whether these findings reflect important biological phenotypes that might inform stratified medicine approaches requires further investigation

Overview of data analysis on COPD cohort.

<p>Overview of data analysis on COPD cohort.</p

Univariate regression analysis of protein analytes versus lung function parameters in COPD subjects with and without metabolic syndrome.

<p>Significance (<i>p</i> values) and effect sizes (spearman correlation) are listed for biomarker associations with lung function parameters. Interaction <i>p</i> values indicate significance of differences in biomarker associations with lung function parameters, between metabolic syndrome and non- metabolic syndrome groups.</p

Correlation network illustrating functional co-clustering of analytes associated with FEV₁, FEV₁/FVC and DLCO.

<p>Analytes are plotted in a network using Cytoscape <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038629#pone.0038629-Shannon1" target="_blank">[83]</a> where nodes represent analytes and edges represent significant correlations (<i>r</i> >0.4, <i>p</i><0.05, corrected for multiple testing). Analytes are colored according to whether they were associated with FEV₁ related parameters (green), DLCO (red) or both DLCO and FEV₁ related parameters (orange) in univariate regression. Node size is proportional to the number of lung function parameters that showed significant association with a given analyte. Clusters of co-expressed analytes with similar function are highlighted by dotted regions in the graph as neutrophil function (orange), systemic inflammation (blue) and growth factor pathways (grey).</p

Multivariate analysis of protein analyte data for COPD subjects.

<p>Spearman correlation and adjusted R squared values were computed using test set samples, in a 5-fold nested cross-validation scheme, averaged over 10 random seeds. R squared values were adjusted for the number of predictor terms in the model.</p

Association of MPO with FEV₁/FVC and Fibrinogen with DLCO in COPD patients with and without metabolic syndrome.

<p>Log2-transformed levels of MPO (A, C) and Fibrinogen (B, D) (ng/ml for MPO and mg/dl for Fibrinogen) are plotted against covariate adjusted values for FEV₁/FVC and DLCO, respectively in COPD patients with (A, B) and without (C, D) metabolic syndrome (<i>r</i> values indicate spearman correlation, covariates include age, sex, BMI, pack years and smoking status).</p

Protein analyte differences between COPD and control disease severity groups.

<p>Data are expressed as median (interquartile range) in ng/ml for individual analytes, except for Fibrinogen which is in mg/dl.</p><p>All analyte data shown are from profiling on the RBM Luminex platform, except for Fibrinogen which was tested at Hospital Grosshansdorf. COPD subjects were grouped as GOLD I/II (mild/moderate) and GOLD III/IV (severe/very severe). ANOVA was used for group-wise comparisons, except for analytes noted with *, which did not follow a normal distribution and a non-parametric Kruskal Wallis test was used.</p

Intraflagellar Transport Gene Expression Associated with Short Cilia in Smoking and COPD

<div><p>Smoking and COPD are associated with decreased mucociliary clearance, and healthy smokers have shorter cilia in the large airway than nonsmokers. We hypothesized that changes in cilia length are consistent throughout the airway, and we further hypothesized that smokers with COPD have shorter cilia than healthy smokers. Because intraflagellar transport (IFT) is the process by which cilia of normal length are produced and maintained, and alterations in IFT lead to short cilia in model organisms, we also hypothesized that smoking induces changes in the expression of IFT-related genes in the airway epithelium of smokers and smokers with COPD. To assess these hypotheses, airway epithelium was obtained via bronchoscopic brushing. Cilia length was assessed by measuring 100 cilia (10 cilia on each of 10 cells) per subject and Affymetrix microarrays were used to evaluate IFT gene expression in nonsmokers and healthy smokers in 2 independent data sets from large and small airway as well as in COPD smokers in a data set from the small airway. In the large and small airway epithelium, cilia were significantly shorter in healthy smokers than nonsmokers, and significantly shorter in COPD smokers than in both healthy smokers and nonsmokers. The gene expression data confirmed that a set of 8 IFT genes were down-regulated in smokers in both data sets; however, no differences were seen in COPD smokers compared to healthy smokers. These results support the concept that loss of cilia length contributes to defective mucociliary clearance in COPD, and that smoking-induced changes in expression of IFT genes may be one mechanism of abnormally short cilia in smokers. Strategies to normalize cilia length may be an important avenue for novel COPD therapies.</p></div

Cilia length in the large and small airway of nonsmokers, healthy smokers, and COPD smokers.

<p>Mean cilia length in micrometers is displayed on the ordinate. The abscissa displays large airway on the left and small airway on the right, with healthy nonsmokers represented in white, healthy smokers in dark gray, and smokers with COPD in black. Error bars show the standard error of the mean for each phenotype. Within the large airway, n = 120 (healthy nonsmokers n = 25, healthy smokers n = 25, smokers with COPD n = 70). Within the small airway, n = 108 (healthy nonsmokers n = 20, healthy smokers n = 32, smokers with COPD n = 56). There are significant differences in mean cilia length between all phenotypes in both the large and small airway (p<0.05). There are significant differences in mean cilia length between airway locations in both healthy nonsmokers and healthy smokers (p<0.05).</p

Expression of intraflagellar transport (IFT)-related genes significantly modified by smoking in the airway epithelium.

<p>Gene expression was assessed in nonsmokers and healthy smokers by microarray analysis in two independent data sets. The ordinate represents relative gene expression and specific genes are displayed on the abscissa. Nonsmokers are represented by light bars and healthy smokers by dark gray bars. Error bars represent standard error of the mean and p values are calculated using the Benjamini-Hochberg correction. Significant p values are displayed in bold font. Where multiple probe sets represent the same gene, the probe set with the lowest p value is displayed; where p values are identical, the probe set with highest fold-change is displayed. <b>A.</b> Expression in the large airway epithelium (nonsmokers n = 21 and healthy smokers n = 31). <b>B.</b> Expression in the small airway epithelium (nonsmokers n = 28 and healthy smokers n = 69).</p