Gene expression in relation to exhaled nitric oxide identifies novel asthma phenotypes with unique biomolecular pathways

Abstract

Rationale: Although asthma is recognized as a heterogeneous disease associated with clinical phenotypes, the molecular basis of these phenotypes remains poorly understood. Although genomic studies have successfully broadened our understanding in diseases such as cancer, they have not been widely used in asthma studies.

Objectives: To link gene expression patterns to clinical asthma phenotypes.

Methods: We used a microarray platform to analyze bronchial airway epithelial cell gene expression in relation to the asthma biomarker fractional exhaled nitric oxide (FeNO) in 155 subjects with asthma and healthy control subjects from the Severe Asthma Research Program (SARP).

Measurements and main results: We first identified a diverse set of 549 genes whose expression correlated with FeNO. We used k-means to cluster the patient samples according to the expression of these genes, identifying five asthma clusters/phenotypes with distinct clinical, physiological, cellular, and gene transcription characteristics-termed "subject clusters" (SCs). To then investigate differences in gene expression between SCs, a total of 1,384 genes were identified that highly differentiated the SCs at an unadjusted P value < 10(-6). Hierarchical clustering of these 1,384 genes identified nine gene clusters or "biclusters," whose coexpression suggested biological characteristics unique to each SC. Although genes related to type 2 inflammation were present, novel pathways, including those related to neuronal function, WNT pathways, and actin cytoskeleton, were noted.

Conclusions: These findings show that bronchial epithelial cell gene expression, as related to the asthma biomarker FeNO, can identify distinct asthma phenotypes, while also suggesting the presence of underlying novel gene pathways relevant to these phenotypes.

Keywords: clustering; exhaled nitric oxide; severe asthma.

Figure 1.

Heat map derived by k-means clustering of subject samples with genes that correlated with exhaled nitric oxide (Fe_NO). Gene expression was correlated with Fe_NO using a Spearman correlation method, identifying 549 genes that correlated strongly (positively and negatively) with Fe_NO. k-means clustering of subject bronchial epithelial brushing samples with these genes identified five unique SCs shown here (x axis, top).

Figure 2.

Composition of subject clusters (SC1–5) according to traditional asthma severity criteria. Gene expression was correlated with exhaled nitric oxide (Fe_NO) using a Spearman correlation method, identifying 549 genes that correlated strongly (positively and negatively) with Fe_NO. k-means clustering of subject bronchial epithelial brushing samples with these genes identified five unique SCs. As shown in the figure, SCs greatly differed according to traditional asthma severity criteria. SC2 and 3 had the highest percentage of patients with severe asthma. HCs = healthy control subjects; ICS = inhaled corticosteroids; Mild-Mod no ICS = mild to moderate asthma not on ICS; Mild + ICS = mild asthma on ICS; Mod + ICS = moderate asthma on ICS; Severe = severe asthma.

Figure 3.

Complete linkage hierarchical clustering of genes that most strongly differentiate the subject clusters (SCs). After establishment of SCs, all genes expressed in the epithelial brushings (total = 19,957) were compared across SCs for differential expression using intergroup Student t tests. Genes differentially expressed to a P value < 10⁻⁶ between at least two of the SCs were used to compose a set of “highly differentiating” genes (n = 1,384) shown in the heat map here. Complete linkage hierarchical clustering of this highly differentiating gene set along the y axis of the heat map identified nine clusters of coexpressed genes using a cutline at the fourth division of the hierarchical tree (y axis, left). SCs remain on the x axis. GC = gene cluster.