Dissecting childhood asthma with nasal transcriptomics distinguishes subphenotypes of disease

Abstract

Background: Bronchial airway expression profiling has identified inflammatory subphenotypes of asthma, but the invasiveness of this technique has limited its application to childhood asthma.

Objectives: We sought to determine whether the nasal transcriptome can proxy expression changes in the lung airway transcriptome in asthmatic patients. We also sought to determine whether the nasal transcriptome can distinguish subphenotypes of asthma.

Methods: Whole-transcriptome RNA sequencing was performed on nasal airway brushings from 10 control subjects and 10 asthmatic subjects, which were compared with established bronchial and small-airway transcriptomes. Targeted RNA sequencing nasal expression analysis was used to profile 105 genes in 50 asthmatic subjects and 50 control subjects for differential expression and clustering analyses.

Results: We found 90.2% overlap in expressed genes and strong correlation in gene expression (ρ = .87) between the nasal and bronchial transcriptomes. Previously observed asthmatic bronchial differential expression was strongly correlated with asthmatic nasal differential expression (ρ = 0.77, P = 5.6 × 10(-9)). Clustering analysis identified TH2-high and TH2-low subjects differentiated by expression of 70 genes, including IL13, IL5, periostin (POSTN), calcium-activated chloride channel regulator 1 (CLCA1), and serpin peptidase inhibitor, clade B (SERPINB2). TH2-high subjects were more likely to have atopy (odds ratio, 10.3; P = 3.5 × 10(-6)), atopic asthma (odds ratio, 32.6; P = 6.9 × 10(-7)), high blood eosinophil counts (odds ratio, 9.1; P = 2.6 × 10(-6)), and rhinitis (odds ratio, 8.3; P = 4.1 × 10(-6)) compared with TH2-low subjects. Nasal IL13 expression levels were 3.9-fold higher in asthmatic participants who experienced an asthma exacerbation in the past year (P = .01). Several differentially expressed nasal genes were specific to asthma and independent of atopic status.

Conclusion: Nasal airway gene expression profiles largely recapitulate expression profiles in the lung airways. Nasal expression profiling can be used to identify subjects with IL13-driven asthma and a TH2-skewed systemic immune response.

Keywords: Nasal airway epithelium; T(H)2; asthma; bronchial airway epithelium; transcriptome.

Figure 1. Comparison of non-ubiquitous gene expression between airway tissues

Overlap of expressed genes between nasal-bronchial (A), nasal-SAE (B), bronchial-SAE (C), and between all tissues (D). Scatter plot of mean expression levels for genes commonly expressed between nasal-bronchial (E), nasal-SAE (F), bronchial-SAE (G). Correspondence-at-top plot for the top 500 genes ranked by expression level from highest to lowest for each tissue (H).

Figure 2. Unsupervised clustering of subjects with atopic asthma and healthy controls using nasal transcriptome expression levels

FPKM expression levels for all genes in the nasal whole transcriptome sequencing data were used for clustering.

Figure 3. Comparison of gene expression fold-changes in asthma between bronchial and nasal airway expression data for bronchial airway biomarker genes

Scatter plot of previously reported bronchial airway gene expression log2 fold-changes in asthma, for the top 20 up- and down-regulated genes, versus the fold-changes in asthma for these genes in the nasal airway transcriptome data. Linear regression best-fit line shown.

Figure 4. Correlation between Ampliseq nasal gene expression of IL13 and the other 47 genes differentially expressed in asthma

Genes are ranked from top to bottom by decreasing Spearman correlation coefficient (ρ). Purple and pink regions correspond to levels of high positive (ρ > 0.5) and negative (ρ < −0.5) correlation, respectively. Significant IL13 correlations=clear bars, Non-significant IL13 correlations=black bars.

Figure 5. Clustering of Ampliseq nasal gene expression levels in study subjects

Clustering was generated using relative nasal expression levels for the 70 genes differentially expressed in atopy (n=99). Heatmap represents normalized expression counts (red=low; green=high) for each gene. The subject presence (blue) or absence (red) of atopy, asthma, eosinophil levels, and rhinitis are displayed directly below the heatmap. White squares=missing data.

Figure 6. Boxplots of genes differentially expressed in asthma but not atopy in the nasal airway

Ampliseq normalized expression counts for 3 of the 6 genes (MUC5B, OSM, KRT5) differential expressed in asthma but not atopy (+1 pseudocount and log10 scale) are plotted according to subject asthma and atopy status.