Dissecting childhood asthma with nasal transcriptomics distinguishes subphenotypes of disease

Alex Poole, Cydney Urbanek, Celeste Eng, Jeoffrey Schageman, Sean Jacobson, Brian P. O'Connor, Joshua M. Galanter, Christopher R. Gignoux, Lindsey A. Roth, Rajesh Kumar, Sharon Lutz, Andrew H. Liu, Tasha E. Fingerlin, Robert A. Setterquist, Esteban G. Burchard, Jose Rodriguez-Santana, Max A. Seibold*

*Corresponding author for this work

Research output: Contribution to journalArticle

102 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)670-678.e12
JournalJournal of Allergy and Clinical Immunology
Volume133
Issue number3
DOIs
StatePublished - Mar 2014

Keywords

  • Nasal airway epithelium
  • asthma
  • bronchial airway epithelium
  • transcriptome

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology

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    Poole, A., Urbanek, C., Eng, C., Schageman, J., Jacobson, S., O'Connor, B. P., Galanter, J. M., Gignoux, C. R., Roth, L. A., Kumar, R., Lutz, S., Liu, A. H., Fingerlin, T. E., Setterquist, R. A., Burchard, E. G., Rodriguez-Santana, J., & Seibold, M. A. (2014). Dissecting childhood asthma with nasal transcriptomics distinguishes subphenotypes of disease. Journal of Allergy and Clinical Immunology, 133(3), 670-678.e12. https://doi.org/10.1016/j.jaci.2013.11.025