Gene expression profiling of asthma phenotypes demonstrates molecular signatures of atopy and asthma control

Abstract

Background: Recent studies have used cluster analysis to identify phenotypic clusters of asthma with differences in clinical traits, as well as differences in response to therapy with anti-inflammatory medications. However, the correspondence between different phenotypic clusters and differences in the underlying molecular mechanisms of asthma pathogenesis remains unclear.

Objective: We sought to determine whether clinical differences among children with asthma in different phenotypic clusters corresponded to differences in levels of gene expression.

Methods: We explored differences in gene expression profiles of CD4(+) lymphocytes isolated from the peripheral blood of 299 young adult participants in the Childhood Asthma Management Program study. We obtained gene expression profiles from study subjects between 9 and 14 years of age after they participated in a randomized, controlled longitudinal study examining the effects of inhaled anti-inflammatory medications over a 48-month study period, and we evaluated the correspondence between our earlier phenotypic cluster analysis and subsequent follow-up clinical and molecular profiles.

Results: We found that differences in clinical characteristics observed between subjects assigned to different phenotypic clusters persisted into young adulthood and that these clinical differences were associated with differences in gene expression patterns between subjects in different clusters. We identified a subset of genes associated with atopic status, validated the presence of an atopic signature among these genes in an independent cohort of asthmatic subjects, and identified the presence of common transcription factor binding sites corresponding to glucocorticoid receptor binding.

Conclusion: These findings suggest that phenotypic clusters are associated with differences in the underlying pathobiology of asthma. Further experiments are necessary to confirm these findings.

Keywords: Childhood asthma; asthma phenotypes; gene expression profiling; longitudinal study; microarray analysis.

Figure 1

Graphical depiction of possible distributions of gene expression patterns for each phenotypic cluster. Each color denotes a particular genome-wide pattern of expression, and each row denotes a distribution of expression patterns across phenotypic clusters. Similar colors denote similar patterns of expression, whereas different colors denote different patterns of expression. For example, the top row of the figure depicts five red segments, indicating similar patterns of genome-wide expression across the five phenotypic clusters. A close-up view of the top five expression pattern distributions as determined by posterior probability is also shown.

Figure 2

Longitudinal trajectories of several clinical traits stratified by gene expression pattern (more atopic vs. less atopic). A) Depicts the trajectory of serum eosinophil levels, B) Depicts the trajectory of serum IgE levels, and C) Depicts the trajectory of methacholine PC₂₀ levels.

Figure 3

Decision Tree Classification Model for Atopic Status. There were five genes that were strongly predictive of atopic status in an independent population. These genes make up the branches of the tree, with each partition determined by the log₂ gene expression level.

Figure 4

Correlation heatmap depicting correlation between clinical traits and gene expression levels. Blue denotes a higher positive level of correlation between gene and clinical trait, while grey denotes a higher negative level of correlation between gene and clinical trait.