Judie A Howrylak1, Matthew Moll2, Scott T Weiss3, Benjamin A Raby3, Wei Wu4, Eric P Xing5. 1. Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Penn State Milton S. Hershey Medical Center, Hershey, Pa. Electronic address: jhowrylak@hmc.psu.edu. 2. Department of Medicine, Boston University, Boston, Mass. 3. Harvard Medical School, Boston, Mass; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Mass; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Mass. 4. Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pa. 5. Department of Machine Learning, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pa.
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.
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.
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