Damien C Croteau-Chonka1, Zhanghua Chen2, Kathleen C Barnes3, Albino Barraza-Villarreal4, Juan C Celedón5, W James Gauderman6, Frank D Gilliland2, Jerry A Krishnan7, Andrew H Liu8, Stephanie J London9, Fernando D Martinez10, Joshua Millstein6, Edward T Naureckas11, Dan L Nicolae12,13,14, Steven R White11, Carole Ober12, Scott T Weiss1,15, Benjamin A Raby1,16. 1. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. 2. Division of Environmental Health, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. 3. Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, Colorado, USA. 4. National Institute of Public Health, Cuernavaca, Morelos, México. 5. Division of Pulmonary Medicine, Allergy and Immunology, Children's Hospital of Pittsburgh of the University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. 6. Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. 7. Division of Pulmonary, Critical Care, Sleep, and Allergy, Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA. 8. Division of Allergy and Clinical Immunology, Department of Pediatrics, National Jewish Health and University of Colorado School of Medicine, Denver, Colorado, USA. 9. Division of Intramural Research, Department of Health and Human Services, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, North Carolina, USA. 10. Arizona Respiratory Center and BIO5 Institute, University of Arizona, Tucson, Arizona, USA. 11. Section of Pulmonary and Critical Care Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, USA. 12. Department of Human Genetics, University of Chicago, Chicago, Illinois, USA. 13. Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, USA. 14. Department of Statistics, University of Chicago, Chicago, Illinois, USA. 15. Partners HealthCare Personalized Medicine, Partners Health Care, Boston, Massachusetts, USA. 16. BWH Pulmonary Genetics Center, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Abstract
OBJECTIVE: Asthmatic children who develop obesity through adolescence have poorer disease outcomes compared with those who do not. This study aimed to characterize the biology of childhood asthma complicated by adult obesity. METHODS: Gene expression networks are powerful statistical tools for characterizing human disease that leverage the putative coregulatory relationships of genes to infer relevant biological pathways. Weighted gene coexpression network analysis of gene expression data was performed in whole blood from 514 adult asthmatic subjects. Then, module preservation and association replication analyses were performed in 418 subjects from two independent asthma cohorts (one pediatric and one adult). RESULTS: A multivariate model was identified in which three gene coexpression network modules were associated with incident obesity in the discovery cohort (each P < 0.05). Two module memberships were enriched for genes in pathways related to platelets, integrins, extracellular matrix, smooth muscle, NF-κB signaling, and Hedgehog signaling. The network structures of each of the obesity modules were significantly preserved in both replication cohorts (permutation P = 9.999E-05). The corresponding module gene sets were significantly enriched for differential expression in subjects with obesity in both replication cohorts (each P < 0.05). CONCLUSIONS: The gene coexpression network profiles thus implicate multiple interrelated pathways in the biology of an important endotype of asthma with obesity.
OBJECTIVE: Asthmatic children who develop obesity through adolescence have poorer disease outcomes compared with those who do not. This study aimed to characterize the biology of childhood asthma complicated by adult obesity. METHODS: Gene expression networks are powerful statistical tools for characterizing human disease that leverage the putative coregulatory relationships of genes to infer relevant biological pathways. Weighted gene coexpression network analysis of gene expression data was performed in whole blood from 514 adult asthmatic subjects. Then, module preservation and association replication analyses were performed in 418 subjects from two independent asthma cohorts (one pediatric and one adult). RESULTS: A multivariate model was identified in which three gene coexpression network modules were associated with incident obesity in the discovery cohort (each P < 0.05). Two module memberships were enriched for genes in pathways related to platelets, integrins, extracellular matrix, smooth muscle, NF-κB signaling, and Hedgehog signaling. The network structures of each of the obesity modules were significantly preserved in both replication cohorts (permutation P = 9.999E-05). The corresponding module gene sets were significantly enriched for differential expression in subjects with obesity in both replication cohorts (each P < 0.05). CONCLUSIONS: The gene coexpression network profiles thus implicate multiple interrelated pathways in the biology of an important endotype of asthma with obesity.
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