The metabolomics of asthma control: a promising link between genetics and disease

Immunity, Inflammation and Disease

Michael J. McGeachie¹  Amber Dahlin¹  Weiliang Qiu¹  Damien C. Croteau-Chonka¹  Jessica Savage¹  Ann Chen Wu¹  Emily S. Wan¹  Joanne E. Sordillo¹  Amal Al-Garawi¹  Fernando D. Martinez²  Robert C. Strunk⁵  Robert F. Lemanske Jr⁴  Andrew H. Liu⁶  Benjamin A. Raby¹  Scott Weiss¹  Clary B. Clish³ 

[1] Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA;Arizona Respiratory Center and BIO5 Institute, University of Arizona, Tucson, Arizona, USA;Broad Institute, Cambridge, Massachusetts, USA;University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA;Department of Pediatrics, Division of Allergy, Immunology and Pulmonary Medicine, Washington University School of Medicine, St. Louis, Missouri, USA;Department of Pediatrics, Division of Allergy and Clinical Immunology, National Jewish Health and University of Colorado School of Medicine, Denver, Colorado, USA

关键词: Albuterol;    asthma;    epigenetics;    genetics;    metabolomics;   

DOI  :  10.1002/iid3.61

来源: Wiley

【 摘 要 】

Abstract

Short-acting β agonists (e.g., albuterol) are the most commonly used medications for asthma, a disease that affects over 300 million people in the world. Metabolomic profiling of asthmatics taking β agonists presents a new and promising resource for identifying the molecular determinants of asthma control. The objective is to identify novel genetic and biochemical predictors of asthma control using an integrative “omics” approach. We generated lipidomic data by liquid chromatography tandem mass spectrometry (LC-MS), ­ using plasma samples from 20 individuals with asthma. The outcome of interest was a binary indicator of asthma control defined by the use of albuterol inhalers in the preceding week. We integrated metabolomic data with genome-wide genotype, gene expression, and methylation data of this cohort to identify genomic and molecular indicators of asthma control. A Conditional Gaussian Bayesian Network (CGBN) was generated using the strongest predictors from each of these analyses. Integrative and metabolic pathway over-representation analyses (ORA) identified enrichment of known biological pathways within the strongest molecular determinants. Of the 64 metabolites measured, 32 had known identities. The CGBN model based on four SNPs (rs9522789, rs7147228, rs2701423, rs759582) and two metabolites—monoHETE_0863 and sphingosine-1-phosphate (S1P) could predict asthma control with an AUC of 95%. Integrative ORA identified 17 significantly enriched pathways related to cellular immune response, interferon signaling, and cytokine-related signaling, for which arachidonic acid, PGE2 and S1P, in addition to six genes (CHN1, PRKCE, GNA12, OASL, OAS1, and IFIT3) appeared to drive the pathway results. Of these predictors, S1P, GNA12, and PRKCE were enriched in the results from integrative and metabolic ORAs. Through an integrative analysis of metabolomic, genomic, and methylation data from a small cohort of asthmatics, we implicate altered metabolic pathways, related to sphingolipid metabolism, in asthma control. These results provide insight into the pathophysiology of asthma control.

【 授权许可】

CC BY   
© 2015 The Authors. Immunity, Inflammation and Disease Published by John Wiley & Sons Ltd.

Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.