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

Michael J. McGeachie, Amber Dahlin, Weiliang Qiu, Damien C. Croteau-Chonka, Jessica Savage, Ann Chen Wu, Emily S. Wan, Joanne E. Sordillo, Amal Al-Garawi, Fernando Martinez, Robert C. Strunk, Robert F. Lemanske, Andrew H. Liu, Benjamin A. Raby, Scott Weiss, Clary B. Clish, Jessica A. Lasky-Su

Research output: Contribution to journalArticle

36 Citations (Scopus)

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), usingplasma samples from 20individualswith 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.

Original languageEnglish (US)
Pages (from-to)224-238
Number of pages15
JournalImmunity Inflammation and Disease
Volume3
Issue number3
DOIs
StatePublished - Jan 1 2015

Fingerprint

Inborn Genetic Diseases
Metabolomics
Asthma
Albuterol
Metabolic Networks and Pathways
Methylation
Sphingolipids
Nebulizers and Vaporizers
Tandem Mass Spectrometry
Dinoprostone
Arachidonic Acid
Cellular Immunity
Liquid Chromatography
Interferon-gamma
Area Under Curve
Single Nucleotide Polymorphism
Molecular Biology
Genotype
Genome
Cytokines

Keywords

  • Albuterol
  • Asthma
  • Epigenetics
  • Genetics
  • Metabolomics

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology

Cite this

McGeachie, M. J., Dahlin, A., Qiu, W., Croteau-Chonka, D. C., Savage, J., Wu, A. C., ... Lasky-Su, J. A. (2015). The metabolomics of asthma control: A promising link between genetics and disease. Immunity Inflammation and Disease, 3(3), 224-238. https://doi.org/10.1002/iid3.61

The metabolomics of asthma control : A promising link between genetics and disease. / McGeachie, Michael J.; Dahlin, Amber; Qiu, Weiliang; Croteau-Chonka, Damien C.; Savage, Jessica; Wu, Ann Chen; Wan, Emily S.; Sordillo, Joanne E.; Al-Garawi, Amal; Martinez, Fernando; Strunk, Robert C.; Lemanske, Robert F.; Liu, Andrew H.; Raby, Benjamin A.; Weiss, Scott; Clish, Clary B.; Lasky-Su, Jessica A.

In: Immunity Inflammation and Disease, Vol. 3, No. 3, 01.01.2015, p. 224-238.

Research output: Contribution to journalArticle

McGeachie, MJ, Dahlin, A, Qiu, W, Croteau-Chonka, DC, Savage, J, Wu, AC, Wan, ES, Sordillo, JE, Al-Garawi, A, Martinez, F, Strunk, RC, Lemanske, RF, Liu, AH, Raby, BA, Weiss, S, Clish, CB & Lasky-Su, JA 2015, 'The metabolomics of asthma control: A promising link between genetics and disease', Immunity Inflammation and Disease, vol. 3, no. 3, pp. 224-238. https://doi.org/10.1002/iid3.61
McGeachie MJ, Dahlin A, Qiu W, Croteau-Chonka DC, Savage J, Wu AC et al. The metabolomics of asthma control: A promising link between genetics and disease. Immunity Inflammation and Disease. 2015 Jan 1;3(3):224-238. https://doi.org/10.1002/iid3.61
McGeachie, Michael J. ; Dahlin, Amber ; Qiu, Weiliang ; Croteau-Chonka, Damien C. ; Savage, Jessica ; Wu, Ann Chen ; Wan, Emily S. ; Sordillo, Joanne E. ; Al-Garawi, Amal ; Martinez, Fernando ; Strunk, Robert C. ; Lemanske, Robert F. ; Liu, Andrew H. ; Raby, Benjamin A. ; Weiss, Scott ; Clish, Clary B. ; Lasky-Su, Jessica A. / The metabolomics of asthma control : A promising link between genetics and disease. In: Immunity Inflammation and Disease. 2015 ; Vol. 3, No. 3. pp. 224-238.
@article{d17ef33133e04ecfa46c12bd823ef1a3,
title = "The metabolomics of asthma control: A promising link between genetics and disease",
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), usingplasma samples from 20individualswith 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.",
keywords = "Albuterol, Asthma, Epigenetics, Genetics, Metabolomics",
author = "McGeachie, {Michael J.} and Amber Dahlin and Weiliang Qiu and Croteau-Chonka, {Damien C.} and Jessica Savage and Wu, {Ann Chen} and Wan, {Emily S.} and Sordillo, {Joanne E.} and Amal Al-Garawi and Fernando Martinez and Strunk, {Robert C.} and Lemanske, {Robert F.} and Liu, {Andrew H.} and Raby, {Benjamin A.} and Scott Weiss and Clish, {Clary B.} and Lasky-Su, {Jessica A.}",
year = "2015",
month = "1",
day = "1",
doi = "10.1002/iid3.61",
language = "English (US)",
volume = "3",
pages = "224--238",
journal = "Immunity, inflammation and disease",
issn = "2050-4527",
publisher = "John Wiley and Sons Ltd",
number = "3",

}

TY - JOUR

T1 - The metabolomics of asthma control

T2 - A promising link between genetics and disease

AU - McGeachie, Michael J.

AU - Dahlin, Amber

AU - Qiu, Weiliang

AU - Croteau-Chonka, Damien C.

AU - Savage, Jessica

AU - Wu, Ann Chen

AU - Wan, Emily S.

AU - Sordillo, Joanne E.

AU - Al-Garawi, Amal

AU - Martinez, Fernando

AU - Strunk, Robert C.

AU - Lemanske, Robert F.

AU - Liu, Andrew H.

AU - Raby, Benjamin A.

AU - Weiss, Scott

AU - Clish, Clary B.

AU - Lasky-Su, Jessica A.

PY - 2015/1/1

Y1 - 2015/1/1

N2 - 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), usingplasma samples from 20individualswith 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.

AB - 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), usingplasma samples from 20individualswith 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.

KW - Albuterol

KW - Asthma

KW - Epigenetics

KW - Genetics

KW - Metabolomics

UR - http://www.scopus.com/inward/record.url?scp=85011570000&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85011570000&partnerID=8YFLogxK

U2 - 10.1002/iid3.61

DO - 10.1002/iid3.61

M3 - Article

AN - SCOPUS:85011570000

VL - 3

SP - 224

EP - 238

JO - Immunity, inflammation and disease

JF - Immunity, inflammation and disease

SN - 2050-4527

IS - 3

ER -