DNA methylation and childhood asthma in the inner city

Abstract

Background: Epigenetic marks are heritable, influenced by the environment, direct the maturation of T lymphocytes, and in mice enhance the development of allergic airway disease. Thus it is important to define epigenetic alterations in asthmatic populations.

Objective: We hypothesize that epigenetic alterations in circulating PBMCs are associated with allergic asthma.

Methods: We compared DNA methylation patterns and gene expression in inner-city children with persistent atopic asthma versus healthy control subjects by using DNA and RNA from PBMCs. Results were validated in an independent population of asthmatic patients.

Results: Comparing asthmatic patients (n = 97) with control subjects (n = 97), we identified 81 regions that were differentially methylated. Several immune genes were hypomethylated in asthma, including IL13, RUNX3, and specific genes relevant to T lymphocytes (TIGIT). Among asthmatic patients, 11 differentially methylated regions were associated with higher serum IgE concentrations, and 16 were associated with percent predicted FEV1. Hypomethylated and hypermethylated regions were associated with increased and decreased gene expression, respectively (P < 6 × 10(-12) for asthma and P < .01 for IgE). We further explored the relationship between DNA methylation and gene expression using an integrative analysis and identified additional candidates relevant to asthma (IL4 and ST2). Methylation marks involved in T-cell maturation (RUNX3), TH2 immunity (IL4), and oxidative stress (catalase) were validated in an independent asthmatic cohort of children living in the inner city.

Conclusions: Our results demonstrate that DNA methylation marks in specific gene loci are associated with asthma and suggest that epigenetic changes might play a role in establishing the immune phenotype associated with asthma.

Keywords: DNA methylation; T(H)2 immunity; atopic asthma; epigenetics; inner city.

Conflict of interest statement

Disclosure of potential conflict of interest: I. V. Yang has received research support from the National Institutes of Health (NIH). A. Liu has received payment for lectures from Merck and is on the data safety monitoring committee from GlaxoSmithKline. G. T. O’Connor has received research support from the NIH. S. J. Teach has received research and travel support from the NIH/National Institute of Allergy and Infectious Diseases (NIAID), Patient-Centered Outcome Research Institute, Fight for Children, the DC Department of Health, and the Kellogg Foundation; is employed by Children’s National Health System; and receives royalties from Up-To-Date. M. Kattan has received research support from the NIH and is a member of the Novartis Advisory Board. R. Gruchalla has received research and travel support from the NIAID. S. J. Szefler has received research support from the NIAID and GlaxoSmithKline; has consultant arrangements with Merck, Boehringer Ingelheim, GlaxoSmithKline, and Genentech; has received payment for lectures from Merck; and has submitted a patent for β-adrenengic receptor polymorphism for the National Heart, Lung, and Blood Institute CARE Network. M. A. Gill has received research support from the NIH/ NIAID. A. Calatroni has received research support from the NIH/NIAID. G. David has a contract with the NIH/NIAID. W. W. Busse has received research support from the NIH/NIAID and the National Heart, Lung, and Blood Institute; is a board member for Merck; has consultant arrangements with Novartis, GlaxoSmithKline, Roche, Pfizer, Boston Scientific, Circassia, ICON, AstraZeneca, Sanofi, Amgen, Med-Immune, NeoStem, Takeda, and Boehringer Ingelheim; and has received royalties from Elsevier. D. A. Schwartz has received research support from the NIH and the Veterans Administration; has consultant arrangements with Novartis and Boehringer-Ingelheim; is employed by the University of Colorado Medical School and the Department of Veterans Affairs; has provided expert testimony from Weitz and Luxenberg Law Firm, Brayton and Purcell Law Firm, and Wallace and Graham Law Firm; has patents for TLR2 single nucleotide polymorphism, MUC5b single nucleotide polymorphism, and has patent applications (61/248,505, 61/666,233, 60/ 992,079); and has received royalties from Springer. The rest of the authors declare that they have no relevant conflicts of interest.

Published by Elsevier Inc.

Figures

FIG 1

DMRs are associated with asthma after controlling for age, sex, race, technical variables, and batch effects. A, Manhattan plot of adjusted P values for disease status (asthma/control) from the linear model. Each dot represents a P value for a probe on the Illumina 450k array that has been adjusted by the significance of neighboring probes within 300 bases according to their correlation. Probes within statistically significant DMRs are identified by a darker and slightly larger symbol after adjustment for genome-wide comparisons. B, A representative transcriptional network of genes with associated DMRs from Ingenuity Pathway Analysis. Network analysis was performed with only direct interactions (solid lines) and networks with a score of greater than 20. Genes are intensity colored red (hypermethylated) or green (hypomethylated). Horizontal ellipse, Transcriptional regulator; square, cytokine; double circle, group/complex; triangle, phosphatase; vertical ellipse, transmembrane receptor; rectangle, ion channel.

FIG 2

Differentially methylated marks are associated with higher total serum IgE concentrations (A) and percent predicted FEV1 (B) after controlling for age, sex, race, technical variables, and batch effects among the asthmatic patients. Manhattan plots were constructed in the same fashion as in Fig 1, A Probes within statistically significant DMRs are identified by a darker and slightly larger symbol after adjustment for genome-wide comparisons.

FIG 3

Expression changes in genes within 3 kb of the nearest DMR associated with asthma in the entire study population (A), IgE levels among asthmatic patients (B), and genome-wide methylation expression in asthmatic patients (C). Fig 3, A, x-axis, The methylation difference is represented by the mean percentage methylation difference in asthmatic patients compared with control subjects. Fig 3, A, y-axis, The expression difference is represented by the mean fold change in asthmatic patients compared with control subjects (on the log base 2 scale). Fig 3, B, The percentage methylation difference on the x-axis is represented by the mean correlation coefficient between IgE and methylation in asthmatic patients. The y-axis is represented by the slope of the IgE covariate in the linear model for expression in asthmatic patients. Blue symbols represent hypomethylated genes associated with increased gene expression, as well as some hypermethylated genes associated with decreased gene expression. Red symbols represent methylation changes that were not associated with expected gene expression differences. Upward triangles indicate DMR location upstream of the gene, circles represent DMRs in the gene body, and downward triangles refer to DMRs downstream of the gene. In Fig 3, C, the t statistic for expression change of all genes in the genome in asthmatic patients compared with control subjects from PBMCs is plotted against the t statistic for methylation changes in these genes (single CpG within the 3-kb promoter). Red dots represent the 207 genes with posterior probability from the joint model of methylation and expression of greater than 0.95 (corresponding to q < 0.05), a methylation t statistic of less than 21.5, and an expression t statistic of greater than 1.5 (see Table E10, A). Additionally, 82 genes have posterior probability of greater than 0.95, a methylation t statistic of less than 21.5, and an expression t statistic of less than 21.5 (lower left quadrant, see Table E10, B). No significant expression/methylation changes were identified in the right 2 quadrants on this plot.