Open Access

Interaction of prenatal maternal smoking, interleukin 13 genetic variants and DNA methylation influencing airflow and airway reactivity

  • Veeresh K Patil1, 2Email author,
  • John W Holloway2, 3,
  • Hongmei Zhang4,
  • Nelis Soto-Ramirez4,
  • Susan Ewart5,
  • S Hasan Arshad1, 2 and
  • Wilfried Karmaus4
Clinical EpigeneticsThe official journal of the Clinical Epigenetics Society20135:22

DOI: 10.1186/1868-7083-5-22

Received: 25 June 2013

Accepted: 1 November 2013

Published: 6 December 2013

Abstract

Background

Asthma is characterized by airflow limitation and airway reactivity (AR). Interleukin-13 (IL-13) is involved in the pathogenesis of asthma. Two functional SNPs, rs20541 and rs1800925, of the IL-13 gene (IL13) have been frequently associated with asthma-related lung functions. However, genetic variation alone does not fully explain asthma risk. DNA-methylation (DNA-M) is an epigenetic mechanism that regulates gene expression and can be influenced by both environment and genetic variants. To explore the interplay of prenatal maternal smoking, genetic variants and DNA-M, we used a two-stage model: (1) identifying cytosine phosphate guanine (CpG) sites where DNA-M is influenced by the interaction between genetic variants and maternal smoking during pregnancy (conditional methQTL (methylation quantitative trait loci)); and (2) determining the effect of the interaction between DNA-M of CpG (from stage 1) and SNPs (modifying genetic variants; modGV) on airflow limitation and AR in 245 female participants of the Isle of Wight birth cohort. DNA-M was assessed using the Illumina Infinium HumanMethylation450 BeadChip.

Findings

Six CpG sites were analyzed in stage 1. DNA-M at cg13566430 was influenced by interaction of maternal smoking during pregnancy and rs20541. In stage 2, genotype at rs1800925 interacted with DNA-M at cg13566430 significantly affecting airflow limitation (P = 0.042) and AR (P = 0.01).

Conclusion

Both genetic variants and environment affect DNA-M. This study supports the proposed two-stage model (methQTL and modGV) to study genetic variants, environment and DNA-M interactions in asthma-related lung function.

Keywords

Asthma genetics and epigenetics Airway reactivity DNA methylation IL13 gene Lung functions Maternal smoking during pregnancy

Findings

Asthma is a chronic airway disease characterized by airflow limitation and airway reactivity (AR) and exhibits wide heterogeneity in disease susceptibly and phenotypes. Multiple genes, gene-gene and gene-environment interactions have been shown to play a role in determining susceptibility to asthma and associated phenotypes of lung function and AR. Maternal smoking during pregnancy is a significant risk factor for developing asthma in offspring [1] and can affect offspring lung function [2]. Interleukin-13 (IL-13) is a recognized effector in airway inflammation, reactivity and remodeling. IL13 is located on chromosome 5q31, and has been consistently associated with asthma [3, 4]. Several functional genetic variants occur in IL13 including rs1800925 (−1112C/T) and rs20541 (R130Q, +2044A/G). Recent meta-analyses have shown that both SNPs are associated with asthma risk [5, 6] and with forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC) in asthmatics [7, 8]. In severe asthma, monoclonal antibody to IL-13 use is associated with improvement in lung function in humans [9] and airway hyper-responsiveness in a murine model [10].

DNA-methylation (DNA-M) represents a site of molecular interaction between the environment and genome. There is growing evidence that DNA-M plays a role in complex diseases like asthma [11] and can be modified by environmental exposures such as tobacco smoke [12] as well as by disease-associated genotypes [13]. We have previously shown that IL13 polymorphism modifies the impact of in utero tobacco smoke exposure on childhood asthma, suggesting a role for gene-environment interaction [14]. Recently we have also shown that genetic variants in the IL-4 receptor interact with DNA-M to determine risk of asthma [15]. DNA-M is a potential integrator of different signals affecting disease susceptibility, with both environment and genotype influencing methylation levels. Karmaus and colleagues [16] proposed a two-stage model to incorporate the role of genetic variants, environment and DNA-M interactions in asthma. In stage 1, an environmental exposure and genetic variant interact to influence DNA-M at a specific site in an adjacent locus. This stage identifies the conditional methQTL (methylation quantitative trait loci) and the change in DNA-M once established can differentially regulate gene activity. In stage 2, the phenotypic effects of sequence variants of the gene (modifiable genetic variants (modGVs)) can be modified by the pre-established methylation by the conditional methQTL. This two stage model for asthma-related lung function is depicted in Figure 1. Given our previous observation of the interaction between maternal smoke exposure and genotype in determining asthma, we hypothesize that this interaction would occur through a mechanism involving methQTL and/or modGVs.
https://static-content.springer.com/image/art%3A10.1186%2F1868-7083-5-22/MediaObjects/13148_2013_Article_67_Fig1_HTML.jpg
Figure 1

Two-stage model to investigate environment, SNP and DNA methylation interactions influencing asthma-related lung functions (Modified from Karmaus et al. [16]. CpG, cytosine phosphate guanine; methQTL, methylation, quantitative trait loci (genetic variants that change the susceptibility for DNA methylation); modGV, modifiable genetic variants (genetic variants that are modified by DNA methylation).

Study population and assessments

The Isle of Wight birth cohort (n = 1,456) was established in 1989 and has been followed up through childhood. At 18 years of age, spirometry and bronchial challenge tests were done according to American Thoracic Society guidelines [17, 18]. We analyzed lung function measurements and DNA-M data of 245 females aged 18 years who were randomly selected from the cohort population for epigenetic studies. Comparison of the analyzed sample with whole cohort females in terms of risk factors have been described elsewhere [15]. A continuous measure of AR (dose response slope (DRS)) was used. DRS is the gradient of FEV1 drop from baseline with each successive incremental dose of methacholine administered. A transformation of Log10 (DRS + 10) was required to satisfy the distributional assumption of normal data. A higher positive value of this transformation signifies greater AR. A description of the 18-year follow-up of the cohort is previously reported [19].

Genotyping and SNP selection

DNA was isolated from peripheral blood leucocytes collected at age 18. IL13 polymorphisms were genotyped as described by Sadeghnejad and colleagues [14]. Two known functional SNPs, rs1800925 (n = 234) and rs20541 (n = 235), frequently associated with asthma and related lung functions were used in this analysis. These SNPs were not in linkage disequilibrium in this cohort as described elsewhere [14].

DNA-methylation

One microgram of DNA was bisulfite-treated using the EZ 96-DNA-methylation kit (Zymo Research, Irvine, CA, USA), following the manufacturer’s protocol. Genome-wide DNA- M was assessed using the Illumina Infinium-HumanMethylation450 BeadChip (Illumina, Inc., Hayward, CA, USA) as described previously [15].

Statistical methods

The pre-processed DNA-M beta (β) values, presented as the proportion of intensity of methylated (M) over the sum of methylated (M) and unmethylated (U) sites (β = M/[c + M + U] with c being a constant to prevent dividing by zero), were used to estimate the effect of DNA-M [20]. The R-package IMA in Bioconductor (IMA is implemented in the R language and is freely available from http://​www.​rforge.​net/​IMA) was used for the pre-processing [21]. SNP genotype-dependent methylation was analyzed using the Kruskal-Wallis test. Interaction was tested using multiple linear regressions. Statistical analyses were performed using IBM SPSS Statistics, Version-19.0 (IBM SPSS Statistics for Windows, Version 19.0. Armonk, NY, USA: IBM Corp,). The statistical significance was set at 0.05.

Results

Six cytosine phosphate guanine (CpG) sites were identified in the promoter region of IL13. The location and description of the CpG sites and of SNPs are shown in Table 1. All the six CpG sites spanning the promoter region of IL13 were analyzed in Stage 1. The effect of maternal smoking during pregnancy interacting with both SNPs was explored independently for DNA-M at each CpG site. DNA-M at cg13566430 was influenced by the interaction of rs20541 and maternal smoking during pregnancy (P = 0.043); this remained significant after correcting for personal smoking at 18 years (P = 0.041). DNA-M at cg13566430 also showed a genotype dependent methylation for rs1800925 (Kruskall Wallis test P < 0.001; Figure 2).
Table 1

Location, position and description of rs1800925, rs20541 and CpG sites on the promoter region of IL13 gene

SNP

Chromosomal location

Position

Genotypes

n (%)

rs1800925

5:131992809

5′ Promoter upstream

TT

8 (3.4%)

CT

68 (28.8%)

CC

158 (67.8%)

rs20541

5:131995964

Exon 4

AA

10 (4.3%)

AG

66 (28.1%)

GG

159 (67.7%)

CpG

  

Median

Percentiles (5%, 95%)

cg13566430

5:131992455

TSS1500

0.18

0.14

0.23

cg04303330

5: 131992430

TSS1500

0.30

0.23

0.36

cg06584121

5: 131993818

TSS200

0.80

0.730

0.84

cg06967316

5: 131993853

TSS200

0.74

0.66

0.80

cg14523284

5: 131993614

TSS1500

0.86

0.83

0.89

cg15329179

5: 131993728

TSS200

0.87

0.81

0.90

CpG, cytosine phosphate guanine; TSS200, 200 base pairs from transcription start site; TSS1500, 1500 base pairs from transcription start site.

https://static-content.springer.com/image/art%3A10.1186%2F1868-7083-5-22/MediaObjects/13148_2013_Article_67_Fig2_HTML.jpg
Figure 2

Whisker plot showing IL13 rs1800925 genotype-dependant DNA methylation of cg13566430.

In the next step, the interaction between DNA-M at cg13566430 and rs1800925 genotype on lung function (FEV1/FVC and DRS) was explored. DNA-M at cg13566430 significantly interacted with rs1800925 genotype to determine FEV1/FVC and DRS (Table 2). In subjects with the TT genotype, FEV1/FVC increases with increasing methylation of cg13566430 (coefficient 3.274, P = 0.042), for the CT genotype group the increase was smaller (coefficient 0.799, P = 0.086) and CC was the reference genotype. The graph in Figure 3 provides a visual description of the effect of DNA-M at cg13566430 and rs1800925 on FEV1/FVC. DRS decreases quickly with increasing methylation of cg13566430 in TT genotype (coefficient −27.497, P = 0.010), while the drop in DRS in CT is insignificant (coefficient −0.809, P = 0.742).
Table 2

Interaction of methylation at cg13566430 and rs1800925 genotypes on FEV 1 /FVC and DRS

  

FEV1/FVC

DRS

  

Estimate

95% CI

P

Estimate

95% CI

P

Main effects

       

cg13566430

 

0.175

−0.166 to 0.517

0.313

−0.568

−2.361 to 1.226

0.533

rs20541

AA

0.020

−0.024 to 0.064

0.369

−0.075

−0.278 to 0.129

0.469

 

AG

0.006

−0.014 to 0.026

0.548

0.036

−0.076 to 0.149

0.523

 

GG

 

Reference

  

Reference

 

rs1800925

TT

0.030

−0.019 to 0.079

0.231

0.299

0.045 to 0.554

0.021

 

CT

−0.001

−0.021 to 0.019

−0.001

−0.026

−0.134 to 0.083

0.644

 

CC

 

Reference

  

Reference

 

Interaction

       

rs1800925

TT

−0.412

−0.855 to 0.030

0.068

3.989

1.194 to 6.785

0.005

 

CT

−0.134

−0.296 to 0.028

0.106

0.124

−0.733 to 0.982

0.775

 

CC

 

Reference

  

Reference

 

cg13566430

 

0.174

−0.301 to 0.648

0.472

0.624

−1.926 to 3.174

0.629

rs1800925× cg13566430

TT

3.274

0.114 to 6.434

0.042

−27.497

−48.283 to −6.710

0.010

 

CT

0.799

−0.114 to 1.712

0.086

−0.809

−5.661 to 4.043

0.742

 

CC

 

Reference

  

Reference

 

DRS, dose response slope; FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity.

https://static-content.springer.com/image/art%3A10.1186%2F1868-7083-5-22/MediaObjects/13148_2013_Article_67_Fig3_HTML.jpg
Figure 3

Graph showing interaction of rs1800925 and methylation at cg13566430 influencing FEV 1 /FVC. FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity.

Discussion

This study tested a two-stage model for integrating the interactions of maternal smoking during pregnancy, genetic variants and DNA-M for an asthma candidate gene IL13. We show that interaction of a functional IL13 SNP, rs20541, and maternal smoking during pregnancy influenced DNA-M at cg13566430. We also show that DNA-M at cg13566430 interacts with genotype of another functional SNP, rs1800925, to affect airflow limitation and AR. Michel and colleagues [22] examined the effect of farm exposure on DNA-M of ten asthma candidate genes and found that DNA-M at one IL13 site (spanning rs1800925; similar to the site in our study) was more methylated in the exposed group compared to the non-exposed group. They did not see significant differential methylation of IL13 between asthmatic and non-asthmatic children but asthma-related traits were not tested and also the interaction with genetic variants was not examined. Our approach identifies the effect of environmental exposure and genetic variants on DNA-M and then also combines the interaction of other modifiable genetic variants with DNA-M on the outcomes. We show genotype-dependent DNA-M in the IL13 promoter region; methylation at cg13566430 revealed variation in levels dependent on the genotype of rs1800925, as seen in other genes [23]. This varying distribution of DNA-M across genotypes supports the plausible role for DNA-M in the pathway between genotype and phenotype.

There are some limitations to this study. While DNA-M measurement using Illumina Infinium-HumanMethylation450 arrays has been shown to have reproducibility and high validity [24], technical replication of the DNA-M measurements has not been undertaken. We have not measured DNA-M in airway tissue; however, the major source of IL-13 production in the airways is inflammatory cells, and methylation measurements in peripheral blood are likely to better reflect asthma-related immune mechanisms. Cell composition in the peripheral blood can influence DNA-M; however, cell composition alone cannot explain the differential methylation observed [22]. Focusing only on female participants is a limitation; however, the results should be encouraging for further studies to replicate the model. The interaction effects seen may not imply a direct relationship between genotype and altered methylation. Genotype may also alter other epigenetic processes such as chromatin remodeling and this may then lead to altered DNA-M.

The effect of environment, genotype and DNA-M was seen for two different, but equally important and objective, characteristics of asthma: airflow limitation and AR. Similar to Michel and colleagues [22] we did not see an effect on asthma as an outcome (results not shown); however, the observed effect on asthma-related objective measures avoids plausible bias of reported asthma. Methylation of the promoter region can regulate gene transcription [25] and we have shown that promoter region methylation is dependent on genotype, and the interplay of the environment, genotype and DNA-M influence the phenotype.

While requiring replication in an independent cohort, the results show the interplay of prenatal maternal smoking, genetic variants and DNA-M of IL13 influencing asthma-related lung function. This highlights the need to consider environment, genotype and DNA-M together when seeking to understand the pathogenesis of complex disease, as DNA-M plays a role of integrator of multiple disease pathway signals.

Abbreviations

AR: 

Airway reactivity

CpG: 

Cytosine phosphate guanine

DNA-M: 

DNA methylation

DRS: 

Dose response slope

FEV1: 

Forced expiratory volume in 1 second

FVC: 

Forced vital capacity

IL: 

Interleukin

methQTL: 

Methylation, quantitative trait loci

modGV: 

Modifiable genetic variant

SNP: 

Single nucleotide polymorphism.

Declarations

Acknowledgements

We would like to thank all the participants of the Isle of Wight birth cohort, Dr A Raza and the research team at David Hide Asthma & Allergy Research Centre, Isle of Wight, for collecting the data and Nikki Graham for technical support. We thank the High-Throughput Genomics Group at the Wellcome Trust Centre for Human Genetics (funded by Wellcome Trust grant reference 090532/Z/09/Z and MRC Hub grant G0900747 91070) for the generation of the methylation data.

Funding sources

The epigenetic investigation was supported by National Institutes of Health, USA (R01-AI091905, R01-AI061471, and R21-AI099367). The 18-year assessment of the 1989 Isle of Wight birth cohort was funded by grants from the National Institutes of Health, USA (R01-HL082925) and National Eczema Society/British Dermatological Nursing Group Research Awards.

Authors’ Affiliations

(1)
David Hide Asthma and Allergy Research Centre, St Mary’s Hospital
(2)
Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton
(3)
Human Development and Health, Faculty of Medicine, University of Southampton
(4)
Division of Epidemiology, Biostatistics, & Environmental Health, School of Public Health, University of Memphis
(5)
Department of Large Animal Clinical Sciences, Michigan State University

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© Patil et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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