Open Access

Epigenetic regulation of AXL and risk of childhood asthma symptoms

  • Lu Gao1,
  • Joshua Millstein1,
  • Kimberly D. Siegmund1,
  • Louis Dubeau1,
  • Rachel Maguire2,
  • Frank D. Gilliland1,
  • Susan K. Murphy3,
  • Cathrine Hoyo2 and
  • Carrie V. Breton1Email author
Clinical EpigeneticsThe official journal of the Clinical Epigenetics Society20179:121

https://doi.org/10.1186/s13148-017-0421-8

Received: 8 August 2017

Accepted: 1 November 2017

Published: 7 November 2017

Abstract

Background

AXL is one of the TAM (TYRO3, AXL and MERTK) receptor tyrosine kinases and may affect numerous immune-related health conditions. However, the role for AXL in asthma, including its epigenetic regulation, has not been extensively studied.

Methods

We investigated the association between AXL DNA methylation at birth and risk of childhood asthma symptoms at age 6 years. DNA methylation of multiple CpG loci across the regulatory regions of AXL was measured in newborn bloodspots using the Illumina HumanMethylation450 array on a subset of 246 children from the Children’s Health Study (CHS). Logistic regression models were fitted to assess the association between asthma symptoms and DNA methylation. Findings were evaluated for replication in a separate population of 1038 CHS subjects using Pyrosequencing on newborn bloodspot samples. AXL genotypes were extracted from genome-wide data.

Results

Higher average methylation of CpGs in the AXL gene at birth was associated with higher risk of parent-reported wheezing, and the association was stronger in girls than in boys. This relationship reflected the methylation status of the gene-body region near the 5′ end, for which a 1% higher methylation level was significantly associated with a 72% increased risk of ever having wheezed by 6 years. The association of one CpG locus, cg00360107 was replicated using Pyrosequencing. Increased AXL methylation was also associated with lower mRNA expression level of this gene in lung tissue from the Cancer Genome Atlas (TCGA) dataset. Furthermore, AXL DNA methylation was strongly linked to underlying genetic polymorphisms.

Conclusions

AXL DNA methylation at birth was associated with higher risk for asthma-related symptoms in early childhood.

Keywords

MethylationChildrenEpigenetics

Background

Asthma is the most common chronic disease in childhood [1, 2]. It is a complex disease determined by the interplay between genetic and environmental factors [37]. The pathogenesis of childhood asthma is characterized by both structural features in the airway wall, such as breach in epithelium integrity, and immunological features like airway inflammation [811]. We and others have shown that early life exposure to tobacco smoke and air pollution are associated with increased risk of childhood asthma and asthma symptoms [1217]. In addition, epigenetic modifications, including DNA methylation, can alter regulation of genes involved in airways development or immune-mediated inflammatory pathways and may play a role in mediating the effects of environmental exposures [1822]. Many studies have now been conducted to investigate the effects of epigenetic variation on risk of asthma-related phenotypes [23, 24]. Although alterations in DNA methylation patterns can occur throughout life, important patterns in the methylome are established during embryogenesis and early life [25]. However, few studies have examined the effects of DNA methylation in immune cells at birth on asthma pathogenesis.

In previous work, we identified a gene—AXL, one of the TAM (TYRO3, AXL and MERTK) family receptor tyrosine kinases—in which methylation status of certain CpG loci varied based on prenatal exposure to tobacco smoke [26, 27]. Given the known associations between prenatal tobacco smoke exposure and asthma risk, as well as prenatal tobacco smoke and DNA methylation in AXL, we sought to investigate whether methylation in AXL at birth was associated with childhood asthma or asthma-related symptoms.

TAM genes are key signaling molecules in innate immune responses and may affect numerous immune-related health conditions [28]. They act as pivotal inhibitors of immuno-regulatory factors and prevent unrestrained signaling of inflammatory responses by these factors [29, 30]. Growth-arrest-specific 6 (GAS6) and protein S are the ligands that bind and activate the TAM receptors [31], and GAS6 showed higher expression in subjects with severe asthma during exacerbation [32]. Moreover, genetic polymorphisms in TAM genes and their ligands have also been implicated in inflammation and autoimmune diseases [33, 34]. Genetic variants may affect DNA methylation of CpG sites in their genomic surroundings and gene expression through altering the affinity of DNA binding factors, enhancer activity, or chromatin formation [35, 36]. Increased CpG methylation in the promoter region may modulate expression or silence AXL entirely and lead to overstimulation of the immune system. The identification of epigenetic and genetic variation associated with childhood asthma symptoms may shed light on the etiology of this complex disease and the biological role of AXL.

In this study, we investigated the association between methylation of multiple CpG sites across the regulatory regions of AXL at birth and risk of childhood asthma symptoms, taking into consideration the underlying genetic variation in AXL. We first assessed the association in a subset of 246 subjects from the Children’s Health Study (CHS), then sought to replicate the associations in a separate population of 1038 CHS subjects. Correlations between DNA methylation and expression of AXL were also evaluated in two tissue types, cord blood from 235 subjects of the Newborn Epigenetic STudy (NEST) and lung tissue samples from the Cancer Genome Atlas (TCGA) dataset [3739]. To further investigate the epigenetic control of AXL and address potential confounding effects from genetic polymorphisms, the association between AXL genetic polymorphisms and methylation was also evaluated.

Methods

Study population

This study was conducted in subsets of participants in the Children’s Health Study, a longitudinal study of respiratory health of children in southern California [6, 4042]. A subset of 737 children were initially sampled to participate in a study of atherosclerosis [43] of whom 689 could be linked to California birth records. Of these, we randomly selected 246 children from participants for whom at least 700 ng of DNA was available from a dried newborn bloodspot. DNA methylation was assessed in the newborn bloodspots using the Infinium HumanMethylation450 BeadChip (HM450) arrays. A separate subset of 1038 CHS subjects who were not participants in the atherosclerosis study, but who were enriched in children with asthma, was selected to have newborn bloodspot DNA methylation measured by Pyrosequencing. By design, neither population had exposure to in utero tobacco smoke.

Personal, parental, socio-demographic characteristics including maternal smoking during pregnancy and medical history for all CHS subjects were obtained from parent-completed questionnaires at study entry. Asthma and related symptoms at age 6 years were evaluated through these questionnaires and included (a) asthma (defined by a “yes” answer to the question “Has a doctor ever diagnosed this child as having asthma?”); (b) wheeze (defined by a “yes” answer to the question “Has your child’s chest ever sounded wheezy or whistling?”); and (c) wheeze in the previous 12 months; (d) bronchitic symptoms in the previous 12 months (defined by the parent’s report of a daily cough for 3 months in a row, congestion of phlegm other than when accompanied by a cold, or bronchitis).

A subset of 235 Newborn Epigenetics STudy (NEST) subjects was evaluated for the association between methylation at several AXL CpG loci and its mRNA level in cord blood. The NEST is a prospective study of women and their children [44]. It was designed to identify exposures during pregnancy and early life associated with stable epigenetic alterations in infants that may alter chronic disease susceptibility later in life. Women were eligible if they were aged 18 years and older, were pregnant, and spoke English. The catchment area for Duke Maternal Fetal Medicine prenatal care clinic largely includes three contiguous counties in central North Carolina (NC): Durham, Orange, and Wake. Women who met eligibility criteria were either consented and interviewed in-person in consultation rooms during the visit or given the questionnaire to self-administer and mail back to the study office. Smokers were preferentially enrolled to the extent possible, identified through medical records.

DNA methylation

DNA methylation was measured in newborn bloodspots (NBS) that were obtained as part of the routine California Newborn Screening Program from the California Department of Public Health Genetic Disease Screening Program. The NBS were stored by the state of California at − 20 °C. A single complete newborn bloodspot for each requested participant was mailed to us and then stored in our lab at − 80 °C upon receipt. Laboratory personnel performing DNA methylation analysis were blinded to study subject information. DNA was extracted from whole blood cells using the QiaAmp DNA blood kit (Qiagen Inc., Valencia, CA) and stored at − 80 °C. Seven hundred nanograms to 1 μg of genomic DNA from each sample was treated with bisulfite using the EZ-96 DNA Methylation Kit™ (Zymo Research, Irvine, CA, USA), according to the manufacturer’s recommended protocol and eluted in 18 μl. The Infinium HM450 data was compiled for each locus and was expressed as beta (β) values. Minfi package (version 1.16.0) in R was used to process the HM450 array data [45], applying a normal exponential background correction to the raw intensities to reduce array-level background noise followed by dye-bias correction [46]. We then normalized each sample’s methylation values to the same quantiles to address sample-to-sample variability [47]. Seven cord blood cell sub-populations (CD8+ T-lymphocytes, CD4+ T-lymphocytes, natural killer cells, B-lymphocytes, monocytes, granulocytes, and nucleated red blood cells) were estimated using regression calibration approach algorithm described by Bakulski et al. [48, 49]. After preprocessing, CpG loci containing single-nucleotide polymorphisms (SNPs) were removed from analyses. DNA methylation was studied for a total of 12 features on the HM450 array spanning the AXL gene, identified according to their genomic positions (Fig. 1).
Fig. 1

Genomic location of AXL CpG sites and SNPs under investigation. Solid black box: CpG sites in the near-TSS region (cg10564498, cg03247049, cg12722469, cg02372201, cg19848291 and cg14892768); dashed gray box: CpG sites in the gene-body region (cg27579501, cg00360107, cg19270050, cg24901063, and cg26521562); dashed black box: CpG site in the 3′ untranslated region (cg20964856). TSS = transcription start site

For Pyrosequencing assays, three CpG loci (cg10564498, cg12722469, and cg00360107) were selected for replication based on results in the primary population. For NEST subjects, genomic DNA from buffy coat specimens was extracted from umbilical cord blood using Puregene Reagents (Qiagen, Valencia, CA). PCR primers were designed by EpigenDx Inc. (http://www.epigendx.com) to cover the loci of interest, and the specificity of the primer sequences was confirmed using in silico PCR. Five hundred nanograms of genomic DNA extracted from NEST and CHS samples (randomized together) was bisulfite treated using the EZ DNA Methylation Kit™ (Zymo Research, Irvine, CA, USA) and was purified according to the manufacturer’s protocol. Methylation assays (assay ADS8097-FS) were performed by EpigenDx Inc. using the PSQ96HS system (Pyrosequencing, Qiagen) according to standard procedures as described in previous work [50, 51]. The methylation level was determined using QCpG software (Pyrosequencing, Qiagen) and was reported as percent of DNA methylation at each CpG locus. Each experiment included cytosines not part of a CpG dinucleotide as internal controls to evaluate incomplete bisulfite conversion of the input DNA. A series of unmethylated and methylated DNA were included as controls in each assay. Furthermore, PCR bias testing was performed by mixing unmethylated control DNA with in vitro methylated DNA at different ratios (0, 5, 10, 25, 50, 75, and 100%), followed by bisulfite modification, PCR, and Pyrosequencing analysis.

mRNA expression in NEST

Origene’s qStar mRNA detection system (Origene, Rockville, MD) was used in the quantification of AXL mRNA in cord blood in NEST subjects. qPCR primers for the major AXL transcript (#HK228780) and its corresponding copy number standard (#HK201002) were designed by qStar. All measurements of expression were conducted in duplicate in cord blood samples from 235 participants in the NEST cohort. AXL mRNA was isolated from stored PAXgene tubes of cord blood using the PAXgene blood miRNA isolation kit (Qiagen, Valencia, CA). First strand cDNA conversion of mRNA was performed using Origene’s cDNA synthesis kit (#NP100042). qPCR reactions were run with Kappa Sybr Fast qPCR kit (# KK4604; KapaBiosystems, Boston, MA) in the ABI 7900HT thermocycler (Thermofisher). Ten percent repeats were included to evaluate reproducibility.

In silico analyses in publicly available data

To assess the association between DNA methylation and gene expression in lung tissue, we downloaded AXL methylation profiling data of 29 histologically normal tissue samples from cases with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC) from the TCGA dataset [37, 39]. All samples had both methylation profiling (Illumina Infinium HumanMethylation450 Beadchip) and RNA-seq (Illumina HiSeq) data. The mean age was 65.9 years (SD 12.39), and 75.9% of the subjects were male. 51.7% of the subjects were moderate to heavy smokers.

To visualize epigenetics marks and regulatory regions of the AXL gene in relevant tissues and cell types, we used the WashU EpiGenome Browser [52, 53].

Genotyping

Buccal scrapes were collected from CHS subjects beginning in 1998 using standard protocols [54]. A customized package including three buccal kits with instructions on buccal cell collection was sent to each participant. Genomic DNA was isolated from buccal cells using a Puregene™ DNA isolation Kit (Gentra Systems, Minneapolis, MN), and genotyping was performed using the Illumina HumanHap550, HumanHap550-Duo, or Human610-Quad BeadChip microarrays as described previously [55]. Data was phased using SHAPEIT and imputed using IMPUTE2 with 1000 Genomes Phase 1 integrated variant v3 phased reference (April 2012). Genotypes of SNPs in AXL and its surrounding region (1 kb upstream and downstream) were extracted from the CHS genome-wide genotypic data. SNPs with minor allele frequency (MAF) less than 5% or missing in more than 5% of the samples were removed, leaving 90 SNPs for analyses. RS numbers, minor allele frequencies, and genomic locations of all 90 SNPs under investigation were shown in Additional file 1: Table S1. Twenty-eight tagged SNPs were identified with a pair tag r 2 > 0.8 in Haploview using all available CHS samples (N = 3845) and were included in the analyses [56]. In addition, we performed principal component (PC) analysis on the 28 AXL tagged SNPs, and the top 7 PCs, which represented 80% of the total variation cumulatively, were added as covariates in regression models to test confounding effects from gene polymorphisms. Genotype data was available for 207 of the 246 subjects in the primary population and for 728 of the 1038 subjects in the replication study. Admixture was assessed using the program STRUCTURE from a set of ancestral informative markers that were scaled to represent the proportion of African American, Asian, Native American, and white admixture [57].

Statistical analyses

Descriptive analyses were performed to examine the distribution of methylation and subject characteristics. Spearman correlations of methylation between each CpG site were calculated and shown in Additional file 1: Table S2. We took the average of methylation at CpG sites in the same genomic region to represent regional methylation status (Fig. 1). To evaluate the association between AXL methylation and asthma symptoms, we fitted logistic regression models for each outcome and CpG individually, adjusted for child’s age, sex, ethnicity, city of residence at study entry, and plate effect, while history of doctor-diagnosed asthma was additionally adjusted for wheezing and bronchitic outcomes. Additional adjustment for genetic polymorphisms, methylation slide, estimated cord blood cell type proportions, parental education level, allergy history, birth weight, mode of delivery, gestational age, environmental exposures (pets, pests, cockroaches, mildew and carpet), asthma medication use, and admixture did not change the effect estimates by more than 10% and were removed from final models. The results of sensitivity analyses assessing the confounding effects of admixture and top 7 PCs from AXL SNPs were shown in Additional file 1: Table S4 and Table S5, respectively. Confounding effects from each of the 28 AXL tagged SNPs were also tested one by one and were found to be minimal (results not shown). To estimate if the associations between AXL methylation and asthma symptoms were modified by sex, we included an interaction term between sex and methylation in the regression models. Wald tests were used to compute interaction p values.

A similar logistic regression model was used in the replication population to evaluate the association between asthma symptoms and methylation at each of the three CpG sites (cg10564498, cg12722469, and cg00360107) measured by Pyrosequencing, with adjustment for child’s age, sex, ethnicity, city of residence at study recruitment, and asthma history (for wheezing and bronchitic outcomes). Adjusting for methylation plate had no effect on results and was not included in the final model. Effect modification by sex was also assessed.

Linear regression models were used to evaluate the associations between genetic polymorphisms and DNA methylation at AXL CpG sites, adjusting for sex, admixture, and gestational age. All SNPs were coded additively by the number of minor alleles. Logistic regression models were used to assess the association between AXL SNPs and asthma symptoms, adjusting for child’s age, sex, ethnicity, and admixture. We controlled the false discovery rate (FDR) at the 0.05 level using the Benjamini-Hochberg procedure [58], accounting for multiple tests across CpG sites and SNPs in AXL.

All tests assumed a two-sided alternative hypothesis and were conducted using the R programming language, version 3.3.1.

Results

DNA methylation of AXL and risk of asthma symptoms

Demographic characteristics of the primary and replication study populations are shown in Table 1. The primary population had fewer males, more Hispanic subjects, and lower parental education level. There were more subjects having doctor-diagnosed asthma and related symptoms in the replication population by design. Prevalence of asthma was 16% in the primary study population and 28% in the replication population. Participants were 6 years old on average in the primary population and 7 years old in the replication population at the time of asthma symptoms assessment. Many of the 12 CpG loci were significantly correlated (Additional file 1: Table S2), with CpG sites closer in proximity showing stronger correlations.
Table 1

Demographic characteristics of participants

 

Primary study population (N = 246)

Replication population (N = 1038)

p valuea

Male sex, n (%)

98 (39.8)

541 (52.1)

0.0005

Ethnicity, n (%)

  

0.05

 Hispanic

147 (59.8)

531 (51.3)

 

 Non-Hispanic White

72 (29.3)

381 (36.8)

 

 Asian/Black/Other

27 (11.0)

124 (12.0)

 

Ever MD-diagnosed asthma, n (%)

39 (15.9)

295 (28.4)

< 0.0001

Ever wheezing, n (%)

66 (26.8)

455 (43.8)

< 0.0001

Wheezing in the previous 12 months, n (%)

37 (15.0)

250 (24.1)

0.001

Bronchitic symptoms in the previous 12 months, n (%)

40 (16.3)

229 (22.1)

0.04

Parental education, n (%)

  

0.002

 High school or less

89 (36.6)

316 (31.3)

 

 Some college

76 (31.3)

438 (43.5)

 

 Finished college/some graduate school

78 (32.1)

254 (25.2)

 

Age years, mean (sd)

6.4 (0.6)

7.2 (1.3)

< 0.0001

Gestational age days, mean (sd)

277.5 (11.0)

272.7 (11.3)

< 0.0001

aDerived from a Pearson’s Chi-squared test for categorical variables and from an unequal variance 2-sample t test for continuous variables

We first investigated whether average DNA methylation in AXL was associated with childhood asthma symptoms (Table 2). Average methylation of all 12 CpG sites was positively associated with ever wheezing (OR 1.46, 95% CI 1.12–1.91), and the association remained significant after adjusting for multiple testing at these genomic regions (FDR-adjusted p value 0.008). This was mainly driven by methylation status of the gene-body region near the 5′ end, for which a 1% higher methylation level was significantly associated with a 72% higher risk of ever wheezing (OR 1.72, 95% CI 1.30–2.28) and a 109% higher risk of wheezing in the previous 12 months (OR 2.09, 95% CI 1.32–3.30). Moreover, the effects of average AXL methylation on risk of wheezing in the previous 12 months were limited to girls (OR 1.88, 95% CI 1.09–3.24) and not boys (OR = 0.75, 95% CI 0.40–1.39; p int = 0.03). Increased AXL methylation was also associated with higher risk for acute bronchitic symptoms, although effects were not significant.
Table 2

Association between average DNA methylation levels at AXL CpG sites and risk of asthma and related symptoms in childhood in the primary study population (N = 246)

 

Average of near-TSS CpG sitesa

Average of gene-body CpG sitesb

Average of all 12 CpG sitesc

OR

p

OR

p

OR

p

Ever MD-diagnosed asthma

 Overall

1.10

0.16

1.05

0.71

1.17

0.19

 By sex

  Boys

1.17

0.10

1.12

0.58

1.34

0.09

   Girls

1.03

0.79

1.00

1.00

1.02

0.90

 Interaction p value

0.33

0.69

0.28

Ever wheezing

 Overall

1.10

0.19

1.72

0.0002

1.46

0.005

By sex

 Boys

1.06

0.60

2.70

0.001

1.42

0.12

 Girls

1.12

0.20

1.51

0.01

1.48

0.02

 Interaction p value

0.69

0.08

0.88

Wheezing in the previous 12 months

 Overall

0.97

0.79

2.09

0.002

1.26

0.25

 By sex

  Boys

0.73

0.10

1.85

0.11

0.75

0.35

  Girls

1.16

0.31

2.20

0.005

1.88

0.02

  Interaction p value

0.05

0.70

0.03

Bronchitic symptoms in the previous 12 months

 Overall

0.99

0.90

1.23

0.14

1.04

0.74

 By sex

  Boys

0.94

0.51

1.18

0.46

0.92

0.64

  Girls

1.05

0.63

1.26

0.19

1.18

0.35

  Interaction p value

0.41

0.82

0.32

The primary study population (N = 246) is adjusted for child’s age, sex, ethnicity, methylation plate and city of residence at study recruitment; additionally adjusted for ever had MD-diagnosed asthma for wheezing and bronchitic outcomes. Odds ratios are presented for an increase in 1% of DNA methylation level at birth. For all comparisons, the reference group is children not having the corresponding outcome. Significant p values (< 0.05) are marked in italics

TSS transcription start site

aAverage of cg10564498, cg03247049, cg12722469, cg02372201, cg19848291 and cg14892768

bAverage of cg27579501, cg00360107, cg19270050, cg24901063 and cg26521562

cAverage of all 12 CpG sites

We sought to replicate results of four individual CpG loci in the primary analysis (Table 3 and Additional file 1: Table S3). We chose the two loci in the gene body with the most consistent associations (cg00360107 and cg19270050) and the two loci showing significant interactions with sex (cg10564498 and cg12722469) (Additional file 1: Table S3). These loci were evaluated in a separate population of 1038 CHS subjects using Pyrosequencing. A successful PCR primer design could not be found for cg19270050; therefore, only cg00360107, cg10564498, and cg12722469 were evaluated in the replication population (Table 3). Consistent with primary results, methylation at cg00360107 was also negatively associated with asthma-related symptoms, especially the risk of ever wheezing (Table 3; OR 0.90, 95% CI 0.82–0.99). The differences in associations by sex were marginally replicated for cg10564498 (p int = 0.06), but not for cg12722469 (Table 3). In both populations, higher cg10564498 methylation was associated with higher risk for ever wheezing and wheezing in the previous 12 months in girls but lower risk in boys, with similar magnitudes of sex-stratified effects.
Table 3

Association between DNA methylation levels at selected AXL CpG sites and risk of asthma and related symptoms in childhood in the primary study population (N = 246) and replication population (N = 1038)

 

cg10564498

cg12722469

cg00360107

Primary study population

Replication population

Primary study population

Replication population

Primary study population

Replication population

Distance to TSS (bp)

− 455

− 455

− 55

− 55

6826

6826

Mean methylation (%)

26.71

19.60

17.05

11.32

6.68

4.35

 

OR

p

OR

p

OR

p

OR

p

OR

p

OR

p

Ever MD-diagnosed asthma

 Overall

1.02

0.28

1.00

0.86

1.05

0.24

0.99

0.72

0.73

0.04

0.99

0.86

 By sex

  Boys

1.04

0.11

1.01

0.60

1.08

0.24

0.99

0.73

0.72

0.16

1.02

0.76

  Girls

0.99

0.80

0.98

0.39

1.03

0.59

0.99

0.87

0.73

0.13

0.96

0.54

  Interaction p value

0.24

0.32

0.59

0.92

0.94

0.51

Ever wheezing

 Overall

1.03

0.21

0.97

0.13

1.07

0.09

0.95

0.05

0.78

0.07

0.90

0.04

 By sex

  Boys

0.98

0.59

0.92

0.006

1.07

0.38

0.92

0.01

1.05

0.87

0.92

0.20

  Girls

1.07

0.04

1.02

0.44

1.08

0.14

0.99

0.86

0.69

0.04

0.89

0.10

  Interaction p value

0.09

0.01

0.94

0.10

0.20

0.73

Wheezing in the previous 12 months

 Overall

1.02

0.60

0.96

0.03

0.99

0.93

0.93

0.007

0.55

0.04

0.95

0.34

 By sex

  Boys

0.92

0.13

0.92

0.004

0.82

0.10

0.92

0.02

0.74

0.37

0.90

0.12

  Girls

1.15

0.02

1.00

0.94

1.11

0.22

0.95

0.16

0.40

0.03

1.03

0.68

  Interaction p value

0.009

0.06

0.04

0.58

0.23

0.19

Bronchitic symptoms in the previous 12 months

 Overall

0.99

0.66

0.95

0.02

1.06

0.19

0.95

0.05

0.75

0.07

0.93

0.15

 By sex

  Boys

0.96

0.24

0.95

0.04

1.02

0.74

0.94

0.07

0.80

0.31

0.93

0.24

  Girls

1.03

0.46

0.97

0.25

1.09

0.14

0.97

0.37

0.70

0.12

0.93

0.40

  Interaction p value

0.18

0.60

0.47

0.60

0.67

0.96

The primary study population (N = 246) is adjusted for child’s age, sex, ethnicity, methylation plate and city of residence at study recruitment; additionally adjusted for ever had MD-diagnosed asthma for wheezing and bronchitic outcomes

The replication population (N = 1038) is adjusted for child’s age, sex, ethnicity and city of residence at study recruitment; additionally adjusted for ever had MD-diagnosed asthma for wheezing and bronchitic outcomes

Odds ratios are presented for an increase in 1% of DNA methylation level at birth. For all comparisons, the reference group is children not having the corresponding outcome. Significant p values (< 0.05) are marked in italics

TSS transcription start site

AXL methylation and expression in cord blood and lung

Next, we sought to identify whether DNA methylation in AXL was associated with its mRNA expression level in the cord blood and in the lung tissue. To do so, we evaluated the correlations between paired data in NEST and TCGA datasets. While transcripts of AXL mRNA were detectable in the cord blood in NEST, overall expression was very low and we did not find evidence to support a correlation. We then evaluated the correlations with gene expression for AXL methylation using 29 histologically normal lung tissue samples based on HM450 array and RNA sequencing data (Additional file 1: Figure S1). Average methylation of the whole AXL gene as represented by 12 CpG loci, showed negative correlation with expression (r = − 0.42, p value = 0.03). These data, albeit in a population of adult males some of whom have a history of smoking, lend preliminary support to the notion that increased methylation may lead to lower AXL expression level in the lung, a more pathologically relevant tissue for asthma and related phenotypes than evaluation of peripheral blood.

Genetic variants and DNA methylation of AXL

We also tested whether SNPs in AXL and the surrounding regions (1 kb upstream and downstream) were associated with average DNA methylation in the primary study population (Table 4). A few tagging SNPs were significantly associated with average AXL DNA methylation in the near-transcription start site (TSS) region and the whole gene after FDR adjustment. We further tested if SNPs were associated with DNA methylation at individual CpG sites in the replication population and found that AXL DNA methylation was strongly linked to underlying genetic polymorphisms (Table 5). The associations between cg10564498 methylation and tagging SNPs are shown in Additional file 1: Figure S2, suggesting that SNPs were having stronger associations with CpG sites in closer proximity. The SNPs under investigation were tagging SNPs; thus, linkage disequilibrium (LD) was low by design (Additional file 1: Figure S2). None of these SNPs were confounders to the association between AXL methylation and asthma-related symptoms (Additional file 1: Table S5) or statistically significantly associated with asthma and related symptoms in childhood (Additional file 1: Table S6).
Table 4

Association between DNA methylation levels at multiple CpG sites and gene polymorphisms in AXL in the primary study population (N = 207)

  

Average of near-TSS CpG sitesa

Average of gene-body CpG sitesb

Average of all 12 CpG sitesc

RS Number

Location

β

p

Adjusted p

β

p

Adjusted p

β

p

Adjusted p

rs2301235

41724671

0.60

0.14

0.29

0.09

0.64

0.79

0.35

0.11

0.26

rs2569692

41724687

1.55

2.9E−04

0.01

0.24

0.24

0.44

0.84

2.1E−04

0.01

rs28364580

41724885

1.16

0.005

0.05

0.34

0.07

0.22

0.67

0.002

0.03

rs1709122

41725754

1.21

4.2E−04

0.01

0.05

0.76

0.87

0.59

0.001

0.03

rs4803446

41726167

0.66

0.09

0.25

0.22

0.23

0.44

0.41

0.05

0.19

rs2271546

41727197

0.87

0.12

0.28

0.46

0.08

0.24

0.63

0.03

0.16

rs186235601

41728703

1.34

0.05

0.22

0.52

0.11

0.26

0.89

0.02

0.10

rs10409940

41728765

0.78

0.03

0.16

− 0.06

0.73

0.86

0.33

0.09

0.25

rs11083613

41729505

0.03

0.93

0.95

− 0.33

0.06

0.22

− 0.18

0.38

0.56

rs4803447

41731175

1.75

0.009

0.08

0.52

0.10

0.25

1.01

0.004

0.05

rs59423102

41731749

− 0.13

0.81

0.89

0.22

0.38

0.56

0.07

0.81

0.89

rs12462203

41732423

0.18

0.59

0.75

− 0.05

0.74

0.87

− 0.02

0.91

0.95

rs12984621

41732727

− 0.15

0.68

0.82

− 0.14

0.41

0.60

− 0.23

0.25

0.44

rs4802112

41734490

0.89

0.02

0.11

0.04

0.84

0.92

0.34

0.09

0.25

rs4803449

41734666

1.02

0.005

0.05

0.17

0.31

0.50

0.47

0.01

0.10

rs76249126

41737410

− 1.22

0.06

0.22

− 0.28

0.36

0.56

− 0.71

0.04

0.18

rs75955910

41737414

− 0.65

0.32

0.50

− 0.52

0.08

0.24

− 0.54

0.11

0.26

rs7246896

41738212

0.11

0.86

0.92

0.22

0.42

0.60

0.18

0.57

0.74

rs77287588

41739574

− 0.48

0.29

0.49

− 0.22

0.29

0.49

− 0.27

0.27

0.46

rs4802114

41741278

0.36

0.32

0.50

0.07

0.66

0.81

0.14

0.47

0.64

rs4637024

41743454

− 0.76

0.13

0.29

0.02

0.95

0.96

− 0.36

0.17

0.34

rs3786555

41748153

0.70

0.10

0.25

− 0.23

0.24

0.44

0.13

0.55

0.72

rs2304234

41748753

0.51

0.17

0.34

0.00

0.99

0.99

0.14

0.48

0.64

rs55841050

41750550

1.43

0.04

0.19

0.80

0.01

0.10

0.94

0.01

0.10

rs12983027

41753634

0.79

0.07

0.22

− 0.18

0.36

0.56

0.18

0.43

0.61

rs12978323

41756038

0.89

0.03

0.15

0.09

0.63

0.79

0.33

0.13

0.28

rs116056574

41759637

0.10

0.77

0.88

0.12

0.45

0.63

0.02

0.92

0.95

rs35546772

41764758

1.12

0.07

0.22

0.05

0.86

0.92

0.43

0.18

0.34

SNP data was only available for a subset of subjects. SNPs were modeled as ordinal variables (0 = major allele, 1 = heterozygote, and 2 = minor allele), and models were adjusted for child’s sex, admixture, and gestational age. Beta values are showing the percent change in methylation per one unit increase in SNP. Tagging SNPs were defined with a pair tag r 2 > 0.8 in Haploview with all CHS samples (N = 3845). FDR was used to adjust for all tests performed at the 3 methylation averages (28 × 3 tests). Significant FDR-adjusted p values (< 0.05) are marked in italics

TSS transcription start site

aAverage of cg10564498, cg03247049, cg12722469, cg02372201, cg19848291, and cg14892768

bAverage of cg27579501, cg00360107, cg19270050, cg24901063, and cg26521562

cAverage of all 12 CpG sites

Table 5

Association between DNA methylation levels at selected CpG sites and gene polymorphisms in AXL in the replication population (N = 728)

  

cg10564498 (location: 41724653)

cg12722469 (location: 41725053)

cg00360107 (location: 41731934)

RS Number

Location

β

p

Adjusted p

β

p

Adjusted p

β

p

Adjusted p

rs2301235

41724671

0.94

0.002

0.004

0.39

0.11

0.15

− 0.07

0.57

0.63

rs2569692

41724687

1.75

1.2E07

7.7E−07

1.83

8.8E12

3.7E−10

0.37

0.004

0.007

rs28364580

41724885

1.57

5.6E07

2.9E−06

1.66

8.2E11

2.3E−09

0.31

0.01

0.02

rs1709122

41725754

1.80

4.2E12

3.5E−10

1.31

7.9E10

1.7E−08

0.19

0.06

0.08

rs4803446

41726167

1.43

2.0E07

1.2E−06

1.02

6.4E06

2.5E−05

0.21

0.05

0.07

rs2271546

41727197

2.21

2.8E08

2.3E−07

1.93

2.5E09

4.1E−08

0.33

0.03

0.05

rs186235601

41728703

1.91

5.0E04

0.001

2.13

1.9E06

8.5E−06

0.57

0.007

0.01

rs10409940

41728765

1.46

2.1E08

2.0E−07

1.18

3.0E08

2.3E−07

0.18

0.07

0.10

rs11083613

41729505

0.10

0.74

0.77

0.06

0.79

0.79

0.08

0.46

0.53

rs4803447

41731175

0.57

0.22

0.27

1.04

0.006

0.01

0.38

0.03

0.05

rs59423102

41731749

0.57

0.16

0.20

0.58

0.08

0.10

0.06

0.70

0.75

rs12462203

41732423

0.77

0.002

0.004

0.81

5.1E05

1.7E−04

0.47

5.1E07

2.8E−06

rs12984621

41732727

0.55

0.03

0.05

0.58

0.006

0.01

0.36

1.9E04

5.6E−04

rs4802112

41734490

0.87

9.8E04

0.002

0.93

1.5E05

5.6E−05

0.56

2.0E08

2.0E−07

rs4803449

41734666

1.08

2.4E05

8.4E−05

1.11

9.3E08

6.5E−07

0.54

1.7E08

2.0E−07

rs76249126

41737410

− 0.93

0.03

0.05

− 0.49

0.17

0.21

− 0.08

0.62

0.67

rs75955910

41737414

− 1.42

0.003

0.005

− 1.34

5.7E04

0.001

− 0.07

0.72

0.76

rs7246896

41738212

0.79

0.10

0.13

0.22

0.57

0.63

− 0.05

0.78

0.79

rs77287588

41739574

− 0.46

0.17

0.21

− 0.84

0.002

0.005

0.04

0.73

0.77

rs4802114

41741278

0.47

0.08

0.10

0.68

0.001

0.003

0.33

8.9E04

0.002

rs4637024

41743454

− 0.32

0.39

0.45

− 0.04

0.90

0.90

− 0.09

0.54

0.61

rs3786555

41748153

0.85

0.003

0.005

0.79

6.9E04

0.002

0.50

3.2E06

1.4E−05

rs2304234

41748753

0.37

0.13

0.16

0.66

9.6E04

0.002

0.46

7.2E07

3.6E−06

rs55841050

41750550

0.58

0.23

0.28

1.08

0.007

0.01

0.57

0.002

0.005

rs12983027

41753634

1.03

0.001

0.002

1.12

1.3E05

5.0E−05

0.48

5.2E05

1.7E−04

rs12978323

41756038

0.99

2.1E04

5.8E−04

1.06

1.2E06

5.6E−06

0.59

6.4E09

8.9E−08

rs116056574

41759637

0.77

0.003

0.005

0.79

1.3E04

3.9E−04

0.35

3.5E04

9.2E−04

rs35546772

41764758

1.31

0.006

0.01

1.45

2.1E04

5.8E−04

0.69

1.4E04

4.2E−04

SNP data was only available for a subset of subjects. SNPs were modeled as ordinal variables (0 = major allele, 1 = heterozygote, and 2 = minor allele) and models were adjusted for child’s sex, admixture and gestational age. Beta values are showing the percent changes in methylation at each CpG site per one unit increase in SNP. Tagging SNPs were defined with a pair tag r 2 > 0.8 in Haploview with all CHS samples (N = 3845). FDR was used to adjust for all tests performed at the 3 CpG sites (28 × 3 tests) in each study population. Significant FDR-adjusted p values (< 0.05) are marked in italics

Lastly, we used the WashU EpiGenome Browser to conduct an in silico investigation of epigenetic regulatory traits in tissues and cell types relevant to our fetal programming hypothesis and potential involvement of immune cells. We contrasted histone marks, regulatory regions, and transcription factor binding sites in the IMR90 fetal lung cell line, adult lung fibroblast cells, and adult CD4 naïve primary cells (Fig. 2). The CpG loci evaluated in the AXL gene body region are located adjacent to enhancers in both fetal and adult lung fibroblast cells, and adult CD4 naïve primary cells (light green and yellow in chromHMM tracks). The gene-body region under investigation also contains transcription factor binding sites, indicated by peaks from ChIP-Seq input, and marks for active transcription (green in chromHMM tracks). There are more transcription factor binding sites and active histone marks associated with this region in fetal lung than in adult lung and blood, suggesting the active transcription and regulation of AXL in fetal lung. Additionally, the combination of H3K4me1 and H3K27ac, which marks active enhancers [59, 60], was observed in the same genomic position (CpG sites cg00360107-cg26521562) in both fetal and adult lung fibroblast cells, suggesting this region may act as an enhancer throughout life. The epigenetic marks in CD4 naïve primary cells were fewer, although the pattern of H3K4me1 in particular was generally consistent with lung cells and suggests that these epigenetic marks observed in blood may reflect patterns in fetal and adult lung.
Fig. 2

Illustration of epigenetic marks in AXL gene-body region (yellow box) and CpG sites (red and green bars) in multiple cell lines. This region contains a putative enhancer in IMR90 fetal lung fibroblast cells (light green in chromHMM track) and adult CD4 Naïve Primary cells (yellow in chromHMM track) and is adjacent to a putative enhancer in NHLF adult lung fibroblast cells (light green in chromHMM track). There are also transcription factor binding sites located within this region in all three cell lines from ChIP-Seq input. This region is enriched with epigenetic marks for poised enhancer (indicated by H3K4me1), active enhancer (indicated by H3K27ac), and active transcription (green in chromHMM track). CpG site 1: cg27579501; CpG site 2: cg00360107; CpG site 3: cg19270050; CpG site 4: cg24901063; CpG site 5: cg26521562. IMR90: fetal lung fibroblast cell; NHLF: normal adult lung fibroblast cell; CD4 Naïve Primary cells: obtained from adult blood

Discussion

Our results show that average methylation in AXL at birth was associated with higher risk for asthma-related phenotypes in childhood, especially wheezing. The effects of average AXL methylation on wheezing symptoms were magnified in girls compared to boys. One CpG locus, cg00360107, which was inversely correlated with its nearest neighbors, was associated with marginally significantly reduced wheezing risk, and the result was replicated by Pyrosequencing in a separate population of 1038 CHS subjects.

The AXL CpG region showing the strongest association with wheeze is located in a region of the gene body harboring histone marks for active transcription and enhancers in fetal and adult lung cells and CD4 immune cells. This region was predicted from the UCSC genome browser [61, 62] to have binding sites for the transcription factor IRF7 (interferon regulatory factor 7), which is involved in transcriptional activation of virus-inducible cellular genes, the transcriptional activator ISGF3 (interferon-stimulated gene factor 3), and AP-1 (activator protein 1) that regulates gene expression in response to a variety of pathogenic stimuli [6365]. Alterations in CpG methylation levels in this region during fetal development may modify the transcriptional activity of AXL and the binding of transcription factors in response to stimuli, particularly in the lung.

AXL has been well characterized in the pathogenesis of numerous cancers and cardiovascular events [6671] but is rarely addressed in asthma. Key elements in asthma pathogenesis include the accumulation of polarized CD4+ T helper (TH)2 cells and exaggeration of pro-inflammatory TH2 cells over the infection-fighting TH1 cells in the T cell repertoire, accompanied by an upregulation of the TH2 inflammatory cytokines [72]. In the key antigen-presenting cells including dendritic cells (DCs) and macrophages, AXL and other TAM proteins function to inhibit production of pro-inflammatory cytokines that are induced by Toll-like receptors (TLRs), while activating the inflammation-inhibitory genes encoding the suppressor of cytokine signaling (SOCS) 1 and 3 [29, 73]. Taken together, these concepts illuminate a carefully regulated feedback control process that switches a pro-inflammatory signaling complex to one that inhibits inflammation. Many of the genes inhibited or activated by AXL in this process are involved in asthma pathogenesis.

This information suggests that AXL signaling may be associated with the suppression of inflammatory responses and lower risk for asthma and related phenotypes. In our study, we observed average DNA methylation at AXL was positively associated with wheezing symptoms. The association was stronger in girls, where a 1% increase in average methylation was associated with an 88% increase in risk of wheezing symptoms while no effect was seen in boys. Previous research has reported sex-specific associations between DNA methylation and various health outcomes including autoimmune diseases [7476], although the mechanisms behind these are unclear. The association between AXL methylation and higher risk for wheezing symptoms was observed as early as the first few days of life, indicating that methylation status of AXL may be reflecting epigenetic changes programmed in utero that make the child more susceptible to symptoms in later childhood. Higher average AXL methylation was also associated with higher risk for childhood bronchitic symptoms, which are suggestive of chronic symptoms that may follow an illness or acute exacerbation of asthma, or chronic inflammation in the airway. However, due to the small sample size in the primary population, we were not able to detect significant associations.

Underlying genetic variants are known to influence epigenetic variation. Therefore, we evaluated SNPs in AXL to understand whether genetic variation influenced DNA methylation directly, to address potential confounding of observed associations between DNA methylation and asthma and wheeze risk, and to test whether SNPs independently predicted asthma and wheeze risk. Genome-wide studies have revealed quantitative trait loci (QTLs) for DNA methylation, known as methylation QTLs (metQTLs) in multiple human tissues [7783]. MetQTLs are usually located in intergenic or intragenic regions and affect DNA methylation levels at nearby CpG sites [84]. In one study of metQTLs in human lung, the authors identified 34,304 cis- and 585 trans-metQTLs, which were enriched in CTCF-binding sites, DNaseI hypersensitivity regions, and histone marks [81]. In this study, we found that average DNA methylation in AXL was highly correlated with genetic variation in nearby sites acting in cis.

The above evidence implies that alterations in the methylation landscape of AXL may be attributable partially to genetic polymorphisms in nearby regions. Most of the SNPs under investigation in this paper and the reported methylation-associated SNPs were located in gene-body intragenic regions [84], suggesting an interaction between gene-body methylation and proximal genetic variants. However, none of the SNPs under investigation were implicated in asthma and related symptoms in childhood in this study and none of the SNPs acted as confounders of the observed associated between DNA methylation and asthma-related symptoms.

One of the strengths of this study is the temporal separation of DNA methylation assessment (at birth) and respiratory health outcomes assessment (at 6-7 years of age), which enables the investigation of fetal factors associated with asthma predisposition while overcoming the concern for reverse causation. However, several limitations should also be noted. First, DNA methylation of AXL was measured from newborn blood which is a mixed cell population. Since AXL is expressed at very low levels in blood [85, 86], it may not be the ideal tissue to study AXL gene activity. Nonetheless, methylation levels systemically altered during fetal development ought to be reflected across multiple tissues, and therefore, evaluating methylation in newborn blood can serve as a useful biomarker of early life exposure relevant to the target tissue. Indeed, the lack of AXL expression in cord blood, but its presence in lung tissue, coupled with our in vitro assessment of the epigenetic landscape in CD4 naïve primary cells compared to lung cells supports this notion for AXL. Future evidence from human- or animal-based designs is warranted to demonstrate the consistent pattern of AXL methylation across somatic tissues and in which tissues the methylation correlates with expression. Second, characterization of asthma and related phenotypes was based on parent-completed questionnaires, potentially introducing recall bias or misclassification bias. Lastly, although we made every effort to control for potential confounders, we cannot exclude the possibility of residual confounding by some unknown factors associated with AXL DNA methylation levels and asthma-related phenotypes.

Conclusions

In conclusion, AXL DNA methylation at birth, which was strongly linked to underlying genetic variation, was also associated with higher risk for asthma-related phenotypes in early childhood. The effects on wheezing were stronger in girls than in boys.

Abbreviations

CHS: 

Children’s Health Study

CI: 

Confidence interval

CpG: 

Cytosine-guanine dinucleotide sites

DNA: 

Deoxyribonucleic acid

FDR: 

False discovery rate

HM450: 

HumanMethylation450 BeadChip

LD: 

Linkage disequilibrium

MAF: 

Minor allele frequency

metQTL: 

Methylation quantitative trait loci

NBS: 

Newborn bloodspot

NEST: 

Newborn Epigenetic STudy

NHLF: 

Normal human lung fibroblasts

OR: 

Odds ratio

PC: 

Principal component

PCR: 

Polymerase chain reaction

SNP: 

Single-nucleotide polymorphism

TCGA: 

The Cancer Genome Atlas

TH1: 

Type 1 T helper;

TH2: 

Type 2 T helper

TSS: 

Transcription start site

Declarations

Acknowledgements

We would like to express our sincere gratitude to Steve Graham and Robin Cooley at the California Biobank Program and Genetic Disease Screening Program within the California Department of Public Health for their assistance and advice regarding newborn bloodspots. The biospecimens and/or data used in this study were obtained from the California Biobank Program, (SIS request number(s) 479)” Section 6555(b), 17 CCR. The California Department of Public Health is not responsible for the results or conclusions drawn by the authors of this publication.

Funding

This research was supported by NIEHS grants 4R01ES022216, K01ES017801, and P30ES007048.

Availability of data and materials

Please contact author for data requests.

Authors’ contributions

CB conceived and designed the study. RM, SKM, and CH aided in the design of the study and acquired data for the Newborn Epigeneic STudy (NEST). CB, JM, KS, LD, and FG supervised the project. LG analyzed the data and wrote the manuscript. All authors edited and approved the manuscript.

Ethics approval and consent to participate

The study was approved by the Institutional Review Board of the University of Southern California. The NEST study protocol was approved by the Duke University Institutional Review Board.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Preventive Medicine, USC Keck School of Medicine
(2)
Department of Biological Sciences, Center for Human Health and the Environment, North Carolina State University
(3)
Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Duke University School of Medicine

References

  1. Moorman JE, Rudd RA, Johnson CA, King M, Minor P, Bailey C, et al. National surveillance for asthma—United States, 1980–2004. MMWR Surveill Summ. 2007;56(8):1–54.PubMedGoogle Scholar
  2. Mannino DM, Homa DM, Akinbami LJ, Moorman JE, Gwynn C, Redd SC. Surveillance for asthma—United States, 1980–1999. MMWR Surveill Summ. 2002;51(1):1–13.Google Scholar
  3. Lim RH, Kobzik L, Dahl M. Risk for asthma in offspring of asthmatic mothers versus fathers: a meta-analysis. PLoS One. 2010;5(4):e10134.PubMedPubMed CentralGoogle Scholar
  4. Duijts L. Fetal and infant origins of asthma. Eur J Epidemiol. 2012;27(1):5–14.PubMedPubMed CentralGoogle Scholar
  5. Duijts L, Jaddoe VW, van der Valk RJ, Henderson JA, Hofman A, Raat H, et al. Fetal exposure to maternal and paternal smoking and the risks of wheezing in preschool children: the Generation R Study. Chest. 2012;141(4):876–85.PubMedGoogle Scholar
  6. McConnell R, Berhane K, Yao L, Jerrett M, Lurmann F, Gilliland F, et al. Traffic, susceptibility, and childhood asthma. Environ Health Perspect. 2006;114(5):766–72.PubMedPubMed CentralGoogle Scholar
  7. Islam T, Berhane K, McConnell R, Gauderman WJ, Avol E, Peters JM, et al. Glutathione-S-transferase (GST) P1, GSTM1, exercise, ozone and asthma incidence in school children. Thorax. 2009;64(3):197–202.PubMedGoogle Scholar
  8. Pijnenburg MW, De Jongste JC. Exhaled nitric oxide in childhood asthma: a review. Clin Exp Allergy. 2008;38(2):246–59.PubMedGoogle Scholar
  9. Holgate ST. Epithelium dysfunction in asthma. J Allergy Clin Immunol. 2007;120(6):1233–44. quiz 45-6PubMedGoogle Scholar
  10. Lambrecht BN, Hammad H. The airway epithelium in asthma. Nat Med. 2012;18(5):684–92.PubMedGoogle Scholar
  11. Xiao C, Puddicombe SM, Field S, Haywood J, Broughton-Head V, Puxeddu I, et al. Defective epithelial barrier function in asthma. J Allergy Clin Immunol. 2011;128(3):549–56. e1-12PubMedGoogle Scholar
  12. Li YF, Langholz B, Salam MT, Gilliland FD. Maternal and grandmaternal smoking patterns are associated with early childhood asthma. Chest. 2005;127(4):1232–41.PubMedGoogle Scholar
  13. Gauderman WJ, Avol E, Lurmann F, Kuenzli N, Gilliland F, Peters J, et al. Childhood asthma and exposure to traffic and nitrogen dioxide. Epidemiology. 2005;16(6):737–43.PubMedGoogle Scholar
  14. Jerrett M, Shankardass K, Berhane K, Gauderman WJ, Kunzli N, Avol E, et al. Traffic-related air pollution and asthma onset in children: a prospective cohort study with individual exposure measurement. Environ Health Perspect. 2008;116(10):1433–8.PubMedPubMed CentralGoogle Scholar
  15. Berhane K, Chang CC, McConnell R, Gauderman WJ, Avol E, Rapapport E, et al. Association of changes in air quality with bronchitic symptoms in children in California, 1993–2012. JAMA. 2016;315(14):1491–501.PubMedGoogle Scholar
  16. Cook DG, Strachan DP. Health effects of passive smoking. 3. Parental smoking and prevalence of respiratory symptoms and asthma in school age children. Thorax. 1997;52(12):1081–94.PubMedPubMed CentralGoogle Scholar
  17. Jaakkola JJ, Gissler M. Maternal smoking in pregnancy, fetal development, and childhood asthma. Am J Public Health. 2004;94(1):136–40.PubMedPubMed CentralGoogle Scholar
  18. Jung KH, Lovinsky-Desir S, Yan B, Torrone D, Lawrence J, Jezioro JR, et al. Effect of personal exposure to black carbon on changes in allergic asthma gene methylation measured 5 days later in urban children: importance of allergic sensitization. Clin Epigenetics. 2017;9:61.PubMedPubMed CentralGoogle Scholar
  19. Martino D, Prescott S. Epigenetics and prenatal influences on asthma and allergic airways disease. Chest. 2011;139(3):640–7.PubMedGoogle Scholar
  20. Ozanne SE, Constancia M. Mechanisms of disease: the developmental origins of disease and the role of the epigenotype. Nat Clin Pract Endocrinol Metab. 2007;3(7):539–46.PubMedGoogle Scholar
  21. Perera F, Tang WY, Herbstman J, Tang D, Levin L, Miller R, et al. Relation of DNA methylation of 5′-CpG island of ACSL3 to transplacental exposure to airborne polycyclic aromatic hydrocarbons and childhood asthma. PLoS One. 2009;4(2):e4488.PubMedPubMed CentralGoogle Scholar
  22. Wang IJ, Karmaus WJ, Chen SL, Holloway JW, Ewart S. Effects of phthalate exposure on asthma may be mediated through alterations in DNA methylation. Clin Epigenetics. 2015;7:27.PubMedPubMed CentralGoogle Scholar
  23. Yang IV, Pedersen BS, Liu A, O'Connor GT, Teach SJ, Kattan M, et al. DNA methylation and childhood asthma in the inner city. J Allergy Clin Immunol. 2015;136(1):69–80.PubMedPubMed CentralGoogle Scholar
  24. Morales E, Bustamante M, Vilahur N, Escaramis G, Montfort M, de Cid R, et al. DNA hypomethylation at ALOX12 is associated with persistent wheezing in childhood. Am J Respir Crit Care Med. 2012;185(9):937–43.PubMedGoogle Scholar
  25. Reik W. Stability and flexibility of epigenetic gene regulation in mammalian development. Nature. 2007;447(7143):425–32.PubMedGoogle Scholar
  26. Breton CV, Byun HM, Wenten M, Pan F, Yang A, Gilliland FD. Prenatal tobacco smoke exposure affects global and gene-specific DNA methylation. Am J Respir Crit Care Med. 2009;180(5):462–7.PubMedPubMed CentralGoogle Scholar
  27. Breton CV, Salam MT, Gilliland FD. Heritability and role for the environment in DNA methylation in AXL receptor tyrosine kinase. Epigenetics. 2011;6(7):895–8.PubMedPubMed CentralGoogle Scholar
  28. Lemke G, Rothlin CV. Immunobiology of the TAM receptors. Nat Rev Immunol. 2008;8(5):327–36.PubMedPubMed CentralGoogle Scholar
  29. Rothlin CV, Ghosh S, Zuniga EI, Oldstone MB, Lemke GTAM. Receptors are pleiotropic inhibitors of the innate immune response. Cell. 2007;131(6):1124–36.PubMedGoogle Scholar
  30. Sharif MN, Sosic D, Rothlin CV, Kelly E, Lemke G, Olson EN, et al. Twist mediates suppression of inflammation by type I IFNs and Axl. J Exp Med. 2006;203(8):1891–901.PubMedPubMed CentralGoogle Scholar
  31. Stitt TN, Conn G, Gore M, Lai C, Bruno J, Radziejewski C, et al. The anticoagulation factor protein S and its relative, Gas6, are ligands for the Tyro 3/Axl family of receptor tyrosine kinases. Cell. 1995;80(4):661–70.PubMedGoogle Scholar
  32. Aoki T, Matsumoto Y, Hirata K, Ochiai K, Okada M, Ichikawa K, et al. Expression profiling of genes related to asthma exacerbations. Clin Exp Allergy. 2009;39(2):213–21.PubMedGoogle Scholar
  33. Hsiao FC, Lin YF, Hsieh PS, Chu NF, Chen YD, Shieh YS, et al. Effect of GAS6 and AXL gene polymorphisms on adiposity, systemic inflammation, and insulin resistance in adolescents. Int J Endocrinol. 2014;674069:2014.Google Scholar
  34. Recarte-Pelz P, Tassies D, Espinosa G, Hurtado B, Sala N, Cervera R, et al. Vitamin K-dependent proteins GAS6 and Protein S and TAM receptors in patients of systemic lupus erythematosus: correlation with common genetic variants and disease activity. Arthritis Res Ther. 2013;15(2):R41.PubMedPubMed CentralGoogle Scholar
  35. Kasowski M, Grubert F, Heffelfinger C, Hariharan M, Asabere A, Waszak SM, et al. Variation in transcription factor binding among humans. Science. 2010;328(5975):232–5.PubMedPubMed CentralGoogle Scholar
  36. McDaniell R, Lee BK, Song L, Liu Z, Boyle AP, Erdos MR, et al. Heritable individual-specific and allele-specific chromatin signatures in humans. Science. 2010;328(5975):235–9.PubMedPubMed CentralGoogle Scholar
  37. Cancer Genome Atlas Research. N. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455(7216):1061–8.Google Scholar
  38. Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR, Kibbe WA, et al. Toward a shared vision for cancer genomic data. N Engl J Med. 2016;375(12):1109–12.PubMedGoogle Scholar
  39. The Cancer Genome Atlas. https://cancergenome.nih.gov/ (2009). Accessed 20 Mar 2017.
  40. Peters JM, Avol E, Gauderman WJ, Linn WS, Navidi W, London SJ, et al. A study of twelve Southern California communities with differing levels and types of air pollution. II. Effects on pulmonary function. Am J Respir Crit Care Med. 1999;159(3):768–75.PubMedGoogle Scholar
  41. Peters JM, Avol E, Navidi W, London SJ, Gauderman WJ, Lurmann F, et al. A study of twelve Southern California communities with differing levels and types of air pollution. I. Prevalence of respiratory morbidity. Am J Respir Crit Care Med. 1999;159(3):760–7.PubMedGoogle Scholar
  42. Gauderman WJ, Gilliland GF, Vora H, Avol E, Stram D, McConnell R, et al. Association between air pollution and lung function growth in southern California children: results from a second cohort. Am J Respir Crit Care Med. 2002;166(1):76–84.PubMedGoogle Scholar
  43. Dratva J, Breton CV, Hodis HN, Mack WJ, Salam MT, Zemp E, et al. Birth weight and carotid artery intima-media thickness. J Pediatr. 2013;162(5):906–11. e1-2PubMedGoogle Scholar
  44. Hoyo C, Murtha AP, Schildkraut JM, Forman MR, Calingaert B, Demark-Wahnefried W, et al. Folic acid supplementation before and during pregnancy in the Newborn Epigenetics STudy (NEST). BMC Public Health. 2011;11(1):46.PubMedPubMed CentralGoogle Scholar
  45. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363–9.PubMedPubMed CentralGoogle Scholar
  46. Triche TJ, Weisenberger DJ, Van Den Berg D, Laird PW, Siegmund KD. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 2013; in pressGoogle Scholar
  47. Bolstad BM, Irizarry RA, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003;19(2):185–93.PubMedGoogle Scholar
  48. Jaffe AE, Irizarry RA. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol. 2014;15(2):R31.PubMedPubMed CentralGoogle Scholar
  49. Bakulski KM, Feinberg JI, Andrews SV, Yang J, Brown S, LM S, et al. DNA methylation of cord blood cell types: applications for mixed cell birth studies. Epigenetics. 2016;11(5):354–62.PubMedPubMed CentralGoogle Scholar
  50. Tost J, Dunker J, Gut IG. Analysis and quantification of multiple methylation variable positions in CpG islands by Pyrosequencing. BioTechniques. 2003;35(1):152–6.PubMedGoogle Scholar
  51. Brakensiek K, Wingen LU, Langer F, Kreipe H, Lehmann U. Quantitative high-resolution CpG island mapping with Pyrosequencing reveals disease-specific methylation patterns of the CDKN2B gene in myelodysplastic syndrome and myeloid leukemia. Clin Chem. 2007;53(1):17–23.PubMedGoogle Scholar
  52. Zhou X, Maricque B, Xie M, Li D, Sundaram V, Martin EA, et al. The Human Epigenome Browser at Washington University. Nat Methods. 2011;8(12):989–90.PubMedPubMed CentralGoogle Scholar
  53. Human Epigenome Browser at Washington University in St. Louis. http://epigenomegateway.wustl.edu/ (2010). Accessed 25 Feb 2017.
  54. Li YF, Gauderman WJ, Conti DV, Lin PC, Avol E, Gilliland FD. Glutathione S-transferase P1, maternal smoking, and asthma in children: a haplotype-based analysis. Environ Health Perspect 2008; 116(3):409-415.Google Scholar
  55. Torgerson DG, Ampleford EJ, Chiu GY, Gauderman WJ, Gignoux CR, Graves PE, et al. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet. 2011;43(9):887–92.PubMedPubMed CentralGoogle Scholar
  56. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21(2):263–5.PubMedGoogle Scholar
  57. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945–59.PubMedPubMed CentralGoogle Scholar
  58. Hochberg Y, Benjamini Y. More powerful procedures for multiple significance testing. Stat Med. 1990;9(7):811–8.PubMedGoogle Scholar
  59. Zentner GE, Tesar PJ, Scacheri PC. Epigenetic signatures distinguish multiple classes of enhancers with distinct cellular functions. Genome Res. 2011;21(8):1273–83.PubMedPubMed CentralGoogle Scholar
  60. Creyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ, et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc Natl Acad Sci U S A. 2010;107(50):21931–6.PubMedPubMed CentralGoogle Scholar
  61. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The human genome browser at UCSC. Genome Res. 2002;12(6):996–1006.PubMedPubMed CentralGoogle Scholar
  62. The UCSC Genome Browser. http://genome.ucsc.edu/cite.html (2000). Accessed 10 Feb 2017.
  63. Matsumoto M, Tanaka N, Harada H, Kimura T, Yokochi T, Kitagawa M, et al. Activation of the transcription factor ISGF3 by interferon-gamma. Biol Chem. 1999;380(6):699–703.PubMedGoogle Scholar
  64. Hess J, Angel P, Schorpp-Kistner M. AP-1 subunits: quarrel and harmony among siblings. J Cell Sci. 2004;117(Pt 25):5965–73.PubMedGoogle Scholar
  65. Ning S, Pagano JS, Barber GN. IRF7: activation, regulation, modification and function. Genes Immun. 2011;12(6):399–414.PubMedPubMed CentralGoogle Scholar
  66. Janssen JW, Schulz AS, Steenvoorden AC, Schmidberger M, Strehl S, Ambros PF, et al. A novel putative tyrosine kinase receptor with oncogenic potential. Oncogene. 1991;6(11):2113–20.PubMedGoogle Scholar
  67. Holland SJ, Pan A, Franci C, Hu Y, Chang B, Li W, et al. R428, a selective small molecule inhibitor of Axl kinase, blocks tumor spread and prolongs survival in models of metastatic breast cancer. Cancer Res. 2010;70(4):1544–54.PubMedGoogle Scholar
  68. Hong CC, Lay JD, Huang JS, Cheng AL, Tang JL, Lin MT, et al. Receptor tyrosine kinase AXL is induced by chemotherapy drugs and overexpression of AXL confers drug resistance in acute myeloid leukemia. Cancer Lett. 2008;268(2):314–24.PubMedGoogle Scholar
  69. Melaragno MG, Wuthrich DA, Poppa V, Gill D, Lindner V, Berk BC, et al. Increased expression of Axl tyrosine kinase after vascular injury and regulation by G protein-coupled receptor agonists in rats. Circ Res. 1998;83(7):697–704.PubMedGoogle Scholar
  70. Healy AM, Schwartz JJ, Zhu X, Herrick BE, Varnum B, Farber HW. Gas 6 promotes Axl-mediated survival in pulmonary endothelial cells. Am J Physiol Lung Cell Mol Physiol. 2001;280(6):L1273–81.PubMedGoogle Scholar
  71. Melaragno MG, Cavet ME, Yan C, Tai LK, Jin ZG, Haendeler J, et al. Gas6 inhibits apoptosis in vascular smooth muscle: role of Axl kinase and Akt. J Mol Cell Cardiol. 2004;37(4):881–7.PubMedGoogle Scholar
  72. Ray A, Khare A, Krishnamoorthy N, Qi Z, Ray P. Regulatory T cells in many flavors control asthma. Mucosal Immunol. 2010;3(3):216–29.PubMedPubMed CentralGoogle Scholar
  73. Yoshimura A, Naka T, Kubo M. SOCS proteins, cytokine signalling and immune regulation. Nat Rev Immunol. 2007;7(6):454–65.PubMedGoogle Scholar
  74. Lesseur C, Armstrong DA, Murphy MA, Appleton AA, Koestler DC, Paquette AG, et al. Sex-specific associations between placental leptin promoter DNA methylation and infant neurobehavior. Psychoneuroendocrinology. 2014;40:1–9.PubMedGoogle Scholar
  75. Sawalha AH, Wang L, Nadig A, Somers EC, McCune WJ, Hughes T, et al. Sex-specific differences in the relationship between genetic susceptibility, T cell DNA demethylation and lupus flare severity. J Autoimmun. 2012;38(2-3):J216–22.PubMedPubMed CentralGoogle Scholar
  76. Shimabukuro M, Sasaki T, Imamura A, Tsujita T, Fuke C, Umekage T, et al. Global hypomethylation of peripheral leukocyte DNA in male patients with schizophrenia: a potential link between epigenetics and schizophrenia. J Psychiatr Res. 2007;41(12):1042–6.PubMedGoogle Scholar
  77. Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai SL, et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 2010;6(5):e1000952.PubMedPubMed CentralGoogle Scholar
  78. Davies MN, Volta M, Pidsley R, Lunnon K, Dixit A, Lovestone S, et al. Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biol. 2012;13(6):R43.PubMedPubMed CentralGoogle Scholar
  79. Grundberg E, Meduri E, Sandling JK, Hedman AK, Keildson S, Buil A, et al. Global analysis of DNA methylation variation in adipose tissue from twins reveals links to disease-associated variants in distal regulatory elements. Am J Hum Genet. 2013;93(5):876–90.PubMedPubMed CentralGoogle Scholar
  80. van Eijk KR, de Jong S, Boks MP, Langeveld T, Colas F, Veldink JH, et al. Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects. BMC Genomics. 2012;13:636.PubMedPubMed CentralGoogle Scholar
  81. Shi J, Marconett CN, Duan J, Hyland PL, Li P, Wang Z, et al. Characterizing the genetic basis of methylome diversity in histologically normal human lung tissue. Nat Commun. 2014;5:3365.PubMedPubMed CentralGoogle Scholar
  82. Wagner JR, Busche S, Ge B, Kwan T, Pastinen T, Blanchette M. The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts. Genome Biol. 2014;15(2):R37.PubMedPubMed CentralGoogle Scholar
  83. Gutierrez-Arcelus M, Lappalainen T, Montgomery SB, Buil A, Ongen H, Yurovsky A, et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. elife. 2013;2:e00523.PubMedPubMed CentralGoogle Scholar
  84. Voisin S, Almen MS, Zheleznyakova GY, Lundberg L, Zarei S, Castillo S, et al. Many obesity-associated SNPs strongly associate with DNA methylation changes at proximal promoters and enhancers. Genome Med. 2015;7:103.PubMedPubMed CentralGoogle Scholar
  85. Carithers LJ, Moore HM. The Genotype-Tissue Expression (GTEx) Project. Biopreserv Biobank. 2015;13(5):307–8.PubMedPubMed CentralGoogle Scholar
  86. The GTEx Portal. https://gtexportal.org/home/ (2010). Accessed 10 Feb 2017.

Copyright

© The Author(s). 2017