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Erschienen in: Respiratory Research 1/2019

Open Access 01.12.2019 | Research

DNA methylation is associated with lung function in never smokers

verfasst von: Maaike de Vries, Ivana Nedeljkovic, Diana A. van der Plaat, Alexandra Zhernakova, Lies Lahousse, Guy G. Brusselle, Najaf Amin, Cornelia M. van Duijn, Judith M. Vonk, H. Marike Boezen, BIOS Consortium

Erschienen in: Respiratory Research | Ausgabe 1/2019

Abstract

Background

Active smoking is the main risk factor for COPD. Here, epigenetic mechanisms may play a role, since cigarette smoking is associated with differential DNA methylation in whole blood. So far, it is unclear whether epigenetics also play a role in subjects with COPD who never smoked. Therefore, we aimed to identify differential DNA methylation associated with lung function in never smokers.

Methods

We determined epigenome-wide DNA methylation levels of 396,243 CpG-sites (Illumina 450 K) in blood of never smokers in four independent cohorts, LifeLines COPD&C (N = 903), LifeLines DEEP (N = 166), Rotterdam Study (RS)-III (N = 150) and RS-BIOS (N = 206). We meta-analyzed the cohort-specific methylation results to identify differentially methylated CpG-sites with FEV1/FVC. Expression Quantitative Trait Methylation (eQTM) analysis was performed in the Biobank-based Integrative Omics Studies (BIOS).

Results

A total of 36 CpG-sites were associated with FEV1/FVC in never smokers at p-value< 0.0001, but the meta-analysis did not reveal any epigenome-wide significant CpG-sites. Of interest, 35 of these 36 CpG-sites have not been associated with lung function before in studies including subjects irrespective of smoking history. Among the top hits were cg10012512, cg02885771, annotated to the gene LTV1 Ribosome Biogenesis factor (LTV1), and cg25105536, annotated to Kelch Like Family Member 32 (KLHL32). Moreover, a total of 11 eQTMS were identified.

Conclusions

With the identification of 35 CpG-sites that are unique for never smokers, our study shows that DNA methylation is also associated with FEV1/FVC in subjects that never smoked and therefore not merely related to smoking.
Hinweise

Supplementary information

Supplementary information accompanies this paper at https://​doi.​org/​10.​1186/​s12931-019-1222-8.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Abkürzungen
ATS
American Thoracic Society
BIOS
Biobank-based Integrative Omics Studies
COPD
Chronic Obstructive Pulmonary Disease
CpG
Cytosine-phosphate-Guanine
DNA
Deoxyribonucleic acid
eQTM
Expression Quantitative Trait Methylation
ERS
European Respiratory Society
ETS
Environmental tobacco smoke
EWAS
Epigenome-wide association study
FEV1
Forced expiratory volume in 1 s
FVC
Forced vital capacity
GTEx
Genotype tissue expression

Background

Chronic Obstructive Pulmonary Disease (COPD) is a progressive inflammatory lung disease characterized by persistent airway obstruction that causes severe respiratory symptoms and poor quality of life [1]. Although smoking is generally considered the main environmental risk factor, estimations are that 25–45% of patients with COPD have never smoked [2]. Despite extensive research, the etiology of COPD remains incompletely understood. It is known that the development of this complex heterogeneous disease is influenced by both genetic and environmental factors, as well as their interactions [36]. As interface between the inherited genome and environmental exposures, an important role has been postulated for the epigenome [7]. The epigenome includes multiple epigenetic mechanisms that affect gene expression without modifying the DNA sequence. These epigenetic mechanisms are highly dynamic and respond to environmental exposures, ageing and diseases [8]. One such epigenetic mechanism is DNA methylation, which involves the binding of a methyl group to a cytosine base located adjacent to a guanine base. Methylation of these so called CpG-sites in regulatory regions of the DNA generally result in decreased expression of a particular gene [9].
So far, only a few studies have investigated the association between DNA methylation in peripheral blood and COPD or lung function using an epigenome-wide hypothesis free approach [1017]. Although findings across the studies are not consistent, there is suggestive evidence that alterations in DNA methylation might play a role in the etiology of COPD. However, in previous studies, subjects were mainly included irrespective of smoking status, thus including current smokers, ex-smokers and never smokers. As a consequence, it is currently not known if there are differences in DNA methylation between healthy individuals and patients with COPD who have never smoked. Recently, we studied the association between epigenome-wide DNA methylation and COPD in both current smokers and never smokers [16]. Although we did not find any epigenome-wide significant association in current smokers nor in never smokers, the associations between DNA methylation and COPD were different between both groups. Hence, by further exploring the role of DNA methylation in a much larger set of never smokers together with a continuous measurement of lung function, we might be able to reveal important novel insights in the etiology of COPD. In this study, we aim to assess the association between DNA methylation and lung function in never smokers, meta-analyzing four independent population-based cohorts.

Methods

Study population

To study the association between epigenome-wide DNA methylation and lung function, defined as the ratio between the Forced Expiratory Volume in 1 s (FEV1) and Forced Vital Capacity (FVC), in never smokers, we performed a meta-analysis in four different cohorts. Two cohorts originated from the LifeLines population-based cohort study [18]: the LifeLines COPD & Controls DNA methylation study [16, 19] (LL COPD&C, n = 903) and the LifeLines DEEP study [20] (LLDEEP, n = 166). The two other cohorts originated from the population-based Rotterdam study (RS) [21]: The first visit of the third RS cohort (RS-III-1, n = 150) and a cohort selected for the Biobank-based Integrative Omics Studies (BIOS) project (RS-BIOS, n = 206). Both population-based cohort studies were approved by the local university medical hospital ethical committees and all participants signed written informed consent. In all cohorts, never smoking was defined based on self-reported never smoking history and 0 pack years included in the standardized questionnaires.

Measurements

Lung function

Within the LifeLines population-based cohort study, pre-bronchodilator spirometry was performed with a Welch Allyn Version 1.6.0.489, PC-based Spiroperfect with CA Workstation software according to ATS/ERS guidelines. Technical quality and results were evaluated by well-trained assistants and difficult to interpret results were re-evaluated by a lung physician. Within the population-based Rotterdam study, pre-bronchodilator spirometry was performed during the research center visit using a SpiroPro portable spirometer (RS-III-1) or a Master Screen® PFT Pro (RS-BIOS) by trained paramedical staff according to the ERS/ATS Guidelines. Spirometry results were analyzed by two researchers and verified by a specialist in pulmonary medicine.

DNA methylation

In all four cohorts, DNA methylation levels in whole blood were determined with the Illumina Infinium Methylation 450 K array. Data was presented as beta values (ratio of methylated probe intensity and the overall intensity) ranging from 0 to 1. Quality control has been performed for all datasets separately as described before [19, 22]. After quality control, data was available on 396,243 CpG-sites in all four datasets.

Statistical analysis

Epigenome-wide association study and meta-analysis

We performed an epigenome-wide association study (EWAS) on lung function defined as FEV1/FVC in all four cohorts separately using robust linear regression analysis in R. The analysis was adjusted for the potential confounders age and sex. To adjust for the cellular heterogeneity of the whole blood samples, we included proportional white blood cell counts of mononuclear cells, lymphocytes, neutrophils and eosinophils, obtained by standard laboratory techniques. For LL COPD&C, we adjusted for technical variation by performing a principal components analysis using the 220 control probes incorporated in the Illumina 450 k Chip. The 7 principal components that explained > 1% of the technical variation were included in the analysis. For LLDEEP, data on technical variance was not accessible. For the two RS cohorts, we included the position on the array and array number to adjust for technical variation. Regression estimates from all four individual EWA studies were combined by a weighted by the inverse of the variance random-effect meta-analysis using the effect estimates and standard errors in “rmeta” package in R. CpG-sites with a p-value below 1.26 × 10^− 7 (Bonferroni corrected p-value by number of CpG-sites 0.05/396243) were considered epigenome-wide significant. CpG-sites with a p-value below 0.0001 in the meta-analysis were defined as top associations in our study.

Expression quantitative trait methylation (eQTM) analysis

To assess whether top associations were also associated with gene expression levels, we used the never smokers included in the Biobank-based Integrative Omics Studies (BIOS). For all cohorts separately, reads were normalized to counts per million. To adjust for technical variation for gene expression and DNA methylation, principal component analysis was conducted on the residual normalized counts and beta-values excluding the potential confounders age and gender. Principal components that explained more than 5% of the technical variation in gene expression or DNA methylation were included in the analysis. Subsequently, robust linear regression analysis was performed on the CpG-sites and the genes within 1 MB around the CpG-sites. The analyses were adjusted for the potential confounders age, sex and technical variation by principal components as stated before. The individuals eQTM analysis were combined by a random-effect meta-analysis using the effect estimates and standard errors in RMeta. An eQTM was considered significant when the Bonferroni-adjusted p-value for the number of genes within 1 MB around the CpG-sites was below 0.05.

Results

Subject characteristics

An overview of the characteristics of the subjects included in the study is shown in Table 1. LL COPD&C was the largest cohort included in this meta-analysis. Notably, since this cohort is a non-random selection from the LifeLines cohort study with COPD (defined as FEV1/FVC < 0.70) as one of the selection criteria, the percentages of COPD cases should not be interpreted as prevalence.
Table 1
Subject characteristics of the subjects from the four different DNA methylation datasets
 
LL COPD&C
LLDEEP
RS-III-1
RS-BIOS
Number of subjects, N (%)
903
166
150
206
Male, N (%)
508 (56.3)
71 (42.8)
74 (49.3)
80 (38.8)
Age (yrs), median (min-max)
46 (18–80)
42 (20–78)
63 (53–93)
68 (52–79)
Airway obstruction (FEV1/FVC< 70%), N (%)
316 (35.0)
15 (9.0)
13 (8.7)
19 (9.0)
 - FEV1 (L), mean (SE)
3.5 (0.9)
3.6 (0.9)
3.2 (0.8)
2.7 (0.7)
 - FEV1/FVC, mean (SE)
84.5 (8.2)
78.6 (6.2)
77.8 (5.9)
77.9 (5.9)

Meta-analysis of the four epigenome-wide association studies

The meta-analysis of the four different cohorts did not reveal CpG-sites that were epigenome wide significantly associated with FEV1/FVC. We identified 36 CpG-sites as our top associations (Table 2). The Manhattan plot of the meta-analysis is shown in Fig. 1a. Forest plots of the three most significant CpG-sites cg10012512, located in the intergenic region of chromosome 7q36.3 (p=5.94 × 10^− 7), cg02285771, annotated to LTV1 Ribosome Biogenesis Factor (LTV1) (p=4.10 × 10^− 6) and cg25105536, annotated to Kelch Like Family Member 32 (KLHL32) (p=9.09 × 10^− 6) are shown in Fig. 1b-d. An overview of all CpG-sites associated with FEV1/FVC at nominal p-value of 0.05 can be found in Additional file 1: Table S1.
Table 2
Results of the meta-analysis and individual EWA studies on FEV1/FCV in never smokers
 
Meta-analysis
LL COPD&C
LLDEEP
RS-III-1
RS-BIOS
Beta
SE
P-value
Beta
SE
P-value
Beta
SE
P-value
Beta
SE
P-value
Beta
SE
P-value
cg10012512
Intergenic
−38.27
7.67
5.94E-07
−45.54
12.14
1.76E-04
−16.71
26.68
5.31E-01
−33.86
15.33
2.72E-02
−38.23
14.78
9.71E-03
cg02885771
LTV1
20.66
4.48
4.10E-06
21.53
8.76
1.40E-02
27.73
15.33
7.05E-02
21.95
6.05
2.86E-04
5.67
13.95
6.84E-01
cg25105536
KLHL32
−59.71
13.46
9.09E-06
−76.36
44.35
8.51E-02
−97.80
235.46
6.78E-01
−54.41
14.81
2.38E-04
−94.28
47.91
4.91E-02
cg20102034
RTKN
36.14
8.28
1.28E-05
42.57
15.29
5.35E-03
29.70
15.94
6.25E-02
40.85
14.65
5.29E-03
22.02
24.20
3.63E-01
cg03703840
KIAA1731
84.04
19.38
1.45E-05
100.48
42.84
1.90E-02
−43.70
187.80
8.16E-01
88.13
23.36
1.61E-04
33.87
62.55
5.88E-01
cg21614201
SYNPO2
−22.66
5.23
1.45E-05
−28.17
13.55
3.76E-02
−25.53
28.56
3.71E-01
−21.10
6.11
5.58E-04
−25.22
17.72
1.55E-01
cg07957088
PRIC285
35.48
8.33
2.06E-05
49.48
15.72
1.64E-03
31.33
16.68
6.03E-02
38.68
13.97
5.62E-03
−0.10
24.74
9.97E-01
cg05304461
C1orf127
−80.31
19.00
2.37E-05
−95.35
36.04
8.16E-03
152.12
153.04
3.20E-01
−82.63
25.66
1.28E-03
−68.52
47.73
1.51E-01
cg11749902
Intergenic
−22.32
5.30
2.55E-05
−26.22
7.75
7.17E-04
−16.37
12.44
1.88E-01
−12.69
14.61
3.85E-01
−24.69
11.32
2.91E-02
cg02207312
PRPF19
75.53
18.05
2.87E-05
79.32
53.44
1.38E-01
− 177.08
222.75
4.27E-01
77.18
20.22
1.35E-04
74.46
63.10
2.38E-01
cg19734370
NPTX1
12.65
3.04
3.19E-05
12.29
4.11
2.76E-03
12.09
6.95
8.21E-02
9.23
8.85
2.97E-01
17.64
8.07
2.88E-02
cg03077331
FN3K
14.19
3.45
3.99E-05
16.08
4.94
1.14E-03
9.62
8.41
2.52E-01
29.01
16.49
7.85E-02
11.51
6.31
6.84E-02
cg18387671
ANKRD13B
−88.73
21.86
4.92E-05
− 110.71
69.61
1.12E-01
4.44
272.02
9.87E-01
−87.37
24.33
3.30E-04
−83.43
73.78
2.58E-01
cg03224276
ZFHX3
37.55
9.26
5.00E-05
52.17
19.25
6.73E-03
16.06
44.59
7.19E-01
28.97
11.60
1.25E-02
71.59
31.14
2.15E-02
cg02137691
FGFR3
28.80
7.11
5.11E-05
13.24
13.60
3.30E-01
40.83
15.87
1.01E-02
35.10
10.64
9.74E-04
16.63
25.22
5.10E-01
cg25884324
UNC45A
−36.97
9.16
5.45E-05
−42.03
19.42
3.05E-02
−32.96
50.06
5.10E-01
−35.47
11.31
1.71E-03
−36.84
30.86
2.32E-01
cg27158523
PPIL4
−49.97
12.40
5.54E-05
−62.31
22.65
5.94E-03
− 241.34
161.10
1.34E-01
−37.48
14.71
1.09E-02
−83.47
40.23
3.80E-02
cg01157143
NAV2
−23.11
5.74
5.63E-05
−31.05
15.70
4.80E-02
−10.87
23.51
6.44E-01
−24.64
6.82
3.03E-04
−8.89
18.20
6.25E-01
cg07160694
DCAF5
77.84
19.34
5.69E-05
63.24
40.81
1.21E-01
54.41
155.03
7.26E-01
73.37
27.79
8.29E-03
98.91
36.83
7.24E-03
cg22127773
KDM6B
−48.39
12.03
5.75E-05
−58.63
19.17
2.22E-03
3.55
81.11
9.65E-01
−56.26
21.72
9.60E-03
−29.26
22.85
2.00E-01
cg20939319
TEX15
−14.90
3.71
5.84E-05
−17.12
8.37
4.07E-02
−26.90
17.30
1.20E-01
−13.61
4.55
2.80E-03
−13.49
12.02
2.62E-01
cg02206852
PROCA1
23.87
5.97
6.39E-05
28.18
16.23
8.24E-02
26.98
20.97
1.98E-01
22.38
7.02
1.45E-03
27.78
24.10
2.49E-01
cg17075019
Intergenic
35.53
8.90
6.56E-05
49.59
13.38
2.12E-04
26.62
17.55
1.29E-01
13.65
25.97
5.99E-01
28.14
20.81
1.76E-01
cg25556432
Intergenic
23.02
5.78
6.75E-05
25.96
8.69
2.82E-03
21.69
13.17
9.95E-02
32.14
17.96
7.36E-02
15.46
11.29
1.71E-01
cg22742965
TMEFF2
−17.79
4.47
6.76E-05
−24.96
11.10
2.45E-02
0.42
20.86
9.84E-01
−17.82
5.43
1.03E-03
−14.83
13.14
2.59E-01
cg16734845
CTDSPL2
−33.94
8.52
6.82E-05
−54.67
21.90
1.26E-02
−38.26
26.03
1.42E-01
−31.88
10.86
3.32E-03
−15.33
24.10
5.25E-01
cg09108394
PRKCB
−14.93
3.76
7.11E-05
−16.43
8.33
4.84E-02
−27.78
14.95
6.31E-02
−14.34
4.92
3.55E-03
−9.74
9.71
3.16E-01
cg10034572
Intergenic
−20.08
5.08
7.77E-05
−19.86
13.39
1.38E-01
−56.52
27.77
4.18E-02
−19.29
5.90
1.09E-03
−12.71
17.73
4.73E-01
cg20066227
C1QL3
32.20
8.16
7.92E-05
26.51
18.29
1.47E-01
24.42
30.70
4.26E-01
40.00
10.35
1.12E-04
3.19
24.73
8.97E-01
cg07148038
TNXB
44.32
11.26
8.23E-05
51.79
16.72
1.95E-03
41.06
24.11
8.85E-02
55.29
30.47
6.96E-02
22.61
25.67
3.78E-01
cg23396786
SFXN5
20.16
5.12
8.26E-05
22.48
7.68
3.43E-03
13.97
10.89
2.00E-01
45.93
18.48
1.30E-02
13.79
10.08
1.71E-01
cg06218079
TBCD
8.18
2.08
8.34E-05
5.68
3.00
5.79E-02
12.74
3.45
2.26E-04
3.33
8.96
7.10E-01
6.35
6.52
3.30E-01
cg06982745
ADAMTS14
−40.80
10.44
9.37E-05
−36.77
18.57
4.77E-02
13.29
44.30
7.64E-01
−48.83
14.67
8.71E-04
−42.55
30.04
1.57E-01
cg05946118
Intergenic
−20.27
5.19
9.38E-05
−17.24
6.98
1.35E-02
−23.39
14.23
1.00E-01
−25.24
13.56
6.28E-02
−23.41
12.66
6.46E-02
cg08065963
Intergenic
−16.72
4.28
9.56E-05
−18.12
5.84
1.93E-03
−9.56
11.07
3.88E-01
−29.63
11.66
1.10E-02
−8.68
10.18
3.94E-01
cg12064372
Intergenic
32.85
8.43
9.75E-05
48.15
18.52
9.33E-03
26.64
92.88
7.74E-01
31.50
10.10
1.81E-03
7.96
28.48
7.80E-01
Ranking of CpG-sites is based on the P-value of the meta-analysis
The direction of the effect of the 36 top CpG-sites did not change in a sensitivity analysis in the LL COPD&C cohort excluding the subjects that were exposed to environmental tobacco smoke (ETS)(N=659 subjects) (Additional file 2: Table S2).

Expression quantitative trait methylation (eQTM) analysis

In total, 803 genes were located within 2 MB of the 36 CpG-sites. The expression of 11 genes was significantly associated with DNA methylation levels at the 9 different CpG-sites (Table 3). DNA methylation at cg25105536, annotated to KLHL32, was significantly associated with gene expression levels of KLHL32. DNA methylation levels at cg08065963, located in the intergenic region on chromosome 16 and not yet annotated to a gene, showed a significant association with gene expression levels of 4-Aminobutyrate Aminotransferase (ABAT). For the other 7 CpG-sites, DNA methylation levels were associated with gene expression levels of one or two genes other than the previously annotated genes. An overview of the association between DNA methylation and gene expression levels of all genes can be found in Additional file 3: Table S3.
Table 3
Overview of the results of the meta-analysis of the eQTM analysis
CpG-site
Gene annotation CpG-site
Genes located within 1 MB (N)
Gene (expression)
Beta
SE
p-value
Adjusted p-value
cg02137691
FGFR3
31
SLC26A1
0.0156
0.0038
3.53E-05
0.0011
cg02206852
PROCA1
52
NUFIP2
0.0084
0.0022
1.06E-04
0.0055
cg02206852
PROCA1
52
GIT1
0.0080
0.0023
6.11E-04
0.0318
cg02885771
LTV1
11
VDAC1P8
0.0096
0.0033
3.51E-03
0.0386
cg07148038
TNXB
89
ATP6V1G2
0.0074
0.0021
3.79E-04
0.0337
cg07148038
TNXB
89
STK19B
0.0035
0.0010
3.77E-04
0.0335
cg08065963
 
12
ABAT
0.0127
0.0034
1.85E-04
0.0022
cg20939319
TEX15
10
SARAF
−0.0029
0.0010
3.36E-03
0.0336
cg22127773
KDM6B
80
TMEM88
0.0011
0.0003
1.82E-04
0.0146
cg23396786
SFXN5
18
CYP26B1
0.0024
0.0008
1.78E-03
0.0321
cg25105536
KLHL32
4
KLHL32
−0.0004
0.0002
5.52E-03
0.0221

Discussion

This study is the first large general population-based EWA study on lung function in never smokers. So far, virtually all EWA studies on the origin of COPD included subjects with a history of cigarette smoking. As a consequence, these studies mainly addressed the origins of COPD in response to smoking. It is unclear if the results of these studies help to explain the etiology of COPD or rather explain the contribution of cigarette smoke towards the disease. Therefore, our study importantly contributes to the current understanding of COPD in never smokers.
We identified 36 CpG-sites that were significantly associated with FEV1/FVC at p-value below 0.0001. The top hit of our meta-analysis, cg10012512, is located in the intergenic region of chromosome 7q36.3. It is therefore not possible to speculate on the functional effect of differences in DNA methylation at this specific CpG-site and how these differences may affect FEV1/FVC. While associations found with an eQTM analysis may help to get more insight in the function of a CpG-site, our eQTM analysis did not reveal any nominal significant associations for cg10012512. However, this CpG-site was differentially methylated between never smokers and current smokers [23]. Presumably, this CpG-site does also respond to other inhaled deleterious substances, which in turn affects lung function. The second top hit, cg02885771 located on chromosome 6q24.2 is annotated LTV1. Previously, this CpG-site has been associated with asthma in airway epithelial cells [24] and LTV1 was shown to be expressed in lung tissue in the Genotype Tissue Expression (GTEx) project. Although studies in yeast describe LTV1 as a conserved 40S-associated biogenesis factor that functions in small subunit nuclear export, a specific role for LTV1 in respiratory diseases is not known [25]. The third top hit, cg25105536, is annotated to KLHL32 on chromosome 6q16.1 and we found a significant association between DNA methylation levels of cg25105536 and gene expression levels of KLHL32. The function of KLHL32 is poorly understood, however, four genetic variants in the KLHL32 gene have been associated with FEV1 and FEV1/FVC in African American subjects with COPD and a history of smoking [26]. Notwithstanding the fact that these associations were only identified in a specific group, it might suggest a role for KLHL32 in the respiratory system. Next to KLHL32, we found that gene expression levels of 10 additional genes were significantly associated with DNA methylation levels at one of the 36 CpG-sites. cg08065963, which was not yet annotated to a gene, was significantly associated with 4-Aminobutyrate Aminotransferase (ABAT). Interestingly, a role for ABAT in COPD has not been described before. The remaining nine genes were other genes than the annotated genes of the particular CpG-sites. This suggest that the CpG-sites may also regulate distant genes within a region of 2 MB, which complicates the functional assessment of differences in DNA methylation even further.
To the best of our knowledge, there are eight studies in literature describing the association between DNA methylation and lung function (Table 4). Six of these studies included both subjects with and without a history of cigarette smoking and, except for the study by Qui et al., adjusted for smoking status in the statistical analysis. In addition, the recent study by Imboden et al. performed analyses with and without adjustment for smoking status and pack years. Altogether, these seven studies identified 462 unique CpG-sites. Interestingly, none of the 36 CpG-sites from our meta-analysis in never smokers were among these 462 previously identified CpG-sites (Table 5). Apparently these 36 CpG-sites are only associated with lung function level in never smokers. The fact that 17 CpG-sites (47%) were associated at nominal p-value < 0.05 with COPD (dichotomously defined as the ratio of FEV1/FVC below 70%) in our previously EWAS stratified for never smoking, further underscores this assumption [16]. There is, however, one exception, since cg22742965, annotated to Transmembrane Protein With EGF Like And Two Follistatin Like Domains 2 (TMEFF2), was also significantly associated with COPD in smokers. Most likely, this CpG-site shows a general response to inhaled deleterious substances such as cigarette smoke and other yet unknown substances.
Table 4
Overview of studies reporting results of differential DNA methylation with lung function or COPD in whole blood
Study
Study population
Trait
Adjustment included in model
DNA methylation platform
Number of CpG-sites available for comparison
Epigenome-wide association study of lung function level and its change
Imboden et al., 2019 [17]
Discovery-replication approach. Discovery included 3 cohorts (N=2043) and replication included 7 cohorts (Adult: N=3327, Childhood: N=420)
- Smoking status: self-reported, subjects with and without smoking history; never smokers only
- FEV1
- FVC
- FEV1/FVC
Analyses were performed twice: with and without adjustment for smoking status and pack years
- Age
- Age2
- Height
- Height2 deviation
- Sex
- Sex Age, Age2, height, Height2 deviation
- Education
- BMI
- Spirometer type
- Study Center
- Blood cell composition
Discovery: Illumina Infinium Human Methylation 450 K BeadChip and EPIC BeadChip
Replication: various arrays for the discovery-identified CpG-sites only
Without smoking adjustment: 56a
With smoking adjustment: 12a
Never smokers: 8 (from discovery).
None of the CpG sites were replicateda
No association between DNA methylation and COPD in never and current smokers
De Vries et al., 2018 [16]
Non-random selection from LifeLines cohort (N=1561 subjects)
- Smoking status: Stratified for smoking (658 smokers and 903 never smokers)
- COPD (defined as FEV1/FVC ≤ 0.7)
- Sex
- Age
- Pack years (in smoking stratified analysis)
- Batch effects
- Blood cell composition
Illumina Infinium Human Methylation450K BeadChip array
- Number of included probes: 420,938
Smokers: 19492b
Never smokers: 19393b
Lung function discordance in monozygotic twins and ssociated differences in blood DNA methylation
Bolund et al., 2017 [11]
Sub-population of twins from the Middle-Aged Danish Twin (MADT) study (N=169 twin pairs)
- Smoking status: subjects with and without smoking history
Intra-pair difference in z-score calculated as “superior” minus “inferior” twin at baseline and during follow-up period for:
- FEV1
- FVC
- FEV1/FVC
- Sex
- Age
- BMI
- Pack years
- Smoking status at follow-up
- Blood cell composition
Intra-pair difference was calculated for all the variables
Illumina Infinium Human Methylation450K BeadChip array
- Number of included probes: 453,014
37a
Epigenome-wide association study of chronic obstructive pulmonary disease and lung function in Koreans
Lee et al., 2017 [12]
Sample of Korean COPD cohort (N=100 subjects)
- Smoking status: subjects with and without smoking history
- COPD status (defined as FEV1/FVC < 0.7)
- FEV1
- FVC
- FEV1/FVC
- Sex
- Age
- Height
- Smoking status
- Pack years
- Blood cell composition
Illumina Infinium Human Methylation450K BeadChip array
- Number of included probes: 402,508
16a
Differential DNA methylation marks and gene comethylation of COPD in African-Americans with COPD exacerbations
Busch et al., 2016 [13]
Sample of PA-SCOPE AA study population (N=362 subjects)
- Smoking status: smokers > 20 pack years
- COPD (defined as FEV1/FVC ≤ 0.7 and FEV1 ≤ 80%)
- Sex
- Age
- Pack years
- Batch number
- Blood cell composition
Illumina Infinium Human Methylation27K BeadChip array
- Number of included probes: 19,302
12a
The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort
Marioni et al., 2015 [15]
The Lothian Birth Cohort of 1936 (N=1091)
- Smoking status: self-reported, subjects with and without smoking history
- FEV1
- Sex
- Age
- Height
- Smoking status
- Blood cell composition
Illumina Infinium Human Methylation450K BeadChip array
- Number of included probes: 450,726
2a
Variable DNA methylation is associated with chronic obstructive pulmonary disease and lung function
Qiu et al., 2012 [10]
Test-replication approach in 2 family-based cohorts (N=1085 and 369 subjects)
- Smoking status: subjects with and without smoking history
- COPD status (FEV1/FVC ≤0.7 and FEV1 ≤70%)
- FEV1/FVC
- FEV1
- Random family effect
Illumina Infinium Human Methylation27K BeadChip array
- Number of included probes: 26,485
349a
Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population
Bell et al., 2012 [14]
Sample of the TwinsUK cohort (N=172 female twin pairs)
- Smoking status: unknown
- FEV1
- FVC
- Age
- Batch effects
Illumina Infinium Human Methylation27K BeadChip array
- Number of included probes: 24,641
1a
COPD Chronic Obstructive Pulmonary Disease, FEV1 Forced Expiratory Volume in 1 s, FVC Forced Expiratory Capacity
aCpG-sites obtained from the online available data
bCpG-sites selected at nominal p-value < 0.05 available from self-performed analyses
Table 5
Overview of CpG location, gene annotation, gene function and literature comparison of the top 36 CpG-sites of the meta analysis
CpG-site
CpG location
Gene annotation
Gene function
Previously associated with lung function
cg10012512
7:157224041
Intergenic
NA
Yesa
cg02885771
6:144163654
LTV1
Involved in ribosome biogenesis
No
cg25105536
6:97372436
KLHL32
Only described as protein coding gene
No
cg20102034
2:74653166
RTKN
Negative regulator of GTPase activity of Rho proteins
Yesa
cg03703840
11:93394809
KIAA1731
Mediating of centriole-to-centrosome conversion at late mitosis
No
cg21614201
4:119888794
SYNPO2
Only described as protein coding gene
No
cg07957088
20:62196387
PRIC285
Nuclear transcriptional co-activator for peroxisome proliferator activated receptor alpha
Yesa
cg05304461
1:11019377
C1orf127
Only described as protein coding gene
No
cg11749902
8:41093619
Intergenic
NA
Yesa
cg02207312
11:60674164
PRPF19
Involved in cell survival and DNA repair
No
cg19734370
17:78444348
NPTX1
Exclusively localized to the nervous system as binding protein for taipoxin
Yesa
cg03077331
17:80693076
FN3K
Catalyzes the phosphorylation of fructosamines
Yesa
cg18387671
17:27920246
ANKRD13B
Only described as protein coding gene
Yesa
cg03224276
16:72829831
ZFHX3
Regulates myogenic and neuronal differentiation
No
cg02137691
4:1805671
FGFR3
Involved in bone development and maintenance
No
cg25884324
15:91482502
UNC45A
Regulator of the progesterone receptor chaperoning pathway
No
cg27158523
6:149867355
PPIL4
Involved in protein folding, immunosuppression and infection of HIV-1 virions
Yesa
cg01157143
11:19478542
NAV2
Plays a role in cellular growth and migration
No
cg07160694
14:69619856
DCAF5
Only described as protein coding gene
No
cg22127773
17:7754785
KDM6B
Demethylation of di- or tri-methylated lysine 27 of histone H3
Yesa
cg20939319
8:30707701
TEX15
Involved in cell cycle processes of spermatocytes
No
cg02206852
17:27030540
PROCA1
Only described as protein coding gene
No
cg17075019
10:79541650
Intergenic
NA
Yesa
cg25556432
2:239628926
Intergenic
NA
Yesa
cg22742965
2:192891657
TMEFF2
Cellular context-dependent oncogene or tumor suppressor
Yes
cg16734845
15:44781962
CTDSPL2
Only described as protein coding gene
No
cg09108394
16:23850106
PRKCB
As kinase involved in diverse cellular signaling pathways
No
cg10034572
2:160921789
Intergenic
NA
No
cg20066227
10:16564552
C1QL3
Only described as protein coding gene
No
cg07148038
6:32061160
TNXB
Anti-adhesive protein involved in matrix maturation during wound healing
Yesa
cg23396786
2:73299151
SFXN5
Only described as protein coding gene
Yesa
cg06218079
17:80834228
TBCD
As co-factor D involved in the correct folding of beta-tubulin
No
cg06982745
10:72454006
ADAMTS14
The matured enzyme is involved in the formation of collagen fibers
No
cg05946118
16:8985638
Intergenic
NA
Yesa
cg08065963
16:8985593
Intergenic
NA
Yesa
cg12064372
12:30948792
Intergenic
NA
Yesa
aOnly observed in study by de Vries et al. in never smokers; Gene function obtained by www.​genecards.​org
Assuming that the observed differential DNA methylation at the majority of the CpG-sites in our study occurs without exposure to smoking, the question arises why this differential DNA methylation is observed. One possible explanation may be that other factors within the environment such as air pollution and job-related exposures are responsible for the observed differences in DNA methylation. Recently, we studied the epigenome-wide association between DNA methylation and exposure to air pollution and job-related exposures in a selection of the LifeLines population cohort including both never and current smokers [19, 27]. While we did find significant associations, none of them were replicated in independent cohorts. Additional analyses in never smokers for this paper did not reveal novel associations between DNA methylation and environmental exposures (Additional file 4: Table S4 and Additional file 5: Figure S1). This might potentially be due to lack of power, since only a small percentage of the subjects that have never smoked in the LL COPD&C cohort have been exposed to environmental exposures. Moreover, exposure levels to air pollution in the LL COPD&C are relatively low compared to the average Dutch levels determined within the 2012 Dutch national health survey as described by Strak et al [28]. Next to environmental exposures, another explanation may be that a reduced lung function level precedes the differences in DNA methylation. However, with the cross-sectional design of this study, we cannot derive conclusions on the direction of the association and causality. Large longitudinal studies are required to investigate causality between DNA methylation and FEV1/FVC. Moreover, this will give the opportunity to investigate if low levels of FEV1 and decline in FEV1 over the years is associated with DNA methylation in never smokers.

Conclusions

With this study we show that epigenetics indeed may be associated with FEV1/FVC in subjects who never smoked. Moreover, since 35 out of the 36 identified CpG-sites are unique for never smokers, our data suggest that factors other than smoking affect FEV1/FVC via DNA methylation.

Supplementary information

Supplementary information accompanies this paper at https://​doi.​org/​10.​1186/​s12931-019-1222-8.

Acknowledgements

The Biobank-Based Integrative Omics Studies (BIOS) Consortium is funded by BBMRI-NL, a research infrastructure financed by the Dutch government (NWO 184.021.007).
LifeLines population-based cohort study
Written informed consents was provided by all included subjects and the study was approved by the Medical Ethics Committee of the University Medical Center Groningen (2007/152).
The Rotterdam Study
Written informed consents to participate in the study and to obtain information from their treating physicians was provided by all participants. The study has been approved by the Medial Ethics Committee of the Erasmus Medical Center and by the Ministry of Health, Welfare and Sport of the Netherlands, implementing the Population Studies Act: Rotterdam Study.
Not applicable

Competing interests

The authors declare that they have no competing interests.
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.

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Metadaten
Titel
DNA methylation is associated with lung function in never smokers
verfasst von
Maaike de Vries
Ivana Nedeljkovic
Diana A. van der Plaat
Alexandra Zhernakova
Lies Lahousse
Guy G. Brusselle
Najaf Amin
Cornelia M. van Duijn
Judith M. Vonk
H. Marike Boezen
BIOS Consortium
Publikationsdatum
01.12.2019
Verlag
BioMed Central
Erschienen in
Respiratory Research / Ausgabe 1/2019
Elektronische ISSN: 1465-993X
DOI
https://doi.org/10.1186/s12931-019-1222-8

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