Mutation Research 779 (2015) 57–64

Contents lists available at ScienceDirect

Mutation Research/Genetic Toxicology and Environmental Mutagenesis journal homepage: www.elsevier.com/locate/gentox Community address: www.elsevier.com/locate/mutres

Gene promoter methylation and DNA repair capacity in monozygotic twins with discordant smoking habits Laura Ottini a , Piera Rizzolo a , Ester Siniscalchi b , Andrea Zijno b , Valentina Silvestri a , Riccardo Crebelli b,∗ , Francesca Marcon b a b

Department of Molecular Medicine, University of Rome, La Sapienza, Rome, Italy Department of Environment and Primary Prevention, Istituto Superiore di Sanità, Rome, Italy

a r t i c l e

i n f o

Article history: Received 27 October 2014 Received in revised form 11 December 2014 Accepted 13 January 2015 Available online 15 January 2015 Keywords: DNA methylation DNA repair capacity Tobacco smoke Folic acid Twins

a b s t r a c t The influence of DNA repair capacity, plasma nutrients and tobacco smoke exposure on DNA methylation was investigated in blood cells of twenty-one couples of monozygotic twins with discordant smoking habits. All study subjects had previously been characterized for mutagen sensitivity with challenge assays with ionizing radiation in peripheral blood lymphocytes. Plasma levels of folic acid, vitamin B12 and homocysteine were also available from a previous investigation. In this work DNA methylation in the promoter region of a panel of ten genes involved in cell cycle control, differentiation, apoptosis and DNA repair (p16, FHIT, RAR, CDH1, DAPK1, hTERT, RASSF1A, MGMT, BRCA1 and PALB2) was assessed in the same batches of cells isolated for previous studies, using the methylation-sensitive high-resolution melting technique. Fairly similar profiles of gene promoter methylation were observed within co-twins compared to unrelated subjects (p = 1.23 × 10−7 ), with no significant difference related to smoking habits (p = 0.23). In a regression analysis the methylation index of study subjects, used as synthetic descriptor of overall promoter methylation, displayed a significant inverse correlation with radiation-induced micronuclei (p = 0.021) and plasma folic acid level (p = 0.007) both in smokers and in non-smokers. The observed association between repair of radiation-induced DNA damage and promoter methylation suggests the involvement of the DNA repair machinery in DNA modification. Data also highlight the possible modulating effect of folate deficiency on DNA methylation and the strong influence of familiarity on the individual epigenetic profile. © 2015 Elsevier B.V. All rights reserved.

1. Introduction The regulation of gene expression and the protection of genome integrity are the main functions of DNA methylation. The alteration of DNA methylation is recognized as a critical epigenetic modification in carcinogenesis, leading to the deregulation of the expression of multiple genes involved in basic cell functions [1]. Altered DNA methylation may also have a prognostic significance for cancer development, since gene promoter hypermethylation is detectable in tumor target cells, as well as in surrogate non cancer tissues, several years before the clinical diagnosis of cancer [2–6].

Abbreviations: IR, ionizing radiation; MN, micronuclei; MS-HRM, methylationsensitive high-resolution melting; MZ, monozygotic; PCR, polymerase chain reaction. ∗ Corresponding author at: Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy. Tel.: +39 6 49902840; fax: +39 6 49903650. E-mail address: [email protected] (R. Crebelli). http://dx.doi.org/10.1016/j.mrgentox.2015.01.006 1383-5718/© 2015 Elsevier B.V. All rights reserved.

Twin studies show that the pattern of DNA methylation is largely under genetic control, even though a divergence in methylation profile is observed during lifetime, highlighting the modifying effect of non-shared environmental factors [7]. Among lifestyle factors, cigarette smoke is likely to play a major role as modifier of DNA methylation. Altered methylation of cancer-related genes is, in fact, frequently observed in lung tumors of smokers [8–11], and a progressive accumulation of epigenetic alterations is also observed in the respiratory epithelium of cancer-free heavy smokers [12–14] and in exfoliated cells of smokers sputum [15]. Moreover, changes in the methylation profile of cancer related genes have been observed in plasma DNA from cancer-free heavy smokers [16,17], and smoking-related changes in methylation at a number of CpG sites have been identified in epigenome-wide investigations in blood cells of subjects with different smoking habits [18–21]. The mechanism(s) by which tobacco smoke could affect DNA methylation is not elucidated. A direct interference of tobacco smoke components with the enzymatic machinery responsible for

58

L. Ottini et al. / Mutation Research 779 (2015) 57–64

epigenetic DNA modifications has been proposed, based on the observed down-regulation of DNA-methyltransferase 1 (DNMT1) expression by nicotine [22]; other proposed indirect mechanisms rely on the action of cigarette smoke on DNA binding factors, resulting in the inhibition of de novo methylation of CpG, or on the effect of tobacco smoke on the methyl donor S-adenosylmethionine, as well as on the local hypoxia generated by tobacco smoke inhalation (reviewed in Ref. [23]). Also the DNA damaging activity of tobacco smoke components has been implicated in DNA methylation in view of the partial overlapping between DNA repair and modification pathways, as shown by the involvement of base excision repair components in DNA demethylation [24,25], and by the effective recruitment of DNMT1 at sites of double strand DNA damage [26–28]. These mechanistic consideration are supported by the results of a study on heavy smokers, in which an association between double strand break repair capacity and gene promoter methylation in sputum has been reported [29]. Despite the interest and mechanistic implications of the association between DNA repair and its methylation, only scanty confirmatory data are available from population study. To our knowledge, following the study by Leng et al. quoted above [29], no other investigations specifically addressing this task have been performed. Thus, this work was undertaken to further investigate the association between DNA methylation and repair capacity in blood cells of a study population previously well characterized for the sensitivity to the clastogenic effect of ionizing radiation [30]. The study subjects consisted of monozygotic twins with discordant smoking habits. Thus, the study design allowed to probe multiple associations, i.e., the influence of DNA repair capacity on DNA methylation, comparing subjects with different radiation sensitivity, the effect of tobacco smoke, comparing twins with discordant smoking habits, and the possible interactive effect of tobacco smoke and DNA repair capacity on DNA methylation, comparing the methylation profile within smokers and non-smokers with different radiation sensitivity. In view of the proposed modifying effect of diet and micronutrients involved in one-carbon metabolism on epigenetic profile [14,17], folate status (viz., plasma folic acid, homocysteine and vitamin B12) was considered as covariate. For the analysis of the methylation profile, a panel of ten genes (p16, FHIT, MGMT, BRCA1, PALB2, hTERT, RASSF1A, DAPK1, CDH1, and RAR) involved in cell cycle control, cell differentiation, apoptosis and DNA repair was selected. Previous data show that these genes are frequently hypermethylated in the aerodigestive tract and in plasma DNA of cancer-free smokers [16,31], and they were considered as biologically relevant targets for smoke-mediated changes in DNA methylation in blood cells too.

2. Materials and methods 2.1. Study population Twenty-one pairs of MZ twins discordant for smoking habits were enrolled for a cross-sectional study conducted in accordance with the principles of Good Clinical Practice and approved by the local ethics committee. Written informed consent was obtained from all subjects according to the Declaration of Helsinki. Information on demographic data, medical history, lifestyle, dietary habits, occupational and environmental exposures was collected by questionnaire. Individual DNA repair capacity of study subjects was previously determined by mutagen sensitivity assays on blood cells treated ex vivo with gamma rays using the Cytome assay, and expressed as ionizing radiation (IR)-induced micronucleated cells (MN‰) [30]. Saliva samples were taken from all subjects to verify the zygosity status. Testing for zygosity was performed using the

AmpFiSTRs Identifier Kit (Applied Biosystems, Carlsbad, California, USA). All the analyses were carried out on coded samples. Smoking status of the study subjects was assigned as previously reported [30]. Information on folate status of study subjects was previously determined, using the same blood samples used for DNA isolation in this study [30]. 2.2. Blood sample collection From each donor, a blood sample was taken by venipuncture into tubes containing Ficoll (BD Vacutainer® CPTTM ). Members of the same couple of twins were sampled on the same day, and blood samples were processes in parallel. 2.3. DNA extraction and bisulfite modification Genomic DNA was isolated from whole blood samples, stored at −80 ◦ C, using Puregene Core Kit (Qiagen, Germany), according to manufacturer’s instructions. DNA (1 ␮g) was subject to bisulfite conversion with the EpiTec Plus Bisulfite Kit (Qiagen, Germany) according to the manufacturer’s instructions. The purified bisulfite converted samples were eluted in 40 ␮L volume and stored at –20 ◦ C no longer than three months. CpGenome Universal Methylated DNA and CpGenome Universal Unmethylated DNA (Chemicon; Billerica, MA, USA) were used as fully methylated and unmethylated controls. Standard dilution series of 100%, 80%, 60%, 50%, 40%, 20%, 10%, and 0% methylation levels were prepared by diluting the fully methylated DNA into unmethylated DNA to perform a standard curve. 2.4. Methylation-specific high-resolution melting analysis (MS-HRM) Promoter methylation of p16, FHIT, RAR, CDH1, DAPK1, hTERT, RASSF1A, MGMT, BRCA1 and PALB2 genes was assessed by methylation-sensitive high-resolution melting (MS-HRM). MSHRM combines PCR (polymerase chain reaction) amplification using methylation independent primers with subsequent HRM analyses of the PCR products. The PCR product generated from methylated (bisulfite modified) template has a relatively higher GC content and therefore higher melting temperature than PCR product generated from unmethylated (GC-poor) variant of the same template. The HRM analyses allow for highly sensitive monitoring of the melting temperature of PCR product and hence distinction between methylated and unmethylated PCR products [32]. MS-HRM was performed on 7500 Fast Real Time PCR System (Life Technologies, Carlsbad, California, USA). The promoter regions analyzed were selected based on literature data. The primers were designed using Applied Biosystems Methyl Primer Express® software v.1, following Applied Biosystems protocols and Wojdacz et al. guidelines [33]. The range of amplification products was between 106 base pairs (bps) and 154 bps, and 6–15 CpG sites were included in the amplicons. Information on primer sequences is available on request. Reaction was carried out in a 10 L total volume containing 1× MeltDoctor HRM Master Mix (Life Technologies, Carlsbad, California, USA), 0.3 M of each primer and 1 L of bisulphite modified template (20 ng/L). The cycling conditions were as follows: 1 cycle of 95 ◦ C for 10 min, 40 cycles of 95 ◦ C for 15 s, 60 ◦ C for 1 min, 1 cycle of 95 ◦ C for 10 s, followed by an HRM step from 60 to 95 ◦ C rising at 0.2 ◦ C per second and holding for 1 s after each stepwise increment. A standard curve with known methylation ratios was included in each assay, and was used to calculate the methylation ratio of each sample. Melting curves were normalized by calculation of the ‘line of best fit’ in between two normalization regions before and after

L. Ottini et al. / Mutation Research 779 (2015) 57–64

the major fluorescence decrease representing the melting of the PCR product using High Resolution Melting Software (Life Technologies, Carlsbad California, USA). This algorithm allows the direct comparison of the samples that have different starting fluorescence levels. 2.5. Statistical analyses Methylation levels for each gene were expressed as mean percentage (±standard deviation, SD) of gene promoter methylation. A promoter was considered methylated for a methylation level >10%. This cut off was chosen on the basis of the sensitivity of the technique applied [32]. The methylation index, calculated as proportion of methylated genes (total number of methylated genes divided by the total number of genes examined), was used as synthetic descriptor of individual methylation status. The non-parametric Wilcoxon signed-rank test was used to compare DNA methylation levels for each gene and methylation indexes within twin pairs. In order to compare the frequencies of individuals with gene promoter hypermethylation by smoking status and smoking habits, the odds ratio (OR) and its corresponding 95% confidence interval (CI) were determined. The non-parametric two-tailed Mann–Whitney U-test was used to assess differences in DNA methylation levels for each gene, and in methylation index between individuals stratified according to plasma nutrients, and IR-induced MN‰ levels, dichotomized around the median. P-values were adjusted for multiple testing using the Bonferroni correction method. The influence of IRinduced MN‰ and plasma micronutrients on methylation index was also evaluated by linear regression analysis. To evaluate the similarity in DNA methylation within twin pairs, a mean divergence index was calculated as the sum of the differences in the percentage of methylation between the smoker twin and the non-smoker twin for each gene, divided by the number of genes analyzed. The similarity in DNA methylation among each twin of a pair and all other unrelated individuals with discording smoking habits was expressed as the mean of their divergence indexes, calculated for each twin pair as the mean of the differences for each gene in the methylation percentage between the non-smoker twin of the pair and the smoker twin of all the other pairs, plus the differences between the smoker twin of the pair and the non-smoker twin of all the other pairs, divided by the number of genes analyzed. The within pair and inter-individual diverge indexes were compared by paired Student’s t-test. For all the analyses, a p-value ≤0.05 was considered statistically significant. Statistical analyses were performed with R (www.r-project.org) software.

59

Table 1 Characteristics of the study population. Smokers

Non-smokers

Age (years)

31.7 ± 6.4 (23–46)a

31.7 ± 6.4 (23–46)

Gender Male Female

12 (57.1%)b 9 (42.9%)

12 (57.1%) 9 (42.9%)

Smoking habits Cigarettes/day Years of smoking Pack/year

13.7 ± 5.5 (7–20)a 10.4 ± 5.1 (2–23) 7.9 ± 5.1 (1–23)

– – –

Plasma nutrients Folic acid (ng/mL) Vitamin B12 (pg/mL) Homocysteine (uM/L)

4.8 ± 1.5 (2.5–9.7)a 567.1 ± 154.2 (283–988) 13.1 ± 9.1 (7.2–49.6)

5.5 ± 2.8 (1.9–12.9) 627.4 ± 308.4 (189–1469) 11.1 ± 2.9 (5.0–15.0)

IR induced MN cells (‰)

205.8 ± 57.3 (134–309)a

200.4 ± 42.4(124–290)

a b

Mean ± standard deviation (min–max in brackets). Number of subjects (%).

homocysteine and are summarized in Table 1. IR-induced MN‰ and plasma nutrients levels did not differ significantly between smoker and non smoker twins, although a trend to lower folic acid and vitamin B12, and higher homocysteine level, was observed in smokers. 3.2. Gene promoter methylation and smoking habits The results of the MS-HRM analysis of gene promoters methylation in blood cells of smokers and non-smokers are summarized Table 2. No significant difference in methylation levels in any of the genes investigated was observed comparing smoker and nonsmoker twins by Wilcoxon signed-rank test. The same result was obtained considering the quantitative descriptors of smoking habits, i.e., number of cigarettes/day, years of smoking and pack/year (data not shown). The overall frequency of individuals with promoter hypermethylation varied remarkably depending on the gene investigated, ranging from 19% for p16 up to 90% for RASSF1A and PALB2. However, no difference in the prevalence of individuals with hypermethylated gene promoters was observed in relation to smoking status and smoking habits (Table 3). In order to account for the overall methylation profile, a methylation index was calculated for each individual as described in Section 2. As shown in Fig. 1, comparable profiles of gene promoter methylation were observed within twins despite their discordant smoking habits (p = 0.23).

3. Results

3.3. DNA methylation and other demographic and lifestyle factors

3.1. Characteristics of the study population

Data on gene promoter methylation within the whole study population (smokers and nonsmokers) stratified according to the other study variables are summarized in Table 4. Higher levels of FHIT promoter methylation were observed in younger individuals than in aged subjects (p = 0.03), while higher levels of promoter methylation of DAPK1, RARˇ and RASSF1A were observed in older subjects (p < 0.0001, p = 0.02 and p = 0.005, respec-

The study population consisted of 21 pairs of MZ twins (24 males and 18 females) with discordant smoking habits. The mean age of the study subjects was 31.7 years, ranging from 23 to 46 years. Information on smoking habits, IR-induced micronuclei per thousand (MN‰), and plasma levels of folic acid, vitamin B12 and Table 2 Gene promoter methylation levelsa in smokers and nonsmokers.

Smokers Non-smokers p-valueb a b

No. of subjects

RARˇ

RASSF1A

FHIT

21 21

58.1 ± 40.1 69.5 ± 30.9 0.09

77.1 ± 10.1 72.9 ± 18.5 0.20

14.3 ± 10.8 13.3 ± 24.6 20 ± 14.8 8.1 ± 14.4 0.06 0.32

Mean ± SD. Wilcoxon signed-rank test.

p16

MGMT

BRCA1

hTERT

DAPK1

CDH1

PALB2

26.2 ± 19.1 22.0 ± 13.6 0.31

2.9 ± 6.4 9.0 ± 21.2 0.39

37.1 ± 33.3 44.8 ± 29.9 0.20

24.3 ± 27.1 25.2 ± 29.3 0.92

28.6 ± 23.3 21.9 ± 20.6 0.19

47.1 ± 20.0 50.9 ± 17.3 0.18

60

Table 3 Frequencies of individuals with gene promoter hypermethylationa by smoking status and smoking habits. RARb (%)

RASSF1 (%)

FHIT (%)

p16 (%)

MGMT (%)

BRCA1 (%)

hTERT (%)

DAPK1 (%)

CDH1 (%)

PALB2 (%)

21 21

17 (81) 14 (66.7) 0.47 (0.11–1.94)

20 (95.2) 21 (100) nd

9 (42.9) 4 (19) 0.31 (0.08–1.26)

3 (14.3) 5 (23.8) 1.87 (0.38–9.12)

17 (81) 16 (76.2) 0.75 (0.17–3.31)

4 (19) 2 (9.5) 0.45 (0.07–2.74)

16 (76.2) 14 (66.7) 0.62 (0.16–2.42)

11 (52.4) 11 (52.4) 1.00 (0.30–3.36)

8 (38.1) 11 (52.4) 1.79 (0.52–6.11)

20 (95.2) 18 (85.7) 0.30 (0.29–3.15)

11 10

6 (54.5) 8 (80) 3.33(0.47–23.5)

11 (100) 10 (100) nd

11 (100) 4 (40) nd

3 (27.3) 2 (20) 0.67 (0.09–5.13)

7 (63.6) 9 (90) 5.14 (0.46–56.9)

1 (9.1) 1 (10) 1.11 (0.06–20.5)

6 (54.5) 8 (80) 3.33 (0.47–23.5)

6 (54.5) 5 (50) 0.83 (0.15–4.64)

6 (54.5) 5 (50) 0.83 (0.15–4.64)

8 (72.7) 10 (100) nd

Years of smoking ≤10 (Ref) >10 OR (95% CI)

13 8

9 (69.2) 5 (62.5) 0.74(0.12–4.73)

13 (100) 8 (100) nd

3 (23.1) 1 (12.5) 0.48 (0.04–5.58)

2 (15.4) 3 (37.5) 3.30 (0.41–26.4)

11 (84.6) 5 (62.5) 0.30 (0.04–2.42)

1 (7.7) 1 (12.5) 1.71 (0.09–31.9)

10 (76.9) 4 (50) 0.30 (0.04–1.99)

5 (38.5) 6 (75) 4.80 (0.68–33.8)

6 (46.2) 5 (62.5) 1.94 (0.32–11.8)

12 (92.3) 6 (75) 0.25 (0.02–3.34)

Pack/year ≤7.3 (Ref) >7.3 OR (95% CI)

11 10

6 (54.5) 8 (80) 3.33 (0.47–23.5)

11 (100) 10 (100) nd

1 (9.1) 3 (30) 4.29 (0.37–50.2)

2 (18.2) 3 (30) 1.93 (0.25–14.9)

8 (72.7) 8 (80) 1.50 (0.19–11.5)

11 (100) 2 (20) nd

7 (63.6) 7 (70) 1.33 (0.21–8.29)

5 (45.5) 6 (60) 1.80 (0.32–10.2)

6 (54.5) 5 (50) 0.83 (0.15–4.64)

9 (81.8) 9 (90) 2.00 (0.15–26.2)

Smoking status Non-smokers (Ref) Smokers OR (95% CI) Smoking habits No. of cig/day ≤12 (Ref) >12 OR (95% CI)

Ref: reference category; OR: odd ratio; CI: confidence interval; nd: not determined. a Gene promoters were considered to be hypermethylated for any methylation level >10%.

L. Ottini et al. / Mutation Research 779 (2015) 57–64

No. of subjects

Variable

L. Ottini et al. / Mutation Research 779 (2015) 57–64

61

Table 4 Gene promoter methylation in the twin population by study variables. Variablea

No. of subjects

Age (y)

≤30 >30 p-valuea

22 20

52.7 ± 35.6 69.5 ± 16.5 76 ± 32.7 81 ± 10.2 0.02 0.005

20.9 ± 14.8 15 ± 23.9 13 ± 9.8 6 ± 13.9 0.03 0.21

Male Female p-valueb

24 18

62.9 ± 39.5 73.6 ± 17.9 65 ± 31.3 76.7 ± 9.7 0.88 0.87

Folic acid (ng/mL) ≤4.6 >4.6 p-value

22 20

Vitamin B12 (pg/mL) ≤563 >563 p-value

RARˇ

RASSF1A

FHIT

p16

MGMT

BRCA1

CDH1

PALB2

21.4 ± 23.8 29.5 ± 19.6 0.09

48.2 ± 20.8 50 ± 16.2 0.88

17.9 ± 14.4 14.2 ± 23.2 22.1 ± 16.7 10 ± 20 16.1 ± 11.4 6.1 ± 14.2 26.7 ± 16.4 0.6 ± 2.4 0.78 0.34 0.26 0.03

47.5 ± 32.1 34.2 ± 28.3 27.9 ± 23.2 32.2 ± 29.4 12.2 ± 22.4 21.7 ± 20.4 0.17 0.0008 0.48

49.2 ± 21.7 48.9 ± 14.1 0.61

73.2 ± 36.8 75.9 ± 17.9 53.5 ± 32.5 74 ± 11 0.04 0.27

16.8 ± 13.2 10 ± 21.8 21.8 ± 14.7 4.5 ± 15.3 17.5 ± 13.3 11.5 ± 18.4 26.5 ± 18.4 7.5 ± 16.5 0.88 0.24 0.52 0.22

48.2 ± 34.5 32.7 ± 31.3 33.2 ± 25 33 ± 26.6 16 ± 20.9 16.5 ± 14.2 0.15 0.11 0.03

50.9 ± 18 47 ± 19.5 0.46

21 21

70.9 ± 37.3 75.2 ± 18.1 56.7 ± 33.7 74.8 ± 11.2 0.14 0.50

16.7 ± 13.5 8.6 ± 21.3 17.6 ± 13 12.9 ± 19 0.64 0.10

4.3 ± 15.7 7.6 ± 16.1 0.09

49 ± 34.6 30 ± 31.6 32.9 ± 25.5 32.9 ± 26.5 19.5 ± 23.1 17.6 ± 14.8 0.12 0.45 0.07

52.4 ± 18.1 45.7 ± 18.9 0.14

Homocysteine (uM/L) ≤10.8 >10.8 p-value

21 21

57.1 ± 35.1 72.4 ± 10.4 70.5 ± 36.1 77.6 ± 18.1 0.14 0.03

19.5 ± 14.7 11.4 ± 19.3 27.1 ± 18.2 5.2 ± 15.7 14.8 ± 11.2 10 ± 21.2 21 ± 14.5 6.7 ± 16.2 0.21 0.60 0.28 0.66

35.7 ± 28.7 15.7 ± 22.5 17.6 ± 19.5 46.2 ± 34 33.8 ± 30.2 32.9 ± 22.2 0.34 0.04 0.02

52.4 ± 16.1 45.7 ± 20.6 0.42

IR-induced MN‰ ≤200 >200 p-value

20 20

62 ± 42 66 ± 31.5 0.92

19.5 ± 15 12 ± 22.4 15.5 ± 11.5 10 ± 18.9 0.44 0.77

52.5 ± 28.8 30.5 ± 28.4 33.5 ± 21.3 32.5 ± 31.4 19.5 ± 28.4 18.5 ± 21.1 0.03 0.26 0.01

52.5 ± 20.5 46.5 ± 17.3 0.19

26.4 ± 20.4 8.2 ± 20.6 21.5 ± 10.9 3.5 ± 7.5 0.76 0.80

hTERT

DAPK1

42.3 ± 30.5 9.1 ± 19 39.5 ± 33.3 42 ± 26.1 0.70 10.8 uM/L (p = 0.03, p = 0.04 and p = 0.02 respectively). However, after Bonferroni correction, only the association between DAPK1 promoter methylation and age remained statistically significant.

The association between study variables and methylation index was evaluated by Mann–Whitney U-test. In a preliminary assessment, the whole panel of DNA damage end-points evaluated in Marcon et al. [30], was taken into account. No remarkable association was observed for the other markers of DNA damage, and thus only radiation-induced micronuclei were considered in further analyses as robust indicator of DNA damage repair. Significant associations between methylation index and gender (p = 0.02), folic acid levels (p = 0.01) and IR-induced MN‰ (p = 0.04) emerged.These associations were further investigated by linear regression analysis. The results obtained confirmed plasma levels of folic acid and radiation-induced MN‰ as variables most significantly related to the methylation index, which displayed a

Fig. 1. Methylation index in smoking (black) and non-smoking (white) co-twins. The methylation index represents fraction of genes with methylated promoter out of the total number of genes analyzed.

62

L. Ottini et al. / Mutation Research 779 (2015) 57–64

Table 5 Linear regression analysis of the association between methylation index and study variables. Included variables c

Costant Folic acid IR-induced MN‰

Ba

Pb

95% CI

0.711 −0.024 −0.001

0.007 0.021

−0.042 −0.002

−0.007 −0.000

Variables evaluated but not included in the model: gender, age, body mass index smoking habits. Significance of the model: P < 0.001; F = 7.965; R2 = 0.263 a Slope of the regression line. b Significance of the variable. c Estimated intercept value.

Fig. 2. Similarity of methylation profile between twins and among unrelated individuals: box plot of the divergence index within co-twins (“within pairs”) and among unrelated individuals with different smoking habits (“between pairs”).

significant inverse correlation with folic acid (p = 0.007) and IRinduced MN‰ (p = 0.021) both in smokers and in non-smokers (Table 5). These variables explained about 26% of the variance observed (R2 of the model = 0.263; p < 0.001). 3.4. Comparison within and between twin pairs A further analysis was performed to compare the degree of similarity (or divergency) of methylation profile of smokers and non-smokers within twins belonging to the same couple and among unrelated subjects (Fig. 2). The average divergence in DNA methylation within twins with different smoking habits (“Within pair”) was 9.95%, ranging from 0 (no difference in methylation for any gene) to 29%. The divergence in DNA methylation among unrelated individuals with different smoking habits (“Between pairs”) was 22.71%, ranging from 18% to 34%. Such difference was very highly significant on statistical grounds (p = 1.23 × 10−7 ), highlighting the strong influence of familiarity on individual epigenetic profile. 4. Discussion Cigarette smoking is associated with epigenetic modifications in the upper aerodigestive tract of smokers [13,14,34,35], and epigenetic changes are supposed to play a key role in the malignant transformation of lung epithelial cells in smokers [15,36]. Smokerelated changes in DNA methylation have also been detected in blood cells [18–21] and in newborns [37] in recent epigenomewide association studies on DNA methylation and smoking habits. Even though only a relatively low number of CpG sites attained the high statistical significance required to be identified as differently methylated in such untargeted studies, this does not rule out

the possibility that smoking could elicit milder effects also in other genes, which may be important when critical genes are affected. In this work promoter methylation of a panel of ten genes involved in cell cycle control, differentiation, apoptosis and DNA repair (p16, FHIT, RAR, CDH1, DAPK1, hTERT, RASSF1A, MGMT, BRCA1 and PALB2), frequenty hypermethylated in the aerodigestive tract of cancer-free heavy smokers [16,31,38], was further investigated in monozygotic twins with discordant smoking habits. Monozygotic twins represent the optimal study model to assess the influence of environmental factors on the epigenome, as the comparison of genetically identical individuals excludes any possible bias due to genetic heterogeneity, e.g. in polymorphism(s) affecting CpG methylation rates [20,39–42]. The results obtained do not highlight any significant influence of tobacco smoking on promoter methylation in the set of genes analyzed. A word of caution is necessary, however, bearing in mind that the study population mainly included light smokers (less than 15 cigarettes/day) with a relatively short duration of addiction to tobacco (10 years as the average). In addition to tobacco smoke, the role of a few other biological and lifestyle factors as modifiers of DNA methylation was analyzed. Individual DNA repair capacity was considered in view of the reported association between double strand break repair and DNA methylation in smokers [29]. Also the folate status of study subjects (i.e., plasma levels of folic acid, homocysteine and vitamin B12) was considered as possible covariate, as folate status is negatively modulated by smoke and known to influence DNA methylation [43,44]. Individual DNA repair capacity was estimated based on the incidence of micronuclei induced in blood cells by an ex vivo challenge with gamma-rays [30]. The choice of ionizing radiation as challenging agent fits well with the purpose of this investigation, as the primary lesion induced by radiation, DNA double strand break, is the main lesion implicated in the recruitment of dimethyltransferases (DNMTs) at the site of DNA damage, as discussed below. The results obtained show a significant positive association between DNA repair capacity (or lower radiation sensitivity) and number of genes with methylated promoters, independently of smoking habits, in agreement with the suggested functional association between DNA methylation and repair. DNA damage and the subsequent recruitment of DNMT1, in fact, has been proposed as a key mechanism modulating DNA methylation [23] since DNMT1 colocalizes at sites of DNA repair where it plays a role both in DNA damage response [26,27,45–48] and in the methylation of CpGs adjacent to the repaired nucleotides [28,49]. A functional link between DNMT activity and DNA repair is also indicated by the concurrent suppression of DNA repair activity elicited by DNMTs inhibitors [48], while other in vitro studies point to the direct involvement of DMNTs in DNA repair through DNA methylation independent mechanisms [50]. This hypothesis is supported by the results of a population study in which DNA sequence variations in DNMTs genes was shown to affect negatively the sensitivity to benzo[a]pyrene diol epoxide in challenge assays ex vivo, possibly highlighting a direct role of DNMTs in the signaling and repair of double strand breaks [38]. The indication of a positive association between DNA repair capacity (expressed as lower incidence of radiation-induced micronuclei) and higher methylation obtained in this work is mechanistically plausible, but at variance from the results reported in another study on smokers, in which an inverse association between DNA repair capacity and gene promoter methylation was observed [29]. This discrepancy could be due to differences in study populations, in the target genes analyzed, in the intensity and duration of exposure to tobacco smoke or to other unidentified variables. It is remarkable, however, that both studies highlight a significant association between DNA repair capacity and DNA methylation,

L. Ottini et al. / Mutation Research 779 (2015) 57–64

a finding which deserves further consideration and investigation. The results obtained in this work also confirm the role of folate status as possible modifier of DNA methylation: low plasma levels of folic acid were associated with a higher methylation index, in agreement with previous studies showing an association between folate deficiency and hypermethylation in the airway epithelium [14]. The negative correlation between folate status and DNA methylation, observed herein and in previous studies, seems to indicate a paradoxical effect of folate deficiency on DNA hypermethylation. Folates play a key role in the conversion of homocysteine to methionine, the precursor of S-adenosylmethionine (SAM), the primary methyl group donor for most biological methylation reaction [44]. It has been hypothesized that folate deficiency may lead to a deregulation of the intracellular concentration of free SAM and S-adenosylhomocysteine (SAH, an inhibitor of SAM) [51], or to the compensatory up-regulation of DNA methyltransferase (DNMT) [52]: in both cases these events would result in DNA hypermethylation. This hypothesis is in agreement with the observed up-regulation of DNMT1 and DNMT3A in mice subjected to a folate deficient diet [53]. On the other hand, an indirect effect of folate status on DNA methylation via its influence on DRC capacity can be ruled out, as plasma folic acid proved not to affect DRC in this study population [30]. Overall, the results obtained show large difference in the degree of promoter methylation among different genes, and among individuals for the same gene. This finding, which is in agreement with the results of previous investigations on cancer-free population [16,54], could not be ascribed to different smoking habits, nor to any of the other study variables. However, the comparison of the divergence in methylation within twins and among unrelated subjects points to a strong role of familiarity in determining a similar epigenetic profile. This may be related to some extent to the familiarity in DNA damage response observed in this twin cohort (as reported in 30), and highlighted by previous twin studies [55–57], even though the design of this study, only involving MZ twins, did not allow not disentangle the role of heritability from that of shared environment. In summary, the results of this study suggest that exposure to tobacco smoke does not modify to a detectable extent promoter methylation in a set of cancer-related genes in blood cells. Conversely, a significant modulation of methylation by individual DNA repair capacity and folate status was observed. The study also pointed to a distinct influence of familiarity as a modifier of methylation profile. Conflict of interest

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15] [16]

[17]

[18]

None declared [19]

Acknowledgements This work was supported by the Istituto Superiore di Sanità (Italian National Institute of Health) (cap.524, fiscal year 2012–2013, to R.C.), and by the Associazione Italiana per la Ricerca sul Cancro (AIRC IG12780 to L.O).

[20]

[21]

References [1] S.A. Belinsky, Silencing of genes by promoter hypermethylayion: key event in rodent and human lung cancer, Carcinogenesis 26 (2005) 1481–1487. [2] S.A. Belinsky, D.M. Klinge, J.D. Dekker, M.W. Smith, T.J. Bocklage, F.D. Gilliland, R.E. Crowell, D.D. Karp, C.A. Stidley, M.A. Picchi, Gene promoter methylation in plasma and sputum increases with lung cancer risk, Clin. Cancer Res. 11 (2005) 6505–6510. [3] A.L. Russo, A. Thiagalingam, H. Pan, J. Califano, K.H. Cheng, J.F. Ponte, D. Chinnappan, P. Nemani, D. Sidransky, S. Thiagalingam, Differential DNA hypermethylation of critical genes mediates the stage-specific tobacco

[22]

[23] [24]

63

smoke-induced neoplastic progression of lung cancer, Clin. Cancer Res. 11 (2005) 2466–2470. E. Baryshnikova, A. Destro, M.V. Infante, S. Cavuto, U. Cariboni, M. Alloisio, G.L. Ceresoli, R. Lutman, G. Brambilla, G. Chiesa, G. Ravasi, M. Roncalli, Molecular alterations in spontaneous sputum of cancer-free heavy smokers: results from a large screening program, Clin. Cancer Res. 14 (2008) 1913–1919. A. Salskov, S.E. Hawes, J.E. Stern, Q. Feng, C.D. Jordan, L. Wiens, J. Rasey, H. Lu, N.B. Kiviat, H. Vesselle, Hypermethylation of CCND2 may reflect a smoking-induced precancerous change in the lung, J. Oncology 2011 (2011) 9, ID:950140. H. Heyn, F.J. Carmona, A. Gomez, H.J. Ferreira, J.T. Bell, S. Sayols, K. Ward, O.A. Stefansson, S. Moran, J. Sandoval, J.E. Eyfjord, T.D. Spector, M. Esteller, DNA methylation profiling in breast cancer discordant identical twins identifies DOK7 as novel epigenetic biomarker, Carcinogenesis 34 (2013) 102–108. M.F. Fraga, E. Ballestar, M.F. Paz, S. Ropero, F. Setien, M.L. Ballestar, D. ˜ Heine-Suner, J.C. Cigudosa, M. Urioste, J. Benitez, M. Boix-Chornet, A. Sanchez-Aguilera, C. Ling, E. Carlsson, P. Poulsen, A. Vaag, Z. Stephan, T.D. Spector, Y.Z. Wu, C. Plass, M. Esteller, Epigenetic differences arise during the lifetime of monozygotic twins, Proc. Natl. Acad. Sci. U. S. A. 102 (2005) 10604–10609. H. Enokida, H. Shiina, S. Urakami, M. Terashima, T. Ogishima, L.C. Li, M. Kawahara, M. Nakagawa, C.J. Kane, P.R. Carroll, M. Igawa, R. Dahiya, Smoking influences aberrant CpG hypermethylation of multiple genes in human prostate carcinoma, Cancer 106 (2006) 79–86. I.M. Smith, W.K. Mydlarz, S.K. Mithani, J.A. Califano, DNA global hypomethylation in squamous cell head and neck cancer associated with smoking, alcohol consumption and stage, Int. J. Cancer 121 (2007) 1724–1728. C.S. Furniss, C.J. Marsit, E.A. Houseman, K. Eddy, K.T. Kelsey, Line region hypomethylation is associated with lifestyle and differs by human papillomavirus status in head and neck squamous cell carcinomas, Cancer Epidemiol. Biomarkers Prev. 17 (2008) 966–971. J.Y. Wu, J. Wang, J.C. Lai, Y.W. Cheng, K.T. Yeh, T.C. Wu, C.Y. Chen, H. Lee, Association of O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation with p53 mutation occurrence in non-small cell lung cancer with different histology, gender, and smoking status, Ann. Surg. Oncol. 15 (2008) 3272–3277. W.A. Palmisano, K.K. Divine, G. Saccomanno, F.D. Gilliland, S.B. Baylin, J.G. Herman, S.A. Belinsky, Predicting lung cancer by detecting aberrant promoter methylation in sputum, Cancer Res. 60 (2000) 5954–5958. S. Zöchbauer-Müller, S. Lam, S. Toyooka, A.K. Virmani, K.O. Toyooka, S. Seidl, J.D. Minna, A.F. Gazdar, Aberrant methylation of multiple genes in the upper aerodigestive tract epithelium of heavy smokers, Int. J. Cancer 107 (2003) 612–616. C.A. Stidley, M.A. Picchi, S. Leng, R. Willink, R.E. Crowell, K.G. Flores, H. Kang, T. Byers, F.D. Gilliland, S.A. Belinsky, Multivitamins, folate, and green vegetables protect against gene promoter methylation in the aerodigestive tract of smokers, Cancer Res. 70 (2010) 568–574. S.A. Belinsky, Gene-promoter hypermethylation as a biomarker in lung cancer, Nat. Rev. Cancer 4 (2004) 707–717. M. Brait, J.G. Ford, S. Papaiahgari, M.A. Garza, J.I. Lee, M. Loyo, L. Maldonado, S. Begum, L. McCaffrey, M. Howerton, D. Sidransky, M.R. Emerson, S. Ahmed, C.D. Williams, M.O. Hoque, Association between lifestyle factors and CpG island methylation in a cancer-free population, Cancer Epidemiol. Biomarkers Prev. 18 (2009) 2984–2991. F.F. Zhang, R.M. Santella, M. Wolff, M.A. Kappil, S.B. Markowitz, A. Morabia, White blood cell global methylation and IL-6 promoter methylation in association with diet and lifestyle risk factors in a cancer-free population, Epigenetics 7 (2012) 606–614. L.P. Breitling, R. Yang, B. Korn, B. Burwinkel, H. Brenner, Tobacco-smoking-related differential DNA methylation: 27 K discovery and replication, Am. J. Hum. Genet. 88 (2011) 450–457. E.S. Wan, W. Qiu, A. Baccarelli, V.J. Carey, H. Bacherman, S.I. Rennard, A. Agusti, W. Anderson, D.A. Lomas, D.L. Demeo, Cigarette smoking behaviors and time since quitting are associated with differential DNA methylation across the human genome, Hum. Mol. Genet. 21 (2012) 3073–3082. N.S. Shenker, S. Polidoro, K. van Veldhoven, C. Sacerdote, F. Ricceri, M.A. Birrell, M.G. Belvisi, R. Brown, P. Vineis, J.M. Flanagan, Epigenome-wide association study in the European prospective investigation into cancer and nutrition (EPIC-Turin) identifies novel genetic loci associated with smoking, Hum. Mol. Genet. 22 (2013) 843–851. S. Harlid, Z. Xu, V. Panduri, D.P. Sandler, J.A. Taylor, CpG sites associated with cigarette smoking: analysis of epigenome-wide data from the sister study, Environ. Health Perspect. 122 (7) (2014) 673–678, http://dx.doi.org/10.1289/ehp.1307480, Epub 2014 Apr 2. R. Satta, E. Maloku, A. Zhubi, F. Pibiri, M. Hajos, E. Costa, A. Guidotti, Nicotine decreases DNA methyltransferase 1 expression and glutamic acid decarboxylase 67 promoter methylation in GABAergic interneurons, Proc. Natl. Acad. Sci. U. S. A. 105 (2008) 16356–16361. K.W. Lee, Z. Pausova, Cigarette smoking and DNA methylation, Front Genet. 4 (2013) 1–11. N. Bhutani, J.J. Brady, M. Damian, A. Sacco, S.Y. Corbel, H.M. Blau, Reprogramming towards pluripotency requires AID-dependent DNA demethylation, Nature 463 (2010) 1042–1047.

64

L. Ottini et al. / Mutation Research 779 (2015) 57–64

[25] K. Rai, I.J. Huggins, S.R. James, A.R. Karpf, D.A. Jones, B.R. Cairns, DNA demethylation in zebrafish involves the coupling of a deaminase, a glycosylase, and gadd45, Cell 135 (2008) 1201–1212. [26] P.O. Estève, H.G. Chin, S. Pradhan, Human maintenance DNA (cytosine-5)-methyltransferase and p53 modulate expression of p53-repressed promoters, Proc. Natl. Acad. Sci. U. S. A. 102 (2005) 1000–1005. [27] O. Mortusewicz, L. Schermelleh, J. Walter, M.C. Cardoso, H. Leonhardt, Recruitment of DNA methyltransferase I to DNA repair sites, Proc. Natl. Acad. Sci. U. S. A. 102 (2005) 8905–8909. [28] C. Cuozzo, A. Porcellini, T. Angrisano, A. Morano, B. Lee, A. Di Pardo, S. Messina, R. Iuliano, A. Fusco, M.R. Santillo, M.T. Muller, L. Chiariotti, M.E. Gottesman, E.V. Avvedimento, DNA damage, homology-directed repair, and DNA methylation, PLoS Genet. 3 (7) (2007) e110. [29] S. Leng, C.A. Stidley, R. Willink, A. Bernauer, K. Do, M.A. Picchi, X. Sheng, M.A. Frasco, D. Van Den Berg, F.D. Gilliland, C. Zima, R.E. Crowell, S.A. Belinsky, Double-strand break damage and associated DNA repair genes predispose smokers to gene methylation, Cancer Res. 68 (2008) 3049–3056. [30] F. Marcon, D. Carotti, C. Andreoli, E. Siniscalchi, P. Leopardi, S. Caiola, M. Biffoni, A. Zijno, E. Medda, L. Nisticò, S. Rossi, R. Crebelli, DNA damage response in monozygotic twins discordant for smoking habits, Mutagenesis 28 (2013) 135–144. [31] S.A. Belinsky, W.A. Palmisano, F.D. Gilliland, L.A. Crooks, K.K. Divine, S.A. Winters, M.J. Grimes, H.J. Harms, C.S. Tellez, T.M. Smith, P.P. Moots, J.F. Lechner, C.A. Stidley, R.E. Crowell, Aberrant promoter methylation in bronchial epithelium and sputum from current and former smokers, Cancer Res. 62 (2002) 2370–2377. [32] T.K. Wojdacz, A. Dobrovic, L.L. Hansen, Methylation-sensitive high-resolution melting, Nat. Protoc. 3 (2008) 1903–1908. [33] T.K. Wojdacz, T. Borgbo, L.L. Hansen, Primer design versus PCR bias in methylation independent PCR amplifications, Epigenetics 4 (2009) 231–234. [34] J.C. Soria, M. Rodriguez, D.D. Liu, J.J. Lee, W.K. Hong, L. Mao, Aberrant promoter methylation of multiple genes in bronchial brush samples from former cigarette smokers, Cancer Res. 62 (2002) 351–355. [35] H.W. Chang, G.S. Ling, W.I. Wei, A.P. Yuen, Smoking and drinking can induce p15 methylation in the upper aerodigestive tract of healthy individuals and patients with head and neck squamous cell carcinoma, Cancer 101 (2004) 125–132. [36] A. Merlo, J.G. Herman, L. Mao, D.J. Lee, E. Gabrielson, P.C. Burger, S.B. Baylin, D. Sidransky, 5 CpG island methylation is associated with transcriptional silencing of the tumour suppressor p16/CDKN2/MTS1 in human cancers, Nat. Med. 1 (1995) 686–692. [37] C.A. Markunas, Z. Xu, S. Harlid, P.A. Wade, R.T. Lie, J.A. Taylor, A.J. Wilcox, Identification of DNA methylation changes in newborns related to maternal smoking during pregnancy, Environ. Health Perspect. 122 (10) (2014) 1147–1153. [38] S. Leng, C.A. Stidley, A.M. Bernauer, M.A. Picchi, X. Sheng, M.A. Frasco, D. Van Den Berg, F.D. Gilliland, R.E. Crowell, S.A. Belinsky, Haplotypes of DNMT1 and DNMT3B are associated with mutagen sensitivity induced by benzo(a) pyrene diol epoxide among smokers, Carcinogenesis 29 (2008) 1380–1385. [39] J.T. Bell, T.D. Spector, A twin approach to unraveling epigenetics, Trends Genet. 27 (2011) 116–125. [40] J.T. Bell, A.A. Pai, J.K. Pickrell, D.J. Gaffney, R. Pique-Regi, J.F. Degner, Y. Gilad, J.K. Pritchard, DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines, Genome Biol. 12 (2011) R10, http://www.ncbi.nlm.nih.gov/pubmed?term=Pique-Regi%20R%5BAuthor %5D&cauthor=true&cauthor uid=21251332 [41] J.T. Bell, P.C. Tsai, T.P. Yang, R. Pidsley, J. Nisbet, D. Glass, M. Mangino, G. Zhai, F. Zhang, A. Valdes, S.Y. Shin, E.L. Dempster, R.M. Murray, E. Grundberg, A.K.

[42]

[43] [44] [45]

[46]

[47]

[48]

[49]

[50] [51] [52]

[53]

[54]

[55]

[56]

[57]

Hedman, A. Nica, K.S. Small, MuTHER Consortium, E.T. Dermitzakis, M.I. McCarthy, J. Mill, T.D. Spector, P. Deloukas, Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population, PLoS Genet. 8 (2012) e1002629. M.R. Munafò, M.N. Timofeeva, R.W. Morris, D. Prieto-Merino, N. Sattar, P. Brennan, E.C. Johnstone, C. Relton, P.C. Johnson, D. Walther, P.H. Whincup, J.P. Casas, G.R. Uhl, P. Vineis, S. Padmanabhan, B.J. Jefferis, A. Amuzu, E. Riboli, M.N. Upton, P. Aveyard, S. Ebrahim, A.D. Hingorani, G. Watt, T.M. Palmer, N.J. Timpson, E.P.I.C. Study Group, G. Davey Smith, Association between genetic variants on chromosome 15q25 locus and objective measures of tobacco exposure, J. Natl. Cancer Inst. 104 (2012) 740–748. K. Okumura, H. Tsukamoto, Folate in smokers, Clin. Chim. Acta 412 (2011) 521–552. A. Ly, L. Hoyt, J. Crowell, Y.I. Kim, Folate and DNA methylation, Antioxid. Redox Signal. 17 (2012) 302–326. G. Le Gac, P.O. Estève, C. Ferec, S. Pradhan, DNA damage-induced down-regulation of human Cdc25C and Cdc2 is mediated by cooperation between p53 and maintenance DNA (cytosine-5) methyltransferase 1, J. Biol. Chem. 281 (2006) 24161–24170. S.S. Palii, B.O. Van Emburgh, U.T. Sankpal, K.D. Brown, K.D. Robertson, DNA methylation inhibitor 5-Aza-2 -deoxycytidine induces reversible genome-wide DNA damage that is distinctly influenced by DNA methyltransferases 1 and 3B, Mol. Cell Biol. 28 (2008) 752–771. K. Ha, G.E. Lee, S.S. Palii, K.D. Brown, Y. Takeda, K. Liu, K.N. Bhalla, K.D. Robertson, Rapid and transient recruitment of DNMT1 to DNA double-strand breaks is mediated by its interaction with multiple components of the DNA damage response machinery, Hum. Mol. Genet. 20 (2011) 126–140. H.J. Kim, J.H. Kim, E.K. Chie, P.D. Young, I.A. Kim, I.H. Kim, DNMT (DNA methyltransferase) inhibitors radiosensitize human cancer cells by suppressing DNA repair activity, Radiat. Oncol. 7 (2012) 39. G.E. Lee, J.H. Kim, M. Taylor, M.T. Muller, DNA methyltransferase 1-associated protein (DMAP1) is a co-repressor that stimulates DNA methylation globally and locally at sites of double strand break repair, J. Biol. Chem. 285 (2010) 37630–37640. B. Jin, K.D. Robertson, DNA methyltransferases, DNA damage repair, and cancer, Adv. Exp. Med. Biol. 754 (2013) 3–29. M.S. Jhaveri, C. Wagner, J.B. Trepel, Impact of extracellular folate levels on global gene expression, Mol. Pharmacol. 60 (2001) 1288–1295. K.J. Sohn, J.M. Stempak, S. Reid, S. Shirwadkar, J.B. Mason, Y.I. Kim, The effect of dietary folate on genomic and p53-specific DNA methylation in rat colon, Carcinogenesis 24 (2003) 81–90. Y.B. Ding, J.L. He, X.Q. Liu, X.M. Chen, C.L. Long, Y.X. Wang, Expression of DNA methyltransferases in the mouse uterus during early pregnancy and susceptibility to dietary folate deficiency, Reproduction 144 (2012) 91–100. S.A. Belinsky, K.C. Liechty, F.D. Gentry, H.J. Wolf, J. Rogers, K. Vu, J. Haney, T.C. Kennedy, F.R. Hirsch, Y. Miller, W.A. Franklin, J.G. Herman, S.B. Baylin, P.A. Bunn, T. Byers, Promoter hypermethylation of multiple genes in sputum precedes lung cancer incidence in a high-risk cohort, Cancer Res. 66 (2006) 3338–3344. X. Wu, M.R. Spitz, C.I. Amos, J. Lin, L. Shao, J. Gu, M. de Andrade, N.L. Benowitz, P.G. Shields, G.E. Swan, Mutagen sensitivity has high heritability: evidence from a twin study, Cancer Res. 66 (2006) 5993–5996. K. Borgmann, D. Haeberle, T. Doerk, A. Busjahn, G. Stephan, E. Dikomey, Genetic determination of chromosomal radiosensitivities in G0- and G2-phase human lymphocytes, Radiother. Oncol. 83 (2007) 196–202. H. Surowy, A. Rinckleb, M. Luedeke, M. Stuber, A. Wecker, D. Varga, C. Maier, J. Hoegel, W. Vogel, Heritability of baseline and induced micronucleus frequencies, Mutagenesis 26 (2011) 111–117.