Journal of Exposure Science and Environmental Epidemiology (2014) 24, 145–149 & 2014 Nature America, Inc. All rights reserved 1559-0631/14
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ORIGINAL ARTICLE
Blood methylomics in response to arsenic exposure in a low-exposed US population Xin Liu1,2, Yinan Zheng3, Wei Zhang4, Xiao Zhang2, Donald M. LIoyd-Jones2, Andrea A. Baccarelli5, Hongyan Ning2, Myriam Fornage6, Ka He7, Kiang Liu2 and Lifang Hou2,8 Exposure to arsenic (As) has been associated with a number of diseases such as cancers, cardiovascular diseases (CVD), and neurological disorders. To explore the possible underlying epigenetic mechanisms, a nested case–control study was conducted within the Coronary Artery Risk Development in Young Adults (CARDIA) study by randomly selecting 46 non-smoker and non-diabetic White participants with low (N ¼ 23) and high (N ¼ 23) As exposure based on toenail total As measures at examination year 2. We conducted methylomic profiling of white blood cell (WBC) DNA collected at examination year 15 using the Illumina HumanMethylation450 BeadChip, and performed association tests using multiple linear regression models adjusting for age, sex, and estimated WBC proportions. We observed 22 CpG sites with methylation levels associated with high As exposure at a nominal significance level of 10 4. However, the statistical significance disappeared after correction for multiple testing. Some genes annotated by these 22 CpG sites are known to be involved in As-associated diseases. Replication in larger samples of individuals with low levels of As exposure will be required. Journal of Exposure Science and Environmental Epidemiology (2014) 24, 145–149; doi:10.1038/jes.2013.89; published online 25 December 2013 Keywords: arsenic exposure; methylomic profiling; prospective association
INTRODUCTION Arsenic (As) is a naturally occurring ubiquitous element found in foods and the environment, such as water, soil, and air. Chronic As exposure is a worldwide health problem.1 It was classified as a Group 1 carcinogen in 1987 by the International Agency for Research on Cancer (IARC).2 Cumulative experimental data and epidemiological evidence indicate that As exposure is associated with various cancers3 such as lung, bladder, kidney, liver, and skin,4–6 and other chronic diseases such as diabetes,7–9 cardiovascular diseases (CVD),10–12 and neurological disorders.13,14 The mechanisms linking As with disease remain largely unknown. Recent evidence suggests that environmental chemicals may cause diseases via epigenetic mechanisms that regulate gene expression without changing DNA sequencing, such as DNA methylation (i.e., cytosine modification at CpG dinucleotides), the most well-studied epigenetic mechanism in the etiology of disease.15,16 Although some studies have been conducted using tissue or cell line samples,17–20 evidence for As exposure and DNA methylation in blood DNA has also begun to accumulate.21–27 A global dose-dependent hypermethylation of blood DNA was observed in a Bangladeshi population with chronic exposure to As-contaminated drinking water.21 In a cross-sectional study of Indian individuals, As levels in contaminated water were also associated with global DNA hypermethylation in blood mononuclear cells.22 Chanda et al.23 observed blood DNA
hypermethylation in the promoter of p53 and p16 in As-exposed Indian individuals. Consistently, in a Chinese population, increased DNA methylation in the p16 promoter was observed in arseniasis patients compared with people without a history of As exposure.27 However, these previous studies have been limited to the evaluation of global methylation markers or methylation alterations in a small group of genes. Two recent genome-wide DNA methylation analyses were conducted in 16 female Mexicans aged 12–59 years, with half showing signs of arsenicosis24 or pre-diabetes and diabetes.25 Multiple comparisons were not controlled for in this small sized study, and thus spurious signals due to chance alone could not be eliminated. A US birth cohort study also found no statistically significant differences in methylation in response to low levels of As in utero after adjusting for multiple comparisons.26 The US population has a much lower level of exposure to As compared with Asians and Australians with a mean toenail As level of B0.1–0.29 mg/g vs B3– 32 mg/g;28–31 however, there have been no prospective genomewide studies to evaluate the effect of long-term low As exposure on DNA methylation alterations. In the Coronary Artery Risk Development in Young Adults (CARDIA) study, we previously measured year (Y) 2 toenail As levels. We further examined associations between Y2 toenail As levels and subclinical atherosclerotic markers, that is, carotid intima-media thickness (CIMT) and coronary artery
1 Mary Ann and J. Milburn Smith Child Health Research Program, Ann and Robert H. Lurie Children’s Hospital of Chicago Research Center, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; 2Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; 3 Driskill Graduate Program (DGP) in Life Sciences, Northwestern University Biomedical Informatics Center (NUBIC), Northwestern University Clinical and Translational Sciences Institute (NUCATS), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; 4Department of Pediatrics, Institute of Human Genetics, University of Illinois, Chicago, Illinois, USA; 5Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA; 6Institute of Molecular Medicine and School of Public Health, Division of Epidemiology Human Genetics and Environmental Sciences, University of Texas Health Sciences Center at Houston, Houston, Texas, USA; 7Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, Indiana, USA and 8The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. Correspondence to: Dr. Lifang Hou, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 North Lake Shore Drive, Suite 1400, Chicago, IL 60611, USA. Tel.: +1 312 503 4798. Fax: +1 312 908 9588. E-mail: [email protected] Received 29 May 2013; revised 4 October 2013; accepted 5 October 2013; published online 25 December 2013
Arsenic exposure and blood methylomics Liu et al
146 calcification (CAC). Our results show that the toenail As concentration was positively associated with the prevalence of the CAC score, common CIMT, and bulb CIMT at Y15 respectively (data not shown). We therefore hypothesize that exposure to As from environmental sources induces methylomic changes that persist with continuous exposure and may be a biomarker of Asassociated diseases, such as atherosclerosis. In this study, we performed a prospective genome-wide examination to evaluate whether exposure to As induces DNA methylation alterations in 46 apparently young, middle-aged healthy non-smoker, non-diabetic, White individuals derived from the CARDIA study, a large prospective study of young adults. MATERIALS AND METHODS Study Population CARDIA is a multi-center prospective study of risk factors for coronary artery disease (CAD) development in young adults free from CVD (N ¼ 5115) and aged 18–30 years at baseline (1985–6). Participants have undergone eight examinations to date, including a baseline examination at year 0 and follow-up examinations at Y2, 5, 7, 10, 15, 20, and 25, with a 72% examination rate at Y20 (2005–6). A detailed description of the study design, sampling, and initial response rates was previously published.32 Institutional Review Boards at each study site reviewed the protocol and procedures, and approved the research. All participants provided written informed consent. This study included 46 White participants who had available data for Y2 toenail total As level and Y15 blood DNA.
As Exposure Measurement in CARDIA and Study Subject Selection At examination Y2, CARDIA participants were mailed the instructions and materials for collecting toenail samples. Toenail clippings were collected and As level was measured in 4362 CARDIA participants by Neutron Activation Analysis (NAA)33 at the University of Missouri Research Reactor. A total of 46 White non-smoker, non-diabetic healthy age- and sexmatched study subjects (23 high- and 23 low-exposed) for the present study were randomly selected from the highest (Z0.1442 mg/g: high As exposure) and lowest (o0.0649 mg/g: low As exposure) quartile exposure groups.
Genome-Wide Examination of DNA Methylation Alterations We performed genome-wide DNA methylation examination in 46 white blood cell (WBC) DNA samples that passed the DNA quality test for our assay using the Illumina Infinium Human Methylation450 BeadChip, which targets B486,000 CpG sites. These samples were randomly plated on each chip with regard to ‘‘high’’ and ‘‘low’’ As levels. A 500-ng DNA sample from each selected CARDIA participant was used to perform bisulfite conversion followed by Illumina’s protocol for methylation profiling. BeadChips were scanned with an Illumina iScan and then analyzed using the Illumina GenomeStudio software. All experiments were conducted following the manufacturer’s protocols in the Genomics Core Facility at the Center for Genetic Medicine at Northwestern University. For the purpose of quality control (QC), in addition to Illumina’s built-in QC, we included commercially available known unmethylated (normal B lymphocytes (NA10923 from Coriell Institute), Camden, NJ, USA) and methylated (colon cancer cells (ATCC: HTB-38), Manassas, VA, USA) control samples, as previously described.34
Bioinformatic and Statistical Analysis Illumina 450K probe filtering. We first excluded the CpG probes that are ambiguously mapped to the human genome (hg19, Build 37). A total of 340,658 probes passed the analysis using Bowtie.35 To avoid potential bias due to genetic polymorphisms, we also filtered 6893 CpG probes with the presence of common single-nucleotide polymorphisms (SNPs) (i.e., minor allele frequency (MAF)Z0.01) within each probe. The MAF was based on the HapMap European origin populations (CEU: Caucasian residents from Utah, USA) in the dbSNP database (v135). To reduce the effects of differential methylation between males and females on the sex chromosomes, 7953 CpG sites on chromosomes X and Y were excluded. Finally, we removed 2921 probes having bead count o3 in 5% of samples or having 41% of samples with a detection P-value of 40.05. Note that, one sample was dropped due to 41% of the CpG sites having a detection P-value of 40.05.
Figure 1. Filter process for Illumina 450K Infinium Methylation BeadChip. In the end, a total of 322,891 CpG sites on autosomes were tested with regard to As exposure in the remaining 45 samples (Figure 1). Normalization and batch effect correction. Methylation intensities were first background adjusted between Type I and Type II probes, then between-sample quantile normalization of methylated and unmethylated intensities was performed separately, which applied to Type I and Type II probes, separately. No dye bias correction was performed. This normalization pipeline was conducted using R package ‘‘wateRmelon’’, which has been proven to have high sensitivity and accuracy for detection of differences between experimental groups.36 Next, b-values were computed from the normalized intensities and transformed into Mvalues as described previously.34 The data were corrected for potential chip effect (four chips) using empirical Bayes correction.37 WBC proportion estimation. To control for the effect of relative proportions of different WBC types on methylation level, cell proportions for each sample were estimated using an established algorithm.38 Among the 500 most informative CpG probes for distinguishing cell types chosen from the Illumina Infinium 27K array, all 421 probes that also presented in our filtered Illumina 450K array were used to estimate the cell proportions. A total of five different WBC type proportions were estimated, including T cells (CD8 þ and CD4 þ ), NK cells, B cells, monocytes, and granulocytes. Genome-wide association analysis. We then tested the associations between As exposure and DNA methylation of the pre-processed methylation levels. For each CpG site, a multiple linear regression model was fitted with adjustment for estimated WBC proportions as noted above, as well as age and gender, two known factors that influence DNA methylation patterns.39–41 We then annotated the significant As-associated CpG sites to the corresponding genes based on an Illumina-designed document (http://www.illumina.com/). For the identified differentially methylated genes, we searched for their functions and related disease involvements in the Ingenuity Knowledge Base (http://www.ingenuity.com/ products/pathways_analysis.html). A heatmap was plotted to visualize the different methylation levels across As exposure status using hierarchical clustering. All of the analyses were carried out using the R package and SAS software (version9.3; SAS Institute, Cary, NC, USA).
RESULTS Table 1 shows the characteristics of the study subjects with low (N ¼ 22) and high (N ¼ 23) As exposure status. There was no significant difference in age, education, alcohol drinking, marriage status, or body mass index (BMI) between the two groups. Table 2 provides the means (SD) of estimated proportions of WBC types by As exposure status 13 years ago. There was no statistically significant effect of As exposure on any WBC types.
Journal of Exposure Science and Environmental Epidemiology (2014), 145 – 149
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Arsenic exposure and blood methylomics Liu et al
147 Subjects exposed to relatively high As at year 2 appeared to have higher proportions of CD8 þ , CD4 þ , and monocytes but lower proportions of NK cells, B cells, and granulocytes at year 15 than those exposed to low As. Table 3 lists 22 CpG sites with methylation levels associated with As exposure status at a nominal P-value of o10 4. The absolute and percentage difference of mean methylation levels between the high and low As exposure groups ranged from 0.01 to 0.12 and 1– 41%, respectively. An overview of the sample relations based on a heatmap of these top CpG sites shows distinct DNA methylation patterns between subjects with high and low As exposure (Figure 2), with over half of the top CpG sites showing elevated methylation levels in high As exposure groups compared with low Table 1. Characteristics of 45 study subjects in CARDIA by arsenic exposure status. Variables
Male, n (%) Age (years), mean (SD) Education (years), mean (SD) Married, n (%) Alcohol drinker, n (%) Body mass index (kg/m2), mean (SD)
As high exposed (N ¼ 23)
As low exposed (N ¼ 22)
P-valuea
12 (52.1) 41.2±2.6
11 (50.0) 41.6±2.8
0.99 0.61
15.7±2.2
16.2±2.4
0.40
8 (35.8) 17 (73.9) 25.9±4.3
11 (50.0) 16 (72.7) 25.5±4.7
0.37 0.99 0.78
exposure groups. Some genes annotated by these 22 CpG sites are involved in cancers (BSG, SNRNP200, MICA, FRYI, CEP112, CDC7, PAK2, BSDC1, and ANK3), Type I diabetes (MICA and BTNL2), and neurological disorders (ANK3, EGLN1, and MAG).
DISCUSSION This is the first prospective genome-wide methylomic study linking DNA methylation markers with As exposure status assessed in toenails collected 13 years ago. We found that As exposure may affect DNA methylation levels in genes known to be involved in Asassociated diseases. These findings, once confirmed by a large independent human sample, should stimulate future investigations to better understand the underlying molecular mechanisms of how As exposure might contribute to the pathogenesis of As-associated diseases via DNA methylation changes. Table 2. Estimated proportions of white blood cell types (mean±SD) by arsenic exposure status 13 years ago. Cell types CD8 þ CD4 þ NK cells B cells Monocytes Granulocytes
a
P-values were calculated using Student’s t-test and Fisher’s exact test for continuous and categorical variables, respectively.
Table 3.
As high exposed (N ¼ 23)
As low exposed (N ¼ 22)
P-valuea
11.12±7.68 19.38±5.60 2.20±2.22 4.13±3.28 10.24±3.10 51.96±10.54
8.56±4.61 17.68±7.20 2.48±2.83 7.14±12.55 9.38±3.32 53.69±12.75
0.27 0.63 0.75 0.82 0.26 0.42
a
P-values were calculated using exact Wilcoxon two-sample test.
Top CpG sites with methylation levels associated with As exposure (Po10 4).
IllumID
Gene symbola
Gene features
cg05622915
BSG
TSS200 TSS1500
cg00088989
SNRNP200
Body
cg07175191d cg06696800
SUPT6H/SDF2 FAM176A (EVA1A) MAG ADCK1 MICA
TSS1500/Body 5’UTR; first exon
TSS200 TSS1500 Body
cg17428496 cg23417171 cg25417223 cg06722407 cg10014338
FRYL CCDC46 (CEP112) AP3D1 — NFIX LOC100101938 BTNL2
TSS1500 — 3’UTR Body Body
cg07604512 cg13611173 cg20682143 cg01040960 cg02276490 cg16793757
CDC7 NAA30 EGLN1 PAK2 BSDC1 ANK3
cg22489759 cg10389982
ELL WDFY3
cg04690840 cg23997365 cg05651282 cg13311357 cg02824793
Body Body TSS1500
Mean diff. (% diff.)b 0.01 (15)
P-value 1.89E 06
0.04 (5)
3.37E 06
0.01 (16) 0.02 (41)
1.15E 05 1.64E 05
0.02 (2) 0.03 (3) 0.03 (21)
3.08E 05 3.22E 05 4.44E 05
0.01 (21) 0.02 (2)
4.64E 05 4.70E 05
0.01 0.04 0.02 0.12 0.01
(12) (5) (13) (31) (1)
4.90E 05 5.25E 05 5.72E 05 7.16E 05 7.19E 05
TSS1500 5’UTR; first exon TSS1500 5’UTR Body Body
0.02 0.01 0.06 0.02 0.01 0.03
(2) (19) (8) (2) (13) (4)
7.20E 05 7.76E 05 7.79E 05 8.05E 05 8.07E 05 8.21E 05
Body 5’UTR
0.02 (2) 0.02 (2)
8.69E 05 9.51E 05
Associated diseasesc Endometriosis; hepatocellular carcinoma; measles virus infection; liver cancer; osteolytic bone disease; primary biliary cirrhosis, sclerosing cholangitis; renal cancer; renal-cell carcinoma; Crohn’s disease Rheumatoid arthritis; retinitis pigmentosa type 33; adenocarcinoma in lung — — Schizophrenia, Alzheimer’s disease — Type I diabetes; rheumatoid arthritis; systemic lupus erythematosus; celiac disease; multiple myeloma Adenocarcinoma in lung Adenocarcinoma in lung — — Sotos syndrome; Marshall–Smith syndrome — Rheumatoid arthritis; Type I diabetes; multiple sclerosis; primary biliary cirrhosis; Crohn’s disease Uterine leiomyoma; adenocarcinoma in stomach — Parkinson’s disease; Familial erythrocytosis Head and neck cancer Prostate cancer Bipolar disorder; schizophrenia; adenocarcinoma in lung; metastatic colorectal cancer — Crohn’s disease
a
Genes were sorted by nominal P-values from the regression models with the adjustment of age, sex, and estimated white blood cell proportions. Mean difference ¼ mean methylation values in As high exposed mean values in As low exposed; % Diff. ¼ ((absolute mean difference between methylation values in As high- and low-exposed subjects)/(summation of these two means/2)) 100%. c From Ingenuity Knowledge Base and PubMed search; gene expression or protein levels or genetic polymorphisms are associated with diseases in humans. d cg07175191 is annotated to two adjacent genes. b
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Journal of Exposure Science and Environmental Epidemiology (2014), 145 – 149
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148 et al.26 reported in a US study of 134 mother–infant pairs, which could be due to the relatively small sample size (45 vs 134), age of the studied subjects (adults vs newborns), As exposure status assessment (toenails vs maternal urine), biospecimen used for DNA methylation (venous blood vs cord blood), and so on. Finally, we assumed that As exposure status at Y2 was stable over the 13-year follow-up period given that the majority of the CARDIA participants lived in the same areas (87%) and all of the 46 participants provided the same addresses when their blood samples were collected at Y15. It is possible that other risk factors present between Y2 and Y15 could have contributed to the observed As-associated epigenetic changes. Replication in larger samples of individuals with low levels of As-exposure will be required. CONFLICT OF INTEREST The authors declare no conflict of interest.
ACKNOWLEDGEMENTS Figure 2. Heatmap of 22 top CpG sites with methylation levels associated with As exposure. Note. DNA methylation heatmap of methylated genes (nominal Po10 4) in white blood cell (WBC) DNA from As highly exposed subjects compared with that from lowexposed subjects. Each row represents a CpG site with columns corresponding to each sample. Higher methylation levels are shaded in red and lower levels are in green. The dendrogram shows the results of unsupervised hierarchical clustering of 45 samples based on 22 CpG sites, which separates As highly exposed subjects (labeled as H at the bottom) from As low-exposed subjects (labeled as L).
As exposure has repeatedly been associated with a variety of common diseases, including lung, bladder, kidney, liver, and skin cancers,4–6 CVD,10–12 and neurological diseases.13,14 Oxidative stress has been proposed as a link between As exposure and these chronic diseases. A recent study demonstrated that reactive oxygen species (ROS) production can alter the expression of genes belonging to DNA methylation machinery,42 and thus could induce altered DNA methylation patterns. In addition, inorganic As is enzymatically methylated for detoxification using S-adenosyl methionine (SAM) in the process.43 Therefore, SAM insufficiency might be another possible mechanism underlying As-induced DNA methylation, given that both As metabolism and DNA methylation need SAM as the methyl donor. Furthermore, As exposure often occurs in relatively resource-poor populations with low dietary intake of methionine, an essential amino acid required for SAM synthesis.44 As has also been shown to decrease DNA methyltransferase (DNMT) gene expression17 and enzyme activities.3 All of these As-induced cellular processes may independently or cooperatively interact to contribute to related DNA methylation changes. Different genes may behave differently with respect to As exposure-associated diseases, and As may cause hypo- or hyper-methylation in each individual gene depending on the role of the gene in cancer and other disease development. Our findings suggest that As exposure may be associated with DNA methylation levels in genes implicated in cancers, diabetes, CVD, and neurological disorders. Nevertheless, these associations should be interpreted with caution because of our small sample size and multiple testing issues as well as the flaws inherent in the 450K BeadChips. With a total of 45 study subjects, none of the top 22 CpG sites remained statistically significant after correction for multiple testing, which is consistent with previous findings in both high24,25and low26As exposed populations. Of note, we did not observe any statistically significant differences in the estimated proportion of CD8 þ cells across As exposure status as Koestler
The Coronary Artery Risk Development in Young Adults Study (CARDIA) is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with the University of Alabama at Birmingham (HHSN268201300025C and HHSN268201300026C), Northwestern University (HHSN268201300027C), University of Minnesota (HHSN268201300028C), Kaiser Foundation Research Institute (HHSN268201300029C), and Johns Hopkins University School of Medicine (HHSN268200900041C). CARDIA is also partially supported by the Intramural Research Program of the National Institute on Aging. This study is supported in part by George M Eisenberg Foundation and an NHLBI grant (R01HL081572). This manuscript has been reviewed by CARDIA for scientific content.
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ORIGINAL ARTICLE
Blood methylomics in response to arsenic exposure in a low-exposed US population Xin Liu1,2, Yinan Zheng3, Wei Zhang4, Xiao Zhang2, Donald M. LIoyd-Jones2, Andrea A. Baccarelli5, Hongyan Ning2, Myriam Fornage6, Ka He7, Kiang Liu2 and Lifang Hou2,8 Exposure to arsenic (As) has been associated with a number of diseases such as cancers, cardiovascular diseases (CVD), and neurological disorders. To explore the possible underlying epigenetic mechanisms, a nested case–control study was conducted within the Coronary Artery Risk Development in Young Adults (CARDIA) study by randomly selecting 46 non-smoker and non-diabetic White participants with low (N ¼ 23) and high (N ¼ 23) As exposure based on toenail total As measures at examination year 2. We conducted methylomic profiling of white blood cell (WBC) DNA collected at examination year 15 using the Illumina HumanMethylation450 BeadChip, and performed association tests using multiple linear regression models adjusting for age, sex, and estimated WBC proportions. We observed 22 CpG sites with methylation levels associated with high As exposure at a nominal significance level of 10 4. However, the statistical significance disappeared after correction for multiple testing. Some genes annotated by these 22 CpG sites are known to be involved in As-associated diseases. Replication in larger samples of individuals with low levels of As exposure will be required. Journal of Exposure Science and Environmental Epidemiology (2014) 24, 145–149; doi:10.1038/jes.2013.89; published online 25 December 2013 Keywords: arsenic exposure; methylomic profiling; prospective association
INTRODUCTION Arsenic (As) is a naturally occurring ubiquitous element found in foods and the environment, such as water, soil, and air. Chronic As exposure is a worldwide health problem.1 It was classified as a Group 1 carcinogen in 1987 by the International Agency for Research on Cancer (IARC).2 Cumulative experimental data and epidemiological evidence indicate that As exposure is associated with various cancers3 such as lung, bladder, kidney, liver, and skin,4–6 and other chronic diseases such as diabetes,7–9 cardiovascular diseases (CVD),10–12 and neurological disorders.13,14 The mechanisms linking As with disease remain largely unknown. Recent evidence suggests that environmental chemicals may cause diseases via epigenetic mechanisms that regulate gene expression without changing DNA sequencing, such as DNA methylation (i.e., cytosine modification at CpG dinucleotides), the most well-studied epigenetic mechanism in the etiology of disease.15,16 Although some studies have been conducted using tissue or cell line samples,17–20 evidence for As exposure and DNA methylation in blood DNA has also begun to accumulate.21–27 A global dose-dependent hypermethylation of blood DNA was observed in a Bangladeshi population with chronic exposure to As-contaminated drinking water.21 In a cross-sectional study of Indian individuals, As levels in contaminated water were also associated with global DNA hypermethylation in blood mononuclear cells.22 Chanda et al.23 observed blood DNA
hypermethylation in the promoter of p53 and p16 in As-exposed Indian individuals. Consistently, in a Chinese population, increased DNA methylation in the p16 promoter was observed in arseniasis patients compared with people without a history of As exposure.27 However, these previous studies have been limited to the evaluation of global methylation markers or methylation alterations in a small group of genes. Two recent genome-wide DNA methylation analyses were conducted in 16 female Mexicans aged 12–59 years, with half showing signs of arsenicosis24 or pre-diabetes and diabetes.25 Multiple comparisons were not controlled for in this small sized study, and thus spurious signals due to chance alone could not be eliminated. A US birth cohort study also found no statistically significant differences in methylation in response to low levels of As in utero after adjusting for multiple comparisons.26 The US population has a much lower level of exposure to As compared with Asians and Australians with a mean toenail As level of B0.1–0.29 mg/g vs B3– 32 mg/g;28–31 however, there have been no prospective genomewide studies to evaluate the effect of long-term low As exposure on DNA methylation alterations. In the Coronary Artery Risk Development in Young Adults (CARDIA) study, we previously measured year (Y) 2 toenail As levels. We further examined associations between Y2 toenail As levels and subclinical atherosclerotic markers, that is, carotid intima-media thickness (CIMT) and coronary artery
1 Mary Ann and J. Milburn Smith Child Health Research Program, Ann and Robert H. Lurie Children’s Hospital of Chicago Research Center, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; 2Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; 3 Driskill Graduate Program (DGP) in Life Sciences, Northwestern University Biomedical Informatics Center (NUBIC), Northwestern University Clinical and Translational Sciences Institute (NUCATS), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; 4Department of Pediatrics, Institute of Human Genetics, University of Illinois, Chicago, Illinois, USA; 5Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA; 6Institute of Molecular Medicine and School of Public Health, Division of Epidemiology Human Genetics and Environmental Sciences, University of Texas Health Sciences Center at Houston, Houston, Texas, USA; 7Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, Indiana, USA and 8The Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. Correspondence to: Dr. Lifang Hou, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 North Lake Shore Drive, Suite 1400, Chicago, IL 60611, USA. Tel.: +1 312 503 4798. Fax: +1 312 908 9588. E-mail: [email protected] Received 29 May 2013; revised 4 October 2013; accepted 5 October 2013; published online 25 December 2013
Arsenic exposure and blood methylomics Liu et al
146 calcification (CAC). Our results show that the toenail As concentration was positively associated with the prevalence of the CAC score, common CIMT, and bulb CIMT at Y15 respectively (data not shown). We therefore hypothesize that exposure to As from environmental sources induces methylomic changes that persist with continuous exposure and may be a biomarker of Asassociated diseases, such as atherosclerosis. In this study, we performed a prospective genome-wide examination to evaluate whether exposure to As induces DNA methylation alterations in 46 apparently young, middle-aged healthy non-smoker, non-diabetic, White individuals derived from the CARDIA study, a large prospective study of young adults. MATERIALS AND METHODS Study Population CARDIA is a multi-center prospective study of risk factors for coronary artery disease (CAD) development in young adults free from CVD (N ¼ 5115) and aged 18–30 years at baseline (1985–6). Participants have undergone eight examinations to date, including a baseline examination at year 0 and follow-up examinations at Y2, 5, 7, 10, 15, 20, and 25, with a 72% examination rate at Y20 (2005–6). A detailed description of the study design, sampling, and initial response rates was previously published.32 Institutional Review Boards at each study site reviewed the protocol and procedures, and approved the research. All participants provided written informed consent. This study included 46 White participants who had available data for Y2 toenail total As level and Y15 blood DNA.
As Exposure Measurement in CARDIA and Study Subject Selection At examination Y2, CARDIA participants were mailed the instructions and materials for collecting toenail samples. Toenail clippings were collected and As level was measured in 4362 CARDIA participants by Neutron Activation Analysis (NAA)33 at the University of Missouri Research Reactor. A total of 46 White non-smoker, non-diabetic healthy age- and sexmatched study subjects (23 high- and 23 low-exposed) for the present study were randomly selected from the highest (Z0.1442 mg/g: high As exposure) and lowest (o0.0649 mg/g: low As exposure) quartile exposure groups.
Genome-Wide Examination of DNA Methylation Alterations We performed genome-wide DNA methylation examination in 46 white blood cell (WBC) DNA samples that passed the DNA quality test for our assay using the Illumina Infinium Human Methylation450 BeadChip, which targets B486,000 CpG sites. These samples were randomly plated on each chip with regard to ‘‘high’’ and ‘‘low’’ As levels. A 500-ng DNA sample from each selected CARDIA participant was used to perform bisulfite conversion followed by Illumina’s protocol for methylation profiling. BeadChips were scanned with an Illumina iScan and then analyzed using the Illumina GenomeStudio software. All experiments were conducted following the manufacturer’s protocols in the Genomics Core Facility at the Center for Genetic Medicine at Northwestern University. For the purpose of quality control (QC), in addition to Illumina’s built-in QC, we included commercially available known unmethylated (normal B lymphocytes (NA10923 from Coriell Institute), Camden, NJ, USA) and methylated (colon cancer cells (ATCC: HTB-38), Manassas, VA, USA) control samples, as previously described.34
Bioinformatic and Statistical Analysis Illumina 450K probe filtering. We first excluded the CpG probes that are ambiguously mapped to the human genome (hg19, Build 37). A total of 340,658 probes passed the analysis using Bowtie.35 To avoid potential bias due to genetic polymorphisms, we also filtered 6893 CpG probes with the presence of common single-nucleotide polymorphisms (SNPs) (i.e., minor allele frequency (MAF)Z0.01) within each probe. The MAF was based on the HapMap European origin populations (CEU: Caucasian residents from Utah, USA) in the dbSNP database (v135). To reduce the effects of differential methylation between males and females on the sex chromosomes, 7953 CpG sites on chromosomes X and Y were excluded. Finally, we removed 2921 probes having bead count o3 in 5% of samples or having 41% of samples with a detection P-value of 40.05. Note that, one sample was dropped due to 41% of the CpG sites having a detection P-value of 40.05.
Figure 1. Filter process for Illumina 450K Infinium Methylation BeadChip. In the end, a total of 322,891 CpG sites on autosomes were tested with regard to As exposure in the remaining 45 samples (Figure 1). Normalization and batch effect correction. Methylation intensities were first background adjusted between Type I and Type II probes, then between-sample quantile normalization of methylated and unmethylated intensities was performed separately, which applied to Type I and Type II probes, separately. No dye bias correction was performed. This normalization pipeline was conducted using R package ‘‘wateRmelon’’, which has been proven to have high sensitivity and accuracy for detection of differences between experimental groups.36 Next, b-values were computed from the normalized intensities and transformed into Mvalues as described previously.34 The data were corrected for potential chip effect (four chips) using empirical Bayes correction.37 WBC proportion estimation. To control for the effect of relative proportions of different WBC types on methylation level, cell proportions for each sample were estimated using an established algorithm.38 Among the 500 most informative CpG probes for distinguishing cell types chosen from the Illumina Infinium 27K array, all 421 probes that also presented in our filtered Illumina 450K array were used to estimate the cell proportions. A total of five different WBC type proportions were estimated, including T cells (CD8 þ and CD4 þ ), NK cells, B cells, monocytes, and granulocytes. Genome-wide association analysis. We then tested the associations between As exposure and DNA methylation of the pre-processed methylation levels. For each CpG site, a multiple linear regression model was fitted with adjustment for estimated WBC proportions as noted above, as well as age and gender, two known factors that influence DNA methylation patterns.39–41 We then annotated the significant As-associated CpG sites to the corresponding genes based on an Illumina-designed document (http://www.illumina.com/). For the identified differentially methylated genes, we searched for their functions and related disease involvements in the Ingenuity Knowledge Base (http://www.ingenuity.com/ products/pathways_analysis.html). A heatmap was plotted to visualize the different methylation levels across As exposure status using hierarchical clustering. All of the analyses were carried out using the R package and SAS software (version9.3; SAS Institute, Cary, NC, USA).
RESULTS Table 1 shows the characteristics of the study subjects with low (N ¼ 22) and high (N ¼ 23) As exposure status. There was no significant difference in age, education, alcohol drinking, marriage status, or body mass index (BMI) between the two groups. Table 2 provides the means (SD) of estimated proportions of WBC types by As exposure status 13 years ago. There was no statistically significant effect of As exposure on any WBC types.
Journal of Exposure Science and Environmental Epidemiology (2014), 145 – 149
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Arsenic exposure and blood methylomics Liu et al
147 Subjects exposed to relatively high As at year 2 appeared to have higher proportions of CD8 þ , CD4 þ , and monocytes but lower proportions of NK cells, B cells, and granulocytes at year 15 than those exposed to low As. Table 3 lists 22 CpG sites with methylation levels associated with As exposure status at a nominal P-value of o10 4. The absolute and percentage difference of mean methylation levels between the high and low As exposure groups ranged from 0.01 to 0.12 and 1– 41%, respectively. An overview of the sample relations based on a heatmap of these top CpG sites shows distinct DNA methylation patterns between subjects with high and low As exposure (Figure 2), with over half of the top CpG sites showing elevated methylation levels in high As exposure groups compared with low Table 1. Characteristics of 45 study subjects in CARDIA by arsenic exposure status. Variables
Male, n (%) Age (years), mean (SD) Education (years), mean (SD) Married, n (%) Alcohol drinker, n (%) Body mass index (kg/m2), mean (SD)
As high exposed (N ¼ 23)
As low exposed (N ¼ 22)
P-valuea
12 (52.1) 41.2±2.6
11 (50.0) 41.6±2.8
0.99 0.61
15.7±2.2
16.2±2.4
0.40
8 (35.8) 17 (73.9) 25.9±4.3
11 (50.0) 16 (72.7) 25.5±4.7
0.37 0.99 0.78
exposure groups. Some genes annotated by these 22 CpG sites are involved in cancers (BSG, SNRNP200, MICA, FRYI, CEP112, CDC7, PAK2, BSDC1, and ANK3), Type I diabetes (MICA and BTNL2), and neurological disorders (ANK3, EGLN1, and MAG).
DISCUSSION This is the first prospective genome-wide methylomic study linking DNA methylation markers with As exposure status assessed in toenails collected 13 years ago. We found that As exposure may affect DNA methylation levels in genes known to be involved in Asassociated diseases. These findings, once confirmed by a large independent human sample, should stimulate future investigations to better understand the underlying molecular mechanisms of how As exposure might contribute to the pathogenesis of As-associated diseases via DNA methylation changes. Table 2. Estimated proportions of white blood cell types (mean±SD) by arsenic exposure status 13 years ago. Cell types CD8 þ CD4 þ NK cells B cells Monocytes Granulocytes
a
P-values were calculated using Student’s t-test and Fisher’s exact test for continuous and categorical variables, respectively.
Table 3.
As high exposed (N ¼ 23)
As low exposed (N ¼ 22)
P-valuea
11.12±7.68 19.38±5.60 2.20±2.22 4.13±3.28 10.24±3.10 51.96±10.54
8.56±4.61 17.68±7.20 2.48±2.83 7.14±12.55 9.38±3.32 53.69±12.75
0.27 0.63 0.75 0.82 0.26 0.42
a
P-values were calculated using exact Wilcoxon two-sample test.
Top CpG sites with methylation levels associated with As exposure (Po10 4).
IllumID
Gene symbola
Gene features
cg05622915
BSG
TSS200 TSS1500
cg00088989
SNRNP200
Body
cg07175191d cg06696800
SUPT6H/SDF2 FAM176A (EVA1A) MAG ADCK1 MICA
TSS1500/Body 5’UTR; first exon
TSS200 TSS1500 Body
cg17428496 cg23417171 cg25417223 cg06722407 cg10014338
FRYL CCDC46 (CEP112) AP3D1 — NFIX LOC100101938 BTNL2
TSS1500 — 3’UTR Body Body
cg07604512 cg13611173 cg20682143 cg01040960 cg02276490 cg16793757
CDC7 NAA30 EGLN1 PAK2 BSDC1 ANK3
cg22489759 cg10389982
ELL WDFY3
cg04690840 cg23997365 cg05651282 cg13311357 cg02824793
Body Body TSS1500
Mean diff. (% diff.)b 0.01 (15)
P-value 1.89E 06
0.04 (5)
3.37E 06
0.01 (16) 0.02 (41)
1.15E 05 1.64E 05
0.02 (2) 0.03 (3) 0.03 (21)
3.08E 05 3.22E 05 4.44E 05
0.01 (21) 0.02 (2)
4.64E 05 4.70E 05
0.01 0.04 0.02 0.12 0.01
(12) (5) (13) (31) (1)
4.90E 05 5.25E 05 5.72E 05 7.16E 05 7.19E 05
TSS1500 5’UTR; first exon TSS1500 5’UTR Body Body
0.02 0.01 0.06 0.02 0.01 0.03
(2) (19) (8) (2) (13) (4)
7.20E 05 7.76E 05 7.79E 05 8.05E 05 8.07E 05 8.21E 05
Body 5’UTR
0.02 (2) 0.02 (2)
8.69E 05 9.51E 05
Associated diseasesc Endometriosis; hepatocellular carcinoma; measles virus infection; liver cancer; osteolytic bone disease; primary biliary cirrhosis, sclerosing cholangitis; renal cancer; renal-cell carcinoma; Crohn’s disease Rheumatoid arthritis; retinitis pigmentosa type 33; adenocarcinoma in lung — — Schizophrenia, Alzheimer’s disease — Type I diabetes; rheumatoid arthritis; systemic lupus erythematosus; celiac disease; multiple myeloma Adenocarcinoma in lung Adenocarcinoma in lung — — Sotos syndrome; Marshall–Smith syndrome — Rheumatoid arthritis; Type I diabetes; multiple sclerosis; primary biliary cirrhosis; Crohn’s disease Uterine leiomyoma; adenocarcinoma in stomach — Parkinson’s disease; Familial erythrocytosis Head and neck cancer Prostate cancer Bipolar disorder; schizophrenia; adenocarcinoma in lung; metastatic colorectal cancer — Crohn’s disease
a
Genes were sorted by nominal P-values from the regression models with the adjustment of age, sex, and estimated white blood cell proportions. Mean difference ¼ mean methylation values in As high exposed mean values in As low exposed; % Diff. ¼ ((absolute mean difference between methylation values in As high- and low-exposed subjects)/(summation of these two means/2)) 100%. c From Ingenuity Knowledge Base and PubMed search; gene expression or protein levels or genetic polymorphisms are associated with diseases in humans. d cg07175191 is annotated to two adjacent genes. b
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Journal of Exposure Science and Environmental Epidemiology (2014), 145 – 149
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148 et al.26 reported in a US study of 134 mother–infant pairs, which could be due to the relatively small sample size (45 vs 134), age of the studied subjects (adults vs newborns), As exposure status assessment (toenails vs maternal urine), biospecimen used for DNA methylation (venous blood vs cord blood), and so on. Finally, we assumed that As exposure status at Y2 was stable over the 13-year follow-up period given that the majority of the CARDIA participants lived in the same areas (87%) and all of the 46 participants provided the same addresses when their blood samples were collected at Y15. It is possible that other risk factors present between Y2 and Y15 could have contributed to the observed As-associated epigenetic changes. Replication in larger samples of individuals with low levels of As-exposure will be required. CONFLICT OF INTEREST The authors declare no conflict of interest.
ACKNOWLEDGEMENTS Figure 2. Heatmap of 22 top CpG sites with methylation levels associated with As exposure. Note. DNA methylation heatmap of methylated genes (nominal Po10 4) in white blood cell (WBC) DNA from As highly exposed subjects compared with that from lowexposed subjects. Each row represents a CpG site with columns corresponding to each sample. Higher methylation levels are shaded in red and lower levels are in green. The dendrogram shows the results of unsupervised hierarchical clustering of 45 samples based on 22 CpG sites, which separates As highly exposed subjects (labeled as H at the bottom) from As low-exposed subjects (labeled as L).
As exposure has repeatedly been associated with a variety of common diseases, including lung, bladder, kidney, liver, and skin cancers,4–6 CVD,10–12 and neurological diseases.13,14 Oxidative stress has been proposed as a link between As exposure and these chronic diseases. A recent study demonstrated that reactive oxygen species (ROS) production can alter the expression of genes belonging to DNA methylation machinery,42 and thus could induce altered DNA methylation patterns. In addition, inorganic As is enzymatically methylated for detoxification using S-adenosyl methionine (SAM) in the process.43 Therefore, SAM insufficiency might be another possible mechanism underlying As-induced DNA methylation, given that both As metabolism and DNA methylation need SAM as the methyl donor. Furthermore, As exposure often occurs in relatively resource-poor populations with low dietary intake of methionine, an essential amino acid required for SAM synthesis.44 As has also been shown to decrease DNA methyltransferase (DNMT) gene expression17 and enzyme activities.3 All of these As-induced cellular processes may independently or cooperatively interact to contribute to related DNA methylation changes. Different genes may behave differently with respect to As exposure-associated diseases, and As may cause hypo- or hyper-methylation in each individual gene depending on the role of the gene in cancer and other disease development. Our findings suggest that As exposure may be associated with DNA methylation levels in genes implicated in cancers, diabetes, CVD, and neurological disorders. Nevertheless, these associations should be interpreted with caution because of our small sample size and multiple testing issues as well as the flaws inherent in the 450K BeadChips. With a total of 45 study subjects, none of the top 22 CpG sites remained statistically significant after correction for multiple testing, which is consistent with previous findings in both high24,25and low26As exposed populations. Of note, we did not observe any statistically significant differences in the estimated proportion of CD8 þ cells across As exposure status as Koestler
The Coronary Artery Risk Development in Young Adults Study (CARDIA) is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with the University of Alabama at Birmingham (HHSN268201300025C and HHSN268201300026C), Northwestern University (HHSN268201300027C), University of Minnesota (HHSN268201300028C), Kaiser Foundation Research Institute (HHSN268201300029C), and Johns Hopkins University School of Medicine (HHSN268200900041C). CARDIA is also partially supported by the Intramural Research Program of the National Institute on Aging. This study is supported in part by George M Eisenberg Foundation and an NHLBI grant (R01HL081572). This manuscript has been reviewed by CARDIA for scientific content.
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