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Arch ToxicolDOI 10.1007/s00204-014-1351-2

INORGANIC COMPOUNDS

Epigenetic mechanisms underlying arsenic‑associated lung carcinogenesis

Simone G. J. van Breda · Sandra M. H. Claessen · Ken Lo · Marcel van Herwijnen · Karen J. J. Brauers · Sofia Lisanti · Daniël H. J. Theunissen · Danyel G. J. Jennen · Stan Gaj · Theo M. C. M. de Kok · Jos C. S. Kleinjans

Received: 13 April 2014 / Accepted: 25 August 2014 © Springer-Verlag Berlin Heidelberg 2014

time-dependent manner. By combining whole-genome DNA methylation and gene expression data with possibly involved transcription factors, a large molecular interaction network was created based on transcription factor-target gene pairs, consisting of 216 genes. A tumor protein p53 (TP53) subnetwork was identified, showing the interactions of TP53 with other genes affected by arsenic. Furthermore, multiple other new genes were discovered showing altered DNA methylation and gene expression. In particular, arse-nic modulated genes which function as transcription factor, thereby affecting target genes which are known to play a role in lung cancer promotion and progression.

Keywords Epigenomics · Transcriptomics · Arsenic · Lung cancer · Genetic pathways · Data integration

Introduction

Arsenic is an environmental toxicant and classified as a class I carcinogen by the International Agency for Research on Cancer (IARC 1987). Epidemiological stud-ies have indicated that exposure to high levels of arse-nic leads to a multitude of malignant tumors including cancers of the lung, skin, kidney, liver, and bladder, but also to noncancerous health effects such as skin lesions, peripheral vascular diseases, reproductive toxicity, and neurological effects. Exposure to arsenic occurs mainly through oral consumption of contaminated drinking water, soil and food, or via occupational exposure through inha-lation of contaminated dust particles and causes a world-wide health problem, but in particular in areas of Taiwan, South America, India, Pakistan, and Bangladesh, where concentrations in drinking water are relatively high (Tapio et al. 2006).

Abstract Arsenic is an established human carcino-gen, but the mechanisms through which it contributes to for instance lung cancer development are still unclear. As arsenic is methylated during its metabolism, it may inter-fere with the DNA methylation process, and is therefore considered to be an epigenetic carcinogen. In the present study, we hypothesize that arsenic is able to induce DNA methylation changes, which lead to changes in specific gene expression, in pathways associated with lung cancer promotion and progression. A549 human adenocarcinoma lung cells were exposed to a low (0.08 µM), intermediate (0.4 µM) and high (2 µM) concentration of sodium arsenite for 1, 2 and 8 weeks. DNA was isolated for whole-genome DNA methylation analyses using NimbleGen 2.1 M deluxe promoter arrays. In addition, RNA was isolated for whole-genome transcriptomic analysis using Affymetrix micro-arrays. Arsenic modulated DNA methylation and expres-sion levels of hundreds of genes in a dose-dependent and

Electronic supplementary material The online version of this article (doi:10.1007/s00204-014-1351-2) contains supplementary material, which is available to authorized users.

S. G. J. van Breda (*) · S. M. H. Claessen · M. van Herwijnen · K. J. J. Brauers · D. H. J. Theunissen · D. G. J. Jennen · S. Gaj · T. M. C. M. de Kok · J. C. S. Kleinjans Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, P.O. Box 616, 6200 MD Maastricht, The Netherlandse-mail: s.vanbreda@maastrichtuniversity.nl

K. Lo Roche Applied Science, Madison, WI 53719, USA

S. Lisanti Human Nutrition Research Centre, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne NE4 5PL, UK

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Recently, it was found that sediment levels of arse-nic were significantly associated with an increase in lung cancer incidence rates in the US, even after controlling for smoking and income. This indicates that low-level expo-sure to arsenic via drinking water is responsible for excess lung cancer cases, an effect which is at the moment under-estimated due to the pervasiveness of smoking and other tobacco use in the US (Putila et al. 2011). The impact of arsenic exposure on lung cancer incidence may therefore be higher than is estimated, not only in the US, but also in other parts of the Western World where exposure to arse-nic has already been decreased as a result of limitations of arsenic levels in drinking water.

Recent mechanistic studies have shown that the contri-bution of epigenetic control mechanisms of arsenic-induced carcinogenicity has become more evident [reviewed in (Bustaffa et al. 2014; Ren et al. 2011)]. Arsenic is meth-ylated during its metabolism, thereby depleting the intra-cellular methyl donor S-adenosyl-methionine, which may lead to disturbances in DNA methylation patterns, lead-ing to altered gene expression (Baccarelli et al. 2009; Ren et al. 2011). In particular, arsenic is able to modulate gene expression and/or DNA-binding activities of several key transcription factors in carcinogenesis, including nuclear factor kappa B, tumor protein 53, and activating protein-1 [reviewed in (Tchounwou et al. 2003)], thereby modifying signal transduction pathways involved in cell growth and proliferation.

Epigenetic mechanisms in lung cancer have been inves-tigated only recently, and alterations in DNA methylation have been shown to play a role in lung tumorigenesis. Only a couple of studies have investigated the effect of arsenic on epigenetic changes in the lung as target tissue. Few studies have been performed, i.e, in lung cancer cells (Mass et al. 1997; Zhou et al. 2008) and mouse lung tis-sue (Cui et al. 2006), and these studies mainly analyzed a limited number of epigenetic endpoints (Cui et al. 2006; Mass and Wang 1997; Zhou et al. 2008). There is a big gap in our knowledge of the mechanisms by which arse-nic can contribute to the development and progression of lung cancer, especially at the epigenetic level. Studies are needed in which the effect of chemical carcinogens like arsenic on a genome-wide level in cell lines and target tis-sues at different doses and exposure times is investigated in order to get more insight into the influence on epige-netic endpoints in relation to lung carcinogenesis (Ren et al. 2011).

In the present study, in which we use arsenic as a model compound of epigenetic carcinogens, we hypothesize that arsenic is able to induce DNA methylation changes, which lead to changes in specific gene expression, in pathways associated with lung cancer promotion and progression. Therefore, we investigated the dose-dependent (0.08, 0.4

and 2.0 μM) and time-dependent (1, 2, and 8 weeks expo-sure) effect of arsenic on DNA methylation changes in A549 human lung adenoma cells using NimbleGen 2.1 deluxe promoter arrays. In addition, whole-genome tran-scriptomic analysis is performed using Affymetrix whole-genome gene expression microarrays in order to corre-late alterations in DNA methylation and subsequent gene expression changes, and to perform transcription factor analyses. Extensive software applications were used in order to investigate methylation differences between vari-ous experimental conditions and to integrate these with transcriptomics data.

Materials and methods

Cell culture and sodium arsenite treatment

A549 cells (human epithelial lung carcinoma cells; obtained from the American Type Culture Collection, Manassas, VA) were cultured in Dulbecco’s modified Eagle’s medium (Sigma) supplemented with 10 % heat-inactivated fetal calf serum (Invitrogen) and 1 % penicillin/streptomycin (Sigma) and maintained at 37 °C in a 5 % CO2 atmosphere. Cells were exposed to sodium arsenite (NaAsO2, Sigma) at concentrations of 0.08, 0.4 and 2 µM for 1, 2 and 8 weeks. Exposure to sodium arsenite started 6 h after the cells were seeded into the culture flasks to ensure that sodium arsen-ite did not affect attachment. The final concentrations of sodium arsenite were obtained by the appropriate dilution with media. Medium was changed after 3 days, thereby providing a new dose of sodium arsenite to the cells. All experiments were performed in duplicate.

DNA isolation

Cells were washed twice with Hank’s Buffered Salt Solu-tion (Invitrogen) and trypsinized. Cell pellets were resus-pended in 500 μl digestion buffer (1 mM EDTA; 50 mM Tris–HCl, pH 8.0; 5 % SDS), and proteinase K (1 mg/ml) (Ambion) was added. After incubation of 1 h at 55 °C, the proteinase K was inactivated at 80 °C. RNAse A (400 μg/ml) (Qiagen) treatment was performed for 1 h at 37 °C. An equal amount of phenol–chloroform–isoamylalcohol (PCI; 25:24:1) (Sigma) was added and shaken manually for 5 min. The upper phase was precipitated with 50 µl 3 M NaAc pH 5.6 and 1,250 µl cold 100 % ethanol. The DNA pellet was washed with cold 70 % ETOH, dissolved in 50 µl nuclease free water and quantified spectrophoto-metrically using the NanoDrop 1000 (Thermo Scientific, Waltham, MA). The total amount was at least 10 µg DNA, the 260/280 ratio ranged between 1.7 and 1.9, and the 260/230 was higher than 1.6.

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Methylated DNA immunoprecipitation (MeDIP), whole-genome amplification and methylation enrichment assessment

Genomic DNAs were sonicated to obtain fragments rang-ing from 200 to 1,000 bp, cleaned up using silica columns (Zymo Research) and eluted in TE buffer. 4.4 µg of each sonicated sample was spiked with 40 ng of a positive (methylated) control and 40 ng of a negative (unmeth-ylated) control (Lisanti et al. 2012) in a total volume of 495 µL TE buffer. Ten percent of such prepared mixtures was kept as Input sample (Input) at 4 °C, while the remain-ing was immunoprecipitated with 12 µL of a monoclonal antibody against 5′-methylcytidine (Eurogentec) as previ-ously described (Weber et al. 2005, 2007). Immunoprecipi-tated DNAs (MeDIP) were purified using silica columns (Zymo Research) and eluted in TE buffer. Forty ng of both Input and MeDIP samples were whole-genome amplified (WGA) using the WGA2 kit (Sigma-Aldrich) following the manufacturer’s instruction, but omitting the fragmen-tation step. WGA reactions were cleaned up using silica columns (Sigma-Aldrich) and eluted in water. Methylation enrichment in the paired samples MeDIP/Input was cal-culated from qPCR data as the ratio of positive control to negative control, applying the ΔΔCq method. The primers used were the following: Pos. Ctrl. FOR: TAC AGA AAG ACG GAC GAA GG and REV: TGG TGG GCG TTT TCA TAC AT; Neg. Ctrl. FOR: TTC GTG ATA TTC CGT CGC TG and REV: AGT TTT TTG CCG CTT TAC CG (Lisanti et al. 2012).

MeDIP-chip

For analyses of DNA methylation levels, the Human DNA Methylation 2.1 M Deluxe Promoter Array (Roche Nimble-Gen) was used. Labeling and hybridization of arrays were performed according to the manufactures’ protocol. Slides were washed using the NimbleGen wash buffer kit and scanned using the 2-µm high-resolution NimbleGen MS 200 microarray scanner.

DNA methylation data analysis

Signal intensity data were extracted from the scanned images of each array using NimbleScan v2.6 software and quantile normalized on a per channel basis. Log2 ratios of the intensities were computed (ratio of MeDIP signal/Input signal), and for each array, centering is performed by sub-tracting the global array bi-weight mean of the log2 ratios such that the computed log2 ratios are centered around 0.

Detection of differential methylation was performed using the probe sliding window–ANOVA algorithm (PSW-ANOVA). PSW–ANOVA is implemented in the R

statistical programming environment as a custom script and was provided by Roche NimbleGen in collaboration. Briefly, for each probe, a sliding window is determined based on the sliding window size parameter, centering on the probe in question. Within each sliding window, a repeated-measures ANOVA is used to assess the difference between the various experimental groups for all probes located within the sliding window, using the probe ID as the reference to reduce the amount of error arising from different probes within a sliding window. The ANOVA algorithm contains a log linear model in which the differ-ent exposure conditions are incorporated as independent categorical variables.

PSW–ANOVA (sliding window of 750 bp comprising 7 probes, and a FDR adjusted p value < 0.05) was used to identify differential methylated regions (DMR), which were statistically significantly different between the differ-ent conditions tested in the experiment, i.e, concentration (CONC) and the interaction between time and concentra-tion (TIME*CONC), enabling identification of a dose–response (CONC) and a time-dependent dose–response effect of arsenic (TIME*CONC).

Peaks were identified in the DMR by searching for the number of probes (default = 2) above a p value minimum cutoff (−log10, default = 1.3) within 150-bp distance of each other. Peaks within 500 bp were merged. Peaks were mapped to promoter regions [from 3 kb upstream to 1 kb downstream of the transcription start site (Young et al. 2011)] and CpG islands of genes using the NimbleScan v2.6 software. A control corrected mean log2 ratio was calculated for the peaks mapped to these genes for each dose and time point. Log2 ratios >0 indicate hypermeth-ylation and log2 ratios <0 indicate hypomethylation of the DMR by arsenic treatment. The DNA methylation micro-array data from this publication have been submitted to the NCBI’s Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=ndctlgsygwwmkda&acc=GSE44174) and assigned the identifier GSE44174.

MeDIP qPCR: validation of DNA methylation array

Standard quantitative PCR analyses were carried out using 5 ng of MeDIP and Input samples on a Bio-Rad MyiQ™. A list of primers is available in supplementary data Table 1.

Total RNA isolation and microarray experiments

After removal of the medium, cells were lysed with TRI-zol (Invitrogen), and total RNA was extracted using 0.5 ml TRIzol according to the manufacturer’s instructions. RNe-asy Mini Kits (Qiagen, Westburg bv, The Netherlands) were used to purify total RNA from salts and residual DNA. All

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samples had on OD 260/280 ratio between 1.8 and 2.0, and held an RNA integrity number >8.

Sample preparation, hybridization, washing, staining and scanning of the Affymetrix Human Genome U133 Plus 2.0 GeneChip arrays were conducted according to the manufacturer’s manual as previously described (Jennen et al. 2010). The gene expression data from this publica-tion have been submitted to the NCBI’s GEO database (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=ndctlgsygwwmkda&acc=GSE44174) and assigned the identifier GSE44174.

Microarray gene expression data analyses

The Affymetrix CEL files were imported into R v2.15.0 (Team 2011) using the ‘affy’ library (Gautier et al. 2004) within BioConductor (v2.9) (Gentleman et al. 2004). Probe-sets were reannotated to EntrezGene genes using the Brain-Array custom CDF v15.0 annotations (Dai et al. 2005). The quality of the arrays was assessed through box plots, fitPLM, NUSE, RLE, clustering/heat maps, PCA and correlation plots. No technically deviating arrays were detected. Probe-set intensities were normalized using the RMA algorithm. For detecting DEG, the ‘limma’ library (Smyth 2005) was used to construct a linear model containing coefficients for all factor combinations. The resulting p values were FWER-corrected using the false discovery rate (FDR) approach. A gene was called differentially expressed when all three con-ditions were met: (1) average expression in at least one of the experimental groups >6 (log2-scale); (2) absolute fold change treated versus control≥1.5; and (3) FDR adjusted p value <0.05. Information on gene function was retrieved from EntrezGene (http://www.ncbi.nlm.nih.gov/gene/).

Integration of DNA methylation and Gene expression data

Correlation analyses

In order to link DNA methylation changes with gene expression changes, Pearson correlation analyses were per-formed. For each gene for which a significant DNA meth-ylation change was found, methylation differences were correlated with the changes in gene expression for the dose and exposure time range. As this is a very stringent analy-sis, an additional correlation analysis between DNA meth-ylation and gene expression changes per time point was performed.

Transcription factor analyses

The list with differentially methylated genes (DMG) and the list with differentially expressed genes (DEG) were used as input list in the TRANScription FACtor database

(TRANSFAC) database (Release 2012.3) (BIOBASE Bio-logical databases, Beverly, USA). For both gene sets, two different queries were run: (1) binding factor for gene (BFG): for each gene in the input list, a corresponding transcription factor(s) is searched for in the TRANSFAC database; and (2) gene bound by factor (GBF): searches for genes in the input list who serve as transcription factor and searches for target genes of these transcription factors in the TRANSFAC database. Output data were subsequently filtered on the following criteria: (1) the identified BFG or GBF should exist in the union of the DMG and DEG input list; (2) the identified BFG or GBF should be Homo sapi-ens of origin; and (3) the identified BFG should exist in the union of the DMG and DEG output data. The last filtering performed was to include BFG who were found in the out-put list for both input lists, but were not present in the input lists. The BFG thereby connects the DMG and DEG input data. Output data of TRANSFAC consist of pairs of genes, i.e, a list of transcription factors coupled to genes, and a list of target genes coupled to transcription factors.

Output data from TRANSFAC were uploaded in Cytoscape version 2.8.3, an open source bioinformatics software platform (www.cytoscape.org) used for visual-izing molecular interaction networks (Cline et al. 2007). Inputs for the network were the transcription factor—target gene pairs that were extracted from TRANSFAC.

The Transcription Regulation algorithm from the online commercially software suite MetaCore™ version 6.1 (GeneGo, San Diego, CA) was used in order to identify tran-scription factors in the TRANSFAC output data list for which the number of targets was significantly higher than expected based on the number of known targets for the particular tran-scription factor. Transcription factors were ranked according to their p value (p value <0.05 and FDR <0.05), based on hypergeometric distribution. The ranking represents the prob-ability of picking up a transcription factor by chance, taken into account the number of target genes it mapped to in the TRANSFAC output list versus the number of genes within the full set of all genes in the Metacore database.

The DMG present in the output data lists of TRANS-FAC was uploaded in the online software suite GenePat-tern version 3.1 (http://www.broadinstitute.org/cancer/software/genepattern/) (Reich et al. 2006) for hierarchical clustering analyses in order to visualize time-dependent and dose-dependent effects of a low (0.08 µM), interme-diate (0.4 µM) and high (2 µM) concentration of sodium arsenite on DNA methylation.

Text mining using the comparative toxicogenomics database

The Comparative Toxicogenomics Database (CTD) (Mount Desert Island Biological Laboratory, Salisbury

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Cove, Maine. http://ctdbase.org/) is a public resource that provides information on the interaction between environ-mental chemicals with gene products and their effect on human disease (Davis et al. 2013). Curated (arsenic–gene and sodium arsenite–gene interactions referred to as ‘arse-nic’; and lung neoplasm–gene interactions referred to as ‘lung cancer’) data were retrieved. Identified gene list was uploaded into VENNY (Oliveros 2007) creating Venn dia-grams for the gene lists for ‘arsenic’ and ‘lung cancer,’ and the identified DMG and DEG of the present study, thereby confirming identified genes in the current data set with pub-lished data, and enabling identification of new genes.

Results

Exposure of A549 cells to a low (0.08 µM), intermediate (0.4 µM) and high (2 µM) concentration of sodium arsen-ite for 1, 2 and 8 weeks resulted in statistically significant different methylation patterns between the different condi-tions. A dose–response effect was detected for 851 genes at all three time points. Moreover, for 94 genes, a time-dependent dose–response in methylation was identified (Supplementary data Table 2A and 2B for log ratios per condition, and Supplementary data Table 2C and 2D for comparisons between different conditions including statis-tics). In addition to DNA methylation, whole-genome gene expression analyses identified 673 statistically significantly modulated expressed genes after sodium arsenite treatment (Supplementary data Table 3). A Venn diagram was created comparing the differentially methylated gene list, the dif-ferentially expressed gene list, and the gene lists retrieved from CTD for ‘lung cancer’ and ‘arsenic’ (Supplementary data Fig. 1). Lists of shared genes were exported (Supple-mentary data Table 4). An arsenic–gene interaction was found in CTD for 109 differentially methylated genes. For 95 of these 109 genes, arsenic exposure resulted in a change in expression of the gene or its encoded protein. For 14 genes, our study established the modulating effect of arsenic exposure on DNA methylation and included among others homeobox B5 (HOXB5) (hypermethylation), protein tyrosine phosphatase, nonreceptor type 9 (PTPN9) (hypermethylation) and tumor protein p53 (TP53) (hypo-methylation). Four of the 109 genes which showed an interaction with ‘arsenic’ were also found to play a role in lung cancer and included telomerase reverse transcriptase (TERT) (hypermethylation), caspase 8, apoptosis-related cysteine peptidase (CASP8) (hypomethylation), ubiqui-tin carboxyl-terminal esterase L1 (ubiquitin thiolesterase) (UCHL1) (hypomethylation) and TP53. In addition to these four genes involved in lung cancer, an additional 6 DMG were identified in our data set, which play a role in lung carcinogenesis, i.e, solute carrier family 22 (organic cation

transporter), member 18 antisense (SLC22A18) (hyper-methylation), N-myc downstream regulated 1 (NDRG1) (hypermethylation), echinoderm microtubule associated protein like 4 (EML4) (hypomethylation), DAB2 interact-ing protein (DAB2IP) (hypomethylation), ribonucleotide reductase M1 (RRM1) (hypomethylation) and oxytocin receptor (OXTR) (hypermethylation).

For only 24 genes which were differentially methylated, a significant change in gene expression was also detected (Supplementary data Table 5). Only one gene (SH3 and multiple ankyrin repeat domains 2 (SHANK2) showed an inverse correlation (Pearson R < −0.7). Furthermore, for one gene, i.e., N-acetyltransferase 1 (arylamine N-acetyl-transferase) (NAT1) (hypermethylation), an interaction was found with arsenic exposure.

In addition to these gene expression changes, an inter-action with ‘arsenic’ and/or ‘lung cancer’ was found for in total 146 gene expression changes. For 14 genes, an interaction for both ‘arsenic’ and ‘lung cancer’ was found and included, e.g., heme oxygenase (HMOX1) (upregu-lated), cyclin-dependent kinase inhibitor 1A (p21, Cip1) (CDKN1A) (upregulated), interleukin 8 (IL8) (upregu-lated), FBJ murine osteosarcoma viral oncogene homolog (FOS) (downregulated) and tumor protein tp63 (TP63) (downregulated).

Correlation analyses between significant DNA meth-ylation changes and gene expression revealed few overall associations. Only 3.7 % (i.e., 20 of 544 genes) showed an overall inverse correlation (Pearsons R ≤ −0.7) between methylation and transcription (Supplementary data Table 6A) using all doses and time points. This percent-age is relatively low, but not an unusual outcome in this type of evaluation (Boellmann et al. 2010; Eckhardt et al. 2006). As the performed correlation analysis was car-ried out on the full dose and time range, we performed an additional and less stringent correlation analyses per time point, which revealed an increase of correlation up to 30 % (Pearsons R ≤ −0.7) (Supplementary data Table 6B). This implies that DNA methylation and gene expression changes are inversely correlated quite well at a specific time point, but do not show this relationship over time. Genes for which there was an inverse relation between treatment-related changes in DNA methylation and gene expres-sion were found to include caspase 6 apoptosis-related cysteine peptidase (CASP6), homeobox A3 (HOXA3), NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 4, 9 kDa (NDUFA4), ornithine aminotransferase (OAT), protein kinase, AMP-activated, beta 1 noncatalytic subunit (PRKAB1).

As it is known that arsenic is able to modulate expression and/or DNA-binding activities of several key transcription factors of carcinogenesis [reviewed in (Tchounwou et al. 2003)], the relation between DNA methylation changes and

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transcriptomics was further assessed by searching for tran-scription factors and their target genes in both gene lists. Therefore, gene lists were uploaded into TRANSFAC reveal-ing 436 transcription factors—target gene pairs representing 218 unique genes encoding for transcription factors (Sup-plementary data Table 7). Gene pairs were visualized using CYTOSCAPE showing a large molecular interaction net-work (Supplementary data Fig. 2). For six DMG encoding for transcription factors, the number of gene targets in the TRANSFAC output data list was significantly higher than expected and included TP53, transcription factor 7-like 2 (T-cell specific, HMG-box) (TCF7L2), neural retina leucine zipper (NRL), transcription factor 7 (T-cell specific, HMG-box) (TCF7), transcription factor 3 (E2A immunoglobulin enhancer binding factors E12/E47) (E2A) and vitamin D (1,25-dihydroxyvitamin D3) receptor (VDR) (Supplemen-tary data Table 8). The number of targets for TCF7L2, NRL, TCF7, E2A and VDR was relatively low (6, 3, 3, 4 and 4, respectively), resulting in small interaction networks. On the other hand, for TP53, a molecular interaction network (Fig. 1) was identified in the large molecular interaction network consisting of 43 transcription factors—target gene pairs representing 23 genes among which are TP53, TP63 (downregulated), dual specificity phosphatase 4 (DUSP4) (hypomethylated), TERT (hypermethylated), FOS (down-regulated), CDKN1A (upregulated), NDRG1 (hypermeth-ylated), NDRG family member 2 (NDRG2) (hypomethyl-ated), insulin-like growth factor binding protein 3 (IGFBP3) (downregulated), actin, alpha 2, smooth muscle, aorta (ACTA2) (hypermethylated), interferon, gamma-inducible protein 16 (IFI16) (upregulated), tripartite motif containing 22 (TRIM22) (upregulation), and Fas (TNF receptor super-family, member 6) (FAS) (upregulated) (Supplementary data Table 9). For the genes showing a significant effect upon treatment of sodium arsenite, the control corrected mean log2 ratio data for DNA methylation and the control corrected log2 fold changes for gene expression are shown in Tables 1 and 2, respectively, for each dose and time point.

MeDIP and Input samples were used for validation of the array results of the four genes AKT2, TP53, DUSP4 and FOXP3. Q-PCR analyses confirmed the losing or gaining of methylation in each case (Supplementary data Fig. 3).

Looking into more detail to the genes for which a signif-icant treatment effect of sodium arsenite on DNA methyla-tion was found and which were present in the large molecu-lar interaction network, the DNA methylation patterns were visualized using the hierarchical clustering algorithm in GenePattern. For these 61 genes, at each time point, genes were clustered showing a similar dose–response effect of sodium arsenite on DNA methylation (Fig. 2). A strong lin-ear dose-dependent increase in methylation (hypermethyla-tion) was found for, e.g., NDRG1, TERT and OXTR. A clear reduction in methylation (hypomethylation) with increasing

Fig. 1 Tumor protein p53 molecular interaction network of 43 transcription—target gene pairs representing 23 genes identified by TRANSFAC of significantly differentially methylated genes and significantly modulated gene expressions in A549 cells after treatment with 0.08 µM, 0.4 µM and 2 µM sodium arsenite for 1, 2 and 8 weeks. Legend: triangle transcription factor is found in the TRANSFAC output lists for both differentially methylated and DEG; diamond gene is found as target gene in the union of the input lists of differentially methylated and DEG for transcription factors present in the TRANSFAC database; rectangle gene is present as transcription factor as well as target gene in the input lists of differentially meth-ylated and DEG. Specification of the 43 transcription factor—target gene pairs as well as the NCBI gene ID and gene name of the genes can be found in Supplementary data Table 9. For the genes showing a significant effect upon treatment of sodium arsenite, the control cor-rected mean log2 ratio data for DNA methylation and the control cor-rected log2 fold changes for gene expression are shown in Table 1 and 2, respectively, for each dose and time point

dose could be observed for, e.g., v-akt murine thymoma viral oncogene homolog 2 (AKT2), TP53, NDRG2 and CD247 molecule (CD247). Supplementary data Tables 2C and 2D provide a full overview of all significant dose–response and time-dependent dose–response effects.

Discussion

Arsenic is able to modulate the methylation and expression level of hundreds of genes in a dose-dependent and time-dependent manner in A549 cells exposed to sodium arsenite. For a small number of genes, changes in both DNA meth-ylation and gene expression were detected, and for only one of these genes, an interaction was found with arsenic, i.e, NAT1. Modulation of DNA methylation of this gene by arsenic has not been reported so far. In addition to this gene, 114 genes were identified showing altered DNA methyla-tion upon arsenic treatment for which a relation with arse-nic and/or with lung cancer has already been described in

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Table 1 Control corrected log2 mean ratios of the significant DNA methylation genes present in the tumor protein p53 molecular interac-tion network identified by TRANSFAC of significantly differentially

methylated genes and significantly modulated gene expressions in A549 cells after treatment with 0.08, 0.4 and 2 µM sodium arsenite for 1, 2 and 8 weeks

a Abbreviation of the geneb T time in weeks of exposure

Symbola Entrez gene ID Gene name Tb = 1 Tb = 2 Tb = 8

0.08 µM 0.4 µM 2 µM 0.08 µM 0.4 µM 2 µM 0.08 µM 0.4 µM 2 µM

NDRG2 57447 NDRG family member 2 −0.28 −0.04 −0.34 0.01 0.20 −0.32 −0.38 −0.46 −0.58

NDRG1 10397 N-myc downstream regulated 1 0.10 0.28 0.55 0.06 0.04 0.18 0.24 0.31 0.45

TP53 7157 Tumor protein p53 −0.30 −0.26 −0.44 −0.35 0.10 −0.36 0.10 −0.03 −0.14

TERT 7015 Telomerase reverse transcriptase 0.46 0.66 1.17 −0.01 0.53 0.37 0.35 0.68 0.56

DUSP4 1846 Dual specificity phosphatase 4 −0.41 −0.29 −0.41 −0.17 0.12 −0.12 −0.03 −0.05 −0.27

ACTA2 59 Actin, alpha 2, smooth muscle, aorta

0.15 0.06 0.23 0.10 0.35 0.02 0.01 −0.18 −0.20

Table 2 Control corrected log2 fold change of the genes showing a significant change in gene expression present in the tumor protein p53 molecular interaction network identified by TRANSFAC of sig-

nificantly differentially methylated genes and significantly modulated gene expressions in A549 cells after treatment with 0.08, 0.4 and 2 µM sodium arsenite for 1, 2 and 8 weeks

a Abbreviation of the geneb T time in weeks of exposure

Symbola Entrez gene ID Gene name Tb = 1 Tb = 2 Tb = 8

0.08 µM 0.4 µM 2 µM 0.08 µM 0.4 µM 2 µM 0.08 µM 0.4 µM 2 µM

TP63 8626 Tumor protein p63 −0.14 −0.20 −0.71 −0.02 −0.08 −0.02 0.04 0.07 −0.05

IGFBP3 3486 Insulin-like growth factor binding protein 3

−0.01 −0.14 −0.71 0.08 0.13 −0.27 0.01 0.16 −0.42

IFI16 3428 Interferon, gamma-inducible protein 16

0.30 0.22 0.67 −0.01 0.04 0.32 0.23 0.16 1.29

CDKN1A 1026 Cyclin-dependent kinase inhibitor 1A (p21, Cip1)

−0.13 0.08 0.82 0.14 0.30 0.51 0.01 0.19 0.45

FAS 355 Fas (TNF receptor superfamily, member 6)

0.51 0.25 0.92 −0.01 0.21 0.07 −0.02 −0.05 0.08

FOS 2353 FBJ murine osteosarcoma viral oncogene homolog

−0.29 −0.31 −0.81 0.09 0.10 −0.19 0.18 0.17 0.10

TRIM22 10346 Tripartite motif containing 22 0.28 0.25 0.97 0.05 0.33 0.56 0.05 0.40 0.89

literature, such as CASP8 (Shivapurkar et al. 2002a), thiore-doxin reductase 1 (TXNRD1) (Meno et al. 2009; Selenius et al. 2008), TP53 [reviewed in (Ren et al. 2011)] and heat shock 60 kDa protein 1 (chaperonin) (HSPD1) (Rubporn et al. 2009). However, for 100 of these 114 genes, these reported changes involved expression of either genes or proteins. For CASP8 (Shivapurkar et al. 2002b) and TERT (Guilleret et al. 2002), an effect on DNA methylation of its promoter region was found in relation to lung cancer risk, but not after arsenic exposure. This is the first study show-ing an effect on DNA methylation of these genes, imply-ing that the reported changes in gene or protein expression could be due to epigenetic modulation by arsenic. These findings provide new clues for understanding the role of arsenic in lung cancer promotion and progression.

Correlation analyses between DNA methylation and gene expression analyses revealed a low percentage of inverse relationships, even at specific time points. There are several reasons that may explain why DNA methylation and gene expression changes are not inversely correlated, and also why there is no clear relationship between the proportionality of DNA methylation change and the amount of gene expression change. Firstly, gene expression regulation is determined by a complex interplay between other DNA methylation, histone modifications, presence of miRNA(s) and availability of tran-scription factors. The role of the individual players is becom-ing clearer, but the result on gene expression of the complex interplay of these determinants is hard to predict. Secondly, the specific location of DNA methylation in the genome in relation to particular histone marks is thought to play an

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essential role in gene expression regulation (Maunakea et al. 2010; Wagner et al. 2014). DNA methylation which occurs at intragenic gene bodies might play an additional role in gene expression regulation compared to the status of DNA meth-ylation at the promoter region. The DNA methylation status of alternative gene promoters in gene bodies shows a high inverse correlation with histone H3 lysine 4 trimethylation status, supporting a role of intragenic DNA methylation in regulat-ing transcription from alternative promoters (Maunakea et al. 2010; Wagner et al. 2014). Thirdly, baseline methylation level plays a role in the effect of subsequent methylation changes on gene expression. It was shown by Boellmann et al. (2010) that genes with low absolute methylation levels showed a trend

toward increased arsenite-related methylation and decreased gene expression, and genes with high levels of DNA methyla-tion showed a trend toward decreased arsenite-related meth-ylation, but no change in expression. It implies that baseline methylation levels play a role in determining which genes are susceptible to arsenite-related methylation (Boellmann et al. 2010). Altogether, this implies that site-specific DNA meth-ylation is very important, in particular in combination with chromatin structural features like histone modifications, in determining gene expression.

Our study demonstrated altered methylation of TP53 after arsenic exposure in A549 lung cells. Also, in other cell types and in human subjects, changes in DNA

Fig. 2 Hierarchical clustering analyses of the 61 differentially methylated genes present in the output data lists of TRANS-FAC using the online software suite GenePattern version 3.1 (http://www.broadinstitute.org/cancer/software/genepattern/) (Reich et al. 2006) visualizing the dose-dependent effects of a low (0.08 µM), intermediate (0.4 µM) and high (2 µM) concentration of sodium arsenite for 1 week (a), 2 weeks (b) and 8 weeks (c) of expo-

sure. Blue and red colors indicate hypomethylation and hypermeth-ylation, respectively. NCBI gene ID, full gene name of the genes, and control corrected mean log2 ratios for each gene can be found in Sup-plementary data Tables 2A and 2B. In Supplementary data Tables 2C and 2D, a complete overview of the differences in log ratio’s between the different conditions and a p value for these comparisons is shown (color figure online)

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methylation of TP53 after arsenic exposure have been reported. Hypermethylation and hypomethylation effects have been described (Mass and Wang 1997; Ren et al. 2011; Woodson et al. 2001; Zhang et al. 2011). Although alterations in TP53 expression and mutations play a major role in processes associated with tumor initiation, recent studies have highlighted roles for TP53 in other processes including metabolism, stem cell maintenance, invasion and metastasis, as well as communication with the tumor microenvironment, as recently reviewed by Bieging et al. (2014) (Bieging et al. 2014). In our study, TP53 was dose-dependently hypomethylated, showing an overall decrease in methylation at all time points (Fig. 2, Supplementary data Table 2C). These results are in line with what has been reported by others. In a study by Woodson et al. (2001), it was shown in human subjects that hypomethylation of TP53 in peripheral blood DNA was associated with the development of lung cancer (Woodson et al. 2001). The induction of hypomethylation has been associated with the development of DNA strand breaks (Pogribny et al. 1995), which in turn is highly related to cell death, chromosomal aberrations, mutations and neoplastic transformation (Obe et al. 1992). Furthermore, (Zhang et al. 2011), showed that in DNA isolated from blood from human subjects suffering from arsenism due to exposure to arsenic caused by coal-burning, hypomethylation of the TP53 gene was related to its mutations, which is a frequent event in tumorigenesis.

With the introduction of whole-genome gene investiga-tions, single-gene effects are combined in molecular net-works describing biological pathways, thereby providing more insight into underlying modes of action. The number of whole-genome investigations after arsenic exposure is, how-ever, limited. In a study by Smeester et al. (2011), 183 genes were differentially methylated in lymphocytes of subjects exposed to elevated levels of arsenic (Smeester et al. 2011). Methylation of seven of these 183 genes was also detected in our study and include HOXB5, potassium inwardly recti-fying channel, subfamily J, member 14 (KCNJ14), PTPN9, RGS9, BTBD3, tryptophanyl tRNA synthetase 2, mito-chondrial (WARS2), protocadherin gamma subfamily A, 12 (PCDHGA12), and family with sequence similarity 156, member A (FAM156A). Although in their study TP53 was not differentially methylated, they identified a p53-associ-ated network of which a number of genes were found to be modulated in our study, including TP53, fragile X mental retardation 1 (FMR1), MAD1 mitotic arrest deficient-like 1 (yeast) (MAD1L1), protocadherin alpha 4 (PCDHA4) and PCDHGA12. TP53 thus seems to be an important player in arsenic-mediated carcinogenicity, probably due to changes in DNA methylation of the gene. We also identified a TP53 molecular interaction network combining DNA methylation and gene expression data, for which our data were highly sta-tistically significant. It shows multiple interactions between

TP53 and other relevant genes for carcinogenesis in the net-work, as also interactions among other genes present in the network (Fig. 1). TP53, FOS and TP63 act as transcription factor as well as target gene in this network and are able to regulate themselves. FOS also acts as transcription factor for TP53. TP53 regulates numerous other genes present in the network, of which the expression or methylation was mod-ulated by arsenic exposure. Target genes of TP53 of which methylation was changed include TERT, NDRG1, NDRG2, DUPS4 and ACTA2. It was shown that hypermethylation of TERT was positively correlated with telomerase activity in various human tumor cell lines and tumor tissue including lung (Guilleret et al. 2002). Elevated TERT expression was detected in blood sampled from humans having skin hyper-keratosis due to exposure to high levels of arsenic via drink-ing water (Mo et al. 2009). Overexpression of NDRG1 and reduced expression of NDRG2 are associated with increased proliferation, invasion and metastasis in several cancer types (Melotte et al. 2010), including lung cancer (Azuma et al. 2012). Until now, no effects of arsenic exposure on NDRG1 and NDRG2 have been described. The protein product of DUSP4 negatively regulates members of the mitogen-acti-vated protein (MAP) kinase superfamily, which are associ-ated with cellular proliferation and differentiation. In lung cancer tissue, it was shown that loss of DUSP4 was found in EGFR-mutant lung tumors (Chitale et al. 2009). Downregu-lation of DUSP4 induced by arsenic trioxide has been shown in myeloma cells in vitro who were resistance to chemother-apy (Zhou et al. 2005). ACTA2 belongs to the actin family of proteins, and no clear role of this gene has been reported in carcinogenesis. In addition, no effect of arsenic on ACTA2 expression has been described. In conclusion, except for ACTA2, these genes play a role in lung cancer promotion and progression, and for TERT and DUSP4, it has already been shown that expression can be affected by arsenic. However, this is the first study showing that arsenic is able to modu-late DNA methylation of these genes. Moreover, for TERT, NDRG1 and NDRG2, clear dose–response relationships were found after arsenic exposure, implying an unambigu-ous effect of arsenic on the methylation status of these genes.

In addition to DNA methylation, a number of tar-get genes of TP53 in the molecular interaction network were differentially expressed, i.e., TP63, TRIM22, IFI16, IGFBP3, FAS and CDKN1A. FOS was also differentially expressed and acts as a transcription factor for TP53. FOS is a known oncogene, and it has been shown that downreg-ulation of FOS may be involved in the pathogenesis of lung cancer (Levin et al. 1995). Modulation of FOS by arsenic has been described and involved upregulation (Cavigelli et al. 1996). In the present study, however, FOS expression was downregulated after arsenic exposure. FOS and TP53 both regulate transcription of FAS, which was upregulated. The protein product of FAS plays a role in regulation of

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programmed cell death. However, it was shown that expres-sion of this gene in lung cancer tissue was unable to induce apoptosis. This loss of apoptotic function was linked to TP53 alterations, which contribute to the self-maintenance of cancer cells. Arsenic is able to induce FAS expression, thereby inducing apoptosis (Zhu et al. 2003). Induced FAS gene expression was also found in the present study and confirms these previous findings. In addition to FAS, TP53 acts as a transcription factor for TP63. TP63 is a family member of TP53, and genomic amplification plays an early role in lung tumorigenesis. Patients of which lung cancer tissue shows overexpression of TP63 have prolonged sur-vival (Massion et al. 2003). In our study, TP63 was down-regulated after arsenic exposure. Both TP63 and TP53 are transcription factors for CDKNA1. The expression of CDKN1A is tightly controlled by the TP53 protein and induces cell cycle arrest after stress stimuli. High expres-sion of the protein is associated with a good prognosis for lung cancer patients. Modulation of CDKN1A gene and protein expression by arsenic has been reported, although not in lung (cancer) cells or tissue (Liu et al. 2006). Fur-thermore, TRIM22 has been shown to restrict the replica-tion of a number of viruses and has been implicated in cel-lular differentiation and proliferation and may play a role in certain cancers and autoimmune diseases. An association with lung cancer promotion and progression has not been reported so far. Also, no relation with arsenic has been described. Also, for IFI16, no relation with arsenic expo-sure is known. The protein product of this gene belongs to the cytokine family and is involved in cell growth. No specific role in lung cancer development has been reported. In contrast, for IGFBP3, reduced expression is associated with a poor prognosis of non-small cell lung cancer (Chang et al. 2002), but also no effect on gene function has been described after arsenic exposure. The protein product of this gene is the most abundant IGFBP in the circulation and plays a role in regulating cell proliferation, differentiation and apoptosis. To summarize, these genes showing altered gene expression, all play a role in carcinogenesis, and for TP63, IGFBP3, TRIM22 and IFI16, modulation of gene expression by arsenic has not been described so far.

Overall, this TP53 molecular interaction network shows new insight into the arsenic response in lung cancer cells. By modulating DNA methylation (TP53) and gene expres-sion (TP63 and FOS) of the transcription factors TP53, TP63 and FOS, downstream genes are affected which play a role in different processes involved in (lung) car-cinogenesis such as survival (TERT, FAS), cell cycle arrest (CDKN1A), cell proliferation (NDRG1, NDRG2, DUSP4, TRIM22, IFI16, IGFBP3, FOS) and cell differentiation (DUSP4, TRIM22, IGFBP3).

In addition to this TP53 molecular interaction net-work (Fig. 1), many more genes are present in the large

molecular interaction network (Supplementary data Fig. 2) showing altered DNA methylation upon arsenic treatment which could be of importance in understanding arsenic-induced lung cancer promotion and progression. A clear dose-dependent and time-dependent effect of arsenic on DNA methylation has been shown for several genes, e.g., AKT2 (hypomethylation at week 2 dose 2 µM, and week 8 dose 0.4 and 2 µM) and FOXP3 (hypermethylation at week 1 dose 2 µM, week 2 dose 2 µM, and week 8 at all expo-sures) (Fig. 2, and Supplementary data Tables 2C and 2D). AKT2 is a putative oncogene and part of the PIK3/AKT pathway affecting cell growth, cell cycle entry and cell survival, and plays a role in the development of several cancers including lung cancer. Also, FOXP3 is involved in lung cancer development. This gene encodes for a pro-tein involved in immune system response by regulating the development and function of regulatory T cells. Overex-pression leads to antitumor immune dysfunction, especially in early stages. For both AKT2 and FOXP3, this is the first study showing an effect of arsenic on DNA methylation.

In conclusion, our approach for analyzing epigenomic dose–response data has gained new knowledge and resulted in better understanding of the complex effects of the epi-genetic carcinogen arsenic on the genome. We identified many new genes for which an effect after arsenic exposure in A549 cells has not been described before. Arsenic was able to affect DNA methylation of genes in a dose-depend-ent and time-dependent manner, which was accompanied by changes in gene expression. By combining DNA meth-ylation and gene expression data, a large molecular inter-action network was created, showing the relations between transcription factors and their target genes. A TP53 subnet-work was identified in this large network, displaying the interactions of TP53 with other genes affected by arsenic. This network identifies new genes affected by arsenic-mediated TP53 hypomethylation. As for most of these genes it is known that they play a role in (lung) carcinogen-esis, these gene modulations might contribute to arsenic-induced lung cancer promotion and progression.

Conflict of interest The authors declare no conflicts of interest.

References

Azuma K et al (2012) NDRG1/Cap43/Drg-1 may predict tumor angi-ogenesis and poor outcome in patients with lung cancer. J Thorac Oncol 7(5):779–789

Baccarelli A et al (2009) Epigenetics and environmental chemicals. Curr Opin Pediatr 21(2):243–251

Bieging KT et al (2014) Unravelling mechanisms of p53-mediated tumour suppression. Nat Rev Cancer 14(5):359–370

Boellmann F et al (2010) Genome-wide analysis of DNA methyla-tion and gene expression changes in the mouse lung following subchronic arsenate exposure. Toxicol Sci 117(2):404–417

Arch Toxicol

1 3

Bustaffa E et al (2014) Genotoxic and epigenetic mechanisms in arse-nic carcinogenicity. Arch toxicol

Cavigelli M et al (1996) The tumor promoter arsenite stimulates AP-1 activity by inhibiting a JNK phosphatase. EMBO J 15(22):6269–6279

Chang YS et al (2002) Correlation between insulin-like growth fac-tor-binding protein-3 promoter methylation and prognosis of patients with stage I non-small cell lung cancer. Clin Cancer Res 8(12):3669–3675

Chitale D et al (2009) An integrated genomic analysis of lung can-cer reveals loss of DUSP4 in EGFR-mutant tumors. Oncogene 28(31):2773–2783

Cline MS et al (2007) Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2(10):2366–2382

Cui X et al (2006) Chronic oral exposure to inorganic arsenate inter-feres with methylation status of p16INK4a and RASSF1A and induces lung cancer in A/J mice. Toxicol Sci 91(2):372–381

Dai M et al (2005) Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res 33(20):e175

Davis AP et al (2013) The comparative toxicogenomics database: update 2013. Nucleic Acids Res 41(D1):D1104–D1114

Eckhardt F et al (2006) DNA methylation profiling of human chromo-somes 6, 20 and 22. Nat Genet 38(12):1378–1385

Gautier L et al (2004) Affy–analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20(3):307–315

Gentleman RC et al (2004) Bioconductor: open software develop-ment for computational biology and bioinformatics. Genome Biol 5(10):R80

Guilleret I et al (2002) Hypermethylation of the human telomerase catalytic subunit (hTERT) gene correlates with telomerase activ-ity. Int J Cancer 101(4):335–341

IARC (1987) Overall evaluations of carcinogenicity: an updating of IARC Monographs volumes 1 to 42. In: IARC Monogr Eval Car-cinog Risks Hum Suppl, 1–440

Jennen DG et al (2010) Comparison of HepG2 and HepaRG by whole-genome gene expression analysis for the purpose of chem-ical hazard identification. Toxicol Sci 115(1):66–79

Levin WJ et al (1995) Expression patterns of immediate early tran-scription factors in human non-small cell lung cancer. The lung cancer study group. Oncogene 11(7):1261–1269

Lisanti S et al (2012) Standardization and quality controls for the methylated DNA immunoprecipitation technique. Epigenetics 7(6):1–11

Liu J et al (2006) Further studies on aberrant gene expression asso-ciated with arsenic-induced malignant transformation in rat liver TRL1215 cells. Toxicol Appl Pharmacol 216(3):407–415

Mass MJ et al (1997) Arsenic alters cytosine methylation patterns of the promoter of the tumor suppressor gene p53 in human lung cells: a model for a mechanism of carcinogenesis. Mutat Res 386(3):263–277

Massion PP et al (2003) Significance of p63 amplification and overex-pression in lung cancer development and prognosis. Cancer Res 63(21):7113–7121

Maunakea AK et al (2010) Conserved role of intragenic DNA methyla-tion in regulating alternative promoters. Nature 466(7303):253–257

Melotte V et al (2010) The N-myc downstream regulated gene (NDRG) family: diverse functions, multiple applications. FASEB J 24(11):4153–4166

Meno SR et al (2009) Exposure to monomethylarsonous acid (MMA(III)) leads to altered selenoprotein synthesis in a primary human lung cell model. Toxicol Appl Pharmacol 239(2):130–136

Mo J et al (2009) Elevated human telomerase reverse transcriptase gene expression in blood cells associated with chronic arsenic exposure in Inner Mongolia China. Environ Health Perspect 117(3):354–360

Obe G et al (1992) DNA double-strand breaks induced by sparsely ionizing radiation and endonucleases as critical lesions for cell

death, chromosomal aberrations, mutations and oncogenic trans-formation. Mutagenesis 7(1):3–12

Oliveros JC (2007) VENNY. An interactive tool for comparing lists with Venn Diagrams

Pogribny IP et al (1995) Differential sensitivity to loss of cytosine methyl groups within the hepatic p53 gene of folate/methyl defi-cient rats. Carcinogenesis 16(11):2863–2867

Putila JJ et al (2011) Association of arsenic exposure with lung cancer incidence rates in the United States. PLoS One 6(10):e25886

Reich M et al (2006) GenePattern 2.0. Nat Genet 38(5):500–501Ren X et al (2011) An emerging role for epigenetic dysregulation in arsenic

toxicity and carcinogenesis. Environ Health Perspect 119(1):11–19Rubporn A et al (2009) Comparative proteomic analysis of lung can-

cer cell line and lung fibroblast cell line. Cancer Genomics Prot-eomics 6(4):229–237

Selenius M et al (2008) Treatment of lung cancer cells with cytotoxic levels of sodium selenite: effects on the thioredoxin system. Bio-chem Pharmacol 75(11):2092–2099

Shivapurkar N et al (2002a) Loss of expression of death-inducing sign-aling complex (DISC) components in lung cancer cell lines and the influence of MYC amplification. Oncogene 21(55):8510–8514

Shivapurkar N et al (2002b) Differential inactivation of caspase-8 in lung cancers. Cancer Biol Ther 1(1):65–69

Smeester L et al (2011) Epigenetic changes in individuals with arsenicosis. Chem Res Toxicol 24(2):165–167

Smyth GK (2005) Limma: linear models for microarray data. In: R. Gentleman VC, S. Dudoit, R. Irizarry, W. Huber (ed) Bioinfor-matics and Computational Biology Solutions using R and Bio-conductor, Springer, New York, pp pages

Tapio S et al (2006) Arsenic in the aetiology of cancer. Mutat Res 612(3):215–246

Tchounwou PB et al (2003) Carcinogenic and systemic health effects associated with arsenic exposure–a critical review. Toxicol Pathol 31(6):575–588

Team RDC (2011) R: A language and environment for statisti-cal computing. R Foundation for Statistical Computing. URL http://www.R-project.org/. Vienna, Austria

Wagner JR et al (2014) The relationship between DNA methylation, genetic and expression inter-individual variation in untrans-formed human fibroblasts. Genome Biol 15(2):R37

Weber M et al (2005) Chromosome-wide and promoter-specific anal-yses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet 37(8):853–862

Weber M et al (2007) Distribution, silencing potential and evolution-ary impact of promoter DNA methylation in the human genome. Nat Genet 39(4):457–466

Woodson K et al (2001) Hypomethylation of p53 in peripheral blood DNA is associated with the development of lung cancer. Cancer Epidemiol Biomarkers Prev 10(1):69–74

Young MD et al (2011) ChIP-seq analysis reveals distinct H3K27me3 profiles that correlate with transcriptional activity. Nucleic Acids Res 39(17):7415–7427

Zhang AH et al (2011) Relationship between the methylation and mutation of p53 gene and endemic arsenism caused by coal-burn-ing. Zhonghua yu fang yi xue za zhi Chinese journal of preven-tive medicine 45(5): 393–398

Zhou P et al (2005) Changes in gene expression profiles of multiple mye-loma cells induced by arsenic trioxide (ATO): possible mechanisms to explain ATO resistance in vivo. Br J Haematol 128(5):636–644

Zhou X et al (2008) Arsenite alters global histone H3 methylation. Carcinogenesis 29(9):1831–1836

Zhu J et al (2003) Arsenic trioxide induces apoptosis in leukemia/lymphoma cell lines via the CD95/CD95L system. Oncol Rep 10(3):705–709