Dynamic Interplay between the Transcriptome and Methylome in

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Dynamic interplay between the transcriptome and methylome in response to oxidative and alkylating stress Lize Deferme, Jarno E.J. Wolters, Sandra M.H. Claessen, Daniel H.J. Theunissen, Twan van den Beucken, James Richard Wagner, Simone G.J. van Breda, Jos C.S. Kleinjans, and Jacco Jan Briede Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/acs.chemrestox.6b00090 • Publication Date (Web): 10 Aug 2016 Downloaded from http://pubs.acs.org on August 12, 2016

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Chemical Research in Toxicology

Dynamic Interplay between the Transcriptome and Methylome in Response to Oxidative and Alkylating Stress Lize Deferme1,2, Jarno E.J. Wolters1, Sandra M.H. Claessen1, Daniel H.J. Theunissen1, Twan van den Beucken1, J. Richard Wagner3, Simone G. van Breda1, Jos C.S. Kleinjans1, Jacco J. Briedé1*

1

Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW)

Maastricht University, 6200 MD Maastricht, The Netherlands 2

ExxonMobil petroleum and Chemicals, Hermeslaan 2, 1831 Machelen, Belgium

3

Département de médecine nucléaire et radiobiologie, Faculté de médecine et des sciences de la

santé, Université de Sherbrooke, Sherbrooke, Québec, Canada J1H 5N4.

* To whom correspondence should be addressed at Department of Toxicogenomics, Faculty of Health, Medicine and Life Sciences, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands. Fax: 0031 43 3884146 E-mail [email protected]

1 ACS Paragon Plus Environment


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ABSTRACT In recent years, it has been shown that free radicals do not only react directly with DNA, but also regulate epigenetic processes such as DNA methylation which may be relevant within the context of e.g. tumorigenesis. However, how these free radicals impact on the epigenome, remains unclear. We therefore investigated whether methyl- and hydroxyl radicals, formed by tert-butyl hydroperoxide (TBH), change temporal DNA methylation patterns and how this interferes with genome-wide gene expression. At 3 time points, TBH-induced radicals in HepG2 cells were identified by ESR spectroscopy. Total 5-methylcytosine (5mC) levels were determined by LC-MS/MS and genome-wide changes in 5mC and gene expression by microarrays. Induced methylome changes rather represent an adaptive response to the oxidative stress-related reactions observed in the transcriptome. More specifically, we found that methyl radicals did not induce DNA methylation directly. An initial oxidative and alkylating stressrelated response of the transcriptome during the early phase of TBH treatment was followed by an epigenetic response associated with cell-survival signaling. Also, we identified genes of which the expression seems directly regulated by DNA methylation. This work suggests an important role of the methylome in counter-regulating primary oxidative- and alkylating stress responses in the transcriptome in order to restore normal cell function. Altogether, the methylome may play an important role in counter-regulating primary oxidative- and alkylating stress responses in the transcriptome presumably to restore normal cell function.

Keywords: tert-butyl hydroperoxide, transcriptome, methylome, liver cells, oxidative stress, radicals, pathways.



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INTRODUCTION Cancer is traditionally described as a disease induced by accumulations of gene mutations that favor the cell into immortality by stimulating cell growth and by inhibiting apoptosis.1 Recently, genomic instability has been described as an additional hallmark of cancer which may occur through non-mutational mechanisms such as changes in chromatin structures induced by the deregulation of DNA methylation and histone modifications.2 Methylation of DNA occurs through the enzymatic addition of methyl groups to the fifth position of cytosine, by DNA methyltransferases (DNMTs), using S-adenosylmethionine (SAM) as the methyl donor. Conversely, these methyl groups can be enzymatically removed by demethylases (or dioxygenases), in particular from the TET enzyme family.3 Aberrantly high CpG methylation in promoter and exon 1 regions has been associated to transcriptional repression and loss of gene function.4-6 CpG islands in promoters are found to be aberrantly hypermethylated in many cancers,7 as well as in pre-cancerous tissue.8 For example, CDO1 promoter methylation is associated with a significantly poorer survival in clear-cell renal cell cancer9 and hypermethylation of SYNPO2 is associated with shorter survival rates from melanoma.10 In hepatocellular carcinoma it has been shown that expression of the cadherin FAT1 is regulated by methylation in the promoter region.11 Enzymatic processes of DNA methylation and demethylation, essential for the maintenance of normal cellular function, may be disrupted by a number of extracellular and intracellular factors. These factors include free radicals such as oxygen radicals (hydroxyl radicals, superoxide anion radicals) and carbon-centered radicals (methyl radicals).12,

13

These can be produced as

byproducts of intracellular processes as well as induced by extracellular stressors such as oxidative compounds, e.g. hydroperoxides, chemical carcinogens, UV or ionizing radiation. This


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may result in S phase arrest, lipid peroxidation, and DNA damage, and may be accompanied by upregulation of the NRF2 pathway and subsequent antioxidant genes.14-16 Failure to activate an adequate antioxidant response however may subsequently lead to chronic oxidative stress.17 The resulting damage leads to accumulation of oxidation products, DNA mutations and chromosomal aberrations, and might thus contribute to tumorigenesis.18 Oxidative stress-induced mechanisms have for instance been related to chronic liver diseases and hepatocellular carcinoma (HCC).19 Previously, it has been shown that free radicals can directly act on DNA methylation.13 In this context, it has been proposed that in HCC, free radicals induce transcriptional silencing of tumor suppressor genes, such as SOCS1 and CDH1, by hypermethylation of their promoter regions.20, 21

In particular, it has been suggested that oxidative stress in HCC induces these alterations in

DNA methylation status by affecting the activity of DNMTs.13, 22 However, in general, the mode of action by which free radicals impact on the methylome remains unclear. Therefore, in this study, we sought to obtain a better understanding of the response of the methylome following exposure of the liver cell line HepG2 to exogenously generated free radicals, by applying microarray-based technologies. We specifically analyzed the interplay between global gene expression changes and 5-methylcytosine (5mC) status, by focusing on the promoter sites of genes strongly induced in HepG2 cells in response to oxidative and alkylating stress. In HepG2, TBH is metabolized into hydroxyl- and methyl radicals,15 and is thus capable of inducing respectively cellular oxidative and alkylating damage. Furthermore, using a cell free system,12 it was observed that methyl radicals, formed by TBH, randomly attack cytosine residues to form 5mC in DNA with a low yield and not specifically to CpG sequences.



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However, it is not clear whether methyl radicals can directly react with genomic DNA in a cellsystem such as HepG2 cells, which contain an active antioxidant machinery and active DNA repair15 as well as the capability of metabolizing methionine to form the methyl donor, SAM, in combination with high DNMT activity.23, 24 Therefore, the aim of this study was to obtain new insights into the dynamic cross-talk between changes in the transcriptome and methylome in a cellular liver model, HepG2 cells, in response to oxidative and alkylating stress. Temporal exposure analysis (1, 8 and 24 h) demonstrated an early primary antioxidative response of the transcriptome to TBH exposure subsequently followed by a response of the methylome. Overall, this study provides new knowledge into the transient oxidative stress-related response in HepG2 cells after TBH treatment controlled by the interplay between gene expression and DNA methylation. Insights into the relationship between oxidative stress, genome-wide methylation and gene expression is important to increase our understanding in the mechanisms of liver carcinogenesis25 to support early diagnosis and treatment.

EXPERIMENTAL PROCEDURES Cell culture HepG2 cells (ATCC, LGC logistics) were cultured in 6-well plates in the presence of minimal essential medium supplemented with 1% nonessential amino acids, 1% sodium pyruvate, 1% penicillin/streptomycin, and 10% fetal bovine serum (all from Gibco BRL, Breda, The Netherlands). The cells were incubated at 37°C and 5% CO2. When cells were 80% confluent, the medium was replaced with medium containing 200 µM TBH (Sigma-Aldrich, Zwijndrecht, The Netherlands) and exposed for 1, 8 and 24h in triplicate. As a solvent control, medium was used. Time-matched control cells were treated in an identical manner without addition of TBH. 6 ACS Paragon Plus Environment

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Identification and levels of radical formation HepG2 cells were washed with PBS, preincubated for 30 min with 50mM 5,5-dimethyl-1pyrolline N-oxide (DMPO), and washed with PBS again. After exposure to TBH as described above, cells were scraped. ESR spectra from cells were recorded on a Bruker EMX 1273 spectrometer with instrumental conditions and quantification of DMPO spin adduct peak signals as described previously.26

LC-MS/MS detection of 5-methylcytosine After DNA isolation and digestion using NaI and P1 nuclease as described before,27 a HPLC system (Shimadzu LC-10AD pumps, DGU-10A degasser, SIL-HTc autoinjector, CTO-10AS column heater, SPD-20A UV detector) coupled to a tandem mass spectrometer (MS/MS; API 3000 with Turbo Ionspray; AB-Sciex) was used to analyze nucleotides including 5mC. The products were separated using an octadecylsilicagel (ODS) column (250 length x 2.0 mm I.D. particle size =5um; YMC) and eluted with a gradient buffer solution (5 mM formate buffer, pH 5) with increasing acetonitrile (Optima, Fisher Scientific) from 0% to 30% in 15 min at a total flow rate of 0.2 mL/min. The duration of analysis was 30 min, which included the gradient program for separation (15 min), a column wash with 70% acetonitrile (10 min) and equilibration with initial buffer solution (5 min). The eluent was split to the MS/MS instrument (~85%) and the UV detector (~15%). The products were detected by MS using positive ionization in the multiple reaction mode (MRM) with optimized mass to mass transitions and collision energies for each nucleoside. MRM analysis included the appropriate transitions for natural and isotopically-labeled compounds and the yield of products was determined from the



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ratio of ion signals for the natural and corresponding isotopic standards (+2 or +3 a.m.u.) (Figure S1 and S2). The amount of digested DNA injected was estimated by UV detection using a standard solution of nucleosides. A sample containing a known amount of enzymatically digested calf thymus DNA was run in parallel with extracted DNA samples to correct for variations in the efficiency of digestion, the separation, UV absorption and MS detection.

Total RNA isolation and whole genome gene expression Total RNA was extracted using 0.5 ml QIAZOL (Qiagen, Westburg, The Netherlands) according to the manufacturer's instructions. MiRNeasy Mini Kits (Qiagen, Westburg, The Netherlands) were used to purify total RNA. cRNA was prepared using Affymetrix synthesis and labeling kits as described before (Affymetrix, Santa Clara).28 cRNA was hybridized on high-density oligonucleotide GeneTitan chips (Affymetrix Human Genome U133 Plus PM GeneTitan 24 arrays) as described before.15

Purification of 5mC enriched DNA fragments Cells were collected in digestion buffer (1mM EDTA; 50mM Tris–HCl, pH 8.0; 5% SDS) and proteinase K (1mg/ml) (Ambion, Bleiswijk, The Netherlands). 1:1 phenol-chloroformisoamylalcohol (PCI) (Sigma-Aldricht, Zwijndrecht, The Netherlands) was added and the upper phase was precipitated using 3M NaAc pH 5.6 and cold 100% ethanol. Genomic DNA was fragmented to range between 200bp to 600bp and purified using silica columns (Zymo Research, Freiburg, Germany) and eluted in TE buffer. MeDIP was performed using the MagMeDIP kit (Diagenode, Liege, Belgium) according to the manufacturer’s protocol. Briefly, IP incubation mix was added to 1.2 µg sonicated DNA sample and denaturated at 95°C.


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10% of this was used as reference samples. The remaining sample was immunoprecipitated overnight using antibody mix containing the 5-methylcytidine antibody and magnetic beads. Following purification using the Ipure kit (Diagenode) according to manufacturer’s protocol, reference and MeDIP samples were prepared for microarray analysis by whole genome amplification (WGA) using the WGA2 kit (Sigma-Aldrich) as described by the manufacturer’s protocol without performing the fragmentation step. Methylation enrichment in the paired samples MeDIP/Input was derived from qPCR data by calculating the ratio positive control/negative control, applying the ∆∆Ct method.

MeDIP-Chip For whole genome analysis of DNA methylation levels, the Human DNA Methylation 2.1M Deluxe Promoter Array (Roche NimbleGen, Basel, Switzerland) was used. This platform has already been applied successfully in previous in vitro and in vivo studies.29, 30 These arrays have a density of 2.1 million probes (50-75 oligonucleotides long, median probe spacing 100 bp) that represent all annotated human promoters (~ 26,210), 27,867 CpG islands, and 750 miRNA promoters per slide. Labeling and hybridization of arrays was performed according to the manufactures’ protocol. Briefly, reference and MeDIP DNA were labeled with Cy3 and Cy5 respectively by random priming using the Dual Color DNA labeling kit (Roche NimbleGen) and hybridized using the NimbleGen hybridization kit (Roche, NimbleGen). Samples were hybridized overnight on the 2.1M Deluxe Promoter Arrays using the HX1 mixers and the NimbleGen Hybridization system 4. Slides were washed using the NimbleGen wash buffer kit and scanned using the 2µm high resolution NimbleGen MS 200 micro array scanner.



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Whole genome gene expression data analysis Data normalization and filtering Data from eighteen arrays were obtained (samples in biological triplicate for three time points/treated and untreated (=3x3x2=18) HepG2 cells), and Robust Multi-array Average (RMA) normalized and re-annotated to custom CDF files (version 14.1) using the arrayanalysis tool (http://arrayanalysis.org/). Using the bioConductor package LIMMA version 3.18.3,31 differentially expressed genes (DEGs) were determined. A linear model was fitted to the gene expression data whereby replicate information (paired) was treated as random effect. Next, contrasts were defined that estimated the effect of TBH over medium controls. A moderate t-test was computed and corrected for multiple testing (FDR < 0.05). Using this highly stringent statistic approach, we were able to obtain DEGs coherently with the stringent identification of differentially methylated regions (DMR), as described later. The data discussed in this publication have been deposited in NCBI’s gene expression omnibus32 and

are

accessible

through

GEO

series

accession

number

GSE39291:

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39291

Pathway analysis ConsensusPathDB33 was used to identify and visualize the involvement of the DEGs and biological processes that may be affected at the level of pathways, by selecting significant pathways with a p value5 in the time-matched control (Table S5). This material is available free of charge via the Internet at http://pubs.acs.org.

Funding N.A.

Author Contributions L.D. and J.E.J.W contributed equally to this work.

ABBREVIATIONS Bp, base pairs; DEGs, differentially expressed genes; DMPO, 5,5-dimethyl-1-pyrolline N-oxide; DMR,

differentially

methylated

regions;

DNMTs,

DNA

methyltransferases;

EDTA,

Ethylenediaminetetraacetic acid, ESR, Electron spin resonance; FDR, false discovery rate; HCC, hepatocellular carcinoma; 5mC, 5 methylcytosine; NCBI, National Center for Biotechnology Information; ODS, octadecylsilicagel; PSW, Probe Sliding Window; RMA, Robust Multi-array 23 ACS Paragon Plus Environment


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Average; RRBS, reduced-representation bisulfite sequencing; SAM, S-adenosylmethionine; SDS,

Sodium

dodecyl

sulfate;

Tris(hydroxymethyl)aminomethane

TBH,

hydrochloride;

tert-butyl TET,

hydroperoxide; Ten-eleven

Tris,

translocation

methylcytosine dioxygenase; WGA, whole genome amplification

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Figure legends Figure 1 (A) Spectrum of methyl (DMPO•-CH3, indicates by “open circles”) and hydroxyl (DMPO•-OH, indicated by “closed circles”) radicals as measured by ESR spectroscopy after 30 minutes TBH treatment in the presence of HepG2 cells. (B) Spectrum of 30 minutes TBH treatment in the absence of HepG2 cells, which only produced hydroxyl radicals (DMPO•-OH, indicated by “closed circles”). (C) Levels in percentage of 5mC per cytosine in complete DNA of TBH-treated and untreated HepG2 cells as detected by LC-MS/MS (n=3, p

Dynamic Interplay between the Transcriptome and Methylome in

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