Dual inhibition of DNA and histone methyltransferases increases viral mimicry in ovarian cancer cells

Abstract

Ovarian cancer ranks as the most deadly gynecologic cancer and there is an urgent need to develop more effective therapies. Previous studies have shown that G9A, a histone methyltransferase that catalyzes mono- and di-methylation of histone H3 lysine9, is highly expressed in ovarian cancer tumors, and its overexpression is associated with poor prognosis. Here we report that pharmacological inhibition of G9A in ovarian cancer cell lines with high levels of G9A expression induces synergistic anti-tumor effects when combined with the DNA methylation inhibitor (DNMTi) 5-aza-2’-deoxycytidine (5-aza-CdR). These anti-tumor effects included upregulation of endogenous retroviruses (ERV), activation of the viral defense response, and induction of cell death, which have been termed “viral mimicry” effects induced by DNMTi. G9Ai treatment further reduced H3K9me2 levels within the long terminal repeat (LTR) regions of ERV, resulting in further increases of ERV expression and enhancing “viral mimicry” effects. In contrast, G9Ai and 5-aza-CdR were not synergistic in cell lines with low basal G9A levels. Taken together, our results suggest that the synergistic effects of combination treatment with DNMTi and G9Ai may serve as a novel therapeutic strategy for ovarian cancer patients with high levels of G9A expression.

INTRODUCTION

Recent advances in cancer genomics and epigenomics have revealed that the vast majority of human cancers harbor both genetic and epigenetic alterations (1–3). Epigenetic mechanisms, including DNA and histone modifications, as well as chromatin accessibility, determine how cells express genes and play a crucial role in regulating normal cellular functions (4). Aberrant DNA methylation occurs in almost every cancer type and is among the earliest and most common event during tumorigenesis (5). Unlike genetic abnormalities, which are difficult to reverse, epigenetic alterations are readily reversible, making them attractive therapeutic targets for cancer treatment (6).

A group of inhibitors that target epigenetic modifiers has emerged as an exciting group of compounds for use in the clinic (6). For example, the Food and Drug Administration (FDA) has approved DNA methyltransferase inhibitors (DNMTi), such as 5-aza-2’-deoxycytidine (5-aza-CdR), for the treatment of myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). Currently, there are ongoing clinical trials for solid tumors including colon, ovarian, liver, lung, and breast cancer (6,7). Proposed mechanisms underlying the clinical efficacies of DNMTis include demethylation of the promoters of tumor suppressor genes and bodies of oncogenes, thereby restoring more normal expression levels of these genes (8). Moreover, recent work has suggested a novel mechanism of action for DNMTis, termed “viral mimicry” (9–11). Viral mimicry is characterized by the up-regulation of endogenous retrovirus (ERV) transcripts, formation of cytoplasmic double-stranded RNA, and the induction of viral defense pathways (9–11). Importantly, the sensing of these ERVs by viral defense proteins leads to the death of colon cancer stem cells (11). The response to DNMTis in a subset of patients was impressive, with long-term durable anti-tumor effects (12–15).

However, primary and secondary resistance to the epigenetic therapies is common, likely because epigenetic processes are established and reinforced by both positive and negative feedback loops (16,17). For example, while DNMTis induce immediate and genome-wide demethylation of DNA, their effectiveness can be limited by a fairly rapid remethylation after the drug is removed (8), and/or by the involvement of alternative silencing mechanisms such as histone modifications (18). The future of epigenetic therapies, especially for solid tumors, relies on the rational design of combination treatments taking alternative silencing mechanisms into consideration.

DNA methylation plays an important role in silencing ERVs in somatic cells and in the male differentiating germline (19–22), yet it is dispensable for the repression of a subset of ERVs in the early germline, early embryo, and embryonic stem (ES) cells (23–25). Silencing of ERVs in early embryo and germline development depends primarily on histone methylation, notably at lysine 9 of histone H3 (H3K9). Many studies have shown that H3K9 lysine methyltransferases, including G9A, GLP, and SETDB1, are essential for repressing ERV expression in these stages (24–27). Importantly, G9A, which is responsible for catalyzing mono- or di-methylation of H3K9, is up-regulated in many types of cancers (28–30). G9A and H3K9me2 are present at DNA hypermethylated promoters of tumor suppressor genes in cancers, and are lost from the promoters when these genes are reactivated by DNMTis or in DKO cells (31,32). The level of G9A overexpression has been associated with poor prognosis of ovarian, colon, breast, and lung cancers (30,33–36). We have previously shown that knocking down G9A sensitizes a colon cancer cell line to 5-aza-CdR treatment (37). In addition, G9Ai in combination with DNMTis has been proposed as a potential therapy for sickle cell disease, since this combination further activates fetal hemoglobin genes (38). This combination has also been shown to effectively increase the expression of immune genes in a colon cell line (39). These findings urged us to evaluate this novel combination as a potential epigenetic therapy approach for ovarian cancer. Here, we show that pharmacological inhibition of G9A synergistically enhances the efficacy of 5-aza-CdR by restoring strong promoter activity of ERVs and enhancing the viral mimicry effects in ovarian cancer cells with high levels of G9A and GLP expression.

MATERIALS AND METHODS

Cell lines and drugs

A2780, CAOV3, PEO14, and OAW42 ovarian cancer cell lines were authenticated using the American Type Culture Collection (ATCC) human cell line authentication service. All cell lines were cultured according to standard mammalian tissue culture protocols using sterile technique. A2780 were maintained in McCoy’s 5A medium, PEO14 cells in Roswell Park Memorial Institute (RPMI) 1640 medium and CAOV3 and OAW42 in Dulbecco’s Modified Eagle Medium (DMEM). All media (from GIBCO) were supplemented with 10% fetal bovine serum (Sigma-Aldrich) and 1% penicillin/streptomycin (GIBCO). Venor™ GeM Mycoplasma Detection Kit (Sigma-Aldrich) was used every three months to confirm all cell lines were mycoplasma-free. 5-Aza-CdR and G9Ai (UNC0638) were purchased from Sigma-Aldrich and APExBIO, respectively.

Cell viability assay

To test for dose-dependent response, 400 A2780 cells or 1000 CAOV3, PEO14, and OAW42 cells were plated in each well of the 96-well plates 24 h prior to the treatment. A2780 and OAW42 cells were exposed to a dose of 5-aza-CdR (from 25 to 4800 nM) for 48 h, while CAOV3 and PEO14 cells were treated with two consecutive daily doses of 5-aza-CdR (from 25 to 4800 nM) for 48 h. Subsequently G9Ai UNC0638 (from 100 to 20,000 nM) was added to the culture media until 7 d after the treatment. Cells were then incubated with CellTiter-Glo assay reagent (Promega) for 10 min and luminescence was measured using a Synergy HT multi-mode microplate reader (BioTek).

Evaluation of combination effect

Cell viability data were normalized to their corresponding untreated controls for each treatment condition and were expressed as percentage fractional affect (Fa). CompuSyn software (ComboSyn, Inc.) was use to calculate combination index (CI) values of Fa under different conditions using the Chou-Talalay equation (40) CI = (D)_Vc/((D_m)_Vc (Fa/(1-Fa))^1/m1) + (D)_Aza/((D_m)_Aza (Fa/(1-Fa))^1/m2), where D is the concentration of G9Ai and 5-aza-CdR either alone or in combination to achieve a given Fa. The median effect dose (D_m), m1 (G9Ai), and m2 (5-aza-CdR) values were determined using the median-effect equation (41) (Fa)/(1-Fa) = ((D)/(D_m))^m for G9Ai and 5-Aza-CdR treatment alone. The CI values define synergistic effect (42) when CI < 1, additive effect when CI = 1, and antagonism when CI > 1.

Cell-cycle analysis and quantification of dead cell percentages

Cells were harvested and then stained with the amine reactive viability dye Ghost Dye Violet 450 (Tonbo Biosciences) at 4^oC for 30 minutes according to the manufacturer’s protocol. Cells were then fixed with 66% ethanol and stored at 4^oC until ready to stain with propidium iodide (PI). Fixed cells were pelleted and resuspended in 500 ul of PI staining solution (PBS + 100 ug/ml RNase A + 50 ug/ml PI) and incubated overnight at 4^oC. For flow cytometry analysis, cells were filtered through cell strainer snap caps (Fisher Scientific) and then analyzed on a CytoFLEX S (Beckman Coulter). Cell cycle was analyzed using ModFit LT software (Verify Software House, www.vsh.com) and the percentage of cells alive and dead was measured using FlowJo v10.0.7 (FlowJo, LLC).

Chromatin fractionation and western blot analysis

Cell lysis and washing steps were performed in cold buffer containing 10 mM PIPES, pH 7.0, 300 mM sucrose, 100 mM NaCl, 3 mM MgCl2, 1× EDTA-free protease inhibitor (Roche), 1× phosphatase inhibitor cocktail (Sigma), and 0.1% Triton X-100. Whole cell and chromatin fractions were treated with benzonase (Sigma-Aldrich) prior to western blot analysis. Whole cell, chromatin associated or soluble fractions were mixed with SDS/β-mercaptoethanol loading buffer and resolved on a Biorad 4–15% gradient SDS/PAGE gel. Antibodies against G9A (Perseus Proteomics Inc, PP-A8620A-00), Tubulin (Cell Signaling, 86298S), TBP (Santa Cruz Biotech sc-74596), H3K9me1 (Epicypher 13–0014), H3K9me2 (Abcam #1220), H3K9me3 (Active Motif #39161) and total H3 (Abcam #12079) were used. Proteins were visualized using the Clarity Western ECL substrate (Bio-Rad) and ChemiDoc™ XRS+ imaging system (BioRad).

RNA sequencing

Total RNA was extracted with Trizol reagent (Invitrogen), followed by clean-up and Turbo DNase I (Invitrogen) treatment with Zymo Direct-Zol RNA mini prep kit (Zymo Research) according to the manufacturer’s protocol. RNA quality was assessed using Agilent 2100 bioanalyzer with RNA Nano chips (Agilent Technologies, Inc.). For directional RNA-seq with ribosomal RNA (rRNA) reduction, libraries were prepared using KAPA Stranded RNA-Seq Kit with RiboErase (HMR) (KapaBiosystems) and sequenced as single-end 75 bases on a NextSeq 500 instrument (Illumina) at the Van Andel Research Institute Genomics Core. The RNA-seq reads were mapped to the human transcriptome using TopHat version 2.1.0 with NCBI RefSeq as the reference annotation of transcripts. The transcripts were assembled and quantified using Cufflinks version 2.2.1. Differential expression is measured using edgeR package from the Bioconductor project.

Identification of bidirectionally transcribed ERVs

To quantify the transcription of repetitive element at a specific locus, we used Repeatmasker (http://www.repeatmasker.org) annotation as our input and considered only uniquely mapped reads (with mapping quality threshold 10) as previously described (10). For each transcript, we separated reads mapped to the two strands. We considered a transcript as bidirectionally transcribed if the smaller read count divided by the greater read count is over 0.5.

MethyLight assay and bisulfite sequencing

On day 5 after treatment, A2780, CAOV3, PEO14, and OAW42 cells were harvested and genomic DNA was purified by phenol–chloroform extraction and ethanol precipitation. Bisulfite conversion was performed using the EZ DNA Methylation Kit (Zymo). MethyLight assays were then performed as previously described (43) using the primers listed in Supplementary Table 1. For A2780 cells, bisulfite PCR was performed using bisulfite-converted DNA with primers listed in Supplementary Table 1, and the products were cloned using the pGEM-T Vector System I (Promega) as previously described (10).

Chromatin immunoprecipitation

Single nucleosome preparation was performed according to the Dilworth lab native ChIP protocol (44). Briefly, A2780 cells (10⁷ cells) before and after 5-aza-CdR, G9Ai, or combination treatment were harvested and washed twice and resuspended in ice-cold Buffer N (15 mM Tris pH7.5, 15 mM NaCl, 60 mM KCl, 8.5%(w/v) Surcose, 5 mM MgCl₂, 1 mM CaCl₂, 1 mM DTT, 200 μM PMSF, 1X cOmplete™ Mini EDTA-free Protease Inhibitor Cocktail (Roche)). To prepare nuclei, cells were lysed in 1 mL Lysis Buffer (Buffer N supplemented with 0.3% NP-40 substitute (Sigma)) for 10 min at 4°C, and nuclei were collected by centrifugation (500 × g for 5 min at 4°C), resuspended in 1 mL of Buffer N, then sedimented through 7.5 mL sucrose cushion (10 mM HEPES pH7.9, 30%(w/v) sucrose, 1.5 mM MgCl₂ and centrifuged 13000 × g using Sorvall swinging bucket for 12 min at 4°C). To isolate single nucleosomes, the nuclei were digested with MNase (1U Worthington MNase per 70 μg of chromatin at 37°C for 10 min), the nucleosomes were then purified by hydroxyapatite chromatography, and adjusted to a concentration of 20 μg/mL with ChIP Buffer 1(25 mM Tris pH 7.5, 5 mM MgCl2, 100 mM KCl, 10% (v/v) glycerol, 0.1% (v/v) NP-40 substitute) and analyzed using 2% agrose gel.

H3K9me2, H3K4me3 and H3K27ac ChIP were performed as previously described (45), using 5 μg of nucleosome pulled down with 10 μg of anti-H3K9me2 (Abcam ab1220), anti-H3K4me3 (Abcam ab1012), and anti-H3K27ac (Active Motif AM39133) antibody on Dynabeads Protein G (Invitrogen) for 2 h at 4°C. Initial chromatin (10%) for each IP was set aside to serve as ChIP input. The beads were washed 3 times with ChIP Buffer 2 (10 mM Tris pH 7.5, 5 mM MgCl2, 300 mM KCl, 10% (v/v) glycerol, 0.1% (v/v) NP-40 substitute), twice with ChIP Buffer 3 (10 mM Tris pH 7.5, 250 mM LiCl, 1mM EDTA, 0.5% Na•Deoxycholate, 0.5%(v/v) NP 40 substitute), twice with 1 × TE buffer followed with two elution steps in Elution buffer (50 mM Tris pH 7.5, 1 mM EDTA, 1% w/v SDS). After proteinase K (Roche) digestion (65°C 1h), sample DNA was purified using Agencourt AMPure XP beads (Beckman Coulter) prior to qPCR analysis. Primers used in this study are listed in Supplementary Table 1.

Accession codes

All data have been deposited at the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/)) with accession code GSE108223.

RESULTS

Combination treatment with G9Ai and 5-aza-CdR induces synergistic effects to promote cell death in ovarian cancer lines A2780 and CAOV3, but not in PEO14 and OAW42.

RNA sequencing data from The Cancer Genome Atlas (TCGA) project reveals that G9A expression is up-regulated in most types of cancer, and that the highest levels are found in primary ovarian tumors amongst 33 cancer types (Figure S1A). In addition, the related histone methyltransferase GLP is also significantly overexpressed in ovarian tumors compared to normal tissues (Figure S1B). We therefore used four ovarian cancer cell lines A2780, CAOV3, PEO14 and OAW42 as our in vitro models for treatment. We first tested various concentrations of a G9A inhibitor (UNC0638) in A2780 cells and found that the global levels of H3K9me1 and H3K9me2 were efficiently reduced by 400 nM G9Ai at 48 h after the treatment (Figure 1A). In addition, UNC0638 at 400 nM exhibited low cellular toxicity for all four ovarian cancer cell lines (Figure S2). This is consistent with a previous report that UNC0638 has high G9A/GLP inhibition potency and low cellular toxicity with concentrations in the nanomolar range (46). We therefore used 400 nM UNC0638 to determine the optimum dosing schedules for combination treatments with low dose 5-aza-CdR (100 nM) in A2780 cells. We explored different time points to add G9Ai relative to 5-aza-CdR and varied the duration of exposure to these compounds (Figure 1B). The expression levels of HERV-Fc1, a marker for the effects of DNMTis (9,10), were used as read-out and were measured by quantitative RT-PCR. The dosing schedule in which A2780 cells were exposed to 5-aza-CdR for 48 h and subsequently G9Ai until harvest achieved the greatest effect on upregulating HERV-Fc1 (Figure 1B). Therefore this schedule was applied to A2780 and OAW42 cells in subsequent experiments (Figure 2A). Since the doubling times for CAOV3 and PEO14 cells were greater than 24 h, we treated these cells with two consecutive daily doses of 5-aza-CdR for 48 h to compensate for the fact that incorporation of the drug requires cell doubling (Figure 2B).

Figure 1.

Optimization of treatment schedule by combination of G9Ai and 5-aza-CdR in A2780 cells. A) Western blot analysis of H3K9me1 and H3K9me2 levels in response to increasing concentrations of UNC0638 in A2780 cells 48 h after treatment. Total histone H3 was used as a loading control. Representative blots from 3 independent experiments were shown. (B) Effects of dosing schedule by combination treatment with G9Ai and 5-aza-CdR on HERV-Fc1 expression. A2780 cells (2.5×10⁵) were seeded in 100 mm dishes at d −1, treated with 400 nM G9Ai, 100 nM 5-aza-CdR or their combinations according to the schedule shown at the top panel. Cells were harvested at d 5 after 5-aza-CdR treatment. HERV-Fc1 expression levels were then assayed by quantitative RT-PCR using the expression levels of TATA-binding protein (TBP) as a loading control, and normalized to the level of HERV-Fc1 expression after 5-aza-CdR treatment alone. Values are presented as mean ± SEM of three independent experiments. A one-way repeated measures ANOVA was used for statistical analysis. *P <0.05.

Figure 2.

Effects of combination treatment with 5-aza-CdR and G9Ai on proliferation, cell death, and cell cycle in A2780, CAOV3, PEO14 and OAW42 cells. A) Dosing schedule for the combination treatment in A2780 and OAW42 cells. B) Dosing schedule for the combination treatment in CAOV3 and PEO14 cells. C) Chou-Talalay model of the effects of combination treatment with varying concentrations of G9Ai and 5-aza-CdR. The fraction affected (Fa) values were determined using CellTiter-Glo luminescent cell viability assay as shown in Figure S3. Combination index across the Fa values were calculated by CompuSyn software (ComboSyn, Inc.). CI<1, CI=1, and CI>1 indicate synergism, additive effect, and antagonism, respectively. Values are presented as mean ± SEM of three independent experiments. D) Amine-based viability assay using Ghost Dye Violet 450 (Tonbo Biosciences) analyzed by flow cytometry. Values are mean ± SEM of three independent experiments. One-way repeated measures ANOVA with Geisser-Greenhouse’s epsilon correction was used for statistical analysis. *P <0.05, stars in black, blue and red indicate comparison to untreated, G9Ai treated, and 5-aza-CdR treated samples, respectively. E) Bar graphs show the percentages for G1, S, and G2/M cells by propidium iodide (PI) staining and analyzed by flow cytometry. Values are mean with 95% CI from three independent experiments. Beta mixed-effects regression with a random intercept to account for experimental (batch) differences were used to compare the differences between cell phases and treatments. FDR multiple testing adjustments were performed to account for multiple testing. R v3.4.1 was used to fit these regressions (https://cran.r-project.org/). *P <0.05 for all cell cycle phases. Stars in black and blue indicate comparison to untreated, and G9Ai treated samples, respectively.

Following these dosing schedules, we tested whether the combinations of G9Ai and 5-aza-CdR could induce synergistic effects in ovarian cancer cells. We measured dose-dependent inhibition of cell proliferation (as fraction affected (f_a)) with various concentrations of 5-aza-CdR and G9Ai alone or in combination using the CellTiter-Glo luminescent cell viability assay (Figure S3). Combination treatments resulted in increased inhibition of cell proliferation (increased f_a) compared to single compound-treatment in A2780 and CAOV3 cells (Figure S3). The Chou-Talalay analyses (47) determined that the two compounds indeed acted synergistically (combination index (CI) values <1) under the majority of conditions in A2780 and CAOV3 cells (Figure 2C). In contrast, the synergistic effects were limited in PEO14 and OAW42 cells (Figure 2C and S3).

As further characterization of the effects by combination treatments, we next monitored changes of total cell counts upon treatments using a Coulter counter. In this experiment, A2780, CAOV3, PEO14, and OAW42 cells were cultured in 100 mm dishes and treated with 400 nM G9Ai alone or in combination with 100 nM 5-aza-CdR following the dosing schedules outlined above. The results showed that A2780 and CAOV3 cells exhibited significantly reduced total cell counts by combination treatment compared to untreated or single compound-treated cells (Figure S4). Since this effect could be due to reduced cell proliferation rate or increased cell death, we next examined the cellular phenotypes by flow cytometry using an amine reactive viability dye and performed cell cycle analysis to test these possibilities. The reduced total cell counts in A2780 and CAOV3 cells were associated with increased cell death by combination treatment (Figure 2D), while cell cycle parameters remained unchanged compared to 5-aza-CdR treatment (Figure 2E). These data indicated that low dose of G9Ai and 5-aza-CdR could act synergistically by promoting cell death in A2780 and CAOV3 cells. To the contrary, no significant differences in total cell counts (Figure S4), percentages of dead cells (Figure 2D) and cell cycle parameters (Figure 2E) were found between combination treatment and 5-aza-CdR treated PEO14 and OAW42 cells. These data further confirmed that the two compounds have no synergistic effects on cell growth or inducing cell death in PEO14 and OAW42 cells.

Combination treatment with G9Ai and 5-aza-CdR synergistically up-regulate viral defense genes in A2780 and CAOV3, but not in PEO14 and OAW42 cells.

To investigate gene expression changes underlying the synergistic anti-tumor effects of combination treatments, we sequenced total RNA in A2780, CAOV3, PEO14, and OAW42 cells after treatments. A p-value of 0.05 and two-fold expression change were used as cut-offs to identify differential expressed genes between treated and untreated cells from two independent experiments. In general, G9Ai treatment alone did not induce global gene expression changes in the four ovarian cancer cell lines (Figure S5), with only a few genes significantly up-regulated in A2780 and CAOV3 cells (Figure 3A). Although genes up-regulated by 5-aza-CdR and combination treatment largely overlapped, more genes were up-regulated by combination treatment compared to 5-aza-CdR treatment alone in all four cell lines (Figure 3A, S5 and S6A-C). We therefore performed gene ontology (GO) analysis to identify the functions of genes that were uniquely up-regulated by combination treatment. Interestingly, the immune response pathways, especially the interferon pathways as cellular defense response to virus were overrepresented in the set of genes uniquely up-regulated by combination treatments in A2780 and CAOV3 cells (Figure 3B). We and others have previously shown that these viral defense genes can be up-regulated by 5-aza-CdR treatment and are responsible for inducing apoptosis or inhibiting proliferation of cancer cells (9–11). Therefore, the expression status of a panel of 24 viral defense genes were examined after G9Ai, 5-aza-CdR, and combination treatment compared to untreated cells (Figure 3C). Consistent with previous reports (9–11), 5-aza-CdR treatment up-regulated these viral defense genes in a cell type specific pattern (Figure 3C and S7A-B); whereas G9Ai treatment alone slightly increased the expression of these genes less than 2 fold relative to untreated cells (Figure 3C and S7A-B). These viral defense genes were further up-regulated by the combination treatment (Figure 3C and S7A-B), suggesting that the synergistic anti-tumor effects by the combination treatment were contributed in part by the increased viral defense response in A2780 and CAOV3 cells. In contrast, the same analysis showed that the viral defense pathway were not further up-regulated by combination treatments in PEO14 and OAW42 cells (Figure S6B-C and S7C-D), in which no synergy was found (Figure 2C-E). This observation strengthens our conclusion that the synergistic effects between G9Ai and 5-aza-CdR are associated with further induction of the viral defense pathway.

Figure 3.

Combination treatments further up regulated viral defense genes in A2780 and CAOV3 cells. A) Venn diagrams show the up-regulated genes (greater than two-fold change, p <0.05) after G9Ai, 5-aza-CdR, and combination treatment compared to untreated cells. B) Bar graphs show the top ten Gene Ontology Biological Processes (GOBP) terms of genes uniquely up-regulated by combination treatment (greater than two-fold change, p <0.05). C) Bar graphs show the expression fold change (in log2 values) of 24 viral defense genes as previously published (9,10) in A2780 and CAOV3 cells after G9Ai, 5-aza-CdR, and combination treatment compared to untreated cells.

ERVs were up-regulated by G9Ai, 5-aza-CdR, and combination treatment in a cell line specific pattern.

Since induction of ERV expression, especially as dsRNA, is key for triggering the viral defense response by 5-aza-CdR (9–11), we next tested whether ERVs were further up-regulated by combination treatment. We mapped transcripts unique to ERV loci in two replicates of the RNA-seq data for all four cell types after the various treatment regimens. ERVs located within coding genes (including introns) were removed from analysis to focus on expression induced by their LTRs rather than as part of host genes. G9Ai treatment alone increased the total reads of intergenic ERVs in CAOV3 but not in A2780 cells (Figure 4A). While 5-aza-CdR increased the total intergenic ERV counts compared to untreated A2780 and CAOV3 cells, a further increase after combination treatment was found (Figure 4A).

Figure 4.

Combination treatments further up regulated ERVs in A2780 and CAOV3 cells. A) Bar graphs show percentage of ERV base counts mapped uniquely to intergenic region relative to total base counts in two independent RNA sequencing experiments. *P <0.05 by χ² test for equality of proportions without continuity correction. B) Bar graphs show percentage of bi-directionally transcribed ERV base counts mapped uniquely to intergenic region relative to total base counts in two independent RNA sequencing experiments. *P <0.05 by χ² test for equality of proportions without continuity correction. Stars in black, blue, and red indicate comparison to untreated, G9Ai treated, and 5-aza-CdR treated samples, respectively. C) Venn diagrams show the up-regulated ERVs (greater than two-fold change, p <0.05) after G9Ai, 5-aza-CdR, and combination treatment compared to untreated cells. D) Top 10 most up-regulated ERV families in A2780 and CAOV3 cells. Values are log2 fold change of transcripts uniquely mapped to individual ERV family after G9Ai, 5-aza-CdR, and combination treatment compared to untreated cells.

Our previous data also showed that some ERVs could be bidirectionally transcribed and that these overlapping transcripts can pair to form dsRNA (10). In addition, dsRNA are preferred substrates for the RIG-I-like receptors (RLRs), such as RIG-I or MDA5, and are important inducers of antiviral immunity (9,11,48). We therefore analyzed the strand-specific RNA sequencing data and detected increases in bi-directional transcribed ERVs mainly by 5-aza-CdR and combination treatment, similar to total intergenic ERVs (Figure 4B). G9Ai treatment alone, however, significantly increased the total counts of the bi-directionally transcribed ERVs in both A2780 and CAOV3 cells (Figure 4B). These data support a correlation between total bi-directionally transcribed ERVs and the up-regulation of viral-defense genes (Figure 3C and 4B). Therefore, combination treatment of G9Ai and 5-aza-CdR enhanced the previously reported “viral mimicry” response by 5-aza-CdR alone, with increased total ERV counts, especially the dsERV counts, that further up-regulated an anti-viral immune response in these cells.

Interestingly, we observed that G9A and DNA methylation appear to repress a distinct set of ERVs, since the majority of ERVs up-regulated by G9Ai treatment did not overlap with those up-regulated by 5-aza-CdR (Figure 4C). This is consistent with our recent finding that there is a switch in silencing mechanisms depending on the evolutionary age of ERVs (49). Evolutionary “young” ERVs tend to be CpG-rich and are mainly repressed by DNA methylation, while “intermediate aged” ERVs with lower CpG density are predominantly repressed by histone modifications, particularly H3K9 methylation (49). The combination treatment of G9Ai with 5-aza-CdR resulted in the activation of more ERVs than single compound-treated A2780 and CAOV3 cells (Figure 4C and S8), the majority of which were uniquely up-regulated by the combination treatment (Figure 4C). These data suggest that these ERVs might be silenced by both G9A and DNA methylation. Alternatively, an “epigenetic switch” might happen at these ERVs which become silenced by histone methyltransferases such as G9A upon loss of DNA methylation. Similar mechanisms have been described previously (18,49–51). We also observed that different ERVs were up-regulated in A2780 and CAOV3 cells, showing a cell line specific pattern of ERV up-regulation (Figure 4D). Using quantitative RT-PCR, we confirmed that HERV-Fc1 and MLT1N2 were robustly up-regulated by 5-aza-CdR and further by combination treatments in both cells (Figure 4D and S9A-B). However, LTR12C, a family of ERVs previously found to be induced by 5-aza-CdR treatment (10,52), were not further up-regulated by combination treatment (Figure S9A-B).

In PEO14 and OAW42 cells, however, no further increases in intergenic ERVs or bi-directionally transcribed ERVs were found with combination treatments compared to 5-aza-CdR treated cells (Figure S10A-B), which might explain why no further up-regulation of the viral defense genes is elicited by combination treatments. In summary, the cell lines exhibited variable responses to the combination treatments with respect to ERV up-regulation.

G9Ai treatment does not alter DNA methylation levels at LINE-1 elements and 5’LTR of HERV-Fc1.

To address the cell-line dependent effects on ERV up-regulation by the combination treatments, we next examined the effectiveness of 5-aza-CdR in inhibiting DNA methylation. We previously observed that 5-aza-CdR treatment in HCT116 cells with stable knock-down of G9A causes a decrease in DNA methylation levels at the promoters of the G9A-targeted genes (37). We therefore used a MethyLight assay (43) to determine DNA methylation level at LINE-1 elements, an approximate measure of global DNA methylation levels. DNA methylation was not inhibited by G9Ai alone, but was more strongly inhibited by 5-aza-CdR in A2780 and CAOV3 than in PEO14 and OAW42 cells (Figure 5A and S11). No synergy in inhibition of DNA methylation was seen in any of the combination treatments (Figure 5A). We then performed bisulfite sequencing to examine the DNA methylation levels at the 5’ LTR of HERV-Fc1, which serves as its promoter (illustrated in Figure 5B), since the expression levels of HERV-Fc1 were further up-regulated by combination treatment compared to 5-aza-CdR treatment alone in A2780 cells (Figure 1B). Consistent with the MethyLight assay results, bisulfite sequencing data showed no robust changes in DNA methylation induced by G9Ai treatment alone or in combination with 5-aza-CdR in A2780 cells (Figure 5C). Thus the enzymatic activity of G9A was dispensable for the maintenance or restoration of DNA methylation status at LINE-1 elements or the LTR of HERV-Fc1 after 5-aza-CdR treatment. To summarize, although DNA demethylation was required for up-regulation of a group of ERVs such as HERV-Fc1, the synergistic effects of the combination treatment to further up-regulate these ERVs did not involve further DNA demethylation by G9Ai.

Figure 5.

DNA methylation levels at LINE-1 elements and the 5’LTR of HERV-Fc1 before and after treatments. A) MethyLight analysis of DNA methylation at LINE-1 elements in A2780, CAOV3, PEO14, and OAW42 cells before and after treatment. MethyLight assay was performed using primers against methylated LINE-1 elements in bisulfite-converted DNA from these cells, alongside genomic DNA from DKO1 cells as a negative control. Methylation levels were calculated in triplicate by ΔΔCt method using unbiased Alu reactions as copy number controls for each sample and normalized to fully methylated reference DNA (M.SssI treated genomic DNA). Values are mean ± SEM, and one-way repeated measures ANOVA was used for statistical analysis. *P <0.05, stars in black, blue, and red indicate comparison to untreated, G9Ai treated, and 5-aza-CdR treated samples, respectively. B) A schematic diagram of HERV-Fc1 showing the structure of LTRs and the locations of primers for bisulfite sequencing and ChIP-qPCR. C) Bisulfite sequencing of the 5’LTR of HERV-Fc1 at d 5 after treatments in A2780 cells. Bubble maps show the methylation status of cells before and after treatment, with each row representing a different read and each column representing a different CpG position. Filled and open circles indicate methylated and unmethylated CpG sites, respectively. The numbers below each bubble map show the respective CpG methylation percentages.

Combination treatment further reduces the level of chromatin bound G9A protein and global levels of H3K9me1/2/3 in A2780 cells.

Reduction of global and gene locus-specific G9A protein and H3K9 di-methylation levels by 5-aza-CdR treatment alone have been reported previously (30–32). Since the G9Ai (UNC0638) acts as a competitive inhibitor, the combination treatment with G9Ai and 5-aza-CdR could further reduce the chromatin bound G9A protein and H3K9 methylation levels to increase promoter activity synergistically. To test this possibility, we performed chromatin association assays to quantify chromatin-bound G9A protein levels at d 5 after the treatments in A2780 cells. G9Ai treatment alone did not significantly change the chromatin-bound G9A protein levels (Figure 6A-B), but increases free G9A proteins in both whole cell and soluble fractions (Figure S12A-C). 5-Aza-CdR treatment resulted in a significant reduction of chromatin-bound G9A protein levels, which were further reduced by combination treatment in A2780 cells (Figure 6A-B), despite the fact that more soluble G9A proteins were found after combination treatment than 5-aza-CdR treatment alone (Figure S12A and C).

Figure 6.

Changes in G9A protein and histone modification levels in A2780 cells at d 5 of the treatment schedule. A) Western blot analysis of G9A protein levels in whole cell, chromatin, and soluble fractions. TATA-binding protein (TBP) and beta tubulin proteins were used as controls. Representative blots were shown from three independent experiments. B) Bar graph shows the quantification of the chromatin-associated G9A protein levels relative to TBP protein levels. The values were normalized to the levels of G9A protein before treatment and are represented as mean ± SEM in three independent experiments. One-way repeated measures ANOVA with Geisser-Greenhouse’s epsilon correction was used for statistical analysis. *P <0.05; stars in black, blue and red indicate comparison to untreated, G9Ai treated, and 5-aza-CdR treated samples, respectively. C) Western blot analysis of total H3K9me1/2/3 modification levels in A2780 cells after treatments. Representative blots were shown from three independent experiments. D) Bar graphs show the quantification of H3K9me1/2/3 modification levels relative to total H3 levels and normalized to the levels in untreated samples in three independent experiments. Statistical analysis was performed as in B). E) Quantification of H3K9me2, H3K4me3 and H3K27ac levels by ChIP-qPCR at LTR regions of HERV-Fc1 (Fc1 LTRa and b) and MLT1N2. The intergenic spacer region of the 35S ribosomal DNA genes (IGS rDNA) and the promoter region of beta-actin (ACTBp) were used as controls. Values are mean ± SEM (n=3). One-way repeated measures ANOVA was used for statistical analysis. *P <0.05; stars in black, blue and red indicate comparison to untreated, G9Ai treated, and 5-aza-CdR treated samples, respectively.

We next quantified the levels of H3K9 methylation by Western blot in A2780 cells, and found that both G9Ai and 5-aza-CdR significantly reduced global H3K9me1/2/3 levels, especially for H3K9me2, and the combination treatment further reduced the global levels of these repressive marks (Figure 6C-D). Interestingly, global H3K9me3 levels were also reduced by the treatments in A2780 cells (Figure 6C-D). Although the G9A/GLP complex does not catalyze tri-methylation of H3K9, as the levels of H3K9me1/2 decreased, other lysine methyltransferase activities might have been reduced by depleting their substrates. Taken together, we observed a global reduction of chromatin-bound G9A protein and H3K9me1/2/3 levels by combination treatment compared to treatment with either compound alone, which might serve as the underlying mechanism for the synergistic effects of combination treatments in A2780 cells.

We also examined the H3K9me1/2 levels in OAW42 cells, in which no synergy was found between G9Ai and 5-aza-CdR. Interestingly, the results showed that 5-aza-CdR treatment alone efficiently reduced H3K9me1/2 levels to less than 20% of those in untreated cells (Figure S13A-B), compared to about 75% in A2780 cells (Figure 6C-D). The combination treatment only slightly reduced the H3K9me1/2 levels compared to those after 5-aza-CdR treatment alone (Figure S13A-B). These data suggested that in OAW42 cells, 5-aza-CdR treatment is sufficient to remove most of the repressive H3K9me1/2 marks. The addition of G9Ai therefore might not be effective in further removing these marks globally. A search in our RNA seq data revealed that the A2780 and CAOV3 cells showed higher levels of G9A/GLP expression, while in the cells which did not respond synergistically, lower levels of G9A/GLP transcripts were found (Figure S13C). These data suggested that the synergy between G9Ai and 5-aza-CdR might be in part associated with G9A/GLP expression levels.

Combination treatment further decreases H3K9me2 levels at LTRs of ERVs in A2780 cells.

The RNA-seq data revealed that a cluster of ERVs could be further up-regulated by combination treatments making it likely that the LTRs of these ERVs were suppressed by H3K9me1/2 in addition to DNA methylation. Although our results demonstrated strong decreases in global H3K9me2 by G9Ai or combination treatment (Figure 6C-D), it was still not clear whether these changes also occur at ERV LTRs. We therefore performed chromatin immunoprecipitation assays followed by quantitative PCR (ChIP-qPCR) to detect the chromatin modification status at the LTRs of HERV-Fc1 and MLT1N2, as two examples in A2780 cells. There was enrichment of the repressive mark H3K9me2 at LTRs of these ERVs compared to the promoters of β-actin (ACTB) (Figure 6E). Indeed, the repressive H3K9me2 mark was dramatically reduced by G9Ai treatment alone. 5-Aza-CdR treatment resulted in increases, rather than decreases, of H3K9me2 levels at LTRs of these ERVs (Figure 6E), suggesting an “epigenetic switch” to silencing the ERVs by histone modifications upon loss of DNA methylation (49,51). H3K9me2 levels were further reduced by the combination treatment at LTRs of these ERVs, associated with an increase in active marks H3K4me3 and H3K27ac (Figure 6E). Taken together, DNA demethylation by 5-aza-CdR and the additional chromatin alterations at ERV LTRs (decreasing H3K9me2 and increasing H3K4me3 and H3K27ac) induced by the combination treatment likely contributes to the synergistic up-regulation of ERVs.

DISSCUSSION

Our study shows that inhibiting both DNA methylation and the histone methyltransferase G9A synergistically induces anti-tumor effects and enhances the “viral mimicry” response in ovarian cancer cells with high levels of G9A/GLP expression. Importantly, we have shown that G9A was overexpressed in primary ovarian tumors, and was the highest amongst 33 TCGA cancer types. In addition, over expression of G9A has been associated with poor prognosis of EOC (29). Therefore, a treatment strategy targeting both G9A and DNA methylation provides a promising treatment option since DNA methylation inhibitors have already been tested in clinical trials with promising outcomes (53,54). Our dual inhibition approach suggests a potentially new epigenetic therapy combination that may serve as a novel therapeutic strategy for ovarian cancer patients.

A key mechanism underlying the synergistic effects of G9Ai and 5-aza-CdR on ERV up-regulation may involve the “epigenetic switch” in which a group of ERVs were repressed by G9A upon loss of DNA methylation. This is in line with recent work from our laboratories that there is a gain in repressive marks such as H3K9me2/3 or H3K27me3 after DNA demethylation to maintain the silencing of some ERV LTRs (49–51). Since H3K9me2 levels at LTRs of HERV-Fc1 and MLT1N2 increased after 5-aza-CdR treatment, this histone mark may well be responsible for repressing these ERVs. This is different from the situation at promoters of protein coding genes where 5-aza-CdR effectively reduced H3K9me2 levels and depleted G9A (31,32). Therefore, the addition of G9Ai after 5-aza-CdR treatment could enhance the expression of these ERVs by further reducing the repressive H3K9me2 marks. It is worth noting that the “epigenetic switch” might be dependent on the expression levels of G9A/GLP or other silencing complexes such as the polycomb repressive complex 2 (PRC2) (51). In addition, different responses to DNA demethylation by 5-aza-CdR might also contribute to the variable synergistic effects observed in these cell lines after the combination treatment.

An interesting finding in this study is that G9A and DNA methylation appear to repress distinct sets of ERVs. This is consistent with our recent finding that DNA methylation tends to predominate in silencing the evolutionarily younger ERVs, which have higher CpG densities, while histone methyltransferases mainly target the middle-aged ERVs with less CpG density (49). Therefore, the combination treatment results in up-regulation of more ERVs that include those repressed by G9A or DNA methylation alone. For evolutionary “young” ERVs such as HERV-Fc1 and MLTN1, DNA methylation plays a major silencing role, and demethylation by 5-aza-CdR can induce their expression while G9Ai treatment alone is not sufficient to up-regulate these ERVs. However, they were still repressed by G9A after 5-aza-CdR treatment (termed as the “epigenetic switch” mentioned above). Combination treatment simultaneously removed both repressive mechanisms, resulting in further up-regulation of these ERVs. In this way, combination treatment with the two inhibitors synergistically strengthens the “viral mimicry” response and leads to increased anti-tumor effects.

Our findings also show that not all ERVs were equal with respect to their abilities to activate the viral defense pathway. In CAOV3 cells, where G9Ai treatment alone up-regulated equivalent numbers of ERVs as 5-aza-CdR (Figure 4A-C), the levels of induced viral defense genes were lower than those up-regulated by 5-aza-CdR treatment (Figure 3C). This observation reinforces our previous findings that the intermediate age ERVs mainly silenced by histone methyltransferases did not elicit a strong antiviral response (49). The strength of the viral mimicry response may be determined by the expression of evolutionary “younger” ERVs silenced by DNA methylation. Further studies are needed to characterize the effects of specific ERVs in inducing the antiviral response, which may be crucial for patient responses to the combination epigenetic therapy.

Our results also suggest that G9A plays different roles in various tissues or cell types. For example, it has been shown that G9A is essential in repressing developmental genes at euchromatic regions during early embryogenesis (55). In breast cancer cells, G9A has been shown to be responsible for aberrant silencing of tumor suppressor genes (30). In our study, G9Ai treatment alone did not induce up-regulation of tumor suppressor genes. In fact, very few genes were up-regulated, instead, a subset of ERVs were up-regulated by G9Ai treatment alone in some ovarian cancer cells, highlighting the important role of G9A in suppressing ERVs in ovarian cancer cells.

UNC0638 was designed based on the structure of G9Ai BIX01294 with improved in vitro potency and increased cell membrane permeability (46). UNC0638 exhibited high potency and specificity for inhibiting G9A and GLP with very low off-target toxicities (46). However, its potential for clinical application is limited by poor pharmacokinetics in vivo. The development of next generation G9Ai with comparable potency is needed to improve in vivo pharmacokinetics. Our current preclinical study has provided a rationale for the future clinical application using the combination of inhibitors for both G9A and DNMTs. We predict that this combination would be very helpful for those cancer types that exhibit overexpression of G9A or GLP proteins, especially for those cancers with poor prognosis correlated with higher G9A expression levels, such as ovarian cancer. Our results are limited to the four cell lines tested so that the generality of the results need to be replicated in a larger set. However, they are consistent with recent data we have obtained with knockdowns of histone methyltransferases, and point to the need for patient stratification based on the levels of G9A/GLP before combination treatment is initiated.

Conclusions:

DNA methylation and the histone modifications H3K9me1/2 are both important for silencing ERVs. Dual inhibition of these processes results in synergistic up-regulation of ERVs and induces an anti-viral response in some but not all cell lines, serving as a basis for exploring this novel combination treatment in the clinic especially for ovarian cancer patients with high levels of H3K9me1/2 histone methyltransferases G9A/GLP expression.

SIGNIFICANCE.

Dual inhibition of DNA methylation and histone H3 lysine 9 di-methylation by 5-aza-CdR and G9Ai results in synergistic upregulation of ERV and induces an anti-viral response, serving as a basis for exploring this novel combination treatment in ovarian cancer patients.