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Environmental Epigenetics logoLink to Environmental Epigenetics
. 2017 Sep 6;3(3):dvx013. doi: 10.1093/eep/dvx013

Trichloroethylene-induced alterations in DNA methylation were enriched in polycomb protein binding sites in effector/memory CD4+ T cells

Kathleen M Gilbert 1,*, Sarah J Blossom 1, Brad Reisfeld 2, Stephen W Erickson 1,4, Kanan Vyas 1, Mary Maher 1, Brannon Broadfoot 1, Kirk West 1, Shasha Bai 1, Craig A Cooney 3,‡,1, Sudeepa Bhattacharyya 1,‡,2
PMCID: PMC5676456  NIHMSID: NIHMS914820  PMID: 29129997

Abstract

Exposure to industrial solvent and water pollutant trichloroethylene (TCE) can promote autoimmunity, and expand effector/memory (CD62L) CD4+ T cells. In order to better understand etiology reduced representation bisulfite sequencing was used to study how a 40-week exposure to TCE in drinking water altered methylation of ∼337 770 CpG sites across the entire genome of effector/memory CD4+ T cells from MRL+/+ mice. Regardless of TCE exposure, 62% of CpG sites in autosomal chromosomes were hypomethylated (0–15% methylation), and 25% were hypermethylated (85–100% methylation). In contrast, only 6% of the CpGs on the X chromosome were hypomethylated, and 51% had mid-range methylation levels. In terms of TCE impact, TCE altered (≥ 10%) the methylation of 233 CpG sites in effector/memory CD4+ T cells. Approximately 31.7% of these differentially methylated sites occurred in regions known to bind one or more Polycomb group (PcG) proteins, namely Ezh2, Suz12, Mtf2 or Jarid2. In comparison, only 23.3% of CpG sites not differentially methylated by TCE were found in PcG protein binding regions. Transcriptomics revealed that TCE altered the expression of ∼560 genes in the same effector/memory CD4+ T cells. At least 80% of the immune genes altered by TCE had binding sites for PcG proteins flanking their transcription start site, or were regulated by other transcription factors that were in turn ordered by PcG proteins at their own transcription start site. Thus, PcG proteins, and the differential methylation of their binding sites, may represent a new mechanism by which TCE could alter the function of effector/memory CD4+ T cells.

Keywords: trichloroethylene, immunotoxicity, autoimmunity, DNA methylation, polycomb proteins

Introduction

Approximately 24 million Americans have one or more autoimmune disease (e.g. Type I diabetes, systemic lupus erythematosus, autoimmune hepatitis). These chronic and incurable diseases disproportionately affect women, and are among the leading causes of death for young and middle-age women. In order to prevent these chronic incurable diseases we need to know more about the factors that trigger and maintain their pathology.

Effector/memory CD4+ T cells that secrete IFN-γ or IL-17 are critical mediators of both idiopathic and experimental autoimmune disease [1, 2]. These CD4+ T cells can persist for years in humans and animals without causing disease, while maintaining a memory phenotype, a stable cytokine response pattern, and the capacity for induced autoimmune attack [1]. Several adoptive transfer studies have shown that memory CD4+ T cells that have differentiated into Th1 or Th17 cells and reactivated can provide crucial help to cytotoxic CD8+ T cells, promote generation of pathogenic autoantibodies, and secrete tissue-damaging pro-inflammatory cytokines [3, 4].

Understanding how autoimmune disease triggers differentiation of naïve CD4+ T cells into pro-inflammatory effector/memoryTh1 or Th17 cells is important for defining etiology. Twin concordance studies have shown that although genetics may increase susceptibility to autoimmunity, environmental triggers are required to initiate disease. Our work examining the link between the environment and CD4+ T cell differentiation has focused on the volatile organic compound trichloroethylene (TCE). Although the use of TCE as a solvent in the USA has declined as its toxicity became more apparent, over 31 million pounds of TCE-containing waste was released or disposed of in this country in the last decade alone (https://www.epa.gov/toxics-release-inventory-tri-program). Because of its improper disposal over the years, TCE has contaminated many of the water systems in the USA [5].

TCE is one of the first 10 chemicals selected for risk evaluation by the EPA under the newly revised TSCA (Toxic Substances Control Act) (https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/evaluating-risk-existing-chemicals-under-tsca#chemicalnames). This list was compiled based on the highest combined hazard, exposure, persistence and bioaccumulation characteristics. One of the most sensitive non-cancer outcomes of TCE exposure is immunotoxicity [6]. Specifically, chronic exposure to TCE (occupational or environmental) has been linked to a variety of autoimmune diseases and other hypersensitivity disorders [7–15].

CD4+ T cells are especially susceptible to the effects of TCE. Even if overt disease is not diagnosed, increased numbers of activated CD4+ T cells are often found in humans exposed to TCE [7, 16–19]. An expansion of peripheral blood CD4+ T cells is also a biomarker for patients with TCE-induced hypersensitivity [20]. Finally, as shown by ourselves and others, TCE exposure increased the percentage of effector/memory IFN-γ- and Th17-secreting CD4+ T cells in mice that went on to develop CD4+ T cell-mediated autoimmune hepatitis [21–23].

It has recently been reported that CD4+ T cell differentiation into different effector/memory CD4+ T cell subsets (Th1, Th2, Th17 and Treg) is at least partially regulated by gene-specific (i.e. Ifng, Il17A, Ctla, Tnfsf14, and Foxp3) increases or decreases in DNA methylation [24, 25]. This differentiation process can be disrupted during the development of autoimmunity, resulting in inappropriate DNA methylation and associated expression of genes that encode pro-inflammatory cytokines, chemokines, adhesion molecules, or suppressive mediators (e.g. LTA, CD11α, CD70, CD40L, FOXP3) [26–34]. The dysregulated methylome in autoimmune disease can enhance heterogeneity or plasticity in CD4+ T cell subsets that can increase disease severity [35, 36]. For example, the most pathogenic CD4+ T cells in models of type 1 diabetes mellitus, arthritis, and multiple sclerosis are those that secrete both IL-17 and IFN-γ, i.e. exhibit a dual Th1/Th17 phenotype [37]. Thus, the development of autoimmune disease may represent a breakdown in normal DNA methylation patterns in a manner that increases the differentiation of pathogenic effector/memory CD4+ T cells. Demonstrating that a toxicant such as TCE can disrupt the methylome is important for understanding how toxicants promote autoimmunity.

We reported previously that a 12-week exposure to TCE in drinking water altered global methylation in effector/memory CD4+ T cells from MRL+/+ mice [38]. A subsequent study, using targeted bisulfite next generation sequencing of amplicons generated on a Fluidigm Access Array, examined TCE- and time-dependent changes in DNA methylation associated with 16 functionally important genes in CD4+ T cells. TCE was found to increase gene-specific methylation variance in effector/memory CD4+ cells [6], and to induce a time-dependent cumulative increase in DNA methylation in the CpG sites of the promoter of the Ifng gene [39].

The methylome is even larger than the transcriptome. Thus, although a gene targeted evaluation of DNA methylation may provide important information about TCE-induced epigenetic alterations of specific genes, it may not recognize more global alterations in the methylome induced by TCE exposure. Consequently, the current study used reduced representation bisulfite sequencing (RRBS) [40], for a more comprehensive look at the impact of TCE exposure on CpG sites across the entire genome. A concurrent transcriptomic analysis was conducted so that TCE-induced alterations in DNA methylation could be compared with associated changes in gene expression.

Materials and Methods

Ethics Statement

All work was approved by the Animal Care and Use Committee at the University of Arkansas for Medical Sciences, and conformed to the USDA Animal Welfare Act and Regulations.

Mouse Treatment

Female MRL+/+ mice were selected for this study. Autoimmune disease in humans is known to involve an ill-defined genetic predisposition, and is most often found in women. Young adult female MRL+/+ mice, with a propensity for autoimmunity but absence of overt disease, can be used to mimic these requirements, and are used to test for the ability of different toxicants to trigger or augment autoimmunity as previously described [38]. Eight week-old female MRL+/+ mice (Jackson Laboratories; Bar Harbor, ME, USA) were housed in polycarbonate ventilated cages and provided with lab chow (Harlan 7027) and drinking water ad libitum. TCE (purity 99+ %; Aldrich Chemical Co. Inc.; Milwaukee, WI, USA) was suspended in drinking water with 1% emulsifier Alkamuls EL-620 from Rhone-Poulenc (Cranbury, NJ). The mice (8–9 mice/group) received either 0 or 0.5 mg/ml TCE in their drinking water for 40 weeks. Freshly made TCE-containing drinking water was provided every 3–4 days. The mice were weighed once a month. On the basis of water intake, body weight and measured TCE degradation in the water bottles the mice were exposed to an average of 40–50 mg/kg/day TCE. This does is occupationally relevant based on the current 8-h Permissible Exposure Limit [established by the Occupational Safety and Health Administration (OSHA)] for TCE is 100 ppm or ∼76 mg/kg/day.

CD4+ T Cells

Mice were sacrificed after 40 weeks, and splenic CD4+ T cells were isolated using Dynabeads FlowComp Mouse CD4 kit (Invitrogen). The CD4+ T cells were then further separated into CD62Llo or CD62Lhi CD4+ T cell populations using Dynabeads M-280 Streptavidin (Invitrogen) conjugated with biotinylated anti-CD62L antibody (eBiosciences, 13-0621-85). The resulting CD62Llo CD4+ T cells (effector/memory CD4+ T cells) were stimulated with immobilized anti-CD3 antibody and anti-CD28 antibody for 18 has described [41], and the activated CD4+ T cells were frozen for subsequent examination of DNA methylation or transcriptomics. To ensure sufficient cells for use in all the assays each sample of CD4+ T cells used in the study originated in an equal number of pooled spleen cells from 2 to 3 mice resulting in four samples/treatment group.

DNA Methylation Analysis by RRBS

DNA from the CD4+ T cells (four control samples and four TCE samples) was isolated using the PureLink Genomic DNA Mini Kit (Thermo Fisher Scientific). The resulting DNA is tested on the NanoDrop 2000c (Thermo Fisher Scientific) for an A260/A280 range of 1.8–2.0. DNA quality is then confirmed using standard gel electrophoresis. The DNA was then restriction digested, end-repaired, purified, and attached with barcode adapters [42]. The RRBS libraries were generated, bisulfite converted, PCR enriched, size selected, purified, and sequenced (2 × 100 paired end) using an Illumina HiSeq sequencer (UAMS Translational Research Institute Genomics Core).

The Fasta files containing sequenced reads were first quality checked using FastQC program v0.11.5 and trimmed for adapter and low-quality sequences using trim galore (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) with a Phred score of 20 as cut-off. Sequences were mapped using paired end mapping to mouse genome assembly mm9 using Bismark software v0.16.1 with Bowtie 2 v2.2.8 short read aligner. In the final step of bismark_methylation_extractor module, C in CpG, CHH and CHG were extracted with the parameter—no_overlap, to ensure that overlapping reads from the paired reads were not measured twice in the final analysis. Additionally, due to detection of consistently higher methylation rate at the ends of the sequencing reads in M-bias plots (data not shown), three bases from the ends of each pair of reads were discounted for methylation extraction. Bismark’s methylation_extracter output files were then read into MethylKit R package [43] for further statistical analyses.

Differential Methylation Analysis

A Phred Quality Score (Q) is used to represent the confidence level in assignment of each base call by the sequencer. It is logarithmically related to error probability and gives an estimated probability of a base call being wrong. In bisulfite sequencing a Phred score of 20 is normally used as a cut-off, and is the default value used in many open source sequencing tools [44]. Similarly, 10 reads is the minimum number of reads required for accurate determination of DNA methylation if individual CpG sites are analyzed for methylation differences [45]. Consequently, only sequence reads with a Phred score >20 and a minimum of 10 reads per CpG were accepted for downstream statistical analysis. Reads above the 99.9 percentile were also filtered out since these reads are either mapped against repeat elements or have very high PCR bias. A logistic regression model was fitted per CpG site to test for TCE effect on methylation level using a FDR cut off of 5% and a methylation difference of at least 10%.

In order to test the difference between TCE and control mice in terms of the relationship between mean methylation variance and mean percent methylation, quadratic regression models including the quadratic and linear interaction between groups and average percent methylation were fit to the data. A likelihood ratio test was used to compare the TCE and control curves by fitting and comparing a full model and a reduced model. The full model includes quadratic and linear interaction between groups (TCE vs control) and average percent methylation, while the reduced model includes common quadratic and linear terms of average percent methylation for both TCE and control.

qRT-PCR

Fluorescence-based quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) was conducted using RNA isolated from effector/memory CD4+ T cells using techniques and primers as described [21]. Fold differences (log2 scale) in expression were determined using expression levels of resting (unactivated) CD4+ T cells of the appropriate subset of control mice as the control (1×) expression level. The threshold for statistical significance in fold change was set at P < 0.05. Differences between experimental groups were tested first with analysis of variance (ANOVA), and where the F test was significant, subsequent pairwise contrasts were tested using a two-sample t-test. CD4+ T cell concentration and gene expression values were right-skewed, and therefore these data were log-transformed for statistical analyzes. Adjusting for multiple comparisons, P-values from pairwise comparisons that were smaller than the Bonferroni-adjusted significance level indicated statistical significance.

Gene Arrays

This assessment was conducted by the Genomics Core at the University of Arkansas for Medical Sciences. All RNA samples extracted from CD4+ T cells had RIN (RNA integrity number) values of 8.0 or above. Total RNA (500 ng) was converted to cDNA, amplified and biotinylated by use of the Ambion Illumina TotalPrep™-96RNA Amplification Kit (Life Technologies, Carlsbad, CA, USA). Gene expression profiling was performed using the Expression BeadChip System from Illumina (Illumina Inc., San Diego, CA, USA) following the manufacturer’s instructions. Raw data were log2 transformed and normalized to the median intensity signal of 47 231 genes on the array. After normalization and filtering of low intensity spots, two-sample Student’s t-tests were performed and these data were plotted against fold-change measurements. Statistical significance was set at false discovery rate (FDR) < 0.05. Ingenuity Pathway Analysis software (Redwood City, CA, USA) was used for network identification.

Modeling

Fractional Polynomials were fit to model the percentage of total CpG sites that displayed a particular level of mean methylation (e.g. CpG sites that averaged 0–5% methylation or 40–45% methylation). This model has more flexibility to obtain a wide range of shapes of the distribution of the data than regular polynomial models. The power of mean methylation binning were chosen among {-2, -1, -0.5, 0, 0.5, 1, 2, 3} and were allowed to be repeated. The best-fitting first-degree FP model was the one with the lowest deviance among all first-degree powers. The best-fitting second-degree FP model was determined the same way after searching through all possible second-degree power combinations. The final best-fitting model was decided among the four models: null, linear, best-fitting first-degree FP, and best-fitting second-degree FP using a close testing procedure [46]. The final sets of powers selected for the best-fitting model for each chromosome in control or TCE groups were then compared. Having the same set of power suggests that the distribution is similar while a different set of powers suggests the distribution is different.

Annotation of the CpGs and Regulatory Elements was done using the University of California, Santa Cruz, Genome Browser (mouse NCBI37/mm9).

Results

Large-Scale Effects of TCE on DNA Methylation in Effector/Memory CD4+ T Cells

RRBS analysis of the effector/memory CD4+ T cells collected after 40 weeks of adult exposure to TCE was conducted. The analysis incorporated ∼337 770 CpGs sites that were assayed with at least 10× coverage. Bisulfite conversion efficiency in all samples was > 99%. Figure 1A presents histograms showing the average methylation of all the CpG sites examined after binning for average methylation (e.g. 0–5% methylation or 20–25% methylation). These profiles demonstrated that 54.7% and 16.5% of CpG sites from control mice were hypomethylated (0–5% methylation) or hypermethylated (95–100% methylation), respectively. These values were slightly altered in CD4+ T cells from TCE-treated mice; with 54.2% hypomethylated and 15.9% hypermethylated CpG sites respectively. Representing the binned mean methylation results without the hyper- and hypo-methylation skewing the extreme ends of the histogram (Fig. 1B) illustrated TCE-induced differences in the percentage of CpGs methylated at 90–95% (4.9% vs 6.0%; P < 0.05).

Figure 1:

Figure 1:

Average DNA methylation levels of all CpGs interrogated. RRBS analysis of the effector/memory CD4+ T cells collected after 40 weeks of adult exposure to TCE was conducted. (A) Histograms show the average methylation of all 337 770 CpG sites examined in CD4+ T cells from either control or TCE-treated mice after binning for average methylation (e.g. 0–5% methylation or 20–25% methylation). (B) The same histograms are shown without inclusion of the CpGs that were either 0–5% or 95–100% methylated

The average methylation levels of CpGs on individual chromosomes were also examined. With the exception of the X chromosome, most of the chromosomes showed very similar methylation profiles (Fig. 2A). In other words, most had similar percentages of CpGs with hypo-, hyper- and mid-range methylation levels, regardless of whether they came from CD4+ T cells from control or TCE-treated mice. Unlike the 19 autosomal chromosomes, very few (∼2.4%) of the CpGs interrogated in the X chromosome displayed methylation levels between 0 and 5%. Instead, the X chromosome had a slightly higher percentage (18.3%) of CpGs with mean methylation levels between 95 and 100%, and a considerably higher percentage (51%) of CpGs with mean methylation levels between 15 and 60%, peaking around 40% mean methylation. This effect was more visually striking after removing from the histograms all CpGs with 0–5% or 95–100% methylation (Fig. 2B). The difference in the average methylation histogram of the X chromosome was confirmed by Fractional Polynomial modeling (Supplementary Table S1 and Supplementary Fig. S1). This modeling showed that the basic shape of the methylation distribution did not differ among representative autosomal chromosomes (chromosomes 1, 3 or 12), regardless of TCE exposure, but that all are different from the X chromosome. Figure 2B also reveals other apparent chromosome-specific differences in the shape of the methylation distribution histograms. For example, chromosome 17 has increased mid-range DNA methylation levels, while several other chromosomes (e.g. 16, 18 and 19) had flattened U-shape histograms indicated less skewing toward hypo- or hyper-methylation status. The chromosome-specific differences in CpG mean methylation appeared to be inherent, i.e. were not induced by TCE exposure.

Figure 2:

Figure 2:

Chromosome-specific mean DNA methylation levels. (A) The results from the RRBS analysis described in Fig. 1 were sorted into individual chromosomes, and presented after binning for average methylation of the CpGs. The area of each histogram was normalized to one to make it easier to compare the chromosomes. (B) The RRBS results were presented (as total number of CpG sites in the different bins) after excluding the CpG sites that were either 0–5% or 95–100% methylated

When the mean methylation of thousands of CpG sites was examined, as shown in Figs 1 and 2, no TCE-induced effect was detected. However, global effects on DNA methylation can also occur at the level of methylation variance [47]. Methylation variance reflects the group-specific inter-sample variation in the methylation of each CpG site, rather than mean methylation of each CpG site. An examination of all the CpGs interrogated showed that mean methylation variance detected in effector/memory CD4+ T cells, regardless of TCE treatment, was substantially lower than the theoretical variance that would be achieved by random distribution in a percent mean methylation bin (Fig. 3). However, as we and others have previously reported, inter-sample methylation variance at the CpG sites examined in effector/memory CD4+ T cells correlated with distance to either end of the 0–100% methylation scale [6, 48]. Thus, mean methylation variance in both control and TCE samples was highest at those CpG sites that averaged 30–80% methylation (Fig. 3). Interestingly, exposure to TCE decreased the methylation variance at CpGs at almost all levels of mean methylation. This was true for the X chromosome as well, despite its different mean methylation distribution (data not shown). A likelihood test statistic of 75.8 (P-value < 0.0001) suggested that the curves documenting methylation variance for all the CpGs interrogated (Fig. 3) are significantly different between the samples from control and TCE-treated mice. We evaluated the methylation variance for each of the CpG sites that averaged between 50 and 60% methylation for four individual control samples (Supplementary Fig. S2). This revealed that the variance in the control group could not be attributed to an outlier sample, but appeared to represent a consistent difference among all the samples. Thus, the ability of TCE to impact CpG methylation on a genome-scale, as evidenced by this decrease in methylation variance, did not appear to be artefactual.

Figure 3:

Figure 3:

TCE exposure decreased total methylation variance. The RRBS results for the effector/memory CD4+ T cells from control or TCE-treated mice were sorted by treatment group and binned for mean methylation. The inter-sample methylation variance at all the CpG sites in the different bins was then calculated. The dotted line represents a prediction of highest possible methylation variance based on a theoretical value spread of four samples in each bin

Effects of TCE at the Individual CpG Level

In addition to assessing the genome-scale effects of TCE on DNA methylation, individual CpG sites that were differentially methylated by TCE exposure were identified. Comparison of effector/memory CD4+ T cells from control and TCE-treated mice revealed 233 differentially methylated sites (DMS, q value < 0.005, and methylation differences ≥ 10%). Hierarchical clustering of the DMS is shown in Fig. 4A. Annotation of the DMS indicated that the 233 DMS were associated with 216 genes after taking into account those instances in which two or more CpGs were associated with the same gene. Further evaluation for potential functional significance narrowed down the list to 157 DMS that were actually located in a gene, or within 5 kb upstream of the transcription start site (TSS), and thus in a possible promoter region. As shown in Fig. 4B, more DMS were found downstream compared with upstream of the TSS. Distribution from TSS was not normally distributed (P-value of Shapiro–Wilk tests is <0.001, rejects the null hypothesis which is data are normally distributed). Examination of symmetry plot and quantile–quantile plot shows that the data are heavy-tailed. The greatest number of DMS were located within 5 kB downstream of the TSS. Thus, TCE tended to alter methylation of CpG sites in the gene body close to the TSS more often than it altered sites in a traditional promoter region.

Figure 4:

Figure 4:

Identification of CpG sites differentially methylated by TCE exposure. (A) RRBS analysis of effector/memory CD4+ T cells from control and TCE-treated mice revealed 233 DMS (methylation difference ≥ 10%, q value <0.005). Hierarchical clustering of the gene-associated DMS is shown here. (B) The genomic location of the 233 DMS detected in the effector/memory CD4+ T cells relative to the nearest transcription start site (TSS) is shown. (C) The genes associated with the DMS identified by the RRBS were subjected to a gene list functional analysis by the Panther Gene Ontology Classification System

Pathway analysis of the CpG sites differentially methylated by TCE indicated that, in terms of molecular function, TCE primarily altered methylation of genes associated with binding (GO:0005488) (Fig. 4C). Drilling down in the binding molecular function category showed that TCE effects were focused on genes associated with nucleic acid binding (GO:003676), that were in turn enriched for genes associated with DNA binding (GO:0003677). TCE also differentially methylated genes associated with protein binding (GO:0005515), specifically transcription factor binding activity (GO:0000988). Taken together, TCE exposure altered DNA methylation in a manner that seemed primed to impact epigenetic function and gene expression.

When the CpG sites that were differentially methylated between control and TCE samples were binned by average percent methylation in effector/memory CD4+ T cells from control mice the profile included many CpG sites with mid-range methylation (Fig. 5). The percentage of DMS with hypo-methylated (0–20%) and hyper-methylated (80–100%) status was much lower than those found in the evaluation of all the CpGs interrogated (as shown in Fig. 2). When the differentially methylated CpG sites were binned by average percent methylation in effector/memory CD4+ T cells from TCE-treated mice, it showed that, compared with controls, TCE decreased the number of CpG sites with 60–80% methylation, and increased the number of CpG sites that averaged 80–100% methylation. This suggested that the effect of TCE on DNA methylation was skewed toward inducing hyper- rather than hypo-methylation.

Figure 5:

Figure 5:

Average percent methylation of CpG sites differentially methylated by TCE. RRBS analysis of effector/memory CD4+ T cells from control and TCE-treated mice revealed 233 DMS (methylation difference ≥ 10%, q value < 0.005). These DMS were sorted separately for control and TCE-treated samples and binned for mean methylation

Enrichment for Polycomb Protein Binding Sites

A single CpG may indicate the DNA methylation status of the surrounding region in which differential methylation of other individual sites may not reach the level of statistical significance. Thus, alterations in methylation of single CpGs in regulatory regions can have potential functional importance. The Mouse NCB137/mm9 genome in the UCSC Genome Browser was used to determine whether the DMS were located in regulatory elements that bound transcription factors, and thus might impact transcription. Of the 233 DMS, 87 (37.3%) were found in regulatory elements with annotated transcription factor binding sites. Analysis of these 87 DMS revealed that 85% were found in regions exclusively used to bind one or more of four different Polycomb group (PcG) proteins, namely Ezh2, Suz12, Mtf2 or Jarid2 (Fig. 6; Table 1). The remaining 15% DMS in regulatory regions were found in binding sites for other transcription factors (e.g. Ebf1, Gata1, Nfe212, and ATOH). This distribution was somewhat surprising since only ∼19 000 (4.5%) or the ∼399 000 unique transcription factor binding sites in the whole mouse genome are thought to bind one or more PcG protein. The TCE-induced modifications of CpG sites in the PcG protein binding regions were evenly divided between increased and decreased methylation. Of the DMS in polycomb binding sites, most (85%) flanked a TSS. In comparison, none of the DMS in binding sites for other transcription factors occurred in a region that flanked a TSS. Indeed, an evaluation of all 337 770 CpGs sites interrogated in the effector/memory CD4+ T cells revealed that only 23.2% were found in PcG protein binding regions, while only 7.2% were found in regulatory regions targeted by other transcription factors. Thus, our evaluation of effector/memory CD4+ T cells suggests that PcG protein binding regions are enriched for CpG sites. It is possible that the CpG sites in these regions of effector/memory CD4+ T cells may be particularly sensitive to TCE-induced alterations.

Figure 6:

Figure 6:

Many CpG sites differentially methylated by TCE are found in PcG protein binding sites. RRBS analysis of effector/memory CD4+ T cells from control and TCE-treated mice revealed 233 DMS (methylation difference ≥ 10%, q value < 0.005). Annotation of these DMS described whether they were found outside of a transcription binding site (NRE: no regulatory element), or in a regulatory element that bound PcG proteins or other transcription factors. The percentage of CpG sites differentially methylated by TCE was compared with 400 randomly selected CpG sites not altered by TCE (Random CpGs), and to the total number of individual OREG sites known to bind Suz12, EZH2, Mtf2 or Jarid2 or other transcription factors (as identified in Mouse NCB137/mm9 genome in the UCSC Genome Browser) (All Regulatory Elements in Genome)

Table 1:

DMS found in genes or gene promoter regions (q value < 0.005 and differential methylation ≥ 10%)

Chr Position Difference between TCE and control methylation Refseq ID Feature name PcG Other TF with PcG
chr1 52733087 19.80 NM_133829 Mfsd6 None None
chr1 72871428 10.11 NM_008342 Igfbp2 None None
chr1 84692366 –12.74 NM_152915 Dner Mtf2
chr1 87915123 12.38 NM_027029 Spata3 Mtf2, Suz12, EZH2, Jarid2
chr1 87915176 10.91 NM_027029 Spata3 Mtf2, Suz12, EZH2, Jarid2
chr1 88566769 –11.67 NM_010933 Nppc Suz12, EZH2, Jarid2
chr1 95079010 21.05 NM_001310428 Crocc2 None None
chr10 57695435 19.10 NM_001081954 Dux Mtf2
chr10 59348501 38.10 NM_019965 Dnajb12 None Ebf1
chr10 70622501 12.01 NM_031397 Bicc1 Mtf2, Suz12, EZH2, Jarid2
chr10 80297554 26.59 NM_001013758 Lingo3 Mtf2, Suz12, EZH2
chr10 80306503 13.08 NM_001013758 Lingo3 Mtf2, Suz12, EZH2
chr10 114239413 –17.84 NM_146241 POL2 Mtf2, Suz12, EZH2, Jarid2
chr10 126954849 24.03 NM_001098789 Shmt2 None
chr10 126962175 –15.64 NM_028230 Nxph4 Mtf2, Suz12
chr10 127733967 –12.19 NM_031252 Il23a None
chr11 8496658 22.56 NM_001083587 Tns3 None None
chr11 50416921 –11.01 NM_175643 Adamts2 Mtf2, Suz12, EZH2, Jarid2
chr11 53271010 –23.32 NM_027917 Schroom1 Mtf2, Suz12
chr11 61267631 27.93 NM_009548 Rnf112 None None
chr11 92958940 –13.88 NM_028296 Car 10 Mtf2, Suz12
chr11 95692072 29.58 NM_025659 Abi3 Mtf2, Suz12, EZH2
chr11 103222902 10.07 NM_001205236 h3d20 None
chr11 113664146 –20.41 NM_172800 Sdk2 None
chr11 117829957 –13.33 NM_007707 Socs3 None
chr11 121691565 22.39 NM_029049 Ptchd3 Mtf2, Suz12
chr12 25366541 16.22 NM_001004455 Cys1 Mtf2, EZH2
chr12 28026694 18.76 NM_009234 Sox11 Mtf2, Suz12, EZH2
chr12 51749480 14.75 NM_008858 Prdk1 Mtf2, Jarid2
chr12 51749594 22.12 NM_008858 Prdk1 Mtf2, Jarid2
chr12 77506059 13.22 NM_008301 Hspa2 Mtf2, Jarid2, EZH2, Jarid2
chr12 81071379 17.08 NM_001252562 Rad51b None Ebf1
chr12 81216463 –18.69 NM_007564 Zfp36l1 Mtf2
chr12 81794135 20.44 NM_001177503 Plekhd1 Mtf2, Suz12
chr12 84828460 27.36 NM_001267625 Dpf3 Mtf2, Jarid2, EZH2, Jarid2
chr12 84828462 32.76 NM_001267625 Dpf3 Mtf2, Jarid2, EZH2,Suz12
chr12 85758595 20.16 NM_025525 Bbof1 None None
chr12 86630006 –21.49 NM_172414 Zc2hc1c None None
chr12 111468610 28.44 NM_012023 Ppp2r5c None None
chr13 49168362 –23.74 NM_001290313 Wnk2 None None
chr13 53560005 –23.88 NM_013601 Msx2 Mtf2
chr13 55097204 –16.10 NM_001206390 Unca5 None None
chr13 69137738 17.13 NM_153534 Adcy2 EZH2, Mtf2
chr13 104899736 –10.66 NM_029447 N1n None None
chr13 110249606 –21.25 NM_011056 Pde4d None None
chr14 55195182 –22.04 NM_010590 Ajuba None None
chr14 64478548 –26.58 NM_028228 Pinx1 None None
chr15 7763733 –10.39 NM_001301333 Gdnf Mtf2, Jarid2, EZH2, Suz12
chr15 39751326 26.27 NM_172814 Lrp12 None None
chr15 78548459 –21.71 NM_183141 Elfn2 Mtf2, Jarid2, EZH2, Suz12
chr15 78873745 –10.38 NM_015738 Galr3 Suz12, EZH2
chr15 79920093 22.09 NM_009303 Syngr1 None None
chr15 91729910 11.19 NM_198927 Muc19 None None
chr15 101858889 –20.17 NM_010664 Krt18 Mtf2
chr15 22440834 24.56 NM_023794 Etv5 None None
chr16 65815534 17.40 NM_028572 Vg113 None ATOH1
chr16 72682095 11.64 NM_019413 Robo1 None None
chr16 84989074 13.29 NM_001198823 App None None
chr17 11806358 26.52 NM_016694 Park2 None None
chr17 26204880 –17.06 NM_001162868 Rab11fip3 Mtf1
chr17 27787774 30.42 NM_001286743 Pascin1 None None
chr17 32326330 14.78 NM_001033163 Ephx3 Mtf2
chr17 35033560 20.63 NM_001286575 Zbtb12 None None
chr17 47809520 26.89 NM_198421 Usp49 None None
chr17 80112667 10.22 NM_009994 Cyb1b1 Mtf2, Jarid2, EZH2, Suz12
chr17 86014548 20.07 NM_198421 SIX30S1 Mtf2, Jarid2, EZH2, Suz12
chr18 7170227 –15.42 NM_001081393 Armc4 None None
chr18 11997673 11.79 NM_001146287 Cables1 Mtf2, Jarid2, EZH2, Suz12
chr18 37926514 26.30 NM_033595 Pcdhga12 Suz12
chr18 46372273 –16.68 NM_178872 Trim36 Suz12, Mtf2
chr18 74603497 –26.02 NM_201600 Myo5b EZH2, Mtf2
chr18 75980831 –36.51 NM_145356 Zbtb7c Mtf2, Jarid2, EZH2, Suz12
chr19 5332047 –28.01 NM_139301 Catsper1 None None
chr19 47388615 22.47 NM_008018 Sh3pxd2a None None
chr19 47388661 24.44 NM_008018 Sh3pxd2a None None
chr2 5636284 11.52 NM_177343 Camkid None None
chr2 37649110 20.86 NM_001163566 Crb2 Mtf2, Jarid2, EZH2, Suz12
chr2 37649121 20.12 NM_001163566 Crb2 Mtf2, Jarid2, EZH2, Suz12
chr2 76176253 15.65 NM_001081033 Pdella Mtf2
chr2 91316809 –23.84 NM_172668 Lrp4 None None
chr2 91475664 16.00 NM_010168 F2 None None
chr2 126379285 –22.21 NM_011978 Slc27a2 None None
chr2 147874612 –14.49 NM_010446 Foxa2 Mtf2, Jarid2, EZH2, Suz12
chr2 165242677 16.72 NM_054055 Slc13a3 None None
chr3 69120629 –12.04 NM_178726 Ppm11 Mtf2, Jarid2, EZH2, Suz12
chr3 89229249 12.43 NM_001113331 Shc1 None None
chr3 128906125 15.67 NM_011098 Pitx2 Mtf2, Jarid2, EZH2, Suz12
chr3 151928358 –26.03 NM_199465 Nexn Mtf2, Jarid2, EZH2, Suz12
chr4 46728412 –11.11 NM_001081141 Gabbr2 None None
chr4 65065259 –33.06 NM_019514 Astn2 None None
chr4 80557862 10.89 NM_026821 Lurap11 None None
chr4 117928909 15.15 NM_011213 Ptprf None None
chr4 124682634 –26.36 NM_138683 Rspo1 None None
chr4 133430503 –18.16 NM_001285506 Rps6ka1 None None
chr4 134711341 26.82 NM_019732 Runx3 Suz12
chr4 134711350 23.31 NM_019732 Runx3 Suz12
chr4 139379960 –22.47 NM_011039 Pax7 Mtf2, Jarid2, EZH2, Suz12
chr4 141640595 –21.15 NM_145402 GM10565 None None
chr4 148423617 29.44 NM_019781 Pex14 None None
chr4 149745860 17.35 NM_001085492 Rere None None
chr4 151089350 26.29 NM_001081557 Camta1 None None
chr4 152834168 18.58 NM_001099299 Ajap1 None None
chr5 34066416 11.04 NM_001163217 Fgfr3 Mtf2, Jarid2, EZH2, Suz12
chr5 37187480 10.53 NM_026242 Mrfap1 None None
chr5 114723943 –11.11 NM_148935 Foxn4 None None
chr5 122278672 22.15 NM_001306126 Sh2b3 Mtf2
chr5 131782939 –23.02 NM_145218 Wbscr17 Mtf2, Jarid2, EZH2, Suz12
chr5 140447989 17.64 NM_175522 Rlfn1 None None
chr5 148138165 –13.33 NM_001039678 Urad None None
chr6 23210525 –18.28 NM_028462 Cadps2 Mtf2
chr6 63207478 17.79 NM_008167 Grid2 Mtf2, Jarid2, EZH2, Suz12
chr6 83135914 –26.38 NM_007835 Dctn1 None None
chr6 85324297 –10.98 NM_001003955 Rab11fip5 None None
chr6 98926364 30.44 NM_001197322 Foxp1 None None
chr6 113342775 33.59 NM_133923 Tt113 None None
chr6 125262312 –21.28 NM_010736 Tltpr Mtf2
chr7 13628786 –15.20 NM_145819 Mzf1 Suz12, Mtf2
chr7 16726237 –11.83 NM_148946 Slc8a2 None None
chr7 24096775 –16.75 NM_001004194 Nirpfe None None
chr7 29606786 –24.74 NM_016772 Hnrnpl None Ebf1
chr7 35015176 13.38 NM_008155 Gpi1 None None
chr7 52940127 18.03 NM_001289693 Sec1/Ntn5 None None
chr7 63520836 –24.05 NM_021879 Oca2 None None
chr7 87481393 –12.53 NM_133952 Unc45a None None
chr7 93301399 28.53 NM_001102578 Vmn2r75 None None
chr7 104376316 21.11 NM_001177412 Gab2 None None
chr7 133957534 27.42 NM_026884 Fam57b None ATOH1
chr7 138078229 –22.20 NM_019564 Htra1 Mtf2, EZH2, Suz12
chr8 12430585 –10.48 NM_009233 GM5607 Mtf2
chr8 72406588 18.95 NM_026818 Cilp2 Mtf2, Jarid2, EZH2, Suz12
chr8 72898699 14.06 NM_016685 Comp Mtf2, EZH2, Suz12
chr8 73296263 –11.49 NM_008841 Pik3r2 None None
chr8 83263159 –24.64 NM_053124 Smarca5 None None
chr8 94882246 –12.94 NM_018826 Irx5 Mtf2, EZH2, Suz12
chr8 107880886 –12.28 NM_001081332 Slc9a5 None None
chr8 116729816 13.57 NM_173016 Vat1l Mtf2, Suz12
chr8 121971793 –19.02 NM_054095 Necab2 Jarid2, EZH2, Suz12
chr8 125088900 26.17 NM_026014 Cdt1 None Ebf1
chr8 125389116 –36.05 NM_007662 Cadherin 15 None Ebf1
chr8 125389194 –30.65 NM_007662 Cadherin 15 None Ebf1
chr8 125389244 –29.38 NM_007662 Cadherin 15 None Ebf1
chr8 125389246 –28.44 NM_007662 Cadherin 15 None Ebf1
chr9 21549579 12.84 NM_026282 Ldlr None None
chr9 31720357 –14.51 NM_013800 Barx2 Jarid2, EZH2, Suz12
chr9 56994574 –14.04 NM_028347 Neil1 None None
chr9 107611227 –21.43 NM_011349 Sema3f Mtf2, EZH2, Suz12
chrX 11655662 –14.96 NM_029510 Bcor Mtf2, Jarid2, EZH2, Suz12
chrX 11658411 –23.91 NM_175046 Bcor Mtf2, Jarid2, EZH2, Suz12
chrX 34415040 22.79 NM_019668 Ube2a None None
chrX 34415077 21.67 NM_019668 Ube2a None None
chrX 56387270 27.18 NM_010200 Fgf13 Mtf2, EZH2
chrX 68917537 –24.71 NM_010340 Gpr50 Mtf2, Jarid2, EZH2, Suz12
chrX 97454071 21.48 NM_001177943 Eda None None
chrX 110412278 23.36 NM_033605 Dach2 None None
chrX 130221716 30.33 NM_001105245 Pcdh19 Mtf2, Jarid2, Suz12
chrX 158346841 –25.22 NM_198409 Nhs None None
chrX 160347121 27.82 NM_001290379 Ap1s2 None None

TCE Alters CD4+ T-Cell Gene Expression

The functional effects of TCE exposure were further assessed at the gene expression level. This was accomplished using a microarray assessment of the same cells as those profiled in the RRBS analysis: i.e. effector/memory (CD62Llo) CD4+ T cells collected from control mice or from mice exposed to TCE for 40 weeks. Gene expression was examined 20 h after activation of the CD4+ T cells in vitro. At a cutoff of FDR < 0.05 and a fold-change > 1.25, the expression of ∼560 genes was found to be significantly altered in the activated effector/memory CD4+ T cells of mice exposed to TCE compared with similarly activated effector/memory CD4+ T cells from control mice (data not shown). Of these differentially expressed genes, those associated with immune function are listed in Table 2. A network evaluation suggested that pathways with the most number of genes altered by TCE after 40 weeks were those with decreases in gene expression that centered on Ifng and Tnf (Supplementary Fig. S3A). qRT-PCR analysis confirmed the TCE-induced decrease in the expression of Ifng and Tnf in the effector/memory CD4+ T cells (Supplementary Fig. S3B). Of the genes altered by TCE in the effector/memory CD4+ T cells, none contained the previously described DMS.

Table 2:

TCE vs Control annotated immune gene expression

Symbol REFSEQ_ID FC P value adj. P value q-Value PcG Other TF with PcG
Cytokines
Ifit2 NM_008332.2 –2.91422 6.49E–06 0.008901 0.007645 None Cdx1, Myod1
Tnf NM_013693.1 –2.43709 3.44E–05 0.015926 0.01368 None None
Ifitm3 NM_025378.2 –2.04188 0.000198 0.031075 0.026692 None Stat5a
Amica1 NM_001005421.3 –2.01204 0.000128 0.025726 0.022097 None None
Ifi202b NM_008327.1 –2.00414 1.99E–06 0.005447 0.004679 None None
Il4 NM_021283.1 –1.98215 0.00038 0.041971 0.03605 None Nfatc2
Ifitm1 NM_026820.2 –1.86847 0.00039 0.04252 0.036523 None None
Irf7 NM_016850.2 –1.84292 0.000657 0.054756 0.047032 None None
Isg20 NM_020583.4 –1.75296 1.48E–05 0.011839 0.010169 None Foxa2
Il17a NM_010552.3 –1.68794 0.000793 0.060267 0.051766 None Bhlhe40
Lif NM_001039537.1 –1.50454 1.05E–05 0.01061 0.009113 None Pax6
Il16 NM_010551 1.305869 6.96E–07 0.00394 0.003384 None Foxa2, ATOH1
Tnfrsf26 NM_175649.5 1.346837 0.00032 0.038826 0.03335 None Cdx1
Ing4 NM_133345.2 1.366442 0.000634 0.054238 0.046587 None Rxra, Sox3
Traf1 NM_009421.3 1.401221 0.000837 0.062156 0.053389 None Bhlhe40, Myod1
Ncf4 NM_008677.1 1.405497 8.09E–05 0.021965 0.018867 None Ebf1, Myod1, Bhlhe40
Il1r2 NM_010555.4 1.459921 0.00052 0.049157 0.042223 None Foxa2
Chemokines
Cxcl10 NM_021274.1 –1.76465 0.000219 0.032733 0.028116 None None
Ccrl2 NM_017466.4 –1.66005 1.25E–05 0.010725 0.009212 None None
Cxcr4 NM_009911.2 1.634843 0.000334 0.020502 0.016849 Suz12, Jarid2, Mtf2 ATOH1
Transcription factors & enzymes
Mycbp2 NM_207215.2 –1.64382 0.000189 0.030347 0.026066 Mtf2 Meis1, Bhlhe40, Ebf1
Trafd1 NM_172275.1 –1.58643 7.10E–05 0.021467 0.018439 None Foxa2, Nkx2-5
Sp100 NM_013673.2 –1.31215 0.000377 0.041921 0.036008 None Bhlhe40, Cdx1
Cxxc1 NM_028868.3 1.321214 7.64E–05 0.021965 0.018867 None Bhlhe40
Csk NM_007783.2 1.344270 0.0003704 0.041861 0.035956 None Ebf1, ATOH1
Mapk11 NM_011161.4 1.341871 0.000238 0.034005 0.029208 None Egr2, Sox3
Rap1gap NM_001081155.1 1.344891 0.000792 0.060267 0.051766 Mtf2 Bhlhe40
Elk3 NM_205536.1 1.3791 1.47E–05 0.011839 0.010169 None Ebf1, Stat5a
Nfkbib NM_010908.3 1.410298 0.000119 0.025478 0.021884 None Foxa2
Rag1ap1 NM_009057.2 1.42048 0.000158 0.027165 0.023333 None Ebf1
Mt1 NM_013602.2 2.12539 7.29E–06 0.009447 0.008114 None None
Cell cycle
Gadd45g NM_011817.1 –1.30109 7.79E–05 0.021965 0.018867 Suz12, Mtf2 ATOH1
Cdc23 NM_178347 1.340161 5.13E–06 0.0083 0.007129 None Foxa2
Apoptosis
Daxx NM_007829.3 –1.79108 3.82E–05 0.016024 0.013764 None None
Fasl NM_010177.3 –1.45356 0.000115 0.025294 0.021726 None Cdx1
Bcl11b NM_021399.2 1.311508 0.000262 0.035376 0.030386 EZH2, Suz12, Jarid2, Mtf2
Pdcd2 NM_008799.2 1.356754 3.22E–06 0.006902 0.005928 None Bhlhe40, Meis1
Pdcd4 NM_011050.3 1.407455 0.000546 0.05012 0.04305 None Foxa2, Myod1
Integrins
Sdc3 NM_011520.3 –1.84389 0.000208 0.031629 0.027168 None Hoxc9, Sox3
Ly6c1 NM_010741.2 –1.50191 0.000146 0.026013 0.022343 None Cdx1, Bhlhe40
Cd247 NM_031162.1 1.300788 0.000203 0.031216 0.026812 None Foxa2, Creg1, Tal1
Leng9 NM_175529.3 1.303316 0.000444 0.045017 0.038667 Mtf2 None
Mic2l1 NM_138309.1 1.33617 9.20E–05 0.022878 0.019651 None Stat5a
Hist1h2ai NM_178182.1 1.419197 0.000898 0.063308 0.054378 None None
Hist1h1c NM_015786.1 1.592885 0.000622 0.054158 0.046519 None Cdx1, Bhlhe40
Ctla4 NM_009843.3 1.472701 0.000221 0.03284 0.028208 None None
Miscellaneous
Birc2 NM_007465.1 1.372856 1.75E–05 0.012794 0.010989 None Rxra, Cdx1
Ddb2 NM_028119.4 1.37404 1.22E–05 0.010725 0.009212 None Cdx1, Rxra, Tal1
Rfx1 NM_009055.2 1.519909 0.000101 0.023946 0.020568 None Myod1

Although TCE-induced gene changes and DMS did not coincide they did share some common features. Evaluation of the first intron and 5 kb region upstream of the TSS for the immune genes altered by TCE (Table 2) showed that 10.2% had binding sites for PcG proteins, while an additional 69.4% had binding sites for other transcription factors that were in turn regulated by PcG proteins at their own TSS. This suggests that at least 80% of the genes altered by TCE in the effector/memory CD4+ T cells had the potential for PcG protein regulation.

Discussion

Our RRBS evaluation of CpG methylation distributions in autosomal chromosomes in effector/memory CD4+ T cells conformed to the general trend: hypomethylation > hypermethylation > mid-range methylation. On a genome-scale TCE appeared to decrease methylation variance of CpG sites that averaged <95%, or >5% methylation. Increased methylation variance at CpG sites that average between 30 and 60% methylation is thought to be important for maintaining flexibility in the methylation and associated expression of functionally important genes [47]. Such regions may be protected from suppressed, fully methylated states or permissive, unmethylated states. When we examined all the CpGs interrogated by RRBS in the effector/memory CD4+ T cells from control mice, the variance was highest for CpGs with intermediate methylation levels. This has been described previously [6] and is related to the fact that percentage scales tend to restrict variability near the edges of the scale. In the current study, we found that TCE exposure decreased variance in CpG sites with intermediate methylation levels. The ability of TCE to impact intermediate methylation may have more functional significance than effects at the ends of the methylation scale; increasing DNA methylation from 5 to 20%, or decreasing methylation from 95 to 80% is less likely to alter gene expression.

Using a methylation difference of 10% between the two groups as the cutoff, only 233 CpGs (216 genes) of the 337 770 CpGs interrogated in effector/memory CD4+ T cells were differentially methylated by TCE exposure. The relatively small TCE affect (0.07% of the CpGs examined) compares to the 0.83% of total CpGs that were found to be differentially methylated when naïve CD4+ T cells were contrasted to memory CD4+ T cells in humans [24]. It is perhaps not surprising that two populations of effector/memory CD4+ R cells that differed only in relatively low level adult exposure to a toxicant would demonstrate less epigenetic modifications than that which accompanies CD4+ T cell differentiation. A transcriptomic analysis of the same effector/memory CD4+ T cells used to generate the RRBS data identified a number of immune-associated changes following TCE exposure. However, none of the differentially expressed genes overlapped with DMS in the CD4+ T cells from the TCE-treated mice despite the fact that many of the DMS were found in gene bodies within 5 kb downstream of the TSS. Although cytosine methylation of promoters is negatively correlated with gene expression, the question of whether methylation of a particular cytosine impacts expression is still unclear. Others have also seen a lack of correlation between gene expression and treatment-related changes in methylation status [49–53]. In one study of over 230 000 cytosines, only 16.6% demonstrated a significant association between methylation and expression of a closely located TSS [54]. The association between gene body methylation and gene expression appears to be complex and context dependent [55, 56]. Even in cancerous cells with their often more robust changes in methylation, associations between gene expression and methylation are surprisingly small, and include both positive and negative correlations [57]. This may be attributed to the time-dependent sensitivity of gene expression. Alternatively, the epigenetic impact of exogenous factors such as TCE on gene expression may be indirect; via methylation-induced changes in the expression or function of some upstream regulator, and thus not obviously correlative. Alterations in methylation may only play a permissive, rather than direct, role in regulating gene expression.

Compared with the 19 autosomal chromosomes, the X chromosome had a very different profile of mean methylation levels, regardless of TCE exposure. In dramatic contrast to the autosomal chromosomes the X chromosome had very few hypomethylated CpG sites, and a much larger percentage of CpG sites with mid-range DNA methylation. Differences in X chromosome DNA methylation profiles are not surprising due to the epigenetically regulated X chromosome silencing that occurs in the blastocyst. This silencing is accomplished by a combination of epigenetic modifications involving histone deacetylation, RNA methylation, and DNA methylation [58, 59]. Comparing the methylation status of individual CpGs in the X chromosome from peripheral blood leukocytes of males and females indicated both had a set of highly methylated CpGs, while CpGs that were hypomethylated in males (under 11%) tended to be methylated in the 30–40% range in females [60]. This is largely in agreement with our analysis of the X chromosome from effector/memory CD4+ T cells of female mice. The mean methylation distribution observed in the X chromosome does not reflect averaging of two chromosomes, one of which was completely hypermethylated. Instead, the profile indicates the presence of a more complex methylation pattern. It remains to be determined whether this pattern is due to the DNA methylation on the inactivated X chromosome, the active X chromosome, or a combination of both.

One surprising result was the apparent connection between TCE-altered CpGs and PcG protein binding regions. PcG proteins were first identified as regulators of embryonic development and stem cell pluripotency [61]. PcG proteins form two complexes in mammals; polycomb-repressive complex 1 (PRC1) and PRC2. PRC2 mediates H3K27me3, which is thought to inhibit transcription by a mechanism involving H2A ubiquitination and/or chromatin compaction [62, 63]. The initial work on PcG proteins conducted in embryonic stem cells identified a complex interaction between PRC2 binding and DNA methylation. Although the majority of CpG islands do not normally recruit PcG proteins, there is an anomalous conservation of CpG sites at PRC2-binding domains [64, 65]. This connection seems to involve a certain level of reciprocal regulation. For example, PRC2 binding represses DNA methylation at the PRC2 target regions in embryonic stem cells [66]. Similarly, DNA methylation appears to regulate PRC2 binding. A high density of unmethylated CpG sites reportedly promotes PcG protein binding [67]. Removal of DNA methylation promotes the accumulation of the PRC2 complex in inappropriate genomic loci, indicating that DNA methylation is capable of attenuating PRC2 binding [68]. However, there is evidence in somatic cells and cancer cells that DNA methylation and PRC2 binding may not be mutually exclusive, and may in fact work together to suppress specific gene expression [65].

The role of PcG proteins in the regulation of immune function specifically is still being defined. Late stages of human B cell differentiation showed methylation gain at PcG-repressed areas, thus suggesting a need for DNA methylation to block PcG protein binding in non-transformed lymphocytes [69]. In terms of T cells, PcG proteins have been shown to form a complex with the Ikaros transcription factor to regulate thymocyte development [70]. They can also regulate the function of mature peripheral T cells [71]. For example, EZH2, a component of PRC2, has recently been shown by others to be highly expressed in CD4+ T cells [72], where it reportedly associates with Foxp3 to mediate gene repression and suppressive function [73]. Loss of EZH2 in vivo caused increase immune pathology, including colitis, in part due to a lack of functional Treg cells [73, 74]. EZH2 also controls differentiation and plasticity of CD4+ Th1 and Th2 cells by binding and controlling expression of Tbx21 and Gata3 [75]. Deletion of EZH2 leads to increased generation of effector/memory CD4+ T cells with an increased production of effector cytokines including IFN-γ [73]. Cell differentiation is accompanied by losses and gains of H3K27me3 at many promoters at many stages of the process, while DNA methylation is altered at only a relatively small number of promoters during differentiation. This suggests that PcG protein binding represents a more robust suppression than DNA methylation.

There were some limitations associated with the current study. RRBS analysis does not distinguish between 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). The ten-eleven translocation (TET) family of proteins can oxidize 5mC to 5hmC, a mark not effectively maintained by Dnmt1, thus leading to demethylation as cells divide. Polarization of CD4+ T cells toward Th1 and Th2 lineages is accompanied by changes in 5hmc-mediated DNA de-methylation of key genes [76], and defects in DNA hydroxymethylation have been demonstrated in both thymocytes and peripheral CD4+ T cells from patients with autoimmune diseases [77, 78]. A direct correlation between levels of 5hmc and H3K27me has been described in a variety of somatic tissues [79]. It will be important to distinguish whether the enrichment of PcG protein binding sites in the current study are associated with TCE-induced alterations DNA methylation or DNA hydroxymethylation.

Despite its limitations, the current study has demonstrated effects of TCE on genome-wide and gene-specific DNA methylation. This included a TCE-induced decrease in methylation variance, and the observation that TCE-induced changes in CpG methylation tended to occur in regulatory elements that bound suppressive PcG proteins. These effects may be mechanistically important since many autoimmune diseases are driven by effector/memory CD4+ T cells which are resistant to several mechanisms designed to guard against the expansion of autoreactive CD4+ T cells. Thus, any epigenetic mechanism that targeted effector/memory CD4+ T cells could have important functional consequences. Activation and subsequent gene expression in CD4+ T cells is a complex process. Aside from epigenetic mechanisms such as DNA methylation and histone acetylation, this process is also regulated by the levels and/or phosphorylation state of transcription factors and other signaling molecules. Understanding the contribution of all these factors toward CD4+ T cell activation is going to require complex modeling. The epigenetic alteration of polycomb protein binding may be another component in this process. The possibility that TCE alters DNA methylation in PcG protein binding sites, suggests that an associated alteration in PRC2 binding, and downstream upregulation of proinflammatory Th1 cytokines could play a role in the ability of TCE to promote autoimmunity.

Supplementary Material

Supplementary Data

Acknowledgments

We thank Dr Damir Herman for initial coaching on the RRBS method, Dr Kartik Shankar for recommendations on RRBS sample preparation, and Dr Stewart Macleod for helpful advice on next generation sequencing, and for performing the Illumina sequencing.

Data Availability

Data are available in the Supplementary Material.

Funding

This work was supported by grants from the Arkansas Biosciences Institute, the National Institutes of Health (R01ES021484), and the UAMS Translational Research Institute (National Institutes of Health UL1RR029884).

Supplementary Data

Supplementary data is available at EnvEpig online.

Conflict of interest statement. None declared.

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