Genome-wide DNA methylation analysis identifies hypomethylated genes regulated by FOXP3 in human regulatory T cells

Key Points

• Human naive CD4⁺ T cells and resting nTreg are differentially methylated at 127 regions in their genomic DNA.

• Forkhead-binding motifs are present in promoter-associated differentially methylated regions, inferring broader epigenetic control of Treg.

Abstract

Regulatory T cells (Treg) prevent the emergence of autoimmune disease. Prototypic natural Treg (nTreg) can be reliably identified by demethylation at the Forkhead-box P3 (FOXP3) locus. To explore the methylation landscape of nTreg, we analyzed genome-wide methylation in human naive nTreg (rTreg) and conventional naive CD4⁺ T cells (Naive). We detected 2315 differentially methylated cytosine–guanosine dinucleotides (CpGs) between these 2 cell types, many of which clustered into 127 regions of differential methylation (RDMs). Activation changed the methylation status of 466 CpGs and 18 RDMs in Naive but did not alter DNA methylation in rTreg. Gene-set testing of the 127 RDMs showed that promoter methylation and gene expression were reciprocally related. RDMs were enriched for putative FOXP3-binding motifs. Moreover, CpGs within known FOXP3-binding regions in the genome were hypomethylated. In support of the view that methylation limits access of FOXP3 to its DNA targets, we showed that increased expression of the immune suppressive receptor T-cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT), which delineated Treg from activated effector T cells, was associated with hypomethylation and FOXP3 binding at the TIGIT locus. Differential methylation analysis provides insight into previously undefined human Treg signature genes and their mode of regulation.

Introduction

Regulatory T cells (Treg) are pivotal in maintaining T-cell homeostasis and preventing autoimmune disease.¹ Prototypic natural CD4⁺ Treg (nTreg) are derived from the thymus and programmed by the transcription factor Forkhead-box P3 (FOXP3),^2,3 which is required for both their development and suppressive function. In humans, naive resting nTreg (rTreg) are relatively homogenous and can be isolated based on surface expression of CD45RA and CD25. Activated nTreg (aTreg), derived from rTreg after antigen activation in vivo, mostly comprise the CD25^high fraction of memory (CD45RO⁺) CD4⁺ T cells.⁴ However, there is no clear phenotypic distinction in human blood between aTreg and activated CD45RO⁺ CD25⁺ conventional T cells (Tconv)^4,5 or from antigen-activated naive CD4⁺ T cells (Naive) that also transiently express FOXP3.⁵ Thus, markers that better identify nTreg would aid assessment of immune status in health and disease. nTreg have reduced surface expression of the interleukin (IL)-7 receptor alpha chain (CD127)^6,7 and express the Ikaros transcription factor family member Helios.⁸ However, activation of Tconv also leads to downregulation of CD127⁵ and expression of HELIOS.⁹ Lack of consensus about the status of nTreg in human disease, for example, in autoimmune diseases such as type 1 diabetes,¹⁰ underscores the need for specific and stable nTreg markers.

Demethylation at the FOXP3 locus allows identification of T cells with a stable nTreg phenotype. Demethylation within a highly conserved region, the Treg-specific demethylated region (TSDR; or conserved noncoding sequence 2, CNS2), is restricted to nTreg and is not seen in recently activated Tconv that transiently express FOXP3.^11,12 Demethylation allows chromosome relaxation and binding of FOXP3 itself as well as other transcription factors (CREB/ATF, STAT5, ETs-1, Cbf-β-Runx1) in order to maintain transcriptional activity at the FOXP3 locus.^13-16 To further explore the epigenetic landscape in shaping nTreg phenotype and function, we performed global DNA methylation profiling to identify differentially methylated cytosine–guanosine dinucleotides (CpGs) between human rTreg and Naive cells before and after activation in vitro. We describe regions of differential methylation (RDMs) that are potentially able to bind FOXP3 and regulate expression of other Treg signature genes, illustrated directly for TIGIT, which is a suppressive receptor expressed by Treg.

Materials and methods

Human blood T-cell isolation, activation, and phenotyping

This study was conducted in accordance with the Declaration of Helsinki. Approval was obtained from the Walter and Eliza Hall Institute’s human research ethics committee. Blood buffy coat samples were donated by the Australian Red Cross Blood Service. CD4⁺ T cells were flow-sorted into Naive (CD45RA⁺ CD45RO⁻ CD25⁻) and rTreg (CD45RA⁺ CD45RO⁻CD25⁺), CellTrace Violet (CTV) dye labeled, activated for 3 days with anti-CD3 (soluble, 100 ng/mL; OKT3) and anti-CD28 (200 ng/mL; CD28.2; BD) antibodies and irradiated autologous CD4⁺ T-cell–depleted peripheral blood mononuclear cells in IP5 medium (Iscove’s modified Dulbecco’s medium [Gibco] supplemented with 5% pooled human serum). On day 4, fresh IP5 containing recombinant human IL-2 (final of 20 U/mL) was added, and cells were cultured for an additional 2 days. On day 6, proliferated and CD25⁺ (CTV^dim CD25⁺) cells were sorted.

Suppressor assay

rTreg were added to CTV-labeled Naive (5 × 10⁴) at 1:1 and 1:2 (suppressor:responder) ratios together with autologous antigen-presenting cells, in duplicate. Cells were stimulated with anti-CD3 antibody (soluble, 200 ng/mL) for 4 days and analyzed for CTV dilution. Suppression was calculated as the percent repression of Naive proliferation.

Chromatin immunoprecipitation and quantitative, reverse-transcription polymerase chain reaction

Naive and rTreg were activated with anti-CD3 (plate-bound, 3 μg/mL), anti-CD28 (soluble, 1 μg/mL) antibodies and IL-2 (200 U/mL) for 6–10 days to obtain Act-Naive and Act-rTreg. Induced Treg (iTreg) were differentiated from Naive with the same stimuli plus transforming growth factor-β1 (5 ng/mL). FOXP3 ChIP, 10–20 million cells, were used. Relative FOXP3 enrichment was first normalized to input DNA, and fold change was calculated compared with an intergenic region. Primers are listed in supplemental Table 1 (available on the Blood Web site).

Genomic DNA isolation, bisulfite conversion, and sequencing

Genomic DNA was isolated with a blood and tissue kit (DNeasy; Qiagen), subjected to sodium bisulfite conversion with a DNA methylation kit (EZ; Zymo Research). Converted DNA was used as the template to amplify the fragments for sequencing and methylation Taqman assay.

Reverse-transcription polymerase chain reaction, quantitative polymerase chain reaction, and Taqman quantitative, reverse-transcription polymerase chain reaction

Total RNA was extracted with reagent (TRI; Invitrogen). For reverse transcription, random hexamers (Qiagen) were used. Quantitative polymerase chain reaction (qPCR) was performed in duplicate with 200 ng first-strand cDNA. Hypoxanthine phosphoribosyltransferase-1 was used as a control. miRNAs were detected using Taqman miRNA assays (Ambion), and U6 snRNA was used as a reference gene. Methylated CpGs were detected using bisulfite-converted genomic DNA and 6-carboxy-2′,4,4′,5′,7,7′–hexachlorofluorescein– and 6-carboxy fluorescein (FAM)-labeled probes. Methylation was calculated using the following formula: % methylation = 100/[1 + 2^{Ct(methylated - unmethylated)}].

Illumina 450k arrays

Bisulfite-converted DNA was used to hybridize the Infinium HumanMethylation450 BeadChips as previously described.¹⁷ Illumina iScan was used to scan the BeadChips. The raw intensity data (IDAT) were imported into the R (2.15.2; http://cran.r-project.org/), processed using the minfi (1.4.0) Bioconductor package,¹⁸ and normalized for technical variations using SWAN.¹⁹ Poorly performing probes (P > .01 in 1 or more samples) and probes interrogating single nucleotide polymorphisms on the array were removed from further analysis. This resulted in a final dataset of 481 473 probes.

Differential methylation analysis

The proportion of methylation at each CpG is represented by the β value, defined as the proportion of the methylated signal to the total signal and calculated from the normalized intensity values. Statistical analyses were performed on M values [M = log2 (methylated/unmethylated)].²⁰ For probe-wise differential methylation analysis, a linear model was fitted, taking into account the paired sample within an individual and using the limma (3.14.4) package.²¹ P values were adjusted to control for the false discovery rate (FDR) using the Benjamini–Hochberg method.²² Significantly differentially methylated probes (DMPs) were defined as those having an adjusted P < .05 and an absolute difference in mean β values |Δβ| ≥ 0.2.

Identification of RDMs

RDMs were identified using the “dmrFind” in the Bioconductor package charm (2.4.0).²³ FDRs were calculated using the “qval” function in the charm package, with numiter = 1000 permutations. RDMs were further refined by the exclusion of regions with an absolute average percentage methylation difference across probes of <15%.

Functional annotation

Functional annotation was performed using the online Database for Annotation, Visualization and Integrated Discovery (DAVID), version 6.7,^24,25 on the level of “genes” using default parameters.

Motif identification

Promoter-associated RDM sequences were extracted from the human genome (hg19) and submitted to the MEME motif discovery tool.²⁶ The identified motifs were analyzed using the TOMTOM motif comparison tool¹⁸ in conjunction with the JASPAR FAM motifs database.²⁷

Gene set testing

Gene sets include FOXP3 targets in mice^28,29 and humans³⁰ based on FOXP3 ChIP experiments, genes regulated by FOXP3 in mice³¹ and in humans,³² and genes encoding mouse cytokines and transcription factors³³ (supplemental Table 2). Gene set testing was performed on all RDMs annotated to genes using 1-tailed Fisher exact tests.

ChIP-seq data analysis

Human FOXP3 ChIP-seq data³⁴ were obtained from the Sequence Read Archive, and the quality was assessed using FastQC (0.10.1). The 35bp reads were then trimmed using Trimmomatic (0.30).³⁵ Bowtie (0.12.7) in best mode with default parameters³⁶ was used to map reads to the human genome (hg19). MACS (1.4.2) with default parameters³⁷ was subsequently used to identify FOXP3 binding peaks.

Details of the methods can be found in the supplemental Methods section. All primers and probes are listed in supplemental Table 1. HumanMethylation450 BeadChips raw data were deposited at Gene Expression Omnibus with accession number GSE49667.

Results

CD4⁺ T-cell subsets: phenotype, function, and DNA methylation

Human rTreg are a relatively homogenous population that, after activation in vitro, resemble the activated nTreg (aTreg) observed in peripheral blood.⁴ We sorted Naive and rTreg from 3 healthy male blood donors (M28, M29, M30; Figure 1A, top panel) and activated them for 6 days with anti-CD3 and anti-CD28 antibodies, supplemented with IL-2 at day 4. After activation, both populations proliferated and were CD25⁺ (Figure 1A, middle panel). The majority (>85%) of activated rTreg (Act-rTreg) and some of the activated Naive (about 30%; Act-Naive) expressed FOXP3 (Figure 1B, bottom panel). In an autologous suppression assay, rTreg inhibited Naive proliferation (Figure 1B).

Figure 1.

CD4⁺ T-cell subsets: phenotype, function, and DNA methylation. (A) Naive and rTreg from male blood donors were sorted (top panel) and activated with soluble anti-CD3 and -28 antibodies (100 and 200 ng/mL, respectively) and autologous irradiated CD4⁺ cell-depleted peripheral blood mononuclear cells for 4 days, supplemented with 20 U/mL IL-2 for an additional 2 days. Act-Naive and Act-rTreg were sorted as proliferating CTV^dim CD25⁺ cells (middle panel). FOXP3 expression was examined by intracellular staining (bottom panel). (B) Sorted CTV^dim CD25⁺ cells were incubated with Naive CD4⁺ T cells (5 × 10⁴) at 1:1, 1:2, 1:4, 1:8, and 1:16 ratios with anti-CD3 antibody (100 ng/mL), with or without IL-2 (20 U/mL) for 5 days. (C) Multidimensional scaling (MDS) analysis of the normalized, filtered data. The MDS plot is analogous to a principal components analysis plot. The axes represent the major sources of variation in the data based on the top 1000 genes with the largest standard deviations between samples; dimension 1 represents the largest source of variation, dimension 2 represents the next largest orthogonal source of variation, followed by dimension 3, etc. The left-hand MDS plot shows that the largest source of variation between samples was the difference in baseline methylation between the donors. Examination of further sources of variation reveals that methylation differences between cell types and activation status are also present, as shown in the right-hand MDS plot. Each sample is labeled with the donor ID and colored by cell type.

We prepared DNA from rTreg, Naive, Act-Naive, and Act-rTreg for methylation analysis on Illumina Infinium HumanMethylation450 (450k) arrays. Following quality control and preprocessing (see “Materials and methods” section), multidimensional scaling (MDS) analysis of the normalized, filtered data indicated that the largest source of variation between the samples was between the donors (Figure 1C, left panel). However, a more thorough examination of other sources of variation revealed consistent differences between cell types and activation status (Figure 1C, right panel). Our statistical analysis therefore accounted for interindividual differences when testing for methylation differences between cell types.

Differential DNA methylation between Naive and rTreg

Between Naive and rTreg, 2315 CpGs were significantly differentially methylated (FDR adjusted P < .05, |Δβ| ≥0.2; supplemental Table 3). These represented <0.5% of the total CpG probes (n = 481 473). More than 70% (n = 1681) of the DMPs were associated with genes (n = 1137), while the remaining 634 were not annotated. With respect to genomic context, 33% (n = 747) of the DMPs were in promoter regions, 40% (n = 920) in gene bodies, 4% (n = 86) in 3′ untranslated regions (UTRs) of known genes, and 24% (n = 541) were not assigned to a genomic feature. Compared with the probes on the 450k array, DMPs showed a significant enrichment in the gene body (P < 2.2 × 10⁻¹⁶, Fisher exact test [FET]) and a deficiency in promoter (P < 2.2 × 10⁻¹⁶, FET), while the proportions in the 3′ UTR and unannotated regions were not significantly different from a randomly selected set (Figure 2A). The most significant clusters identified by DAVID functional annotation clustering analysis^24,25 of the genes associated with DMPs were terms T-cell activation and differentiation and regulation of cell death (supplemental Table 4).

Figure 2.

Differential DNA methylation between Naive and rTreg. (A) Functional annotations of the 450k array probes (left panel), 2315 DMPs between Naive and rTreg cells (middle panel), and probes associated with 127 RDMs between Naive and rTreg (right panel). (B) Unsupervised hierarchical clustering of the β values of both the significant DMPs (left panel) and RDM probes (right panel) resulted in distinct groupings of the Naive and rTreg samples. Each horizontal row in the heat map represents a CpG probe and each vertical column is a sample. The color indicates the level of methylation at each CpG in each sample, as described by the color key.

Promoter methylation is inversely correlated with gene expression

Neighboring CpGs are often correlated in methylation status,^38,39 and genomic regions are more functionally relevant.^40-43 Thus, we performed an analysis to identify RDMs, which can be defined as clusters of physically proximal CpGs (within ∼300bp) that are significantly different in their methylation between cell types.⁴⁴ One hundred and twenty-seven RDMs were identified between Naive and rTreg (supplemental Table 5). The RDMs encompassed 608 individual CpG probes and were associated with 97 genes and 23 unannotated genomic regions (supplemental Table 6). Of the 608 individual probes, 65% (n = 385) were located in promoter regions, 23% (n = 134) in gene bodies, 2% (n = 11) in 3′ UTRs, and 11% (n = 65) were unannotated (Figure 2A, right panel). In contrast to the individual DMPs, the probes located in RDMs were significantly enriched in promoter annotations (P < 2.2 × 10⁻¹⁶, FET) and a corresponding decrease in gene body (P < 1.823 × 10⁻⁰⁵, FET) and unannotated regions (P < 1.348 × 10⁻¹⁴, FET), while the proportion in 3′ UTR did not differ significantly. The top clusters from DAVID analysis^24,25 were the associated with the terms T-cell activation and differentiation of the genes associated with RDMs. However, in contrast to the DMPs, the next 2 most significant clusters contained terms associated with repressor activity and plasma membrane (supplemental Table 7).

Unsupervised clustering of the β values of both the DMPs and RDM probes resulted in distinct groupings of the Naive and rTreg samples (Figure 2B). In both cases, hypermethylation and hypomethylation were distributed evenly between rTreg and Naive, which is in contrast to the previous report of relative hypomethylation in nTreg obtained using other methods.⁴⁵

To explore the relationship between methylation changes and gene expression, we correlated the methylation status of promoter-associated RDMs with gene expression data from Schmidl et al.⁴⁶ We observed a negative correlation between methylation and gene expression (Pearson correlation coefficient, −0.5; P = .0026; supplemental Figure 1), suggesting that changes in promoter methylation are related to gene expression. The 12Mb methylation data from Schmidl et al⁴⁶ showed minimal (<0.01%) overlap with the 450k array CpGs. However, 5 RDMs intersected with their regions of interest, of which 3 were consistently differentially methylated in both studies (data not shown).

Activation-induced changes in DNA methylation profiles

The ability of the immune system to combat diverse, potentially pathogenic insults and still maintain tolerance to self is reflected by the lineage and functional plasticity of T cells, including Treg. We therefore examined the fidelity of distinct DNA methylation profiles after antigen activation in Naive and rTreg. Act-rTreg and Act-Naive displayed significant differences in 1689 CpG probes (supplemental Table 8), the majority of which overlapped with the differences found between these 2 T-cell subsets before activation (Figure 3A). Thus, the DNA methylation profiles that distinguished Naive and rTreg were largely unaffected by activation.

Figure 3.

Activation-induced changes in DNA methylation profiles. (A) Act-Naive and Act-rTreg displayed significant differences in 1679 CpG probes (green cross), the majority of which overlapped with the differences found between these 2 T-cell subsets before activation (red circle). The distinct DNA methylation profiles between Naive and rTreg are largely unaffected by activation. (B) Activation-induced methylation changes in 466 CpGs in Naive CD4+T cells (blue dots). Of these, 171 overlapped with the methylation patterns of rTreg and 283 were unique to activation. (C) Activation-induced demethylation at the MIR21 locus. (D) Expression of miR-21 in Naive and Treg before and after activation. Data are represented as mean ± standard error of the mean of 3 donors. *P = .05 (Mann–Whitney test, 1-tailed).

Next, we examined the activation-induced DNA methylation differences in Naive and rTreg. In Naive, methylation at 466 CpGs was significantly different after activation (supplemental Table 9). Of these probes, 36.7% (n = 171) acquired methylation patterns that were similar to those of rTreg and 60.7% (n = 283) were unique to activation (Figure 3B). The genes with activation-associated DMPs were enriched for terms related to transcription, DNA binding, and positive regulation of various cellular processes in the most significant cluster, followed by clusters containing terms DNA binding, nucleotide binding, phosphorylation, and kinase activity (supplemental Table 10). Eighteen RDMs were also identified, 13 of which were associated with known genes (KSR1, MIR21, GFI1, CSF2, NCF4, VMP1, HSD17B8, IL13, ITGAX, RPTOR, AIM2, CCL5, CD59) and 5 were unannotated (supplemental Table 11). The genes with activation-associated RDMs were enriched for terms related to cytokine–cytokine receptor interaction and regulation of T-cell differentiation (supplemental Table 12), indicating that changes in DNA methylation are associated with Naive activation and plasticity. Interestingly, activation did not lead to significant DNA methylation changes in rTreg.

The methylation status of activation-associated RDMs may regulate gene expression. Activation of Naive induced significant demethylation at the microRNA MIR21 locus (Figure 3C). Therefore, we examined expression of miR-21 in Naive and rTreg before and after activation. miR-21 was significantly upregulated after activation in Naive. Activation in vitro did not induce significant changes in rTreg, but aTreg isolated directly ex vivo exhibited significantly increased miR-21 expression (Figure 3D).

Identification of Forkhead-binding motifs in RDMs

We were interested to know if putative transcription factor binding sites were present in promoter-associated RDMs. An overrepresented motif was identified using MEME²⁶ (supplemental Figure 2A). When compared with known binding motif models using TOMTOM,¹⁸ this motif was identified as a candidate Forkhead-binding motif (supplemental Figure 2B), similar to one previously described by Sadlon et al.³⁰ Therefore, we investigated the relationship between the RDMs and genomic FOXP3 binding sites. We compared the genomic positions of our 127 RDMs with human ChIP-chip³⁰ and ChIP-seq³⁴ data. The ChIP-chip data identified 8305 FOXP3 binding peaks and the ChIP-seq data identified 26 483 peaks (see “Materials and methods” section). Almost 60% of the ChIP-chip peaks were also covered by the ChIP-seq peaks. Creating a union of the ChIP-chip and ChIP-seq peak regions resulted in 28 885 unique FOXP3 binding loci. Using this list, we found a direct overlap with 24 of our RDMs (supplemental Figure 3). Although this number appears small, the combined list of peaks only covers 33 342 (<7%) of the 450k CpGs. Given the number of genomic bases covered by each RDM and ChIP peak, in the context of the whole genome the number of bases covered by both RDMs and ChIP peaks is significantly overrepresented (P < 2.2 × 10⁻¹⁶; Pearson χ² test), implying a relationship between methylation status and FOXP3 binding in nTreg. Furthermore, we found that RDMs overlapping FOXP3 peaks were significantly more likely to be hypomethylated in nTreg (P = .0051; 1-sided FET). The positions and methylation status of the RDMs, along with evidence for FOXP3 binding, are summarized in Table 1. We subsequently selected and validated by quantitative reverse transcription-PCR (qRT-PCR) the expression of several other hypomethylated genes in Treg (supplemental Figure 2C). Our findings are consistent with the view that demethylation, especially in promoter regions of Treg genes, increases gene expression.

Table 1.

Genomic coordinates of RDMs and putative FOXP3 binding site association

Gene	Full name	Chr.	Start	End	nTreg Meth.	FOXP3 ChIP
HIVEP3	Human immunodeficiency virus type 1 enhancer binding protein 3	chr1	42384310	42384564	Hyper	Genomic
TNFRSF9	Tumor necrosis factor receptor superfamily, member 9	chr1	8001027	8001309	Hypo
AIM2	Absent in melanoma 2	chr1	159046773	159047163	Hypo	Genomic
PYHIN1	Pyrin and HIN domain family, member 1	chr1	158900088	158900644	Hypo	Genomic
NA	NA	chr1	38941882	38942403	Hyper
RUNX3	Runt-related transcription factor 3	chr1	25291385	25291546	Hyper	Genomic
PRKCZ	Protein kinase C, ζ	chr1	2003864	2004346	Hypo
VPS13D	Vacuolar protein sorting 13 homolog D (Saccharomyces cerevisiae)	chr1	12538341	12538678	Hyper
TNFRSF4	Tumor necrosis factor receptor superfamily, member 4	chr1	1150085	1150416	Hypo
CD160	CD160 molecule	chr1	145715571	145715719	Hypo
PAOX	Polyamine oxidase (exo-N4-amino)	chr10	135202522	135203200	Hypo
NA	NA	chr10	8373416	8373659	Hyper	Genomic
NET1	Neuroepithelial cell transforming 1	chr10	5488366	5488628	Hyper
ADO	2-aminoethanethiol (cysteamine) dioxygenase	chr10	64565642	64565772	Hypo
NA	NA	chr10	90749920	90750176	Hypo
FBXO18	F-box protein, helicase, 18	chr10	5935953	5936426	Hypo
CXCR5	Chemokine (C-X-C motif) receptor 5	chr11	118754280	118754593	Hypo
CD248	CD248 molecule, endosialin	chr11	66084469	66084841	Hyper
LRRC32	Leucine-rich repeat containing 32	chr11	76380921	76381236	Hypo
APLP2	Amyloid β (A4) precursor-like protein 2	chr11	129992297	129992368	Hypo
BCL9L	B-cell CLL/lymphoma 9-like	chr11	118781728	118781978	Hyper
CRY2	Cryptochrome 2 (photolyase-like)	chr11	45868398	45868501	Hypo
NA	NA	chr11	62621178	62621406	Hypo	Genomic
ACER3	Alkaline ceramidase 3	chr11	76571458	76571610	Hyper
DUSP6	Dual-specificity phosphatase 6	chr12	89744471	89744701	Hyper
IKZF4	IKAROS family zinc finger 4 (Eos)	chr12	56414442	56414533	Hypo
KRT72	Keratin 72	chr12	52994933	52995295	Hypo
NA	NA	chr12	54413000	54413384	Hyper
NCOR2	Nuclear receptor corepressor 2	chr12	124990897	124991139	Hyper
C12orf42	Chromosome 12 open reading frame 42	chr12	103695883	103696381	Hyper
LOC338799	Uncharacterized LOC338799	chr12	122235169	122235560	Hypo
NA	NA	chr12	125258948	125259531	Hypo	Genomic
NCOR2	Nuclear receptor corepressor 2	chr12	124941307	124941423	Hypo
SCNN1A	Sodium channel, nonvoltage-gated 1 α subunit	chr12	6486598	6486709	Hyper
NA	NA	chr13	34184932	34185311	Hypo	Genomic
EDNRB	Endothelin receptor type B	chr13	78494064	78494272	Hypo
TFDP1	Transcription factor Dp-1	chr13	114287374	114287735	Hyper
NA	NA	chr13	40746370	40746648	Hyper
TC2N	Tandem C2 domains, nuclear	chr14	92333765	92334271	Hyper	Genomic
BCL11B	B-cell CLL/lymphoma 11B (zinc finger protein)	chr14	99664107	99664344	Hyper
MMP14	Matrix metallopeptidase 14 (membrane-inserted)	chr14	23305780	23305957	Hyper
APBA2	Amyloid β (A4) precursor protein-binding, family A, member 2	chr15	29213626	29213860	Hyper
SMAD3	SMAD family member 3	chr15	67356310	67356942	Hyper
THBS1	Thrombospondin 1	chr15	39871808	39872071	Hypo
LOC91450	Uncharacterized LOC91450	chr15	78286521	78286737	Hypo
NA	NA	chr15	70767183	70767649	Hyper
ANKRD11	Ankyrin repeat domain 11	chr16	89421700	89422212	Hypo
ITGAL	Integrin, α L (antigen CD11A (p180), lymphocyte function-associated antigen 1; α polypeptide)	chr16	30485383	30485966	Hypo
NA	NA	chr16	84552874	84553585	Hyper
ANKRD11	Ankyrin repeat domain 11	chr16	89390609	89390968	Hypo
ACSF3	acyl-CoA synthetase family member 3	chr16	89163343	89163800	Hyper
CA5A	Carbonic anhydrase VA, mitochondrial	chr16	87958145	87958493	Hypo
MIR21	microRNA 21	chr17	57917974	57918682	Hypo	Genomic/qPCR
KSR1	Kinase suppressor of ras 1	chr17	25798741	25799447	Hypo	Genomic
KCNAB3	Potassium voltage-gated channel, shaker-related subfamily, β member 3	chr17	7832764	7833237	Hypo
VMP1	Vacuole membrane protein 1	chr17	57915665	57915773	Hypo	Genomic
RPTOR	Regulatory associated protein of MTOR, complex 1	chr17	78755379	78755604	Hyper
ST6GALNAC1	ST6 (α-N-acetyl-neuraminyl-2,3-β-galactosyl-1,3)-N-acetylgalactosaminide α-2,6-sialyltransferase 1	chr17	74639731	74640078	Hyper
ZPBP2	Zona pellucida binding protein 2	chr17	38023993	38024636	Hypo	Genomic
MEOX1	Mesenchyme homeobox 1	chr17	41739130	41739326	Hypo	Genomic
SCARF1	Scavenger receptor class F, member 1	chr17	1548881	1549108	Hyper
SEPT9	Septin 9	chr17	75473577	75474070	Hyper
AXIN2	Axin 2	chr17	63534340	63534688	Hyper
SEPT9	Septin 9	chr17	75451779	75452100	Hyper
TNFRSF13B	Tumor necrosis factor receptor superfamily, member 13B	chr17	16875349	16875596	Hypo
LAMA3	Laminin, α 3	chr18	21452730	21452895	Hyper
TGIF1	TGFB-induced factor homeobox 1	chr18	3447454	3447713	Hyper
SLC1A5	Solute carrier family 1 (neutral amino acid transporter), member 5	chr19	47288039	47288263	Hypo
EMR3	egf-like module containing, mucin-like, hormone receptor-like 3	chr19	14785593	14785849	Hyper
TYROBP	TYRO protein tyrosine kinase binding protein	chr19	36400049	36400437	Hyper
GNG8	Guanine nucleotide binding protein (G protein), γ 8	chr19	47139338	47139761	Hypo
KCNN4	Potassium intermediate/small conductance calcium-activated channel, subfamily N, member 4	chr19	44285297	44285594	Hypo	Genomic
NA	NA	chr2	43398079	43398271	Hyper
GALNT5	UDP-N-acetyl-α-d-galactosamine:polypeptide N-Acetylgalactosaminyltransferase 5 (GalNAc-T5)	chr2	158114069	158114300	Hyper
CASP10	Caspase 10, apoptosis-related cysteine peptidase	chr2	202047246	202047754	Hyper	Genomic
HDAC4	Histone deacetylase 4	chr2	240196769	240197131	Hypo	Genomic
FAM124B	Family with sequence similarity 124B	chr2	225265963	225266346	Hyper
SP140	SP140 nuclear body protein	chr2	231090376	231090745	Hypo	Genomic
NA	NA	chr2	242805838	242806119	Hypo	Genomic
SNED1	Sushi, nidogen, and EGF-like domains 1	chr2	241974858	241975140	Hypo
BCL11A	B-cell CLL/lymphoma 11A (zinc finger protein)	chr2	60763331	60763808	Hypo
CD8A	CD8a molecule	chr2	87018944	87019192	Hyper
NA	NA	chr21	46077454	46077731	Hyper	Genomic
HORMAD2	HORMA domain containing 2	chr22	30476089	30476525	Hyper
TIGIT	T-cell immunoreceptor with Ig and ITIM domains	chr3	114012659	114012912	Hypo	Genomic/qPCR
TGFBR2	Transforming growth factor, β receptor II (70/80 kDa)	chr3	30647378	30647725	Hyper
VGLL4	Vestigial like 4 (Drosophila)	chr3	11623526	11624023	Hyper
CD47	CD47 molecule	chr3	107810678	107810791	Hyper
NA	NA	chr3	151985426	151985869	Hyper
PRDM8	PR domain containing 8	chr4	81119178	81119473	Hyper
NA	NA	chr4	1202509	1202780	Hypo
PRDM8	PR domain containing 8	chr4	81118343	81118794	Hyper
HPGDS	Hematopoietic prostaglandin D synthase	chr4	95264019	95264373	Hyper
RGS12	Regulator of G-protein signaling 12	chr4	3371236	3371652	Hyper
NA	NA	chr4	155661691	155661929	Hypo
NDNF	Neuron-derived neurotrophic factor	chr4	121991345	121991672	Hypo
CUTA	cutA divalent cation tolerance homolog (Escherichia coli)	chr6	33384179	33384537	Hypo
TNF	Tumor necrosis factor	chr6	31544694	31545473	Hypo
ZC3H12D	Zinc finger CCCH-type containing 12D	chr6	149806331	149806732	Hypo	Genomic
RPS6KA2	Ribosomal protein S6 kinase, 90 kDa, polypeptide 2	chr6	166908379	166908811	Hypo
FLOT1	Flotillin 1	chr6	30706619	30706810	Hyper
ZBTB12	Zinc finger and BTB domain containing 12	chr6	31867906	31868025	Hyper
NA	NA	chr6	33386967	33387089	Hypo
C6orf222	Chromosome 6 open reading frame 222	chr6	36304456	36305155	Hypo
NA	NA	chr6	6588693	6589075	Hyper
HSD17B8	Hydroxysteroid (17-β) dehydrogenase 8	chr6	33173307	33173489	Hypo
TAGAP	T-cell activation RhoGTPase activating protein	chr6	159462964	159463300	Hypo	Genomic
PLAGL1	Pleiomorphic adenoma gene-like 1	chr6	144386457	144386528	Hypo
TAP1	Transporter 1, ATP-binding cassette, subfamily B (MDR/TAP)	chr6	32820102	32820249	Hypo	Genomic
TAP2	Transporter 2, ATP-binding cassette, subfamily B (MDR/TAP)	chr6	32805398	32805570	Hypo
AIF1	Allograft inflammatory factor 1	chr6	31583915	31584223	Hyper
NA	NA	chr7	149569883	149570128	Hyper
ATXN7L1	Ataxin 7-like 1	chr7	105319437	105319679	Hyper
GIMAP7	GTPase, IMAP family member 7	chr7	150216872	150217081	Hyper
UPP1	Uridine phosphorylase 1	chr7	48129797	48129992	Hypo
NA	NA	chr7	73241729	73242028	Hyper
WIPI2	WD repeat domain, phosphoinositide interacting 2	chr7	5271480	5271655	Hyper
GNA12	Guanine nucleotide binding protein (G protein) α 12	chr7	2774414	2774654	Hyper
NA	NA	chr7	157306340	157306592	Hypo
NA	NA	chr7	4652671	4652842	Hyper
ZHX1	Zinc fingers and homeoboxes 1	chr8	124284176	124284568	Hypo
NA	NA	chr8	61822740	61823275	Hyper
ANGPT1	Angiopoietin 1	chr8	108510286	108510343	Hyper
PLEC	Plectin	chr8	145028170	145028257	Hyper
EIF2C2	Eukaryotic translation initiation factor 2C, 2	chr8	141636174	141636798	Hypo
LOC286467	Family with sequence similarity 195, member A pseudogene	chrX	130964695	130965143	Hyper
MAOB	Monoamine oxidase B	chrX	43741826	43741945	Hypo

Genes bound by FOXP3 in FOXP3-ChIP dataset are in boldface.

NA, not available; ITIM, immunoreceptor tyrosine-based inhibitory motif.

FOXP3 binding coincides with hypomethylation in the genome

FOXP3 can function either as a transcriptional activator (eg, to FOXP3 itself) or suppressor (eg, to IL2 and IFNG). To explore how DNA methylation may be associated with different modes of FOXP3 function, we further examined the methylation status of FOXP3 binding sites in the genome using the published human ChIP-chip³⁰ and ChIP-seq³⁴ data. CpG probes within FOXP3–ChIP peaks were significantly hypomethylated compared with the probes not in the peaks (supplemental Figure 4). As a transcriptional activator, FOXP3 binds to its own demethylated TSDR in Treg to maintain its transcription.¹⁰ To investigate whether FOXP3 potentially utilizes similar mechanisms to regulate the expression of other genes, we performed gene set tests on the genes associated with RDMs (see “Materials and methods” section). The differentially expressed genes in Treg and the FOXP3 target genes were significantly enriched in the RDMs (supplemental Table 13). Interestingly, genes directly activated by FOXP3 were enriched in the hypomethylated RDMs in Treg. However, genes directly repressed by FOXP3 were not differentially methylated between rTreg and Naive (supplemental Figure 5). Thus, a range of other factors, including the context of DNA methylation (eg, within or outside a CpG island) and cofactors bound to FOXP3, may ultimately determine the transcriptional regulatory function of FOXP3.

Hypomethylation is required for FOXP3 binding

To examine how methylation affects FOXP3 binding, we took advantage of differential DNA methylation between Act-rTreg and Naive-derived Act-Naive and iTreg lineages, which all express FOXP3 (Figure 4A). By ChIP-qPCR, we observed enrichment of FOXP3 at the demethylated TIGIT and MIR21 loci in Act-rTreg, as well as at other hypomethylated loci (FOXP3 TSDR, CTLA4, and IL2Ra) known to bind to FOXP3. In contrast, at the IL7RA (CD127) locus, repressed by FOXP3, enrichment of FOXP3 was marginal, suggesting indirect or weak binding. As a control, no enrichment of FOXP3 binding was observed at intron 1 of AFM (Figure 4B). Overall, our findings indicate that methylation (and its context) limits FOXP3 binding, thereby influencing Treg function.

Figure 4.

Hypomethylation is required for FOXP3 binding. (A) Illustration of subsets of activated T cells (left panel) and expression of FOXP3 (right panel). (B) Enrichment of FOXP3 binding in hypomethylated Treg gene loci upregulated by FOXP3 and at the FOXP3-repressed IL7RA locus. AFM intron 1 was used as a control. Data are represented as mean + standard deviation of 3 donors; paired t test (2-tailed).

Treg-specific demethylation at the T-cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain locus

T-cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT) is a recently discovered receptor that suppresses activation of T cells, dendritic cells, and natural killer (NK) cells.^47-49 We identified an overlapping RDM and FOXP3 binding peak in the TIGIT promoter (supplemental Figure 3). Demethylation at the TIGIT locus was one of the most significantly differentially methylated regions that distinguished Naive and rTreg, not altered by activation, and is required for FOXP3 binding (Figures 4B and 5A). To validate our array data and evaluate the potential of using TIGIT as a new Treg marker, we extended DNA methylation across the TIGIT locus in 5 donors by clonal bisulfite sequencing of CpG dinucleotides proximal to the DMPs. As a control, we also analyzed methylation of the FOXP3 promoter and TSDR on the same samples (supplemental Figure 6). The CpG dinucleotides of TIGIT were hypomethylated in rTreg across several regions, most apparent in biF1, 2, and 5 where putative promoter and enhancer regions are located (Figure 5B, left panel). Demethylation of the TSDR in the FOXP3 locus indicated the purity of isolated Naive and rTreg (Figure 5B). In addition, activation and differentiation of Naive into Act-Naive or iTreg cells did not change the methylation status of TIGIT or FOXP3 (Figure 5C), as measured by methylation-sensitive Taqman-qPCR. TIGIT was described as being expressed in human Treg and activated Tconv cells.⁴⁷ We found that significantly increased TIGIT protein expression occurred only in Act-rTreg cells, which is in contrast to Act-Naive and iTreg (Figure 5D). Thus, TIGIT, unlike CD25 and FOXP3, can distinguish activated nTreg cells from in vitro activated conventional T cells.

Figure 5.

Treg-specific hypomethylation at the TIGIT locus. (A) DMPs and RDM in TIGIT locus. (B) Location of the PCR fragments neighboring DMPs in TIGIT (biF1-5) and FOXP3 loci and clonal bisulfite sequencing results. Ref. seq, reference sequence; Bis. seq, clonal bisulfite sequencing fragments. (Arrows) DMPs identified from 450k array. Data are represented as the mean of 5 donors. (C) Methylation-sensitive Taqman-qPCR showing methylation at TIGIT (RDM) and FOXP3 (TSDR) loci in Act-Naive and iTreg and demethylation in Act-rTreg. Data are represented as mean + standard deviation of 3 donors; paired t test (2-tailed). (D) Expression of TIGIT and CD25 in Act-Naive, iTreg, and Act-rTreg. Data are representative of 10 healthy individuals all showing similar results.

Delineation of human nTreg based on TIGIT expression

Finally, we determined whether TIGIT expression could further delineate human nTreg. CD4⁺ rTreg, normally identified by the expression of CD45RA and CD25, were divided by TIGIT into 2 compartments. In the memory fraction, the combination of TIGIT and CD25 also improved the gating strategy for isolating aTreg ex vivo (Figure 6A). In an autologous suppression assay (Figure 6B), these Treg subsets further upregulated TIGIT after activation and suppressed Naive proliferation (Figure 6C-D). However, a fraction (∼30%) of TIGIT^lo rTreg cells did not upregulate TIGIT (Figure 6C, upper panel), and these cells also showed reduced FOXP3 and increased interferon-gamma (IFN-γ) expression (Figure 6E).

Figure 6.

Expression of TIGIT on Treg and the Treg suppressor function. (A) TIGIT delineates human rTreg and aTreg. (B) Naive proliferation at day 4 in the absence of Treg. (C) TIGIT expression on Treg and Naive (top and middle panel) was analyzed at day 4 during a suppressor assay (bottom panel). (D) Suppression by different Treg subsets presented as percent inhibition of Naive proliferation. Data are presented as mean + standard deviation of 3 donors. (E) Naive, TIGIT^hi rTreg, and TIGIT^lo rTreg activated in vitro with plate-bound anti-CD3 (3 μg/mL) and soluble anti-CD28 (1 μg/mL) antibodies and IL-2 (200 U/mL) for 3 days. Cells were supplemented with IL-2 at day 4 and analyzed for FOXP3 and IFN-γ production by intracellular staining at day 6. Data are representative of results from 3 individuals.

Discussion

Demethylation of CpGs in the FOXP3 promoter^12,50 and TSDR^11,51 are stable epigenetic markers for nTreg. This has important implications for assessing T-cell status in humans in health and disease, illustrated by the fact that while expression of CD25 and FOXP3 leads to variable estimates of Treg frequency in people with type 1 diabetes¹⁰ or immune dysregulation, polyendocrinopathy, enteropathy, X-linked–like syndrome,⁵² TSDR demethylation quantitatively defines Treg frequency.^52,53 Apart from being a useful lineage mark, demethylation at the FOXP3 locus is required to maintain the suppressor function of Treg. In this regard, FOXP3 binds to the demethylated TSDR to maintain its own transcription.^13-16 We reasoned that other differences in DNA methylation across the genome might further elucidate the lineage and function of Treg. We detected 2315 individual DMPs comprising 127 RDMs between human rTreg and Naive cells and showed that DNA methylation in Naive was plastic and could be altered by cell activation. The latter was also recently described for mouse naive CD4⁺ T cells.⁵⁴ Nevertheless, Act-Naive and rTreg retained the majority of the DNA methylation profile of their parent cells, and activation did not induce significant changes in DNA methylation in rTreg.

Inspired by the knowledge that FOXP3 binds to its own demethylated TSDR to maintain its expression, we examined whether FOXP3 was involved in regulating the expression of other Treg genes in a similar fashion. We found that CpGs within the genomic FOXP3 binding sites were significantly hypomethylated. Interestingly, genes activated by FOXP3 were enriched in the hypomethylated RDMs. Genes that are repressed by FOXP3 were not differentially methylated between rTreg and Naive. Ohkura et al⁴⁵ reported that demethylation was “causative” for increased expression of some Treg signature genes, such as Helios and Eos. Other genes such as Il2, Ifng, and Zap70 were repressed by Foxp3 but showed no difference in DNA methylation. Complementary to their finding, we found that FOXP3 does not change DNA methylation at the TIGIT and FOXP3 loci in Act-Naive and iTreg. Instead, demethylation (within or outside CpG islands) allows FOXP3 to access its targets and exert its function. FOXP3 can act as a transcriptional activator and repressor. We propose that FOXP3 uses different mechanisms to regulate gene activation compared with repression. FOXP3 may directly enhance gene expression by binding to demethylated regions that contain a Forkhead-binding motif, as we demonstrated for TIGIT, MIR21, FOXP3, CTLA4, and CD25. In contrast, cofactors (eg, recruited to CpG island) may be required for FOXP3-mediated gene repression. Different modes of FOXP3 target binding and gene expression regulation have also been suggested by Samstein et al.⁵⁵

Differential methylation could be associated with heterogeneity of Treg function, for which TIGIT may be illustrative. TIGIT is expressed by Treg⁴⁷ and suppresses a range of immune cells, including T cells, dendritic cells, and NK cells.^47-49 We found that hypomethylation at the TIGIT locus characterizes human Treg and that TIGIT expression allows activated Treg to be better distinguished from effector T cells ex vivo or after activation in vitro. It is likely that demethylation and FOXP3 binding regulate TIGIT expression and that TIGIT in turn prevents nTreg from becoming IFN-γ–secreting effector T cells. MIR21 is another example. The MIR21 locus is demethylated in Treg. In Naive CD4⁺ T cells, active demethylation occurs after T-cell activation. MIR21 overexpression is associated with human autoimmune diseases such as systemic lupus erythematosus, which is also characterized by aberrant DNA methylation of CD4⁺ T cells.⁵⁶

In conclusion, it is sobering to consider that despite the broad genomic coverage of the Infinium 450k array (most RefSeq genes, promoters, 5′ UTRs, first exons, gene bodies, and 3′ UTR regions), the vast majority of CpGs in the genome are not probed by the array. Further characterization of individual DMPs and neighboring regions is required to fully appreciate the methylation landscape of human Treg and to identify potential biomarkers for the assessment of T-cell status in human disease.

Authorship

Contribution: Y.Z., M.E.B., and L.C.H. designed the experiments; Y.Z., G.N., and J.Q. performed all the experiments; J.M. and A.O. performed all the analysis; Y.Z. and J.M. drafted the manuscript; L.C.H., A.O., and M.E.B. contributed to the rewriting; and all authors contributed to discussions.

Conflict-of-interest disclosure: The authors declare no competing financial interests.

Correspondence: Alicia Oshlack, Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville 3052, Australia; e-mail: alicia.oshlack@mcri.edu.au; and Leonard C. Harrison, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria, 3052, Australia; e-mail: harrison@wehi.edu.au.