Open Access

Genome-wide DNA methylation profiling of CD8+ T cells shows a distinct epigenetic signature to CD4+ T cells in multiple sclerosis patients

  • Vicki E. Maltby1, 2,
  • Moira C. Graves1, 3,
  • Rodney A. Lea1, 4,
  • Miles C. Benton4,
  • Katherine A. Sanders1, 5,
  • Lotti Tajouri5,
  • Rodney J. Scott1, 6 and
  • Jeannette Lechner-Scott1, 3, 7Email author
Contributed equally
Clinical EpigeneticsThe official journal of the Clinical Epigenetics Society20157:118

https://doi.org/10.1186/s13148-015-0152-7

Received: 30 August 2015

Accepted: 29 October 2015

Published: 5 November 2015

Abstract

Background

Multiple sclerosis (MS) is thought to be a T cell-mediated autoimmune disorder. MS pathogenesis is likely due to a genetic predisposition triggered by a variety of environmental factors. Epigenetics, particularly DNA methylation, provide a logical interface for environmental factors to influence the genome. In this study we aim to identify DNA methylation changes associated with MS in CD8+ T cells in 30 relapsing remitting MS patients and 28 healthy blood donors using Illumina 450K methylation arrays.

Findings

Seventy-nine differentially methylated CpGs were associated with MS. The methylation profile of CD8+ T cells was distinctive from our previously published data on CD4+ T cells in the same cohort. Most notably, there was no major CpG effect at the MS risk gene HLA-DRB1 locus in the CD8+ T cells.

Conclusion

CD8+ T cells and CD4+ T cells have distinct DNA methylation profiles. This case–control study highlights the importance of distinctive cell subtypes when investigating epigenetic changes in MS and other complex diseases.

Keywords

Multiple sclerosis DNA methylation CD8+ T cells HLA-DRB1

Findings

Multiple sclerosis (MS) susceptibility is influenced by a combination of genetic factors and environmental exposures. CD4+ T cells have long been favoured as the most important immune cell subset in the pathogenesis of disease, but there is increasing evidence that CD8+ T cells play a substantial role in central nervous system damage (reviewed in [1]).

Despite several large genome-wide association studies (GWAS), there remains a large proportion of unexplained heritability in terms of MS risk. Epigenetics can influence the genome without changes to the DNA sequence. Environmental exposures such as smoking and vitamin D levels have been demonstrated to modify epigenetic mechanisms, providing a plausible link between environmental factors and disease [2, 3]. One such epigenetic mechanism is DNA methylation, which is the addition of a methyl group to CpG dinucleotides. We, and others, have used genome-wide DNA methylation technologies to assess differentially methylated regions (DMRs) of CD4+ T cells in MS patients compared to healthy controls [46]. We found a striking methylation signal located on chromosome 6p21 with a peak signal at HLA-DRB1, which remained after controlling for background SNP effects, as well as 55 non-HLA CpGs that localise to genes previously linked with MS.

In an effort to determine if these previously identified DMRs were specific to CD4+ T cells, we performed a genome-wide methylation study of CD8+ T cells using the same cohort, workflow and data analysis as described in our previous study [5]. Briefly, DNA from total CD8+ T cells was extracted from 30 MS patients and 28 healthy age- and sex-matched controls. The DNA was bisulphite-converted and hybridised to Illumina 450K arrays. Raw fluorescence data were processed using a combination of R/Bioconductor and custom scripts of a total of 442,672 probes representing individual CpG sites that passed quality control (QC) steps. These CpGs were analysed by statistical modelling of methylation levels (β values) between MS cases and controls.

Figure 1 shows the genome-wide distribution of differential methylation scores for all CpG sites that passed the nominal p value cut-off of 0.05. We conducted a stepwise prioritisation strategy to extract the most robust CpG loci associated with MS. Based on the criteria of (i) FDR p < 0.05 and (ii) Δ meth ≥ ± 0.1 thresholds, 111 CpGs were extracted. To filter out potential effects of gender and treatment, we performed a subgroup analysis of the methylation statistics as previously described [5]. This process reduced the number of associated CpG sites down to a core panel of 79 (Table 1).
Fig. 1

A genome-wide differential methylation plot based on sites passing a nominal p value of 0.05. Data points outside the circle represent increased methylation in multiple sclerosis (MS) patients compared to controls (i.e. Δmeth), whereas points inside the circle represent methylation in the MS group

Table 1

MS-associated CpGs in CD8+ T cells

Probe IDa

CHRb

Position

Genec

Feature

Median (case)

Median (control)

Δ meth d

p valuee

cg03431738

21

40031295

ERG

5′UTR

0.81

0.68

0.13

0.004033

cg12026095

19

49468461

FTL

TSS200

0.30

0.49

−0.18

0.004033

cg26228123

14

73392919

DCAF4

TSS200

0.09

0.20

−0.11

0.004033

cg10478035

13

80919503

 

-

0.75

0.64

0.11

0.004033

cg04474988

10

131770171

 

-

0.34

0.46

−0.11

0.03549

cg25152348

22

50946712

NCAPH2

1st exon

0.30

0.47

−0.17

0.03549

cg08206623

11

2907334

CDKN1C

TSS1500

0.29

0.44

−0.15

0.004033

cg13738615

9

109624741

ZNF462

TSS1500

0.18

0.31

−0.13

0.004033

cg01525244

22

39548611

CBX7

TSS200

0.14

0.24

−0.10

0.004033

cg12702165

12

95228136

MIR492

TSS200

0.65

0.54

0.11

0.004033

cg06443542

10

100206752

HPS1

TSS200

0.14

0.25

−0.11

0.03549

cg00380172

6

148663585

SASH1

TSS200

0.21

0.33

−0.12

0.03549

cg19095187

6

108437051

 

-

0.17

0.31

−0.14

0.03549

cg04488145

3

46899455

MYL3

3′UTR

0.83

0.73

0.11

0.03549

cg03027241

20

49620453

KCNG1

3′UTR

0.50

0.32

0.18

0.004033

cg11700985

10

82127205

DYDC2

3′UTR

0.85

0.74

0.11

0.03549

cg07886142

5

126793022

MEGF10

3′UTR

0.59

0.46

0.13

0.03549

cg18183163

2

171574141

SP5

3′UTR

0.12

0.26

−0.14

0.03549

cg01181415

12

16757954

LMO3

5′UTR

0.22

0.36

−0.14

0.03549

cg10143811

12

16757985

LMO3

5′UTR

0.12

0.22

−0.10

0.03549

cg23274123

1

229478617

C1orf96

5′UTR

0.10

0.22

−0.12

0.004033

cg00095276

5

1068111

SLC12A7

Body

0.77

0.63

0.15

0.004033

cg03447557

1

2273735

MORN1

Body

0.80

0.70

0.10

0.03549

cg02745847

17

47075880

IGF2BP1

Body

0.17

0.31

−0.13

0.03549

cg09406795

11

64019655

PLCB3

Body

0.25

0.38

−0.13

0.000358

cg18016288

13

95834131

ABCC4

Body

0.47

0.32

0.15

0.000358

cg14486346

2

102000131

CREG2

Body

0.78

0.66

0.12

0.03549

cg21937244

14

103406412

CDC42BPB

Body

0.75

0.61

0.14

0.03549

cg11811840

2

234669166

UGT1A10

Body

0.84

0.72

0.12

0.03549

cg25756617

1

43734917

TMEM125

TSS1500

0.69

0.58

0.11

0.03549

cg03768916

10

49813307

ARHGAP22

TSS200

0.30

0.43

−0.14

0.004033

cg06524757

13

72441523

DACH1

TSS200

0.25

0.35

−0.11

0.03549

cg03168749

11

124413574

OR8B12

TSS200

0.82

0.68

0.14

0.03549

cg21276022

9

136390236

TMEM8C

TSS200

0.74

0.61

0.13

0.004033

cg09851596

8

143545214

BAI1

TSS200

0.60

0.49

0.11

0.03549

cg25296222

11

2037173

 

-

0.76

0.65

0.11

0.03549

cg00878533

1

2848864

 

-

0.72

0.62

0.11

0.000358

cg03612700

17

18970610

 

-

0.64

0.52

0.12

0.004033

cg03310594

7

22704316

 

-

0.82

0.69

0.13

2.34E-05

cg05854694

14

61123243

 

-

0.12

0.22

−0.10

0.000358

cg12384499

15

89949617

 

-

0.19

0.31

−0.11

0.004033

cg22509113

2

91777482

 

-

0.41

0.51

−0.10

0.004033

cg10495084

15

96889416

 

-

0.24

0.36

−0.12

0.004033

cg18008019

13

100641646

 

-

0.10

0.23

−0.12

0.03549

cg12093775

13

112548065

 

-

0.15

0.26

−0.11

0.000358

cg12787323

10

119494959

 

-

0.16

0.27

−0.11

0.004033

cg22792862

14

67827087

EIF2S1

1st exon

0.23

0.38

−0.15

0.004033

cg08969532

10

99790438

CRTAC1

1st exon

0.05

0.15

−0.10

0.004033

cg18185028

3

154042079

DHX36

1st exon

0.30

0.41

−0.11

0.000358

cg23059965

19

50655862

C19orf41

3′UTR

0.81

0.70

0.11

0.004033

cg02192678

8

1495185

DLGAP2

5′UTR

0.78

0.68

0.11

0.004033

cg02976009

6

32068226

TNXB

5′UTR

0.71

0.59

0.12

0.03549

cg18073471

4

81119198

PRDM8

5′UTR

0.18

0.29

−0.11

0.03549

cg00945810

7

814391

HEATR2

Body

0.67

0.56

0.11

0.03549

cg04875614

4

2008706

WHSC2

Body

0.80

0.69

0.10

2.34E-05

cg26920627

1

7319248

CAMTA1

Body

0.75

0.63

0.12

0.004033

cg26647242

2

30040525

ALK

Body

0.78

0.67

0.11

0.004033

cg04605816

20

62092443

KCNQ2

Body

0.83

0.71

0.12

0.004033

cg10944063

2

120233706

SCTR

Body

0.58

0.46

0.12

0.004033

cg14595269

7

151216272

RHEB

Body

0.14

0.24

−0.10

2.34E-05

cg23720125

5

177097760

LOC202181

Body

0.85

0.73

0.12

0.004033

cg02047661

3

51976883

RRP9

TSS1500

0.64

0.52

0.11

0.004033

cg07925549

12

52828840

KRT75

TSS1500

0.75

0.63

0.12

0.03549

cg06697094

17

54911185

DGKE

TSS1500

0.16

0.28

−0.12

0.03549

cg18789663

1

242688591

PLD5

TSS1500

0.09

0.20

−0.11

0.03549

cg03468541

14

89029199

ZC3H14

TSS200

0.17

0.30

−0.13

0.004033

cg13526221

8

987389

 

-

0.79

0.69

0.11

0.004033

cg03313895

4

24803042

 

-

0.65

0.54

0.10

0.03549

cg19442593

2

26252851

 

-

0.85

0.74

0.11

0.004033

cg04851089

6

28953923

 

-

0.39

0.54

−0.15

0.004033

cg24520975

6

31651362

 

-

0.86

0.75

0.11

0.03549

cg01932076

21

47394659

 

-

0.18

0.30

−0.12

2.34E-05

cg17555825

5

76924190

 

-

0.16

0.26

−0.10

0.03549

cg23154781

15

80634195

 

-

0.81

0.69

0.12

0.004033

cg00792513

6

100066698

 

-

0.34

0.47

−0.14

0.03549

cg23708569

14

106058450

 

-

0.63

0.51

0.13

2.34E-05

cg09579989

12

110685438

 

-

0.81

0.71

0.10

0.03549

cg12077664

12

125145446

 

-

0.78

0.64

0.14

0.000358

cg24824082

2

133030701

 

-

0.24

0.35

−0.11

0.000358

Dash indicates intergenic

UTR untranslated region, TSS transcription start site

aProbe ID on 450K chip

bChromosome

cGene annotated to probe

dDifferential-methylated score

e p value for specified probe in CD8+ T cells

Of the 79 CpGs showing differential methylation in MS patients after filtering, all resided outside the MHC locus on chr 6p21. Of these, 27 were intergenic (34 %), have no gene association, or map to genes of unknown function. Of the remaining 52 loci, 26 % are promoter associated, 9 % are in the 5′UTR, 5 % are in the 1st exon, 20 % are in gene bodies and 8 % are in the 3′UTR. Interestingly, none of these CpGs maps to genes that have previously been reported to have a relationship with MS [7, 8]. There was no overlap between these results and our previous results, and, unlike in CD4+ T cells, there was no gene that contained multiple differentially methylated sites. MORN1 has a single hypermethylated CpG in both CD4+ and CD8+ T cells; however, it was a different site in each study, making it unlikely that this is a significant finding. Our observations are consistent with the recent study by Bos et al., who also identified minimal overlap between the methylation profiles of CD4+ and CD8+ T cells of MS patients [4].

Using GSEA with WebGestalt, our patient cohort did not have prominent pathways in the KEGG Pathway analysis or disease association analysis. The most significant promoter associated with differential methylation was the ferritin light chain (FTL) gene. The MS cohort displayed decreased methylation at this CpG locus compared to controls. The gene’s biological function is cation transport. One of the statistically significant genes, ERG (ETS-related gene), had a single hypermethylated CpG in the MS cohort compared to controls. ERG is a member of the transcription factor family involved in activities such as cell proliferation, differentiation, apoptosis and inflammation. FTL is a component of ferritin, and defects in this subunit are associated with other neurodegenerative diseases where mutations result in accumulation of iron in the brain [9]. Relapsing–remitting multiple sclerosis (RRMS) patients have increased iron deposits in their grey matter as compared to healthy controls; thus, misregulation of FTL could be important in disease pathology [10, 11]. Mutations in DCAF4 are associated with leucocyte telomere length, and there is evidence that shortened telomere length in leucocytes is associated with other neurodegenerative diseases, such as Parkinson and Alzheimer’s disease [1214]. In addition, one study found a shorted telomere length in primary progressive MS patients, but no correlation between RRMS and differing telomere length has been established [15].

Interestingly, we did not see a cluster of differentially methylated CpGs within HLA-DRB1 as seen in CD4+ T cells [5]. It is well known that the HLA region is notoriously difficult to investigate with many molecular techniques due to increased genetic variation. To minimise the possibility that our observed methylation profile was due to the probes in this region not meeting QC, we used targeted pyrosequencing on available case and control DNA samples. This assay covered seven of the ten differentially methylated CpGs identified in our previous study, but due to high sequence variability, only five of the seven sites returned data. We calculated the median beta values across the five CpG sites using the K–S test. Results showed that the median methylation level in the cases (median = 3.6) and controls (median = 3.6) was not significantly different (p = 0.72). This supports a conclusion that this MS-related DMR at HLA-DRB1 does not exist in CD8+ T cells but is unique to CD4+ T cells.

A recent study by Bos et al. (2015) also found no major effect loci or clusters of differentially methylated CpGs in the CD8+ T cells of MS patients. However, of the top 40 CpG sites, none overlaps with the top 79 sites found in our study. In addition, we found that approximately half the differentially methylated sites were hypermethylated. This is also in contrast to Bos et al., who found nearly 95 % of sites were hypermethylated in CD8+ T cells. Unlike Bos et al., we chose not to filter out probes that are known to contain SNPs. We reasoned that any false positive signals exclusively due to SNP effects would be subsequently identified by genotyping at the key loci. In support of this notion, pyrosequencing of the key HLA-DRB1 locus did not alter our array-based findings. Additionally, we did not observe a signal at the HLA-DRB1 locus in CD8+ T cells but did in CD4+ T cells, providing further support that SNPs are not influencing the findings at this locus.

One important consideration of our study is that the patients were being, or had been, treated with various immunomodulatory therapies at the time of recruitment. In particular, eight patients were being treated with fingolimod, which prevents CD4+ lymphocyte egress from lymphoid tissue. As part of our analysis, we stratified our case–control analysis based on treatment groups in an effort to determine whether overall differential methylation signal may be confounded. None of the patient treatment groups shows a distinct methylation signature, including fingolimod (data not shown), which supports the notion that the small number of treated patients in our cohort is not affecting our results. We do note that this does not necessarily mean that fingolimod is not acting on the methylome, but we can conclude that the small number of patients being treated with fingolimod in our study is not confounding the findings. Future studies will benefit from treatment-naïve patients or will be limiting the study to patients on a particular treatment group.

In this study, we identified 79 CpGs showing minor association with MS. None of these hits was observed in the CD4+ T cells from the same cohort, including the major CD4+ DMR at HLA-DRB1. All genome-wide DNA methylation studies to date have used relatively small sample sizes. This has resulted in identification of large-effect regions only. Large-scale studies are needed to identify minor-effect DMRs. Future studies should also examine the functional consequences of these changes through transcript analysis. Primarily, the results of this study highlight the need to focus on individual cell types when assessing DNA methylation associated with MS susceptibility.

Ethics statement

The Hunter New England Health Research Ethics Committee and University of Newcastle Human Ethics committee approved this study (05/04/13.09 and H-505-0607, respectively). MS patients gave written and verbal consent. The Australian Red Cross Blood Service ethics committee approved the use of blood from healthy donors.

Notes

Abbreviations

DMR: 

differentially methylated region

DNA: 

deoxyribonucleic acid

FDR: 

false discovery rate

GSEA: 

gene set enrichment assay

GWAS: 

genome-wide association study

MHC: 

major histocompatibility complex

MS: 

multiple sclerosis

QC: 

quality control

SNP: 

single nucleotide polymorphism

Declarations

Acknowledgements

This study was supported by the John Hunter Charitable Trust. Rodney Lea, Vicki Maltby and Katherine Sanders are supported by fellowships from Multiple Sclerosis Research Australia. We would like to thank the MS patients and clinical team at the John Hunter Hospital MS clinic who participated in this study and the Australia Red Cross Blood Service for providing healthy control samples. We also acknowledge the Analytical Biomolecular Research Facility at the University of Newcastle for flow cytometry support, EpigenDx for pyrosequencing and the Australian Genome Research Facility for performing the bisulfite conversions and hybridisations to the Illumina 450K arrays.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Centre for Information Based Medicine, Hunter Medical Research Institute
(2)
School of Biomedical Sciences and Pharmacy, University of Newcastle
(3)
School of Medicine and Public Health, Univeristy of Newcastle
(4)
Insitute of Health and Biomedical Innovation, Queensland University of Technology
(5)
Faculty of Health Sciences and Medicine, Bond University
(6)
Division of Molecular Medicine, Pathology North
(7)
Department of Neurology, Devision of Medicine, John Hunter Hospital

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Copyright

© Maltby et al. 2015

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