Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Epigenome-wide differences in pathology-free regions of multiple sclerosis–affected brains

Abstract

Using the Illumina 450K array and a stringent statistical analysis with age and gender correction, we report genome-wide differences in DNA methylation between pathology-free regions derived from human multiple sclerosis–affected and control brains. Differences were subtle, but widespread and reproducible in an independent validation cohort. The transcriptional consequences of differential DNA methylation were further defined by genome-wide RNA-sequencing analysis and validated in two independent cohorts. Genes regulating oligodendrocyte survival, such as BCL2L2 and NDRG1, were hypermethylated and expressed at lower levels in multiple sclerosis–affected brains than in controls, while genes related to proteolytic processing (for example, LGMN, CTSZ) were hypomethylated and expressed at higher levels. These results were not due to differences in cellular composition between multiple sclerosis and controls. Thus, epigenomic changes in genes affecting oligodendrocyte susceptibility to damage are detected in pathology-free areas of multiple sclerosis–affected brains.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Differential distribution of DNA methylation in multiple sclerosis–affected brains.
Figure 2: Changes in DNA methylation are concordant within a differentially methylated region.
Figure 3: Circos plot of genome-wide DNA methylation changes between brains of individuals with multiple sclerosis and controls without neurological disease.
Figure 4: Identification of genes with coordinated changes in DNA methylation and gene expression.
Figure 5: Cell composition does not affect changes detected by immunohistochemistry.
Figure 6: Validation of methylation and gene expression changes in an independent cohort.

Accession codes

Primary accessions

Gene Expression Omnibus

References

  1. 1

    Hafler, D.A. et al. Risk alleles for multiple sclerosis identified by a genomewide study. N. Engl. J. Med. 357, 851–862 (2007).

    CAS  PubMed  Google Scholar 

  2. 2

    Sawcer, S. et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214–219 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3

    Patsopoulos, N.A. et al. Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci. Ann. Neurol. 70, 897–912 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4

    Ebers, G.C. Environmental factors and multiple sclerosis. Lancet Neurol. 7, 268–277 (2008).

    PubMed  Google Scholar 

  5. 5

    Esteller, M. Epigenetics in cancer. N. Engl. J. Med. 358, 1148–1159 (2008).

    CAS  PubMed  Google Scholar 

  6. 6

    Amir, R.E. et al. Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-binding protein 2. Nat. Genet. 23, 185–188 (1999).

    CAS  PubMed  Google Scholar 

  7. 7

    Hansen, R.S. et al. The DNMT3B DNA methyltransferase gene is mutated in the ICF immunodeficiency syndrome. Proc. Natl. Acad. Sci. USA 96, 14412–14417 (1999).

    CAS  PubMed  Google Scholar 

  8. 8

    Dempster, E.L. et al. Disease-associated epigenetic changes in monozygotic twins discordant for schizophrenia and bipolar disorder. Hum. Mol. Genet. 20, 4786–4796 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9

    Baranzini, S.E. et al. Genome, epigenome and RNA sequences of monozygotic twins discordant for multiple sclerosis. Nature 464, 1351–1356 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Bock, C. et al. DNA methylation dynamics during in vivo differentiation of blood and skin stem cells. Mol. Cell 47, 633–647 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Mastronardi, F.G. et al. Increased citrullination of histone H3 in multiple sclerosis brain and animal models of demyelination: a role for tumor necrosis factor-induced peptidylarginine deiminase 4 translocation. J. Neurosci. 26, 11387–11396 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Pedre, X. et al. Changed histone acetylation patterns in normal-appearing white matter and early multiple sclerosis lesions. J. Neurosci. 31, 3435–3445 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Sandoval, J. et al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6, 692–702 (2011).

    CAS  PubMed  Google Scholar 

  14. 14

    Bock, C. Analysing and interpreting DNA methylation data. Nat. Rev. Genet. 13, 705–719 (2012).

    CAS  Google Scholar 

  15. 15

    Hernandez, D.G. et al. Distinct DNA methylation changes highly correlated with chronological age in the human brain. Hum. Mol. Genet. 20, 1164–1172 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    Numata, S. et al. DNA methylation signatures in development and aging of the human prefrontal cortex. Am. J. Hum. Genet. 90, 260–272 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Irizarry, R.A. et al. The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat. Genet. 41, 178–186 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Doi, A. et al. Differential methylation of tissue- and cancer-specific CpG island shores distinguishes human induced pluripotent stem cells, embryonic stem cells and fibroblasts. Nat. Genet. 41, 1350–1353 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Stadler, M.B. et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature 480, 490–495 (2011).

    CAS  PubMed  Google Scholar 

  20. 20

    Sharp, A.J. et al. DNA methylation profiles of human active and inactive X chromosomes. Genome Res. 21, 1592–1600 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Noonan, J.P. & McCallion, A.S. Genomics of long-range regulatory elements. Annu. Rev. Genomics Hum. Genet. 11, 1–23 (2010).

    CAS  PubMed  Google Scholar 

  22. 22

    Sheffield, N.C. et al. Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions. Genome Res. 23, 777–788 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Guintivano, J., Aryee, M.J. & Kaminsky, Z.A. A cell epigenotype specific model for the correction of brain cellular heterogeneity bias and its application to age, brain region and major depression. Epigenetics 8, 290–302 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Ebers, G.C. et al. Parent-of-origin effect in multiple sclerosis: observations in half-siblings. Lancet 363, 1773–1774 (2004).

    CAS  PubMed  Google Scholar 

  25. 25

    Ascherio, A., Munger, K.L. & Lunemann, J.D. The initiation and prevention of multiple sclerosis. Nat Rev Neurol. 8, 602–612 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Hedström, A.K., Baarnhielm, M., Olsson, T. & Alfredsson, L. Exposure to environmental tobacco smoke is associated with increased risk for multiple sclerosis. Mult. Scler. 17, 788–793 (2011).

    PubMed  Google Scholar 

  27. 27

    Swank, R.L. & Dugan, B.B. Effect of low saturated fat diet in early and late cases of multiple sclerosis. Lancet 336, 37–39 (1990).

    CAS  PubMed  Google Scholar 

  28. 28

    von Geldern, G. & Mowry, E.M. The influence of nutritional factors on the prognosis of multiple sclerosis. Nat. Rev. Neurol. 8, 678–689 (2012).

    CAS  PubMed  Google Scholar 

  29. 29

    Ramagopalan, S.V., Dobson, R., Meier, U.C. & Giovannoni, G. Multiple sclerosis: risk factors, prodromes, and potential causal pathways. Lancet Neurol. 9, 727–739 (2010).

    PubMed  Google Scholar 

  30. 30

    Lim, A.L. et al. Epigenetic state and expression of imprinted genes in umbilical cord correlates with growth parameters in human pregnancy. J. Med. Genet. 49, 689–697 (2012).

    CAS  PubMed  Google Scholar 

  31. 31

    Bakulski, K.M. et al. Genome-wide DNA methylation differences between late-onset Alzheimer's disease and cognitively normal controls in human frontal cortex. J. Alzheimers Dis. 29, 571–588 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Liu, Y. et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat. Biotechnol. 31, 142–147 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Ross, A.J. et al. BCLW mediates survival of postmitotic Sertoli cells by regulating BAX activity. Dev. Biol. 239, 295–308 (2001).

    CAS  PubMed  Google Scholar 

  34. 34

    Principato, G.B., Rosi, G., Talesa, V., Bocchini, V. & Giovannini, E. Purification of S-2-hydroxyacylglutathione hydrolase (glyoxalase II) from calf brain. Biochem. Int. 9, 351–359 (1984).

    CAS  PubMed  Google Scholar 

  35. 35

    Melotte, V. et al. The N-myc downstream regulated gene (NDRG) family: diverse functions, multiple applications. FASEB J. 24, 4153–4166 (2010).

    CAS  PubMed  Google Scholar 

  36. 36

    Yao, M., Nguyen, T.V. & Pike, C.J. β-amyloid-induced neuronal apoptosis involves c-Jun N-terminal kinase-dependent downregulation of Bcl-w. J. Neurosci. 25, 1149–1158 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Murphy, B. et al. Bcl-w protects hippocampus during experimental status epilepticus. Am. J. Pathol. 171, 1258–1268 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

    Hu, X.L., Olsson, T., Johansson, I.M., Brannstrom, T. & Wester, P. Dynamic changes of the anti- and pro-apoptotic proteins Bcl-w, Bcl-2, and Bax with Smac/Diablo mitochondrial release after photothrombotic ring stroke in rats. Eur. J. Neurosci. 20, 1177–1188 (2004).

    PubMed  Google Scholar 

  39. 39

    Cosker, K.E., Pazyra-Murphy, M.F., Fenstermacher, S.J. & Segal, R.A. Target-derived neurotrophins coordinate transcription and transport of bclw to prevent axonal degeneration. J. Neurosci. 33, 5195–5207 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Chapman, H.A. Endosomal proteolysis and MHC class II function. Curr. Opin. Immunol. 10, 93–102 (1998).

    CAS  PubMed  Google Scholar 

  41. 41

    Santamaría, I., Velasco, G., Pendas, A.M., Fueyo, A. & Lopez-Otin, C. Cathepsin Z, a novel human cysteine proteinase with a short propeptide domain and a unique chromosomal location. J. Biol. Chem. 273, 16816–16823 (1998).

    PubMed  Google Scholar 

  42. 42

    Ratovitski, T., Chighladze, E., Waldron, E., Hirschhorn, R.R. & Ross, C.A. Cysteine proteases bleomycin hydrolase and cathepsin Z mediate N-terminal proteolysis and toxicity of mutant huntingtin. J. Biol. Chem. 286, 12578–12589 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Kos, J., Jevnikar, Z. & Obermajer, N. The role of cathepsin X in cell signaling. Cell Adh. Migr. 3, 164–166 (2009).

    PubMed  PubMed Central  Google Scholar 

  44. 44

    Obermajer, N., Doljak, B., Jamnik, P., Fonovic, U.P. & Kos, J. Cathepsin X cleaves the C-terminal dipeptide of alpha- and gamma-enolase and impairs survival and neuritogenesis of neuronal cells. Int. J. Biochem. Cell Biol. 41, 1685–1696 (2009).

    CAS  PubMed  Google Scholar 

  45. 45

    Jevnikar, Z., Obermajer, N., Bogyo, M. & Kos, J. The role of cathepsin X in the migration and invasiveness of T lymphocytes. J. Cell Sci. 121, 2652–2661 (2008).

    CAS  PubMed  Google Scholar 

  46. 46

    Obermajer, N., Jevnikar, Z., Doljak, B. & Kos, J. Role of cysteine cathepsins in matrix degradation and cell signalling. Connect. Tissue Res. 49, 193–196 (2008).

    CAS  PubMed  Google Scholar 

  47. 47

    Wendt, W., Zhu, X.R., Lubbert, H. & Stichel, C.C. Differential expression of cathepsin X in aging and pathological central nervous system of mice. Exp. Neurol. 204, 525–540 (2007).

    CAS  PubMed  Google Scholar 

  48. 48

    Beck, H. et al. Cathepsin S and an asparagine-specific endoprotease dominate the proteolytic processing of human myelin basic protein in vitro. Eur. J. Immunol. 31, 3726–3736 (2001).

    CAS  PubMed  Google Scholar 

  49. 49

    Graumann, U., Reynolds, R., Steck, A.J. & Schaeren-Wiemers, N. Molecular changes in normal appearing white matter in multiple sclerosis are characteristic of neuroprotective mechanisms against hypoxic insult. Brain Pathol. 13, 554–573 (2003).

    CAS  PubMed  Google Scholar 

  50. 50

    Lindberg, R.L. et al. Multiple sclerosis as a generalized CNS disease–comparative microarray analysis of normal appearing white matter and lesions in secondary progressive MS. J. Neuroimmunol. 152, 154–167 (2004).

    CAS  PubMed  Google Scholar 

  51. 51

    Xi, Y. & Li, W. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics 10, 232 (2009).

    PubMed  PubMed Central  Google Scholar 

  52. 52

    Sun, Z. et al. Batch effect correction for genome-wide methylation data with Illumina Infinium platform. BMC Med. Genomics 4, 84 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  55. 55

    Thompson, R.F., Suzuki, M., Lau, K.W. & Greally, J.M. A pipeline for the quantitative analysis of CG dinucleotide methylation using mass spectrometry. Bioinformatics 25, 2164–2170 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Huber, A.K. et al. Genetically driven target tissue overexpression of CD40: a novel mechanism in autoimmune disease. J. Immunol. 189, 3043–3053 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58

    Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59

    Geiss, G.K. et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat. Biotechnol. 26, 317–325 (2008).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We are grateful to all the members of the Casaccia and Sharp laboratory for technical help, to C. Watson for assistance with the CETS analysis, to F. Zhang and W. Zhang for advice and assistance with the RNA-seq alignment, to F. Lublin and G. John for critical reading of the manuscript and to P.L. De Jager (Brigham and Women's Hospital), S. Baranzini (University of California at San Francisco) and N. Schaeren-Wiemers (University Hospital, Basel) for discussions. The work has been funded by Icahn School of Medicine seed funds and grants from the US National Institutes of Health (NIH) NINDS (R01NS052738-06 and R37NS042925-10) to P.C., from NIH NIDA (R01DA033660) and NIH NHGRI (R01HG006696) to A.J.S., from NIH NIMH (R01MH090948-01) to J.Z., from NIH NINDS (R01NS38667) to B.D.T. and from NIH NIA (P01AG02219), NIH NIMH (P50MH066392) and VA-MIRECC to V.H. J.L.H. is the recipient of NIH Fellowship F31NS077504-01 and a scholarship from the Foundation of the Consortium of Multiple Sclerosis Centers' MS Workforce of the Future. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. We thank R. Reynolds and the UK Multiple Sclerosis Tissue Bank at Imperial College London (funded by the UK MS Society, grant no. 910/09) for the provision of multiple sclerosis tissue samples, as well as the Human Brain and Spinal Fluid Resource Center, Veterans Affairs West Los Angeles Healthcare Center, which is sponsored by NINDS, NIMH, National Multiple Sclerosis Society and Department of Veterans Affairs.

Author information

Affiliations

Authors

Contributions

J.L.H. isolated the DNA and RNA; designed and conducted the array, verification, validation and expression studies; and drafted the manuscript. P.G. and A.J.S. conducted statistical analyses and drafted the manuscript. T.H.T. performed the immunohistochemistry and M.J.D. assisted with the histology and immunohistochemistry analysis. S.Y. and J.Z. performed the gene ontology analysis. V.H. provided the second cohort of controls without neurological disease. R.D. and B.D.T. provided the second cohort of multiple sclerosis samples and expression analysis from a third cohort. P.C. conceived and designed the study and wrote the manuscript.

Corresponding author

Correspondence to Patrizia Casaccia.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Hematoxylin and eosin and Luxol fast blue staining show absence of overt inflammation and demyelinating lesions.

(a) Representative hematoxylin and eosin staining for control (n=5 brain samples) and multiple sclerosis (n=15 brain samples) sections shows an absence of inflammatory infiltrates. (b) Representative Luxol fast blue staining for control (n=5 brain samples) and multiple sclerosis (n=15 brain samples) sections shows an absence of demyelinating lesions. Scale bar = 100 μm. NAWM, normal-appearing white matter.

Supplementary Figure 2 Pairwise comparison of neighboring CpGs shows high correlation within a 1-kb window.

(a) Schematic of pairwise comparison between the central CpG within DMRs and neighboring CpGs. (b) A ±5 kb window was extended around each CpG within the DMRs. Pearson correlation was calculated for each CpG (central CpG) and the neighboring CpGs within each respective window. Vertical dashed lines demarcate 1-kb window (±500 bp) around the central CpG, which shows the strongest level of correlation. Heatmap shows density of correlations. (c) DNA methylation block shows pairwise Pearson correlation between CpGs in the promoter and gene body of CTSZ. Lines connect with the gene structure below at the location of the corresponding CpG. (d) Plot of DNA methylation levels for individual CpGs within a hypomethylated DMR in the promoter of CTSZ as measured by Illumina array. Above the plot is the complete gene structure, with dashed lines indicating the zoomed region containing the DMR shown in the plot. Methylation levels for individual MS cases and controls are plotted in red and black, respectively, with red and black lines connecting the mean methylation level for each consecutive CpG assayed. MS, multiple sclerosis.

Supplementary Figure 3 Minimal association of disease duration with methylation levels in normal-appearing white matter.

The x-axis shows autosomal chromosomes and y-axis the –log10(p-value). Each dot represents nominal Fisher's combined p-value for CpGs within a 1-kb sliding window, where individual p-values were generated using the correlation between disease duration and methylation across the genome. The red line denotes 5% FDR.

Supplementary Figure 4 Distribution of methylation difference between multiple sclerosis and controls shows most changes to be subtle.

(a) Density plots based on β-values obtained from 461,272 autosomal CpGs show a bimodal distribution of DNA methylation levels in the genome (black). Density plots of CpGs within hypomethylated (red) and hypermethylated (green) DMRs show a significantly different distribution (p<10–10, Kolmogorov-Smirnov test), with an excess of intermediate methylation levels. (b) Difference taken as the β-value average of MS minus the β-value average of controls. Distribution of methylation level difference for each CpG identified in differentially methylated regions are divided into three categories: <0.05, 0.05–0.10, or >0.10.

Supplementary Figure 5 Validation of changes in DNA methylation between multiple sclerosis and controls in an independent cohort of samples.

(a) Plot of DNA methylation levels for individual CpGs within a window of the CTSZ DMR as measured by Illumina array of the discovery cohort. Methylation levels for individual MS cases and controls are plotted in red and black, respectively, with red and black lines connecting the mean methylation level for each consecutive CpG assayed. Arrowheads indicate CpGs that were assayed in a second cohort (b). (b) Methylation levels for individual CpGs assessed using MassARRAY EpiTYPER of a second cohort. Plots are as shown in (a) for the CpGs marked by arrowheads. Arrowheads indicate CpGs with corresponding Illumina probes in (a). (c–d) Same as described in (a,b) for a window of the SLC47A2 DMR. MS, multiple sclerosis.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 and Supplementary Tables 1, 4 and 5 (PDF 768 kb)

Supplementary Table 2

List of differentially methylated regions. (XLSX 534 kb)

Supplementary Table 3

RNA-Seq results following DESeq2 analysis. (XLSX 2051 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Huynh, J., Garg, P., Thin, T. et al. Epigenome-wide differences in pathology-free regions of multiple sclerosis–affected brains. Nat Neurosci 17, 121–130 (2014). https://doi.org/10.1038/nn.3588

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing