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.
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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.
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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.
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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.
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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)
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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
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DOI: https://doi.org/10.1038/nn.3588
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