Genotype data

Genotype data at 1170 loci for all 467 individual sticklebac

Genome-Wide DNA Methylation Analysis of Systemic Lupus Erythematosus Reveals Persistent Hypomethylation of Interferon Genes and Compositional Changes to CD4+ T-cell Populations

<div><p>Systemic lupus erythematosus (SLE) is an autoimmune disease with known genetic, epigenetic, and environmental risk factors. To assess the role of DNA methylation in SLE, we collected CD4+ T-cells, CD19+ B-cells, and CD14+ monocytes from 49 SLE patients and 58 controls, and performed genome-wide DNA methylation analysis with Illumina Methylation450 microarrays. We identified 166 CpGs in B-cells, 97 CpGs in monocytes, and 1,033 CpGs in T-cells with highly significant changes in DNA methylation levels (p<1×10⁻⁸) among SLE patients. Common to all three cell-types were widespread and severe hypomethylation events near genes involved in interferon signaling (type I). These interferon-related changes were apparent in patients collected during active and quiescent stages of the disease, suggesting that epigenetically-mediated hypersensitivity to interferon persists beyond acute stages of the disease and is independent of circulating interferon levels. This interferon hypersensitivity was apparent in memory, naïve and regulatory T-cells, suggesting that this epigenetic state in lupus patients is established in progenitor cell populations. We also identified a widespread, but lower amplitude shift in methylation in CD4+ T-cells (>16,000 CpGs at FDR<1%) near genes involved in cell division and MAPK signaling. These cell type-specific effects are consistent with disease-specific changes in the composition of the CD4+ population and suggest that shifts in the proportion of CD4+ subtypes can be monitored at CpGs with subtype-specific DNA methylation patterns.</p></div

Disease activity QQ-Plots and the persistence of hypomethylation in quiescent patients.

<p><b>A.</b> QQ-Plots of the p-values from the flare versus quiescent association analysis for each cell type, illustrating the lack of activity-dependent DNA methylation. <b>B.</b> Boxplots of the methylation difference between each individual and the mean of all controls at CpGs in IFN-regulated genes among those that were highly significant in the SLE-control tests. The groups are labeled C, Control, F, SLE collected during a flare, and Q, SLE collected during quiescence.</p

Common and cell type-specific DNA methylation changes in SLE.

<p>Differences in mean methylation between SLE and controls are plotted for each cell type at each probe near two genes. <b>A.</b> The IRF7 gene shows hypomethylation across all three cell-types at a CpG island, plus monocyte-specific hypomethylation further into the gene body. <b>B.</b> The IKZF4 gene shows T-cell-specific hypomethylation at the 5′ end of the gene. Red dots indicate p<1×10⁻⁸. Yellow dots indicate FDR<1%.</p

Comparison of the SLE-control methylation differences in sorted T-cell populations.

<p>Each scatter plot represents 1,031 CpGs that had p<1×10⁻⁸ in CD4+ T-cells in our SLE-control association tests. The Y-axis for all plots is the mean SLE-control methylation delta at these CpGs in the initial cohort. The X-axis for each plot is the mean SLE-control methylation delta at the same CpGs in our validation cohort, using (<b>A</b>) total CD4+, (<b>B</b>) CD4+Memory, (<b>C</b>) CD4+Naïve, or (<b>D</b>) CD4+Regulatory cells. The red dots represent CpGs near IFN-regulated genes and the squared correlation coefficients (R²) represent the values for all plotted CpGs (upper left) or IFN CpGs only (lower right).</p

Functional analysis of significant CpGs in three cell types.

<p><a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003678#s2" target="_blank">Results</a> from DAVID/Panther GO term analysis for the highly significant CpGs in each cell type and the mildly significant CpGs in T-cells.</p

Non-linear patterns in age-related DNA methylation may reflect CD4⁺ T cell differentiation

<p>DNA methylation (DNAm) is an important epigenetic process involved in the regulation of gene expression. While many studies have identified thousands of loci associated with age, few have differentiated between linear and non-linear DNAm trends with age. Non-linear trends could indicate early- or late-life gene regulatory processes. Using data from the Illumina 450K array on 336 human peripheral blood samples, we identified 21 CpG sites that associated with age (<i>P<</i>1.03E-7) and exhibited changing rates of DNAm change with age (<i>P<</i>1.94E-6). For 2 of these CpG sites (cg07955995 and cg22285878), DNAm increased with age at an increasing rate, indicating that differential DNAm was greatest among elderly individuals. We observed significant replication for both CpG sites (<i>P<5.0</i>E-8) in a second set of peripheral blood samples. In 8 of 9 additional data sets comprising samples of monocytes, T cell subtypes, and brain tissue, we observed a pattern directionally consistent with DNAm increasing with age at an increasing rate, which was nominally significant in the 3 largest data sets (4.3E-15<<i>P<</i>0.039). cg07955995 and cg22285878 reside in the promoter region of <i>KLF14</i>, which encodes a protein involved in immune cell differentiation via the repression of FOXP3. These findings may suggest a possible role for cg07955995 and cg22285878 in immunosenescence.</p

Manhattan plots for markers with <i>P</i><0.0001 from epigenome-wide association study and genome-wide association study.

<p>Phenotypes include 11 sterols and 35 fatty acids measured at fasting.</p

Top genome-wide association study (GWAS) and epigenome-wide association study (EWAS) results for fasting sterols and fatty acids.

<p>Chr, chromosome; FA, fatty acid.</p

Top genome-wide association study (GWAS) and epigenome-wide association study (EWAS) results for postprandial sterols and fatty acids.

<p>Chr, chromosome; FA, fatty acid.</p