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. 2012 Dec;40(22):11339-51.
doi: 10.1093/nar/gks957. Epub 2012 Oct 16.

A global DNA methylation and gene expression analysis of early human B-cell development reveals a demethylation signature and transcription factor network

Seung-Tae Lee  1 , Yuanyuan Xiao, Marcus O Muench, Jianqiao Xiao, Marina E Fomin, John K Wiencke, Shichun Zheng, Xiaoqin Dou, Adam de Smith, Anand Chokkalingam, Patricia Buffler, Xiaomei Ma, Joseph L Wiemels
Affiliations

A global DNA methylation and gene expression analysis of early human B-cell development reveals a demethylation signature and transcription factor network

Seung-Tae Lee et al. Nucleic Acids Res. 2012 Dec.

Abstract

The epigenetic changes during B-cell development relevant to both normal function and hematologic malignancy are incompletely understood. We examined DNA methylation and RNA expression status during early B-cell development by sorting multiple replicates of four separate stages of pre-B cells derived from normal human fetal bone marrow and applied high-dimension DNA methylation scanning and expression arrays. Features of promoter and gene body DNA methylation were strongly correlated with RNA expression in multipotent progenitors (MPPs) both in a static state and throughout differentiation. As MPPs commit to pre-B cells, a predominantly demethylating phenotype ensues, with 79% of the 2966 differentially methylated regions observed involving demethylation. Demethylation events were more often gene body associated rather than promoter associated; predominantly located outside of CpG islands; and closely associated with EBF1, E2F, PAX5 and other functional transcription factor (TF) sites related to B-cell development. Such demethylation events were accompanied by TF occupancy. After commitment, DNA methylation changes appeared to play a smaller role in B-cell development. We identified a distinct development-dependent demethylation signature which has gene expression regulatory properties for pre-B cells, and provide a catalog reference for the epigenetic changes that occur in pre-B-cell leukemia and other B-cell-related diseases.

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Figures

Figure 1.
Figure 1.
Baseline methylation and expression levels at S1. (A) Methylation levels inside and outside of annotation categories, including histone modifications, Polycomb target genes and TET1 target genes. (B) Association of DNA methylation with gene expression. Median methylation levels of CpG sites mapped to the promoter region and the body region of a gene were obtained together with its gene expression level as measured by the Affymetrix Gene 1.0 ST Array. Methylation levels were then categorized as hypomethylated, semimethylated and hypermethylated based on the following beta cutoffs, respectively: β ≤0.3, 0.3 < β ≤0.7 and β > 0.7. Genes that have no body-associated CpG sites (mainly small housekeeping genes) are plotted at the right side of the figure. The number of genes in each group was labeled at the top of the graph. The most highly expressed group was that with both promoter and body hypomethylated whereas the lowest was with both regions hypermethylated, and the mean difference in gene expression between these two groups was 2.8-fold (P < 2.2 × 10−16, two-sample t-test).
Figure 2.
Figure 2.
Differential methylation analysis of B-cell developmental stages (stages 1–4; S1–S4). DMRs between any two subsequent stages were identified if the FDR-adjusted P-value for the t-test was ≤0.05 and the |Δβ| value was ≥0.2. (A) A volcano plot with maximal –log P and maximal |Δβ| between any two subsequent stages for each CpG site shows a negative skewness of Δβ. (B) Density plots of the methylation level at S1 for CpG loci that are identified as de-DMRs and de novo DMRs. (C) Scatter plots of methylation levels of any two subsequent stages (S1 versus S2, S3 versus S2 and S4 versus S3). Red and blue dots highlight de novo and de-DMRs, respectively. (D) A hierarchical clustering of the 22 samples using the core de-DMRs-classified samples according to their stages. The inset shows boxplots of methylation levels of core de-DMRs. (E) Association of core de-DMRs with non-CGI regions and gene body regions. (F) ChIP-PCR analysis of four BCL11A (top) four EBF1 (bottom) targets. ENCODE-described TF binding sites which were also de-DMRs were pulled down using ChIP techniques in S1 and S3 cells. The amount of each targeted sequence was compared in the pull-down relative to the input DNA using quantitative PCR, and the fold enrichment in S1 and S3 cells shown in the figure. The analysis demonstrated that S3 cells harbored between 10-fold (for CD22/EBF1 pull-down) and 3073-fold (for NACA2/BCL11a pull-down) more sequence bound to the indicated TFs compared to input, in most cases orders of magnitude more than in S1 cells. Non-specific IgG control pull-down displayed at maximum only a 3.1-fold enrichment of target sequence compared to control (data not shown). The experiment was performed in technical quadruplicate and repeated once from the chromatin, with similar results.
Figure 2.
Figure 2.
Differential methylation analysis of B-cell developmental stages (stages 1–4; S1–S4). DMRs between any two subsequent stages were identified if the FDR-adjusted P-value for the t-test was ≤0.05 and the |Δβ| value was ≥0.2. (A) A volcano plot with maximal –log P and maximal |Δβ| between any two subsequent stages for each CpG site shows a negative skewness of Δβ. (B) Density plots of the methylation level at S1 for CpG loci that are identified as de-DMRs and de novo DMRs. (C) Scatter plots of methylation levels of any two subsequent stages (S1 versus S2, S3 versus S2 and S4 versus S3). Red and blue dots highlight de novo and de-DMRs, respectively. (D) A hierarchical clustering of the 22 samples using the core de-DMRs-classified samples according to their stages. The inset shows boxplots of methylation levels of core de-DMRs. (E) Association of core de-DMRs with non-CGI regions and gene body regions. (F) ChIP-PCR analysis of four BCL11A (top) four EBF1 (bottom) targets. ENCODE-described TF binding sites which were also de-DMRs were pulled down using ChIP techniques in S1 and S3 cells. The amount of each targeted sequence was compared in the pull-down relative to the input DNA using quantitative PCR, and the fold enrichment in S1 and S3 cells shown in the figure. The analysis demonstrated that S3 cells harbored between 10-fold (for CD22/EBF1 pull-down) and 3073-fold (for NACA2/BCL11a pull-down) more sequence bound to the indicated TFs compared to input, in most cases orders of magnitude more than in S1 cells. Non-specific IgG control pull-down displayed at maximum only a 3.1-fold enrichment of target sequence compared to control (data not shown). The experiment was performed in technical quadruplicate and repeated once from the chromatin, with similar results.
Figure 3.
Figure 3.
Differential expression analysis during B-cell development. (A) Scatter plots of gene expression levels of two subsequent stages during B-cell development. DEGs(FDR-adjusted P ≤ 0.05 and fold changes ≥1.5) were highlighted with red and blue dots (up- and down-regulated, respectively). (B) A hierarchical clustering of 31 samples based on the expression profiles of the 3913 core DEGs classified each sample into its relevant stages. Heatmap colors indicate log-ratios against expression levels at S1.
Figure 4.
Figure 4.
Association of RNA differential expression with DNA differential methylation during B-cell development. (A) Forest plot of the coefficient of differential methylation in a robust linear regression analysis using the gene-wise methylation changes as the outcome and the gene-wise expression changes as the predictor. Methylation changes were stratified with respect to annotation categories and model fitting was carried out within each category. (B) Differential promoter/non-CGI methylation negatively correlates with gene expression. The 89 genes showing both a change in one or more DMRs in its promoter/non-CGI region and a change in gene expression (>1.5-fold) were plotted. (+) and (−) denote an increase and a decrease in expression or methylation, respectively. Numbers above the bars are percentages.
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