上图所示为人类基因组的动态甲基化情况：图上x轴（左边）相应于在24种人类细胞和组织类型中所观察到的最大甲基化变化，y轴是平均总甲基化，z轴是CpG二核苷酸的密度。胞嘧啶的甲基化（通常发生在CpG上）是基因表达的表观调控的一个常见特征。大多数细胞类型都有相对稳定的CpG二核苷酸甲基化模式，而我们对哪些CpG参与基因组调控的认识是有限的。在这项研究中，Meissner及其同事分析了各种不同人类细胞和组织类型的全基因组“亚硫酸氢盐”序列数据集，发现只有大约22%的CpG在这些类型中改变它们的甲基化状态。这些CpG大多数都位于假想的基因调控元素上，尤其是增强子和“转录因子结合点”上。除了进一步澄清DNA甲基化的分布外，这些具有动态DNA甲基化模式的所选区域还可帮助将效率更高的基因组方法引导到专注于能提供信息的区域，同时也可帮助确定调控元素。（生物谷Bioon.com）
生物谷推荐英文摘要：
Nature doi: 10.1038/nature12433
Charting a dynamic DNA methylation landscape of the human genome
Michael J. Ziller,Hongcang Gu, Fabian Müller,Julie Donaghey,Linus T.-Y. Tsai,Oliver Kohlbacher, Philip L. De Jager,Evan D. Rosen, David A. Bennett, Bradley E. Bernstein, Andreas Gnirke & Alexander Meissner
DNA methylation is a defining feature of mammalian cellular identity and is essential for normal development1, 2. Most cell types, except germ cells and pre-implantation embryos3, 4, 5, display relatively stable DNA methylation patterns, with 70–80% of all CpGs being methylated6. Despite recent advances, we still have a limited understanding of when, where and how many CpGs participate in genomic regulation. Here we report the in-depth analysis of 42 whole-genome bisulphite sequencing data sets across 30 diverse human cell and tissue types. We observe dynamic regulation for only 21.8% of autosomal CpGs within a normal developmental context, most of which are distal to transcription start sites. These dynamic CpGs co-localize with gene regulatory elements, particularly enhancers and transcription-factor-binding sites, which allow identification of key lineage-specific regulators. In addition, differentially methylated regions (DMRs) often contain single nucleotide polymorphisms associated with cell-type-related diseases as determined by genome-wide association studies. The results also highlight the general inefficiency of whole-genome bisulphite sequencing, as 70–80% of the sequencing reads across these data sets provided little or no relevant information about CpG methylation. To demonstrate further the utility of our DMR set, we use it to classify unknown samples and identify representative signature regions that recapitulate major DNA methylation dynamics. In summary, although in theory every CpG can change its methylation state, our results suggest that only a fraction does so as part of coordinated regulatory programs. Therefore, our selected DMRs can serve as a starting point to guide new, more effective reduced representation approaches to capture the most informative fraction of CpGs, as well as further pinpoint putative regulatory elements.
