上圖所示為人類基因組的動態(tài)甲基化情況：圖上x軸（左邊）相應(yīng)于在24種人類細(xì)胞和組織類型中所觀察到的最大甲基化變化,，y軸是平均總甲基化,，z軸是CpG二核苷酸的密度。胞嘧啶的甲基化（通常發(fā)生在CpG上）是基因表達(dá)的表觀調(diào)控的一個(gè)常見特征,。大多數(shù)細(xì)胞類型都有相對穩(wěn)定的CpG二核苷酸甲基化模式,，而我們對哪些CpG參與基因組調(diào)控的認(rèn)識是有限的。在這項(xiàng)研究中,，Meissner及其同事分析了各種不同人類細(xì)胞和組織類型的全基因組“亞硫酸氫鹽”序列數(shù)據(jù)集,，發(fā)現(xiàn)只有大約22%的CpG在這些類型中改變它們的甲基化狀態(tài)。這些CpG大多數(shù)都位于假想的基因調(diào)控元素上,，尤其是增強(qiáng)子和“轉(zhuǎn)錄因子結(jié)合點(diǎn)”上,。除了進(jìn)一步澄清DNA甲基化的分布外，這些具有動態(tài)DNA甲基化模式的所選區(qū)域還可幫助將效率更高的基因組方法引導(dǎo)到專注于能提供信息的區(qū)域,，同時(shí)也可幫助確定調(diào)控元素,。（生物谷Bioon.com）
生物谷推薦英文摘要：
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.
