Systematic prediction of DNA shape changes due to CpG methylation explains epigenetic effects on protein–DNA binding

Abstract

BackgroundDNA shape analysis has demonstrated the potential to reveal structure-based mechanisms of protein–DNA binding. However, information about the influence of chemical modification of DNA is limited. Cytosine methylation, the most frequent modification, represents the addition of a methyl group at the major groove edge of the cytosine base. In mammalian genomes, cytosine methylation most frequently occurs at CpG dinucleotides. In addition to changing the chemical signature of C/G base pairs, cytosine methylation can affect DNA structure. Since the original discovery of DNA methylation, major efforts have been made to understand its effect from a sequence perspective. Compared to unmethylated DNA, however, little structural information is available for methylated DNA, due to the limited number of experimentally determined structures. To achieve a better mechanistic understanding of the effect of CpG methylation on local DNA structure, we developed a high-throughput method, methyl-DNAshape, for predicting the effect of cytosine methylation on DNA shape.ResultsUsing our new method, we found that CpG methylation significantly altered local DNA shape. Four DNA shape features—helix twist, minor groove width, propeller twist, and roll—were considered in this analysis. Distinct distributions of effect size were observed for different features. Roll and propeller twist were the DNA shape features most strongly affected by CpG methylation with an effect size depending on the local sequence context. Methylation-induced changes in DNA shape were predictive of the measured rate of cleavage by DNase I and suggest a possible mechanism for some of the methylation sensitivities that were recently observed for human Pbx-Hox complexes.ConclusionsCpG methylation is an important epigenetic mark in the mammalian genome. Understanding its role in protein–DNA recognition can further our knowledge of gene regulation. Our high-throughput methyl-DNAshape method can be used to predict the effect of cytosine methylation on DNA shape and its subsequent influence on protein–DNA interactions. This approach overcomes the limited availability of experimental DNA structures that contain 5-methylcytosine.Electronic supplementary materialThe online version of this article (10.1186/s13072-018-0174-4) contains supplementary material, which is available to authorized users.

Head and Neck Paragangliomas—A Genetic Overview

“…However, the most frequent of all epigenetic markers in DNA is cytosine methylation. This change in the human genome is referred to as “CpG methylation” or “DNA methylation” [ 52 ]. Inactivation of tumor-suppressor genes (TSGs) is caused by overall DNA hypomethylation and hypermethylation of CpG islands located in the closest vicinity of the promoter.…”

Unique and assay specific features of NOMe-, ATAC- and DNase I-seq data

“…In an earlier line of work it was shown that CpG methylation in DNase I cut sites increases cleavage efficiency by altering the DNA structure (54), which can also be predicted by looking at DNA shapes (55). To systematically compare the enzyme specific sequence preferences within each assay we predicted structural DNA shape features (see Methods) around the GpC methylation sites (NOMe) and the enzyme cutting sites (ATAC, DNase I) (Figure 3B and Figure S6) across a number of available samples and datasets (see Figure S6).…”

Whole slide images reflect DNA methylation patterns of human tumors

“…41 Another study showed that CpG methylation significantly altered local DNA shape. 42 DNA methylation is closely linked with the occupancy patterns of an important genome regulator, CTCF, which binds to insulator regions in genomic DNA and plays a fundamental role in controlling higher order chromatin structure and gene expression. 43 To decipher whether CTCF binding plays a role in the link between DNA methylation and cell morphological changes, more comprehensive DNA methylation datasets including noncoding regions such as bisulfite sequencing together with image data are needed to expand our work.…”