Many anecdotal observations exist of a regulatory effect of DNA methylation on gene expression. However, in general, the underlying mechanisms of this effect are poorly understood. In this review, we summarize what is currently known about how this important, but mysterious, epigenetic mark impacts cellular functions. Cytosine methylation can abrogate or enhance interactions with DNA-binding proteins, or it may have no effect, depending on the context. Despite being only a small chemical change, the addition of a methyl group to cytosine can affect base readout via hydrophobic contacts in the major groove and shape readout via electrostatic contacts in the minor groove. We discuss the recent discovery that CpG methylation increases DNase I cleavage at adjacent positions by an order of magnitude through altering the local 3D DNA shape and the possible implications of this structural insight for understanding the methylation sensitivity of transcription factors (TFs). Additionally, 5-methylcytosines change the stability of nucleosomes and, thus, affect the local chromatin structure and access of TFs to genomic DNA. Given these complexities, it seems unlikely that the influence of DNA methylation on protein–DNA binding can be captured in a small set of general rules. Hence, data-driven approaches may be essential to gain a better understanding of these mechanisms.

Protein–DNA binding is a fundamental component of gene regulatory processes, but it is still not completely understood how proteins recognize their target sites in the genome. Besides hydrogen bonding in the major groove (base readout), proteins recognize minor-groove geometry using positively charged amino acids (shape readout). The underlying mechanism of DNA shape readout involves the correlation between minor-groove width and electrostatic potential (EP). To probe this biophysical effect directly, rather than using minor-groove width as an indirect measure for shape readout, we developed a methodology, DNAphi, for predicting EP in the minor groove and confirmed the direct role of EP in protein–DNA binding using massive sequencing data. The DNAphi method uses a sliding-window approach to mine results from non-linear Poisson–Boltzmann (NLPB) calculations on DNA structures derived from all-atom Monte Carlo simulations. We validated this approach, which only requires nucleotide sequence as input, based on direct comparison with NLPB calculations for available crystal structures. Using statistical machine-learning approaches, we showed that adding EP as a biophysical feature can improve the predictive power of quantitative binding specificity models across 27 transcription factor families. High-throughput prediction of EP offers a novel way to integrate biophysical and genomic studies of protein–DNA binding.

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.

Enhancers harbor binding motifs that recruit transcription factors (TFs) for gene activation.While cooperative binding of TFs at enhancers is known to be critical for transcriptional activation of a handful of developmental enhancers, the extent TF cooperativity genome-wide is unknown. Here, we couple high-resolution nuclease footprinting with single-molecule methylation profiling to characterize TF cooperativity at active enhancers in the Drosophila genome. Enrichment of short MNase-protected DNA segments indicates that the majority of enhancers harbor two or more TF binding sites, and we uncover protected fragments that correspond to co-bound sites in thousands of enhancers. We integrate MNase-seq, methylation accessibility profiling, and CUT&RUN chromatin profiling as a comprehensive strategy to characterize co-binding of the Trithorax-like (TRL) DNA binding protein and multiple other TFs and identify states where an enhancer is bound by no TF, by either single factor, by multiple factors, or where binding sites are occluded by nucleosomes. From the analysis of co-binding, we find that cooperativity dominates TF binding in vivo at a majority of active enhancers. TF cooperativity can occur without apparent protein-protein interactions and provides a mechanism to effectively clear nucleosomes and promote enhancer function..

Cell-free DNA (cfDNA) has the potential to enable non-invasive detection of disease states and progression. Beyond its sequence, cfDNA also represents the nucleosomal landscape of cell(s)-of-origin and captures the dynamics of the epigenome. In this review, we highlight the emergence of cfDNA epigenomic methods that assess disease beyond the scope of mutant tumour genotyping. Detection of tumour mutations is the gold standard for sequencing methods in clinical oncology. However, limitations inherent to mutation targeting in cfDNA, and the possibilities of uncovering molecular mechanisms underlying disease, have made epigenomics of cfDNA an exciting alternative. We discuss the epigenomic information revealed by cfDNA, and how epigenomic methods exploit cfDNA to detect and characterize cancer. Future applications of cfDNA epigenomic methods to act complementarily and orthogonally to current clinical practices has the potential to transform cancer management and improve cancer patient outcomes.