Why is cytosine methylation

Unfortunately, data on genome-wide distribution of 5hmC in humans is available for a very limited set of cell types, mostly developmental [ 82 , 83 ], preventing us from a direct study of the effects of 5hmC on transcription and TFBSs. At the current stage the 5hmC data is not available for inclusion in the manuscript. Yet, we were able to perform an indirect study based on the localization of the studied cytosines in various genomic regions.

It was shown previously that cytosine methylation might change the spatial structure of DNA and thus might affect transcriptional regulation by changes in the affinity of TFs binding to DNA [ 47 — 49 ]. However, the answer to the question of if such a mechanism is widespread in the regulation of transcription remains unclear. For TFBSs prediction we used the remote dependency model RDM [ 85 ], a generalized version of a position weight matrix PWM , which eliminates an assumption on the positional independence of nucleotides and takes into account possible correlations of nucleotides at remote positions within TFBSs.

RDM was shown to decrease false positive rates effectively as compared with the widely used PWM model. It is worth noting that the distribution is clearly bimodal with one mode around 0. To ensure that the results were not caused by a novel method of TFBS prediction i. Results obtained using only the 36 normal cell lines were similar: 11 TFs were significantly depleted of such cytosines Additional file 3 , while most of the others were also depleted, yet insignificantly due to the low number of total predictions.

It was previously shown that cytosine methylation can prevent binding of several TFs such as Sp1 [ 60 ], CTCF [ 53 ] and others and, therefore, methylation may serve as a global regulatory mechanism for cell-specific TF binding. Computational prediction of TFBSs identifies DNA regions of potential binding, which may not be available for a TF in a particular cell type due to chromatin modifications.

To avoid a bias caused by potential TFBSs that are not functional in particular cell types, we used experimentally obtained regions of TF binding. Chromatin immunoprecipitation followed by parallel DNA sequencing ChIP-seq is an effective experimental technique for the identification of regions for DNA-protein interaction [ 86 ].

Therefore, we combined experimental and computational approaches and filtered out the predictions of TFBSs outside of ChiP-seq peak regions. ChIP-seq data for other TFs are available only for the cancer cell lines included in our study, making it impossible to draw conclusions about normal cell functioning. At the current stage the ChiP-seq data for other TFs is not available for inclusion in the manuscript.

There are four possible simple scenarios, as described in Table 3. However, it is worth noting that many TFs can work both as activators and repressors depending on their cofactors. Figure 3 shows the distribution of the ratios for activators, repressors and multifunctional TFs able to function as both activators and repressors.

The figure shows that repressors are more sensitive average observed-to-expected ratio is 0. Multifunctional TFs exhibit a bimodal distribution with one mode similar to repressors observed-to-expected ratio 0. This suggests that some multifunctional TFs act more often as activators while others act more often as repressors. Taking into account that most of the known TFs prefer to bind unmethylated DNA, our results are in concordance with the theoretical scenarios presented in Table 3.

We found that the methylation profiles and expression profiles in In a way, the current common perception of the link between methylation and gene expression is seen in a different light. These observations allow us to suggest that blocking of TFBSs by selective methylation is unlikely to be a general mechanism of methylation-dependent transcription regulation and that such a mechanism is limited to special cases.

We conclude that the regulation of expression via DNA methylation and via TF binding are relatively independent regulatory mechanisms; both mechanisms are thus not in a direct causal relationship. Known cases of interaction between these mechanisms appear mostly because they operate on the same target regions promoters and require intermediate partners, for example, modification of chromatin.

We grouped them into 50 classes of identical or similar biological cell types. Detailed information is provided in Additional file 9. All human samples used in the FANTOM5 project were either exempted material available in public collections or commercially available , or provided under informed consent. For a particular TSS, we referred to a set of expression values across the selected 50 classes of cell types as an expression profile.

Low expressed CAGE-tag clusters may be non-robust to sequencing errors or heterogeneity of the cell population. Overlapping promoters were considered independently. All data included cytosine methylation only in the CCGG context. We excluded cytosines covered by less than 10 reads. For a particular cytosine, we referred to a set of methylation values the proportion of methylated reads relative to all reads across the selected 50 cell types as a methylation profile. Low but not 0 levels of cytosine methylation may appear as a result of experimental errors, and these levels can affect further analysis.

To avoid any bias caused by such cytosines, we used only positions differentially methylated between cell types. In the case of overlapping promoters, it is difficult to estimate which TSS is affected by the methylation of a particular cytosine. In the analysis of TFBSs affected by cytosine methylation, we considered only CpGs differentially methylated across cell types. Binding sites having scores less than PWM thresholds were excluded.

Here f a,i,j is the frequency of dinucleotide a formed of nucleotides at positions i and j , and L is the length of the aligned TFBSs. PWM thresholds were selected according to the P -value of 0. It is noteworthy that average GC-content of the RDM hits was undistinguishable from that of the binding sites in the initial alignments.

However, some positions within binding sites contain CpG dinucleotide more often than do others, so we repeated the analysis for each type of binding site position separately. Information content is defined as DIC Discrete Information Content [ 93 ] separately for different types of binding site positions.

Here x a,j are elements of the position count matrix i. In contrast to classic information content [ 94 ], DIC is based on raw counts instead of per-column nucleotide probabilities, which can be inaccurate for a small set of aligned sequences.

We define two empirical DIC thresholds [ 95 ] Th and th introduced in [ 96 ]. Th corresponds to the DIC of the column having only 3 of 4 possible nucleotides that have the same frequency, th corresponds to the DIC of the column having two nucleotides with the same frequency, f , and the other two nucleotides each with the frequency 2f.

The CpG positions have C and G as major nucleotides with the highest frequency in the neighbouring columns. The summary is presented in Additional files 4 and 5. Nucleic Acids Res. Dynamic RNA modifications in gene expression regulation. Eukaryotic rRNA modification by yeast 5-methylcytosine-methyltransferases and human proliferation-associated antigen p PLoS One.

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