Expression regulation of genes is linked to their CpG density distributions around transcription start sites

Abstract

The CpG dinucleotide and its methylation behaviors play vital roles in gene regulation. Previous studies have divided genes into several categories based on the CpG intensity around transcription starting sites and found that housekeeping genes tend to possess high CpG density, whereas tissue-specific genes are generally characterized by low CpG density. In this study, we investigated how the CpG density distribution of a gene affects its transcription and regulation pattern. Based on the CpG density distribution around transcription starting site, by means of a semi-supervised neural network we designed, which took data augmentation into account, we divided the human genes into three categories, and genes within each cluster shared similar CpG density distribution. Not only sequence properties, these different clusters exhibited distinctly different structural features, regulatory mechanisms, correlation patterns between the expression level and CpG/TpG density, and expression and epigenetic mark variations during tumorigenesis. For instance, the activation of cluster 3 genes relies more on 3D genome reorganization, compared with cluster 1 and 2 genes, whereas cluster 2 genes showed the strongest correlation between gene expression and H3K27me3. Genes exhibiting uncoupled correlation between gene regulation and histone modifications are mainly in cluster 3. These results emphasized that the usage of epigenetic marks in gene regulation is partially rooted in the sequence property of genes such as their CpG density distribution and explained to some extent why the relation between epigenetic marks and gene expression is controversial.

Tet-mediated DNA methylation dynamics affect chromosome organization

“…Previous works have shown that the CpG density distribution is strongly associated with the chromatin organization, particularly the nuclear space could be phase-separated according to CpG island (CGI) enrichment into CGI-rich (CGI forest) or CGI-poor (CGI prairie) domains that correspond to compartments A and B, respectively ( 46 , 47 ). Notably, the DNA methylation difference between CGI-rich and CGI-poor correlates very well with the strength of domain segregation or compartmentalization, suggesting that the degree of the methylation difference between CGI-rich and CGI-poor domains may influence compartment strength ( 47 , 48 ).…”

“…Notably, for these genes losing their enhancer-promoter interactions, we observed a higher degree of hypermethylation in their gene bodies compared to the genic background and the random cases (Figures 4I , J , S4J and S4K ). Many studies have revealed that gene body methylation was positively correlated with gene expression ( 46 , 54 , 55 ), mechanistically by preventing RNA pol II binding at abnormal transcription starting sites to avoid spurious transcription initiation ( 56 , 57 ). Therefore, we speculated that the low correlation between enhancer-promoter interaction and gene expression may be associated with the gene regulatory functions of gene body methylation and further experiments are needed.…”

CpG Island Definition and Methylation Mapping of the T2T-YAO Genome

“…Firstly, it is crucial for genome-wide epigenetic regulation study to know how to use long-read sequencing technology to sequence genome-wide methylation data for more people and construct complete human methylation atlas of different populations. Secondly, since artificial intelligence (AI) has been gradually used in genome-wide methylation-related studies [23, 24], how to use AI techniques such as neural networks to investigate the relationship between sequence features, CGI, methylation, and expression specificity of T2T-YAO becomes one of the future research directions. Lastly, genome-wide CGI prediction and methylation analysis are not only data-intensive, but also time-consuming.…”

Promoter-Adjacent DNA Hypermethylation Can Downmodulate Gene Expression: TBX15 in the Muscle Lineage

“…In contrast TBX4, TBX5, and TBX20 displayed this myoblast DNA hypermethylation bordering on and encroaching into their silent H3K27me3-enriched promoter region ( Figure 1 and Figures S3–S5 ). The high density of CpGs around or in the promoter regions of these eight T-box genes is unlike that of most tissue-specific genes but is found at a higher frequency in genes encoding tissue-specific TFs [ 56 ]. Although we saw evidence of frequent silencing of these T-box genes in cancer cell lines by both polycomb-repressed chromatin (H3K27me3) and DNA hypermethylation ( Figure S6 and data not shown [ 23 ]), one cancer cell line, U87 astrocytoma cells, expressed TBX15 at moderate levels [ 38 ].…”