bioRxivBioinformatics
Distinct genomic and epigenomic features demarcate hypomethylated blocks in colon cancer
Mahfuza SharminiD, Hector Corrada Bravo, Sridhar S. HannenhalliiD
Abstract
Background. Large mega base-pair genomic regions show robust alterations in DNA methylation levels in multiple cancers, a vast majority of which are hypo-methylated in cancers. These regions are generally bounded by CpG islands, overlap with Lamin Associated Domains and Large organized chromatin lysine modifications, and are associated with stochastic variability in gene expression. Given the size and consistency of hypo-methylated blocks (HMB) across cancer types, their immediate causes are likely to be encoded in the genomic region near HMB boundaries, in terms of specific genomic or epigenomic signatures. However, a detailed characterization of the HMB boundaries has not been reported.\n\nMethod. Here, we focused on ~13k HMBs, encompassing approximately half the genome, identified in colon cancer. We analyzed a number of distinguishing features at the HMB boundaries including transcription factor (TF) binding motifs, various epigenomic marks, and chromatin structural features.\n\nResult. We found that the classical promoter epigenomic mark - H3K4me3, is highly enriched at HMB boundaries, as are CTCF bound sites. HMB boundaries harbor distinct combinations of TF motifs. Our Random Forest model based on TF motifs can accurately distinguish boundaries not only from regions inside and outside HMBs, but surprisingly, from active promoters as well. Interestingly, the distinguishing TFs and their interacting proteins are involved in chromatin modification. Finally, HMB boundaries significantly coincide with the boundaries of Topologically Associating Domains of the chromatin.\n\nConclusion. Our analyses suggest that the overall architecture of HMBs is guided by pre-existing chromatin architecture, and are associated with aberrant activity of promoter-like sequences at the boundary.
- organism: human
- assay: DNA methylation
- research focus: chromatin & epigenetic regulation
- approach: statistical model
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