bioRxivGenomicsPreprint
Hi-TrAC reveals fractal nesting of super-enhancers
Yaqiang CaoiD, Shuai LiuiD, Kairong Cui, Qingsong TangiD, Keji ZhaoiD
Abstract
Eukaryotic genome spatial folding plays a key role in genome function. Decoding the principles and dynamics of 3D genome organization depends on improving technologies to achieve higher resolution. Chromatin domains have been suggested as regulatory micro-environments, whose identification is crucial to understand the genome architecture. We report here that our recently developed method, Hi-TrAC, which specializes in detecting chromatin loops among genomic accessible regulatory regions, allows us to examine active domains with limited sequencing depths at a high resolution. Hi-TrAC can detect active sub-TADs with a median size of 100kb, most of which harbor one or two cell specifically expressed genes and regulatory elements such as super-enhancers organized into nested interaction domains. These active sub-TADs are characterized by highly enriched signals of histone mark H3K4me1 and chromatin-binding proteins, including Cohesin complex. We show that knocking down core subunit of the Cohesin complex using shRNAs in human cells or decreasing the H3K4me1 modification by deleting the H3K4 methyltransferase Mll4 gene in mouse Th17 cells disrupted the sub-TADs structure. In summary, Hi-TrAC serves as a compatible and highly responsive approach to studying dynamic changes of active sub-TADs, allowing us more explicit insights into delicate genome structures and functions. Highlights- Hi-TrAC detects active sub-TADs with a median size of 100 kb. - Hi-TrAC reveals a block-to-block interaction pattern between super-enhancers, and fractal structures within super-enhancers. - Active sub-TADs are disrupted by the knockdown of RAD21. - Active sub-TADs interaction densities are decreased by the knockout of Mll4.
- organism: human
- organism: mouse
- assay: ChIP-seq
- assay: Hi-C
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: spatial domain detection
- approach: statistical model
- Citations
- 2
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