bioRxivGenomicsPreprint
Specialised super-enhancer networks in stem cells and neurons
Izabela HarabulaiD, Liam SpeakmaniD, Francesco MusellaiD, Luca FiorilloiD, Luna Zea-RedondoiD, Alexander KukaleviD, Robert A. BeagrieiD, Kelly J Morris, Lucas FernandesiD, Ibai Irastorza-AzcárateiD, Ana M. Fernandes, Sílvia CarvalhoiD, Dominik SzabóiD, Carmelo FerraiiD, Mario NicodemiiD, Lonnie R. WelchiD, Ana PomboiD
Abstract
Super-enhancers (SEs) are clusters of enhancers with high transcriptional activity that play essential roles in defining cell identity through regulation of nearby genes. SEs preferentially form multiway chromatin interactions with other SEs and highly transcribed regions in embryonic stem cells. However, the properties of the interacting SEs and their specific contributions to complex regulatory interactions in differentiated cell types remain poorly understood. Here, we compare the structural and functional properties of SEs between embryonic stem cells (ESCs) and dopaminergic neurons (DNs) by combining Genome Architecture Mapping (GAM), chromatin accessibility, histone modification, and transcriptome data. Most SEs are cell-type specific and establish extensive pairwise and multiway chromatin interactions with other SEs and genes with cell-type specific expression. SE interactions span megabase genomic distances and frequently connect distant topologically associating domains. By applying network centrality analyses, we detected SEs with different hierarchical importance. Highest network centrality SEs contain binding motifs for cell-type specific transcription factors, and are candidate regulatory hubs. The functional heterogeneity of SEs is also highlighted by their organisation into modular sub-networks that differ in structure and spatial scale between ESCs and DNs, with more specific and strongly connected SE modules in post-mitotic neurons. Our results uncover both the high complexity and specificity of SE-based 3D regulatory networks and provide a resource for prioritizing SEs with potential roles in transcriptional regulation and disease.
- organism: human
- assay: ChIP-seq
- assay: Hi-C
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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