Specialised super-enhancer networks in stem cells and neurons

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Item Type:	Preprint
Title:	Specialised super-enhancer networks in stem cells and neurons
Creators Name:	Harabula, Izabela, Speakman, Liam, Musella, Francesco, Forillo, Luca, Zea-Redondo, Luna, Kukalev, Alexander, Beagrie, Robert A., Morris, Kelly J., Fernandes, Lucas, Irastorza-Azcarate, Ibai, Fernandes, Ana M., Carvalho, Silvia, Szabó, Dominik, Ferrai, Carmelo, Nicodemi, Mario, Welch, Lonnie and Pombo, Ana
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.
Keywords:	Super-Enhancers, Transcription Factors, 3D Genome Conformation, Regulatory Networks, Genome Regulation
Source:	bioRxiv
Publisher:	Cold Spring Harbor Laboratory Press
Article Number:	2025.08.13.670083
Date:	13 August 2025
Additional Information:	The external datasets are currently private and scheduled to be released on Dec 01, 2026.
Official Publication:	https://doi.org/10.1101/2025.08.13.670083
Related to:	URL URL Type https://edoc.mdc-berlin.de/25618/ Dataset https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE305152 External Dataset https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE304720 External Dataset https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE304996 External Dataset https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE305012 External Dataset https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE305150 External Dataset https://github.com/pombo-lab/Harabula_Speakman_Superenhancer_networks_2025 Software

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