Decoding super-enhancer regulatory grammar and pathogenic gain-of-function variants directly from DNA sequence
- 1Liangzhu Laboratory, Zhejiang University School of Medicine
Abstract
Super-enhancers (SEs) drive cell-identity gene expression, yet their identification relies on epigenomic profiling (e.g., H3K27ac ChIP-seq), precluding discovery in unprofiled cellular states and leaving their underlying sequence grammar unknown. Here, we present Borzoi-SE, a deep learning framework built upon the genomic transformer Borzoi that formulates SE discovery as hierarchical, cell-type-resolved genomic object detection directly from long-range sequence across 90 human lineages. Base-resolution attribution recovers lineage-defining master transcription factors across cell types. Within SEs shared across lineages, it reveals lineage-dependent motif usage, suggesting that the same regulatory regions can support distinct cell-type-specific programs. Using in silico ablation, Borzoi-SE decoded the functional hierarchy of the {beta}-globin super-enhancer, prioritizing hypersensitive site 2 (HS2) as the dominant constituent, which we experimentally verified via CRISPR interference. Trained without variant-effect labels, Borzoi-SE identified gain-of-super-enhancer variants in different tissues. At the CHRM2 locus, a single-nucleotide variant created a de novo glucocorticoid receptor (GR) motif that recruited activated GR and potentiated adjacent MEF2 regulatory syntax, nucleating a cardiac super-enhancer. Orthogonal multi-omics and population-genetic evidence linked this variant to increased CHRM2 expression and variation in resting heart rate and PR-interval duration. Borzoi-SE establishes a general framework for decoding SE regulatory grammar and prioritizing causal non-coding variants directly from sequence.
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BibTeX
@misc{liang2026decoding,
title = {{Decoding super-enhancer regulatory grammar and pathogenic gain-of-function variants directly from DNA sequence}},
author = {Y. Liang and X. Xu and Y. Mao and Z. Zheng and N. Liu},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.11.756618},
url = {https://www.biorxiv.org/content/10.64898/2026.10.11.756618v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Decoding super-enhancer regulatory grammar and pathogenic gain-of-function variants directly from DNA sequence
AU - Y. Liang
AU - X. Xu
AU - Y. Mao
AU - Z. Zheng
AU - N. Liu
PY - 2026
DA - 2026/10/11
PB - bioRxiv
DO - 10.64898/2026.10.11.756618
UR - https://www.biorxiv.org/content/10.64898/2026.10.11.756618v1
AB - Super-enhancers (SEs) drive cell-identity gene expression, yet their identification relies on epigenomic profiling (e.g., H3K27ac ChIP-seq), precluding discovery in unprofiled cellular states and leaving their underlying sequence grammar unknown. Here, we present Borzoi-SE, a deep learning framework built upon the genomic transformer Borzoi that formulates SE discovery as hierarchical, cell-type-resolved genomic object detection directly from long-range sequence across 90 human lineages. Base-resolution attribution recovers lineage-defining master transcription factors across cell types. Within SEs shared across lineages, it reveals lineage-dependent motif usage, suggesting that the same regulatory regions can support distinct cell-type-specific programs. Using in silico ablation, Borzoi-SE decoded the functional hierarchy of the {beta}-globin super-enhancer, prioritizing hypersensitive site 2 (HS2) as the dominant constituent, which we experimentally verified via CRISPR interference. Trained without variant-effect labels, Borzoi-SE identified gain-of-super-enhancer variants in different tissues. At the CHRM2 locus, a single-nucleotide variant created a de novo glucocorticoid receptor (GR) motif that recruited activated GR and potentiated adjacent MEF2 regulatory syntax, nucleating a cardiac super-enhancer. Orthogonal multi-omics and population-genetic evidence linked this variant to increased CHRM2 expression and variation in resting heart rate and PR-interval duration. Borzoi-SE establishes a general framework for decoding SE regulatory grammar and prioritizing causal non-coding variants directly from sequence.
ER -