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Trans-regulation of heterochromatin underlies genetic variation in 3D genome contacts I

GSE312889 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 11 samples Submitted 2026/01/30 Platform GPL34290
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 11 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Mus musculus (mouse)
Samples
11 samples: 3 × B6 5-1, mouse embryonic stem cells, technical vs 3 × D2 5-5, mouse embryonic stem cells, technical vs 3 × XE 2-4, mouse embryonic stem cells, technical vs 2 × QJ 6-1, mouse embryonic stem cells, technicalGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Trans-regulation of heterochromatin underlies genetic variation in 3D genome contactsbioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
Background Genetic variation drives phenotypic diversity and disease susceptibility. Trans-acting genetic variation coordinates genome-wide chromatin changes, yet the molecular mechanisms underlying this regulation remain largely unknown. Here, we use the power of mouse genetics to investigate how genetic variation at trans-acting loci regulates 3D chromatin interactions.
Read the full summary ↓
Results Genetically variable 3D interactions were identified using HiChIP to map H3K27ac-associated regulatory elements in C57BL/6J (B6) and DBA/2J (D2) embryonic stem cells (ESCs). We identified 4,962 strain-differential interactions (~5% of total interactions), 71% of which overlapped chromatin accessibility quantitative trait loci (caQTL), establishing that chromatin interaction variation is predominantly heritable. These differential interactions showed coordinated changes in chromatin state and gene expression, with stronger interactions associated with increased accessibility and transcription. Notably, loci regulated in trans exhibited a unique chromatin signature where weaker interactions were enriched for H3K9me3-marked heterochromatin. This pattern was 20-fold more enriched at trans-regulated targets compared to cis, implicating heterochromatin in trans-regulation of 3D genome structure. Analysis of F1 hybrids revealed dominant repressive effects, consistent with heterochromatin-mediated trans-regulation. To causally test this mechanism, we generated reciprocal congenic mouse strains carrying a Chr13 trans-QTL region on otherwise inbred genetic backgrounds. Integrated multiomic profiling of congenic ESC lines demonstrated that this single locus coordinates changes in H3K9me3, H3K27ac, chromatin accessibility, and 3D contact frequency at hundreds of distal genomic regions. Remarkably, 73-83% of differential interactions at Chr13 trans-QTL targets changed in the predicted direction, demonstrating that heterochromatin-mediated trans-regulation coordinately regulates hundreds of regulatory loci. Conclusions This work establishes heterochromatin formation as a mechanism by which genetic variation at trans-acting loci coordinates changes across chromatin accessibility, histone modifications, and 3D genome organization, providing a framework for understanding how early developmental chromatin states could generate phenotypic variation while preserving essential developmental programs.
Published in
Trans-regulation of heterochromatin underlies genetic variation in 3D genome contacts
Fortin HJ, Struba AZ, Baker CL · bioRxiv : the preprint server for biology 2025 · PMID 41427289 · doi:10.64898/2025.12.10.693515
This dataset
Open GSE312889 in finder → Find more mouse ChIP / ATAC / CUT&Tag datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE312889_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1377555 and SRA study SRP653431. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41427289) ↗
Study design
3 × B6 5-1, mouse embryonic stem cells, technical vs 3 × D2 5-5, mouse embryonic stem cells, technical vs 3 × XE 2-4, mouse embryonic stem cells, technical vs 2 × QJ 6-1, mouse embryonic stem cells, technical

Supports a between-group comparison across 11 samples.

How we read this: 4 replicated groups read from 11 sample titles; they account for 11 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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