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An eRNA Transcription Checkpoint For Diverse Signal-dependent Enhancer Activation Programs

GSE240599 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 130 samples Submitted 2024/09/17 Platform GPL24676Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 130 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
Homo sapiens (human)
Samples
130 samples: 130 samples, each with a distinct title — no replicated groupsGroups 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
An eRNA transcription checkpoint for diverse signal-dependent enhancer activation programsNature genetics · 2025
Summary (written by the submitters)
While many or most signal and ligand-dependent pathways function by directing binding of specific transcription factors to large cohorts of regulatory enhancers, the possible “universal” strategies underling activation of these lorge enhancer programs by diverse signals remains poorly understood.
Read the full summary ↓
Here, we report that ligands, exemplified by estrogen-17-b (E2), cause the recruitment of DNA-PKcs-phosphorylated KAP1 to estrogen receptor a (ERa)-bound enhancers, inhibiting its E3 SUMO ligase activity for the CDK9 subunit of P-TEFb, which permits formation of active P-TEFb required for eRNA elongation overcoming this checkpoint and permitting enhancer activation. We find that most, or all, signal and ligand enhancer activation programs similarly require the loss of SUMOligase function by phoshorylation of KAP1 to prevent Pol II pausing at regulated enhancers. This is based on activation of P-TEFb consequent to deSUMOylation of CDK9, permitting eRNA synthesis. Overcoming this checkpoint appears to be a widely, if not universally, used strategy for signal-dependent enhancer activation because similar events occur on AR and NF-kB-regulated enhancers and even following depolarization acutely-activated neuronal enhancers. This study reveals an unexpected strategy required to overcome a checkpoint common to most or all signal/ligand-regulated enhancers critical to activate broad programs of biologically-important regulatory enhancers that implement the instructions of the endocrine system.
Published in
An eRNA transcription checkpoint for diverse signal-dependent enhancer activation programs
Wang L, Yuan W, Gamliel A et al. · Nature genetics 2025 · PMID 40186057 · doi:10.1038/s41588-025-02138-w
This dataset
Open GSE240599 in finder → Find more 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 GSE240599_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 PRJNA1004290 and SRA study SRP454518. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40186057) ↗
Study design
130 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 130 sample titles: 130 distinct titles with little repetition. 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

+ 90 more — browse all 130 samples with per-sample file links →

Datasets on a similar topic

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