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Efficient in vivo pharmacological inhibition of ∆FOSB with YL-0441 in brain

GSE319820 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 38 samples Submitted 2026/07/01 Platform GPL21103
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 38 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
38 samples: 38 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
Efficient In Vivo Pharmacological Inhibition of ΔFOSB, an AP-1 Transcription Factor, in the BrainACS chemical neuroscience · 2026
Summary (written by the submitters)
ΔFOSB, an unusually stable member of the AP-1family of transcription factors, mediates long-term maladaptationsthat play a key role in the pathogenesis of drug addiction, cognitive decline, dyskinesias, and several other chronic neurological and psychiatric conditions. We have recently identified that 2-phenoxybenzenesulfonic acid-containing compounds disrupt the binding of ΔFOSB to DNA in vitroin cell-based assays.
Read the full summary ↓
We show that one such analog, YL0441, disrupts the binding of ΔFOSB to DNA in vitroand in vivo, and suppresses ΔFOSB-function in cell-based assays. Importantly, infusion of YL0441 into the hippocampus of APP mice (a mouse model for Alzheimer's diseaseneuropathology) leads to virtually complete loss of ΔFOSB bound to genomic DNA as detected by CUT&RUN sequencing. Our findings corroborate that the binding/release of AP1 transcription factors to DNA can be controlled via small moleculesin vivo, even by analogs of a compound that binds to a groove outside of the DNA-binding cleft, and that our lead can be optimized via medicinal chemistry to yield a much moreefficacious inhibitor of ΔFOSB function in vivo. These findings define a strategy to design small-molecule inhibitors for other AP-1and AP-1-related transcription factors, in particular, those involved in neuropsychiatric and neurological disorders.
Published in
Efficient In Vivo Pharmacological Inhibition of ΔFOSB, an AP-1 Transcription Factor, in the Brain
McNeme S, Kumar A, Yim YY et al. · ACS chemical neuroscience 2026 · PMID 41817117 · doi:10.1021/acschemneuro.5c00890
This dataset
Open GSE319820 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 GSE319820_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 PRJNA1425140 and SRA study SRP677896. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41817117) ↗
Study design
38 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 38 sample titles: 38 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
Datasets on a similar topic

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