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CRISPR activation screens identify the SWI-SNF ATPases as suppressors of ferroptosis [NGS3719_ATAC]

GSE245238 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2024/09/22 Platform GPL20301
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 12 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
12 samples: 12 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
CRISPR activation screens identify the SWI/SNF ATPases as suppressors of ferroptosisCell reports · 2024
Summary (written by the submitters)
Ferroptosis is an iron-dependent cell death mechanism characterized by an accumulation of toxic lipid peroxides and membrane rupture. The glutathione dependent enzyme, GPX4 (glutathione peroxidase 4), prevents ferroptosis by reducing these lipid peroxides into non-toxic lipid alcohols. Ferroptosis induction by GPX4 inhibition has emerged as a vulnerability of cancer cells, thus highlighting the need to identify ferroptosis regulators that may be exploited therapeutically.
Read the full summary ↓
Through genome-wide screens and a series of genetic, genomic, and quantitative imaging approaches, we identify the SWI-SNF ATPases BRM and BRG1 as ferroptosis suppressors. Mechanistically, they directly bind to and catalytically increase chromatin accessibility at NRF2 target loci, thus boosting NRF2 transcriptional output. This primes cells to counter lipid peroxidation and confers resistance to GPX4 inhibition and ferroptosis. Importantly, we demonstrate that the BRM/BRG1-ferroptosis connection can be leveraged to enhance the paralog dependency of BRG1-mutant lung cancer cells on BRM, especially in lines that are less sensitive to BRM inhibition or degradation. Our data reveal ferroptosis induction as a potential avenue for broadening the efficacy of BRM degraders/inhibitors and define a specific genetic context for exploiting GPX4 dependency.
Published in
CRISPR activation screens identify the SWI/SNF ATPases as suppressors of ferroptosis
Bhat KP, Vijay J, Vilas CK et al. · Cell reports 2024 · PMID 38870012 · doi:10.1016/j.celrep.2024.114345
This dataset
Open GSE245238 in finder → Find more human 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 GSE245238_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 PRJNA1027454 and SRA study SRP466045. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38870012) ↗
Study design
12 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 12 sample titles: 12 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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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.