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LINE1 RNA demethylation represses chromatin accessibility and sensitizes cancer cells to blockage of DNA damage repair ChIP-seq

GSE285741 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 32 samples Submitted 2026/04/30 Platform GPL24676Platform GPL34284
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 32 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
32 samples: 16 conditions, mostly in duplicateGroups 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.
Summary (written by the submitters)
Repression of DNA damage repair by PARP inhibitors (PARPi) has shown great efficacy in cancer treatment. However, therapy resistance remains a significant clinical challenge. Here, we demonstrated that the N6-methyladenosine (m6A) modification of long interspersed nucleotide element-1 (LINE1, or L1) facilitates DNA damage repair in cancer cells, whereas removal of L1 m6A modifications enhances the sensitivity of both BRCA-wild-type and BRCA-mutant cells to PARPi.
Read the full summary ↓
Specifically, following olaparib treatment, METTL3 accumulates on chromatin, which results in increased chromatin accessibility. Knockdown of METTL3 or removal of m6A on L1 RNAs increases METTL3 levels, leading to reduced chromatin accessibility. This, in turn, inhibits DNA end resection at damage sites and prevents PARP1 dissociation, ultimately impairing homologous recombination repair and enhancing tumor sensitivity to PARPi. Our findings unveil a novel regulatory mechanism of L1 m6A that governs the DNA damage repair response, providing a potential strategy of targeting METTL3 in combination with PARPi for cancer therapy.
This dataset
Open GSE285741 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 GSE285741_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 PRJNA1205744 and SRA study SRP554986. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 conditions, mostly in duplicate
LOVO_shControl_Input ×2 LOVO_shMETTL3_Input ×2 LOVO_shControl_H3K9me3 ×2 LOVO_shMETTL3_H3K9me3 ×2 RKO_shControl_Input ×2 RKO_shMETTL3_Input ×2 RKO_shControl_H3K9me3 ×2 RKO_shMETTL3_H3K9me3 ×2 +8 more

Supports a between-group comparison across 32 samples.

How we read this: 16 replicated groups read from 32 sample titles; they account for 32 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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