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Dynamics of de novo heterochromatin assembly and disassembly at replication forks ensures fork stability

GSE220073 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2024/11/30 Platform GPL11154
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: 6 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)
Chromatin is dynamically reorganized when DNA replication forks are challenged. However, the process of epigenetic reorganization and its implication for fork stability is poorly understood. Here, we discover a checkpoint regulated cascade of chromatin signaling that activates the histone methyltransferase EHMT2/G9a to catalyze heterochromatin assembly at stressed replication forks.
Read the full summary ↓
Using biochemical and single molecule chromatin fiber approaches, we show that G9a together with SUV39h1 induces chromatin condensation by accumulating the silent mark, H3K9me1/2/3, in the vicinity of stressed replication forks. This closed conformation is also favored by the G9a-dependent exclusion of the H3K9-demethylase JMJD1A/KDM3A, which facilitates heterochromatin disassembly upon fork restart. Untimely heterochromatin disassembly from stressed forks by KDM3A enables PRIMPOL access, triggering ssDNA gaps formation and sensitizing cells towards chemotherapeutic drugs. These findings may help explaining chemotherapy resistance and poor prognosis observed in cancer patients displaying elevated level of G9a/H3K9me3.modifications (PTMs) in a single mass spectrum. Combined with immunoprecipitation, Nuc-MS quantified nucleosome co-occupancy of histone H3.3 with variant H2A.Z (sixfold over bulk) and the co-occurrence of oncogenic H3.3K27M with euchromatic marks (for example, a >15-fold enrichment of dimethylated H3K79me2). Nuc-MS is highly concordant with chromatin immunoprecipitation-sequencing (ChIP-seq) and offers a new readout of nucleosome-level biology.
This dataset
Open GSE220073 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 GSE220073_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 PRJNA897702 and SRA study SRP406022. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in duplicate
TIG3,proliferating,H3K9ME3 ×2 TIG3,plusHU,H3K9ME3 ×2 TIG3,minusHU,H3K9ME3 ×2 TIG3,proliferating,H3 ×2 TIG3,plusHU,H3 ×2 TIG3,minusHU,H3 ×2

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 12 sample titles; they account for 12 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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