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DNA hypomethylation promotes UHRF1- and SUV39H1/H2-dependent crosstalk between H3K18ub and H3K9me3 to reinforce heterochromatin states [RNA-Seq]

GSE282925 Homo sapiens (human) DNA methylation 12 samples Submitted 2024/12/03 Platform GPL24676
At a glance

DNA methylation · human · 12 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Homo sapiens (human)
Samples
12 samples: 3 × RNA-seq_SUV39H2-KO-RKO_DMSO control vs 3 × RNA-seq_SUV39H2-KO-RKO_SUV39H1_KD vs 3 × RNA-seq_SUV39H2-KO-RKO_DNMT1i vs 3 × RNA-seq_SUV39H2-KO-RKO_SUV39H1_KD+DNMT1iGroups 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
DNA hypomethylation promotes UHRF1-and SUV39H1/H2-dependent crosstalk between H3K18ub and H3K9me3 to reinforce heterochromatin statesMolecular cell · 2025
Summary (written by the submitters)
Mono-ubiquitination of lysine 18 on histone H3 (H3K18ub), catalyzed by UHRF1, is a DNMT1 docking site that facilitates replication-coupled DNA methylation maintenance. Its functions beyond this are unknown. Here, we genomically map simultaneous increases in UHRF1-dependent H3K18ub and SUV39H1/H2-dependent H3K9me3 following DNMT1 inhibition.
Read the full summary ↓
Mechanistically, transient accumulation of hemi-methylated DNA at CpG islands facilitates UHRF1 recruitment and E3 ligase activity towards H3K18. Notably, H3K18ub enhances SUV39H1/H2 methyltransferase activity and, in colon cancer cells, nucleates new H3K9me3 domains at CpG island promoters of DNA methylation-silenced tumor suppressor genes. Disrupting UHRF1 enzyme activity prevents H3K9me3 accumulation while promoting PRC2-dependent H3K27me3 as a tertiary layer of gene repression in these regions. In contrast, disrupting H3K18ub-dependent SUV39H1/H2 activity enhances the transcriptional activating and antiproliferative effects of DNMT1 inhibition. Collectively, these findings reveal roles for UHRF1 and H3K18ub in regulating a hierarchy of repressive histone methylation signaling and rationalize a combination strategy for epigenetic cancer therapy.
Published in
DNA hypomethylation promotes UHRF1-and SUV39H1/H2-dependent crosstalk between H3K18ub and H3K9me3 to reinforce heterochromatin states
Liu Y, Hrit JA, Chomiak AA et al. · Molecular cell 2025 · PMID 39631394 · doi:10.1016/j.molcel.2024.11.009
This dataset
Open GSE282925 in finder → Find more human RNA-seq 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 GSE282925_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 beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA1191014 and SRA study SRP547778. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39631394) ↗
Study design
3 × RNA-seq_SUV39H2-KO-RKO_DMSO control vs 3 × RNA-seq_SUV39H2-KO-RKO_SUV39H1_KD vs 3 × RNA-seq_SUV39H2-KO-RKO_DNMT1i vs 3 × RNA-seq_SUV39H2-KO-RKO_SUV39H1_KD+DNMT1i

Supports a between-group comparison across 12 samples.

How we read this: 4 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
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