← BioTransfer GEO Dataset Finder
GEO series

Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3 [ChIP-seq]

GSE237390 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 54 samples Submitted 2024/04/24 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 54 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
54 samples: 21 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.
Paper
Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3Nature cell biology · 2024
Summary (written by the submitters)
Histone lysine methyltransferases KMT2C and KMT2D are among the most commonly mutated genes in the highly metastatic TNBC subtype of breast cancer. However, it is not known if mutations of either of these genes similarly effect epigenomic and transcriptomic landscape or if a specific downstream target might influence metastases. Here, we generated heterogenous Kmt2c or Kmt2d KO murine TNBC cell lines side-by-side and performed in vivo metastases assay in syngeneic immunocompetent mice.
Read the full summary ↓
Deficiency for either Kmt2c or Kmt2d, both, induced brain metastases from formerly non-metastatic cells. scRNAseq showed activation of pro-inflammatory pathways but conversely also increase of immune checkpoint blocking genes. Interestingly, histone mass spectrometry revealed changes of H3K27 but not the main substrate H3K4. However, ChIPseq for both, H3K4 and H3K27 modifications showed significant changes compared to wildtype cells. Strikingly, genome occupancy of H3K27me3 was reduced while H3K27 demethylase KDM6A was enriched on genomes of KO cells. Integration with gene expression data revealed significant correlations with histone and KDM6A ChIPseq, identifying them as a main driver of Kmt2c or Kmt2d KO-specific gene regulation. Although our datasets revealed more unique than shared signatures, we found Mmp3 being a common target upon Kmt2c or Kmt2d KO. Indeed, downregulation of Mmp3 reversed induction of Kmt2c and Kmt2d KO-dependent brain metastases. Finally, we found that Kdm6a knockdown reduces Mmp3 levels, again, leading to reduction of brain metastases of Kmt2c or Kmt2d KO cells.
Published in
Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3
Seehawer M, Li Z, Nishida J et al. · Nature cell biology 2024 · PMID 38926506 · doi:10.1038/s41556-024-01446-3
This dataset
Open GSE237390 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 GSE237390_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 PRJNA994871 and SRA study SRP449552. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38926506) ↗
Study design
21 conditions, mostly in duplicate
168_WT_H3K4me1 ×3 168_KMT2C_KO_H3K4me1 ×3 168_KMT2D_KO_H3K4me1 ×3 168_WT_H3K27ac ×3 168_KMT2C_KO_H3K27ac ×3 168_KMT2D_KO_H3K27ac ×3 168_WT_input ×3 168_KMT2C_KO_input ×3 +16 more

Supports a between-group comparison across 51 samples.

How we read this: 21 replicated groups read from 54 sample titles; they account for 51 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

Search all mouse ChIP / ATAC / CUT&Tag datasets in GEO →

Share this dataset

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.