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Dynamic Chromatin Alteration Induces Oncogenic Hijacking by Essential Transcriptional Factors during Medulloblastoma Tumorigenesis [[CUT&Tag 2]

GSE243608 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 9 samples Submitted 2024/05/03 Platform GPL21626
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 9 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
9 samples: 3 × GNP H3K27Ac vs 3 × PNC H3K27Ac vs 3 × Tumor H3K27AcGroups 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.
Summary (written by the submitters)
Using ATAC-seq analysis from GNPs to MBSHH we discovered a massive modification in chromatin accessibility during MBSHH formation. Next, using integrative bioinformatics, we identified genes of the nuclear factor I (NFI) family that function as oncogenes on the MBSHH epigenome. We demonstrate not only that these genes are essential in the early stages of murine MBSHHs tumorigenesis, but also that their genetic silencing inhibits tumor growth in murine and human MBSHHs in vitro and in vivo.
Read the full summary ↓
Finally, we discovered that NFIA/B are post-translationally modified by an activating methylation in MBSHH, making it possibility to target this pathway. Further studies revealed that pharmacological inhibition of this methylated NFIA/B hindered tumor proliferation, demonstrating the requirement of NFIA/B for tumor progression. Collectively our data shed light on the mechanism underlying the epigenome during the formation of the most malignant pediatric brain tumor and emphasizes the importance of deciphering cancer-specific epigenome for identification of new therapeutic avenues.
This dataset
Download · which file do I need?

Direct links to NCBI, 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.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need.

All supplementary files ↗
3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE243608_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

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 PRJNA1019229. Searching any of these in the dataset finder brings you back here.

Study design
3 × GNP H3K27Ac vs 3 × PNC H3K27Ac vs 3 × Tumor H3K27Ac

Supports a between-group comparison across 9 samples.

How we read this: 3 replicated groups read from 9 sample titles; they account for 9 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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