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Functional Characterization of Cooperating MGA Mutations in RUNX1::RUNX1T1 Acute Myeloid Leukemia [Cut&Run]

GSE253750 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 87 samples Submitted 2024/05/15 Platform GPL24676Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 87 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
87 samples: 87 samples, each with a distinct title — no replicated groupsGroups 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
Functional characterization of cooperating MGA mutations in RUNX1::RUNX1T1 acute myeloid leukemiaLeukemia · 2024
Summary (written by the submitters)
MGA (Max-gene associated) is a dual-specificity transcription factor that negatively regulates MYC-target genes to inhibit proliferation and promote differentiation. Loss-of-function mutations in MGA have been commonly identified in several hematological neoplasms, including acute myeloid leukemia (AML) with RUNX1::RUNX1T1, however, very little is known about the impact of these MGA alterations on normal hematopoiesis or disease progression.
Read the full summary ↓
We show that representative MGA mutations identified in patient samples abolish protein-protein interactions and transcriptional activity. Using a series of human and mouse model systems, including a newly developed conditional knock-out mouse strain, we demonstrate that loss of MGA results in upregulation of MYC and E2F targets, cell cycle genes, mTOR signaling, and oxidative phosphorylation in normal hematopoietic cells, leading to enhanced proliferation. The loss of MGA induces an open chromatin state at promotors of genes involved in cell cycle and proliferation. RUNX1::RUNX1T1 expression in Mga-deficient murine hematopoietic cells leads to a more aggressive AML with a significantly shortened latency. These data show that MGA regulates multiple pro-proliferative pathways in hematopoietic cells and cooperates with the RUNX1::RUNX1T1 fusion oncoprotein to enhance leukemogenesis.
Published in
Functional characterization of cooperating MGA mutations in RUNX1::RUNX1T1 acute myeloid leukemia
Thomas ME 3rd, Qi W, Walsh MP et al. · Leukemia 2024 · PMID 38454121 · doi:10.1038/s41375-024-02193-y
This dataset
Open GSE253750 in finder → Find more 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 GSE253750_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 PRJNA1066869 and SRA study SRP484784. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38454121) ↗
Study design
87 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 87 sample titles: 87 distinct titles with little repetition. 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

+ 47 more — browse all 87 samples with per-sample file links →

Datasets on a similar topic

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