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Serine/Threonine Phosphorylation of RUNX1 Promotes Megakaryocytic Fate in Megakaryocyte-Erythroid Progenitors (Primary Human Bulk mRNA-seq)

GSE232879 Homo sapiens (human) RNA-seq (gene expression) 22 samples Submitted 2024/09/24 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 22 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
22 samples: 6 × CMP vs 6 × MEP vs 6 × ERP vs 4 × MKPGroups 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
CDK9 phosphorylates RUNX1 to promote megakaryocytic fate in megakaryocytic-erythroid progenitorsBlood · 2024
Summary (written by the submitters)
The differentiation of primary human megakaryocyte-erythroid progenitors (MEP) into megakaryocytic (Mk) or erythroid (E) progenitors is critical for maintaining the blood system, yet the mechanisms that regulate this fate specification remain unclear. Here, we analyzed RNA-seq data and found that RUNX1 is a key regulator of differential gene expression in MEP fate specification.
Read the full summary ↓
Through viral transduction of primary MEP with RUNX1, we demonstrated that overexpression of RUNX1 promotes Mk over E specification, whereas pan-RUNX inhibition promotes E fate specification over Mk. We found that although the levels of total RUNX1 do not differ between MkP and ErP, MkP have higher levels of RUNX1 phosphorylated serine residues, and that serine/threonine phosphorylation of RUNX1 dramatically increases its effectiveness. We used human erythroleukemia (HEL) cell lines with expression of HA-tagged WT RUNX1, phosphomimetic (RUNX1-4D) and non-phosphorylatable (RUNX1-4A) mutants to model their effects on MEP. Although the chromatin association was not informative, the three forms of RUNX1 caused differential expression of 2,625 genes. Approximately 40% of these differentially expressed genes (DEGs) showed an increase with both RUNX1-WT and RUNX1-4D while another 40% showed a decrease with both RUNX1-WT and RUNX1-4D. We compared these DEGs with DEGs from primary human MEP, Mk progenitors (MkP), and E progenitors (ErP), and found that the genes upregulated by WT and RUNX1-4D in HEL cells overlapped significantly with the genes upregulated in MkP vs. MEP, whereas genes downregulated by WT and RUNX1-4D in HEL cells overlapped significantly with the genes downregulated in MkP vs. MEP. These results suggest that phosphorylation of RUNX1 enhances RUNX1’s function to activate or repress transcription of target genes that are up/downregulated during MEP fate specification.
Published in
CDK9 phosphorylates RUNX1 to promote megakaryocytic fate in megakaryocytic-erythroid progenitors
Kwon N, Lu YC, Thompson EN et al. · Blood 2024 · PMID 39102635 · doi:10.1182/blood.2024023963
This dataset
Open GSE232879 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 GSE232879_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA974364 and SRA study SRP438600. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39102635) ↗
Study design
6 × CMP vs 6 × MEP vs 6 × ERP vs 4 × MKP

Supports a between-group comparison across 22 samples.

How we read this: 4 replicated groups read from 22 sample titles; they account for 22 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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