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The H3K4 methyltransferase KMT2D is an essential cofactor for GATA1 at erythroid gene enhancers

GSE275664 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2026/04/10 Platform GPL18460
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples: 13 distinct titles, 3 of them repeatedGroups 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.
Summary (written by the submitters)
Gene expression during cellular differentiation is coordinated by combinatorial interactions between transcription factors (TFs) and cofactors at promoters and enhancers. The “master TF” GATA1 coordinates gene transcription in a subset of hematopoietic lineages, including erythroid, megakaryocytic, mast and eosinophil, while repressing the development of other blood lineages.
Read the full summary ↓
However, the specific cofactors required for GATA1-activated gene expression during hematopoiesis are incompletely defined. We identified the cofactor KMT2D in an unbiased CRISPR/Cas9 screen for epigenetic regulators of erythropoiesis. Loss of KMT2D in human erythroid precursors caused developmental arrest with impaired expression of numerous erythroid genes. Mechanistically, KMT2D belongs to a family of H3K4 methyltransferases that collaborate with H3K27 acetyltransferases to activate transcription. KMT2D colocalized with GATA1 on more than one thousand erythroid enhancers associated with over two hundreds erythroid genes. In general, co-occupancy of GATA1 and KMT2D at erythroid enhancers was associated with stronger transcriptional activity than occupancy by GATA1 alone. Acute depletion of KMT2D in erythroid precursors caused rapid reductions of H3K4me1 and H3K27ac on a subset of GATA1-bound enhancers and impaired the expression of canonical erythroid genes including ZFPM1, SLC4A1, and EPOR. Moreover, acute depletion of GATA1 or KMT2D individually caused downregulation of overlapping genes. Thus, KMT2D controls erythropoiesis by selectively activating GATA1-dependent erythroid enhancers. Our studies identify KMT2D as a novel cofactor for transcriptional activation by GATA1 during erythropoiesis. More generally, our findings demonstrate how a lineage-specific TF cooperates with a ubiquitous epigenic regulator to drive lineage-specific gene expression during cellular differentiation.
This dataset
Open GSE275664 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 GSE275664_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 PRJNA1152542 and SRA study SRP528661. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
13 distinct titles, 3 of them repeated
K562 cells; DMSO treated ×2 HUDEP2 cells; DMSO treated ×2 HUDEP2 cells; IAA treated_12h ×2

How we read this: Read from 16 sample titles: 13 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
Datasets on a similar topic

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