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Glycolysis involved in transcription dysregulation through histone lactylation in T-lineage acute lymphoblastic leukemia

GSE263584 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2024/05/31 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 12 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)
Topic
Acute lymphoblastic leukemiaThis series is part of our curated briefing on it.
Samples
12 samples: 12 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.
Summary (written by the submitters)
Tumor cells are known for excessive lactate production, due to Warburg effect, and transcriptional dysregulation. However, how lactate influences the epigenetic modifications at a genome-wide level and its impact on gene transcription in tumor cell remains unclear. Addressing this gap, we analyzed genome-wide modification of H3K18 lactylation (H3K18la) in T-cell acute lymphoblastic leukemia (T-ALL).
Read the full summary ↓
Integrated analysis with histone modifications with established functions show the H3K18la is mainly involved in active regulation of gene transcription. We report increased lactate and H3K18la modification levels in T-ALL as compared to normal T cells. We observe clusters of H3K18la modifications in patterns reminiscent of super-enhancers. We show a notable shift of genome-wide H3K18la modification from T cell immunity in normal T cells to leukemogenesis in T-ALL, correlating with altered gene transcription profiles. By disrupting H3K18la modification, we uncover both synergetic and divergent changes between H3K18la and H3K27ac modifications, suggesting the H3K18la and H3K27ac modifications may have specific regulation mechanisms correspondingly. These findings expand our understanding of metabolic disruptions involvement in transcription dysregulation through epigenetic changes in T-ALL, and emphasize the collective importance of histone modifications in maintaining oncogenic epigenetic stability.
This dataset
Open GSE263584 in finder → Find more human ChIP / ATAC / CUT&Tag datasets → Acute lymphoblastic leukemia landscape →
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 GSE263584_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 PRJNA1098208 and SRA study SRP500584. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 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 12 sample titles: 12 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
More Acute lymphoblastic leukemia datasets

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

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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.