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KMT2D regulates Fanconi anemia pathway genes through regulating promoters/enhances upon glucose deprivation (CHIPseq)

GSE234640 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 14 samples Submitted 2024/07/01 Platform GPL21290
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 14 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
14 samples: 12 distinct titles, 2 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.
Paper
Histone-methyltransferase KMT2D deficiency impairs the Fanconi anemia/BRCA pathway upon glycolytic inhibition in squamous cell carcinomaNature communications · 2024
Summary (written by the submitters)
Histone H3 lysine (H3K4) methyltransferase KMT2D is a key regulator of gene expression, mainly through promoting H3K4 methylation and activating enhancers, and plays critical roles in development, differentiation, metabolism, and tumor suppression. Here, our study showed that KMT2D was recruited to the enhancer regions of several Fanconi anemia (FA) pathway genes, such as ATR, FANCM, REV3L, and TOP3A, in response to glucose deprivation.
Read the full summary ↓
Notably, KMT2D loss led to significant impairment of promoter/enhancer signals, as evidenced by decreased H3K4me1, H3K4me3, and H3K27ac signals, on several FA pathway genes. Our ChIP-Seq results demonstrated that the inactivation of promoters/enhancers due to KMT2D loss is a key contributor to the downregulation of FA pathway genes in glucose-deprived KMT2D-deficient HNSCC.
Published in
Histone-methyltransferase KMT2D deficiency impairs the Fanconi anemia/BRCA pathway upon glycolytic inhibition in squamous cell carcinoma
Liu W, Cao H, Wang J et al. · Nature communications 2024 · PMID 39117659 · doi:10.1038/s41467-024-50861-5
This dataset
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.

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 GSE234640_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA982171 and SRA study SRP442243. Searching any of these in the dataset finder brings you back here.

Study design
12 distinct titles, 2 of them repeated
NGWT-H3K4me ×2 NGKO-H3K4me ×2

How we read this: Read from 14 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
Datasets on a similar topic

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