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METTL14 is a chromatin regulator independent of RNA N6-methyladenosine (ChIP-Seq)

GSE183397 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 16 samples Submitted 2024/08/16 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 16 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
Mus musculus (mouse)
Samples
16 samples: 8 conditions, mostly in duplicateGroups 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)
METTL3 and METTL14 are two components that form the core heterodimer of the main RNA m6A methyltransferase complex (MTC, also known as m6A writer) that installs m6A. Surprisingly, depletion of METTL3 or METTL14 displayed distinct effects on mouse embryonic stem cell (mESC) self-renewal. While comparable global hypo-methylation in RNA m6A was observed in Mettl3 or Mettl14 knockout mESCs, respectively.
Read the full summary ↓
Mettl14 knockout led to a globally decreased nascent RNA synthesis, whereas Mettl3 depletion resulted in transcription upregulation, suggesting that METTL14 might possess an m6A-indenepent role in gene regulation. We found that METTL14 colocalizes with the repressive H3K27me3 modification and PRC2 complex. Mechanically, METTL14, but not METTL3, recognizes H3K27me3 and recruits KDM6B to induce H3K27me3 demethylation independent of METTL3. Depletion of METTL14 thus led to a global increase in H3K27me3 level along with a global gene suppression  . The regulation of H3K27me3 by METTL14 is essential to the transition of mESCs from self-renewal to differentiation. This work reveals a regulation mechanism on heterochromatin by METTL14 in a manner distinct from METTL3 and independently of m6A, and critically impacts transcriptional regulation, stemness maintenance and differentiation of mESCs.
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 GSE183397_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 PRJNA760504 and SRA study SRP335573. Searching any of these in the dataset finder brings you back here.

Study design
8 conditions, mostly in duplicate
Input Mettl14 Control mESCs ×2 Input Mettl14 CKO mESCs ×2 H3K4me3 Mettl14 Control mESCs ×2 H3K4me3 Mettl14 CKO mESCs ×2 H3K27ac Mettl14 Control mESCs ×2 H3K27ac Mettl14 CKO mESCs ×2 H3K27me3 Mettl14 Control mESCs ×2 H3K27me3 Mettl14 CKO mESCs ×2

Supports a between-group comparison across 16 samples.

How we read this: 8 replicated groups read from 16 sample titles; they account for 16 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
Datasets on a similar topic

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