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The U6 snRNA m6A methyltransferase METTL16 regulates SAM synthetase intron retention

GSE90914 Homo sapiens (human) Single-nucleus RNA-seq 12 samples Submitted 2017/05/18 Platform GPL11154
At a glance

Single-nucleus RNA-seq · human · 12 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue).
Organism
Homo sapiens (human)
Samples
12 samples: 3 × siCtrl input vs 3 × siMETTL16 input vs 3 × siCtrl m6A IP vs 3 × siMETT16 m6A IPGroups 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)
Maintenance of the intracellular levels of the methyl donor S-adenosylmethionine (SAM) is essential for a wide variety of biological processes. We demonstrate that the N6-adenosine methyltransferase METTL16 regulates expression of MAT2A, which encodes the only SAM synthetase expressed in most cells. Upon SAM depletion by methionine starvation, cells induce MAT2A expression by enhanced splicing of a retained intron.
Read the full summary ↓
Induction requires METTL16 and its methylation substrate, a vertebrate conserved hairpin (hp1) in the MAT2A 3´ UTR. Increasing METTL16 occupancy on the MAT2A 3´ UTR is sufficient to induce efficient splicing. We propose that under SAM-limiting conditions, METTL16 occupancy on hp1 increases due to inefficient enzymatic turnover, which in turn promotes MAT2A splicing. Interestingly, human and S. pombe METTL16 methylate the U6 spliceosomal snRNA at a sequence identical to hp1. These observations suggest that the conserved U6 snRNA methyltransferase evolved an additional function in vertebrates to regulate SAM homeostasis.
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 GSE90914_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA356403 and SRA study SRP094637. Searching any of these in the dataset finder brings you back here.

Study design
3 × siCtrl input vs 3 × siMETTL16 input vs 3 × siCtrl m6A IP vs 3 × siMETT16 m6A IP

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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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