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An Improved SNAP-ADAR Tool Enables Efficient RNA Base Editing to Interfere with Post-translational Protein Modification

GSE264114 Homo sapiens (human) RNA-seq (gene expression) 26 samples Submitted 2024/04/19 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 26 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
26 samples: 13 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.
Paper
An improved SNAP-ADAR tool enables efficient RNA base editing to interfere with post-translational protein modificationNature communications · 2024
Summary (written by the submitters)
RNA base editing applies endogenous or engineered adenosine deaminases to introduce adenosine-to-inosine changes into a target RNA in a highly programmable manner. Recently, notable success was achieved for the repair of disease-causing guanosine-to-adenosine mutations by means of RNA base editing. Here, we propose that RNA base editing could be broadly applied to perturb protein function by removal of regulatory sites of post-translational modification (PTM), like phosphorylation and/or acetylation sites.
Read the full summary ↓
We demonstrate the feasibility of PTM interference (PTMi) on more than 70 PTM sites in various signaling proteins and identify key determinants for high editing efficiency and potent down-stream effects. Specifically, we demonstrate both negative and positive regulation of the JAK/STAT pathway by PTMi. To identify potent regulatory sites for PTMi, we applied an improved version of the SNAP-ADAR tool, which achieved high editing efficiency over a broad codon scope with tight control of bystander editing. The transient nature of RNA base editing enables the fast, dose-dependent (thus partial) and reversible manipulation of PTM sites, which is a key advantage over DNA editing approaches, where genetic compensation or lethality can conceal a phenotype. In summary, PTM interference might become a valuable field of application of RNA base editing in basic biology and medicine.
Published in
An improved SNAP-ADAR tool enables efficient RNA base editing to interfere with post-translational protein modification
Kiran Kumar KD, Singh S, Schmelzle SM et al. · Nature communications 2024 · PMID 39103360 · doi:10.1038/s41467-024-50395-w
This dataset
Open GSE264114 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 GSE264114_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 PRJNA1101009 and SRA study SRP502067. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39103360) ↗
Study design
13 conditions, mostly in duplicate
ctrl ×2 ctrl + ifna - 24h ×2 ctrl + ifng - 24h ×2 BB180 + ifna - 24h ×2 BB180 + ifng - 24h ×2 BB478 + ifna - 24h ×2 BB478 + ifng - 24h ×2 BB349 + ifna - 24h ×2 +5 more

Supports a between-group comparison across 26 samples.

How we read this: 13 replicated groups read from 26 sample titles; they account for 26 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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