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IGF2BP1 phosphorylation regulates ribonucleoprotein condensate formation by impairing low-affinity protein and RNA interactions (RNA-Seq)

GSE272874 Homo sapiens (human) RNA-seq (gene expression) 26 samples Submitted 2024/10/10 Platform GPL34281
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: 26 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.
Paper
IGF2BP1 phosphorylation in the disordered linkers regulates ribonucleoprotein condensate formation and RNA metabolismNature communications · 2024
Summary (written by the submitters)
The insulin-like growth factor 2 mRNA binding protein (IGF2BP1) is a conserved RNA-binding protein that regulates RNA stability, localization, and translation. IGF2BP1 is part of various ribonucleoprotein (RNP) condensates. However, the mechanism that regulates its assembly into condensates remains unknown. Here we found, using proteomics, that IGF2BP1 phosphorylation at S181 in a disordered linker is regulated in a stress-dependent manner.
Read the full summary ↓
Phosphomimetic mutations in two disordered linkers, S181E and Y396E, modulated RNP condensate formation by IGF2BP1 without impacting its binding affinity for RNA. Intriguingly, the S181E mutant, which lies in linker 1, impaired IGF2BP1 condensate formation in vitro and in cells, whereas a Y396E mutant in the second linker increased condensate size and dynamics. Structural approaches showed that the first linker binds RNAs nonspecifically through its RGG/RG motif, an interaction weakened in the S181E mutant. Notably, linker 2 interacts with IGF2BP1’s folded domains and these interactions were partially impaired in the Y396E mutant. Our data reveal how phosphorylation modulates low affinity interaction networks in disordered linkers to regulate RNP condensate formation.
Published in
IGF2BP1 phosphorylation in the disordered linkers regulates ribonucleoprotein condensate formation and RNA metabolism
Hornegger H, Anisimova AS, Muratovic A et al. · Nature communications 2024 · PMID 39426983 · doi:10.1038/s41467-024-53400-4
This dataset
Open GSE272874 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 GSE272874_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 PRJNA1139158 and SRA study SRP521647. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39426983) ↗
Study design
26 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 26 sample titles: 26 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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