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eIF2D promotes 40S ribosomal subunit recycling during intrinsic ribosome destabilization

GSE288755 Homo sapiens (human) RNA-seq (gene expression) 52 samples Submitted 2025/10/24 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 52 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
52 samples: 25 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
eIF2D promotes 40S ribosomal subunit recycling during intrinsic ribosome destabilizationNucleic acids research · 2025
Summary (written by the submitters)
Although eukaryotic initiation factor 2D (eIF2D) is implicated in translation initiation, re-initiation, and ribosome recycling, its precise role remains unclear. Here, we show that eIF2D promotes 40S ribosome recycling during intrinsic ribosome destabilization (IRD), a process in which ribosomes stochastically destabilize while translating proteins with consecutive acidic amino acids at their NH2-terminus.
Read the full summary ↓
Unrecycled 40S ribosomes accumulate in eIF2D-deficient cells, leading to 80S ribosome stalling. Selective translation complex profiling (TCP-seq) reveals that eIF2D preferentially associates with IRD-prone regions. The winged-helix (WH) domain, unique to eIF2D but absent in MCTS1–DENR, enhances its binding to 40S subunits, but likely clashes with ABCE1 during stop codon-associated recycling. Loss of eIF2D reduces the expression of IRD-inducing proteins, including splicing factors. Together, these findings define a previously unappreciated role for eIF2D in 40S recycling and clarify its mechanistic divergence from the MCTS1–DENR complex.
Published in
eIF2D promotes 40S ribosomal subunit recycling during intrinsic ribosome destabilization
Ichihara K, Shiraishi T, Chadani Y et al. · Nucleic acids research 2025 · PMID 41335470 · doi:10.1093/nar/gkaf1322
This dataset
Open GSE288755 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 GSE288755_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 PRJNA1219200 and SRA study SRP561557. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41335470) ↗
Study design
25 conditions, mostly in duplicate
WT HEK293T TCP-seq ×4 WT HEK293T Ribo-seq replicate # ×2 WT HEK293T RNA-seq replicate # ×2 MCTS1 KO HEK293T #1 Ribo-seq ×2 MCTS1 KO HEK293T #1 RNA-seq ×2 MCTS1 KO HEK293T #2 Ribo-seq ×2 MCTS1 KO HEK293T #2 RNA-seq ×2 siControl HEK293T Ribo-seq replicate # ×2 +17 more

Supports a between-group comparison across 52 samples.

How we read this: 25 replicated groups read from 52 sample titles; they account for 52 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

+ 12 more — browse all 52 samples with per-sample file links →

Datasets on a similar topic

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