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Pan-modification profiling facilitates a cross-evolutionary dissection of the thermoregulated ribosomal epitranscriptome - HsapiScere

GSE284583 Saccharomyces cerevisiae (yeast) RNA-seq (gene expression) 34 samples Submitted 2025/11/13 Platform GPL29595
At a glance

RNA-seq (gene expression) · yeast · 34 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
Saccharomyces cerevisiae (yeast)
Samples
34 samples: 17 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
Pan-modification profiling facilitates a cross-evolutionary dissection of the thermoregulated ribosomal epitranscriptomeCell · 2025
Summary (written by the submitters)
Over 170 modifications are deposited on RNA, across the tree of life. A major bottleneck to systematically dissecting RNA modifications has been the lack of methods allowing to acquire systematic, high-confidence maps of many RNA modifications and at scale. Here we have developed Pan-Mod-seq, which permits de-novo, sensitive and specific identification of 16 distinct modifications in dozens of samples in parallel.
Read the full summary ↓
We applied Pan-Mod-seq to RNA from 14 different species from all 3 domains of life, most of which sampled under highly variable chemical/physical/biological gradients, aiming to systematically explore the plasticity of rRNA modifications. While dynamically modified sites are relatively rare in mesophiles, we find that these are widespread in hyperthermophiles, where ~50% of identified modifications were induced with temperature. We focused on dissection of three key dynamically induced modifications: m5C, ac4C and Ψ. We uncover an m5C program encompassing dozens of targets across both rRNA subunits, all of which are dramatically induced with growth temperature in two of the sampled hyperthermophiles. In both cases m5C is introduced at a single consensus motif, via a single enzyme which is essential for growth at higher temperatures. ac4C in turn is dynamically induced among all three sampled hyperthermophiles at hundreds of sites, also at a single consensus motif. Remarkably, the methylation and acetylation overlap, and together form a tandemly modified temperature induced G-m5C-ac4C-G sequence motif. Temperature-dependent induction of both enzymes can be recapitulated in vitro, establishing that it is an intrinsic property of the two enzymes rather than externally regulated. We obtain high-resolution cryoEM structures of ribosomes from WT strains at low and high temperatures and ones deleted of the methylating enzyme, and identify numerous mechanisms via which m5C confers structural stability. Finally, we also uncover a systematic induction in Ψ levels in T. kodakarensis, and reveal that it is largely coordinated by a single temperature-induced sRNA guiding pseudouridine formation at an unprecedented number of targets, whose loss results in growth deficiency at higher temperatures. Our findings offer a wide and systematic view of rRNA modification plasticity across representative species sampled from the tree of life, dissect the ability of different modifications to contribute to the stability of ribosomes at higher temperatures, and present a methodology allowing an unprecedented view on the the rRNA epitranscriptome in health and in disease.
Published in
Pan-modification profiling facilitates a cross-evolutionary dissection of the thermoregulated ribosomal epitranscriptome
Garcia-Campos MA, Georgeson J, Nir R et al. · Cell 2025 · PMID 41130207 · doi:10.1016/j.cell.2025.09.014
This dataset
Open GSE284583 in finder → Find more 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 GSE284583_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 PRJNA1199589 and SRA study SRP552152. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41130207) ↗
Study design
17 conditions, mostly in duplicate
HsSc, lib553, control, Mock, RTHIV, ×2 HsSc, lib553, control, NaBH4HydBiotin, RTHIV, ×2 HsSc, lib553, control, NaCNBH3, SSIII, ×2 HsSc, lib553, control, CMC, SSIII, ×2 HsSc, lib553, control, NaHCO3+NaCNBH3, SSIII, ×2 HsSc, lib553, control, NaHCO3+Heat, SSIII, ×2 HsSc, lib553, control, Bisulfite, SSIII, ×2 HsSc, lib553, control, Mock, SSIII, ×2 +9 more

Supports a between-group comparison across 34 samples.

How we read this: 17 replicated groups read from 34 sample titles; they account for 34 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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