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Ubiquitin-like proteins NEDD8 and SUMO2 control epithelial homeostasis, regeneration, and inflammation [RNA-Seq]

GSE326487 Homo sapiens (human) RNA-seq (gene expression) 8 samples Submitted 2026/06/25 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 8 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
8 samples: 2 × A431_DMSO vs 2 × A431_NEDD8i vs 2 × CAL27_DMSO vs 2 × CAL27_NEDD8iGroups 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)
Stratified epithelial differentiation involves incompletely understood transcriptional and proteomic remodeling. Multi-omic profiling implicated ubiquitin and related networks in differentiation dynamics. Systematic perturbation of ubiquitin-like machinery uncovered opposite functions of NEDD8 and SUMO2. Knockout mice established essential roles for NEDD8 in progenitor maintenance, skin regeneration, and inflammation, whereas SUMO2 was required for differentiation.
Read the full summary ↓
Beyond ubiquitin-proteasome-concordant changes, NEDD8 directed proteomic regulation correlated with RNA abundance. Integration of IP-MS with genome-wide suppressor screening revealed context-specific NEDDylated dependencies. Among effectors, HNRNPU emerged as a post-transcriptional regulator of epithelial cell state, whose binding repertoire was modulated by NEDDylation, linking NEDD8 induced proteomic remodeling to RNA regulation. Together, these findings define opposite roles for NEDD8 and SUMO2 in orchestrating epithelial homeostasis, regeneration, and inflammation, underscoring how ubiquitin-like networks govern tissue fate.
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 GSE326487_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: 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 PRJNA1445702 and SRA study SRP687979. Searching any of these in the dataset finder brings you back here.

Study design
2 × A431_DMSO vs 2 × A431_NEDD8i vs 2 × CAL27_DMSO vs 2 × CAL27_NEDD8i

Supports a between-group comparison across 8 samples.

How we read this: 4 replicated groups read from 8 sample titles; they account for 8 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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