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NNMT overexpression promotes tubular senescence and fibrosis in human chronic kidney disease (young, old and NAM-supplemented old mice kidney) [Series_4]

GSE280553 Mus musculus (mouse) RNA-seq (gene expression) 22 samples Submitted 2025/11/20 Platform GPL30172
At a glance

RNA-seq (gene expression) · mouse · 22 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
Mus musculus (mouse)
Samples
22 samples: 8 × kidney, Old_mice vs 8 × kidney, Old_mice_supplemented_with_NAM vs 6 × kidney, Young_miceGroups 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
NNMT inhibition counteracts tubular senescence and fibrosis in early stages of chronic kidney diseaseCell reports · 2026
Summary (written by the submitters)
Aging is a major risk factor for chronic kidney disease. Aged mice (21 months old) exhibited enriched transcriptomic signatures of fibrosis and cellular senescence in their kidneys compared to young mice (4 months old), and dietary supplementation with nicotinamide (NAM) was insufficient to reverse these disease signatures.
Published in
NNMT inhibition counteracts tubular senescence and fibrosis in early stages of chronic kidney disease
Chanvillard L, Lantermans HC, Wall C et al. · Cell reports 2026 · PMID 41543936 · doi:10.1016/j.celrep.2025.116823
This dataset
Open GSE280553 in finder → Find more mouse 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 GSE280553_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 PRJNA1179394 and SRA study SRP541775. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41543936) ↗
Study design
8 × kidney, Old_mice vs 8 × kidney, Old_mice_supplemented_with_NAM vs 6 × kidney, Young_mice

Supports a between-group comparison across 22 samples.

How we read this: 3 replicated groups read from 22 sample titles; they account for 22 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
Datasets on a similar topic

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