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Gene Signature-guided Drug Screening Identified Narciclasine as a Potential Therapeutic Drug for Renal Interstitial Fibrosis

GSE269356 Rattus norvegicus (rat) RNA-seq (gene expression) 18 samples Submitted 2025/06/12 Platform GPL25947Platform GPL24247
At a glance

RNA-seq (gene expression) · rat · 18 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
Rattus norvegicus (rat)
Samples
18 samples: 6 conditions, mostly in triplicateGroups 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)
Chronic Kidney Disease (CKD) is characterized by progressive tubulointerstitial fibrosis (TIF), a condition not adequately addressed by existing therapies. To identify potential compounds that could slow or halt the progression of TIF, we established gene signatures of TIF using published human CKD renal transcriptome data, followed by high-throughput screening of compounds that could reverse TIF-associated gene expression using the LINCS L1000 database.
Read the full summary ↓
The natural compound Narciclasine (Ncls), extracted from daffodils, was identified as a promising candidate. We validated the effects of Ncls on a TGF-β1-induced fibrosis model in vitro using mouse primary tubular epithelial cells (TECs) and rat fibroblast cell line (NRK49F). Results demonstrated that Ncls significantly inhibited the pro-inflammatory and pro-fibrotic effects induced by TGF-β1 in TECs. Additionally, in fibroblasts, Ncls markedly suppressed the proliferation and activation induced by TGF-β1. These findings not only confirm the therapeutic potential of Ncls in managing TIF but also highlight the efficacy of gene signature-based screening in identifying drugs for the treatment of CKD.
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 GSE269356_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 PRJNA1121269 and SRA study SRP512519. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in triplicate
Con_TECs ×3 Tgfb_TECs ×3 T+N_TECs ×3 Con_49F ×3 Tgfb_49F ×3 T+N_49F ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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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