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Insights into the Cellular and Molecular Mode of Action behind the Anti-Fibrotic Effects of Nerandomilast

GSE271994 Homo sapiens (human) RNA-seq (gene expression) 42 samples Submitted 2025/04/28 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 42 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
42 samples: 42 samples, each with a distinct title — no replicated groupsGroups 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
Insights into the Cellular and Molecular Mechanisms behind the Antifibrotic Effects of NerandomilastAmerican journal of respiratory cell and molecular biology · 2025
Summary (written by the submitters)
The quest for innovative pharmacologic interventions in idiopathic pulmonary fibrosis (IPF) is a challenging journey. The complexity of the disease demands a comprehensive approach that targets multiple cell types and pathways. In this study, we explored the anti-fibrotic properties of nerandomilast, a preferential phosphodiesterase 4B inhibitor, focusing on its effects on myofibroblasts (MFs) and endothelial cells.
Read the full summary ↓
Our findings demonstrate that nerandomilast induces the dedifferentiation of human lung MFs and diminishes their contractility in vitro by interfering with TGFβ and GPCR signaling pathways. Moreover, nerandomilast effectively mitigates features of vascular dysfunction, such as increased vascular permeability and immune cell infiltration, both in vitro and in vivo. This is accomplished by strengthening endothelial junctions and inhibiting the expression of adhesion molecule proteins. Furthermore, in a rat model of bleomycin-induced pulmonary fibrosis, nerandomilast normalizes the specialization of endothelial cell types. The results of this study significantly contribute to the growing knowledge around the potential therapeutic use of nerandomilast in the treatment of IPF by providing valuable insights into its cellular and molecular mode of action
Published in
Insights into the Cellular and Molecular Mechanisms behind the Antifibrotic Effects of Nerandomilast
Reininger D, Wolf F, Mayr CH et al. · American journal of respiratory cell and molecular biology 2025 · PMID 40239038 · doi:10.1165/rcmb.2024-0614OC
This dataset
Open GSE271994 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 GSE271994_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 PRJNA1134254 and SRA study SRP519196. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40239038) ↗
Study design
42 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 42 sample titles: 42 distinct titles with little repetition. 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

+ 2 more — browse all 42 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.