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Transcriptomic and functional responses of the cystic fibrosis airway epithelium to CFTR modulator therapy

GSE306517 Homo sapiens (human) RNA-seq (gene expression) 76 samples Submitted 2025/09/03 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 76 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
76 samples: 76 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
Transcriptomic and functional responses of the cystic fibrosis airway epithelium to CFTR modulator therapyJCI insight · 2025
Summary (written by the submitters)
Elexacaftor/tezacaftor/ivacaftor (ETI) CFTR modulator therapy has led to rapid and substantial improvements in cystic fibrosis (CF) airway disease. Underlying molecular and cellular mechanisms, long-term efficacy, and ability to reverse airway epithelial remodeling in established disease remain unclear. Longitudinal nasal brushes from an adult CF cohort were used to evaluate gene expression, cellular composition, stem cell function, and microbiome changes at baseline and at six months and two years after ETI.
Read the full summary ↓
The baseline to six month span showed a massive downregulation of extensive neutrophilic inflammatory gene expression programs that correlated with increased pulmonary function and decreased sinusitis. Primary airway epithelial stem cell cultures from matched donor samples showed partially improved differentiation and barrier capacity at six months. Although clinical outcomes remained stable during the six month to two year span, transcriptional changes revealed a resurgence of baseline inflammatory programs. Time course gene expression was consistent with ongoing normalization of epithelial remodeling. Relative abundance of Pseudomonas also decreased during the time course. These data suggest that ETI rectifies inflammation, epithelial remodeling, and bacterial infection in the airways, but resurgence of inflammatory gene expression may indicate ongoing inflammation, potentially presaging disease progression with long-term therapy.
Published in
Transcriptomic and functional responses of the cystic fibrosis airway epithelium to CFTR modulator therapy
Vladar EK, Gillen AE, Yadav S et al. · JCI insight 2025 · PMID 41212059 · doi:10.1172/jci.insight.196018
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 GSE306517_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 PRJNA1311026 and SRA study SRP612710. Searching any of these in the dataset finder brings you back here.

Study design
76 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 76 sample titles: 76 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

+ 36 more — browse all 76 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.