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Effects of Antifibrotic Drugs on Transcriptome of Peripheral Blood Mononuclear Cells in Idiopathic Pulmonary Fibrosis [IPF_PBMC]

GSE248485 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2024/03/28 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 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
Homo sapiens (human)
Samples
18 samples: 18 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
Effects of Anti-Fibrotic Drugs on Transcriptome of Peripheral Blood Mononuclear Cells in Idiopathic Pulmonary FibrosisInternational journal of molecular sciences · 2024
Summary (written by the submitters)
Two antifibrotic drugs, pirfenidone (PFD) and nintedanib (NTD), are currently used to treat idiopathic pulmonary fibrosis (IPF). Peripheral blood mononuclear cells are immunocompetent cells that can predispose to those within the lungs and orchestrate cell-cell interactions associated with IPF pathogenesis. We employed RNA sequencing to examine the transcriptome signature in bulk peripheral blood mononuclear cells of patients with IPF and the effects of antifibrotic drugs on these signatures.
Read the full summary ↓
Differentially expressed genes (DEGs) were analyzed in patients with IPF and healthy controls, before and after antifibrotic treatment. Enrichment analysis suggested that fatty acid elongation interferes with profibrotic TGF-β/ signaling and production of oxidative stress since treatment with NTD upregulated the elongation enzymes ELOVL6 and ELOVL7. Treatment with PFD downregulated COL1A1 which produces wound-healing collagens, as activated monocyte-derived macrophages during tissue damage participate in the production of collagen, type I, and alpha 1. Plasminogen activator inhibitor-1 (PAI-1) regulates wound healing by inhibiting plasmin-mediated matrix metalloproteinases activation, and inhibition of PAI-1 activity attenuates lung fibrosis. DEGs analysis suggested that both PFD and NTD upregulated SERPINE1 which in turn regulates PAI-1 activity. This study detected different DEGs as a result of PFD and NTD treatments, suggesting different mechanistic roles for the two antifibrotic drugs.
Published in
Effects of Anti-Fibrotic Drugs on Transcriptome of Peripheral Blood Mononuclear Cells in Idiopathic Pulmonary Fibrosis
Ishii D, Kawasaki T, Sato H et al. · International journal of molecular sciences 2024 · PMID 38612561 · doi:10.3390/ijms25073750
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 GSE248485_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 PRJNA1044112 and SRA study SRP473749. Searching any of these in the dataset finder brings you back here.

Study design
18 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 18 sample titles: 18 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
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