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Targeting PARK7: Sodium 4-Phenylbutyrate Shields Endothelial Cells from GSDME-Mediated Pyroptosis in Acute Lung Injury

GSE285730 Homo sapiens (human) RNA-seq (gene expression) 15 samples Submitted 2025/01/07 Platform GPL24247Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 15 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
15 samples: 5 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)
Summary Sodium 4-phenylbutyrate (Na-PBA), a histone deacetylase inhibitor, shows potential in mitigating acute lung injury (ALI) by addressing endothelial cell injury. In a lipopolysaccharide (LPS)-induced ALI mouse model, Na-PBA pretreatment reduced lung damage, pulmonary edema, and inflammatory markers, while decreasing oxidative stress and endothelial pyroptosis via the cytochrome c-caspase9-caspase3-GSDME pathway.
Read the full summary ↓
In vitro studies with human umbilical vein endothelial cells (HUVECs) confirmed that Na-PBA enhanced PARK7 expression, which is crucial for its protective effects against oxidative damage and pyroptosis. The mechanism involves PARK7 stabilizing mitochondria and inhibiting cytochrome c leakage, leading to reduced caspase activation. Na-PBA's effects are mediated through upregulation of PARK7, potentially within a positive feedback loop involving NRF2. These findings identify a novel therapeutic strategy to alleviate endothelial injury in lung diseases.
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 GSE285730_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 PRJNA1205726 and SRA study SRP554972. Searching any of these in the dataset finder brings you back here.

Study design
5 conditions, mostly in triplicate
pulmonary endothelial cells_PBS ×3 pulmonary endothelial cells_LPS ×3 HUVEC_CON ×3 HUVEC_LPS_Nigericin ×3 HUVEC_NaPBA_LPS_Nigericin ×3

Supports a between-group comparison across 15 samples.

How we read this: 5 replicated groups read from 15 sample titles; they account for 15 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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