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Iron-induced oxidative stress alters the transcriptome of bronchoalveolar lavage cells in silicosis

GSE264182 Homo sapiens (human) RNA-seq (gene expression) 19 samples Submitted 2024/12/31 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 19 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
19 samples: 8 × BAL Cells, Silica Exposed, Complicated Silicosis, Patient vs 6 × BAL Cells, Silica Exposed, Simple Silicosis, Patient vs 3 × BAL Cells, Silica Exposed No Disease, Control Patient vs 2 × BAL Cells, Chronic Cough, Control PatientGroups 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
Iron Alters the Transcriptome and Volatile Organic Compounds in the BAL of Patients with SilicosisAmerican journal of respiratory cell and molecular biology · 2024
Summary (written by the submitters)
Elevated iron levels are observed in silicosis patients, leading alveolar macrophages to increase ferritin production in response to this cellular stress. Our study employed a multi-omic approach to investigate how excess iron/ferritin influences the molecular profile of bronchoalveolar lavage (BAL) cells. Thirty-one participants were included, with RNA sequencing conducted on BAL cells from stone benchtop industry workers diagnosed with simple or complicated silicosis, compared to a cohort with no disease.
Read the full summary ↓
BAL fluid (BALF) was analysed using gas chromatography-mass spectrometry (GC-MS) to screen for 498 volatile organic compounds (VOCs). Compared to no disease, simple silicosis showed 172 differentially expressed genes (DEGs), while complicated silicosis displayed 531 DEGs. Spearman correlation analysis revealed 1072 genes significantly positively association with BALF ferritin. Gene set variation analysis revealed upregulation in glutathione metabolism pathways in BAL cells, including ferroptosis repressor genes (GCLC/GCLM, GSR, SLC7A11, TXNRD1, and AIFM2). In the BALF, 116 VOCs were detected, with 4 showing reduced levels in silicosis patients, independent of smoking status. Notably, the abundance of these VOCs exhibited an inverse association with ferritin levels. In summary, antioxidant defence pathways, incorporating genes that encode proteins to prevent ferroptosis, an iron-dependent non-apoptotic form of cell death, are upregulated in BAL from silicosis patients. Excess iron/ferritin was also associated with a reduction in multiple VOCs present in BALF, which may reflect their oxidative metabolism. We propose that BAL macrophages loaded with iron experience oxidative stress, leading to the activation of an anti-ferroptotic gene network that prolongs the survival of this dysfunctional BAL cell population.
Published in
Iron Alters the Transcriptome and Volatile Organic Compounds in the BAL of Patients with Silicosis
Aloe CA, Goh NSL, Wang H et al. · American journal of respiratory cell and molecular biology 2024 · PMID 39485079 · doi:10.1165/rcmb.2024-0181LE
This dataset
Open GSE264182 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 GSE264182_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 PRJNA1101401 and SRA study SRP502252. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39485079) ↗
Study design
8 × BAL Cells, Silica Exposed, Complicated Silicosis, Patient vs 6 × BAL Cells, Silica Exposed, Simple Silicosis, Patient vs 3 × BAL Cells, Silica Exposed No Disease, Control Patient vs 2 × BAL Cells, Chronic Cough, Control Patient

Supports a between-group comparison across 19 samples.

How we read this: 4 replicated groups read from 19 sample titles; they account for 19 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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