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HIF-1α and HIF-2α transcription factors differentially regulate lung alveolar macrophage function

GSE325293 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2026/03/23 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 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
Mus musculus (mouse)
Samples
18 samples: 6 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)
Alveolar Macrophages (AMs) reside in the alveoli, therefore maintaining an active degradation of hypoxia-inducible transcription factors (HIF) mediated by Von Hippel-Lindau protein (pVHL). We previously found that Vhl-deficient AMs are immature and functionally impaired. Here we investigated the specific contribution of HIF-1α and HIF-2α isoforms to the regulation of the AM function.
Read the full summary ↓
Our work demonstrates that in Vhl-deficient macrophages, both HIF-1α and HIF-2α contribute to AM defective self-renewal, while HIF-2α plays a central role in regulating the impaired AM maturation associated with pVHL loss. HIF-1α promotes a glycolytic shift in alveolar macrophages, while HIF-2α hinders lipid oxidation and surfactant clearance. Thus, HIF-2α raises as a selective critical factor restraining the therapeutic potential of AMs to degrade surfactant excess in mice that have developed pulmonary alveolar proteinosis (PAP). Overall, regulation of both HIF-1α and HIF-2α isoforms is required for an optimal AM function.
This dataset
Open GSE325293 in finder → Find more mouse 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 GSE325293_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 PRJNA1439399 and SRA study SRP684887. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in triplicate
Vhl-Hif1_KO ×3 Vhl-Hif1_WT ×3 Vhl-Hif2_KO ×3 Vhl-Hif2_WT ×3 Vhl-Hif1-Hif2_KO ×3 Vhl-Hif1-Hif2_WT ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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
Datasets on a similar topic

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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.