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Structural survey of HIF-2α reveals regulation of its subcellular localization and protein interactome

GSE329133 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2026/05/05 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples: 4 × SK-N-BE(2), WT, vs 4 × SK-N-BE(2), FL, vs 4 × SK-N-BE(2), N‑CTAD, vs 4 × SK-N-BE(2), c-IDR,Groups 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)
Hypoxia‑inducible factor 2α (HIF‑2α) is a central regulator of cellular homeostasis and a known oncogenic driver in multiple cancers. Although HIF‑2α is canonically defined as a nuclear transcription factor, its cytoplasmic presence and potential non‑transcriptional functions remain poorly understood. Here, we dissect the structural determinants of HIF‑2α localization, stability, and activity using a deletion‑construct library, transcriptional assays, and in vivo xenograft models.
Read the full summary ↓
We show that the oxygen‑dependent degradation domain (ODD), and N‑terminal intrinsically disordered region (IDR) and transactivation domain (NTAD) promote cytoplasmic localization, whereas the C‑terminal IDR drives nuclear accumulation. Notably, we demonstrate that HIF‑2α nuclear localization occurs independently of ARNT dimerization, resolving a long-standing question in the field. While both NTAD and C‑terminal (C)TAD contribute to transcriptional activation, only CTAD mediates hypoxia responsive element (HRE) transactivation, and neither domain is required for tumor growth in vivo. These data suggest a dominant role for non‑canonical cytoplasmic mechanisms in HIF‑2α-driven tumorigenesis. Proteomic analyses reveal HIF‑2α interactions with regulators of mitochondrial function, translation initiation, RNA splicing, vesicular transport, and DNA replication. Together, these findings uncover previously unrecognized structural and functional complexity of HIF‑2α and expand its role beyond canonical transcriptional regulation
This dataset
Open GSE329133 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 GSE329133_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 PRJNA1458036 and SRA study SRP695136. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
4 × SK-N-BE(2), WT, vs 4 × SK-N-BE(2), FL, vs 4 × SK-N-BE(2), N‑CTAD, vs 4 × SK-N-BE(2), c-IDR,

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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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