Cancer-Causing Mutations in a Novel Transcription-Dependent Nuclear Export Motif of VHL Abrogate Oxygen-Dependent Degradation of Hypoxia-Inducible Factor
Bibliographic record
Abstract
It is thought that degradation of nuclear proteins by the ubiquitylation system requires nuclear-cytoplasmic trafficking of E3 ubiquitin ligases. The von Hippel-Lindau (VHL) tumor suppressor protein is the substrate recognition component of a Cullin-2-containing E3 ubiquitin ligase that recruits hypoxia-inducible factor (HIF) for oxygen-dependent degradation. We demonstrated that VHL engages in nuclear-cytoplasmic trafficking that requires ongoing transcription to promote efficient HIF degradation. Here, we report the identification of a discreet motif, DXGX(2)DX(2)L, that directs transcription-dependent nuclear export of VHL and which is targeted by naturally occurring mutations associated with renal carcinoma and polycythemia in humans. The DXGX(2)DX(2)L motif is also found in other proteins, including poly(A)-binding protein 1, to direct its transcription-dependent nuclear export. We define DXGX(2)DX(2)L as TD-NEM (transcription-dependent nuclear export motif), since inhibition of transcription by actinomycin D or 5,6-dichlorobenzimidazole abrogates its nuclear export activity. Disease-causing mutations of key residues of TD-NEM restrain the ability of VHL to efficiently mediate oxygen-dependent degradation of HIF by altering its nuclear export dynamics without affecting interaction with its substrate. These results identify a novel nuclear export motif, further highlight the role of nuclear-cytoplasmic shuttling of E3 ligases in degradation of nuclear substrates, and provide evidence that disease-causing mutations can target subcellular trafficking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".