Ran-mediated Nuclear Export of the von Hippel-Lindau Tumor Suppressor Protein Occurs Independently of Its Assembly with Cullin-2
Bibliographic record
Abstract
Inactivating mutations of the von Hippel-Lindau (VHL) tumor suppressor gene cause the VHL cancer syndrome and sporadic renal clear cell carcinoma. VHL engages in a nucleocytoplasmic shuttle, which is required for its function. Here, we pursue our investigation to identify mechanisms by which VHL-green fluorescent protein (VHL-GFP) is exported from the nucleus. We show that nuclear export of VHL-GFP in living cells requires ongoing RNA polymerase II activity, and is mediated by mechanisms that are temperature-sensitive and energy-dependent. In vitro nuclear export of VHL-GFP is inhibited by nuclear pore-specific lectins, requires ATP hydrolysis and polyadenylated mRNAs, and occurs with kinetics that are similar to those of proteins containing a nuclear export signal. Biochemical fractionation has revealed that nuclear export of VHL-GFP occurs by way of a Ran-dependent pathway. Size exclusion column chromatography and deletion mutant analysis suggest that VHL-GFP does not require assembly with one of its associated proteins, cullin-2, to engage in nuclear export. These results demonstrate that nuclear export of VHL-GFP is Ran-mediated and ATP hydrolysis-dependent. They also suggest that sequences outside the elongin C binding box may function as a nuclear export domain, potentially providing a novel role for this region of VHL frequently mutated in renal cell carcinoma.
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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".