pVHL Modification by NEDD8 Is Required for Fibronectin Matrix Assembly and Suppression of Tumor Development
Bibliographic record
Abstract
Functional inactivation of the von Hippel-Lindau (VHL) tumor suppressor gene is the cause of the familial VHL disease and most sporadic renal clear-cell carcinomas (RCC). pVHL has been shown to play a role in the destruction of hypoxia-inducible factor alpha (HIF-alpha) subunits via ubiquitin-mediated proteolysis and in the regulation of fibronectin matrix assembly. Although most disease-causing pVHL mutations hinder the regulation of the HIF pathway, every disease-causing pVHL mutant tested to date has failed to promote the assembly of the fibronectin matrix, underscoring its potential importance in VHL disease. Here, we report that a ubiquitin-like molecule called NEDD8 covalently modifies pVHL. A nonneddylateable pVHL mutant, while retaining its ability to ubiquitylate HIF, failed to bind to and promote the assembly of the fibronectin matrix. Expression of the neddylation-defective pVHL in RCC cells, while restoring the regulation of HIF, failed to promote the differentiated morphology in a three-dimensional growth assay and was insufficient to suppress the formation of tumors in SCID mice. These results suggest that NEDD8 modification of pVHL plays an important role in fibronectin matrix assembly and that in the absence of such regulation, an intact HIF pathway is insufficient to prevent VHL-associated tumorigenesis.
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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".