Hypoxia upregulates von Hippel-Lindau tumor-suppressor protein through RhoA-dependent activity in renal cell carcinoma
Bibliographic record
Abstract
A key task for the multifunctional von Hippel-Lindau protein (pVHL) is regulation of the activity of hypoxia-inducible factor-1alpha (HIF-1alpha) by targeting it to the proteasome for degradation under normoxia. pVHL binding to HIF-1alpha is lost under low O2 tension, leading to transcription of several genes involved in the hypoxia response. However, regulation of pVHL by hypoxia remains to be investigated. We evaluated the effects of hypoxia on pVHL expression in carcinoma and endothelial cells. We showed that hypoxia stimulates pVHL levels (2.5-fold) in renal Caki-1 cells expressing wild-type VHL (VHL+/+). This upregulation was independent of VHL status, because hypoxia also increased pVHL expression in renal 786-O cells carrying mutated VHL (VHL-/-). Hypoxia did not affect pVHL expression in endothelial cells. Hypoxia-induced pVHL in Caki-1 cells was RhoA dependent, because inhibition by exotoxin C3 prevented pVHL stimulation. Furthermore, inhibition of Rho kinase by Y-27632 blocked pVHL induction by hypoxia. During normoxia, pVHL expression was also induced in cells transfected with dominant-active RhoA. Furthermore, disruption of actin organization by chemical agents or by hypoxia stimulated pVHL expression in kidney cells. On the other hand, inhibition of MAP kinases p38 and JNK, but not MAP kinase kinase (MEK1/2), reduced pVHL upregulation by 30 and 72%, respectively, during hypoxia, supporting a significant role for these signaling pathways. Expression and phosphorylation of c-Jun were stimulated in cells transfected with dominant-active RhoA. Together, these findings demonstrate that hypoxia induces pVHL expression in renal cancer cells, and this induction is mediated by RhoA-dependent pathways.
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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".