Negative regulation of HIF-1α by an FBW7-mediated degradation pathway during hypoxia
Bibliographic record
Abstract
Hypoxia inducible factor-1α (HIF-1α) stimulates expression of genes associated with angiogenesis and is associated with poor outcomes in ovarian and other cancers. In normoxia, HIF-1α is ubiquitinated and degraded through the E3 ubiquitin ligase, von Hippel-Lindau; however, little is known about the regulation of HIF-1α in hypoxic conditions. FBW7 is an E3 ubiquitin ligase that recognizes proteins phosphorylated by glycogen synthase kinase 3β (GSK3β) and targets them for destruction. This study used an ovarian cancer cell model to test the hypothesis that HIF-1α phosphorylation by GSK3β in hypoxia leads to interaction with FBW7 and ubiquitin-dependent degradation. Expression of constitutively active GSK3β reduced HIF-1α protein and transcriptional activity and increased ubiquitination of HIF-1α in hypoxia, whereas pharmacologic inhibition of GSK3 or expression of siGSK3β promoted HIF-1α stabilization and activity. A mechanism through FBW7 was supported by the observed decrease in HIF-1α stabilization when FBW7 was overexpressed and both the elevation of HIF-1α levels and decrease in ubiquitinated HIF-1α when FBW7 was suppressed. Furthermore, HIF-1α associated with FBW7γ by co-immunoprecipitation, and the interaction was weakened by inhibition of GSK3 or mutation of GSK3β phosphorylation sites. The relevance of this pathway to angiogenic signaling was supported by the finding that endothelial cell tube maturation was increased by conditioned media from hypoxic SK-OV-3 cell lines expressing suppressed GSK3β or FBW7. These data introduce a new mechanism for regulation of HIF-1α during hypoxia that utilizes phosphorylation to target HIF-1α for ubiquitin-dependent degradation through FBW7 and may identify new targets in the regulation of angiogenesis.
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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".