Human HIF‐3α4 is a dominant‐negative regulator of HIF‐1 and is down‐regulated in renal cell carcinoma
Bibliographic record
Abstract
A universal response to changes in cellular oxygen tension is governed by a family of heterodimeric transcription factors called hypoxia-inducible factor (HIF). Tumor hypoxia, as well as various cancer-causing mutations, has been shown to elevate the level of HIF-1alpha, signifying a critical role of the HIF pathway in cancer development. The recently identified third member of the human HIF-alpha family, HIF-3alpha, produces multiple splice variants that contain extra DNA binding elements and protein-protein interaction motifs not found in HIF-1alpha or HIF-2alpha. Here we report the molecular cloning of the alternatively spliced human HIF-3alpha variant HIF-3alpha4 and show that it attenuates the ability of HIF-1 to bind hypoxia-responsive elements located within the enhancer/promoter of HIF target genes. The overexpression of HIF-3alpha4 suppresses the transcriptional activity of HIF-1 and siRNA-mediated knockdown of the endogenous HIF-3alpha4 increases transcription by hypoxia-inducible genes. HIF-3alpha4 itself is oxygen-regulated, suggesting a novel feedback mechanism of controlling HIF-1 activity. Furthermore, the expression of HIF-3alpha4 is dramatically down-regulated in the majority of primary renal carcinomas. These results demonstrate an important dominant-negative regulation of HIF-1-mediated gene transcription by HIF-3alpha4 in vivo and underscore its potential significance in renal epithelial oncogenesis.
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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) | 0.002 | 0.001 |
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".