Induction of Hypoxia-inducible Factor-1α by Transcriptional and Translational Mechanisms
Bibliographic record
Abstract
Hypoxia-inducible factor-1 (HIF-1) regulates the transcription of many genes induced by low oxygen conditions. Recent studies have demonstrated that non-hypoxic stimuli can also activate HIF-1 in a cell-specific manner. Here, we define two key mechanisms that are implicated in increasing the active subunit of the HIF-1 complex, HIF-1alpha, following the stimulation of vascular smooth muscle cells (VSMC) with angiotensin II (Ang II). We show that, in contrast to hypoxia, the induction of HIF-1alpha by Ang II in VSMC is dependent on active transcription and ongoing translation. We demonstrate that stimulation of VSMC by Ang II strongly increases HIF-1alpha gene expression. The activation of diacylglycerol-sensitive protein kinase C (PKC) plays a major role in the increase of HIF-1alpha gene transcription. We also demonstrate that Ang II relies on ongoing translation to maintain elevated HIF-1alpha protein levels. Ang II increases HIF-1alpha translation by a reactive oxygen species (ROS)-dependent activation of the phosphatidylinositol 3-kinase pathway, which acts on the 5'-untranslated region of HIF-1alpha mRNA. These results establish that the non-hypoxic induction of the HIF-1 transcription factor via vasoactive hormones (Ang II and thrombin) is triggered by a dual mechanism, i.e. a PKC-mediated transcriptional action and a ROS-dependent increase in HIF-1alpha protein expression. Elucidation of these signaling pathways that up-regulate the vascular endothelial growth factor (VEGF) could have a strong impact on different aspects of vascular biology.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".