HIF‐1α and HIF‐2α play a central role in stretch‐induced but not shear‐stress‐induced angiogenesis in rat skeletal muscle
Bibliographic record
Abstract
Angiogenesis, which is essential for the physiological adaptation of skeletal muscle to exercise, occurs in response to the mechanical forces of elevated capillary shear stress and cell stretch. Increased production of VEGF is a characteristic of endothelial cells undergoing either stretch- or shear-stress-induced angiogenesis. Because VEGF production is regulated by hypoxia inducible factors (HIFs), we examined whether HIFs play a significant role in the angiogenic process initiated by these mechanical forces. Rat extensor digitorum longus (EDL) muscles were overloaded to induce stretch, or exposed to the dilator prazosin to elevate capillary shear stress, and capillaries from these muscles were isolated by laser capture microdissection for RNA analysis. HIF-1alpha and HIF-2alpha transcript levels increased after 4 and 7 days of stretch, whereas a transient early induction of HIF-1alpha and HIF-2alpha transcripts was detected in capillaries from prazosin-treated muscles. Skeletal muscle microvascular endothelial cells exposed to 10% stretch in vitro showed an elevation in HIF-1alpha and HIF-2alpha mRNA, which was preceded by increases in HIF-binding activity. Conversely, HIF-1alpha and HIF-2alpha mRNA were reduced significantly, and HIF-alpha proteins were undetectable, after 24 h exposure to elevated shear stress (16 dyn cm(-2) (16 x10(-5) N cm(-2)). Given the disparate regulation of HIFs in response to these mechanical stimuli, we tested the requirement of HIF-alpha proteins in stretch- and shear-stress-induced angiogenesis by impeding HIF accumulation through use of the geldanamycin derivative 17-DMAG. Treatment with 17-DMAG significantly impaired stretch-induced, but not shear-stress-induced, angiogenesis. Together, these results illustrate that activation of HIF-1alpha and HIF-2alpha contributes significantly to stretch- but not to shear-stress-induced capillary growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".