A hypoxia response element in the Vegfa promoter is required for basal Vegfa expression in skin and for optimal granulation tissue formation during wound healing in mice
Bibliographic record
Abstract
Hypoxia in skin wounds is thought to contribute to healing through the induction of hypoxia inducible factor-1 (HIF-1). Although HIF-1 can regulate the expression of vascular endothelial growth factor A (Vegfa), whether hypoxia and HIF-1 are required to induce Vegfa expression in the context of wound healing is unknown. To test this hypothesis, we evaluated Vegfa expression and wound healing in mutant mice that lack a functional HIF-1 binding site in the Vegfa promoter. Full-thickness excisional wounds were made using a biopsy punch, left to heal by second intention, and granulation tissue isolated on a time course during healing. mRNA levels of Vegfa and its target genes platelet-derived growth factors B (Pdgfb) and stromal cell-derived factor-1 (Sdf1) were measured by RT-qPCR, and HIF-1alpha and VEGFA protein levels measured by immunoblotting. Lower levels of Vegfa, Pdgf1 and Sdf1 mRNA were found in intact skin of mutant mice relative to wild-type controls (n = 6 mice/genotype), whereas levels in granulation tissue during wound healing were unaltered. VEGFA protein levels were also lower in intact skin of the mutant versus the wild-type mice. Decreased Vegfa mRNA levels in skin of mutant mice could not be attributed to decreased HIF-1alpha protein expression, and were therefore a consequence of the loss of HIF-1 responsiveness of the Vegfa promoter. Comparative histologic analyses of healing wounds in mutant and wild-type mice (n = 8 mice/genotype) revealed significant defects in granulation tissue in the mutant mice, both in terms of quantity and capillary density, although epithelialization and healing rates were unaltered. We conclude that HIF-1 is not a major regulator of Vegfa expression during wound healing; rather, it serves to maintain basal levels of expression of Vegfa and its target genes in intact skin, which are required for optimal granulation tissue formation in response to wounding.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".