HIF-1 inhibition decreases systemic vascular remodelling diseases by promoting apoptosis through a hexokinase 2-dependent mechanism
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Bibliographic record
Abstract
AIMS: Vascular remodelling diseases are characterized by the presence of proliferative and apoptosis-resistant vascular smooth muscle cells (VSMC). There is evidence that pro-proliferative and anti-apoptotic states are characterized by metabolic remodelling (a glycolytic phenotype with hyperpolarized mitochondria) involving Akt pathway activation by circulating growth factors. Hypoxia-inducible factor-1 (HIF-1) is involved in different vascular diseases. Since this transcription factor is implicated in metabolic responses, we hypothesized that HIF-1 activity could be involved in vascular remodelling in response to arterial injury. METHODS AND RESULTS: Our findings indicate that growth factors, such as platelet-derived growth factor (PDGF), activate the Akt pathway (measured by immunoblot) in human carotid artery VSMC. Activation of this pathway increased HIF-1 activation (measured by immunoblot), leading to increased glycolysis in VSMC. Expression and mitochondrial activity of hexokinase 2 (HXK2), a primary initiator of glycolysis, are increased during HIF-1 activation. The mitochondrial activity of HXK2 in VSMC led to the hyperpolarization of mitochondrial membrane potential (measured by tetramethylrhodamine methyl-ester perchlorate) and the suppression of apoptosis (measured by TUNEL assay and 3 activity), effects that are blocked by HIF-1 inhibition. Additionally, HIF-1 inhibition also decreased VSMC proliferation (proliferating cell nuclear antigen and Ki-67 assays). In vivo, we demonstrate that localized HIF-1 inhibition, using a dominant-negative HIF-1α adenoviral construct, prevented carotid artery post-injury remodelling in rats. CONCLUSION: We propose that HIF-1 is centrally involved in carotid artery remodelling in response to arterial injury and that localized inhibition of HIF-1 may be a novel therapeutic strategy to prevent carotid stenosis.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it