HIF-1 inhibition decreases systemic vascular remodelling diseases by promoting apoptosis through a hexokinase 2-dependent mechanism
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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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) | 0.002 | 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 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".