Hyperglycemia-Induced Secretion of Endothelial Heparanase Stimulates a Vascular Endothelial Growth Factor Autocrine Network in Cardiomyocytes That Promotes Recruitment of Lipoprotein Lipase
Bibliographic record
Abstract
OBJECTIVE: During diabetes mellitus, coronary lipoprotein lipase increases to promote the predominant use of fatty acids. We have reported that high glucose stimulates active heparanase secretion from endothelial cells to cleave cardiomyocyte heparan sulfate and release bound lipoprotein lipase for transfer to the vascular lumen. In the current study, we examined whether heparanase also has a function to release cardiomyocyte vascular endothelial growth factor (VEGF), and whether this growth factor influences cardiomyocyte fatty acid delivery in an autocrine manner. APPROACH AND RESULTS: Acute, reversible hyperglycemia was induced in rats, and a modified Langendorff heart perfusion was used to separate the coronary perfusate from the interstitial effluent. Coronary artery endothelial cells were exposed to high glucose to generate conditioned medium, and VEGF release from isolated cardiomyocytes was tested using endothelial cell conditioned medium or purified active and latent heparanase. Autocrine signaling of myocyte-derived VEGF on cardiac metabolism was studied. High glucose promoted latent and active heparanase secretion into endothelial cell conditioned medium, an effective stimulus for releasing cardiomyocyte VEGF. Intriguingly, latent heparanase was more efficient than active heparanase in releasing VEGF from a unique cell surface pool. VEGF augmented cardiomyocyte intracellular calcium and AMP-activated protein kinase phosphorylation and increased heparin-releasable lipoprotein lipase. CONCLUSIONS: Our data suggest that the heparanase-lipoprotein lipase-VEGF axis amplifies fatty acid delivery, a rapid and adaptive mechanism that is geared to overcome the loss of glucose consumption by the diabetic heart. If prolonged, the resultant lipotoxicity could lead to cardiovascular disease in humans.
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 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".