Endothelial Heparanase Regulates Heart Metabolism by Stimulating Lipoprotein Lipase Secretion From Cardiomyocytes
Bibliographic record
Abstract
OBJECTIVE: After diabetes mellitus, transfer of lipoprotein lipase (LPL) from cardiomyocytes to the coronary lumen increases, and this requires liberation of LPL from the myocyte surface heparan sulfate proteoglycans with subsequent replenishment of this reservoir. At the lumen, LPL breaks down triglyceride to meet the increased demand of the heart for fatty acid. Here, we examined the contribution of coronary endothelial cells (ECs) toward regulation of cardiomyocyte LPL secretion. APPROACH AND RESULTS: Bovine coronary artery ECs were exposed to high glucose, and the conditioned medium was used to treat cardiomyocytes. EC-conditioned medium liberated LPL from the myocyte surface, in addition to facilitating its replenishment. This effect was attributed to the increased heparanase content in EC-conditioned medium. Of the 2 forms of heparanase secreted from EC in response to high glucose, active heparanase released LPL from the myocyte surface, whereas latent heparanase stimulated reloading of LPL from an intracellular pool via heparan sulfate proteoglycan-mediated RhoA activation. CONCLUSIONS: Endothelial heparanase is a participant in facilitating LPL increase at the coronary lumen. These observations provide an insight into the cross-talk between ECs and cardiomyocytes to regulate cardiac metabolism after diabetes mellitus.
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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 | 0.000 |
| 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".