Flow‐Induced Secretion of Endothelial Heparanase Regulates Cardiac Lipoprotein Lipase and Changes Following Diabetes
Bibliographic record
Abstract
Background Lipoprotein lipase (LPL)-derived fatty acid is a major source of energy for cardiac contraction. Synthesized in cardiomyocytes, LPL requires translocation to the vascular lumen for hydrolysis of lipoprotein triglyceride, an action mediated by endothelial cell (EC) release of heparanase. We determined whether flow-mediated biophysical forces can cause ECs to secrete heparanase and thus regulate cardiac metabolism. Methods and Results Isolated hearts were retrogradely perfused. Confluent rat aortic ECs were exposed to laminar flow using an orbital shaker. Cathepsin L activity was determined using gelatin-zymography. Diabetes was induced in rats with streptozotocin. Despite the abundance of enzymatically active heparanase in the heart, it was the enzymatically inactive, latent heparanase that was exceptionally responsive to flow-induced release. EC exposed to orbital rotation exhibited a similar pattern of heparanase secretion, an effect that was reproduced by activation of the mechanosensor, Piezo1. The laminar flow-mediated release of heparanase from EC required activation of both the purinergic receptor and protein kinase D, a kinase that assists in vesicular transport of proteins. Heparanase influenced cardiac metabolism by increasing cardiomyocyte LPL displacement along with subsequent replenishment. The flow-induced heparanase secretion was augmented following diabetes and could explain the increased heparin-releasable pool of LPL at the coronary lumen in these diabetic hearts. Conclusions ECs sense fluid shear-stress and communicate this information to subjacent cardiomyocytes with the help of heparanase. This flow-induced mechanosensing and its dynamic control of cardiac metabolism to generate ATP, using LPL-derived fatty acid, is exquisitely adapted to respond to disease conditions, like diabetes.
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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) | 0.001 | 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".