High glucose facilitated endothelial heparanase transfer to the cardiomyocyte modifies its cell death signature
Bibliographic record
Abstract
Aims The secretion of enzymatically active heparanase (HepA) has been implicated as an essential metabolic adaptation in the heart following diabetes. However, the regulation and function of the enzymatically inactive heparanase (HepL) remain poorly understood. We hypothesized that in response to high glucose (HG) and secretion of HepL from the endothelial cell (EC), HepL uptake and function can protect the cardiomyocyte by modifying its cell death signature. Methods and results HG promoted both HepL and HepA secretion from microvascular (rat heart micro vessel endothelial cells, RHMEC) and macrovascular (rat aortic endothelial cells, RAOEC) EC. However, only RAOEC were capable of HepL reuptake. This occurred through a low-density lipoprotein receptor-related protein 1 (LRP1) dependent mechanism, as LRP1 inhibition using small interfering RNA (siRNA), receptor-associated protein, or an LRP1 neutralizing antibody significantly reduced uptake. In cardiomyocytes, which have a negligible amount of heparanase gene expression, LRP1 also participated in the uptake of HepL. Exogenous addition of HepL to rat cardiomyocytes produced a dramatically altered expression of apoptosis-related genes, and protection against HG and H2O2 induced cell death. Cardiomyocytes from acutely diabetic rats demonstrated a robust increase in LRP1 expression and levels of heparanase, a pro-survival gene signature, and limited evidence of cell death, observations that were not apparent following chronic and progressive diabetes. Conclusion Our results highlight EC-to-cardiomyocyte transfer of heparanase to modulate the cardiomyocyte cell death signature. This mechanism was observed in the acutely diabetic heart, and its interruption following chronic diabetes may contribute towards the development of diabetic cardiomyopathy.
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 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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".