Elastolytic activity of cysteine cathepsins K, S, and V promotes vascular calcification
Bibliographic record
Abstract
Elastin plays an important role in maintaining blood vessel integrity. Proteolytic degradation of elastin in the vascular system promotes the development of atherosclerosis, including blood vessel calcification. Cysteine cathepsins have been implicated in this process, however, their role in disease progression and associated complications remains unclear. Here, we showed that the degradation of vascular elastin by cathepsins (Cat) K, S, and V directly stimulates the mineralization of elastin and that mineralized insoluble elastin fibers were ~25-30% more resistant to CatK, S, and V degradation when compared to native elastin. Energy dispersive X-ray spectroscopy investigations showed that insoluble elastin predigested by CatK, S, or V displayed an elemental percentage in calcium and phosphate up to 8-fold higher when compared to non-digested elastin. Cathepsin-generated elastin peptides increased the calcification of MOVAS-1 cells acting through the ERK1/2 pathway by 34-36%. We made similar observations when cathepsin-generated elastin peptides were added to ex vivo mouse aorta rings. Altogether, our data suggest that CatK-, S-, and V-mediated elastolysis directly accelerates the mineralization of the vascular matrix by the generation of nucleation points in the elastin matrix and indirectly by elastin-derived peptides stimulating the calcification by vascular smooth muscle cells. Both processes inversely protect against further extracellular matrix degradation.
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".