The proinflammatory cytokines interleukin-1α and tumor necrosis factor α promote the expression and secretion of proteolytically active cathepsin S from human chondrocytes
Bibliographic record
Abstract
Osteoarthritis and rheumatoid arthritis are destructive joint diseases that involve the loss of articular cartilage. Degradation of cartilage extracellular matrix is believed to occur due to imbalance between the catabolic and anabolic processes of resident chondrocytes. Previous work has suggested that various lysosomal cysteine cathepsins participate in cartilage degeneration; however, their exact roles in disease development and progression have not been elucidated. In order to study degradation processes under conditions resembling the in vivo milieu of the cartilage, we cultivated chondrocytes on a type II collagen-containing matrix. Stimulation of the cultivated chondrocytes with interleukin-1α and/or tumor necrosis factor α resulted in a time-dependent increase in cathepsin S expression and induced its secretion into the conditioned media. Using a novel bioluminescent activity-based probe, we were able to demonstrate a significant increase in proteolytic activity of cathepsin S in the conditioned media of proinflammatory cytokine-stimulated chondrocytes. For the first time, cathepsin S was demonstrated to be secreted from chondrocytes upon stimulation with the proinflammatory cytokines, and displayed proteolytic activity in culture supernatants. Its stability at neutral pH and potent proteolytic activity on extracellular matrix components mean that cathepsin S may contribute significantly to cartilage degradation and may thus be considered a potential drug target in joint diseases.
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) | 0.001 | 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".