Inflammatory Cytokines Induce Production of CHI3L1 by Articular Chondrocytes
Bibliographic record
Abstract
Elevated levels of CHI3L1 (chitinase-3-like protein 1) are associated with disorders exhibiting increased connective tissue turnover, such as rheumatoid arthritis, osteoarthritis, scleroderma, and cirrhosis of the liver. This secreted protein is not synthesized in young healthy cartilage, but is produced in cartilage from old donors or patients with osteoarthritis. The molecular processes governing the induction of CHI3L1 are currently unknown. To elucidate the molecular events involved in CHI3L1 synthesis, we investigated two models of articular chondrocytes: neonatal rat chondrocytes, which do not express CHI3L1, and human chondrocytes, which express CHI3L1 constitutively. In neonatal rat chondrocytes, the inflammatory cytokines tumor necrosis factor-alpha (TNF-alpha) and interleukin-1 potently induced steady-state levels of CHI3L1 mRNA and protein secretion. Treatment of chondrocytes with TNF-alpha for as little as 1 h was sufficient for sustained induction up to 72 h afterward. Using inhibitors selective for the major signaling pathways implicated in mediating the effects of TNF-alpha and interleukin-1, only inhibition of NF-kappaB activation was effective in curtailing cytokine-induced expression, including after removal of the cytokine, indicating that induction and continued production of CHI3L1 are controlled mainly by this transcription factor. Inhibition of NF-kappaB signaling also abolished constitutive expression by human chondrocytes. Thus, induction and continued secretion of CHI3L1 in chondrocytes require sustained activation of NF-kappaB. Selective induction of CHI3L1 by cytokines acting through NF-kappaB coupled with the known restriction of the catabolic responses by CHI3L1 in response to these inflammatory cytokines represents a key regulatory feedback process in controlling connective tissue turnover.
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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) | 0.001 | 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 | 0.001 |
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".