Tumor necrosis factor alpha downregulates bone morphogenetic protein expression and matrix macromolecule synthesis in articular cartilage
Bibliographic record
Abstract
Cartilage breakdown in arthritis is thought to result from disequilibrium of catabolic and anabolic mechanisms. Tumor necrosis factor (TNF) alpha is a potent stimulator of catabolic pathways, whereas its influence on anabolic pathways is currently unknown. Herein, we studied the effect of systemic overexpression of TNF on bone morphogenic protein (BMP) expression in articular cartilage and cartilage matrix synthesis. Analyses were performed in human tumor necrosis factor transgenic (hTNFtg) mice, which suffer from chronic destructive arthritis, and wild-type mice. Expression of cartilage-derived morphogenetic protein (CDMP)-1, CDMP-2, and BMP-6 and BMP-7 in articular cartilage was assessed by immunohistochemistry. Cartilage samples from the knee joints were assessed for DNA content and [ S]sulfate incorporation assays to assess chondrocyte number and matrix synthesis, respectively. Expression of all four BMP family members was significantly decreased in articular cartilage of hTNFtg mice. The numbers of stained cells were reduced by about 60% for CDMP-1, CDMP-2 and BMP-6 ( P < 0.004) and by 44% for BMP-7 ( P < 0.02). There was no difference in DNA content in the investigated cartilage samples, whereas isotope incorporation into newly synthesized matrix macromolecules was significantly decreased by an average of 61% in cartilage derived from hTNFtg mice compared with wild-type controls ( P < 0.001). Chronic overexpression of TNF leads to decreased expression of BMPs and reduced matrix macromolecule synthesis in the articular cartilage. These data suggest that TNF, aside from functioning as a catabolic mediator, potently inhibits anabolic mechanisms, which aim to restore the integrity of articular cartilage.
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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 | 0.000 |
| 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".