Tumor necrosis factor alpha and epidermal growth factor act additively to inhibit matrix gene expression by chondrocyte
Bibliographic record
Abstract
The failure of chondrocytes to replace the lost extracellular matrix contributes to the progression of degenerative disorders of cartilage. Inflammatory mediators present in the joint regulate the breakdown of the established matrix and the synthesis of new extracellular matrix molecules. In the present study, we investigated the effects of tumor necrosis factor alpha (TNF-alpha) and epidermal growth factor (EGF) on chondrocyte morphology and matrix gene expression. Chondrocytes were isolated from distal femoral condyles of neonatal rats. Cells in primary culture displayed a cobblestone appearance. EGF, but not TNF-alpha, increased the number of cells exhibiting an elongated morphology. TNF-alpha potentiated the effect of EGF on chondrocyte morphology. Individually, TNF-alpha and EGF diminished levels of aggrecan and type II collagen mRNA. In combination, the effects of TNF-alpha and EGF were additive, indicating the involvement of discrete signaling pathways. Cell viability was not compromised by TNF-alpha or by EGF, alone or in combination. EGF alone did not activate NF-kappaB or alter NF-kappaB activation by TNF-alpha. Pharmacologic studies indicated that the effects of TNF-alpha and EGF alone or in combination were independent of protein kinase C signaling, but were dependent on MEK1/2 activity. Finally, we analyzed the involvement of Sox-9 using a reporter construct of the 48 base pair minimal enhancer of type II collagen. TNF-alpha attenuated enhancer activity as expected; in contrast, EGF did not alter either the effect of TNF-alpha or basal activity. TNF-alpha and EGF, acting through distinct signaling pathways, thus have additive adverse effects on chondrocyte function. These findings provide critical insights into the control of chondrocytes through the integration of multiple extracellular signals.
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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 | 0.000 |
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".