TGF‐β‐induced expression of tissue inhibitor of metalloproteinases‐3 gene in chondrocytes is mediated by extracellular signal‐regulated kinase pathway and Sp1 transcription factor
Bibliographic record
Abstract
Transforming growth factor (TGF-beta1) is a potent inducer of chondrogenesis and stimulant of cartilage extracellular matrix (ECM) synthesis. Tissue inhibitor of metalloproteinases-3 (TIMP-3) is located in ECM and is the major inhibitor of matrix metalloproteinases (MMPs) and aggrecanase, the principal enzymes implicated in collagen and aggrecan degradation in arthritis. We investigated the role of extracellular-signal-regulated kinase (ERK)-mitogen-activated protein kinases (MAPK) and Sp1 transcription factor in TGF-beta-induced TIMP-3 gene in chondrocytes and chondrosarcoma cells. TGF-beta time-dependently induced a sustained phosphorylation of ERK-MAPKs in primary human or bovine chondrocytes. Inhibitors of this pathway, PD98059 and U0126, downregulated TGF-beta-induced expression of TIMP-3 RNA and protein. Since the ERKs can phosphorylate Sp1, and the promoter of human TIMP-3 gene contains four Sp1-binding sites, we investigated whether Sp1 is a downstream target of this pathway. Mithramycin and WP631, the agents that prevent binding of Sp1 to its consensus site, downregulated TGF-beta-inducible TIMP-3 expression. Indeed, mithramycin blocked TGF-beta-stimulated Sp1 binding activity. Transfection of cytomegalovirus (CMV) promoter-Sp1 plasmid increased TIMP-3 promoter (-940 to +376)-driven luciferase activity. Depletion of Sp1 by transfection of an antisense phosphorothioate oligonucleotide suppressed TGF-beta-induced TIMP-3 protein expression, while its sense homolog had no effect. These results suggest that activation of ERK-MAPK pathway and Sp1 transcription factor play a pivotal role in the induction of TIMP-3 by TGF-beta in chondrocytes.
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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 | 0.001 |
| 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".