Role of Activator Protein 1 Transcriptional Activity in the Regulation of Gene Expression by Transforming Growth Factor β1 and Bone Morphogenetic Protein 2 in ROS 17/2.8 Osteoblast-like Cells
Bibliographic record
Abstract
In osteoblastic cells, transforming growth factor beta1 (TGF-beta1) has been found to regulate the expression of a variety of proto-oncogenes including c-fos, c-jun, and junB. The c-fos in particular has been implicated in the mitogenic effect of TGF-beta1. Here, we examined the role of these early response genes in the regulation of osteoblast (OB) gene expression by two members of the TGF-beta superfamily, TGF-beta1 and bone morphogenetic protein 2 (BMP-2). In ROS 17/2.8 cells, TGF-beta1 as well as BMP-2 up-regulated the expression of junB and c-fos messenger RNAs (mRNAs), and this increase was correlated in both cases with an increase in activator protein 1 (AP-1) DNA-binding activity involving JunB and c-Fos proteins. Protein kinase C (PKC)- and protein tyrosine kinase (PTK)-dependent pathways have been implicated in both TGF-beta1 signaling and AP-1 gene regulation. Therefore, using the kinase inhibitors chelerythrine chloride and genistein, we showed that PKC and PTK activities, respectively, participated in TGF-beta1- and BMP-2-induced increases in junB mRNA levels. Similarly, these kinase activities were involved in the stimulatory effect of BMP-2 on c-fos mRNA expression. Using a natural dominant negative for AP-1 transcriptional activity in ROS 17/2.8 cells, we then showed that AP-1 transcription factors mediated TGF-beta1- and BMP-2-regulated expression of the (alpha1) collagen I gene as well as TGF-beta1-regulated expression of the parathyroid hormone (PTH)/PTH-related peptide (PTHrP) receptor. Our data emphasize the role of the AP-1 transcription factor in TGF-beta1 and BMP-2 signaling and highlight the importance of this transcription factor family in the expression of OB genes.
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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 | 0.001 | 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".