Prostaglandin E2 Stimulates Bone Sialoprotein (BSP) Expression through cAMP and Fibroblast Growth Factor 2 Response Elements in the Proximal Promoter of the Rat BSP Gene
Bibliographic record
Abstract
Bone sialoprotein (BSP), an early marker of osteoblast differentiation, has been implicated in the nucleation of hydroxyapatite during de novo bone formation. Prostaglandin E2 (PGE2) has anabolic effects on proliferation and differentiation of osteoblasts via diverse signal transduction systems. Because PGE2 increases the proportion of functional osteoblasts in fetal rat calvarial cell cultures, we investigated the regulation of BSP, as an osteoblastic marker, by PGE2. Treatment of rat osteosarcoma UMR 106 cells with 3 microm, 300 nm, and 30 nm PGE2 increased the steady state levels of BSP mRNA about 2.7-, 2.5-, and 2.4-fold after 12 h. From transient transfection assays, the constructs including the promoter sequence of nucleotides (nt) -116 to +60 (pLUC3) were found to enhance transcriptional activity 3.8- and 2.2-fold treated with 3 microm and 30 nm PGE2 for 12 h. 2-bp mutations were made in an inverted CCAAT box (between nt -50 and -46), a cAMP response element (CRE; between nt -75 and -68), a fibroblast growth factor 2 response element (FRE; nt -92 to -85), and a pituitary-specific transcription factor-1 motif (between nt -111 and -105) within pLUC3 and pLUC7 constructs. Transcriptional stimulation by PGE2 was almost completed abrogated in constructs that included 2-bp mutations in either the CRE and FRE. In gel shift analyses an increased binding of nuclear extract components to double-stranded oligonucleotide probes containing CRE and FRE was observed following treatment with PGE2. These studies show that PGE2 induces BSP transcription in UMR 106 cells through juxtaposed CRE and FRE elements in the proximal promoter of the BSP gene.
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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".