Overexpression of Fibroblast Growth Factor 23 Suppresses Osteoblast Differentiation and Matrix Mineralization In Vitro
Bibliographic record
Abstract
INTRODUCTION: Fibroblast growth factor (FGF)23 is produced primarily in bone and acts on kidney as a systemic phosphaturic factor; high levels result in rickets and osteomalacia. However, it remains unclear whether FGF23 acts locally and directly on bone formation. MATERIALS AND METHODS: We overexpressed human FGF23 in a stage-specific manner during osteoblast development in fetal rat calvaria (RC) cell cultures by using the adenoviral overexpression system and analyzed its effects on osteoprogenitor proliferation, osteoid nodule formation, and mineralization. Bone formation was also measured by calcein labeling in parietal bone organ cultures. Finally, we addressed the role of tyrosine phosphorylation of FGF receptor (FGFR) in mineralized nodule formation. RESULTS: Nodule formation and mineralization, but not osteoprogenitor proliferation, were independently suppressed by overexpression of FGF23 in RC cells. Increased FGF23 levels also suppressed bone formation in the parietal bone organ culture model. FGF23 overexpression enhanced phosphorylation of FGFR, whereas the impairment of mineralized nodule formation by FGF23 overexpression was abrogated by SU5402, an inhibitor of FGFR1 tyrosine kinase activity. CONCLUSIONS: These studies suggest that FGF23 overexpression suppresses not only osteoblast differentiation but also matrix mineralization independently of its systemic effects on Pi homeostasis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".