Protein isoprenylation regulates osteogenic differentiation of mesenchymal stem cells: effect of alendronate, and farnesyl and geranylgeranyl transferase inhibitors
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Protein isoprenylation is an important step in the intracellular signalling pathway conducting cell growth and differentiation. In bone, protein isoprenylation is required for osteoclast differentiation and activation. However, its role in osteoblast differentiation and function remains unknown. In this study, we assessed the role of protein isoprenylation in osteoblastogenesis in a model of mesenchymal stem cells (MSC) differentiation. EXPERIMENTAL APPROACH: We tested the effect of an inhibitor of farnesylation [farnesyl transferase inhibitor-277 (FTI-277)] and one of geranylgeranylation [geranylgeranyltransferase inhibitor-298 (GGTI-298)] on osteoblast differentiating MSC. In addition, we tested the effect of alendronate on protein isoprenylation in this model either alone or in combination with other inhibitors of isoprenylation. KEY RESULTS: Initially, we found that levels of unfarnesylated proteins (prelamin A and HDJ-2) increased after treatment with FTI-277 concomitantly affecting osteoblastogenesis and increasing nuclear morphological changes without affecting cell survival. Furthermore, inhibition of geranylgeranylation by GGTI-298 alone increased osteoblastogenesis. This effect was enhanced by the combination of GGTI-298 and alendronate in the osteogenic media. CONCLUSIONS AND IMPLICATIONS: Our data indicate that both farnesylation and geranylgeranylation play a role in osteoblastogenesis. In addition, a new mechanism of action for alendronate on protein isoprenylation in osteogenic differentiating MSC in vitro was found. In conclusion, protein isoprenylation is an important component of the osteoblast differentiation process that could constitute a new therapeutic target for osteoporosis in the future.
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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) | 0.001 | 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 | 0.000 |
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".