Effects of BMP-9 and BMP-2 on the PI3K/Akt Pathway in MC3T3-E1 Preosteoblasts
Bibliographic record
Abstract
The bone morphogenetic proteins (BMPs), which are involved in bone formation and repair, play an important role in tissue engineering. For example, BMP-9 and BMP-2, which are members of different BMP subfamilies, are osteoinductive factors. However, several studies have recently shown that BMP-9 is more osteogenic than BMP-2. We have previously shown that fetal bovine serum (FBS) strongly enhances the osteoblast differentiation of murine preosteoblasts (MC3T3-E1) to BMP-9 but not to BMP-2. This effect is mimicked by IGF-2, which primarily activates the PI3K/Akt pathway, but how Akt phosphorylation sites are implicated in such differentiation is unclear. The effects of BMP-9 and BMP-2 with or without FBS or IGF-2 on Akt phosphorylation sites and subsequent osteoblastic differentiation were determined, respectively, by western blot analysis and alkaline phosphatase activity measurements. The involvement of phosphorylated Akt at Thr308 and/or Ser473 on BMP-mediated osteoblast differentiation was further studied using specific inhibitors. In MC3T3-E1 incubated with or without FBS, BMP-9 and BMP-2 activate Akt on Ser473 and Thr308 very differently in a time and dose-dependent manner. Using inhibitors specific to each Akt phosphorylation site, we showed that both Ser473 and Thr308 must be phosphorylated for BMP-9 and/or IGF-2-induced osteoblast differentiation, whereas BMP-2 requires phosphorylation of only Ser473. Furthermore, cells stimulated with BMP-2 in the presence of FBS require the phosphorylation of Akt at Ser473 and the dephosphorylation of Akt at Thr308 to increase the osteoblast differentiation with alkaline phosphatase activity similar to that of BMP-9 plus FBS. These results provide a better understanding into how BMP-9 induces osteoblast differentiation and its synergy with IGF-2 at the signaling level. This knowledge is essential for preparing the serum-free osteogenic media required for bone tissue engineering or developing growth factor delivery systems to improve bone formation.
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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) | 0.001 | 0.001 |
| 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".