MyoD enhances BMP7-induced osteogenic differentiation of myogenic cell cultures
Bibliographic record
Abstract
The muscle-specific, basic helix-loop-helix transcription factor MyoD can induce cells from other mesenchymal lineages to express a skeletal muscle phenotype. Interestingly, MyoD is initially upregulated in myogenic cells incubated with bone morphogenetic proteins (BMPs), a treatment that induces osteogenic differentiation, suggesting that MyoD has a role in BMP-induced osteogenesis of myogenic cells. This possibility is supported by our observations that muscle satellite cells derived from adult MyoD(-/-) mice show severely impaired osteogenic induction by BMP-7 (osteogenic protein 1; OP-1) as indicated by the decreased gene expression of the bone markers alkaline phosphatase, osteocalcin, Runx2/Cbfa1, and Osterix. Ectopic expression of MyoD increased alkaline phosphatase activity and Osterix mRNA expression in response to BMP treatment. Similarly, ectopic expression of MyoD in the pluripotent mesenchymal cell line C3H10T1/2 increased alkaline phosphatase activity induced by BMP-7. Transcription assays showed that transfection with a MyoD-expression vector, but not other myogenic basic helix-loop-helix transcription factors (Myf5, myogenin) increased Runx2/Cbfa1 transactivation of a reporter gene construct containing either six OSE sequences in tandem or a single OSE site. This effect was enhanced by BMP treatment. These studies, therefore, demonstrate that the muscle transcription factor MyoD is required for efficient BMP-induced osteogenesis of myogenic cells and indicate that MyoD might exert its effects through co-operative interactions with Runx2/Cbfa1.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".