c-Src kinase inhibits osteogenic differentiation via enhancing STAT1 stability
Bibliographic record
Abstract
The proto-oncogene Src is ubiquitously expressed and is involved in cellular differentiation. However, the role of Src in embryonic stem (ES) cell osteogenic differentiation is largely unknown. Using the small molecule inhibitor PP2, c-Src specific siRNAs, and tet-inducible lentiviral vectors overexpressing active c-Src, we delineated an inhibitory role of c-Src in osteogenic differentiation of mouse embryonic stem cells (mESCs) and mouse MC3T3-E1s preosteoblasts. Active c-Src was shown to restrict the nuclear residency of Runt-related transcription factor 2 (Runx2) and its transcriptional activity with no detectable effect on Runx2 expression level. Furthermore, we showed Signal Transducer and Activator of Transcription 1 (STAT1) was indispensable to the inhibitory role of c-Src on Runx2 nuclear localization. Specifically, higher levels of active c-Src increased STAT1 half-life by inhibiting its proteasomal degradation, thereby increasing the cytoplasmic abundance of STAT1. More abundant cytoplasmic STAT1 bound and anchored Runx2, which restricted its nucleocytoplasmic shuttling and ultimately reduced Runx2 transcriptional activity. Collectively, this study has defined a new mechanism by which c-Src inhibits the transcriptional regulation of osteogenesis from mESCs in vitro.
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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 | 0.001 |
| 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".