Elevation of protein kinase Cα stimulates osteogenic differentiation of mesenchymal stem cells through the TAT-mediated protein transduction system
Bibliographic record
Abstract
Mesenchymal stem cells (MSCs) can differentiate toward various lineages, including the osteogenic lineage, and thus hold great potential for clinic purposes. By using pharmacological inhibitors, protein kinase C (PKC) signaling has been shown to either negatively or positively regulate differentiation of bone, however, due to the low transfection efficiency in MSCs, the role of individual PKC isoforms is still not fully understood. In this study, we established a TAT peptide-mediated transduction system that efficiently delivered PKCα proteins into MSCs in a non-invasive fashion. The increased PKCα protein levels significantly promoted osteogenic differentiation in the murine mesenchymal C3H10T1/2 cells and in primary MSCs from both human and mouse, as demonstrated by the enhanced activity of the osteoblast marker, alkaline phosphatase, and the enhanced expression of the key transcription factor runx2. Mineralization is an important functional indication for bone differentiation. Our results further showed that PKCα promoted expression of the important osteocalcin gene and the accumulation of calcium minerals. Taken together, this study provides direct evidence showing that PKCα positively regulates osteogenic differentiation and demonstrates that the TAT peptide-mediated method enables functional study of specific PKC isoforms in MSCs without using viral infection. This may promote the application of PKCs in therapeutic treatment.
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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) | 0.002 | 0.001 |
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".