Calcium silicate induces mitophagy-mediated metabolic shifts toward oxidative phosphorylation in BMSCs to facilitate osteogenesis and bone regeneration
Bibliographic record
Abstract
Calcium silicate (CS)-based bioactive materials were widely utilized to promote the therapeutic potential of bone marrow mesenchymal stem cells (BMSCs) in bone tissue engineering. The activation of numerous classic bone formation modulators, including the BMP, Wnt, and MAPK/ERK signaling pathways, contributes to the CS-induced osteogenesis of BMSCs. Mitochondrial metabolic patterns have emerged as key contributors to the osteogenic differentiation of mesenchymal stem cells. However, whether CS affects the mitochondrial metabolic profiles of BMSCs is mostly unclear. Herein, we showed that CS induced the osteogenic differentiation of human BMSCs (hBMSCs) mainly via silicon (Si) ion release. Moreover, CS-stimulated hBMSCs underwent metabolic reprogramming accompanied by increased mitochondrial oxidative phosphorylation (OXPHOS) activity. The inhibition of OXPHOS hindered the CS-induced osteogenic differentiation of hBMSCs and bone regeneration, indicating that CS-induced OXPHOS mediated the observed increase in osteogenesis. Mechanistically, CS induced mitophagy and autophagic flux by increasing the formation of autolysosomes and lysosomal degradation to eliminate dysfunctional mitochondria and mitochondrial reactive oxygen species production, leading to enhanced OXPHOS and osteogenesis in hBMSCs. Furthermore, CS promoted mitochondrial fusion in hBMSCs, which may contribute to OXPHOS activation. Our investigation reveals a previously unclear function of CS in regulating the osteogenesis of BMSCs by inducing mitophagy-mediated metabolic shifts toward OXPHOS.
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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 | 0.000 |
| 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".