Focal Adhesion Kinase Alleviates Simulated Microgravity-Induced Inhibition of Osteoblast Differentiation by Activating Transcriptional Wnt/β-Catenin-BMP2-COL1 and Metabolic SIRT1-PGC-1α-CPT1A Pathways
Bibliographic record
Abstract
The metabolic poise, or balance, between glycolysis and fatty acid oxidation (FAO) has recently been found to play a critical role in osteogenic differentiation and homeostasis. While simulated microgravity (SMG) is known to impede osteoblast differentiation (OBD) by inhibiting the Wnt/β-catenin pathway, how it affects osteoblast metabolism in this context remains unclear. We previously analyzed the effect of SMG on the differentiation of pre-osteoblast MC3T3-E1 cells and found that it reduced focal adhesion kinase (FAK) activity. This, in turn, downregulated Wnt/β-catenin and two of its downstream targets critical for OBD bone morphogenic protein-2 (BMP2) and type-1 collagen (COL1) formation, leading to a reduction in alkaline phosphatase (ALP) activity and cell matrix mineralization. In this study, we further analyzed how SMG-induced alterations in energy metabolism contribute to the inhibition of OBD in MC3T3-E1 cells. Consistent with our earlier findings, we demonstrated that SMG inhibits OBD by downregulating the collective activity of FAK and the Wnt/β-catenin-BMP2-COL1 transcriptional pathway. Interestingly, we observed that SMG also reduces the abundance of sirtuin-1 (SIRT1), peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) and carnitine palmitoyl transferase-1α (CPT1A), which are all key metabolic factors regulating mitochondrial number and FAO capacity. Accordingly, we found that the mitochondrial content and FAO potential of MC3T3-E1 cells were lower upon exposure to SMG but were both rescued upon administration of the FAK activator cytotoxic necrotizing factor-1 (CNF1), thereby allowing cells to overcome SMG-induced inhibition of OBD. Taken together, our study indicates that the metabolic regulator SIRT1 may be a new target for reversing SMG-induced bone loss.
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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) | 0.000 | 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".