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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".