Osteoclast-specific cathepsin K deletion stimulates S1P-dependent bone formation
Bibliographic record
Abstract
Cathepsin K (CTSK) is secreted by osteoclasts to degrade collagen and other matrix proteins during bone resorption. Global deletion of Ctsk in mice decreases bone resorption, leading to osteopetrosis, but also increases the bone formation rate (BFR). To understand how Ctsk deletion increases the BFR, we generated osteoclast- and osteoblast-targeted Ctsk knockout mice using floxed Ctsk alleles. Targeted ablation of Ctsk in hematopoietic cells, or specifically in osteoclasts and cells of the monocyte-osteoclast lineage, resulted in increased bone volume and BFR as well as osteoclast and osteoblast numbers. In contrast, targeted deletion of Ctsk in osteoblasts had no effect on bone resorption or BFR, demonstrating that the increased BFR is osteoclast dependent. Deletion of Ctsk in osteoclasts increased their sphingosine kinase 1 (Sphk1) expression. Conditioned media from Ctsk-deficient osteoclasts, which contained elevated levels of sphingosine-1-phosphate (S1P), increased alkaline phosphatase and mineralized nodules in osteoblast cultures. An S1P1,3 receptor antagonist inhibited these responses. Osteoblasts derived from mice with Ctsk-deficient osteoclasts had an increased RANKL/OPG ratio, providing a positive feedback loop that increased the number of osteoclasts. Our data provide genetic evidence that deletion of CTSK in osteoclasts enhances bone formation in vivo by increasing the generation of osteoclast-derived S1P.
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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".