Regulation of bone formation by osteoclasts involves Wnt/BMP signaling and the chemokine sphingosine-1-phosphate
Bibliographic record
Abstract
Under most conditions, resorbed bone is nearly precisely replaced in location and amount by new bone. Thus, it has long been recognized that bone loss through osteoclast-mediated bone resorption and bone replacement through osteoblast-mediated bone formation are tightly coupled processes. Abundant data conclusively demonstrate that osteoblasts direct osteoclast differentiation. Key questions remain, however, as to how osteoblasts are recruited to the resorption site and how the amount of bone produced is so precisely controlled. We hypothesized that osteoclasts play a crucial role in the promotion of bone formation. We found that osteoclast conditioned medium stimulates human mesenchymal stem (hMS) cell migration and differentiation toward the osteoblast lineage as measured by mineralized nodule formation in vitro. We identified candidate osteoclast-derived coupling factors using the Affymetrix microarray. We observed significant induction of sphingosine kinase 1 (SPHK1), which catalyzes the phosphorylation of sphingosine to form sphingosine 1-phosphate (S1P), in mature multinucleated osteoclasts as compared with preosteoclasts. S1P induces osteoblast precursor recruitment and promotes mature cell survival. Wnt10b and BMP6 also were significantly increased in mature osteoclasts, whereas sclerostin levels decreased during differentiation. Stimulation of hMS cell nodule formation by osteoclast conditioned media was attenuated by the Wnt antagonist Dkk1, a BMP6-neutralizing antibody, and by a S1P antagonist. BMP6 antibodies and the S1P antagonist, but not Dkk1, reduced osteoclast conditioned media-induced hMS chemokinesis. In summary, our findings indicate that osteoclasts may recruit osteoprogenitors to the site of bone remodeling through SIP and BMP6 and stimulate bone formation through increased activation of Wnt/BMP pathways.
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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) | 0.002 | 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".