MCP-1 Is Induced by Receptor Activator of Nuclear Factor-κB Ligand, Promotes Human Osteoclast Fusion, and Rescues Granulocyte Macrophage Colony-stimulating Factor Suppression of Osteoclast Formation
Bibliographic record
Abstract
Human osteoclast formation from monocyte precursors under the action of receptor activator of nuclear factor-kappaB ligand (RANKL) was suppressed by granulocyte macrophage colony-stimulating factor (GM-CSF), with down-regulation of critical osteoclast-related nuclear factors. GM-CSF in the presence of RANKL and macrophage colony-stimulating factor resulted in mononuclear cells that were negative for tartrate-resistant acid phosphatase (TRAP) and negative for bone resorption. CD1a, a dendritic cell marker, was expressed in GM-CSF, RANKL, and macrophage colony-stimulating factor-treated cells and absent in osteoclasts. Microarray showed that the CC chemokine, monocyte chemotactic protein 1 (MCP-1), was profoundly repressed by GM-CSF. Addition of MCP-1 reversed GM-CSF suppression of osteoclast formation, recovering the bone resorption phenotype. MCP-1 and chemokine RANTES (regulated on activation normal T cell expressed and secreted) permitted formation of TRAP-positive multinuclear cells in the absence of RANKL. However, these cells were negative for bone resorption. In the presence of RANKL, MCP-1 significantly increased the number of TRAP-positive multinuclear bone-resorbing osteoclasts (p = 0.008). When RANKL signaling through NFATc1 was blocked with cyclosporin A, both MCP-1 and RANTES expression was down-regulated. Furthermore, addition of MCP-1 and RANTES reversed the effects of cyclosporin A and recovered the TRAP-positive multinuclear cell phenotype. Our model suggests that RANKL-induced chemokines are involved in osteoclast differentiation at the stage of multinucleation of osteoclast precursors and provides a rationale for increased osteoclast activity in inflammatory conditions where chemokines are abundant.
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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".