Surface RANKL of Toll-like receptor 4–stimulated human neutrophils activates osteoclastic bone resorption
Bibliographic record
Abstract
Inflammatory bone loss in septic and inflammatory conditions is due to increased activity of osteoclasts that requires receptor activator of NF-kappa B-ligand (RANKL). Neutrophils are the predominant infiltrating cells in these conditions. Although disease severity is linked to neutrophils, their role in evolution of bony lesions is not clear. We show that lipopolysaccharide (LPS), a toll-like receptor 4 ligand, up-regulated the expression of membrane RANKL in human blood neutrophils and murine air pouch-derived neutrophils. LPS-activated human and murine neutrophils, cocultured with human monocyte-derived osteoclasts and RAW 264.7 cells, respectively, stimulated bone resorption. Transfection of PLB-985 neutrophil-like cells with RANKL antisense RNA reduced osteoclastogenesis. Synovial fluid neutrophils of patients with exacerbation of rheumatoid arthritis strongly expressed RANKL and activated osteoclastogenesis in coculture systems. Osteoprotegerin, the RANKL decoy receptor, suppressed osteoclast activation by neutrophils from these different sources. Moreover, direct cell-cell contact between neutrophils and osteoclasts was visualized by confocal laser microscopy. Activation of neutrophil membrane-bound RANKL was linked to tyrosine phosphorylation of Src-homology domain-containing cytosolic phosphatase 1 with concomitant down-regulation of cytokine production. The demonstration of these novel functions of neutrophils highlights their potential role in osteoimmunology and in therapeutics of inflammatory bone disease.
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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) | 0.004 | 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".