TLR5, a novel mediator of innate immunity‐induced osteoclastogenesis and bone loss
Bibliographic record
Abstract
Accumulating evidence points to the importance of the innate immune system in inflammation-induced bone loss in infectious and autoimmune diseases. TLRs are well known for being activated by ligands expressed by bacteria, viruses, and fungi. Recent findings indicate that also endogenous ligands in inflammatory processes are important, one being a TLR5 agonist present in synovial fluid from patients with rheumatoid arthritis (RA). We found that activation of TLR5 by its specific ligand, flagellin, caused robust osteoclast formation and bone loss in cultured mouse neonatal parietal bones dependent on increased receptor activator of NF-κB ligand (RANKL):osteoprotegerin ratio, with half-maximal stimulation at 0.01 μg/ml. Flagellin enhanced Rankl mRNA in isolated osteoblasts by a myeloid differentiation primary response gene 88 and NF-κB-dependent mechanism. Injection of flagellin locally over skull bones in 5-wk-old mice resulted in increased mRNA expression of Rankl and osteoclastic genes, robust osteoclast formation, and bone loss. The effects in vitro and in vivo were absent in Tlr5(-/-) mice. These data show that TLR5 is a novel activator of RANKL and osteoclast formation and, therefore, a potential key factor in inflammation-induced bone erosions in diseases like RA, reactive arthritis, and periodontitis. TLR5 might be a promising novel treatment target for prevention of inflammatory bone loss.
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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 | 0.000 |
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 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".