IL-1R/TLR2 through MyD88 Divergently Modulates Osteoclastogenesis through Regulation of Nuclear Factor of Activated T Cells c1 (NFATc1) and B Lymphocyte-induced Maturation Protein-1 (Blimp1)
Bibliographic record
Abstract
Toll-like receptors (TLR) and the receptor for interleukin-1 (IL-1R) signaling play an important role in bacteria-mediated bone loss diseases including periodontitis, rheumatoid arthritis, and osteomyelitis. Recent studies have shown that TLR ligands inhibit the receptor activator of NF-κB ligand (RANKL)-induced osteoclast differentiation from un-committed osteoclast precursors, whereas IL-1 potentiates RANKL-induced osteoclast formation. However, IL-1R and TLR belong to the same IL-1R/TLR superfamily, and activate similar intracellular signaling pathways. Here, we investigate the molecular mechanisms underlying the distinct effects of IL-1 and Porphyromonas gingivalis lipopolysaccharide (LPS-PG) on RANKL-induced osteoclast formation. Our results show that LPS-PG and IL-1 differentially regulate RANKL-induced activation of osteoclast genes encoding Car2, Ctsk, MMP9, and TRAP, as well as expression of NFATc1, a master transcription factor of osteoclastogenesis. Regulation of osteoclast genes and NFATc1 by LPS-PG and IL-1 is dependent on MyD88, an important signaling adaptor for both TLR and IL-1R family members. Furthermore, LPS-PG and IL-1 differentially regulate RANKL-costimulatory receptor OSCAR (osteoclast-associated receptor) expression and Ca(2+) oscillations induced by RANKL. Moreover, LPS-PG completely abrogates RANKL-induced gene expression of B lymphocyte-induced maturation protein-1 (Blimp1), a global transcriptional repressor of anti-osteoclastogenic genes encoding Bcl6, IRF8, and MafB. However, IL-1 enhances RANKL-induced blimp1 gene expression but suppresses the gene expression of bcl6, irf8, and mafb. Our study reveals the involvement of multiple signaling molecules in the differential regulation of RANKL-induced osteoclastogenesis by TLR2 and IL-1 signaling. Understanding the signaling cross-talk among TLR, IL-1R, and RANK is critical for identifying therapeutic strategies to control bacteria-mediated 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".