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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".