IL-33 Shifts the Balance from Osteoclast to Alternatively Activated Macrophage Differentiation and Protects from TNF-α–Mediated Bone Loss
Bibliographic record
Abstract
IL-33 is a new member of the IL-1 family, which plays a crucial role in inflammatory response, enhancing the differentiation of dendritic cells and alternatively activated macrophages (AAM). Based on the evidence of IL-33 expression in bone, we hypothesized that IL-33 may shift the balance from osteoclast to AAM differentiation and protect from inflammatory bone loss. Using transgenic mice overexpressing human TNF, which develop spontaneous joint inflammation and cartilage destruction, we show that administration of IL-33 or an IL-33R (ST2L) agonistic Ab inhibited cartilage destruction, systemic bone loss, and osteoclast differentiation. Reconstitution of irradiated hTNFtg mice with ST2(-/-) bone marrow led to more bone loss compared with the chimeras with ST2(+/+) bone marrow, demonstrating an important endogenous role of the IL-33/ST2L pathway in bone turnover. The protective effect of IL-33 on bone was accompanied by a significant increase of antiosteoclastogenic cytokines (GM-CSF, IL-4, and IFN-γ) in the serum. In vitro IL-33 directly inhibits mouse and human M-CSF/receptor activator for NF-κB ligand-driven osteoclast differentiation. IL-33 acts directly on murine osteoclast precursors, shifting their differentiation toward CD206(+) AAMs via GM-CSF in an autocrine fashion. Thus, we show in this study that IL-33 is an important bone-protecting cytokine and may be of therapeutic benefit in treating bone resorption.
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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) | 0.001 | 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".