Immune Interactions with CD4+ T Cells Promote the Development of Functional Osteoclasts from Murine CD11c+ Dendritic Cells
Bibliographic record
Abstract
Dendritic cells (DC) are innate immune effectors and are critically involved in regulating T cell immunity. Osteoclasts (OC) are bone-resorbing cells derived from the monocyte/macrophage lineage in response to receptor activator of NF-kappaB ligand (RANKL). DC and T cells form aggregates in the inflammatory infiltrates at active disease sites in human and in experimental rheumatoid arthritis and periodontitis. We investigated whether DC interactions with T cells in the bone environment can support the development of functional OC. In the present study, we demonstrate that upon proper activation by microbial or protein Ags (namely Actinobacillus actinomycetemcomitans, bovine insulin, and outer membrane protein-1) and during immune interactions with CD4+ T cells in vitro, murine BM-derived and splenic CD11c+ DC (CD11b- F4/80- Ly-6C- CD31-) develop into TRAP+ CT-R+ cathepsin-k+ functional OC in a RANKL/RANK-dependent manner. Rescue and blocking experiments using CD11c+ DC derived from Csf-1(-/-) op/op mice show that M-CSF is required "before" developing such osteoclastogenic potential upstream of RANKL/RANK signaling, suggesting that immature CD11c+ DC can indeed act like OC precursors. In addition, these CD11c+ DC-derived OC are capable of inducing bone loss after adoptive transfer in vivo. These data suggest a direct contribution of DC during immune interactions with CD4+ T cells to inflammation-induced osteoclastogenesis. Therefore, our findings not only provide further evidence for DC plasticity, but also extend the current paradigm of osteoimmunology.
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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) | 0.002 | 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".