Induction of Osteogenesis in Mesenchymal Stem Cells by Activated Monocytes/Macrophages Depends on Oncostatin M Signaling
Bibliographic record
Abstract
Bone resorption by osteoclasts and bone formation by osteoblasts are tightly coupled processes implicating factors in TNF, bone morphogenetic protein, and Wnt families. In osteoimmunology, macrophages were described as another critical cell population regulating bone formation by osteoblasts but the coupling factors were not identified. Using a high-throughput approach, we identified here Oncostatin M (OSM), a cytokine of the IL-6 family, as a major coupling factor produced by activated circulating CD14+ or bone marrow CD11b+ monocytes/macrophages that induce osteoblast differentiation and matrix mineralization from human mesenchymal stem cells while inhibiting adipogenesis. Upon activation of toll-like receptors (TLRs) by lipopolysaccharide or endogenous ligands, OSM was produced in classically activated inflammatory M1 and not M2 macrophages, through a cyclooxygenase-2 and prostaglandin-E2 regulatory loop. Stimulation of osteogenesis by activated monocytes/macrophages was prevented using neutralizing antibodies or siRNA to OSM, OSM receptor subunits gp130 and OSMR, or to the downstream transcription factor STAT3. The induced osteoblast differentiation program culminated with enhanced expression of CCAAT-enhancer-binding protein δ, Cbfa1, and alkaline phosphatase. Overexpression of OSM in the tibia of mice has led to new bone apposition with no sign of bone resorption. Two other cytokines have also a potent role in bone formation induced by monocytes/macrophages and activation of TLRs: IL-6 and leukemia inhibitory factor. We propose that during bone inflammation, infection, or injury, the IL-6 family signaling network activated by macrophages and TLR ligands stimulates bone formation that is largely uncoupled from bone resorption and is thus an important target for anabolic bone therapies.
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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) | 0.001 | 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".