Adenosine A2A Receptor Ligation Inhibits Osteoclast Formation
Bibliographic record
Abstract
Adenosine is generated in increased concentrations at sites of injury/hypoxia and mediates a variety of physiological and pharmacological effects via G protein-coupled receptors (A(1), A(2A), A(2B), and A(3)). Because all adenosine receptors are expressed on osteoclasts, we determined the role of A(2A) receptor in the regulation of osteoclast differentiation. Differentiation and bone resorption were studied as the macrophage colony-stimulating factor-1-receptor activator of NF-κB ligand formation of multinucleated tartrate-resistant acid phosphatase (TRAP)-positive cells from primary murine bone marrow-derived precursors. A(2A) receptor and osteoclast marker expression levels were studied by RT-PCR. Cytokine secretion was assayed by enzyme-linked immunosorbent assay. In vivo examination of A(2A) knockout (KO)/control bones was determined by TRAP staining, micro-computed tomography, and electron microscopy. The A(2A) receptor agonist, CGS21680, inhibited osteoclast differentiation and function (half maximal inhibitory concentration, 50 nmol/L), increased the percentage of immature osteoclast precursors, and decreased IL-1β and tumor necrosis factor-α secretion, an effect that was reversed by the A(2A) antagonist, ZM241385. Cathepsin K and osteopontin mRNA expression increased in control and ZM241385-pretreated osteoclasts, and this was blocked by CGS21680. Micro-computed tomography of A(2A)KO mouse femurs showed a significantly decreased bone volume/trabecular bone volume ratio, decreased trabecular number, and increased trabecular space. A(2A)KO femurs showed an increased TRAP-positive osteoclast. Electron microscopy in A(2A)KO femurs showed marked osteoclast membrane folding and increased bone resorption. Thus, adenosine, acting via the A(2A) receptor, inhibits macrophage colony-stimulating factor-1-receptor activator of NF-κB ligand-stimulated osteoclast differentiation and may regulate bone turnover under conditions in which adenosine levels are elevated.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".