Convergent signaling by acidosis and receptor activator of NF-κB ligand (RANKL) on the calcium/calcineurin/NFAT pathway in osteoclasts
Bibliographic record
Abstract
Systemic acidosis has detrimental effects on the skeleton, and local acidosis coincides with bone destruction in inflammatory and metastatic diseases. Acidification dramatically enhances osteoclastic resorption, although the underlying mechanism has remained elusive. We investigated the effect of acidosis on the osteoclastogenic transcription factor NFATc1, which upon dephosphorylation translocates from the cytoplasm to nuclei. Lowering extracellular pH dramatically increased accumulation of NFATc1 in nuclei of rat and rabbit osteoclasts to levels comparable with those induced by the proresorptive cytokine receptor activator of NF-kappaB ligand (RANKL). Activation of NFATc1 by RANKL was mediated by means of prolonged stimulation of the Ca2+/calmodulin-dependent protein phosphatase, calcineurin. In contrast, NFATc1 activation by acidosis involved stimulation of calcineurin and suppression of NFATc1 inactivation. Acidosis, like RANKL, induced transient elevation of cytosolic free Ca2+ concentration ([Ca2+]i), which persisted in Ca2+-free media and was abolished by inhibition of phospholipase C or depletion of intracellular Ca2+ stores. Real-time-PCR of osteoclast-like cells generated from RAW 264.7 cells revealed high levels of expression of ovarian cancer G protein-coupled receptor 1, which links extracellular acidification to elevation of [Ca2+]i. In addition, the calcineurin inhibitor cyclosporin A suppressed the stimulatory effect of acidification on resorption, implicating NFAT in mediating the actions of acidosis on osteoclast activity. In summary, acidification and RANKL induce signals in osteoclasts that converge on the Ca2+/calcineurin/NFAT pathway. Acidosis acts directly on osteoclasts to activate NFATc1 and stimulate 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".