Defective Osteoclastogenesis by IKKβ-null Precursors Is a Result of Receptor Activator of NF-κB Ligand (RANKL)-induced JNK-dependent Apoptosis and Impaired Differentiation
Bibliographic record
Abstract
It has been reported previously that inhibitory kappaB kinase (IKK) supports osteoclastogenesis through NF-kappaB-mediated prevention of apoptosis. This finding suggests that the ligand for receptor activator of NF-kappaB (RANKL), the master osteoclastogenic cytokine, induces apoptosis of osteoclast precursors (OCPs) in the absence of IKKbeta/NF-kappaB competency. To validate this hypothesis, we sought to determine the pro-apoptotic signaling factors induced by RANKL in IKKbeta-null osteoclast OCPs and to rescue osteoclast differentiation in the absence of IKKbeta through their inhibition. To accomplish this, we generated mice that lack IKKbeta in multiple hematopoietic lineages, including OCPs. We found that these mice possess both in vitro and in vivo defects in osteoclast generation, in concurrence with previous reports, and that this defect is a result of susceptibility to RANKL-mediated apoptosis as a result of gain-of-function of JNK activation. We demonstrate that differentiation of OCPs depends on IKKbeta because reduced IKKbeta mRNA expression correlates with impaired induction of osteoclast differentiation markers in response to RANKL stimulation. We further show that fine-tuned inhibition of JNK activation in these cells inhibits RANKL-induced apoptosis and restores the ability of IKKbeta-null OCPs to become mature osteoclasts. Our data highlight the pro-osteoclastogenic and anti-apoptotic roles of IKKbeta in OCPs and identify a pro-apoptotic mechanism activated within the RANK signalosome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".