Fibrillin-1 directly regulates osteoclast formation and function by a dual mechanism
Bibliographic record
Abstract
Mutations in the fibrillin-1 gene give rise to a number of heritable disorders, which are all characterized by various malformations of bone as well as manifestations in other tissues. However, the role of fibrillin-1 in the development and homeostasis of bone is not well understood. Here, we examined the role of fibrillin-1 in regulating osteoclast differentiation from primary bone-marrow-derived precursors and monocytic RAW 264.7 cells. The soluble N-terminal half of fibrillin-1 (rFBN1-N) strongly inhibited osteoclastogenesis, whereas the C-terminal half (rFBN1-C) did not. By contrast, when rFBN1-N was immobilized on calcium phosphate, it did not affect osteoclastogenesis but modulated osteoclast resorptive activity, which was evident by a larger number of smaller resorption pits. Using a panel of recombinant sub-fragments spanning rFBN1-N, we localized an osteoclast inhibitory activity to the 63 kDa subfragment rF23 comprising the N-terminal region of fibrillin-1. Osteoclastic resorption led to the generation of small fibrillin-1 fragments that were similar to those identified in human vertebral bone extracts. rF23, but not rFBN1-N, was found to inhibit the expression of cathepsin K, matrix metalloproteinase 9 and Dcstamp in differentiating osteoclasts. rFBN1-N, but not rF23, exhibited interaction with RANKL. Excess RANKL rescued the inhibition of osteoclastogenesis by rFBN1-N. By contrast, rF23 disrupted RANKL-induced Ca(2+) signaling and activation of transcription factor NFATc1. These studies highlight a direct dual inhibitory role of N-terminal fibrillin-1 fragments in osteoclastogenesis, the sequestration of RANKL and the inhibition of NFATc1 signaling, demonstrating that osteoclastic degradation of fibrillin-1 provides a potent negative feedback that limits osteoclast formation and function.
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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) | 0.001 | 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".