Moesin controls cell–cell fusion and osteoclast function
Bibliographic record
Abstract
Cell-cell fusion is an evolutionarily conserved process that is essential for many functions, including the formation of bone-resorbing multinucleated osteoclasts. Osteoclast multinucleation involves dynamic interactions between the actin cytoskeleton and the plasma membrane that are still poorly characterized. We found that moesin, a cytoskeletal linker protein member of the Ezrin, radixin, and moesin (ERM) protein family, plays a critical role in both osteoclast fusion and function. Moesin inhibition favors osteoclast multinucleation as well as HIV-1- and inflammation-induced cell fusion. Accordingly, moesin depletion decreases membrane-to-cortex attachment and enhances the formation of tunneling nanotubes, F-actin-based intercellular bridges triggering cell-cell fusion. In addition, moesin regulates the formation of the sealing zone, a key structure determining osteoclast bone resorption area, and thus controls bone degradation via a β3-integrin/RhoA/SLK pathway. Finally, moesin-deficient mice have reduced bone density and increased osteoclast abundance and activity. These findings provide a better understanding of cell-cell fusion and osteoclast biology, opening new opportunities to specifically target osteoclasts in bone disease therapy.
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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 | 0.001 | 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".