Moesin controls cell-cell fusion and osteoclast function
Bibliographic record
Abstract
ABSTRACT Cell-cell fusion is an evolutionarily conserved process that is essential for many functions, including fertilisation and the formation of placenta, muscle and osteoclasts, multinucleated cells that are unique in their ability to resorb bone. The mechanisms of osteoclast multinucleation involve dynamic interactions between the actin cytoskeleton and the plasma membrane that are still poorly characterized. Here, we found that moesin, a cytoskeletal linker protein member of the Ezrin/Radixin/Moesin (ERM) protein family, is activated during osteoclast maturation and plays an instrumental role in both osteoclast fusion and function. In mouse and human osteoclast precursors, moesin inhibition favors their ability to fuse into multinucleated osteoclasts. Accordingly, we demonstrated that moesin depletion decreases membrane-to-cortex attachment and enhances the formation of tunneling nanotubes (TNTs), F-actin-based intercellular bridges that we reveal here to trigger cell-cell fusion. Moesin also controls HIV-1- and inflammation-induced cell fusion. In addition, moesin regulates the formation of the sealing zone, the adhesive structure determining osteoclast bone resorption area, and thus controls bone degradation, via a β3-integrin/RhoA/SLK pathway. Supporting our results, moesin - deficient mice present a reduced density of trabecular bones and increased osteoclast abundance and activity. These findings provide a better understanding of the regulation of cell-cell fusion and osteoclast biology, opening new opportunities to specifically target osteoclast activity 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".