Transglutaminase activity regulates differentiation, migration and fusion of osteoclasts via affecting actin dynamics
Bibliographic record
Abstract
Osteoclasts, bone resorbing cells, derive from monocyte/macrophage cell lineage. Increased osteoclast activity is responsible for bone destruction in diseases such as osteoporosis, periodontitis and rheumatoid arthritis. Transglutaminases (TGs), protein crosslinking enzymes, were recently found involved in osteoclastogenesis in vivo, however their mechanisms of action have remained unknown. In this study, we have investigated the role of TG activity in osteoclastogenesis in vitro using four TG inhibitors, NC9, Z006, T101, and monodansyl cadaverine. Our results showed that all TG inhibitors were capable of blocking the entire osteoclastogenesis process. The most potent of the inhibitors, NC9 when added to cultures at different phases of osteoclastogenesis, inhibited differentiation, migration, and fusion of pre-osteoclasts as well as resorption activity of mature osteoclasts. Further investigation into the mechanisms revealed that NC9 increased RhoA levels and blocked podosome belt formation suggesting that TG activity regulates actin dynamics in pre-osteoclasts. The inhibitory effect of NC9 on osteoclastogenesis as well as podosome belt formation was completely reversed with a Rho-family inhibitor Exoenzyme C3. Microtubule architecture, acetylation, and detyrosination of α-tubulin were not affected. Finally, we demonstrated that macrophages and osteoclasts expressed mRNA of three TGs:TG1, TG2, and Factor XIII-A which were all differentially regulated in these cells during differentiation. Immunofluoresence microscopic analysis showed that all three enzymes co-localized to podosomes in osteoclasts. Taken together, our data suggests that TG activity regulates differentiation, migration and fusion of osteoclasts via affecting actin dynamics and that this may involve contribution from all three TG enzymes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".