Bone matrix proteins and extracellular acidification: Potential co‐regulators of osteoclast morphology
Bibliographic record
Abstract
Osteoclasts are signaled by the bone matrix proteins fibronectin (FN), vitronectin (VN), and osteopontin (OPN) via integrins. To perform their resorptive function, osteoclasts cycle between compact (polarized), spread (non-resorbing) and migratory morphologies. Here we investigate the effects of matrix proteins on osteoclast morphology and how those effects are mediated using RAW 264.7 cells differentiated into osteoclasts on FN, VN, and OPN-coated culture dishes. After 96 h, 80% of osteoclasts on FN were compact while 25% and 16% on VN were in compact and migratory states respectively. In contrast, OPN induced osteoclast spreading. Furthermore, osteoclasts formed on VN and FN were two- to fourfold smaller than those formed on OPN in the 21-30 nuclei/osteoclast group. These effects were not due to defects in cytoskeletal reorganization of osteoclasts on VN and FN, demonstrated by the ability of these cells to spread in response to 35 ng/ml macrophage colony stimulating factor (M-CSF). Conversely, osteoclasts on OPN failed to spread when induced by M-CSF. Moreover, the extracellular pH on FN and VN (7.25 and 7.3, respectively) was significantly lower than that on OPN (∼7.4). We further investigated the role of extracellular pH and found that at pH 7.5 the duration of an osteoclast's compact phase was 25.6 min and that of the spread phase was 62.5 min. Reducing the pH to 7.0 increased the frequency of osteoclast cycling by threefold. These results show that matrix proteins play a role in regulating osteoclast morphology, possibly via altering extracellular and intracellular pH.
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) | 0.000 | 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".