Fibronectin inhibits osteoclastogenesis while enhancing osteoclast activity via nitric oxide and interleukin‐1β‐mediated signaling pathways
Bibliographic record
Abstract
Osteoclasts are bone-resorbing cells formed by fusion of mononuclear precursors. The matrix proteins, fibronectin (FN), vitronectin (VN), and osteopontin (OPN) are implicated in joint destruction and interact with osteoclasts mainly through integrins. To assess the effects of these matrix proteins on osteoclast formation and activity, we used RAW 264.7 (RAW) cells and mouse splenocytes differentiated into osteoclasts on tissue culture polystyrene (TCP) or osteologic™ slides pre-coated with 0.01-20 µg/ml FN, VN, and OPN. At 96 h, osteoclast number and multinucleation were decreased on VN and FN compared to OPN and TCP in both RAW and splenocytes cell cultures. When early differentiation was assessed, VN but not FN decreased cytoplasmic tartrate-resistant acid phosphatase activity and pre-osteoclast number at 48 h. OPN had the opposite effect to FN on osteoclast formation. When RAW cells were differentiated on OPN and treated by FN and OPN, osteoclast number only in the FN treated group was 40-60% lower than the control, while the total number of nuclei was unchanged, suggesting that FN delays osteoclast fusion. In contrast to its inhibitory effect on osteoclastogenesis, FN increased resorption by increasing both osteoclast activity and the percentage of resorbing osteoclasts. This was accompanied by an increase in nitric oxide (NO) levels and interleukin-1β (IL-1β). IL-1β production was inhibited using the NO-synthase inhibitor only on FN indicating a FN-specific cross-talk between NO and IL-1β signaling pathways. We conclude that FN upregulates osteoclast activity despite inhibiting osteoclast formation and that these effects involve NO and IL-1β signaling.
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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".