Rho-ROCK signaling differentially regulates chondrocyte spreading on fibronectin and bone sialoprotein
Bibliographic record
Abstract
The mammalian growth plate is a dynamic structure rich in extracellular matrix (ECM). Interactions of growth plate chondrocytes with ECM proteins regulate cell behavior. In this study, we compared chondrocyte adhesion and spreading dynamics on fibronectin (FN) and bone sialoprotein (BSP). Chondrocyte adhesion and spreading were also compared with fibroblasts to analyze potential cell-type-specific effects. Chondrocyte adhesion to BSP is independent of posttranslational modifications but is dependent on the RGD sequence in BSP. Whereas chondrocytes and fibroblasts adhered at similar levels on FN and BSP, cells displayed more actin-dependent spread on FN despite a 16x molar excess of BSP adsorbed to plastic. To identify intracellular mediators responsible for this difference in spreading, we investigated focal adhesion kinase (FAK)-Src and Rho-Rho kinase (ROCK) signaling. Although activated FAK localized to the vertices of adhered chondrocytes, levels of FAK activation did not correlate with the extent of spreading. Furthermore, Src inhibition reduced chondrocyte spreading on both FN and BSP, suggesting that FAK-Src signaling is not responsible for less cell spreading on BSP. In contrast, inhibition of Rho and ROCK in chondrocytes increased cell spreading on BSP and membrane protrusiveness on FN but did not affect cell adhesion. In fibroblasts, Rho inhibition increased fibroblast spreading on BSP while ROCK inhibition changed membrane protrusiveness of FN and BSP. In summary, we identify a novel role for Rho-ROCK signaling in regulating chondrocyte spreading and demonstrate both cell- and matrix molecule-specific mechanisms controlling cell spreading.
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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".