Molecular characterization of the effects of Y‐27632
Bibliographic record
Abstract
Many key cellular functions, such as cell motility and cellular differentiation are mediated by Rho-associated protein kinases (ROCKs). Numerous studies have been conducted to examine the ROCK signal transduction pathways involved in these motile and contractile events with the aid of pharmacological inhibitors such as Y-27632. However the molecular mechanism of action of Y-27632 has not been fully defined. To assess the relative contribution of these Rho effectors to the effects of Y-27632, we compared the cytoskeletal phenotype, wound healing and neurite outgrowth in cells treated with Y-27632 or subjected to knockdown with ROCK-I, ROCK-II or PRK-2- specific siRNAs. Reduction of ROCK-I enhances the formation of thin actin-rich membrane extensions, a phenotype that closely resembles the effect of Y-27632. Knockdown of ROCK II or PRK-2, leads to the formation of disc-like extensions and thick actin bundles, respectively. The effect of ROCK-I knockdown also mimicked the effect of Y-27632 on wound closer rates. ROCK-I knockdown and Y-27632 enhanced wound closure rates, while ROCK-II and PRK-2 were not appreciably different from control cells. In neurite outgrowth assays, knockdown of ROCK-I, ROCK-II or PRK-2 enhances neurite lengths, however no individual knockdown stimulated neurite outgrowth as robustly as Y-27632. We conclude that several kinases contribute to the global effect of Y-27632 on cellular responses.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".