Inhibition of Rho-Kinase Affects Astrocytoma Morphology, Motility, and Invasion through Activation of Rac1
Bibliographic record
Abstract
Malignant astrocytomas are highly invasive neoplasms infiltrating diffusely into regions of normal brain. Whereas the molecular and cellular mechanisms governing astrocytoma invasion remain poorly understood, evidence in other cell systems has implicated a role for the Rho-GTPases in cell motility and invasion. Here, we examine how the inhibition or activation of Rho-kinase (ROCK) affects astrocytoma morphology, motility, and invasion. ROCK was inhibited in astrocytoma cells by using 5 to 100 mumol/L of Y27632 or by expressing the dominant-negative ROCK mutant, RB/PH TT. ROCK activation was achieved by expressing a constitutively active mutant, CAT. ROCK inhibition led to morphologic and cytoskeletal alterations characterized by an increase in the number and length of cell processes, increased membrane ruffling, and collapse of actin stress fibers. Using two-dimensional radial migration and Boyden chamber assays, we show that astrocytoma migration and invasion were increased at least 2-fold by ROCK inhibition. On the contrary, ROCK activation significantly inhibited migration and invasion of astrocytoma cells. Furthermore, using a Rac-GTP pull-down assay, we show that Rac1 is activated as a consequence of ROCK inhibition. Finally, we show that treatment of astrocytoma cells with small interfering RNA duplexes specific for Rac1-reversed stellation, prevented membrane ruffling formation and abrogated the increased motility observed following treatment with Y27632. Our data show that Rac1 plays a major role in astrocytoma morphology, motility, and invasion. These findings warrant further investigation to determine precisely how the modulation of Rac1 and ROCK can be exploited to inhibit glioma invasion.
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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".