Localized Rho GTPase Activation Regulates RNA Dynamics and Compartmentalization in Tumor Cell Protrusions
Bibliographic record
Abstract
mRNA trafficking and local protein translation are associated with protrusive cellular domains, such as neuronal growth cones, and deregulated control of protein translation is associated with tumor malignancy. We show here that activated RhoA, but not Rac1, is enriched in pseudopodia of MSV-MDCK-INV tumor cells and that Rho, Rho kinase (ROCK), and myosin II regulate the microtubule-independent targeting of RNA to these tumor cell domains. ROCK inhibition does not affect pseudopodial actin turnover but significantly reduces the dynamics of pseudopodial RNA turnover. Gene array analysis shows that 7.3% of the total genes analyzed exhibited a greater than 1.6-fold difference between the pseudopod and cell body fractions. Of these, only 13.2% (261 genes) are enriched in pseudopodia, suggesting that only a limited number of total cellular mRNAs are enriched in tumor cell protrusions. Comparison of the tumor pseudopod mRNA cohort and a cohort of mRNAs enriched in neuronal processes identified tumor pseudopod-specific signaling networks that were defined by expression of M-Ras and the Shp2 protein phosphatase. Pseudopod expression of M-Ras and Shp2 mRNA were diminished by ROCK inhibition linking pseudopodial Rho/ROCK activation to the localized expression of specific mRNAs. Pseudopodial enrichment for mRNAs involved in protein translation and signaling suggests that local mRNA translation regulates pseudopodial expression of less stable signaling molecules as well as the cellular machinery to translate these mRNAs. Pseudopodial Rho/ROCK activation may impact on tumor cell migration and metastasis by stimulating the pseudopodial translocation of mRNAs and thereby regulating the expression of local signaling cascades.
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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".