Identification of Radil as a Ras binding partner and putative activator
Bibliographic record
Abstract
Ras genes are among the most frequently mutated oncogenes in human malignancies. To date, there are no successful anticancer drugs in the clinic that target Ras proteins or their pathways. Therefore, it is imperative to identify and characterize new components that regulate Ras activity or mediate its downstream signaling. To this end, we used a combination of affinity-pulldown and mass spectrometry to search for proteins that are physically associated with KRas. One of the top hits was Radil, a gene product with a Ras-association domain. Radil is known to be a downstream effector of Rap1, inhibiting RhoA signaling to regulate cell adhesion and migration. We demonstrate that Radil interacted with all three isoforms of Ras including HRas, NRas, and KRas, although it exhibited the strongest interaction with KRas. Moreover, Radil interacts with GTP-bound Ras more efficiently, suggesting a possibility that Radil may be involved in Ras activation. Supporting this, ectopic expression of Radil led to transient activation of mitogen-activated protein kinase kinase and extracellular signal-regulated kinase; Radil knockdown resulted in weakened activation of Ras downstream signaling components, which was coupled with decreased cell proliferation and invasion, and reduced expression of mesenchymal cell markers. Moreover, Radil knockdown greatly reduced the number of adhesion foci and depolymerized actin filaments, molecular processes that facilitate cancer cell migration. Taken together, our present studies strongly suggest that Radil is an important player for regulating Ras signaling, cell adhesion, and the epithelial-mesenchymal transition and may provide new directions for Ras-related anticancer drug development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".