Direct interaction between CEP85 and STIL mediates PLK4-driven directed cell migration
Bibliographic record
Abstract
PLK4 has emerged as a prime target for cancer therapeutics, and its overexpression is frequently observed in various types of human cancer. Recent studies have further revealed an unexpected oncogenic activity of PLK4 in regulating cancer cell migration and invasion. However, the molecular basis behind the role of PLK4 in these processes still remains only partly understood. Our previous work has demonstrated that an intact CEP85-STIL binding interface is necessary for robust PLK4 activation and centriole duplication. Here, we show that CEP85 and STIL are also required for directional cancer cell migration. Mutational and functional analyses reveal that the interactions between CEP85, STIL and PLK4 are essential for effective directional cell motility. Mechanistically, we show that PLK4 can drive the recruitment of CEP85 and STIL to the leading edge of cells to promote protrusive activity, and that downregulation of CEP85 and STIL leads to a reduction in ARP2 (also known as ACTR2) phosphorylation and reorganization of the actin cytoskeleton, which in turn impairs cell migration. Collectively, our studies provide molecular insight into the important role of the CEP85-STIL complex in modulating PLK4-driven cancer cell migration.This article has an associated First Person interview with the first author of the paper.
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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) | 0.002 | 0.001 |
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".