Cdc42EP5/BORG3 modulates SEPT9 to promote actomyosin function and melanoma invasion and metastasis
Bibliographic record
Abstract
Abstract Fast amoeboid migration in the invasive fronts of melanoma is controlled by high levels of actomyosin contractility, which underlie its highly metastatic potential. How this migratory behaviour is coupled to other cytoskeletal components is poorly understood. Septins are increasingly recognized as novel cytoskeletal components, but details on their regulation and contribution to cancer migration and metastasis are lacking. Here, we show that the septin regulator Cdc42EP5 is consistently required for melanoma cells to migrate and invade into collagen-rich matrices, and to locally invade and disseminate in vivo . Cdc42EP5 associates with actin structures leading to increased actomyosin contractility and amoeboid migration. Cdc42EP5 effects these functions through SEPT9-dependent F-actin crosslinking, which enables the generation of F-actin bundles required for the sustained stabilisation of highly contractile actomyosin structures. This study provides evidence for Cdc42EP5 as a regulator of cancer cell motility that coordinates actin and septin networks. It also describes a unique role for SEPT9 in invasion and metastasis, and illustrates a mechanism that regulates its function in melanoma.
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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) | 0.003 | 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".