Strongylophorine-26, a Rho-dependent inhibitor of tumor cell invasion that reduces actin stress fibers and induces nonpolarized lamellipodial extensions
Bibliographic record
Abstract
Strongylophorine-26, a new meroditerpenoid, was recently identified as an inhibitor of cancer cell invasion. This study was undertaken to characterize its mechanism of action. We find that strongylophorine-26 inhibits the motility of MDA-MB-231 breast carcinoma cells on a plastic surface. Upon addition of strongylophorine-26, rapid cell contraction and depolarization occurred, followed by spreading and flattening of the entire cell. Treated cells exhibited increased membrane ruffling throughout and extended lamellipodia in all directions. Strongylophorine-26 induced a decrease in actin stress fibers, a dramatic increase in the size and number of focal adhesions, and the appearance of a dense meshwork of actin filaments around the cell periphery. Strongylophorine-26 caused a transient activation of the small GTPase Rho and treatment with the Rho inhibitor C3 exoenzyme abrogated the anti-invasive activity of strongylophorine-26. These effects are distinct from those of many motility and angiogenesis inhibitors that seem to act by a common mechanism involving the induction of actin stress fibers. This difference in mechanism of action sets strongylophorine-26 apart as an experimental anticancer agent and indicates that pharmacologic inhibition of cell migration may be achieved by mechanisms not involving the stabilization of actin stress fibers.
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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".