Molecular Mechanisms of Cell Migration Inhibition by Synthetic Triterpenoids
Bibliographic record
Abstract
Cell migration is an important mediator of cancer metastasis and invasion, which is responsible for 90% of cancer-related premature deaths in Canada. Synthetic triterpenoids are a class of promising anti-cancer compounds that have shown considerable efficacy in targeting various cellular functions including apoptosis, growth, inflammation and cytoprotection in both cell culture and animal tumor models. However, their effect on cell migration, an important event in metastasis, remains poorly understood. This thesis focuses on deciphering the molecular mechanisms whereby the synthetic triterpenoids affect cell migration. I observed that the imidazolide and methyl ester derivatives of the synthetic triterpenoid, 2-cyano-3,12-dioxooleana-1,9-dien-28-oic aic acid (CDDO-Im and CDDO-Me), inhibit cell migration by disrupting microtubule dynamics. In addition, I found that these triterpenoids disrupt cell polarity by displacing proteins at the leading edge of migrating cells. Furthermore, using a two-pronged proteomic approach involving protein arrays and mass spectrometry, I identified numerous triterpenoid-binding targets involved in actin polymerization and focal adhesion maintenance. My data further revealed that triterpenoids inhibit branched actin polymerization by targeting Arp3 in the Arp2/3 complex and target GSK3b activity to alter focal adhesion sizes. Collectively, my studies provided novel insights on the underlying molecular mechanisms by which triterpenoids act to affect cell migration. This knowledge will be important for developing a more efficacious and specific therapeutic triterpenoid compound that targets cancer metastasis.
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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 | 0.001 |
| 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".