Microtubule Dynamics Control Tail Retraction in Migrating Vascular Endothelial Cells
Bibliographic record
Abstract
Drugs that target microtubules are potent inhibitors of angiogenesis, but their mechanism of action is not well understood. To explore this, we treated human umbilical vein endothelial cells with paclitaxel, vinblastine, and colchicine and measured the effects on microtubule dynamics and cell motility. In general, lower drug concentrations suppressed microtubule dynamics and inhibited cell migration whereas higher concentrations were needed to inhibit cell division; however, surprisingly, large drug-dependent differences were seen in the relative concentrations needed to inhibit these two processes. Suppression of microtubule dynamics did not significantly affect excursions of lamellipodia away from the nucleus or prevent cells from elongating; but, it did inhibit retraction of the trailing edges that are normally enriched in dynamic microtubules, thereby limiting cell locomotion. Complete removal of microtubules with a high vinblastine concentration caused a loss of polarity that resulted in roundish, rather than elongated, cells, rapid but nondirectional membrane activity, and little cell movement. The results are consistent with a model in which more static microtubules stabilize the leading edge of migrating cells, whereas more dynamic microtubules locate to the rear where they can remodel and allow tail retraction. Suppressing microtubule dynamics interferes with tail retraction, but removal of microtubules destroys the asymmetry needed for cell elongation and directional motility. The prediction that suppressing microtubule dynamics might be sufficient to prevent angiogenesis was supported by showing that low concentrations of paclitaxel could prevent the formation of capillary-like structures in an in vitro tube formation assay.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".