The Role of Microtubules and Their Dynamics in Cell Migration
Bibliographic record
Abstract
Although microtubules have long been implicated in cell locomotion, the mechanism of their involvement remains controversial. Most studies have concluded that microtubules play a positive role by regulating actin polymerization, transporting membrane vesicles to the leading edge, and/or facilitating the turnover of adhesion plaques. Here we used wild-type and mutant CHO cell lines with alterations in tubulin to demonstrate that microtubules can also act to restrain cell motility. Tubulin mutations or low concentrations of drugs that suppress microtubule dynamics without affecting the amount of microtubule polymer inhibited the rate of migration by preventing microtubule reorganization in the trailing portion of the cells where the more dynamic microtubules are normally found. Under these conditions, cells along the edge of a wound still extended lamellipodia and elongated toward the wound but were inhibited in their ability to retract their tails, thus retarding forward progress. The idea that microtubules normally act to restrain cell locomotion was confirmed by treating cells with high concentrations of nocodazole to depolymerize the microtubule network. In the absence of microtubules, wild-type CHO and HeLa cells could still move at near normal speeds, but the movement became more random. We conclude that microtubules act both to restrain cell movement and to establish directionality.
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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 | 0.001 | 0.001 |
| 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".