Collective effects of XMAP215, EB1, CLASP2, and MCAK lead to robust microtubule treadmilling
Bibliographic record
Abstract
Microtubule network remodeling is essential for fundamental cellular processes including cell division, differentiation, and motility. Microtubules are active biological polymers whose ends stochastically and independently switch between phases of growth and shrinkage. Microtubule treadmilling, in which the microtubule plus end grows while the minus end shrinks, is observed in cells; however, the underlying mechanisms are not known. Here, we use a combination of computational and in vitro reconstitution approaches to determine the conditions leading to robust microtubule treadmilling. We find that microtubules polymerized from tubulin alone can treadmill, albeit with opposite directionality and order-of-magnitude slower rates than observed in cells. We then employ computational simulations to predict that the combinatory effects of four microtubule-associated proteins (MAPs), namely EB1, XMAP215, CLASP2, and MCAK, can promote fast and sustained plus-end-leading treadmilling. Finally, we experimentally confirm the predictions of our computational model using a multi-MAP, in vitro microtubule dynamics assay to reconstitute robust plus-end-leading treadmilling, consistent with observations in cells. Our results demonstrate how microtubule dynamics can be modulated to achieve a dynamic balance between assembly and disassembly at opposite polymer ends, resulting in treadmilling over long periods of time. Overall, we show how the collective effects of multiple components give rise to complex microtubule behavior that may be used for global network remodeling in cells.
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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".