Microtubules Accelerate the Kinase Activity of Aurora-B by a Reduction in Dimensionality
Bibliographic record
Abstract
Aurora-B is the kinase subunit of the Chromosome Passenger Complex (CPC), a key regulator of mitotic progression that corrects improper kinetochore attachments and establishes the spindle midzone. Recent work has demonstrated that the CPC is a microtubule-associated protein complex and that microtubules are able to activate the CPC by contributing to Aurora-B auto-phosphorylation in trans. Aurora-B activation is thought to occur when the local concentration of Aurora-B is high, as occurs when Aurora-B is enriched at centromeres. It is not clear, however, whether distributed binding to large structures such as microtubules would increase the local concentration of Aurora-B. Here we show that microtubules accelerate the kinase activity of Aurora-B by a "reduction in dimensionality." We find that microtubules increase the kinase activity of Aurora-B toward microtubule-associated substrates while reducing the phosphorylation levels of substrates not associated to microtubules. Using the single molecule assay for microtubule-associated proteins, we show that a minimal CPC construct binds to microtubules and diffuses in a one-dimensional (1D) random walk. The binding of Aurora-B to microtubules is salt-dependent and requires the C-terminal tails of tubulin, indicating that the interaction is electrostatic. We show that the rate of Aurora-B auto-activation is faster with increasing concentrations of microtubules. Finally, we demonstrate that microtubules lose their ability to stimulate Aurora-B when their C-terminal tails are removed by proteolysis. We propose a model in which microtubules act as scaffolds for the enzymatic activity of Aurora-B. The scaffolding activity of microtubules enables rapid Aurora-B activation and efficient phosphorylation of microtubule-associated substrates.
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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) | 0.002 | 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".