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.
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
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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".