GTSE1 tunes microtubule stability for chromosome alignment and segregation through MCAK inhibition
Bibliographic record
Abstract
Abstract The dynamic regulation of microtubules during mitosis is critical for accurate chromosome segregation and genome stability. Cancer cell lines with hyperstabilized kinetochore microtubules have increased segregation errors and elevated chromosomal instability (CIN), but the genetic defects responsible remain largely unknown. The microtubule depolymerase MCAK can influence CIN through its impact on microtubule stability, but how its potent activity is controlled in cells remains unclear. Here we show that GTSE1, a protein found overexpressed in aneuploid cancer cell lines and tumours, regulates microtubule stability during mitosis by inhibiting MCAK microtubule depolymerase activity. Cells lacking GTSE1 have defects in chromosome alignment and spindle positioning due to microtubule instability caused by excess MCAK activity. Reducing GTSE1 levels in CIN cancer cell lines reduces chromosome missegregation defects, while artificially inducing GTSE1 levels in chromosomally stable cells elevates chromosome missegregation and CIN. Thus GTSE1 inhibition of MCAK activity regulates the balance of microtubule stability that determines the fidelity of chromosome alignment, segregation, and chromosomal stability.
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 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 | 0.001 |
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".