Regulation of sensitivity of tumor cells to antitubulin drugs by Cdk1-TAZ signalling
Bibliographic record
Abstract
// Yulei Zhao 1 , Xiaolong Yang 1 1 Department of Pathology and Molecular Medicine, Queen’s University, Kingston, ON K7L3N6, Canada Correspondence to: Xiaolong Yang, e-mail: yang@path.queensu.ca Keywords: Hippo, TAZ (WWTR1), Cdk1, antitubulin drug, chemoresistance Received: April 13, 2015 Accepted: May 22, 2015 Published: June 08, 2015 ABSTRACT Antitubulin drugs are commonly used for the treatment of numerous cancers. However, either the intrinsic or acquired resistances of patients to these drugs result in the failure of the treatment and high mortality of cancers. Therefore, identifying genes or signalling pathways involved in antitubulin drug resistances is critical for future successful treatment of cancers. TAZ (Transcriptional coactivator with PDZ-binding motif), which is a core component of the Hippo pathway, is overexpressed in various cancers. We have recently shown that high levels of TAZ in cancer cells result in Taxol resistance through up-regulation of downstream targets Cyr61 and CTGF. However, how TAZ is regulated in response to Taxol is largely unknown. In this study, we found that Cdk1 (Cyclin-dependent kinase 1) directly phosphorylated TAZ on six novel sites independent of the Hippo pathway, which further resulted in TAZ degradation through proteasome system. Phosphorylation-mimicking TAZ mutant was unstable, and therefore abolished TAZ-induced antitubulin drug resistances. This study provides first evidence that Cdk1 is a novel kinase phosphorylating and regulating TAZ stability and suggests that Cdk1-TAZ signalling is a critical regulator of antitubulin drug response in cancer cells and may be a potential target for the treatment of antitubulin-drug resistant cancer patients.
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 | 0.001 | 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".