Identification of Kinases Responsible for p53-Dependent Autophagy
Bibliographic record
Abstract
In cancer, autophagy is upregulated to promote cell survival and tumor growth during times of nutrient stress and can confer resistance to drug treatments. Several major signaling networks control autophagy induction, including the p53 tumor suppressor pathway. In response to DNA damage and other cellular stresses, p53 is stabilized and activated, while HDM2 binds to and ubiquitinates p53 for proteasome degradation. Thus blocking the HDM2-p53 interaction is a promising therapeutic strategy in cancer; however, the potential survival advantage conferred by autophagy induction may limit therapeutic efficacy. In this study, we leveraged an HDM2 inhibitor to identify kinases required for p53-dependent autophagy. Interestingly, we discovered that p53-dependent autophagy requires several kinases, including the myotonic dystrophy protein kinase-like alpha (MRCKα). MRCKα is a CDC42 effector reported to activate actin-myosin cytoskeletal reorganization. Overall, this study provides evidence linking MRCKα to autophagy and reveals additional insights into the role of kinases in p53-dependent autophagy.
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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 | 0.001 |
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".