Cellular senescence and protein degradation
Bibliographic record
Abstract
Autophagy and the ubiquitin-proteasome pathway (UPP) are the major protein degradation systems in eukaryotic cells. Whereas the former mediate a bulk nonspecific degradation, the UPP allows a rapid degradation of specific proteins. Both systems have been shown to play a role in tumorigenesis, and the interest in developing therapeutic agents inhibiting protein degradation is steadily growing. However, emerging data point to a critical role for autophagy in cellular senescence, an established tumor suppressor mechanism. Recently, a selective protein degradation process mediated by the UPP was also shown to contribute to the senescence phenotype. This process is tightly regulated by E3 ubiquitin ligases, deubiquitinases, and several post-translational modifications of target proteins. Illustrating the complexity of UPP, more than 600 human genes have been shown to encode E3 ubiquitin ligases, a number which exceeds that of the protein kinases. Nevertheless, our knowledge of proteasome-dependent protein degradation as a regulated process in cellular contexts such as cancer and senescence remains very limited. Here we discuss the implications of protein degradation in senescence and attempt to relate this function to the protein degradation pattern observed in cancer cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".