Regulating Apoptosis by Degradation: The N-End Rule-Mediated Regulation of Apoptotic Proteolytic Fragments in Mammalian Cells
Bibliographic record
Abstract
A pivotal hallmark of some cancer cells is the evasion of apoptotic cell death. Importantly, the initiation of apoptosis often results in the activation of caspases, which, in turn, culminates in the generation of proteolytically-activated protein fragments with potentially new or altered roles. Recent investigations have revealed that the activity of a significant number of the protease-generated, activated, pro-apoptotic protein fragments can be curbed via their selective degradation by the N-end rule degradation pathways. Of note, previous work revealed that several proteolytically-generated, pro-apoptotic fragments are unstable in cells, as their destabilizing N-termini target them for proteasomal degradation via the N-end rule degradation pathways. Remarkably, previous studies also showed that the proteolytically-generated anti-apoptotic Lyn kinase protein fragment is targeted for degradation by the UBR1/UBR2 E3 ubiquitin ligases of the N-end rule pathway in chronic myeloid leukemia cells. Crucially, the degradation of cleaved fragment of Lyn by the N-end rule counters imatinib resistance in these cells, implicating a possible linkage between the N-end rule degradation pathway and imatinib resistance. Herein, we highlight recent studies on the role of the N-end rule proteolytic pathways in regulating apoptosis in mammalian cells, and also discuss some possible future directions with respect to apoptotic proteolysis signaling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".