The PEA-15 Protein Regulates Autophagy via Activation of JNK
Bibliographic record
Abstract
PEA-15/PED (phosphoprotein enriched in astrocytes 15 kDa/phosphoprotein enriched in diabetes) is a death effector domain-containing protein which is known to modulate apoptotic cell death. The mechanism by which PEA-15 inhibits caspase activation and increases ERK (extracellular-regulated kinase) activity is well characterized. Here, we demonstrate that PEA-15 is not only pivotal in the activation of the ERK pathway but also modulates JNK (c-Jun N-terminal kinase) signaling. Upon overexpression of PEA-15 in malignant glioma cells, JNK is potently activated. The PEA-15-induced JNK activation depends on the phosphorylation of PEA-15 at both phosphorylation sites (serine 104 and serine 116). The activation of JNK is substantially inhibited by siRNA-mediated down-regulation of endogenous PEA-15. Moreover, we demonstrate that glioma cells overexpressing PEA-15 show increased signs of autophagy in response to classical autophagic stimuli such as ionizing irradiation, serum deprivation, or rapamycin treatment. In contrast, the non-phosphorylatable mutants of PEA-15 are not capable of promoting autophagy. The inhibition of JNK abrogates the PEA-15-mediated increase in autophagy. In conclusion, our data show that PEA-15 promotes autophagy in glioma cells in a JNK-dependent manner. This might render glioma cells more resistant to adverse stimuli such as starvation or ionizing irradiation.
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) | 0.001 | 0.000 |
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".