c-Jun N-terminal Kinase 3 Deficiency Protects Neurons from Axotomy-induced Death in Vivo through Mechanisms Independent of c-Jun Phosphorylation
Bibliographic record
Abstract
Both the transcription factor c-Jun and the c-Jun N-terminal kinases (JNKs) have been associated with neuronal loss in several death paradigms. JNK are key regulators of c-Jun and a common accepted model has been that JNKs mediate neuronal death through modulation of c-Jun activation. In the present study, we examined whether JNK2 and -3 (JNK members most associated with neuronal loss) deficiency can rescue neuronal loss caused by facial and sciatic nerve axotomy in the neonate in vivo. JNK2, JNK3, and JNK2/3 double-deficient neurons displayed significantly less death in the facial nerves of the CNS when compared with controls. JNK2 and JNK2/3 double-deficient animals also showed reduced c-Jun phosphorylation and induction following axotomy, consistent with the model that JNK acts to regulate death by activating c-Jun. Of significance, however, protection of facial nerves in JNK3-deficient animals was not accompanied by reduction in c-Jun activation. These results suggest that JNKs can mediate death independently of c-Jun. Importantly, the lack of correlation between JNK3 deficiency and c-Jun induction was not universal. In a sciatic axotomy model of neuronal injury in the neonate, death of DRG neurons was also reduced by JNK3 deficiency. However, in this case, c-Jun activation was also eliminated.
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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) | 0.000 | 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 | 0.001 |
| 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".