Required Roles of Bax and JNKs in Central and Peripheral Nervous System Death of Retinoblastoma-deficient Mice
Bibliographic record
Abstract
Retinoblastoma-deficient mice show massive neuronal damage and deficits in both CNS and PNS tissue. Previous work in the field has shown that death is regulated through distinct processes where CNS tissue undergoes death regulated by the tumor suppressor p53 and the apoptosome component, APAF1. Death in the PNS, however, is independent of p53 and reliant on the death protease, caspase 3. In the present study, we more carefully delineated the common and distinct mechanisms of death regulation by examining the stress-activated kinases, JNK2 and 3, the conserved Bcl-2 member Bax, and the relationship among these elements including p53. By use of genetic modeling, we show that death in various regions of the CNS and DRGs of the PNS is reliant on Bax. In the CNS, Bax acts downstream of p53. The relevance of the JNKs is more complex, however. Surprisingly, JNK3 deficiency by itself does not inhibit c-Jun phosphorylation and instead, aggravates death in both CNS and PNS tissue. However, JNK2/3 double deficiency blocks death due to Rb loss in both the PNS and CNS. Importantly, the relationships between JNKs, p53, and Bax exhibit regional differences. In the medulla region of the hindbrain in the CNS, JNK2/3 deficiency blocks p53 activation. Moreover, Bax deficiency does not affect c-Jun phosphorylation. This indicates that a JNK-p53-Bax pathway is central in the hindbrain. However, in the diencephalon regions of the forebrain (thalamus), Bax deficiency blocks c-Jun activation, indicating that a Bax-JNK pathway of death is more relevant. In the DRGs of the PNS, a third pathway is present. In this case, a JNK-Bax pathway, independent of p53, regulates damage. Accordingly, our results show that a death regulator Bax is common to death in both PNS and CNS tissue. However, it is regulated by or itself regulates different effectors including the JNKs and p53 depending upon the specific region of the nervous system.
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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 | 0.001 | 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) | 0.002 | 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".