ASPP1/2 Regulate p53-Dependent Death of Retinal Ganglion Cells through PUMA and Fas/CD95 Activation<i>In Vivo</i>
Bibliographic record
Abstract
The transcription factor p53 mediates neuronal death in a variety of stress-related and neurodegenerative conditions. The proapoptotic activity of p53 is tightly regulated by the apoptosis-stimulating proteins of p53 (ASPP) family members: ASPP1 and ASPP2. However, whether ASPP1/2 play a role in the regulation of p53-dependent neuronal death in the CNS is currently unknown. To address this, we asked whether ASPP1/2 contribute to the death of retinal ganglion cells (RGCs) using in vivo models of acute optic nerve damage in mice and rats. Here, we show that p53 is activated in RGCs soon after injury and that axotomy-induced RGC death is attenuated in p53 heterozygote and null mice. We demonstrate that ASPP1/2 proteins are abundantly expressed by injured RGCs, and that short interfering (si)RNA-based ASPP1 or ASPP2 knockdown promotes robust RGC survival. Comparative gene expression analysis revealed that siASPP-mediated downregulation of p53-upregulated-modulator-of-apoptosis (PUMA), Fas/CD95, and Noxa depends on p53 transcriptional activity. Furthermore, siRNA against PUMA or Fas/CD95 confers neuroprotection, demonstrating a functional role for these p53 targets in RGC death. Our study demonstrates a novel role for ASPP1 and ASPP2 in the death of RGCs and provides evidence that blockade of the ASPP-p53 pathway is beneficial for central neuron survival after axonal injury.
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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) | 0.000 | 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 | 0.001 |
| 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".