THE P53 FAMILY MEMBER, P63, REGULATES NEURAL PRECURSOR CELL SURVIVAL DURING CORTICAL DEVELOPMENT
Bibliographic record
Abstract
Background: p63, a member of the p53 family of proteins, is involved in the regulation of naturally-occurring apoptosis in sympathetic neurons of the peripheral nervous system. Since data from our laboratory indicated that p63 is also expressed in stem cells and neurons within the developing brain, we hypothesized that p63 is involved in regulating the genesis and survival of developing neurons. Methods: As cortical neurogenesis is initiated at embryonic day 12, we knocked-down p63 levels in isolated murine cortical precursors byusing shRNA against p63 or by transfecting floxed-p63 precursors with Cre recombinase. We performed similar studies in vivo using in uteroelectroporation to express either p63 shRNA or Cre recombinase to acutely knockdown or genetically ablate p63. We then performed immunofluorescence for known markers of apoptosis, cell-division, and differentiation to assess the level of cell death, proliferation and neurogenesis. Results: Knock-down of p63 in vitro resulted in a 2-foldincrease in the death of precursors and neurons, associated with blunted neurogenesis but unaltered precursor proliferation. Coincident knock-down of p63 family members, p53, but not p73, rescued the elevated death suggesting that p63 and p53 antagonize each other to promote survival. Similar results were observed in vivo, where knockdown of p63 caused cell death and a decrease in the proportionof neurons in the cortical plate. Conclusions: These experiments indicate that p63 is required forthe survival of neural precursors and newly-born neurons, and for normal cortical development. Ongoing work will explore the environmental cues that regulate p63 during neurogenesis.
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".