Apoptosis-inducing factor is involved in the regulation of caspase-independent neuronal cell death
Bibliographic record
Abstract
Caspase-independent death mechanisms have been shown to execute apoptosis in many types of neuronal injury. P53 has been identified as a key regulator of neuronal cell death after acute injury such as DNA damage, ischemia, and excitotoxicity. Here, we demonstrate that p53 can induce neuronal cell death via a caspase-mediated process activated by apoptotic activating factor-1 (Apaf1) and via a delayed onset caspase-independent mechanism. In contrast to wild-type cells, Apaf1-deficient neurons exhibit delayed DNA fragmentation and only peripheral chromatin condensation. More importantly, we demonstrate that apoptosis-inducing factor (AIF) is an important factor involved in the regulation of this caspase-independent neuronal cell death. Immunofluorescence studies demonstrate that AIF is released from the mitochondria by a mechanism distinct from that of cytochrome-c in neurons undergoing p53-mediated cell death. The Bcl-2 family regulates this release of AIF and subsequent caspase-independent cell death. In addition, we show that enforced expression of AIF can induce neuronal cell death in a Bax- and caspase-independent manner. Microinjection of neutralizing antibodies against AIF significantly decreased injury-induced neuronal cell death in Apaf1-deficient neurons, indicating its importance in caspase-independent apoptosis. Taken together, our results suggest that AIF may be an important therapeutic target for the treatment of neuronal 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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".