Cyclin D1 Induction Preceding Neuronal Death via the Excitotoxic NMDA Pathway Involves Selective Stimulation of Extrasynaptic NMDA Receptors and JNK Pathway
Bibliographic record
Abstract
The N-methyl-D-aspartate subtype of ionotropic glutamate receptors (NMDARs) signals both prosurvival and death-inducing (excitotoxic) neuronal responses via synaptically (synaptic NMDAR) and extrasynaptically (extrasynaptic NMDAR) located receptor pools, respectively. Both receptor pools share similar, though not identical, postreceptor signaling molecules. The activation of the extrasynaptic NMDAR pathway is predominant. Therefore, in order to inhibit the extrasynaptic death pathway while sparing synaptic responses, it is critical to identify selective postreceptor effectors of extrasynaptic NMDARs. The present study addressed these issues by using primary cultures of rat hippocampal neurons and a pharmacological protocol of selective NMDAR stimulation for Western blot and immunocytochemistry analyses. We found that the activation of extrasynaptic NMDARs, either alone or together with synaptic NMDARs, triggers cyclin-D1-associated re-entry into the cell cycle, which does not proceed beyond the S-phase. This aberrant cell cycle re-entry is particularly associated with neuronal death triggered specifically via extrasynaptic NMDAR-induced c-Jun N-terminal protein kinase (JNK). In addition, NMDA-elicited neuronal death was significantly inhibited by pharmacological blockade of JNK-mediated cyclin D1 expression or by silencing cyclin D1 RNA. Taken together, these data suggest a causal relationship between cyclin D1 induction and extrasynaptic NMDAR-triggered neuronal death along the excitotoxic NMDA pathway. Therefore, cyclin D1 may be a putative target for the development of neuroprotective strategies sparing physiological synaptic NMDAR signaling.
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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 | 0.000 |
| 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".