Specific Targeting of Pro-Death NMDA Receptor Signals with Differing Reliance on the NR2B PDZ Ligand
Bibliographic record
Abstract
NMDA receptors (NMDARs) mediate ischemic brain damage, for which interactions between the C termini of NR2 subunits and PDZ domain proteins within the NMDAR signaling complex (NSC) are emerging therapeutic targets. However, expression of NMDARs in a non-neuronal context, lacking many NSC components, can still induce cell death. Moreover, it is unclear whether targeting the NSC will impair NMDAR-dependent prosurvival and plasticity signaling. We show that the NMDAR can promote death signaling independently of the NR2 PDZ ligand, when expressed in non-neuronal cells lacking PSD-95 and neuronal nitric oxide synthase (nNOS), key PDZ proteins that mediate neuronal NMDAR excitotoxicity. However, in a non-neuronal context, the NMDAR promotes cell death solely via c-Jun N-terminal protein kinase (JNK), whereas NMDAR-dependent cortical neuronal death is promoted by both JNK and p38. NMDAR-dependent pro-death signaling via p38 relies on neuronal context, although death signaling by JNK, triggered by mitochondrial reactive oxygen species production, does not. NMDAR-dependent p38 activation in neurons is triggered by submembranous Ca(2+), and is disrupted by NOS inhibitors and also a peptide mimicking the NR2B PDZ ligand (TAT-NR2B9c). TAT-NR2B9c reduced excitotoxic neuronal death and p38-mediated ischemic damage, without impairing an NMDAR-dependent plasticity model or prosurvival signaling to CREB or Akt. TAT-NR2B9c did not inhibit JNK activation, and synergized with JNK inhibitors to ameliorate severe excitotoxic neuronal loss in vitro and ischemic cortical damage in vivo. Thus, NMDAR-activated signals comprise pro-death pathways with differing requirements for PDZ protein interactions. These signals are amenable to selective inhibition, while sparing synaptic plasticity and prosurvival signaling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".