Calmodulin Kinase <scp>IV</scp>–dependent <scp>CREB</scp> activation is required for neuroprotection via <scp>NMDA</scp> receptor‐<scp>PSD</scp>95 disruption
Bibliographic record
Abstract
NMDA-type glutamate receptors mediate both trophic and excitotoxic signalling in CNS neurons. We have previously shown that blocking NMDAR- post-synaptic density-95 (PSD95) interactions provides significant protection from excitotoxicity and in vivo ischaemia; however, the mechanism of neuroprotection is unclear. Here, we report that blocking PSD-95 interactions with the Tat-NR2B9c peptide enhances a Ca²⁺-dependent protective pathway converging on cAMP Response Element binding protein (CREB) activation. We provide evidence that Tat-NR2B9c neuroprotection from oxygen glucose deprivation and NMDA toxicity occurs in parallel with the activation of calmodulin kinase signalling and is dependent on a sustained phosphorylation of the CREB transcription factor and its activator CaMKIV. Tat-NR2B9c-dependent neuroprotection and CREB phosphorylation are blocked by coapplication of CaM kinase (KN93 and STO-609) or CREB (KG-501) inhibitors, and by siRNA knockdown of CaMKIV. These results are mirrored in vivo in a rat model of permanent focal ischaemia. Tat-NR2B9c application significantly reduces infarct size and causes a selective and sustained elevation in CaMKIV phosphorylation; effects which are blocked by coadministration of KN93. Thus, calcium-dependent nuclear signalling via CaMKIV and CREB is critical for neuroprotection via NMDAR-PSD95 blockade, both in vitro and in vivo. This study highlights the importance of maintaining neuronal function following ischaemic injury. Future stroke research should target neurotrophic and pro-survival signal pathways in the development of novel neuroprotective strategies.
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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) | 0.002 | 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".