Interaction of Postsynaptic Density Protein-95 with NMDA Receptors Influences Excitotoxicity in the Yeast Artificial Chromosome Mouse Model of Huntington's Disease
Bibliographic record
Abstract
Evidence suggests that NMDA-type glutamate receptors contribute to degeneration of striatal medium-sized spiny neurons (MSNs) in Huntington's disease (HD). Previously, we demonstrated that NMDA receptor (NMDAR)-mediated current and/or toxicity is increased in MSNs from the yeast artificial chromosome (YAC) transgenic mouse model expressing polyglutamine (polyQ)-expanded (mutant) full-length human huntingtin (htt). Others have shown that membrane-associated guanylate kinases (MAGUKs), such as PSD-95 and SAP102, modulate NMDAR surface expression and excitotoxicity in hippocampal and cortical neurons and that htt interacts with PSD-95. Here, we tested the hypothesis that an altered association between MAGUKs and NMDARs in mutant huntingtin-expressing cells contributes to increased susceptibility to excitotoxicity. We show that htt coimmunoprecipitated with SAP102 in HEK293T cells and striatal tissue from wild-type and YAC transgenic mice; however, the association of SAP102 with htt or the NMDAR NR2B subunit was unaffected by htt polyQ length, whereas association of PSD-95 with NR2B in striatal tissue was enhanced by increased htt polyQ length. Treatment of cultured MSNs with Tat-NR2B9c peptide blocked binding of NR2B with SAP102 and PSD-95 and reduced NMDAR surface expression by 20% in both YAC transgenic and wild-type MSNs, and also restored susceptibility to NMDAR excitoxicity in YAC HD MSNs to levels observed in wild-type MSNs; a similar effect on excitotoxicity was observed after knockdown of PSD-95 by small interfering RNA. Unlike previous findings in cortical and hippocampal neurons, rescue of NMDA toxicity by Tat-NR2B9c occurred independently of any effect on neuronal nitric oxide synthase activity. Our results elucidate further the mechanisms underlying enhanced excitotoxicity in HD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".