Altered NMDA Receptor Trafficking in a Yeast Artificial Chromosome Transgenic Mouse Model of Huntington's Disease
Bibliographic record
Abstract
Overactivation of NMDA receptors (NMDARs) is believed to play a role in degeneration of striatal medium-sized spiny neurons (MSNs) in Huntington's disease (HD). This hereditary disorder is caused by an expansion >35 in the polyglutamine (polyQ) region of the protein huntingtin (htt). Previous work has shown that NMDAR current, calcium signaling, and/or toxicity are enhanced in striatal MSNs in a variety of transgenic mice and cellular models of HD, but whether the enhancement is specific for MSNs or correlated with mutant htt (mhtt) polyQ length is not known. Furthermore, the mechanism underlying the increase in NMDAR activity has not been elucidated. Here we report polyQ length-dependent enhancement of peak NMDAR current density by mhtt in cultured MSNs, but not cortical neurons, from the yeast artificial chromosome (YAC) transgenic HD mouse model. We also observed a shift of NMDAR subunits NR1 and NR2B from internal pools to the plasma membrane and a significantly faster rate of NMDAR insertion to the surface in YAC72 MSNs. In comparing YAC72 with wild-type striatal tissue, subcellular fractionation revealed a relative enrichment of NR1 C2'-containing NMDARs in the vesicle/microsome-enriched fraction, and coimmunoprecipitation experiments demonstrated an increased proportion of NR1 C2' isoforms associated with NR2 subunits, which may contribute to faster forward trafficking of these receptors. Our results suggest that altered NMDAR trafficking may underlie potentiation of NMDAR-mediated current and toxicity in the YAC72 HD mouse model. This polyQ length-dependent, neuronal-specific change in NMDAR activity induced by mhtt may contribute to selective neuronal degeneration in HD.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".