Glutamate Binding to the GluN2B Subunit Controls Surface Trafficking of N-Methyl-d-aspartate (NMDA) Receptors*
Bibliographic record
Abstract
Trafficking of NMDA receptors to the surface of neurons and to synapses is critical for proper brain function and activity-dependent plasticity. Recent evidence suggests that surface trafficking of other ionotropic glutamate receptors requires ligand binding for exit from the endoplasmic reticulum. Here, we show that glutamate binding to GluN2 is required for trafficking of NMDA receptors to the cell surface. We expressed a panel of GluN2B ligand binding mutants in heterologous cells with GluN1 or in rat cultured neurons and found that surface expression correlates with glutamate efficacy. Such a correlation was found even in the presence of dominant negative dynamin to inhibit endocytosis and surface expression correlated with Golgi localization, indicating differences in forward trafficking. Co-expression of wild type GluN2B did not enhance surface expression of the mutants, suggesting that glutamate must bind to both GluN2 subunits in a tetramer and that surface expression is limited by the least avid of the two glutamate binding sites. Surface trafficking of a constitutively closed cleft GluN2B was indistinguishable from that of wild type, suggesting that glutamate concentrations are typically not limiting for forward trafficking. YFP-GluN2B expressed in hippocampal neurons from GluN2B(-/-) mice rescued synaptic accumulation at similar levels to wild type. Under these conditions, surface synaptic accumulation of YFP-GluN2B mutants also correlated with apparent glutamate affinity. Altogether, these results indicate that glutamate controls forward trafficking of NMDA receptors to the cell surface and to synapses and raise the intriguing idea that NMDA receptors may be functional at intracellular sites.
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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 | 0.001 |
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".