GluN2B-Containing NMDA Receptors Regulate AMPA Receptor Traffic through Anchoring of the Synaptic Proteasome
Bibliographic record
Abstract
NMDA receptors play a central role in shaping the strength of synaptic connections throughout development and in mediating synaptic plasticity mechanisms that underlie some forms of learning and memory formation in the CNS. In the hippocampus and the neocortex, GluN1 is combined primarily with GluN2A and GluN2B, which are differentially expressed during development and confer distinct molecular and physiological properties to NMDA receptors. The contribution of each subunit to the synaptic traffic of NMDA receptors and therefore to their role during development and in synaptic plasticity is still controversial. We report a critical role for the GluN2B subunit in regulating NMDA receptor synaptic targeting. In the absence of GluN2B, the synaptic levels of AMPA receptors are increased and accompanied by decreased constitutive endocytosis of GluA1-AMPA receptor. We used quantitative proteomic analysis to identify changes in the composition of postsynaptic densities from GluN2B(-/-) mouse primary neuronal cultures and found altered levels of several ubiquitin proteasome system components, in particular decreased levels of proteasome subunits. Enhancing the proteasome activity with a novel proteasome activator restored the synaptic levels of AMPA receptors in GluN2B(-/-) neurons and their endocytosis, revealing that GluN2B-mediated anchoring of the synaptic proteasome is responsible for fine tuning AMPA receptor synaptic levels under basal conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".