In Vivo Composition of NMDA Receptor Signaling Complexes Differs between Membrane Subdomains and Is Modulated by PSD-95 And PSD-93
Bibliographic record
Abstract
Lipid rafts are dynamic membrane microdomains enriched in cholesterol and sphingolipids involved in the compartmentalization of signaling pathways, trafficking and sorting of proteins. At synapses, the glutamatergic NMDA receptor and its cytoplasmic scaffold protein PSD-95 move between postsynaptic density (PSD) and rafts following learning or ischemia. However it is not known whether the signaling complexes formed by these proteins are different in rafts nor the molecular mechanisms that govern their localization. To examine these issues in vivo we used mice carrying genetically encoded tags for purification of protein complexes and specific mutations in NMDA receptors, PSD-95 and other postsynaptic scaffold proteins. Isolation of PSD-95 complexes from mice carrying tandem affinity purification tags showed differential composition in lipid rafts, postsynaptic density and detergent-soluble fractions. Raft PSD-95 complexes showed less CaMKIIalpha and SynGAP and enrichment in Src and Arc/Arg3.1 compared with PSD complexes. Mice carrying knock-outs of PSD-95 or PSD-93 show a key role for PSD-95 in localizing NR2A-containing NMDA receptor complexes to rafts. Deletion of the NR2A C terminus or the C-terminal valine residue of NR2B, which prevents all PDZ interactions, reduced the NR1 association with rafts. Interestingly, the deletion of the NR2B valine residue increased the total amount of lipid rafts. These data show critical roles for scaffold proteins and their interactions with NMDA receptor subunits in organizing the differential expression in rafts and postsynaptic densities of synaptic signaling complexes.
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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) | 0.001 | 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".