NMDA Receptors Are Upregulated and Trafficked to the Plasma Membrane after Sigma-1 Receptor Activation in the Rat Hippocampus
Bibliographic record
Abstract
Sigma-1 receptors (σ-1Rs) are endoplasmic reticulum resident chaperone proteins implicated in many physiological and pathological processes in the CNS. A striking feature of σ-1Rs is their ability to interact and modulate a large number of voltage- and ligand-gated ion channels at the plasma membrane. We have reported previously that agonists for σ-1Rs potentiate NMDA receptor (NMDAR) currents, although the mechanism by which this occurs is still unclear. In this study, we show that in vivo administration of the selective σ-1R agonists (+)-SKF 10,047 [2S-(2α,6α,11R*]-1,2,3,4,5,6-hexahydro-6,11-dimethyl-3-(2-propenyl)-2,6-methano-3-benzazocin-8-ol hydrochloride (N-allylnormetazocine) hydrochloride], PRE-084 (2-morpholin-4-ylethyl 1-phenylcyclohexane-1-carboxylate hydrochloride), and (+)-pentazocine increases the expression of GluN2A and GluN2B subunits, as well as postsynaptic density protein 95 in the rat hippocampus. We also demonstrate that σ-1R activation leads to an increased interaction between GluN2 subunits and σ-1Rs and mediates trafficking of NMDARs to the cell surface. These results suggest that σ-1R may play an important role in NMDAR-mediated functions, such as learning and memory. It also opens new avenues for additional studies into a multitude of pathological conditions in which NMDARs are involved, including schizophrenia, dementia, and stroke.
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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) | 0.001 | 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) | 0.002 | 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 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".