Modulation of N-methyl-D-aspartate receptors by Gαs- and Gαi/o-coupled receptors
Bibliographic record
Abstract
The induction of synaptic plasticity at CA1 synapses requires NMDAR activation. Modulation of NMDAR function by various GPCRs can shift the thresholds for LTP and LTD induction and contribute to metaplasticity. Here we showed that the activity of GluN2A- and GluN2B-containing NMDARs is differentially regulated by Gαi/o-coupled, Gαq- and Gαs-coupled receptors. Furthermore, enhancing the relative function of GluN2A-to-GluNB NMDAR activity by GPCRs can alter the balance of LTP and LTD induction and contribute to metaplasticity. In CA1 neurons, activation of the Gαs-coupled D1/D5R selectively recruited Fyn kinase and enhanced GluN2B-mediated NMDAR currents. Biochemical experiments confirmed that D1/D5R stimulation activates Fyn kinase and enhances the tyrosine phosphorylation of GluN2B subunits. In contrast, activation of the Gαq-coupled PAC1R selectively recruited Src kinase to enhance the function of GluN2A-containing NMDARs. Enhancing the functional ratio of GluN2A-to-GluN2B subunits by PAC1R activation lowered the threshold for LTP induction whereas enhancing the functional ratio of GluN2B-to-GluN2A subunits by D1/D5R activation increased the threshold for LTP induction. Unexpectedly, activation of the Gαi/o-coupled mGluR2/3 enhanced NMDAR-mediated function via a previously unidentified mechanism. Inhibition of the cAMP-PKA pathway via mGluR2/3 activation resulted in activation of Src via decreased phosphorylation of its C-terminal Tyr527 by Csk. Stimulation of mGluR2/3 selectively potentiated the function of GluN2A-containing NMDARs but whether it shifted the modification threshold θm to the left requires further investigation.
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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 | 0.000 |
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".