Recent Progress in Understanding Subtype Specific Regulation of NMDA Receptors by G Protein Coupled Receptors (GPCRs)
Bibliographic record
Abstract
G Protein Coupled Receptors (GPCRs) are the largest family of receptors whose ligands constitute nearly a third of prescription drugs in the market. They are widely involved in diverse physiological functions including learning and memory. NMDA receptors (NMDARs), which belong to the ionotropic glutamate receptor family, are likewise ubiquitously expressed in the central nervous system (CNS) and play a pivotal role in learning and memory. Despite its critical contribution to physiological and pathophysiological processes, few pharmacological interventions aimed directly at regulating NMDAR function have been developed to date. However, it is well established that NMDAR function is precisely regulated by cellular signalling cascades recruited downstream of G protein coupled receptor (GPCR) stimulation. Accordingly, the downstream regulation of NMDARs likely represents an important determinant of outcome following treatment with neuropsychiatric agents that target selected GPCRs. Importantly, the functional consequence of such regulation on NMDAR function varies, based not only on the identity of the GPCR, but also on the cell type in which relevant receptors are expressed. Indeed, the mechanisms responsible for regulating NMDARs by GPCRs involve numerous intracellular signalling molecules and regulatory proteins that vary from one cell type to another. In the present article, we highlight recent findings from studies that have uncovered novel mechanisms by which selected GPCRs regulate NMDAR function and consequently NMDAR-dependent plasticity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".