Bibliographic record
Abstract
The N-methyl-d-aspartate receptor (NMDAR), a ligand-gated ionotropic glutamate receptor, plays important roles in normal brain development and a wide range of neurologic disorders, including epilepsy. Here, we evaluate for the first time the functional properties of a de novo GRIN2A missense mutation (p.M817V) in the pre-M4 linker in a child with profound global developmental delay and refractory epilepsy. Electrophysiologic recordings revealed that the mutant GluN2A(M817V)-containing receptors showed enhanced agonist potency, reduced sensitivity to endogenous negative inhibitors (Mg2+, proton, and zinc), prolonged synaptic-like response time course, increased single-channel mean open time, and increased channel open probability. These results suggest that the gain-of-function M817V mutation causes overactivation of NMDAR and drives neuronal hyperexcitability, which may contribute to the patient’s observed epileptic phenotype. Molecular modeling of the closed channel conformation reveals that this mutation weakens the interaction between GluN2 transmembrane helix M4 and two GluN1 transmembrane helices, and increases atomic fluctuation or movement of the pre-M1 region of GluN1 subunit, suggesting a mechanism by which channel function is enhanced. The functional changes of this mutation on agonist potency occur when the mutation is introduced into all other GluN2 subunits, suggesting a conserved role of this residue in control of NMDAR function through interactions of membrane spanning GluN2 and GluN1 helices. A number of NMDAR-targeted drugs including U.S. Food and Drug Association–approved NMDAR channel blockers were evaluated for their ability to inhibit receptors containing GluN2A(M817V) as a first step to exploring the potential for rescue pharmacology and personalized medicine.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".