De novo GRIN variants in M3 helix associated with neurological disorders control channel gating of NMDA receptor
Bibliographic record
Abstract
N-methyl-D-aspartate receptors (NMDARs) are members of the glutamate receptor family and participate in excitatory postsynaptic transmission throughout the central nervous system. Genetic variants in GRIN genes encoding NMDAR subunits are associated with a spectrum of neurological disorders. The M3 transmembrane helices of the NMDAR couple directly to the agonist-binding domains and form a helical bundle crossing in the closed receptors that occludes the pore. The M3 functions as a transduction element whose conformational change couples ligand binding to opening of an ion conducting pore. In this study, we report the functional consequences of 48 de novo missense variants in GRIN1, GRIN2A, and GRIN2B that alter residues in the M3 transmembrane helix. These de novo variants were identified in children with neurological and neuropsychiatric disorders including epilepsy, developmental delay, intellectual disability, hypotonia and attention deficit hyperactivity disorder. All 48 variants in M3 for which comprehensive testing was completed produce a gain-of-function (28/48) compared to loss-of-function (9/48); 11 variants had an indeterminant phenotype. This supports the idea that a key structural feature of the M3 gate exists to stabilize the closed state so that agonist binding can drive channel opening. Given that most M3 variants enhance channel gating, we assessed the potency of FDA-approved NMDAR channel blockers on these variant receptors. These data provide new insight into the structure-function relationship of the NMDAR gate, and suggest that variants within the M3 transmembrane helix produce a gain-of-function.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".