Genetic loss of D‐amino acid oxidase activity reverses schizophrenia‐like phenotypes in mice
Bibliographic record
Abstract
Reduced function of the N-methyl-d-aspartate receptor (NMDAR) has been implicated in the pathophysiology of schizophrenia. The NMDAR contains a glycine binding site in its NR1 subunit that may be a useful target for the treatment of schizophrenia. In this study, we assessed the therapeutic potential of long-term increases in the brain levels of the endogenous NMDAR glycine site agonist D-serine, through the genetic inactivation of its catabolic enzyme D-amino acid oxidase (DAO) in mice. The effects of eliminating DAO function were investigated in mice that display schizophrenia-related behavioral deficits due to a mutation (Grin 1(D481N)) in the NR1 subunit that results in a reduction in NMDAR glycine affinity. Grin 1(D481N) mice show deficits in sociability, prolonged latent inhibition, enhanced startle reactivity and impaired spatial memory. The hypofunctional Dao 1(G181R) mutation elevated brain levels of D-serine, but alone it did not affect performance in the behavioral measures. Compared to animals with only the Grin 1(D481N) mutation, mice with both the Dao1(G181R) and Grin 1(D481N) mutations displayed an improvement in social approach and spatial memory retention, as well as a reversal of abnormally persistent latent inhibition and a partial normalization of startle responses. Thus, an increased level of D-serine resulting from decreased catalysis corrected the performance of mice with deficient NMDAR glycine site activation in behavioral tasks relevant to the negative and cognitive symptoms of schizophrenia. Diminished DAO activity and elevations in D-serine may serve as an effective therapeutic intervention for the treatment of psychiatric symptoms.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".