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Intracellular Modulation of NMDA Receptor Function by Antipsychotic Drugs

2000· article· en· W1499413566 on OpenAlexaff
Jean‐Christophe Leveque, Wendy Macias‐Konstantopoulos, Anjali M. Rajadhyaksha, Richard Carlson, Amy K. Barczak, Stanley Kang, Xin‐Min Li, Joseph T. Coyle, Richard L. Huganir, Stephan Heckers, Christine Konradi

Bibliographic record

VenueJournal of Neuroscience · 2000
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsRoyal University Hospital
FundersNational Institute on Drug AbuseNational Alliance for Research on Schizophrenia and Depression
KeywordsHaloperidolAntipsychoticNMDA receptorTypical antipsychoticPharmacologyClozapineSignal transductionPhencyclidineAtypical antipsychoticReceptorNeuroscienceBiologyCell biologyMedicineSchizophrenia (object-oriented programming)BiochemistryDopaminePsychiatry

Abstract

fetched live from OpenAlex

The present study deals with the functional interaction of antipsychotic drugs and NMDA receptors. We show that both the conventional antipsychotic drug haloperidol and the atypical antipsychotic drug clozapine mediate gene expression via intracellular regulation of NMDA receptors, albeit to different extents. Data obtained in primary striatal culture demonstrate that the intraneuronal signal transduction pathway activated by haloperidol, the cAMP pathway, leads to phosphorylation of the NR1 subtype of the NMDA receptor at (897)Ser. Haloperidol treatment is likewise shown to increase (897)Ser-NR1 phosphorylation in rats in vivo. Mutation of (896)Ser and (897)Ser to alanine, which prevents phosphorylation at both sites, inhibits cAMP-mediated gene expression. We conclude that antipsychotic drugs have the ability to modulate NMDA receptor function by an intraneuronal signal transduction mechanism. This facilitation of NMDA activity is necessary for antipsychotic drug-mediated gene expression and may contribute to the therapeutic benefits as well as side effects of antipsychotic drug treatment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.318
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations149
Published2000
Admission routes1
Has abstractyes

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