Effect of antipsychotic drugs on group II metabotropic glutamate receptor expression and epigenetic control in postmortem brains of schizophrenia subjects
Bibliographic record
Abstract
Abstract Antipsychotic-induced low availability of group II metabotropic glutamate receptors (including mGlu2R and mGlu3R) in brains of schizophrenia patients may explain the limited efficacy of mGlu2/3R ligands in clinical trials. Studies evaluating mGlu2/3R levels in well-designed, large postmortem brain cohorts are needed to address this issue. Postmortem samples from the dorsolateral prefrontal cortex of 96 schizophrenia subjects and matched controls were collected. Toxicological analyses identified cases who were (AP+) or were not (AP-) receiving antipsychotic treatment near the time of death. Protein and mRNA levels of mGlu2R and mGlu3R, as well as GRM2 and GRM3 promoter-attached histone posttranslational modifications, were quantified. Experimental animal models were used to compare with data obtained in human tissues. Compared to matched controls, schizophrenia cortical samples had lower mGlu2R protein amounts, regardless of antipsychotic medication. Downregulation of mGlu3R was observed in AP- schizophrenia subjects only. Greater predicted occupancy values of dopamine D2 and serotonin 5HT2A receptors correlated with higher density of mGlu3R, but not mGlu2R. Clozapine treatment and maternal immune activation in rodents mimicked the mGlu2R, but not mGlu3R regulation observed in schizophrenia brains. mGlu2R and mGlu3R mRNA levels, and the epigenetic control mechanisms did not parallel the alterations at the protein level, and in some groups correlated inversely. Insufficient cortical availability of mGlu2R and mGlu3R may be associated with schizophrenia. Antipsychotic treatment may normalize mGlu3R, but not mGlu2R protein levels. A model in which epigenetic feedback mechanisms controlling mGlu3R expression are activated to counterbalance mGluR loss of function is described.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".