Contrasting Patterns and Cellular Specificity of Transcriptional Regulation of the Nuclear Receptor Nerve Growth Factor‐Inducible B by Haloperidol and Clozapine in the Rat Forebrain
Bibliographic record
Abstract
This study was designed to investigate the possible involvement of members of the nuclear receptor family of transcription factors in the effects of antipsychotic drugs used in the treatment of schizophrenia. We have identified, using RT-PCR screening, an important modulation of nerve growth factor-inducible B (NGFI-B) mRNA levels by typical and atypical neuroleptics in the rat forebrain. NGFI-B, a member of the nuclear receptor family, can be observed in target structures of dopaminergic pathways. Using in situ hybridization, we also demonstrate that typical and atypical antipsychotics induced contrasting patterns of expression of NGFI-B after both acute and chronic administration. An acute treatment with clozapine or haloperidol induces high NGFI-B mRNA levels in the prefrontal and cingulate cortices and in the nucleus accumbens shell. However, haloperidol, but not clozapine, dramatically increases NGFI-B expression in the dorsolateral striatum. In contrast, chronic treatment with clozapine reduces NGFI-B expression below basal levels in the rat forebrain, whereas haloperidol still induces high NGFI-B mRNA levels in the dorsolateral striatum. Finally, using a double in situ hybridization technique, we show that acute administration of both neuroleptics increases NGFI-B expression in neurotensin-containing neurons in the nucleus accumbens shell, whereas the effects of haloperidol in the dorsolateral striatum are mainly observed in enkephalin-containing neurons. These results are the first demonstration that members of the nuclear receptor family of transcription factors could play an important role in the effects of antipsychotic drugs.
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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 | 0.001 | 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".