Elevated peripheral expression of neuregulin-1 (NRG1) mRNA isoforms in clozapine-treated schizophrenia patients
Bibliographic record
Abstract
Abstract Differential expression of neuregulin-1 (NRG1) mRNA isoforms and proteins has been reported in schizophrenia, primarily in post-mortem brain tissue. In this study, we examined 12NRG1SNPs, eightNRG1mRNA isoforms (type I, type I(Ig2), type II, type III, type IV, EGFα, EGFβ, pan-NRG1) in whole blood, and NRG1-β1 protein in serum of clozapine-treated schizophrenia patients (N = 71) and healthy controls (N = 57). In addition, using cultured peripheral blood mononuclear cells (PBMC) from 15 healthy individuals, we examined the effect of clozapine onNRG1mRNA isoform and protein expression. We found elevated levels ofNRG1mRNA, specifically the EGFα (P = 0.0175), EGFβ (P = 0.002) and type I(Ig2)(P = 0.023) containing transcripts, but lower NRG1-β1 serum protein levels (P = 0.019) in schizophrenia patients compared to healthy controls. However, adjusting for smoking status attenuated the difference in NRG1-β1 serum levels (P = 0.050). Examination of clinical factors showedNRG1EGFα (P = 0.02) and EGFβ (P = 0.02) isoform expression was negatively correlated with age of onset. However, we found limited evidence thatNRG1mRNA isoform or protein expression was associated with current chlorpromazine equivalent dose or clozapine plasma levels, the latter corroborated by our PBMC clozapine exposure experiment. Our SNP analysis found no robust expression quantitative trait loci. Our results represent the first comprehensive investigation ofNRG1isoforms and protein expression in the blood of clozapine-treated schizophrenia patients and suggest levels of someNRG1transcripts are upregulated in those with schizophrenia.
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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) | 0.002 | 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".