Peripheral Transcription of NRG-ErbB Pathway Genes Are Upregulated in Treatment-Resistant Schizophrenia
Bibliographic record
Abstract
Investigation of peripheral gene expression patterns of transcripts within the NRG-ErbB signaling pathway, other than neuregulin-1 (NRG1), among patients with schizophrenia and more specifically treatment-resistant schizophrenia (TRS) is limited. The present study built on our previous work demonstrating elevated levels of NRG1 EGFα, EGFβ and type I(Ig2) containing transcripts in TRS by investigating 11 NRG-ErbB signaling pathway mRNA transcripts (NRG2, ErbB1, ErbB2, ErbB3, ErbB4, PIK3CD, PIK3R3, AKT1, mTOR, P70S6K, eIF4EBP1) in whole blood of TRS patients (N=71) and healthy controls (N=57). We also examined the effect of clozapine exposure on transcript levels using cultured peripheral blood mononuclear cells (PBMCs) from 15 healthy individuals. Five transcripts (ErbB3, PIK3CD, AKT1, P70S6K, eIF4EBP1) were significantly elevated in TRS patients compared to healthy controls but only expression of P70S6K (Pcorrected=0.018), a protein kinase linked to protein synthesis, cell growth, and cell proliferation, survived correction for multiple testing using Benjamini-Hochberg method. Investigation of clinical factors revealed that ErbB2, PIK3CD, PIK3R3, AKT1, mTOR, and P70S6K expression was negatively correlated with duration of illness. However, no transcript was associated with chlorpromazine equivalent dose or clozapine plasma levels, the latter supported by in-vitro PBMC clozapine exposure experiment. Taken together with previously published NRG1 results, our findings suggest an overall upregulation of transcripts within the NRG-ErbB signaling pathway among individuals with schizophrenia some of which attenuate over duration of illness. Follow-up studies are needed to determine if the observed peripheral upregulation of transcripts within the NRG-ErbB signaling pathway are specific to TRS or are a general blood-based marker of 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) | 0.001 | 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".