An organelle proteomic method to study neurotransmission‐related proteins, applied to a neurodevelopmental model of schizophrenia
Bibliographic record
Abstract
Limited information is currently available on molecular events that underlie schizophrenia-like behaviors in animal models. Accordingly, we developed an organelle proteomic approach enabling the study of neurotransmission-related proteins in the prefrontal cortex (PFC) of postpubertal (postnatal day 60 (PD60)) neonatally ventral hippocampal (nVH) lesioned rats, an extensively used neurodevelopmental model of schizophrenia-like behaviors. The PFC was chosen because of its purported role in the etiology of the disease. Statistical analysis of 392 reproducible spots on 2-D organelle proteomic patterns revealed significant changes in intensity of 18 proteinous spots in plasma membrane-enriched fractions obtained from postpubertal nVH lesioned rats compared to controls. Mass spectrometric analysis and database searching allowed the identification of a single protein in each of the nine differential spots, including proteins of low abundance, such as neurocalcin delta. Most of the identified dysregulated proteins, including clathrin light chain B, syntaxin binding protein 1b and visinin-like protein 1 are known to be linked to various neurotransmitter systems and to play key roles in plasma membrane receptor expression and recycling as well as synaptic vesicle exocytosis/recycling. Organelle proteomic approaches have hence proved to be most useful to identify key proteins linked to a given behavior in animal models of brain diseases.
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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) | 0.001 | 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".