Shared Genetic Architecture Among Severe Mental Disorders: A System Biology Approach Based on Protein–Protein Interaction
Bibliographic record
Abstract
INTRODUCTION: The study explores shared genetic architecture among major psychiatric disorders-major depressive disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder-emphasizing their overlapping molecular pathways. Using public datasets, we identified shared genes and examined their functional implications through protein-protein interaction (PPI) networks and gene set enrichment analysis (GSEA). METHODS: Genes associated with each disorder were identified through the NCBI Gene database. Intersection analyses of gene sets were conducted using R to identify overlaps among the four disorders. STRING was used to predict PPI and conduct clustering analyses. Gene set enrichment analysis was performed to explore biological pathways, molecular functions, and cellular components. RESULTS: We identified 31 intersected genes across all four disorders. PPI analyses demonstrated significant network enrichment, revealing interconnected pathways related to inflammation, neurotransmission, and synaptic plasticity. Functional enrichment highlighted pathways such as cytokines signaling, dopaminergic transmission, and synaptic vesicle cycling. Tissue expression analysis indicated significant involvement of brain regions, including the anterior cingulate cortex and mesolimbic system. CONCLUSION: This study underscores the shared genetic underpinnings of severe psychiatric disorders, highlighting common biological processes, such as pro-inflammatory markers and synaptic signaling. These findings offer a transdiagnostic perspective, potentially informing novel therapeutic strategies for overlapping psychiatric conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".