Integrative systems neuroimmunology reveals leukocyte-expressing PAX6 as a critical predictor of major depressive disorder
Bibliographic record
Abstract
ABSTRACT Major depressive disorder (MDD) is a complex psychiatric condition with a significant global impact. This study applied a genomic-driven integrative systems neuroimmunology approach to analyze transcriptomic data from 3,114 individuals (1,877 MDD patients and 1,237 controls). The analysis revealed neuroimmunological transcriptomic alterations, indicating cross-talk between the immune and nervous systems in peripheral blood mononuclear cells (PBMCs) and specific brain regions. Among 31 shared genes, NEGR1, PPP6C, SORCS3, and PAX6 emerged as significant predictors of MDD in patients’ PBMCs. Notably, PAX6 was also identified as a differentially expressed gene (DEG) in the amygdala, while NEGR1, PPP6C, and SORCS3 showed no significant differential expression in other central nervous system (CNS) regions. Validation by immunophenotyping in a mouse model of chronic stress demonstrated increased PAX6 expression in PBMCs, a gene previously associated with MDD in GWAS studies. Collectively, our findings suggest the existence of shared transcriptomic modules across the brain and immune system, highlighting PAX6 as a potential therapeutic target in MDD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".