Integrative neuroimmunology reveals leukocyte-expressing PAX6 as a critical predictor of major depressive disorder
Bibliographic record
Abstract
Major depressive disorder (MDD) is a multifaceted psychiatric illness with profound global consequences. To illuminate its molecular underpinnings, we employed a genome-driven integrative systems neuroimmunology approach to analyze transcriptomic profiles from 3114 individuals (1877 MDD patients and 1237 controls). This analysis uncovered coordinated neuroimmune transcriptomic shifts, marked by altered expression of genes involved in innate immune regulation, suppression of inflammatory responses, and pathways related to sensory perception, visual system development, and synaptic signaling. These alterations were consistently observed in both peripheral blood leukocytes (PBLs) and brain regions implicated in MDD. Among 31 genes jointly dysregulated in blood and brain, four stood out as robust predictors of MDD in PBLs: NEGR1, PPP6C, SORCS3, and PAX6. Of these, PAX6, a gene previously linked to MDD by GWAS, also exhibited differential expression in the amygdala and was functionally enriched in pathways governing immune modulation, vesicle trafficking, and neurodevelopmental processes, such as neuronal fate determination. In contrast, NEGR1, PPP6C, and SORCS3 showed no significant changes in brain expression, suggesting a predominantly peripheral role. Importantly, these transcriptomic insights were reinforced in a murine model of chronic stress, where immunophenotyping revealed elevated PAX6 expression in peripheral myeloid cells. Together, these findings reveal a shared neuroimmune signature across the brain and immune system in MDD, highlighting PAX6 as a promising mechanistic link and potential biomarker for this disorder.
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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".