Identifying potential therapeutic targets for major depressive disorder via integration of plasma and cerebrospinal fluid proteomes with the brain transcriptome
Journal of Affective Disorders Reports, 2026
Zhu X., Shu Y., Zhang M., Zhang J.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Neurology | Pathophysiology | Plasma |  Olink Target 96  Olink Explore 3072/384 |
Abstract
Background
Major Depressive Disorder (MDD) remains one leading cause of global health burden. Human proteome represents a major source of therapeutic targets. We aimed to explore novel therapeutic targets for MDD via integration of plasma and cerebrospinal fluid proteomes with the brain transcriptome.
Methods
Using a statistical framework incorporating Mendelian randomization (MR), Bayesian colocalization, summary-data-based MR (SMR) and heterogeneity in dependent instruments (HEIDI) tests, we identified candidate proteins. Steiger filtering and reverse MR analyses were conducted to validate causal directionality, and findings were validated by replacing the exposure or outcome datasets. Then, we mapped candidate proteins to the transcriptomic profiles of 13 brain regions to identify potential targets. The Bonferroni correction was applied to control the family-wise error rate due to multiple testing. Lastly, protein-protein interaction (PPI) networks and druggability evaluation were employed to further prioritize potential antidepressant targets. Additionally, we conducted Spearman correlation analysis to examine the consistency of the shared proteins’ effects on MDD across CSF and plasma.
Results
We identified four potential antidepressant targets (ACAA1, ITIH4, IGLON5 and POR). The drug-gene interaction analysis suggested that ACAA1 may have antidepressant effects. ITIH4 maintained a robust causal association with MDD throughout various tests. The PPI analysis indicated ITIH4 and IGLON5 interacted with the current antidepressant targets. Notably, ITIH4 and POR were novel targets. Besides, Spearman correlation analysis showed that the effects of most overlapping proteins on MDD in CSF exhibited a positive correlation with those in plasma.
Conclusions
Our findings provide insights into the potential of CSF proteomics for the genetic prediction of antidepressant targets.