MDD (Major Depressive Disorder Genes and Processes Database)

compiles and presents key genes, biological pathways, and master regulators associated with the development of major depressive disorder, derived from a comprehensive multi-omics analysis integrating genomic and transcriptomic data.

The database contains structured information on genes identified through a transcriptome-wide association study (TWAS) conducted across 15 brain regions, yielding 371 high-confidence major depressive disorder-associated genes, 269 of which were novel compared to prior TWAS studies. The genomic foundation rests on GWAS summary statistics from the large-scale meta-analysis by Howard et al. (807,553 individuals), from which 4,625 significant SNPs were mapped to genes using FUMA and Ensembl VEP. Gene annotations are enriched with functional data from KEGG, Reactome, and UniProt databases.

Key Functional Categories

Genes in the database are organized into biologically meaningful categories, including:

Database uniqueness

A notable feature of the database is its incorporation of sex-specific transcriptional data. We carried out differential expression analyses separately for males and females using postmortem brain RNA sequencing datasets (GEO: GSE80655, GSE101521, and GSE102556). These analyses revealed that many genes and pathways are altered in opposite directions between the sexes, which is a critical consideration for major depressive disorder diagnosis and pharmacotherapy.

The MDD database serves as a curated reference resource for researchers working on the molecular basis of depression, facilitating target identification for antidepressant drug discovery. By linking SNP-driven gene expression changes to downstream biological processes and master regulators, the database supports hypothesis generation for sex-stratified treatment strategies and the identification of novel drug targets beyond the classical monoamine system.

Practical Use

The MDD database can be directly applied in computational drug discovery workflows targeting major depressive disorder. Researchers can query the database to retrieve MDD-associated genes organized by functional categories - metabolic, signaling, transcriptional, or immune and use them as candidate targets for virtual screening or PASS-based activity prediction available elsewhere on the Way2Drug platform. Clinicians and pharmacologists may cross-reference identified master regulators and sex-specific gene expression profiles with existing antidepressant mechanisms to rationalize polypharmacology or repurposing strategies. The database also supports pathway enrichment analysis, as its gene sets are directly compatible with KEGG, Reactome, and Gene Ontology frameworks, enabling seamless integration into standard bioinformatics workflows.