Combining bioinformatics, network pharmacology and artificial intelligence to predict the target genes of S-ketamine for treating major depressive disorder.

Background: Ketamine has received attention owing to its rapid and long-lasting antidepressant effects; however, its clinical application is restricted by its addictiveness and adverse effects. S-ketamine, which is the S-enantiomer of ketamine, is considered safer and better tolerated by patients than ketamine.

Aims: This study aimed to identify the key gene targets and potential signalling pathways associated with the mechanism of S-ketamine in major depressive disorder (MDD) treatment.

Methods: The GSE98793 dataset was extracted from the Gene Expression Omnibus database, and differentially expressed genes were identified in blood samples from patients with MDD and healthy individuals. The hub genes among the differentially expressed genes were identified and enrichment analysis was performed. The therapeutic targets and related signalling pathways of S-ketamine in MDD treatment were analysed. The 3D structures of the target proteins were predicted using AlphaFold2, and molecular docking was performed to verify whether S-ketamine could be successfully docked to the predicted targets. A quantitative polymerase chain reaction was performed to determine the effect of ketamine on the screened targets. Among 228 target genes annotated using pharmacophore target gene analysis, 3 genes were identified and 2 therapeutic signalling pathways were discovered.

Results: S-ketamine exerts downregulatory effects on TGM2 and HSP90AB1 expression but exerts an up-regulatory effect on ADORA3 expression. The protein structures of the therapeutic targets were successfully predicted using AlphaFold2.

Conclusions: S-ketamine may alleviate depression by targeting specific genes, including TGM2, HSP90AB1 and ADORA3, as well as signalling pathways, including the gonadotropin-releasing hormone and relaxin signalling pathways.