Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms.

Schizophrenia is a disastrous mental disorder. Identification of diagnostic biomarkers and therapeutic targets is of significant importance. In this study, five datasets of schizophrenia post-mortem prefrontal cortex samples were downloaded from the GEO database and then merged and de-batched for the analyses of differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA). The WGCNA analysis showed the six schizophrenia-related modules containing 12,888 genes. The functional enrichment analyses indicated that the DEGs were highly involved in immune-related processes and functions. The immune cell infiltration analysis with the CIBERSORT algorithm revealed 12 types of immune cells that were significantly different between schizophrenia subjects and controls. Additionally, by intersecting DEGs, WGCNA module genes, and an immune gene set obtained from online databases, 151 schizophrenia-associated immune-related genes were obtained. Moreover, machine learning algorithms including LASSO and Random Forest were employed to further screen out 17 signature genes, including GRIN1, P2RX7, CYBB, PTPN4, UBR4, LTF, THBS1, PLXNB3, PLXNB1, PI15, RNF213, CXCL11, IL7, ARHGAP10, TTR, TYROBP, and EIF4A2. Then, SVM-RFE was added, and together with LASSO and Random Forest, a hub gene (EIF4A2) out of the 17 signature genes was revealed. Lastly, in a schizophrenia rat model, the EIF4A2 expression levels were reduced in the model rat brains in a brain-regional dependent manner, but can be reversed by risperidone. In conclusion, by using various bioinformatic and biological methods, this study found 17 immune-related signature genes and a hub gene of schizophrenia that might be potential diagnostic biomarkers and therapeutic targets of schizophrenia.

Transcriptomic Analysis of the Effect of GAT-2 Deficiency on Differentiation of Mice Naïve T Cells Into Th1 Cells In Vitro.

The neurotransmitter γ-aminobutyric acid (GABA) is known to affect the activation and function of immune cells. This study investigated the role of GABA transporter (GAT)-2 in the differentiation of type 1 helper T (Th1) cells. Naïve CD4+ T cells isolated from splenocytes of GAT-2 knockout (KO) and wild-type (WT) mice were cultured; Th1 cell differentiation was induced and transcriptome and bioinformatics analyses were carried out. We found that GAT-2 deficiency promoted the differentiation of naïve T cells into Th1 cells. RNA sequencing revealed 2984 differentially expressed genes including 1616 that were up-regulated and 1368 that were down-regulated in GAT-2 KO cells compared to WT cells, which were associated with 950 enriched Gene Ontology terms and 33 enriched Kyoto Encyclopedia of Genes and Genomes pathways. Notably, 4 signal transduction pathways (hypoxia-inducible factor [HIF]-1, Hippo, phospholipase D, and Janus kinase [JAK]/signal transducer and activator of transcription [STAT]) and one metabolic pathway (glycolysis/gluconeogenesis) were significantly enriched by GAT-2 deficiency, suggesting that these pathways mediate the effect of GABA on T cell differentiation. Our results provide evidence for the immunomodulatory function of GABA signaling in T cell-mediated immunity and can guide future studies on the etiology and management of autoimmune diseases.