Brain differential gene expression and blood cross-validation of a molecular signature of patients with major depressive disorder

Lymphoblast transcriptome analysis in 22q11.2 deletion syndrome individuals with schizophrenia-spectrum disorder

OBJECTIVES

22q11.2 deletion is the most prominent risk factor for schizophrenia (SZ). The aim of the present study was to identify unique transcriptome profile for 22q11.2 deletion syndrome (DS)-related SZ-spectrum disorder (SZ-SD).

METHODS

We performed RNA-Seq screening in lymphoblasts collected from 20 individuals with 22q11.2DS (10 men and 10 women, four of each sex with SZ-SD and six with no psychotic disorders (Np)).

RESULTS

Sex effect in RNA-Seq descriptive analysis led to separating the analyses between men and women. In women, only one differentially expressed gene (DEG), HLA-DQA2, was associated with SZ-SD. In men, 48 DEGs (adjp < 0.05) were found to be associated with SZ-SD. Ingenuity pathway analysis of top 85 DEGs (p < 4.66E - 04) indicated significant enrichment for immune-inflammatory response (IIR) and neuro-inflammatory signalling pathways. Additionally, NFATC2, IFNG, IFN-alpha, STAT1 and IL-4 were identified as upstream regulators. Co-expression network analysis revealed the contribution of endoplasmic reticulum protein processing and N-Glycan biosynthesis. These findings indicate dysregulation of IIR and post-translational protein modification processes in individuals with 22q11.2DS-related SZ-SD.

CONCLUSIONS

Candidate pathways and upstream regulators may serve as novel biomarkers and treatment targets for SZ. Future transcriptome studies, including larger samples and proteomic analysis, are needed to substantiate our findings.

Integrative bioinformatics and artificial intelligence analyses of transcriptomics data identified genes associated with major depressive disorders including NRG1

Major depressive disorder (MDD) is a common mental disorder and is amongst the most prevalent psychiatric disorders. MDD remains challenging to diagnose and predict its onset due to its heterogeneous phenotype and complex etiology. Hence, early detection using diagnostic biomarkers is critical for rapid intervention. In this study, a mixture of AI and bioinformatics were used to mine transcriptomic data from publicly available datasets including 170 MDD patients and 121 healthy controls. Bioinformatics analysis using gene set enrichment analysis (GSEA) and machine learning (ML) algorithms were applied. The GSEA revealed that differentially expressed genes in MDD patients are mainly enriched in pathways related to immune response, inflammatory response, neurodegeneration pathways and cerebellar atrophy pathways. Feature selection methods and ML provided predicted models based on MDD-altered genes with ≥75% of accuracy. The integrative analysis between the bioinformatics and ML approaches identified ten key MDD-related biomarkers including NRG1, CEACAM8, CLEC12B, DEFA4, HP, LCN2, OLFM4, SERPING1, TCN1 and THBS1. Among them, NRG1, active in synaptic plasticity and neurotransmission, was the most robust and reliable to distinguish between MDD patients and healthy controls amongst independent external datasets consisting of a mixture of populations. Further evaluation using saliva samples from an independent cohort of MDD and healthy individuals confirmed the upregulation of NRG1 in patients with MDD compared to healthy controls. Functional mapping to the human brain regions showed NRG1 to have high expression in the main subcortical limbic brain regions implicated in depression. In conclusion, integrative bioinformatics and ML approaches identified putative non-invasive diagnostic MDD-related biomarkers panel for the onset of depression.

Differential Gene Expression and DNA Methylation in the Risk of Depression in LOAD Patients

Depression is common among late-onset Alzheimer's Disease (LOAD) patients. Only a few studies investigated the genetic variability underlying the comorbidity of depression in LOAD. Moreover, the epigenetic and transcriptomic factors that may contribute to comorbid depression in LOAD have yet to be studied. Using transcriptomic and DNA-methylomic datasets from the ROSMAP cohorts, we investigated differential gene expression and DNA-methylation in LOAD patients with and without comorbid depression. Differential expression analysis did not reveal significant association between differences in gene expression and the risk of depression in LOAD. Upon sex-stratification, we identified 25 differential expressed genes (DEG) in males, of which CHI3L2 showed the strongest upregulation, and only 3 DEGs in females. Additionally, testing differences in DNA-methylation found significant hypomethylation of CpG (cg20442550) on chromosome 17 (log2FC = -0.500, p = 0.004). Sex-stratified differential DNA-methylation analysis did not identify any significant CpG probes. Integrating the transcriptomic and DNA-methylomic datasets did not discover relationships underlying the comorbidity of depression and LOAD. Overall, our study is the first multi-omics genome-wide exploration of the role of gene expression and epigenome alterations in the risk of comorbid depression in LOAD patients. Furthermore, we discovered sex-specific differences in gene expression underlying the risk of depression symptoms in LOAD.