RESUMEN
Post-stroke depression (PSD) is a common cerebrovascular complication characterized by complex pathogenesis and poor treatment effects. Here, we tested the influence of differentially expressed genes (DEGs), non-targeted metabolites, and intestinal microbes on the occurrence and development of PSD. We acquired gene expression profiles for stroke patients, depression patients, and healthy controls from the Gene Expression Omnibus database. After screening for DEGs using differential expression analysis, we identified common DEGs in stroke and depression patients that were considered to form the molecular basis of PSD. Functional enrichment analysis of DEGs also revealed that the majority of biological functions were closely related to metabolism, immunity, the nervous system, and microorganisms, and we also collected blood and stool samples from healthy controls, stroke patients, and PSD patients and performed 16S rDNA sequencing and untargeted metabolomics. After evaluating the quality of the sequencing data, we compared the diversity of the metabolites and intestinal flora within and between groups. Metabolic pathway enrichment analysis was used to identify metabolic pathways that were significantly involved in stroke and PSD, and a global metabolic network was constructed to explore the pathogenesis of PSD. Additionally, we constructed a global regulatory network based on 16S rDNA sequencing, non-targeted metabolomics, and transcriptomics to explore the pathogenesis of PSD through correlation analysis. Our results suggest that intestinal flora associates the dysregulation of cerebral cortex gene expression and could potentially promote the occurrence of depression by affecting the metabolism of stroke patients. Our findings may be helpful in identifying new targets for the prevention and treatment of PSD.
RESUMEN
PURPOSE: The purpose of this study was to investigate the potential pathogenic mechanisms of post-intracerebral hemorrhage depression. METHODS: Profiles of gene expression in brain tissue of patients with intracerebral hemorrhage (ICH) or depression were downloaded from the Gene Expression Omnibus (GEO) database. We analyzed differentially expressed genes (DEGs) for the two diseases separately. With these DEGs, we conducted an enrichment analysis based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) as well as cross-talk analysis, then we identified hub bridge genes using integrated bridge landscape analysis. RESULTS: We found 131 DEGs for interaction between ICH and depression. In the enrichment analysis, we found 55 GO terms and KEGG pathways involving interacting genes of ICH and depression, and 10 GO terms and 10 KEGG pathways most significantly related to cross-talk between ICH and depression. In the integrated bridge landscape analysis, we identified 20 hub bridge genes. In further analysis, we found that hub bridge genes HLA-A, HMOX1, and JUN related to endocytosis, cell adhesion, and phagosomes may exert their effects through the dopamine (DA) system and the serotonergic pathway post-ICH depression. HLA-A may play a role in the occurrence and development of ICH and depression through immune mediation and cell adhesion. HMOX1 and JUN may participate in the mechanism by interacting with HLA-A. CONCLUSION: Through bioinformatics analysis, we identified potential hub bridge genes and pathways related to post-ICH depression. Our study provides references for further research on mechanisms on the pathogenesis of post-ICH depression.