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Identification of Differential Expression Genes of Blood Leukocytes for Schizophrenia

ABSTRACT: Identification of Differential Expression Genes of Blood Leukocytes for Schizophrenia

PROVIDER: PRJNA1096042 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR28550725_1.fastq.gz	Fastqsanger.gz
	SRR28550725_2.fastq.gz	Fastqsanger.gz
	SRR28550726_1.fastq.gz	Fastqsanger.gz
	SRR28550726_2.fastq.gz	Fastqsanger.gz
	SRR28550727_1.fastq.gz	Fastqsanger.gz

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Identification of Differential Expression Genes of Blood Leukocytes for Schizophrenia

Project description:Schizophrenia (SCZ) is a severe neurodevelopmental disorder with brain dysfunction. This study aimed to use bioinformatic analysis to identify candidate blood biomarkers for SCZ. Our study identified 1205 statistically significant DEGs, of which 623 genes were upregulated and 582 genes were downregulated. Functional enrichment analysis showed that DEGs were mainly enriched in cell chemotaxis, cell surface, and serine peptidase activity, as well as involved in Natural killer cell-mediated cytotoxicity. WGCNA identified 16 gene co-expression modules, and 5 modules were significantly correlated with SCZ (P < 0.05). There were 106 upregulated genes and 90 downregulated genes in the 5 modules. The top ten genes sorted by the Degree algorithm were RPS28, BRD4, FUS, PABPC1, PCBP1, PCBP2, RPL27A, RPS21, RAG1, and RPL27. RAG1 and the other 9 genes belonged to the turquoise and pink module respectively. Pathway enrichment analysis indicated that these 10 genes were mainly involved in processes such as Ribosome, cytoplasmic translation, RNA binding, and protein binding.

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Identification of differentially expressed genes of blood leukocytes for Schizophrenia.

Project description:BackgroundSchizophrenia (SCZ) is a severe neurodevelopmental disorder with brain dysfunction. This study aimed to use bioinformatic analysis to identify candidate blood biomarkers for SCZ.MethodsThe study collected peripheral blood leukocyte samples of 9 SCZ patients and 20 healthy controls for RNA sequencing analysis. Bioinformatic analyses included differentially expressed genes (DEGs) analysis, pathway enrichment analysis, and weighted gene co-expression network analysis (WGCNA).ResultsThis study identified 1,205 statistically significant DEGs, of which 623 genes were upregulated and 582 genes were downregulated. Functional enrichment analysis showed that DEGs were mainly enriched in cell chemotaxis, cell surface, and serine peptidase activity, as well as involved in Natural killer cell-mediated cytotoxicity. WGCNA identified 16 gene co-expression modules, and five modules were significantly correlated with SCZ (p < 0.05). There were 106 upregulated genes and 90 downregulated genes in the five modules. The top ten genes sorted by the Degree algorithm were RPS28, BRD4, FUS, PABPC1, PCBP1, PCBP2, RPL27A, RPS21, RAG1, and RPL27. RAG1 and the other nine genes belonged to the turquoise and pink module respectively. Pathway enrichment analysis indicated that these 10 genes were mainly involved in processes such as Ribosome, cytoplasmic translation, RNA binding, and protein binding.ConclusionThis study finds that the gene functions in key modules and related enrichment pathways may help to elucidate the molecular pathogenesis of SCZ, and the potential of key genes to become blood biomarkers for SCZ warrants further validation.

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Project description:To address the heterogeneity of gene regulation and expression in cell types of a brain region with strong evidence for relevance to schizophrenia, we carried out whole genome methylation and expression analyses using post-mortem brain tissue from BA46 that underwent fluorescence-activated nuclei sorting (FANS). Importantly, we used whole genome bisulfite sequencing (WGBS) for methylation quantification to have an unbiased assessment of epigenetic modifications associated with schizophrenia, and additionally carried out whole genome sequencing (WGS) of the brain samples to account for genetic background differences. We found that a number of schizophrenia GWAS loci overlap with sites of cell-specific differential methylation in schizophrenia brain tissue. Differential methylation is also associated with differential gene expression in a cell-specific manner. Co-expression network analysis can identify groups of genes that are enriched for differentially methylated regions in each cell type, and these groups of co-expressed genes underscore shared molecular pathways that might be at risk in schizophrenia. Together, these data indicate that the integration of multiple levels of data in a cell-specific manner can provide novel biological insights into complex genetic disorders such as schizophrenia.

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