Exploring the mRNA expression level of RELN in peripheral blood of schizophrenia patients before and after antipsychotic treatment.

BACKGROUND: The Reelin (RELN) gene encodes the protein reelin, which is a large extracellular matrix glycoprotein that plays a key role in brain development. Additionally, this protein may be involved in memory formation, neurotransmission, and synaptic plasticity, which have been shown to be disrupted in schizophrenia (SCZ). A decreasing trend in the expression of RELN mRNA in the brain and peripheral blood of SCZ patients has been observed. There is a need to determine whether changes in RELN mRNA expression in SCZ patients are the result of long-term antipsychotic treatment rather than the etiological characteristics of schizophrenia. The expression levels of RELN mRNA in the peripheral blood of 48 healthy controls and 30 SCZ patients before and after 12-weeks of treatment were measured using quantitative real-time PCR. RESULTS: The expression levels of RELN mRNA in the SCZ group were significantly lower than that of healthy controls; however, after 12-weeks of antipsychotic treatment, RELN mRNA levels were significantly increased in the SCZ group. CONCLUSION: The up-regulation of RELN mRNA expression was current in SCZ patients after antipsychotic treatment, suggesting that the changes in RELN mRNA expression were related to the effect of the antipsychotic treatment.

Genome-wide analysis of DNA methylation in 106 schizophrenia family trios in Han Chinese.

BACKGROUND: Schizophrenia (SCZ) is a severe psychiatric disorder that affects approximately 0.75% of the global population. Both genetic and environmental factors contribute to development of SCZ. SCZ tends to run in family while both genetic and environmental factor contribute to its etiology. Much evidence suggested that alterations in DNA methylations occurred in SCZ patients. METHODS: To investigate potential inheritable pattern of DNA methylation in SCZ family, we performed a genome-wide analysis of DNA methylation of peripheral blood samples from 106 Chinese SCZ family trios. Genome-wide DNA methylations were quantified by Agilent 1 × 244 k Human Methylation Microarray. FINDINGS: In this study, we proposed a loci inheritance frequency model that allows characterization of differential methylated regions as SCZ biomarkers. Based on this model, 112 hypermethylated and 125 hypomethylated regions were identified. Additionally, 121 hypermethylated and 139 hypomethylated genes were annotated. The results of functional enrichment analysis indicated that multiple differentially methylated genes (DMGs) involved in Notch/HH/Wnt signaling, MAPK signaling, GPCR signaling, immune response signaling. Notably, a number of hypomethylated genes were significantly enriched in cerebral cortex and functionally enriched in nervous system development. INTERPRETATION: Our findings not only validated previously discovered risk genes of SCZ but also identified novel candidate DMGs in SCZ. These results may further the understanding of altered DNA methylations in SCZ. FUNDING: None.

Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis.

Type 2 diabetes mellitus (T2DM) is a multifactorial and multigenetic disease, and its pathogenesis is complex and largely unknown. In the present study, microarray data (GSE201966) of ß­cell enriched tissue obtained by laser capture microdissection were downloaded, including 10 control and 10 type 2 diabetic subjects. A comprehensive bioinformatics analysis of microarray data in the context of protein­protein interaction (PPI) networks was employed, combined with subcellular location information to mine the potential candidate genes for T2DM and provide further insight on the possible mechanisms involved. First, differential analysis screened 108 differentially expressed genes. Then, 83 candidate genes were identified in the layered network in the context of PPI via network analysis, which were either directly or indirectly linked to T2DM. Of those genes obtained through literature retrieval analysis, 27 of 83 were involved with the development of T2DM; however, the rest of the 56 genes need to be verified by experiments. The functional analysis of candidate genes involved in a number of biological activities, demonstrated that 46 upregulated candidate genes were involved in 'inflammatory response' and 'lipid metabolic process', and 37 downregulated candidate genes were involved in 'positive regulation of cell death' and 'positive regulation of cell proliferation'. These candidate genes were also involved in different signaling pathways associated with 'PI3K/Akt signaling pathway', 'Rap1 signaling pathway', 'Ras signaling pathway' and 'MAPK signaling pathway', which are highly associated with the development of T2DM. Furthermore, a microRNA (miR)­target gene regulatory network and a transcription factor­target gene regulatory network were constructed based on miRNet and NetworkAnalyst databases, respectively. Notably, hsa­miR­192­5p, hsa­miR­124­5p and hsa­miR­335­5p appeared to be involved in T2DM by potentially regulating the expression of various candidate genes, including procollagen C­endopeptidase enhancer 2, connective tissue growth factor and family with sequence similarity 105, member A, protein phosphatase 1 regulatory inhibitor subunit 1 A and C­C motif chemokine receptor 4. Smad5 and Bcl6, as transcription factors, are regulated by ankyrin repeat domain 23 and transmembrane protein 37, respectively, which might also be used in the molecular diagnosis and targeted therapy of T2DM. Taken together, the results of the present study may offer insight for future genomic­based individualized treatment of T2DM and help determine the underlying molecular mechanisms that lead to T2DM.