Ruijie Geng

Objective To investigate the relation between nicotinic acetylcholine receptor subunit (nAChR) genes and schizophrenia, and the relation between tag single nucleotide polymorphism (rs1317286, rs1044396, rs6494212, rs16969968, and rs684513) and schizophrenia in Han Chinese people.Methods The protein-protein interaction (PPI) network among nAChR protein and 350 proteins encoded by schizophrenia-related susceptibility genes was constructed through the String database to explore whether nAChR genes were associated with schizophrenia in these known databases. Then, five single nucleotide polymorphisms (SNPs) of CHRNA3 (rs1317286), CHRNA4 (rs1044396), CHRNA7 (rs6494212), and CHRNA5 (rs16969968, rs684513) were analyzed in a sample of 1,035 schizophrenic patients and 816 healthy controls. The interaction between the markers was analyzed using multifactor dimensionality reduction (MDR) software. Power analysis was performed using the Quanto program.Results There are no significant differences in genotype or allele distribution were identified between the patients and controls (p>0.05). The haplotypes constructed by four markers rs1317286, rs6494212, rs16969968, and rs684513 were not associated with schizophrenia either. However, a significant association between models made of rs1317286, rs1044396, rs6494212, and rs684513 and schizophrenia was revealed in interaction analysis (p<0.05).Conclusion The nAChR protein may have effects on the development of schizophrenia through the interaction with proteins encoded by schizophrenia-related susceptibility genes, but no relation was found between selected polymorphisms and schizophrenia in the collected Han Chinese people. However, interaction analysis suggested four-SNP model has an important effect on schizophrenia.

Background: Major depressive disorder (MDD) is a leading psychiatric disorder that involves complex abnormal biological functions and neural networks. This study aimed to compare the changes in the network connectivity of different brain tissues under different pathological conditions, analyzed the biological pathways and genes that are significantly related to disease progression, and further predicted the potential therapeutic drug targets.Methods: Expression of differentially expressed genes (DEGs) were analyzed with postmortem cingulate cortex (ACC) and prefrontal cortex (PFC) mRNA expression profile datasets downloaded from the Gene Expression Omnibus (GEO) database, including 76 MDD patients and 76 healthy subjects in ACC and 63 MDD patients and 63 healthy subjects in PFC. The co-expression network construction was based on system network analysis. The function of the genes was annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Human Protein Reference Database (HPRD,http://www.hprd.org/) was used for gene interaction relationship mapping.Results: We filtered 586 DEGs in ACC and 616 DEGs in PFC for further analysis. By constructing the co-expression network, we found that the gene connectivity was significantly reduced under disease conditions (P = 0.04 in PFC andP = 1.227e−09 in ACC). Crosstalk analysis showed that CD19, PTDSS2 and NDST2 were significantly differentially expressed in ACC and PFC of MDD patients. Among them, CD19 and PTDSS2 have been targeted by several drugs in the Drugbank database. KEGG pathway analysis demonstrated that the function of CD19 and PTDSS2 were enriched with the pathway of Glycerophospholipid metabolism and T cell receptor signaling pathway.Conclusion: Co-expression network and tissue comparing analysis can identify signaling pathways and cross talk genes related to MDD, which may provide novel insight for understanding the molecular mechanisms of MDD.

Objective: Major depressive disorder (MDD) is a leading psychiatric disorder that involves complex abnormal biological functions and neural networks. This study aimed to compare the changes in the network connectivity of different brain tissues under different pathological conditions, analyzed the biological pathways and genes that are significantly related to disease progression, and further predicted the potential therapeutic drug targets.Methods: Expression of differentially expressed genes (DEGs) were analyzed with postmortem cingulate cortex (ACC) and prefrontal cortex (PFC) mRNA expression profile datasets downloaded from the Gene Expression Omnibus (GEO) database, including 76 MDD patients and 76 healthy subjects in ACC and 63 MDD patients and 63 healthy subjects in PFC. The co-expression network construction was based on system network analysis. The function of the genes was annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Human Protein Reference Database (HPRD, http://www.hprd.org/) was used for gene interaction relationship mapping.Results: We filtered 586 DEGs in ACC and 616 DEGs in PFC for further analysis. By constructing the Co-expression network, we found that the gene connectivity was significantly reduced under disease conditions (P=0.04 in PFC and P=1.227e-09 in ACC). Crosstalk analysis showed that CD19, PTDSS2 and NDST2 were significantly differentially expressed in ACC and PFC of MDD patients. Among them, CD19 and PTDSS2 have been targeted by several drugs in the Drugbank database. KEGG pathway analysis demonstrated that the function of CD19 and PTDSS2 were enriched with the pathway of Glycerophospholipid metabolism and T cell receptor signaling pathway. Conclusion: Co-expression network and tissue comparing analysis can identify signaling pathways and cross talk genes related to MDD, which may provide novel insight for understanding the molecular mechanisms of MDD.

Objective : Major depressive disorder (MDD) is a neurologic disorder, involving complex abnormal biological functions and the neural network. In this study, we aimed to compare different network connectivity of pathological changes of brain tissues under different conditions, and dynamic analyze the biological pathways and genes significantly associated with disease progression, and further to predict potential drug targets. Methods : Datasets of mRNA expression profiles of samples of postmortem anterior cingulate cortex (ACC) and prefrontal cortex (PFC) from patients with MDD were downloaded from GEO database. We used a method based on system network analysis. Correlation networks based on co-expression were constructed. Topological properties of the networks were analyzed and compared. Results : Our results showed that the lesions of brain tissues in MDD patients were not synchronized and alterations of biological functions were not consistent either. ACC showed a greater degree of abnormality as compared to PFC suggesting a higher correlation with disease progression. We consequently analyzed the signaling pathways enriched by DEGs and further cross talk genes that bridge the multiple pathways were also identified. Through construction of the pathway-gene complex network, the genes and signaling pathways with top10 degrees were extracted, which are more likely to be the potential novel therapeutic targets. We also mined the drugbank database for the top10 cross talk genes to explore their drugable target potential. The results suggested that CACNA1A, PTDSS2 and CD19 differentially expressed in both ACC and PFC may correlate with depression progression or other cerebral nerve system related diseases, are more likely to become the new drug targets. Conclusion : Co-expression network and tissue comparing analysis are capable to identify MDD disease related signaling pathways and cross talk genes with potential to be novel drug targets.