Genetic regulation of Nrnx1 expression: an integrative cross-species analysis of schizophrenia candidate genes

Mozhui, Khyobeni; Wang, X.; Chen, J.; Mulligan, Megan K.; Li, Z.; Ingles, J.; Chen, X.; Lu, Lu; Williams, Robert W.

doi:10.1038/tp.2011.24

Cited by 33 publications

(24 citation statements)

References 92 publications

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“…GSK3B is a proline-directed serine–threonine kinase and involved in neuronal cell development (55). This gene had been reported to play important roles in many neurology diseases, such as Parkinson’s disease (56), bipolar disorder (57) and schizophrenia (58). NFAT5 belongs to the protein family of the nuclear factors of activated T cells (NFAT) and plays a key role in inducible gene transcription during the process of immune response.…”

Section: Resultsmentioning

confidence: 99%

Canonical correlation analysis for RNA-seq co-expression networks

Hong

Chen

Jin

et al. 2013

Nucleic Acids Research

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Digital transcriptome analysis by next-generation sequencing discovers substantial mRNA variants. Variation in gene expression underlies many biological processes and holds a key to unravelling mechanism of common diseases. However, the current methods for construction of co-expression networks using overall gene expression are originally designed for microarray expression data, and they overlook a large number of variations in gene expressions. To use information on exon, genomic positional level and allele-specific expressions, we develop novel component-based methods, single and bivariate canonical correlation analysis, for construction of co-expression networks with RNA-seq data. To evaluate the performance of our methods for co-expression network inference with RNA-seq data, they are applied to lung squamous cell cancer expression data from TCGA database and our bipolar disorder and schizophrenia RNA-seq study. The preliminary results demonstrate that the co-expression networks constructed by canonical correlation analysis and RNA-seq data provide rich genetic and molecular information to gain insight into biological processes and disease mechanism. Our new methods substantially outperform the current statistical methods for co-expression network construction with microarray expression data or RNA-seq data based on overall gene expression levels.

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Section: Resultsmentioning

confidence: 99%

Canonical correlation analysis for RNA-seq co-expression networks

Hong

Chen

Jin

et al. 2013

Nucleic Acids Research

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“…WGCNA was used to show that it was at the level of network structure and not differential expression that one could discriminate the nonhuman primate and human brain transcriptome and the regional brain differences in the human transcriptome [40], [41]. Importantly from a translational prospective, recent work has demonstrated that eQTL in combination with coexpression network analysis can identify novel candidate genes related to schizophrenia [42], [43]. The WGCNA has been used to dissect the transcriptome into modules associated with specific cell types (neurons, oligodendrocytes, astrocytes and microglia), specific organelles and synaptic functions [41]; our data replicated this observation.…”

Section: Discussionmentioning

confidence: 99%

Differential Network Analysis Reveals Genetic Effects on Catalepsy Modules

et al. 2013

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We performed short-term bi-directional selective breeding for haloperidol-induced catalepsy, starting from three mouse populations of increasingly complex genetic structure: an F2 intercross, a heterogeneous stock (HS) formed by crossing four inbred strains (HS4) and a heterogeneous stock (HS-CC) formed from the inbred strain founders of the Collaborative Cross (CC). All three selections were successful, with large differences in haloperidol response emerging within three generations. Using a custom differential network analysis procedure, we found that gene coexpression patterns changed significantly; importantly, a number of these changes were concordant across genetic backgrounds. In contrast, absolute gene-expression changes were modest and not concordant across genetic backgrounds, in spite of the large and similar phenotypic differences. By inferring strain contributions from the parental lines, we are able to identify significant differences in allelic content between the selected lines concurrent with large changes in transcript connectivity. Importantly, this observation implies that genetic polymorphisms can affect transcript and module connectivity without large changes in absolute expression levels. We conclude that, in this case, selective breeding acts at the subnetwork level, with the same modules but not the same transcripts affected across the three selections.

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“…Furthermore, significant numbers of dentate gyrus granule cells were histologically and electrophysiologically immature, more than would be predicted based on proliferation rates and the tempo of normal development, and these mice showed severe impairments in working memory . Neurexins (NRXNs) and neuroligins (NLGNs) are families of synaptic proteins, which are known to be risk factors for schizophrenia (Novak et al 2009;Rujescu et al 2009;Ikeda et al 2010;Gauthier et al 2011;Mozhui et al 2011;Sun et al 2011;Yue et al 2011). These proteins are synaptic cell-adhesion molecules involved in various neuronal processes, including differentiation, maturation, stabilization, and plasticity of both inhibitory and excitatory synapses (Bang and Owczarek 2013).…”

Section: Adult Neurogenesis and Psychiatric Disordersmentioning

confidence: 99%

Adult Neurogenesis and Psychiatric Disorders

Kang

Wen

Song

et al. 2016

Cold Spring Harb Perspect Biol

165

118

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Psychiatric disorders continue to be among the most challenging disorders to diagnose and treat because there is no single genetic or anatomical locus that is causative for the disease. Current treatments are often blunt tools used to ameliorate the most severe symptoms, at the risk of disrupting functional neural systems. There is a critical need to develop new therapeutic strategies that can target circumscribed functional or anatomical domains of pathology. Adult hippocampal neurogenesis may be one such domain. Here, we review the evidence suggesting that adult hippocampal neurogenesis plays a role in emotional regulation and forms of learning and memory that include temporal and spatial memory encoding and context discrimination, and that its dysregulation is associated with psychiatric disorders, such as affective disorders, schizophrenia, and drug addiction. Further, adult neurogenesis has proven to be an effective model to investigate basic processes of neuronal development and converging evidence suggests that aberrant neural development may be an etiological factor, even in late-onset diseases. Constitutive neurogenesis in the hippocampus of the mature brain reflects large-scale plasticity unique to this region and could be a potential hub for modulation of a subset of cognitive and affective behaviors that are affected by multiple psychiatric disorders.

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Genetic regulation of Nrnx1 expression: an integrative cross-species analysis of schizophrenia candidate genes

Cited by 33 publications

References 92 publications

Canonical correlation analysis for RNA-seq co-expression networks

Canonical correlation analysis for RNA-seq co-expression networks

Differential Network Analysis Reveals Genetic Effects on Catalepsy Modules

Adult Neurogenesis and Psychiatric Disorders

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