Coexpression network analysis of neural tissue reveals perturbations in developmental processes in schizophrenia

Abstract

We performed integrated gene coexpression network analysis on two large microarray-based brain gene expression data sets generated from the prefrontal cortex obtained post-mortem from 101 subjects, 47 subjects with schizophrenia and 54 normal control subjects, ranging in age from 19 to 81 years. Twenty-eight modules of coexpressed genes with functional interpretations were detected in both normal subjects and those with schizophrenia. Significant overlap of “case” and “control” module composition was observed, indicating that extensive differences in underlying molecular connectivity are not likely driving pathology in schizophrenia. Modules of coexpressed genes were characterized according to disease association, cell type specificity, and the effects of aging. We find that genes with altered expression in schizophrenia clustered into distinct coexpression networks and that these were associated primarily with neurons. We further identified a robust effect of age on gene expression modules that differentiates normal subjects from those with schizophrenia. In particular, we report that normal age-related decreases in genes related to central nervous system developmental processes, including neurite outgrowth, neuronal differentiation, and dopamine-related cellular signaling, do not occur in subjects with schizophrenia during the aging process. Extrapolating these findings to earlier stages of development supports the concept that schizophrenia pathogenesis begins early in life and is associated with a failure of normal decreases in developmental-related gene expression. These findings provide a novel mechanism for the “developmental” hypothesis of schizophrenia on a molecular level.

An Evolutionary Biology Approach to Understanding Neurological Disorders

“…As might be expected for neurodevelopmental disorders, many ASD-and SCZ-implicated gene products participate in protein-protein interaction networks implicated in neuron function (Bourgeron, 2009;Bill & Geschwind, 2009;Gilman et al, 2011;Sun et al, 2010;Torkamani et al, 2010;Voineagu et al, 2011). These genetic and PPI studies are supported by pathological findings, as structural alterations of dendritic spines are associated with both SCZ and ASD (reviewed by Penzes et al, 2011).…”

Alternative transcription exceeds alternative splicing in generating the transcriptome diversity of cerebellar development

“…Multitranscript genes are associated with neurological disorders and transcript variants are aberrantly expressed in medulloblastoma Developmental gene expression signatures that are disrupted in diseases such as schizophrenia, have been identified by various studies (Torkamani et al 2010). Similarly, certain phenomena that occur during development were also observed in diseases, for example, epithelial mesenchymal transition, which is crucial for normal development and tissue repair and also participates in tumor metastasis (Thiery et al 2009;Hatten and Roussel 2011).…”

“…An autism-related gene list was downloaded from the AutDB database, which collects genes from various published genetics studies on human autism spectrum disorders (ASD) (Basu et al 2009). Human schizophrenia and MB gene lists were obtained from published gene expression studies (Kho et al 2004;Torkamani et al 2010). We found that ;85% of the genes that are connected to abnormal cerebellar development in mouse and the diseases-autism, schizophrenia, and MB in humans-belong to multitranscript genes, with ;50% of the genes being expressed from alternative promoters (Table 4).…”

An Integrative Genomic Study Implicates the Postsynaptic Density in the Pathogenesis of Bipolar Disorder

“…We used weighted gene correlation network analysis (WGCNA) , a widely used method that finds modules of highly correlated genes, relates these modules to one another, and tests the influence of sample phenotypes on gene expression correlations. WGCNA has been widely used to identify co-expressed gene networks in various human brain regions (Oldham et al, 2008), animals (Fuller et al, 2007;Langfelder et al, 2012), and in human phenotypes, including schizophrenia (Torkamani et al, 2010), autism (Voineagu et al, 2011), cancer (Clarke et al, 2013), aggressive behavior (Malki et al, 2014), BD (Chen et al, 2013a), and psoriasis (Li et al, 2014). However, aside from one study of a few gene networks (Hong et al, 2013), WGCNA has not yet been applied to the complete brain transcriptome in BD as revealed by RNA-seq.…”