The organization of the transcriptional network in specific neuronal classes

Interdepartmental Program for Neuroscience, University of California Los Angeles, Los Angeles, CA, USA.
Molecular Systems Biology (Impact Factor: 10.87). 02/2009; 5(1):291. DOI: 10.1038/msb.2009.46
Source: PubMed


Genome-wide expression profiling has aided the understanding of the molecular basis of neuronal diversity, but achieving broad functional insight remains a considerable challenge. Here, we perform the first systems-level analysis of microarray data from single neuronal populations using weighted gene co-expression network analysis to examine how neuronal transcriptome organization relates to neuronal function and diversity. We systematically validate network predictions using published proteomic and genomic data. Several network modules of co-expressed genes correspond to interneuron development programs, in which the hub genes are known to be critical for interneuron specification. Other co-expression modules relate to fundamental cellular functions, such as energy production, firing rate, trafficking, and synapses, suggesting that fundamental aspects of neuronal diversity are produced by quantitative variation in basic metabolic processes. We identify two transcriptionally distinct mitochondrial modules and demonstrate that one corresponds to mitochondria enriched in neuronal processes and synapses, whereas the other represents a population restricted to the soma. Finally, we show that galectin-1 is a new interneuron marker, and we validate network predictions in vivo using Rgs4 and Dlx1/2 knockout mice. These analyses provide a basis for understanding how specific aspects of neuronal phenotypic diversity are organized at the transcriptional level.

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    • "We analyzed whole-transcriptome gene expression data from normal human brain and used weighted gene co-expression network analysis (WGCNA) to group NBIA genes into modules in an unsupervised manner (Langfelder and Horvath, 2008; Oldham et al., 2006, 2008; Zhang and Horvath, 2005). This systems-biology approach (Lee et al., 2004; Stuart et al., 2003) enables the identification of modules of biologically related genes that are co-expressed and co-regulated (Oldham et al., 2008; Konopka, 2011; Rosen et al., 2011; Winden et al., 2009), and can give insights on cell-specific molecular signatures (Oldham et al., 2008; Bettencourt et al., 2014; Forabosco et al., 2013). The main goal of this study was to expand our understanding of these iron overload diseases by identifying relationships and shared molecular pathways between known NBIA genes, and unraveling transcriptionally linked novel candidates to facilitate discovery of new genes associated with these diseases and of possible entry points to therapeutic intervention. "
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    ABSTRACT: Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention.
    Full-text · Article · Dec 2015 · Neurobiology of Disease
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    • "To further examine the functions of this group of genes, we examined the genes that were highly connected within this module because hub genes have been shown to be key drivers of module organization and function (Carlson et al., 2006; Winden et al., 2009). The most highly connected gene within this module is Msantd4, which is a poorly characterized gene that contains a MYB DNA binding domain (Fig. 5). "
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    ABSTRACT: The molecular basis of epileptogenesis is poorly characterized. Studies in humans and animal models have identified an electrophysiological signature that precedes the onset of epilepsy, which has been termed fast ripples (FRs) based on its frequency. Multiple lines of evidence implicate regions generating FRs in epileptogenesis, and FRs appear to demarcate the seizure onset zone, suggesting a role in ictogenesis as well. We performed gene expression analysis comparing areas of the dentate gyrus that generate FRs to those that do not generate FRs in a well-characterized rat model of epilepsy. We identified a small cohort of genes that are differentially expressed in FR versus non-FR brain tissue and used quantitative PCR to validate some of those that modulate neuronal excitability. Gene expression network analysis demonstrated conservation of gene co-expression between non-FR and FR samples, but examination of gene connectivity revealed changes that were most pronounced in the cm-40 module, which contains several genes associated with synaptic function and the differentially expressed genes Kcna4, Kcnv1, and Npy1r that are down-regulated in FRs. We then demonstrate that the genes within the cm-40 module are regulated by seizure activity and enriched for the targets of the RNA binding protein Elavl4. Our data suggest that seizure activity induces co-expression of genes associated with synaptic transmission and that this pattern is attenuated in areas displaying FRs, implicating the failure of this mechanism in the generation of FRs. Copyright © 2015. Published by Elsevier Inc.
    Full-text · Article · Mar 2015 · Neurobiology of Disease
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    • "Variability in gene expression levels can result from variability in the state of expression systems, which often involves stereotyped changes in thousands of genes, commonly referred to as gene expression programs[4], [20], [21]. We, and others, have found that covariance-based data analyses such as principal components analysis (PCA) can be used to study global changes in gene expression systems[4], [22], [23], [24], [25]. Though these methods are frequently used with time course data, variability in the state of gene expression systems, irrespective of the origin of variability, can be detected with PCA[4]. "
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    ABSTRACT: Selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine are the most common form of medication treatment for major depression. However, approximately 50% of depressed patients fail to achieve an effective treatment response. Understanding how gene expression systems respond to treatments may be critical for understanding antidepressant resistance. We take a novel approach to this problem by demonstrating that the gene expression system of the dentate gyrus responds to fluoxetine (FLX), a commonly used antidepressant medication, in a stereotyped-manner involving changes in the expression levels of thousands of genes. The aggregate behavior of this large-scale systemic response was quantified with principal components analysis (PCA) yielding a single quantitative measure of the global gene expression system state. Quantitative measures of system state were highly correlated with variability in levels of antidepressant-sensitive behaviors in a mouse model of depression treated with fluoxetine. Analysis of dorsal and ventral dentate samples in the same mice indicated that system state co-varied across these regions despite their reported functional differences. Aggregate measures of gene expression system state were very robust and remained unchanged when different microarray data processing algorithms were used and even when completely different sets of gene expression levels were used for their calculation. System state measures provide a robust method to quantify and relate global gene expression system state variability to behavior and treatment. State variability also suggests that the diversity of reported changes in gene expression levels in response to treatments such as fluoxetine may represent different perspectives on unified but noisy global gene expression system state level responses. Studying regulation of gene expression systems at the state level may be useful in guiding new approaches to augmentation of traditional antidepressant treatments.
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