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: 14.1). 02/2009; 5:291. DOI: 10.1038/msb.2009.46
Source: PubMed

ABSTRACT 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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    • "Weighted Gene Co-expression Network Analysis identifies a TREM2-containing module, which is highly preserved in all brain regions To gain insights into the functional organization of the brain transcriptome, we used the WGCNA package (Langfelder and Horvath, 2008; Zhang and Horvath, 2005) and focused on TREM2. This analytic approach uses the degree of gene neighbourhood sharing, as defined on the basis of co-expression relationships, to identify groups or modules of genes that are highly co-expressed and (by implication) functionally related (Geschwind and Konopka, 2009; Miller et al., 2008; Oldham et al., 2008; Winden et al., 2009). We used WGCNA to analyze gene expression data on 15,409 transcripts equating to 13,706 genes (passing filtering as described in Methods) to construct signed weighted gene co-expression networks for each of the 10 brain regions (see Methods). "
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    • "A complete list of module assignments and network metrics for all genes is included in File S3. The WGCNA approach has been used to generate robust networks in several diverse applications (Chen et al. 2008; Gargalovic et al. 2006; Ghazalpour et al. 2006; Horvath et al. 2006; Oldham et al. 2008; van Nas et al. 2009; Winden et al. 2009), including experiments with a similar or smaller number of samples relative to this study (Gargalovic et al. 2006; Gong et al. 2007). Most WGCNA analyses, however, use a series of preliminary filtering steps to select the most biologically meaningful genes for network construction (Ghazalpour et al. 2006). "
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    • "The original microarray dataset was deposited by Sugino et al. (2006) in the Gene Expression Omnibus ( with accession number GSE2882. WGCNA was performed as previously described (Winden et al., 2009). More detailed conditions are included in the Supplemental Experimental Procedures section. "
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