The organization of the transcriptional network in specific neuronal classes

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. ### Synopsis Understanding the molecular basis of neuronal diversity has been aided by the ability to perform genome‐wide expression profiling, but achieving broad functional insight remains a considerable challenge. Systems‐level analyses that consider relationships between genes permit association of gene expression variation with specific cell phenotypes. Weighted gene co‐expression network analysis (WGCNA) groups functionally related genes into modules in an unsupervised manner ([Zhang and Horvath, 2005][1]; [Horvath et al , 2006][2]; [Oldham et al , 2006][3], [2008][4]), based on the self‐organizing properties of complex systems ([Barabasi and Albert, 1999][5]; [Ravasz et al , 2002][6]). The modularity of the system allows independent analysis of the components, and the identification of relationships between genes facilitates gene annotation based on network position without assumptions about gene function. Here, we analyzed published microarray data from single neuronal populations ([Sugino et al , 2006][7]) using WGCNA to organize the neuronal transcriptome and examine its relationship to cellular function. We identified 13 modules that had characteristic patterns of gene expression and enrichment for specific gene ontology categories. These modules were systematically validated using both transcriptional and proteomic data, showing that the identified co‐expression relationships are reproducible and biologically relevant on the protein level. We then show that many modules correspond to specific functions related to neuronal biology, allowing large‐scale annotation of function and a new perspective on neuronal diversity. For example, several modules correspond to developmental programs or origins of neuronal classes. One module corresponds to the subset of interneurons derived from the subpallium and contains all of the Distalless transcription factors that are expressed in the brain ([Panganiban and Rubenstein, 2002][8]). A group of genes within this module was specifically regulated in both somatostatin‐ and parvalbumin‐positive interneurons, and one of the most highly connected genes was galectin‐1 ( Lgals1) , which had no known role in these cell populations. Visualization of these genes illustrates that galectin‐1 and somatostatin are closely related, and we confirmed this using immunohistochemistry to show that nearly 80% of the galectin‐1‐positive cells were also somatostatin positive. These data indicate that galectin‐1 may serve as a useful marker for this class of cells. Neurons differ greatly in their characteristic firing activity, and we hypothesized that some modules would be related to this fundamental neuronal phenotype. We tested this by comparing physiological parameters to the gene expression patterns found within the modules. The module that had the highest correlation with firing rate was also enriched for proteins localized to mitochondria and involved in carboxylic acid metabolism. This suggests that the coupling between neuronal activity and oxidative energy production ([Kasischke et al , 2004][9]) extends to the transcriptional level. Neuronal morphology and metabolism are aspects of neuronal phenotypic diversity that we theorized might be reflected at the transcriptional level through variation in organellar composition. We tested this hypothesis by comparing modular organization to proteomic data from a large‐scale analysis of subcellular organelles ([Foster et al , 2006][10]), and other studies that focused on specific neuronal features (i.e. synaptosome ([Schrimpf et al , 2005][11]), postsynaptic density ([Collins et al , 2006][12]), presynaptic fraction ([Phillips et al , 2005][13]), synaptic vesicles ([Morciano et al , 2005][14])). We observed that two modules showed significant overrepresentation of mitochondrial proteins, but had distinct expression profiles and were related to different aspects of neuronal physiology, leading to the hypothesis that they represented different mitochondrial populations. Mitochondrial heterogeneity within neurons has been suggested earlier, with one population localized to the cell body and the other to synapses ([Lai et al , 1977][15]). We examined the hub genes of these modules and tested their ability to differentiate between mitochondrial populations ([Figure 6A][16]). We found that genes in the non‐synaptic mitochondrial module ( Phb...