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.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The development of novel high-throughput technologies has opened up the opportunity to deeply characterize patient tissues at various molecular levels and has given rise to a paradigm shift in medicine towards personalized therapies. Computational analysis plays a pivotal role in integrating the various genome data and understanding the cellular response to a drug. Based on that data, molecular models can be constructed that incorporate the known downstream effects of drug-targeted receptor molecules and that predict optimal therapy decisions. In this article, we describe the different steps in the conceptual framework of computational modeling. We review resources that hold information on molecular pathways that build the basis for constructing the model interaction maps, highlight network analysis concepts that have been helpful in identifying predictive disease patterns, and introduce the basic concepts of kinetic modeling. Finally, we illustrate this framework with selected studies related to the modeling of important target pathways affected by drugs.
    Dialogues in clinical neuroscience 12/2014; 16(4):465-77.
  • Source
  • [Show abstract] [Hide abstract]
    ABSTRACT: Inroads into elucidating the origins of human cognitive specializations have taken many forms, including genetic, genomic, anatomical, and behavioral assays that typically compare humans to non-human primates. While the integration of all of these approaches is essential for ultimately understanding human cognition, here, we review the usefulness of coexpression network analysis for specifically addressing this question. An increasing number of studies have incorporated coexpression networks into brain expression studies comparing species, disease versus control tissue, brain regions, or developmental time periods. A clearer picture has emerged of the key genes driving brain evolution, as well as the developmental and regional contributions of gene expression patterns important for normal brain development and those misregulated in cognitive diseases.
    Current Opinion in Genetics & Development 09/2014; 29C:52-59. DOI:10.1016/j.gde.2014.08.012 · 8.57 Impact Factor

Full-text (2 Sources)

Available from
May 31, 2014