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      Genomic Analysis Reveals Disruption of Striatal Neuronal Development and Therapeutic Targets in Human Huntington’s Disease Neural Stem Cells

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          Summary

          We utilized induced pluripotent stem cells (iPSCs) derived from Huntington’s disease (HD) patients as a human model of HD and determined that the disease phenotypes only manifest in the differentiated neural stem cell (NSC) stage, not in iPSCs. To understand the molecular basis for the CAG repeat expansion-dependent disease phenotypes in NSCs, we performed transcriptomic analysis of HD iPSCs and HD NSCs compared to isogenic controls. Differential gene expression and pathway analysis pointed to transforming growth factor β (TGF-β) and netrin-1 as the top dysregulated pathways. Using data-driven gene coexpression network analysis, we identified seven distinct coexpression modules and focused on two that were correlated with changes in gene expression due to the CAG expansion. Our HD NSC model revealed the dysregulation of genes involved in neuronal development and the formation of the dorsal striatum. The striatal and neuronal networks disrupted could be modulated to correct HD phenotypes and provide therapeutic targets.

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          Highlights

          • HD stem cell models have phenotypes in the differentiated NSC and not the IPSC state

          • Bioinformatic analysis of RNA-seq data identifies TGF-β and netrin-1 in HD phenotypes

          • WGCNA analysis in the HD-NSC linked module to development of the dorsal striatum

          • Modulation of TGF-β and netrin-1 in HD-NSCs is neuroprotective

          Abstract

          Ellerby and colleagues use expression profiling and bioinformatic analysis to identify critical pathways mediating Huntington’s disease (HD) phenotypes in neural stem cells. They identify and show modulation of TFG-β and netrin-1 signaling is relevant to HD phenotypes found in neural stem cells and not iPSCs. Further, HD phenotypes are linked to genes in the dorsal striatum.

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          Most cited references36

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          A translational profiling approach for the molecular characterization of CNS cell types.

          The cellular heterogeneity of the brain confounds efforts to elucidate the biological properties of distinct neuronal populations. Using bacterial artificial chromosome (BAC) transgenic mice that express EGFP-tagged ribosomal protein L10a in defined cell populations, we have developed a methodology for affinity purification of polysomal mRNAs from genetically defined cell populations in the brain. The utility of this approach is illustrated by the comparative analysis of four types of neurons, revealing hundreds of genes that distinguish these four cell populations. We find that even two morphologically indistinguishable, intermixed subclasses of medium spiny neurons display vastly different translational profiles and present examples of the physiological significance of such differences. This genetically targeted translating ribosome affinity purification (TRAP) methodology is a generalizable method useful for the identification of molecular changes in any genetically defined cell type in response to genetic alterations, disease, or pharmacological perturbations.
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            Making sense of latent TGFbeta activation.

            TGFbeta is secreted as part of a latent complex that is targeted to the extracellular matrix. A variety of molecules, 'TGFbeta activators,' release TGFbeta from its latent state. The unusual temporal discontinuity of TGFbeta synthesis and action and the panoply of TGFbeta effects contribute to the interest in TGF-beta. However, the logical connections between TGFbeta synthesis, storage and action are obscure. We consider the latent TGFbeta complex as an extracellular sensor in which the TGFbeta propeptide functions as the detector, latent-TGFbeta-binding protein (LTBP) functions as the localizer, and TGF-beta functions as the effector. Such a view provides a logical continuity for various aspects of TGFbeta biology and allows us to appreciate TGFbeta biology from a new perspective.
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              When Is Hub Gene Selection Better than Standard Meta-Analysis?

              Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when) hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data). Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis) and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility) in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA) in three comprehensive and unbiased empirical studies: (1) Finding genes predictive of lung cancer survival, (2) finding methylation markers related to age, and (3) finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, standard meta-analysis methods perform as good as (if not better than) a consensus network approach in terms of validation success (criterion 2). The article also reports a comparison of meta-analysis techniques applied to gene expression data and presents novel R functions for carrying out consensus network analysis, network based screening, and meta analysis.
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                Author and article information

                Contributors
                Journal
                Stem Cell Reports
                Stem Cell Reports
                Stem Cell Reports
                Elsevier
                2213-6711
                08 December 2015
                08 December 2015
                08 December 2015
                : 5
                : 6
                : 1023-1038
                Affiliations
                [1 ]Buck Institute for Research on Aging, Novato, CA 94945, USA
                [2 ]Departments of Neurology and Psychiatry, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
                Author notes
                []Corresponding author lellerby@ 123456buckinstitute.org
                [3]

                Co-first author

                Article
                S2213-6711(15)00341-0
                10.1016/j.stemcr.2015.11.005
                4682390
                26651603
                8ad02cfa-950c-4bb8-8481-819f576f1540
                © 2015 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 4 May 2015
                : 2 November 2015
                : 12 November 2015
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