Integrated genomics and proteomics to define huntingtin CAG length-dependent networks in HD Mice

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Abstract

To gain insight into how mutant huntingtin ( mHtt) CAG repeat length modifies Huntington’s disease (HD) pathogenesis, we profiled mRNA in over 600 brain and peripheral tissue samples from HD knock-in mice with increasing CAG repeat lengths. We find repeat length dependent transcriptional signatures are prominent in the striatum, less so in cortex, and minimal in the liver. Co-expression network analyses reveal 13 striatal and 5 cortical modules that are highly correlated with CAG length and age, and that are preserved in HD models and some in the patients. Top striatal modules implicate mHtt CAG length and age in graded impairment of striatal medium spiny neuron identity gene expression and in dysregulation of cAMP signaling, cell death, and protocadherin genes. Importantly, we used proteomics to confirm 790 genes and 5 striatal modules with CAG length-dependent dysregulation at both RNA and protein levels, and validated 22 striatal module genes as modifiers of mHtt toxicities in vivo.

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Most cited references 42

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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Yoav Benjamini, Yosef Hochberg (1995)

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A general framework for weighted gene co-expression network analysis.

Bin Zhang, Steve Horvath (2005)

Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for ;soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.

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A map of the cis-regulatory sequences in the mouse genome.

Yin Shen, Feng Yue, David F. McCleary … (2012)

The laboratory mouse is the most widely used mammalian model organism in biomedical research. The 2.6 × 10(9) bases of the mouse genome possess a high degree of conservation with the human genome, so a thorough annotation of the mouse genome will be of significant value to understanding the function of the human genome. So far, most of the functional sequences in the mouse genome have yet to be found, and the cis-regulatory sequences in particular are still poorly annotated. Comparative genomics has been a powerful tool for the discovery of these sequences, but on its own it cannot resolve their temporal and spatial functions. Recently, ChIP-Seq has been developed to identify cis-regulatory elements in the genomes of several organisms including humans, Drosophila melanogaster and Caenorhabditis elegans. Here we apply the same experimental approach to a diverse set of 19 tissues and cell types in the mouse to produce a map of nearly 300,000 murine cis-regulatory sequences. The annotated sequences add up to 11% of the mouse genome, and include more than 70% of conserved non-coding sequences. We define tissue-specific enhancers and identify potential transcription factors regulating gene expression in each tissue or cell type. Finally, we show that much of the mouse genome is organized into domains of coordinately regulated enhancers and promoters. Our results provide a resource for the annotation of functional elements in the mammalian genome and for the study of mechanisms regulating tissue-specific gene expression.

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Author and article information

Journal

Journal ID (nlm-journal-id): 9809671

Journal ID (pubmed-jr-id): 21092

Journal ID (nlm-ta): Nat Neurosci

Journal ID (iso-abbrev): Nat. Neurosci.

Title: Nature neuroscience

ISSN (Print): 1097-6256

ISSN (Electronic): 1546-1726

Publication date Nihms-submitted: 24 May 2018

Publication date (Electronic): 22 February 2016

Publication date (Print): April 2016

Publication date PMC-release: 01 June 2018

Volume: 19

Issue: 4

Pages: 623-633

Affiliations

[1 ]Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA

[2 ]Center for Neurobehavioral Genetics, Semel Institute for Neuroscience & Human Behavior, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA

[3 ]Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA

[4 ]UCLA Brain Research Institute, Los Angeles, CA 90095, USA

[5 ]Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA

[6 ]Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA

[7 ]Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA

[8 ]Evotec (Munchen) GmbH, Am Klopferspitz 19a, 82152 Martinsried, Germany

[9 ]CHDI Foundation/CHDI Management Inc., Princeton NJ 08540, USA

[10 ]Department of Biostatistics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA

Author notes

[* ]Co-corresponding authors: X.W.Y. ( xwyang@ 123456mednet.ucla.edu ); S.H. ( shorvath@ 123456mednet.ucla.edu )

Article

Manuscript ID: NIHMS968393

DOI: 10.1038/nn.4256

PMC ID: 5984042

PubMed ID: 26900923

SO-VID: 524daecf-2151-4b8d-b185-d16c9917f333

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Integrated genomics and proteomics to define huntingtin CAG length-dependent networks in HD Mice

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Most cited references 42

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

A general framework for weighted gene co-expression network analysis.

A map of the cis-regulatory sequences in the mouse genome.

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