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Integrated genomics and proteomics define huntingtin CAG length–dependent networks in mice

Nature Neuroscience volume 19, pages 623–633 (2016)Cite this article

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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 found repeat length-dependent transcriptional signatures to be prominent in the striatum, less so in cortex, and minimal in the liver. Coexpression network analyses revealed 13 striatal and 5 cortical modules that correlated highly with CAG length and age, and that were preserved in HD models and sometimes in patients. Top striatal modules implicated mHtt CAG length and age in graded impairment in the expression of identity genes for striatal medium spiny neurons and in dysregulation of cyclic AMP signaling, cell death and protocadherin genes. We used proteomics to confirm 790 genes and 5 striatal modules with CAG length–dependent dysregulation at the protein level, and validated 22 striatal module genes as modifiers of mHtt toxicities in vivo.

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Figure 1: Workflow and differential expression analysis with respect to Htt CAG length.
Figure 2: Consensus coexpression network analysis of striatum and cortex identifies multiple CAG length–dependent modules.
Figure 3: Association of modules with genotype in mouse and disease status in human data and enrichment in human HD-dependent genes.
Figure 4: Striatal markers in module M2 undergo early and progressive CAG length–dependent changes.
Figure 5: Cell death genes in striatum module M7.
Figure 6: Protocadherin dysregulation across multiple modules.
Figure 7: High-throughput proteomic analysis confirms CAG length–dependent changes in 6-month striatum of HD mice.
Figure 8: Genetic perturbation studies in a fly model expressing an mHTT fragment.

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References

  1. Ross, C.A. et al. Huntington disease: natural history, biomarkers and prospects for therapeutics. Nat. Rev. Neurol. 10, 204–216 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Vonsattel, J.P. & DiFiglia, M. Huntington disease. J. Neuropathol. Exp. Neurol. 57, 369–384 (1998).

    Article  CAS  PubMed  Google Scholar 

  3. The Huntington's Disease Collaborative Research Group. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington's disease chromosomes. Cell 72, 971–983 (1993).

    Article  Google Scholar 

  4. Orr, H.T. & Zoghbi, H.Y. Trinucleotide repeat disorders. Annu. Rev. Neurosci. 30, 575–621 (2007).

    Article  CAS  PubMed  Google Scholar 

  5. Gusella, J.F. & MacDonald, M.E. Molecular genetics: unmasking polyglutamine triggers in neurodegenerative disease. Nat. Rev. Neurosci. 1, 109–115 (2000).

    Article  CAS  PubMed  Google Scholar 

  6. Gusella, J.F. & MacDonald, M.E. Huntington's disease: seeing the pathogenic process through a genetic lens. Trends Biochem. Sci. 31, 533–540 (2006).

    Article  CAS  PubMed  Google Scholar 

  7. Aylward, E.H. et al. PREDICT-HD Investigators and Coordinators of the Huntington Study Group. Regional atrophy associated with cognitive and motor function in prodromal Huntington disease. J. Huntingtons Dis. 2, 477–489 (2013).

    PubMed  PubMed Central  Google Scholar 

  8. Biagioli, M. et al. Htt CAG repeat expansion confers pleiotropic gains of mutant huntingtin function in chromatin regulation. Hum. Mol. Genet. 24, 2442–2457 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Seong, I.S. et al. HD CAG repeat implicates a dominant property of huntingtin in mitochondrial energy metabolism. Hum. Mol. Genet. 14, 2871–2880 (2005).

    Article  CAS  PubMed  Google Scholar 

  10. Wang, N. et al. Neuronal targets for reducing mutant huntingtin expression to ameliorate disease in a mouse model of Huntington's disease. Nat. Med. 20, 536–541 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Pouladi, M.A., Morton, A.J. & Hayden, M.R. Choosing an animal model for the study of Huntington's disease. Nat. Rev. Neurosci. 14, 708–721 (2013).

    Article  CAS  PubMed  Google Scholar 

  12. Menalled, L.B. et al. Comprehensive behavioral and molecular characterization of a new knock-in mouse model of Huntington's disease: zQ175. PLoS One 7, e49838 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Menalled, L.B. et al. Early motor dysfunction and striosomal distribution of huntingtin microaggregates in Huntington's disease knock-in mice. J. Neurosci. 22, 8266–8276 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Smith, G.A. et al. Progressive axonal transport and synaptic protein changes correlate with behavioral and neuropathological abnormalities in the heterozygous Q175 KI mouse model of Huntington's disease. Hum. Mol. Genet. 23, 4510–4527 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Van Raamsdonk, J.M. et al. Testicular degeneration in Huntington disease. Neurobiol. Dis. 26, 512–520 (2007).

    Article  CAS  PubMed  Google Scholar 

  16. Mielcarek, M. et al. Dysfunction of the CNS-heart axis in mouse models of Huntington's disease. PLoS Genet. 10, e1004550 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Langfelder, P. & Horvath, S. Eigengene networks for studying the relationships between co-expression modules. BMC Syst. Biol. 1, 54 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Horvath, S. & Dong, J. Geometric interpretation of gene coexpression network analysis. PLOS Comput. Biol. 4, e1000117 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Langfelder, P., Mischel, P.S. & Horvath, S. When is hub gene selection better than standard meta-analysis? PLoS One 8, e61505 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lu, X.H. et al. Targeting ATM ameliorates mutant Huntingtin toxicity in cell and animal models of Huntington's disease. Sci. Transl. Med. 6, 268ra178 (2014).

    Article  PubMed  CAS  Google Scholar 

  21. Genetic Modifiers of Huntington's Disease (GeM-HD) Consortium. Identification of genetic factors that modify clinical onset of Huntington's disease. Cell 162, 516–526 (2015).

  22. Labbadia, J. & Morimoto, R.I. Huntington's disease: underlying molecular mechanisms and emerging concepts. Trends Biochem. Sci. 38, 378–385 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lin, M.T. & Beal, M.F. Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases. Nature 443, 787–795 (2006).

    Article  CAS  PubMed  Google Scholar 

  24. Hodges, A. et al. Regional and cellular gene expression changes in human Huntington's disease brain. Hum. Mol. Genet. 15, 965–977 (2006).

    Article  CAS  PubMed  Google Scholar 

  25. Durrenberger, P.F. et al. Common mechanisms in neurodegeneration and neuroinflammation: a BrainNet Europe gene expression microarray study. J. Neural Transm. (Vienna) 122, 1055–1068 (2015).

    Article  CAS  Google Scholar 

  26. Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707–720 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kuhn, A. et al. Mutant huntingtin's effects on striatal gene expression in mice recapitulate changes observed in human Huntington's disease brain and do not differ with mutant huntingtin length or wild-type huntingtin dosage. Hum. Mol. Genet. 16, 1845–1861 (2007).

    Article  CAS  PubMed  Google Scholar 

  28. Lein, E.S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    Article  CAS  PubMed  Google Scholar 

  29. Grange, P. et al. Cell-type-based model explaining coexpression patterns of genes in the brain. Proc. Natl. Acad. Sci. USA 111, 5397–5402 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Reiner, A. et al. Differential loss of striatal projection neurons in Huntington disease. Proc. Natl. Acad. Sci. USA 85, 5733–5737 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Heiman, M. et al. A translational profiling approach for the molecular characterization of CNS cell types. Cell 135, 738–748 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Lobo, M.K., Karsten, S.L., Gray, M., Geschwind, D.H. & Yang, X.W. FACS-array profiling of striatal projection neuron subtypes in juvenile and adult mouse brains. Nat. Neurosci. 9, 443–452 (2006).

    Article  CAS  PubMed  Google Scholar 

  33. Fishell, G. & Heintz, N. The neuron identity problem: form meets function. Neuron 80, 602–612 (2013).

    Article  CAS  PubMed  Google Scholar 

  34. Deneris, E.S. & Hobert, O. Maintenance of postmitotic neuronal cell identity. Nat. Neurosci. 17, 899–907 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Chen, W.V. & Maniatis, T. Clustered protocadherins. Development 140, 3297–3302 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Toyoda, S. et al. Developmental epigenetic modification regulates stochastic expression of clustered protocadherin genes, generating single neuron diversity. Neuron 82, 94–108 (2014).

    Article  CAS  PubMed  Google Scholar 

  37. Guo, Y. et al. CTCF/cohesin-mediated DNA looping is required for protocadherin α promoter choice. Proc. Natl. Acad. Sci. USA 109, 21081–21086 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Monahan, K. et al. Role of CCCTC binding factor (CTCF) and cohesin in the generation of single-cell diversity of protocadherin-α gene expression. Proc. Natl. Acad. Sci. USA 109, 9125–9130 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zuccato, C. et al. Huntingtin interacts with REST/NRSF to modulate the transcription of NRSE-controlled neuronal genes. Nat. Genet. 35, 76–83 (2003).

    Article  CAS  PubMed  Google Scholar 

  40. Mann, M., Kulak, N.A., Nagaraj, N. & Cox, J. The coming age of complete, accurate, and ubiquitous proteomes. Mol. Cell 49, 583–590 (2013).

    Article  CAS  PubMed  Google Scholar 

  41. Geiger, T., Wehner, A., Schaab, C., Cox, J. & Mann, M. Comparative proteomic analysis of eleven common cell lines reveals ubiquitous but varying expression of most proteins. Molecular & cellular proteomics: MCP 11, M111 014050 (2012).

    Article  CAS  PubMed Central  Google Scholar 

  42. Shirasaki, D.I. et al. Network organization of the huntingtin proteomic interactome in mammalian brain. Neuron 75, 41–57 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteomics 13, 2513–2526 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Sharma, K. et al. Cell type- and brain region-resolved mouse brain proteome. Nat. Neurosci. 18, 1819–1831 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Pal, A., Severin, F., Lommer, B., Shevchenko, A. & Zerial, M. Huntingtin-HAP40 complex is a novel Rab5 effector that regulates early endosome motility and is up-regulated in Huntington's disease. J. Cell Biol. 172, 605–618 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Valencia, A. et al. Striatal synaptosomes from Hdh140Q/140Q knock-in mice have altered protein levels, novel sites of methionine oxidation, and excess glutamate release after stimulation. J. Huntingtons Dis. 2, 459–475 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Kaltenbach, L.S. et al. Huntingtin interacting proteins are genetic modifiers of neurodegeneration. PLoS Genet. 3, e82 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Lu, T. et al. REST and stress resistance in ageing and Alzheimer's disease. Nature 507, 448–454 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Phillips, J.E. & Corces, V.G. CTCF: master weaver of the genome. Cell 137, 1194–1211 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Dowen, J.M. et al. Control of cell identity genes occurs in insulated neighborhoods in mammalian chromosomes. Cell 159, 374–387 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Jacobsen, J.C. et al. HD CAG-correlated gene expression changes support a simple dominant gain of function. Hum. Mol. Genet. 20, 2846–2860 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Menalled, L.B., Sison, J.D., Dragatsis, I., Zeitlin, S. & Chesselet, M.F. Time course of early motor and neuropathological anomalies in a knock-in mouse model of Huntington's disease with 140 CAG repeats. J. Comp. Neurol. 465, 11–26 (2003).

    Article  CAS  PubMed  Google Scholar 

  53. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

  54. Anders, S., Pyl, P.T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  55. Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Johnson, W.E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Article  PubMed  Google Scholar 

  57. Oldham, M.C., Langfelder, P. & Horvath, S. Network methods for describing sample relationships in genomic datasets: application to Huntington's disease. BMC Syst. Biol. 6, 63 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, e17 (2005).

    Article  Google Scholar 

  59. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Langfelder, P. & Horvath, S. Fast R functions for robust correlations and hierarchical clustering. J. Stat. Softw. 46 (11), 1–17 (2012).

    Article  Google Scholar 

  61. Bolstad, B.M., Irizarry, R.A., Astrand, M. & Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).

    Article  CAS  PubMed  Google Scholar 

  62. Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719–720 (2007).

    Article  PubMed  CAS  Google Scholar 

  63. Stouffer, S.A. The American Soldier (Princeton Univ. Press, 1949).

  64. Zaykin, D.V. Optimally weighted Z-test is a powerful method for combining probabilities in meta-analysis. J. Evol. Biol. 24, 1836–1841 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Miller, J.A. et al. Strategies for aggregating gene expression data: the collapseRows R function. BMC Bioinformatics 12, 322 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Shen, Y. et al. A map of the cis-regulatory sequences in the mouse genome. Nature 488, 116–120 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Gu, X. et al. N17 Modifies mutant Huntingtin nuclear pathogenesis and severity of disease in HD BAC transgenic mice. Neuron 85, 726–741 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Giles, P. et al. Longitudinal analysis of gene expression and behaviour in the HdhQ150 mouse model of Huntington's disease. Brain Res. Bull. 88, 199–209 (2012).

    Article  CAS  PubMed  Google Scholar 

  69. Becanovic, K. et al. Transcriptional changes in Huntington disease identified using genome-wide expression profiling and cross-platform analysis. Hum. Mol. Genet. 19, 1438–1452 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Wang, Y. et al. Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells. Proteomics 11, 2019–2026 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Cox, J. et al. A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics. Nat. Protoc. 4, 698–705 (2009).

    Article  CAS  PubMed  Google Scholar 

  72. Gentleman, R.C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate—a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  74. Gu, X. et al. N17 modifies mutant huntingtin nuclear pathogenesis and severity of disease in HD BAC transgenic mice. Neuron 85, 726–741 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Al-Ramahi, I. et al. CHIP protects from the neurotoxicity of expanded and wild-type ataxin-1 and promotes their ubiquitination and degradation. J. Biol. Chem. 281, 26714–26724 (2006).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank PsychoGenics for help in breeding the knock-in allelic series and dissecting the tissues as part of a contract research agreement with CHDI. The research was supported by CHDI Foundation, Inc. HD research in the Yang laboratory is also supported by NINDS US National Institutes of Health grants (R01NS074312, R01NS049501 and R01NS084298). X.W.Y is also supported by the David Weill fund from Semel Institute, the Carol Moss Spivak Scholarship in Neuroscience from the Brain Research Institute at UCLA, and the Leslie Gehry Brenner Prize from the Hereditary Disease Foundation. We acknowledge the support of the NINDS Informatics Center for Neurogenetics and Neurogenomics (P30 NS062691).

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Authors and Affiliations

  1. Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, California, USA

    Peter Langfelder & Steve Horvath

  2. Center for Neurobehavioral Genetics, Semel Institute for Neuroscience & Human Behavior, University of California, Los Angeles, Los Angeles, California, USA

    Jeffrey P Cantle, Doxa Chatzopoulou, Nan Wang, Fuying Gao, Xiao-Hong Lu, Eliana Marisa Ramos, Yining Zhao, Sandeep Deverasetty, Giovanni Coppola & X William Yang

  3. Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, California, USA

    Jeffrey P Cantle, Nan Wang, Fuying Gao, Xiao-Hong Lu, Eliana Marisa Ramos, Giovanni Coppola & X William Yang

  4. UCLA Brain Research Institute, University of California, Los Angeles, Los Angeles, California, USA

    Jeffrey P Cantle, Nan Wang, Xiao-Hong Lu, Giovanni Coppola & X William Yang

  5. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA

    Ismael Al-Ramahi, Karla El-Zein & Juan Botas

  6. Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas, USA

    Ismael Al-Ramahi, Karla El-Zein & Juan Botas

  7. Evotec Munich GmbH, Martinsried, Germany

    Andreas Tebbe & Christoph Schaab

  8. CHDI Foundation/CHDI Management Inc., Princeton, New Jersey, USA

    Daniel J Lavery, David Howland, Seung Kwak, Jeffrey S Aaronson & Jim Rosinski

  9. Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA

    Giovanni Coppola

  10. Department of Biostatistics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, California, USA

    Steve Horvath

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  1. Peter Langfelder
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Contributions

X.W.Y., P.L., S.H., G.C., J.R. and J.S.A. designed and supervised the study. D.H. and S.K. supervised allelic series HD knock-in mouse tissue collection, RNA-seq and stereological counting of MSNs and astrocytes in Q175 mice. F.G. and G.C. performed RNA-seq data processing. P.L. and S.H. performed WGCNA consensus module analyses, preservation studies and WGCNA analyses of proteomic data sets. J.P.C., N.W., X.-H.L. and X.W.Y. contributed to analyses and generation of data and graphs used in Figures 3,4,5,6 and Supplementary Table 4. J.P.C. performed studies for data shown in Supplementary Figure 4. I.A.-R., K.E.-Z. and J.B. performed the mutant huntingtin Drosophila modifier study. D.C., Y.Z., S.D. and G.C. created the HDinHD database. E.M.R. and G.C. performed Ctcf enrichment analyses. A.T., C.S. and D.J.L. performed striatal tissue proteomic studies for the Htt knock-in mice. X.W.Y., P.L., S.H. and G.C. wrote the manuscript.

Corresponding authors

Correspondence to Steve Horvath or X William Yang.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Numbers of significantly associated genes between allelic series knockin heterozygotes versus wild-type mice

This figure shows, analogously to Figure 1C, numbers of significantly differentially expressed genes in two-group comparisons between wild type samples and knock-in transgenics with specific Q lengths. No wild type controls were available for cortex and liver at 6 months. This figure demonstrates that (1) the numbers of significantly differentially expressed genes between Q20 samples and WT samples are extremely low, and (2) the trend of the numbers of differentially expressed genes increasing with Q in 6- and 10-month striatum and to a lesser degree in the 10-month cortex is also present in this analysis.

Supplementary Figure 2 Unbiased stereological measurement of cell numbers in striata of wild-type and Q175 mice

Individual data for NeuN+ neuron numbers (a) and GFAP+ astrocyte numbers (b) are represented as data points, while group means and standard errors are indicated as the horizontal line and brackets, respectively. One-way ANOVA followed by Tukey's post hoc test revealed no significant difference between WT and Q175 mice at the same time-point. N=6 per group.

Supplementary Figure 3 Protocadherin dysregulation across multiple modules suggests Ctcf-mediated topology changes.

Analysis of ENCODE ChIP-seq data shows striatal modules M2 and M20 are enriched in Ctcf target genes. The barplot represents the enrichment significance (Z score) and the vertical line (Z=3) approximately represents the Bonferroni-corrected significance threshold of p=0.05.

Supplementary Figure 4 qRT-PCR summary of validated striatal transcripts

Twenty dysregulated expressed genes in allelic series are validated by qRT-PCR. Bar graph showed gene expression changes in 6mo Q175 striatum compared to wildtype littermates. Error bars indicate SEM. *, p<0.05; **, p<0.01; ***, p<0.001, N=4 per genotype, student t-test.

Supplementary Figure 5 Additional results of genetic perturbation studies in the fly model expressing mHTT fragment

LOF, loss of function; OE, overexpression. Means were compared using ANOVA followed by Dunnett’s test. Stars indicate pairs of means that are significantly different between NT-HTT[128Q] and NT-HTT[128Q]/modifier (p<0.05). Error bars indicate SEM.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 (PDF 910 kb)

Supplementary Methods Checklist (PDF 417 kb)

Supplementary Table 1

Differential expression statistics in 2-, 6-, and 10-month striatum, cortex and liver. This table contains 9 sheets, each sheet corresponding to one tissue/time point combination. In each sheet, rows correspond to genes. The first 3 columns identify the gene and the other columns provide differential expression statistics for two-group comparisons (Q80, Q92, Q111, Q140, Q175 vs. Q20) and for the association test with Q as a numeric variable. (XLSX 45897 kb)

Supplementary Table 2

Differential expression between Q175 mice and controls in tissue survey. Each of the sheets in this table provides differential expression statistics for one of the 14 tissues in the tissue survey. In each sheet, the first 3 columns identify the gene and the other columns provide differential expression statistics. The last sheet, named ‘Correlation of differential expression Z statistics’, provides the correlations among vectors of differential expression Z statistics that are displayed in graphical form in Figure 1D. (XLS 37811 kb)

Supplementary Table 3

Summary of network analysis results. Sheets ‘Striatum, (Cortex, Liver) module membership’ give, for each gene, its assigned module label and color, meta-analysis Z statistics for module membership in all modules, and module membership (also known as kME) in all modules at each time point. These tables can be used as a resource in two ways: Given a module, one can identify consensus hub genes (genes with highest module membership Z statistics) in the module as candidates for further follow-up; and conversely, given an interesting gene, one can check whether it is a hubgene in any of the modules. The sheets labeled ‘Striatum (Cortex, Liver) module-Q association’ provides a summary of association between module eigengenes (Methods) and genotype. Analogously to differential expression testing, we test association of module eigengenes with Q viewed as a continuous variable as well as in two-group comparisons of Q80, Q92, Q111, Q140 and Q175 vs. Q20. Test statistics reported in these tables include correlation, Student t-test statistics, Kruskal-Wallis test statistics, as well as descriptive statistics (means, standard errors, numbers of observations etc.). We also report the meta-analysis Z and significance statistics that pool test results across the three ages. (XLSX 15793 kb)

Supplementary Table 4

Annotation of top 18 striatal modules from network analyses. (XLSX 20 kb)

Supplementary Table 5

Gene ontology analysis of top modules. Brain modules with Meta Z score greater than 5 were assessed for enrichment using DAVID Gene Ontology Functional Annotation Clustering (Huang et al., 2009). Tabs correspond to individual modules, with color representing sign of the Meta Z score (green, negative; red, positive). (XLSX 1789 kb)

Supplementary Table 6

IPA canonical pathway analysis of top modules. Brain modules with Meta Z score greater than 5 were assessed for pathway enrichment using Ingenuity Pathway Analysis Canonical Pathways (Qiagen, Redwood City, CA; http://www.qiagen.com/ingenuity). Tabs correspond to individual modules, with color representing sign of the Meta Z score (green, negative; red, positive). (XLSX 174 kb)

Supplementary Table 7

Top module enrichment of top modules in HDinHD BrainLists anRicher function. Brain modules with Meta Z score greater than 5 were assessed for enrichment using the HDinHD anRicher function limited to BrainLists (http://www.hdinhd.org). This probes datasets related to brain region and cell types, disease, and aging using the userListEnrichment function (Miller et al., 2011). Tabs correspond to individual modules, with color representing sign of the Meta Z score (green, negative; red, positive). (XLS 144 kb)

Supplementary Table 8

Preservation of association between module genes and genotype or HD status in independent data. This table provides, in a text form, data that are shown in Figure 3. Specifically, for each of the 18 selected striatum and cortex modules, the Table shows weighted mean correlation with genotype (mouse data) or HD status (human data) of module genes across 24 test data sets, as well as the corresponding p-values. (XLS 25 kb)

Supplementary Table 9

Overview of HD-related literature gene expression data sets used for validation. (XLSX 12 kb)

Supplementary Table 10

Genes that change consistently in allelic series and human data. In each sheet (Striatum, Cortex), each row corresponds to a gene that is consistently and significantly expressed in 6-month allelic series and human data. For striatum we report the 6-month allelic series striatum and the human CN data sets by Durrenberger et al. and Hodges et al. Each striatal gene satisfies the following criteria: FDR<0.05 in the allelic series striatum, FDR<0.1 in each of the human data sets, and same sign of fold change across all 3 data sets. For the cortex, we report the allelic series 6-month cortex, BA4 and BA9 data by Hodges et al., and PFC and VC data from the Harvard Brain Tissue Resource Center (Zhang et al., 2014). Each cortical gene satisfies the following criteria: FDR<0.05 in the allelic series cortex, FDR<0.1 in at least 3 of the 4 of the human data sets, and same sign of fold change in the allelic series cortex and at least 3 of the 4 human data sets. (XLS 164 kb)

Supplementary Table 11

Enrichment of selected striatum and cortex modules in informative marker sets. This table contains gene marker sets that show nominally significant (p<0.05) enrichment in selected striatum and cortex modules. For the striatum, the marker sets include top 100 ABA striatal and cortex markers, several D1 and D2-specific gene sets, cadherins/protocadherins, and genes determined to change significantly in HD patients using laser capture microdissection (LCM). For cortex modules, we tested for enrichment in top 100 ABA cortex and striatum markers. (XLS 99 kb)

Supplementary Table 12

Preservation of association between cell death genes in striatum M7 and genotype or HD status in literature data. This table provides, in a text form, data shown in Figures 5C-E. Specifically, this table shows weighted mean correlation of cell death genes in Striatum M7 with genotype (mouse data) or HD status (human data) of module genes across 24 test data sets, as well as the corresponding p-values. (XLS 8 kb)

Supplementary Table 13

Proteomic label-free quantification (LFQ) data and sample information. (XLSX 7839 kb)

Supplementary Table 14

Numbers of significantly differentially abundant proteins across all genotypes. For each comparison, the table lists the number of significantly (FDR<0.1) differentially abundant proteins, as well as the number of significantly differentially abundant proteins whose mRNA is also significantly (FDR<0.1) associated with the genotype variable, as well as the corresponding hypergeometric overlap p-values. (XLS 18 kb)

Supplementary Table 15

Summary statistics of protein network modules. This table includes association of module eigen-proteins with genotype and a summary of functional enrichment analysis. (XLSX 2746 kb)

Supplementary Table 16

Enrichment of CAG-dependent mRNA modules in differentially abundant proteins. (XLS 11 kb)

Supplementary Table 17

Genes tested in Drosophila HD model. (XLS 31 kb)

Supplementary Table 18

Summary of the validation in Drosophila HD model. For each tested gene, columns give gene identification, module number from our WGCNA analysis in the striatum, Allele type (LOF, loss of function, shRNA, shRNA knock-down; O, overexpression) and modifier effect (E, enhancer; S, suppressor). (XLS 8 kb)

Supplementary Table 19

Drosophila HD model p and F values for statistics. (XLS 30 kb)

Supplementary Table 20

Sample numbers across tissues, genotypes and time points. The individual sheets in this table provide sample numbers at each genotype and time point for the fully profiled tissues, the tissue survey at 6 months, and the proteomic data. (XLS 10 kb)

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Langfelder, P., Cantle, J., Chatzopoulou, D. et al. Integrated genomics and proteomics define huntingtin CAG length–dependent networks in mice. Nat Neurosci 19, 623–633 (2016). https://doi.org/10.1038/nn.4256

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