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Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice
Open accessAbstract
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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“nearly all differential expression signatures from HD mice (35 of 37) (Langfelder et al., 2016) failed to overlap with microglial-enriched coexpression modules (Figure S5).”
From Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models (2020) · cites this paper as Langfelder et al., 2016
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
Self-citationHTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 12,082
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
MACDONALD, M. (1993) · Cell · doi:10.1016/0092-8674(93)90585-e · Cited by 8,366
Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,328
Mitochondrial Dysfunction and Oxidative Stress in Neurodegenerative Diseases
Lin, M. T., & Beal, M. F. (2006) · Nature · doi:10.1038/nature05292 · Cited by 6,455
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Self-citationGenome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572
Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031591 · Cited by 5,180
Shah, M. A., Datson, N. A., Srinidhi, L., Stanton, V. P., MacDonald, M. E., Allard, M. W., Youngman, S., Am, F., Mott, R., Draths, K., et al. (1993) · Cell · Cited by 3,055
Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R
Langfelder, P., Zhang, B., & Horvath, S. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btm563 · Cited by 1,892
Self-citationIntegrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease
Zhang, B., Gaiteri, C., Bodea, L.-G., Wang, Z., McElwee, J., Podtelezhnikov, A. A., Zhang, C., Xie, T., Tran, L., Dobrin, R., et al. (2013) · Cell · doi:10.1016/j.cell.2013.03.030 · Cited by 1,657
CTCF: Master Weaver of the Genome
Phillips‐Cremins, J. E., & Corces, V. G. (2009) · Cell · doi:10.1016/j.cell.2009.06.001 · Cited by 1,585
Trinucleotide Repeat Disorders
Orr, H. T., & Zoghbi, H. Y. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113042 · Cited by 1,394
A map of the cis-regulatory sequences in the mouse genome
Shen, Y., Yue, F., McCleary, D. F., Ye, Z., Edsall, L., Kuan, S., Wagner, U., Dixon, J., Lee, L., Lobanenkov, V. V., et al. (2012) · Nature · doi:10.1038/nature11243 · Cited by 1,361 · cited 1×
“A list of Ctcf target genes was created by scanning the canonical promoter region (1,000bp upstream of transcription start site) of Mus musculus RefSeq genes (assembly mm9) for the presence of Ctcf peaks in the “Cortex Adult 8 weeks CTCF TFBS Chip-Seq Peaks from ENCODE/LICR” UCSC track 66 .”
Vonsattel, J. P., & DiFiglia, M. (1998) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/00005072-199805000-00001 · Cited by 1,305
Fast R Functions for Robust Correlations and Hierarchical Clustering
Langfelder, P., & Horvath, S. (2012) · Journal of Statistical Software · doi:10.18637/jss.v046.i11 · Cited by 1,293
Self-citationA translational profiling approach for the molecular characterization of CNS cell types
Heiman, M., Schaefer, A., Gong, S., Peterson, J. D., Day, M., Ramsey, K. E., Suárez-Fariñas, M., Schwarz, C., Stephan, D. A., Surmeier, D. J., et al. (2008) · Cell · doi:10.1016/j.cell.2008.10.028 · Cited by 1,134
Huntington disease: natural history, biomarkers and prospects for therapeutics
Ross, C. A., Aylward, E. H., Wild, E. J., Langbehn, D. R., Long, J. D., Warner, J. H., Scahill, R. I., Leavitt, B. R., Stout, J. C., Paulsen, J. S., et al. (2014) · Nature Reviews Neurology · doi:10.1038/nrneurol.2014.24 · Cited by 1,016
Huntingtin interacts with REST/NRSF to modulate the transcription of NRSE-controlled neuronal genes
Zuccato, C., Tartari, M., Crotti, A., Goffredo, D., Valenza, M., Conti, L., Cataudella, T., Leavitt, B. R., Hayden, M. R., Timmusk, T., et al. (2003) · Nature Genetics · doi:10.1038/ng1219 · Cited by 979
Control of cell identity genes occurs in insulated neighborhoods in mammalian chromosomes
Dowen, J. M., Fan, Z. P., Hnisz, D., Ren, G., Abraham, B. J., Zhang, L. N., Weintraub, A. S., Schuijers, J., Lee, T. I., Zhao, K., et al. (2014) · Cell · doi:10.1016/j.cell.2014.09.030 · Cited by 884
Geometric Interpretation of Gene Coexpression Network Analysis
Horvath, S., & Dong, J. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000117 · Cited by 867
Self-citationEigengene networks for studying the relationships between co-expression modules
Langfelder, P., & Horvath, S. (2007) · BMC Systems Biology · doi:10.1186/1752-0509-1-54 · Cited by 867
Self-citationDifferential loss of striatal projection neurons in Huntington disease.
Reiner, A., Albin, R. L., Anderson, K. D., D'Amato, C. J., Penney, J. B., & Young, A. B. (1988) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.85.15.5733 · Cited by 839
Regional and cellular gene expression changes in human Huntington's disease brain
Hodges, A., Strand, A. D., Aragaki, A. K., Kuhn, A., Sengstag, T., Hughes, G., Elliston, L. A., Hartog, C., Goldstein, D. R., Thu, D., et al. (2006) · Human Molecular Genetics · doi:10.1093/hmg/ddl013 · Cited by 804
Geiger, T., Wehner, A., Schaab, C., Cox, J., & Mann, M. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.014050 · Cited by 797
Self-citationCell type- and brain region-resolved mouse brain proteome
Sharma, K., Schmitt, S., Bergner, C. G., Tyanova, S., Kannaiyan, N., Manrique-Hoyos, N., Kongi, K., Cantuti, L., Hanisch, U.-K., Philips, M.-A., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4160 · Cited by 785
REST and stress resistance in ageing and Alzheimer’s disease
Lu, T., Aron, L., Zullo, J., Pan, Y., Kim, H., Chen, Y., Yang, T.-H., Kim, H., Drake, D., Liu, X., et al. (2014) · Nature · doi:10.1038/nature13163 · Cited by 784
A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics
Cox, J., Matić, I., Hilger, M., Nagaraj, N., Selbach, M., Olsen, J. V., & Mann, M. (2009) · Nature Protocols · doi:10.1038/nprot.2009.36 · Cited by 773
Identification of Genetic Factors that Modify Clinical Onset of Huntington’s Disease
Lee, J.-M., Wheeler, V. C., Chao, M. J., Vonsattel, J. P. G., Pinto, R. M., Lucente, D., Abu-Elneel, K., Ramos, E. M., Mysore, J. S., Gillis, T., et al. (2015) · Cell · doi:10.1016/j.cell.2015.07.003 · Cited by 655
Self-citationWang, Y., Yang, F., Gritsenko, M. A., Wang, Y., Clauss, T. R., Liu, T., Shen, Y., Monroe, M. E., López‐Ferrer, D., Reno, T. A., et al. (2011) · PROTEOMICS · doi:10.1002/pmic.201000722 · Cited by 600
Menalled, L., Sison, J. D., Dragatsis, I., Zeitlin, S. O., & Chesselet, M. (2003) · The Journal of Comparative Neurology · doi:10.1002/cne.10776 · Cited by 528
Menalled, L., Kudwa, A. E., Miller, S., Fitzpatrick, J., Watson-Johnson, J., Keating, N., Ruiz, M. C., Mushlin, R. A., Alosio, W., McConnell, K., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0049838 · Cited by 470
Self-citationHuntingtin Interacting Proteins Are Genetic Modifiers of Neurodegeneration
Kaltenbach, L. S., Romero, E., Becklin, R. R., Chettier, R., Bell, R. N., Phansalkar, A. R., Strand, A. D., Torcassi, C., Savage, J. R., Hurlburt, A. P., et al. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030082 · Cited by 446
Self-citationFACS-array profiling of striatal projection neuron subtypes in juvenile and adult mouse brains
Lobo, M. K., Karsten, S. L., Gray, M., Geschwind, D. H., & Yang, X. W. (2006) · Nature Neuroscience · doi:10.1038/nn1654 · Cited by 411
Self-citationMolecular genetics: Unmasking polyglutamine triggers in neurodegenerative disease
Gusella, J. F., & MacDonald, M. E. (2000) · Nature reviews. Neuroscience · doi:10.1038/35039051 · Cited by 404
Huntington's disease: underlying molecular mechanisms and emerging concepts
Labbadia, J. P., & Morimoto, R. I. (2013) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2013.05.003 · Cited by 383
Choosing an animal model for the study of Huntington's disease
Pouladi, M. A., Morton, A. J., & Hayden, M. R. (2013) · Nature reviews. Neuroscience · doi:10.1038/nrn3570 · Cited by 372
The Coming Age of Complete, Accurate, and Ubiquitous Proteomes
Mann, M., Kulak, N. A., Nagaraj, N., & Cox, J. (2013) · Molecular Cell · doi:10.1016/j.molcel.2013.01.029 · Cited by 350
Kuhn, A., Goldstein, D. R., Hodges, A., Strand, A. D., Sengstag, T., Kooperberg, C. L., Bečanović, K., Pouladi, M. A., Sathasivam, K., Cha, J.-H. J., et al. (2007) · Human Molecular Genetics · doi:10.1093/hmg/ddm133 · Cited by 327
When is hub gene selection better than standard meta-analysis?
Langfelder, P., Mischel, P. S., & Horvath, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061505 · Cited by 313
Self-citationStrategies for aggregating gene expression data: The collapseRows R function
Miller, J. A., Cai, C., Langfelder, P., Geschwind, D. H., Kurian, S. M., Salomon, D. R., & Horvath, S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-322 · Cited by 307
Self-citationNetwork Organization of the Huntingtin Proteomic Interactome in Mammalian Brain
Shirasaki, D. I., Greiner, E. R., Al-Ramahi, I., Gray, M., Boontheung, P., Geschwind, D. H., Botas, J., Coppola, G., Horvath, S., Loo, J. A., et al. (2012) · Neuron · doi:10.1016/j.neuron.2012.05.024 · Cited by 297
Self-citationHD CAG repeat implicates a dominant property of huntingtin in mitochondrial energy metabolism
Seong, I. S., Ivanova, E. V., Lee, J., Choo, Y. S., Fossale, E., Anderson, M., Gusella, J. F., Laramie, J. M., Myers, R. H., Lesort, M. J., et al. (2005) · Human Molecular Genetics · doi:10.1093/hmg/ddi319 · Cited by 291
Optimally weighted Z-test is a powerful method for combining probabilities in meta-analysis
Zaykin, D. V. (2011) · Journal of Evolutionary Biology · doi:10.1111/j.1420-9101.2011.02297.x · Cited by 266
CTCF/cohesin-mediated DNA looping is required for protocadherin α promoter choice
Guo, Y., Monahan, K., Wu, H., Gertz, J., Varley, K. E., Li, W., Myers, R. M., Maniatis, T., & Wu, Q. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1219280110 · Cited by 258
Pal, A., Severin, F. F., Lommer, B. S., Shevchenko, A., & Zerial, M. (2006) · The Journal of Cell Biology · doi:10.1083/jcb.200509091 · Cited by 244
Menalled, L., Sison, J. D., Wu, Y. N., Olivieri, M., Li, X., Li, H., Zeitlin, S. O., & Chesselet, M. (2002) · Journal of Neuroscience · doi:10.1523/jneurosci.22-18-08266.2002 · Cited by 221
Wang, N., Gray, M., Lu, X., Cantle, J. P., Holley, S. M., Greiner, E. R., Gu, X., Shirasaki, D., Cepeda, C., Li, Y., et al. (2014) · Nature Medicine · doi:10.1038/nm.3514 · Cited by 205
Self-citationAl‐Ramahi, I., Lam, Y. C., Chen, H.-K., Gouyon, B., Zhang, M., Perez, A. M., Branco, J., de Haro, M., Patterson, C., Zoghbi, H. Y., et al. (2006) · Journal of Biological Chemistry · doi:10.1074/jbc.m601603200 · Cited by 198
Self-citationMaintenance of postmitotic neuronal cell identity
Deneris, E. S., & Hobert, O. (2014) · Nature Neuroscience · doi:10.1038/nn.3731 · Cited by 182
Chen, W. V., & Maniatis, T. (2013) · Development · doi:10.1242/dev.090621 · Cited by 174
Monahan, K. D., Rudnick, N. D., Kehayova, P. D., Pauli, F., Newberry, K. M., Myers, R., & Maniatis, T. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1205074109 · Cited by 155
Oldham, M. C., Langfelder, P., & Horvath, S. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-63 · Cited by 154
Self-citationDurrenberger, P. F., Fernando, F. S., Kashefi, S. N., Bonnert, T. P., Seilhean, D., Nait‐Oumesmar, B., Schmitt, A., Gebicke‐Haerter, P. J., Falkai, P., Grünblatt, E., et al. (2014) · Journal of Neural Transmission · doi:10.1007/s00702-014-1293-0 · Cited by 144
Lu, X., Mattis, V. B., Wang, N., Al‐Ramahi, I., van den Berg, N., Fratantoni, S. A., Waldvogel, H. J., Greiner, E. R., Osmand, A., Merz, K. E., et al. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3010523 · Cited by 142
Self-citationHuntington's disease: seeing the pathogenic process through a genetic lens
Gusella, J. F., & MacDonald, M. E. (2006) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2006.06.009 · Cited by 141
Toyoda, S., Kawaguchi, M., Kobayashi, T., Tarusawa, E., Toyama, T., Okano, M., Oda, M., Nakauchi, H., Yoshimura, Y., Sanbo, M., et al. (2014) · Neuron · doi:10.1016/j.neuron.2014.02.005 · Cited by 137
The Neuron Identity Problem: Form Meets Function
Fishell, G., & Heintz, N. (2013) · Neuron · doi:10.1016/j.neuron.2013.10.035 · Cited by 115
Testicular degeneration in Huntington disease
Raamsdonk, J. M. V., Murphy, Z., Selva, D. M., Hamidizadeh, R., Pearson, J., Petersén, Å., Björkqvist, M., Muir, C., Mackenzie, I. R., Hammond, G. L., et al. (2007) · Neurobiology of Disease · doi:10.1016/j.nbd.2007.01.006 · Cited by 104
Bečanović, K., Pouladi, M. A., Lim, R. S., Kuhn, A., Pavlidis, P., Luthi‐Carter, R., Hayden, M. R., & Leavitt, B. R. (2010) · Human Molecular Genetics · doi:10.1093/hmg/ddq018 · Cited by 98
Smith, G. A., Rocha, E. M., McLean, J. R., Hayes, M. A., Izen, S. C., Ole Isacson, A., & Hallett, P. J. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu166 · Cited by 97
Dysfunction of the CNS-Heart Axis in Mouse Models of Huntington's Disease
Mielcarek, M., Inuabasi, L., Bondulich, M. K., Muller, T., Osborne, G. F., Franklin, S., Smith, D. L., Neueder, A., Rosinski, J., Rattray, I., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004550 · Cited by 94
Self-citationGu, X., Cantle, J. P., Greiner, E. R., Lee, C. Y. D., Barth, A. M., Gao, F., Park, C. S., Zhang, Z., Sandoval-Miller, S., Zhang, R. L., et al. (2015) · Neuron · doi:10.1016/j.neuron.2015.01.008 · Cited by 77
Self-citationHD CAG-correlated gene expression changes support a simple dominant gain of function
Jacobsen, J. C., Gregory, G. C., Woda, J. M., Thompson, M. N., Coser, K. R., Murthy, V., Kohane, I. S., Gusella, J. F., Seong, I. S., MacDonald, M. E., et al. (2011) · Human Molecular Genetics · doi:10.1093/hmg/ddr195 · Cited by 74
Regional Atrophy Associated with Cognitive and Motor Function in Prodromal Huntington Disease
Aylward, E., the PREDICT-HD Investigators and Coordinators of the Huntington Study Group, Harrington, D. L., Mills, J. A., Nopoulos, P., Ross, C. A., Long, J. D., Liu, D., Westervelt, H. K., & Paulsen, J. S. (2013) · Journal of Huntington s Disease · doi:10.3233/jhd-130076 · Cited by 63
Cell-type–based model explaining coexpression patterns of genes in the brain
Grange, P., Bohland, J. W., Okaty, B. W., Sugino, K., Bokil, H., Nelson, S. B., Ng, L., Hawrylycz, M., & Mitra, P. P. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1312098111 · Cited by 62
Biagioli, M., Ferrari, F., Mendenhall, E. M., Zhang, Y., Erdin, S., Vijayvargia, R., Vallabh, S. M., Solomos, N., Manavalan, P., Ragavendran, A., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv006 · Cited by 60
Valencia, A., Sapp, E., Kimm, J. S., McClory, H., Ansong, K. A., Yohrling, G. J., Kwak, S., Kegel, K. B., Green, K. M., Shaffer, S. A., et al. (2013) · Journal of Huntington s Disease · doi:10.3233/jhd-130080 · Cited by 39
Self-citationGiles, P. J., Elliston, L. A., Higgs, G. V., Brooks, S. P., Dunnett, S. B., & Jones, L. (2011) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2011.10.001 · Cited by 18
Flagged first, then by citations
Research works printed but not linked: 2
- Stouffer, S.A. The American Soldier (Princeton Univ. Press, 1949).
- Gu, X. et al. N17 modifies mutant huntingtin nuclear pathogenesis and severity of disease in HD BAC transgenic mice. Neuron 85, 726–741 (2015). · DOI · Paper page
Cited by
Sorted by citations
Negative feedback control of neuronal activity by microglia
Badimon, A., Strasburger, H. J., Ayata, P., Chen, X., Nair, A., Ikegami, A., Hwang, P., Chan, A. T., Graves, S. M., Uweru, J. O., et al. (2020) · Nature · doi:10.1038/s41586-020-2777-8 · Cited by 979
Protein misfolding in neurodegenerative diseases: Implications and strategies
Sweeney, P., Park, H., Baumann, M., Dunlop, J., Frydman, J., Kopito, R., McCampbell, A., Leblanc, G., Venkateswaran, A., Nurmi, A., et al. (2017) · Translational Neurodegeneration · doi:10.1186/s40035-017-0077-5 · Cited by 675
Lee, C. Y. D., Daggett, A., Gu, X., Jiang, L.-L., Langfelder, P., Li, X., Wang, N., Zhao, Y., Park, C. S., Cooper, Y., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.02.002 · Cited by 396
Self-citationYan, S., Tu, Z., Liu, Z., Fan, N., Yang, H., Yang, S., Yang, W., Zhao, Y., Ouyang, Z., Lai, C., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.005 · Cited by 349
Self-citationTabrizi, S. J., Estevez‐Fraga, C., van Roon‐Mom, W., Flower, M. D., Scahill, R. I., Wild, E. J., Muñoz-Sanjuán, I., Sampaio, C., Rosser, A., & Leavitt, B. R. (2022) · The Lancet Neurology · doi:10.1016/s1474-4422(22)00121-1 · Cited by 354
Metadata
- Authors
- Langfelder, P., Cantle, J. P., Chatzopoulou, D., Wang, N., Gao, F., Al-Ramahi, I., Lu, X.-H., Ramos, E. M., El-Zein, K., Zhao, Y., Deverasetty, S., Tebbe, A., Schaab, C., Lavery, D. J., Howland, D., Kwak, S., Botas, J., Aaronson, J. S., Rosinski, J., Coppola, G., Horvath, S., & Yang, X. W.
- Affiliations
- University of California, Los Angeles (US)
- Evotec (Germany) (DE)
- CHDI Foundation (US)
- Baylor College of Medicine (US)
- Corresponding author
- Horvath, Steve
- Yang, X William
- ORCID
- Venue
- Nature Neuroscience
- Published
- 2016-02-22
- Volume
- 19
- Issue
- 4
- Pages
- 623-633
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC5984042
- OpenAlex
- W2274495055
- Cited by
- 385
- References
- 74
- Citations by year
- 2026: 29 · 2025: 27 · 2024: 30 · 2023: 30 · 2022: 60 · 2021: 40 · 2020: 40 · 2019: 36 · 2018: 36 · 2017: 41 · 2016: 16
- FWCI
- 19.76
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Hereditary Disease Foundation
- CHDI Foundation
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke (R01NS084298, R01NS049501, R01NS074312, P30NS062691)
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Genetic Neurodegenerative Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Life Sciences › Neuroscience › Neurology › Neurological diseases and metabolism
- Keywords
- Huntington's disease, mutant huntingtin, CAG repeat length, repeat expansion, transcriptome, proteome, co-expression network, striatum, medium spiny neurons, cAMP signaling, protocadherins, cell death, age-related gene expression, disease-modifying genes, knock-in mice
- MeSH terms
- Huntingtin Protein, Animals, Cerebral Cortex, Corpus Striatum, Female, Male, Mice, Inbred C57BL, Nerve Tissue Proteins, Nuclear Proteins, Genomics, Proteomics, Mice, Gene Regulatory Networks, Gene Knock-In Techniques
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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