DNA methylation study of Huntington's disease and motor progression in patients and in animal models.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
10 09 2020
Historique:
received: 24 10 2019
accepted: 14 08 2020
entrez: 11 9 2020
pubmed: 12 9 2020
medline: 2 10 2020
Statut: epublish

Résumé

Although Huntington's disease (HD) is a well studied Mendelian genetic disorder, less is known about its associated epigenetic changes. Here, we characterize DNA methylation levels in six different tissues from 3 species: a mouse huntingtin (Htt) gene knock-in model, a transgenic HTT sheep model, and humans. Our epigenome-wide association study (EWAS) of human blood reveals that HD mutation status is significantly (p < 10

Identifiants

pubmed: 32913184
doi: 10.1038/s41467-020-18255-5
pii: 10.1038/s41467-020-18255-5
pmc: PMC7484780
doi:

Substances chimiques

HTT protein, human 0
Htt protein, mouse 0
Huntingtin Protein 0
Recombinant Proteins 0

Types de publication

Journal Article Multicenter Study Observational Study Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

4529

Subventions

Organisme : NINDS NIH HHS
ID : P30 NS062691
Pays : United States

Références

Orr, H. T. & Zoghbi, H. Y. Trinucleotide repeat disorders. Annu. Rev. Neurosci. 30, 575–621 (2007).
pubmed: 17417937 doi: 10.1146/annurev.neuro.29.051605.113042
Djousse, L. et al. Interaction of normal and expanded CAG repeat sizes influences age at onset of Huntington disease. Am. J. Med. Genet. A 119A, 279–282 (2003).
pubmed: 12784292 doi: 10.1002/ajmg.a.20190
Ross, C. A. et al. Huntington disease: natural history, biomarkers and prospects for therapeutics. Nat. Rev. Neurol. 10, 204–216 (2014).
pubmed: 24614516 doi: 10.1038/nrneurol.2014.24
Gusella, J. F. & Macdonald, M. Genetic criteria for Huntington’s disease pathogenesis. Brain Res. Bull. 72, 78–82 (2007).
pubmed: 17352930 doi: 10.1016/j.brainresbull.2006.10.014
Lee, J.-M. et al. CAG repeat not polyglutamine length determines timing of Huntington’s disease onset. Cell 178, 887–900.e14 (2019).
doi: 10.1016/j.cell.2019.06.036
Horvath, S. et al. Huntington’s disease accelerates epigenetic aging of human brain and disrupts DNA methylation levels. Aging 8, 1485–1512 (2016).
pubmed: 27479945 pmcid: 4993344 doi: 10.18632/aging.101005
Villar-Menendez, I. et al. Increased 5-methylcytosine and decreased 5-hydroxymethylcytosine levels are associated with reduced striatal A2AR levels in Huntington’s disease. Neuromol. Med. 15, 295–309 (2013).
doi: 10.1007/s12017-013-8219-0
De Souza, R. A. et al. DNA methylation profiling in human Huntington’s disease brain. Hum. Mol. Genet. 25, 2013–2030 (2016).
Ng, C. W. et al. Extensive changes in DNA methylation are associated with expression of mutant huntingtin. Proc. Natl. Acad. Sci. USA 110, 2354–2359 (2013).
pubmed: 23341638 doi: 10.1073/pnas.1221292110 pmcid: 3568325
Landwehrmeyer, G. B. et al. Data Analytics from Enroll-HD, a global clinical research platform for Huntington’s disease. Mov. Disord. Clin. Pract. 4, 212–224 (2017).
pubmed: 30363395 doi: 10.1002/mdc3.12388
Orth, M. et al. Observing Huntington’s disease: the European Huntington’s Disease Network’s REGISTRY. PLoS Curr. 2, RRN1184 (2010).
pubmed: 20890398
Huntington Study Group. Unified Huntington’s Disease Rating Scale: reliability and consistency. Mov. Disord. 11, 136–142 (1996).
Moss, D. J. H. et al. Identification of genetic variants associated with Huntington’s disease progression: a genome-wide association study. Lancet Neurol. 16, 701–711 (2017).
pubmed: 28642124 doi: 10.1016/S1474-4422(17)30161-8
Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).
pubmed: 24138928 pmcid: 4015143 doi: 10.1186/gb-2013-14-10-r115
Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013).
pubmed: 23177740 doi: 10.1016/j.molcel.2012.10.016
Horvath, S. et al. Epigenetic clock for skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo studies. Aging 10, 1758–1775 (2018).
pubmed: 30048243 pmcid: 6075434 doi: 10.18632/aging.101508
Levine, M. E., Lu, A. T., Bennett, D. A. & Horvath, S. Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer’s disease related cognitive functioning. Aging 7, 1198–1211 (2015).
Lu, A. T. et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging 11, 303–327 (2019).
pubmed: 30669119 pmcid: 6366976 doi: 10.18632/aging.101684
Horvath, S. et al. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol. 17, 171 (2016).
pubmed: 27511193 pmcid: 4980791 doi: 10.1186/s13059-016-1030-0
Mansell, G. et al. Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array. BMC Genomics 20, 366 (2019).
pubmed: 31088362 pmcid: 6518823 doi: 10.1186/s12864-019-5761-7
Panegyres, P. K., Beilby, J., Bulsara, M., Toufexis, K. & Wong, C. A study of potential interactive genetic factors in Huntington’s disease. Eur. Neurol. 55, 189–192 (2006).
pubmed: 16772714 doi: 10.1159/000093867
Ciosi, M. et al. A genetic association study of glutamine-encoding DNA sequence structures, somatic CAG expansion, and DNA repair gene variants, with Huntington disease clinical outcomes. EBioMedicine 48, 568–580 (2019).
pubmed: 31607598 pmcid: 6838430 doi: 10.1016/j.ebiom.2019.09.020
Andrews, S. V., Ladd-Acosta, C., Feinberg, A. P., Hansen, K. D. & Fallin, M. D. “Gap hunting” to characterize clustered probe signals in Illumina methylation array data. Epigenet. Chromatin 9, 56 (2016).
doi: 10.1186/s13072-016-0107-z
Jacobsen, J. C. et al. An ovine transgenic Huntington’s disease model. Hum. Mol. Genet. 19, 1873–1882 (2010).
pubmed: 20154343 pmcid: 2860888 doi: 10.1093/hmg/ddq063
Chiang, C. et al. Complex reorganization and predominant non-homologous repair following chromosomal breakage in karyotypically balanced germline rearrangements and transgenic integration. Nat. Genet. 44(Suppl. 1), 390–397 (2012).
pubmed: 22388000 pmcid: 3340016 doi: 10.1038/ng.2202
Handley, R. R. et al. Metabolic disruption identified in the Huntington’s disease transgenic sheep model. Sci. Rep. 6, 20681 (2016).
pubmed: 26864449 pmcid: 4749952 doi: 10.1038/srep20681
Huntington’s Disease Sheep Collaborative Research, G. et al. Further molecular characterisation of the OVT73 transgenic sheep model of Huntington’s disease identifies cortical aggregates. J. Huntington’s Dis. 2, 279–295 (2013).
doi: 10.3233/JHD-130067
Skene, D. J. et al. Metabolic profiling of presymptomatic Huntington’s disease sheep reveals novel biomarkers. Sci. Rep. 7, 43030 (2017).
pubmed: 28223686 pmcid: 5320451 doi: 10.1038/srep43030
Langfelder, P. et al. Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice. Nat. Neurosci. 19, 623–633 (2016).
pubmed: 26900923 pmcid: 5984042 doi: 10.1038/nn.4256
Phipson, B., Maksimovic, J. & Oshlack, A. missMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform. Bioinformatics 32, 286–288 (2015).
pubmed: 26424855 doi: 10.1093/bioinformatics/btv560
Libertini, E. et al. Saturation analysis for whole-genome bisulfite sequencing data. Nat. Biotechnol. 34, 691–693 (2016).
pubmed: 27347755 doi: 10.1038/nbt.3524
van Eijk, K. et al. Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects. BMC Genomics 13, 636 (2012).
pubmed: 23157493 pmcid: 3583143 doi: 10.1186/1471-2164-13-636
Garrick, D., Fiering, S., Martin, D. I. K. & Whitelaw, E. Repeat-induced gene silencing in mammals. Nat. Genet. 18, 56–59 (1998).
pubmed: 9425901 doi: 10.1038/ng0198-56
Ng, C. W. et al. Extensive changes in DNA methylation are associated with expression of mutant huntingtin. Proc. Natl. Acad. Sci. USA 110, 2354–2359 (2013).
pubmed: 23341638 doi: 10.1073/pnas.1221292110 pmcid: 3568325
Wilson, C., Bellen, H. J. & Gehring, W. J. Position effects on eukaryotic gene expression. Annu. Rev. Cell Biol. 6, 679–714 (1990).
pubmed: 2275824 doi: 10.1146/annurev.cb.06.110190.003335
Rexroad, C. E. Transgenic technology in animal agriculture. Anim. Biotechnol. 3, 1–13 (1992).
doi: 10.1080/10495399209525759
Santoro, M. R., Bray, S. M. & Warren, S. T. Molecular mechanisms of Fragile X syndrome: a twenty-year perspective. Annu. Rev. Pathol. 7, 219–245 (2012).
pubmed: 22017584 doi: 10.1146/annurev-pathol-011811-132457
He, F. & Todd, P. K. Epigenetics in nucleotide repeat expansion disorders. Semin. Neurol. 31, 470–483 (2011).
pubmed: 22266885 doi: 10.1055/s-0031-1299786
Castaldo, I. et al. DNA methylation in intron 1 of the frataxin gene is related to GAA repeat length and age of onset in Friedreich ataxia patients. J. Med. Genet. 45, 808–812 (2008).
pubmed: 18697824 doi: 10.1136/jmg.2008.058594
Sun, J. H. et al. Disease-associated short tandem repeats co-localize with chromatin domain boundaries. Cell 175, 224–238.e15 (2018).
pubmed: 30173918 pmcid: 6175607 doi: 10.1016/j.cell.2018.08.005
Pinto, R. M. et al. Mismatch repair genes Mlh1 and Mlh3 modify CAG instability in Huntington’s disease mice: genome-wide and candidate approaches. PLoS Genet. 9, e1003930 (2013).
pubmed: 24204323 pmcid: 3814320 doi: 10.1371/journal.pgen.1003930
Gillis, J. et al. The DNAJB6 and DNAJB8 protein chaperones prevent intracellular aggregation of polyglutamine peptides. J. Biol. Chem. 288, 17225–17237 (2013).
pubmed: 23612975 pmcid: 3682527 doi: 10.1074/jbc.M112.421685
Kakkar, V. et al. The S/T-rich motif in the DNAJB6 chaperone delays polyglutamine aggregation and the onset of disease in a mouse model. Mol. Cell 62, 272–283 (2016).
pubmed: 27151442 doi: 10.1016/j.molcel.2016.03.017
Mielcarek, M. et al. HDAC4 reduction: a novel therapeutic strategy to target cytoplasmic Huntingtin and ameliorate neurodegeneration. PLoS Biol. 11, e1001717 (2013).
pubmed: 24302884 pmcid: 3841096 doi: 10.1371/journal.pbio.1001717
Lowry, E. R., Kruyer, A., Norris, E. H., Cederroth, C. R. & Strickland, S. The GluK4 kainate receptor subunit regulates memory, mood, and excitotoxic neurodegeneration. Neuroscience 235, 215–225 (2013).
pubmed: 23357115 doi: 10.1016/j.neuroscience.2013.01.029
Triche, T. J., Weisenberger, D. J., Van Den Berg, D., Laird, P. W. & Siegmund, K. D. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 41, e90–e90 (2013).
pubmed: 23476028 pmcid: 3627582 doi: 10.1093/nar/gkt090
Chen, B. H. et al. DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging 8, 1844–1865 (2016).
pubmed: 27690265 pmcid: 5076441 doi: 10.18632/aging.101020
Aulchenko, Y. S., Ripke, S., Isaacs, A. & van Duijn, C. M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007).
pubmed: 17384015 doi: 10.1093/bioinformatics/btm108
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).
pubmed: 23284626 pmcid: 3527464 doi: 10.1371/journal.pone.0049838
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008).
doi: 10.1186/1471-2105-9-559
Martin, T. C., Yet, I., Tsai, P. C. & Bell, J. T. coMET: visualisation of regional epigenome-wide association scan results and DNA co-methylation patterns. BMC Bioinform. 16, 131 (2015).
doi: 10.1186/s12859-015-0568-2
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
pubmed: 20616382 pmcid: 2922887 doi: 10.1093/bioinformatics/btq340
Blake, J. A. et al. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015).
doi: 10.1093/nar/gku1179
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).
doi: 10.1093/nar/gkv1070 pubmed: 26476454
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).
pubmed: 16199517 doi: 10.1073/pnas.0506580102 pmcid: 1239896
Miller, J. A. et al. Strategies for aggregating gene expression data: the collapseRows R function. BMC Bioinform. 12, 322 (2011).
doi: 10.1186/1471-2105-12-322
Aryee, M. J. et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30, 1363–1369 (2014).
pubmed: 24478339 pmcid: 4016708 doi: 10.1093/bioinformatics/btu049

Auteurs

Ake T Lu (AT)

Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.

Pritika Narayan (P)

Applied Translational Genetics Group, School of Biological Sciences, Centre for Brain Research, The University of Auckland, Auckland, 1010, New Zealand.

Matthew J Grant (MJ)

Applied Translational Genetics Group, School of Biological Sciences, Centre for Brain Research, The University of Auckland, Auckland, 1010, New Zealand.

Peter Langfelder (P)

Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA.
Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.

Nan Wang (N)

Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA.
Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.

Seung Kwak (S)

CHDI Management/CHDI Foundation, Princeton, NJ, 08540, USA.

Hilary Wilkinson (H)

CHDI Management/CHDI Foundation, Princeton, NJ, 08540, USA.

Richard Z Chen (RZ)

CHDI Management/CHDI Foundation, Princeton, NJ, 08540, USA.

Jian Chen (J)

CHDI Management/CHDI Foundation, Princeton, NJ, 08540, USA.

C Simon Bawden (C)

Livestock and Farming Systems, South Australian Research and Development Institute, Roseworthy, SA, 5371, Australia.

Skye R Rudiger (SR)

Livestock and Farming Systems, South Australian Research and Development Institute, Roseworthy, SA, 5371, Australia.

Marc Ciosi (M)

Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK.

Afroditi Chatzi (A)

Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK.

Alastair Maxwell (A)

Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK.

Timothy A Hore (TA)

Department of Anatomy, University of Otago, Dunedin, 9016, New Zealand.

Jeff Aaronson (J)

CHDI Management/CHDI Foundation, Princeton, NJ, 08540, USA.

Jim Rosinski (J)

CHDI Management/CHDI Foundation, Princeton, NJ, 08540, USA.

Alicia Preiss (A)

CHDI Management/CHDI Foundation, Princeton, NJ, 08540, USA.

Thomas F Vogt (TF)

CHDI Management/CHDI Foundation, Princeton, NJ, 08540, USA.

Giovanni Coppola (G)

Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA.
Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.

Darren Monckton (D)

Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, UK.

Russell G Snell (RG)

Applied Translational Genetics Group, School of Biological Sciences, Centre for Brain Research, The University of Auckland, Auckland, 1010, New Zealand.

X William Yang (X)

Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA.
Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.

Steve Horvath (S)

Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA. shorvath@mednet.ucla.edu.
Department of Biostatistics, School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA. shorvath@mednet.ucla.edu.

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