DNA methylation study of Huntington's disease and motor progression in patients and in animal models.
Adolescent
Adult
Aged
Aged, 80 and over
Animals
Animals, Genetically Modified
Behavior, Animal
CpG Islands
/ genetics
Cross-Sectional Studies
DNA Methylation
Disease Models, Animal
Disease Progression
Epigenesis, Genetic
Female
Follow-Up Studies
Gene Knock-In Techniques
Genetic Loci
Genome-Wide Association Study
Global Burden of Disease
Humans
Huntingtin Protein
/ genetics
Huntington Disease
/ blood
Longitudinal Studies
Male
Mice
Middle Aged
Mutation
Prospective Studies
Recombinant Proteins
/ genetics
Registries
/ statistics & numerical data
Severity of Illness Index
Sheep
Young Adult
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
10 09 2020
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
4529Subventions
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