Most mutations are stably transmitted from parent to offspring. This reliable genetic principle does not hold, however, for dynamic mutation disorders such as Fragile X syndrome or Huntington disease (HD). In these diseases, a sequence such as a CAG repeat tract can expand during transmission, likely through mechanisms involving replication or transcription (Khristich and Mirkin, 2020). In general, the longer the repeat, the earlier the patient develops overt symptoms and the more aggressive the disease is likely to be (Koshy and Zoghbi, 1997). Thus, in HD, modest expansions of 40 repeats in huntingtin gene (HTT) are associated with the appearance of motor, cognitive, and psychiatric disturbances in mid- or late adulthood, whereas large expansions of over 80 repeats cause childhood onset with additional features such as epilepsy and a more rapidly fatal course (Bates et al., 2015b; Sun et al., 2017). Yet on an individual subject basis, we cannot predict the disease course just from the size of the repeat tract: two individuals with the same length repeat expansion in HTT may experience disease onset decades apart (Andrew et al., 1993). The inherited pathological CAG repeat size accounts for about 42–71% of the age at onset in HD (Squitieri et al., 2006), though the confidence limits narrow for tracts longer than 50 CAGs (Andrew et al., 1993; Bates et al., 2015b; Langbehn et al., 2004; Rubinsztein et al., 1997; Wexler et al., 2004).

Part of the reason for such variability could be that HD is still thought of primarily as a movement disorder, so age at onset in HD is typically defined as the point at which motor symptoms become unequivocal. But abnormalities in the brain are present from early development (Barnat et al., 2020), and mutation carriers may experience cognitive deficits, psychiatric disturbances, or even subtle motor impairments years before diagnosis (Bates et al., 2015b). It is challenging, and somewhat misleading, to pinpoint age at onset in a disease that evolves insidiously like HD.

A more interesting explanation takes into account the fact that CAG repeats do not just expand in the germline. They are also somatically unstable, such that different CAG expansions can be identified in the same sample tissue from various organs and brain regions. Somatic mosaicism occurs both in mouse models of HD (Kennedy and Shelbourne, 2000; Larson et al., 2015; Lee et al., 2010) and in humans with HD (Kennedy et al., 2003; Swami et al., 2009; Telenius et al., 1994). The greatest increases in CAG tract length have been observed in the brain regions most affected in HD, the cerebral cortex and the striatum, whose neurons can harbor repeat tract expansions in the hundreds (Gonitel et al., 2008; Kennedy et al., 2003; Møllersen et al., 2010; Mouro Pinto et al., 2020; Shelbourne et al., 2007). Repeat expansions likely result from the formation of unusual DNA structures that predispose the tract to errors in mismatch repair (Khristich and Mirkin, 2020; Tabrizi et al., 2020). In fact, variants in several different DNA repair genes are associated with somatic instability in both animal models of HD (Dragileva et al., 2009; Pinto et al., 2013; Tomé et al., 2013) and HD patients (Ciosi et al., 2019; Flower et al., 2019; Lee et al., 2019).

Mounting evidence suggests that somatic repeat lengths better explain age at onset than the germline repeat, as their propensity to expand relates to both the baseline allele length and age (Ciosi et al., 2019; Lee et al., 2019). Interestingly, these data lend support to a mathematical model put forth over a decade ago (Kaplan et al., 2007). In brief, Kaplan et al. proposed that the onset and progression of triplet repeat diseases, including HD, are determined by the rate of somatic expansion in disease-relevant cells. Symptoms manifest when a critical proportion of cells (say, 20%) pass a pathogenic threshold, which would differ for different cell types. Their modeling suggests the threshold in striatal neurons for HD would be ~115 repeats. They further posited that at birth, nearly all cells would carry just the inherited number of repeats, but that over time the mutant alleles would further expand at a rate that increased linearly with the number of repeats. The rate of expansion would thus determine how rapidly the pathological state is reached, and thus should influence disease onset and progression.

The Kaplan model is quite compelling, but to test its predictions requires longitudinal data to study the evolution of somatic instability over time within patients. Given that the HTT mutation was discovered less than thirty years ago, such a study is only now becoming feasible. Even so, there are limits to how much of the model can be tested in humans. We cannot, for example, sample neurons over the life span to see how many come to exceed 115 repeats, or tally the proportion of neurons that reach a pathogenic threshold before phenoconversion. Nevertheless, we have been able to measure somatic repeat expansions in blood samples from HD carriers and patients over a twenty-year period and examine cortical tissue from mutation-carrying fetuses and deceased adults. By characterizing the degree of somatic expansion at these different stages, we were able to analyze associations between changes in the somatic expansion, age, and inherited CAG repeat length.

Our longitudinal study provides data that support the Kaplan model in several ways (Kaplan et al., 2007). First, the model predicts that at the beginning of life, all disease-relevant cells begin with the inherited repeat and negligible somatic instability. This turns out to be the case: we found almost no somatic mosaicism at the fetal stage. One might have expected that the high number of mitoses at this stage of brain development would make neural precursors sensitive to double-strand breaks and replication errors (Leija-Salazar et al., 2018; Schwer et al., 2016), but somatic expansions occur through different mechanisms than germline expansions (Khristich and Mirkin, 2020; Tabrizi et al., 2020). Although we did not have samples from embryos at later stages, there is such data for other diseases caused by repeat expansions. For instance, in Friedreich ataxia, which is caused by an expanded GAA repeat in the first intron on both alleles of the FXN gene, levels of instability found in tissues from an 18-week-old fetus were very low compared to adult-derived tissues (De Biase et al., 2007). In myotonic dystrophy, caused by a non-coding CTG repeat expansion in the DMPK gene, repeat instability was not observed at 13 weeks in fetal tissues, but a difference between tissues became detectable after 16 weeks (Martorell et al., 1997). All these studies suggest that, early in life, somatic instability is minimal.

Second, Kaplan et al. posited that somatic expansions should progress with age, even prior to disease onset. This also turns out to be correct: the presymptomatic carrier parents of fetal mutation carriers already showed somatic instability in the blood at the time of the pregnancy. It is remarkable, in fact, that increases in ER were evident despite our limited sample size and despite the fact that we had to derive this calculation from blood cells, which are not involved in HD pathogenesis and completely change over every six months or so. Unfortunately, for this very reason, the EI from the blood is not sufficient to predict AO, which is influenced by not only repeat length and somatic instability but other factors such as variants in DNA repair factors (see below).

Third, the model predicts the rate of allele expansion should increase with time and be a function of the repeat length at that time. This is indeed what we found: not just greater somatic expansions with age and reference repeat length (as represented by EI), but a greater propensity to expand with age (as represented by ER).

One prediction of the Kaplan model we could not test is that there should be different thresholds of somatic expansions that must be reached for different brain regions to become pathological. It is hard to imagine how this particular prediction of the model could be tested, other than by performing extensive neuropathological studies on a great many mice at many different disease stages. In terms of correlation between somatic instability and disease progression, we did find group-level differences in EI and ER between the premanifest and manifest state. We could not establish a correlation at the individual subject level, however, likely because of the limited sample size as well as the difficulty of pinpointing phenoconversion in a disease that continues to unfold over many years. Stronger evidence on this point came from a large study of nearly 750 HD mutation carriers, which showed that larger somatic expansions are associated with worse clinical outcomes (earlier AO, higher motor and progression scores) in HD (Ciosi et al., 2019).

The most interesting questions that remain to be answered have to do with what drives somatic instability. The brain regions that have the greatest repeat expansions in HD, the striatum and cortex, are hypermetabolic from early in the disease course (Tereshchenko et al., 2020), and neurons show greater somatic instability than glial cells in models and post mortem brains (Gonitel et al., 2008; Shelbourne et al., 2007). Metabolic stress may also lead to mitochondrial dysfunction and energy deficit in HD (Mochel et al., 2012; Roze et al., 2008; Tabrizi et al., 1999). An excess of excitatory glutamatergic inputs and NMDA receptor activation creates energy demands that are not sustainable in a context of diminished energy capacity, and may lead to cell death (Milnerwood et al., 2010; Mochel and Haller, 2011).

In fact, an excitotoxicity model of neurodegeneration was proposed for HD many years before the discovery of the genetic basis of the disease (Coyle and Schwarcz, 1976; Mcgeer and Mcgeer, 1976). The medium spiny neurons of the caudate and putamen, which are the most vulnerable in HD, receive their main input from cortical glutamatergic neurons; they are thus particularly susceptible to excitation and, in fact, HD can be mimicked by administering glutamate analogues to the striatum (Coyle and Schwarcz, 1976; Estrada Sánchez et al., 2008; Mcgeer and Mcgeer, 1976). In this context it is worth noting that variants in the GluR6 kainate receptor locus were found to account for 13% of variation in AO that was not provided by CAG repeat number (Rubinsztein et al., 1997). Along similar lines, a recent study showed that absence of the aryl hydrocarbon receptor (AhR), which protects mice from excitotoxicity, greatly reduced behavioral deficits in the R6/1 transgenic model of HD (Angeles-López et al., 2021).

Hypermetabolism would also contribute to oxidative stress, which can cause DNA damage (Iyer and Pluciennik, 2021; Leija-Salazar et al., 2018). Large-scale studies have linked somatic CAG expansions in patients’ blood to the presence of variants in DNA repair genes, not just in HD (Ciosi et al., 2019; Lee et al., 2019) but in other polyglutamine diseases as well (Bettencourt et al., 2016). In HD, somatic instability is influenced by polymorphisms in MSH3, MLH1, MlH3, and FAN1, which are all involved in DNA repair (Ciosi et al., 2019). Counterintuitively, loss of function of some DNA repair factors can be protective: knockout of Msh2 or Msh3 in a knock-in model of HD prevents CAG expansions in the striatum (Pinto et al., 2013). The reason for this may be that transcriptionally active genes elicit mismatch repair activity to guard genomic integrity, but long repeat tracts are difficult to repair accurately (Iyer and Pluciennik, 2021). A different mechanism is at work for FAN1, which actually stabilizes the CAG repeat in HD (Goold et al., 2019); loss of FAN1 function increases repeat instability (Kim et al., 2020; Loupe et al., 2020). Interestingly, there is evidence that double-strand break repair is dysregulated in HD: ATM (ataxia-telengiectasia mutated) is upregulated in brain tissue from HD mice and patients, and its heterozygous loss of function is protective in both mouse and Drosophila models of HD (Lu et al., 2014). It could be that the decline in DNA repair capacity or efficiency that comes with age (Gorbunova et al., 2007) contributes to the increasing somatic instability in blood cells, which, as we noted above, seem too short-lived to accumulate expansions as they do. An extended longitudinal study of the effect of DNA repair gene variants on somatic instability would be of great interest.

Given that somatic instability influences disease progression, targeting the repeat instability is a very appealing disease-modifying strategy (Khristich and Mirkin, 2020). One possibility is to introduce DNA-stabilizing interruptions into the repeat tract via gene editing (Ciosi et al., 2019). Another is to modulate DNA repair activity in HD to retard somatic expansions (Dragileva et al., 2009; Pinto et al., 2013), but this might also run the risk of increasing overall genomic instability. A recent approach using a small molecule that specifically binds CAG slip-out structures was able to contract the expansions and reduce protein aggregates in the striatum of R6/2 mice (Nakamori et al., 2020). Further efforts to stabilize or contract somatic expansions are warranted, particularly if expansions within brain tissue can be reduced. Last but not least, there is much more work to be done to understand the mechanisms that trigger somatic expansions, whether they relate to excitotoxicity, and how they lead to neurodegeneration.

All patient data generated and analyzed are included in the manuscript and available in Tables 1, 2 and 3.

1. 1. Andrew SE
   2. Paul Goldberg Y
   3. Kremer B
   4. Telenius H
   5. Theilmann J
   6. Adam S
   7. Starr E
   8. Squitieri F
   9. Lin B
   10. Kalchman MA
   11. Graham RK
   12. Hayden MR

   (1993) The relationship between trinucleotide (CAG) repeat length and clinical features of Huntington's disease

   Nature Genetics 4:398–403.

   https://doi.org/10.1038/ng0893-398

   • Google Scholar
2. 1. Angeles-López QD
   2. García-Lara L
   3. Aguirre-Pineda N
   4. Castañeda-Arellano R
   5. Elizondo-Azuela G
   6. Pérez-Severiano F
   7. Segovia J

   (2021) The absence of the aryl hydrocarbon receptor in the R6/1 transgenic mouse model of Huntington's disease improves the neurological phenotype

   Behavioural Brain Research 408:113230.

   https://doi.org/10.1016/j.bbr.2021.113230

   • PubMed
   • Google Scholar
3. 1. Barnat M
   2. Capizzi M
   3. Aparicio E
   4. Boluda S
   5. Wennagel D
   6. Kacher R
   7. Kassem R
   8. Lenoir S
   9. Agasse F
   10. Braz BY
   11. Liu J-P
   12. Ighil J
   13. Tessier A
   14. Zeitlin SO
   15. Duyckaerts C
   16. Dommergues M
   17. Durr A
   18. Humbert S

   (2020) Huntington’s disease alters human neurodevelopment

   Science 369:787–793.

   https://doi.org/10.1126/science.aax3338

   • Google Scholar
4. 1. Bates D
   2. Mächler M
   3. Bolker B
   4. Walker S

   (2015a) Fitting linear Mixed-Effects models using lme4

   Journal of Statistical Software 67:1–48.

   https://doi.org/10.18637/jss.v067.i01

   • Google Scholar
5. 1. Bates GP
   2. Dorsey R
   3. Gusella JF
   4. Hayden MR
   5. Kay C
   6. Leavitt BR
   7. Nance M
   8. Ross CA
   9. Scahill RI
   10. Wetzel R
   11. Wild EJ
   12. Tabrizi SJ

   (2015b) Huntington disease

   Nature Reviews Disease Primers 1:1–21.

   https://doi.org/10.1038/nrdp.2015.5

   • Google Scholar
6. 1. Bettencourt C
   2. Hensman-Moss D
   3. Flower M
   4. Wiethoff S
   5. Brice A
   6. Goizet C
   7. Stevanin G
   8. Koutsis G
   9. Karadima G
   10. Panas M
   11. Yescas-Gómez P
   12. García-Velázquez LE
   13. Alonso-Vilatela ME
   14. Lima M
   15. Raposo M
   16. Traynor B
   17. Sweeney M
   18. Wood N
   19. Giunti P
   20. Durr A
   21. Holmans P
   22. Houlden H
   23. Tabrizi SJ
   24. Jones L

   (2016) DNA repair pathways underlie a common genetic mechanism modulating onset in polyglutamine diseases

   Annals of Neurology 79:983–990.

   https://doi.org/10.1002/ana.24656

   • Google Scholar
7. 1. Ciosi M
   2. Maxwell A
   3. Cumming SA
   4. Hensman Moss DJ
   5. Alshammari AM
   6. Flower MD
   7. Durr A
   8. Leavitt BR
   9. Roos RAC
   10. Holmans P
   11. Jones L
   12. Langbehn DR
   13. Kwak S
   14. Tabrizi SJ
   15. Monckton DG
   16. TRACK-HD team, Enroll-HD team

   (2019) 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.

   https://doi.org/10.1016/j.ebiom.2019.09.020

   • PubMed
   • Google Scholar
8. 1. Coyle JT
   2. Schwarcz R

   (1976) Lesion of striatal neurons with kainic acid provides a model for Huntington's chorea

   Nature 263:244–246.

   https://doi.org/10.1038/263244a0

   • Google Scholar
9. 1. De Biase I
   2. Rasmussen A
   3. Monticelli A
   4. Al-Mahdawi S
   5. Pook M
   6. Cocozza S
   7. Bidichandani SI

   (2007) Somatic instability of the expanded GAA triplet-repeat sequence in Friedreich ataxia progresses throughout life

   Genomics 90:1–5.

   https://doi.org/10.1016/j.ygeno.2007.04.001

   • Google Scholar
10. 1. Dragileva E
    2. Hendricks A
    3. Teed A
    4. Gillis T
    5. Lopez ET
    6. Friedberg EC
    7. Kucherlapati R
    8. Edelmann W
    9. Lunetta KL
    10. MacDonald ME
    11. Wheeler VC

    (2009) Intergenerational and striatal CAG repeat instability in Huntington's disease knock-in mice involve different DNA repair genes

    Neurobiology of Disease 33:37–47.

    https://doi.org/10.1016/j.nbd.2008.09.014

    • Google Scholar
11. 1. Estrada Sánchez AM
    2. Mejía-Toiber J
    3. Massieu L

    (2008) Excitotoxic Neuronal Death and the Pathogenesis of Huntington's Disease

    Archives of Medical Research 39:265–276.

    https://doi.org/10.1016/j.arcmed.2007.11.011

    • Google Scholar
12. 1. Flower M
    2. Lomeikaite V
    3. Ciosi M
    4. Cumming S
    5. Morales F
    6. Lo K
    7. Hensman Moss D
    8. Jones L
    9. Holmans P
    10. Monckton DG
    11. Tabrizi SJ
    12. Kraus P
    13. Hoffman R
    14. Tobin A
    15. Borowsky B
    16. Keenan S
    17. Whitlock KB
    18. Queller S
    19. Campbell C
    20. Wang C
    21. Langbehn D
    22. Axelson E
    23. Johnson H
    24. Acharya T
    25. Cash DM
    26. Frost C
    27. Jones R
    28. Jurgens C
    29. ‘t Hart EP
    30. van der Grond J
    31. Witjes- Ane M-NN
    32. Roos RAC
    33. Dumas EM
    34. van den Bogaard SJA
    35. Stopford C
    36. Craufurd D
    37. Callaghan J
    38. Arran N
    39. Rosas DD
    40. Lee S
    41. Monaco W
    42. O’Regan A
    43. Milchman C
    44. Frajman E
    45. Labuschagne I
    46. Stout J
    47. Campbell M
    48. Andrews SC
    49. Bechtel N
    50. Reilmann R
    51. Bohlen S
    52. Kennard C
    53. Berna C
    54. Hicks S
    55. Durr A
    56. Pourchot C
    57. Bardinet E
    58. Nigaud K
    59. Valabre R
    60. gue `
    61. Lehericy S
    62. Marelli C
    63. Jauffret C
    64. Justo D
    65. Leavitt B
    66. Decolongon J
    67. Sturrock A
    68. Coleman A
    69. Santos RD
    70. Patel A
    71. Gibbard C
    72. Whitehead D
    73. Wild E
    74. Owen G
    75. Crawford H
    76. Malone I
    77. Lahiri N
    78. Fox NC
    79. Hobbs NZ
    80. Scahill RI
    81. Ordidge R
    82. Pepple T
    83. Read J
    84. Say MJ
    85. Landwehrmeyer B
    86. Daidj F
    87. Bassez G
    88. Lignier B
    89. Couppey F
    90. Delmas S
    91. Deux J-F
    92. Hankiewicz K
    93. Dogan C
    94. Minier L
    95. Chevalier P
    96. Hamadouche A
    97. Catt M
    98. van Hees V
    99. Catt S
    100. Schwalber A
    101. Dittrich J
    102. Kierkegaard M
    103. Wenninger S
    104. Schoser B
    105. Schüller A
    106. Stahl K
    107. Künzel H
    108. Wolff M
    109. Jellinek A
    110. Moreno CJ
    111. Gorman G
    112. Lochmüller H
    113. Trenell M
    114. van Laar S
    115. Wood L
    116. Cassidy S
    117. Newman J
    118. Charman S
    119. Steffaneti R
    120. Taylor L
    121. Brownrigg A
    122. Day S
    123. Atalaia A
    124. Raaphorst J
    125. Okkersen K
    126. van Engelen B
    127. Nikolaus S
    128. Cornelissen Y
    129. van Nimwegen M
    130. Maas D
    131. Klerks E
    132. Bouman S
    133. Knoop H
    134. Heskamp L
    135. Heerschap A
    136. Rahmadi R
    137. Groot P
    138. Heskes T
    139. Kapusta K
    140. Glennon J
    141. Abghari S
    142. Aschrafi A
    143. Poelmans G
    144. Treweek S
    145. Hogarth F
    146. Littleford R
    147. Donnan P
    148. Hapca A
    149. Hannah M
    150. McKenzie E
    151. Rauchhaus P
    152. Cumming SA
    153. Monckton DG
    154. Adam B
    155. Faber C
    156. Merkies I

    (2019) MSH3 modifies somatic instability and disease severity in Huntington’s and myotonic dystrophy type 1

    Brain 142:1876–1886.

    https://doi.org/10.1093/brain/awz115

    • Google Scholar
13. 1. Gonitel R
    2. Moffitt H
    3. Sathasivam K
    4. Woodman B
    5. Detloff PJ
    6. Faull RLM
    7. Bates GP

    (2008) DNA instability in postmitotic neurons

    PNAS 105:3467–3472.

    https://doi.org/10.1073/pnas.0800048105

    • Google Scholar
14. 1. Goold R
    2. Flower M
    3. Moss DH
    4. Medway C
    5. Wood-Kaczmar A
    6. Andre R
    7. Farshim P
    8. Bates GP
    9. Holmans P
    10. Jones L
    11. Tabrizi SJ

    (2019) FAN1 modifies Huntington’s disease progression by stabilizing the expanded HTT CAG repeat

    Human Molecular Genetics 28:650–661.

    https://doi.org/10.1093/hmg/ddy375

    • Google Scholar
15. 1. Gorbunova V
    2. Seluanov A
    3. Mao Z
    4. Hine C

    (2007) Changes in DNA repair during aging

    Nucleic Acids Research 35:7466–7474.

    https://doi.org/10.1093/nar/gkm756

    • Google Scholar
16. 1. Iyer RR
    2. Pluciennik A

    (2021) DNA Mismatch Repair and its Role in Huntington’s Disease

    Journal of Huntington's Disease 10:75–94.

    https://doi.org/10.3233/JHD-200438

    • Google Scholar
17. 1. Kaplan S
    2. Itzkovitz S
    3. Shapiro E

    (2007) A Universal Mechanism Ties Genotype to Phenotype in Trinucleotide Diseases

    PLOS Computational Biology 3:e235.

    https://doi.org/10.1371/journal.pcbi.0030235

    • Google Scholar
18. 1. Kennedy L
    2. Evans E
    3. Chen C-M
    4. Craven L
    5. Detloff PJ
    6. Ennis M
    7. Shelbourne PF

    (2003) Dramatic tissue-specific mutation length increases are an early molecular event in Huntington disease pathogenesis

    Human Molecular Genetics 12:3359–3367.

    https://doi.org/10.1093/hmg/ddg352

    • Google Scholar
19. 1. Kennedy L
    2. Shelbourne PF

    (2000) Dramatic mutation instability in HD mouse striatum: does polyglutamine load contribute to cell-specific vulnerability in Huntington's disease?

    Human Molecular Genetics 9:2539–2544.

    https://doi.org/10.1093/hmg/9.17.2539

    • Google Scholar
20. 1. Khristich AN
    2. Mirkin SM

    (2020) On the wrong DNA track: Molecular mechanisms of repeat-mediated genome instability

    Journal of Biological Chemistry 295:4134–4170.

    https://doi.org/10.1074/jbc.REV119.007678

    • Google Scholar
21. 1. Kim K-H
    2. Hong EP
    3. Shin JW
    4. Chao MJ
    5. Loupe J
    6. Gillis T
    7. Mysore JS
    8. Holmans P
    9. Jones L
    10. Orth M
    11. Monckton DG
    12. Long JD
    13. Kwak S
    14. Lee R
    15. Gusella JF
    16. MacDonald ME
    17. Lee J-M

    (2020) Genetic and Functional Analyses Point to FAN1 as the Source of Multiple Huntington Disease Modifier Effects

    The American Journal of Human Genetics 107:96–110.

    https://doi.org/10.1016/j.ajhg.2020.05.012

    • Google Scholar
22. 1. Koshy BT
    2. Zoghbi HY

    (1997) The CAG/Polyglutamine Tract Diseases: Gene Products and Molecular Pathogenesis

    Brain Pathology 7:927–942.

    https://doi.org/10.1111/j.1750-3639.1997.tb00894.x

    • Google Scholar
23. 1. Langbehn DR
    2. Brinkman RR
    3. Falush D
    4. Paulsen JS
    5. Hayden MR
    6. on behalf of an International Huntington's Disease Collaborative Group

    (2004) A new model for prediction of the age of onset and penetrance for Huntington's disease based on CAG length

    Clinical Genetics 65:267–277.

    https://doi.org/10.1111/j.1399-0004.2004.00241.x

    • Google Scholar
24. 1. Larson E
    2. Fyfe I
    3. Morton AJ
    4. Monckton DG

    (2015) Age-, tissue- and length-dependent bidirectional somatic CAG•CTG repeat instability in an allelic series of R6/2 Huntington disease mice

    Neurobiology of Disease 76:98–111.

    https://doi.org/10.1016/j.nbd.2015.01.004

    • Google Scholar
25. 1. Lee J-M
    2. Zhang J
    3. Su AI
    4. Walker JR
    5. Wiltshire T
    6. Kang K
    7. Dragileva E
    8. Gillis T
    9. Lopez ET
    10. Boily M-J
    11. Cyr M
    12. Kohane I
    13. Gusella JF
    14. MacDonald ME
    15. Wheeler VC

    (2010) A novel approach to investigate tissue-specific trinucleotide repeat instability

    BMC Systems Biology 4:29.

    https://doi.org/10.1186/1752-0509-4-29

    • Google Scholar
26. 1. Lee J-M
    2. Correia K
    3. Loupe J
    4. Kim K-H
    5. Barker D
    6. Hong EP
    7. Chao MJ
    8. Long JD
    9. Lucente D
    10. Vonsattel JPG
    11. Pinto RM
    12. Abu Elneel K
    13. Ramos EM
    14. Mysore JS
    15. Gillis T
    16. Wheeler VC
    17. MacDonald ME
    18. Gusella JF
    19. McAllister B
    20. Massey T
    21. Medway C
    22. Stone TC
    23. Hall L
    24. Jones L
    25. Holmans P
    26. Kwak S
    27. Ehrhardt AG
    28. Sampaio C
    29. Ciosi M
    30. Maxwell A
    31. Chatzi A
    32. Monckton DG
    33. Orth M
    34. Landwehrmeyer GB
    35. Paulsen JS
    36. Dorsey ER
    37. Shoulson I
    38. Myers RH

    (2019) CAG Repeat Not Polyglutamine Length Determines Timing of Huntington’s Disease Onset

    Cell 178:887–900.

    https://doi.org/10.1016/j.cell.2019.06.036

    • Google Scholar
27. 1. Leija-Salazar M
    2. Piette C
    3. Proukakis C

    (2018) Review: Somatic mutations in neurodegeneration

    Neuropathology and Applied Neurobiology 44:267–285.

    https://doi.org/10.1111/nan.12465

    • Google Scholar
28. 1. Loupe JM
    2. Pinto RM
    3. Kim K-H
    4. Gillis T
    5. Mysore JS
    6. Andrew MA
    7. Kovalenko M
    8. Murtha R
    9. Seong I
    10. Gusella JF
    11. Kwak S
    12. Howland D
    13. Lee R
    14. Lee J-M
    15. Wheeler VC
    16. MacDonald ME

    (2020) Promotion of somatic CAG repeat expansion by Fan1 knock-out in Huntington’s disease knock-in mice is blocked by Mlh1 knock-out

    Human Molecular Genetics 29:3044–3053.

    https://doi.org/10.1093/hmg/ddaa196

    • Google Scholar
29. 1. Lu XH
    2. Mattis VB
    3. Wang N
    4. Al-Ramahi I
    5. van den Berg N
    6. Fratantoni SA
    7. Waldvogel H
    8. Greiner E
    9. Osmand A
    10. Elzein K
    11. Xiao J
    12. Dijkstra S
    13. de Pril R
    14. Vinters HV
    15. Faull R
    16. Signer E
    17. Kwak S
    18. Marugan JJ
    19. Botas J
    20. Fischer DF
    21. Svendsen CN
    22. Munoz-Sanjuan I
    23. Yang XW

    (2014) Targeting ATM ameliorates mutant huntingtin toxicity in cell and animal models of Huntington's disease

    Science Translational Medicine 6:268ra178.

    https://doi.org/10.1126/scitranslmed.3010523

    • PubMed
    • Google Scholar
30. 1. Martorell L
    2. Johnson K
    3. Boucher CA
    4. Baiget M

    (1997) Somatic Instability of the Myotonic Dystrophy (CTG)n Repeat during Human Fetal Development

    Human Molecular Genetics 6:877–880.

    https://doi.org/10.1093/hmg/6.6.877

    • Google Scholar
31. 1. Mcgeer EG
    2. Mcgeer PL

    (1976) Duplication of biochemical changes of Huntington's chorea by intrastriatal injections of glutamic and kainic acids

    Nature 263:517–519.

    https://doi.org/10.1038/263517a0

    • Google Scholar
32. 1. Milnerwood AJ
    2. Gladding CM
    3. Pouladi MA
    4. Kaufman AM
    5. Hines RM
    6. Boyd JD
    7. Ko RW
    8. Vasuta OC
    9. Graham RK
    10. Hayden MR
    11. Murphy TH
    12. Raymond LA

    (2010) Early increase in extrasynaptic NMDA receptor signaling and expression contributes to phenotype onset in Huntington's disease mice

    Neuron 65:178–190.

    https://doi.org/10.1016/j.neuron.2010.01.008

    • PubMed
    • Google Scholar
33. 1. Mochel F
    2. Durant B
    3. Meng X
    4. O'Callaghan J
    5. Yu H
    6. Brouillet E
    7. Wheeler VC
    8. Humbert S
    9. Schiffmann R
    10. Durr A

    (2012) Early Alterations of Brain Cellular Energy Homeostasis in Huntington Disease Models*

    Journal of Biological Chemistry 287:1361–1370.

    https://doi.org/10.1074/jbc.M111.309849

    • Google Scholar
34. 1. Mochel F
    2. Haller RG

    (2011) Energy deficit in Huntington disease: why it matters

    Journal of Clinical Investigation 121:493–499.

    https://doi.org/10.1172/JCI45691

    • Google Scholar
35. 1. Møllersen L
    2. Rowe AD
    3. Larsen E
    4. Rognes T
    5. Klungland A

    (2010) Continuous and Periodic Expansion of CAG Repeats in Huntington's Disease R6/1 Mice

    PLOS Genetics 6:e1001242.

    https://doi.org/10.1371/journal.pgen.1001242

    • Google Scholar
36. 1. Mouro Pinto R
    2. Arning L
    3. Giordano JV
    4. Razghandi P
    5. Andrew MA
    6. Gillis T
    7. Correia K
    8. Mysore JS
    9. Grote Urtubey D-M
    10. Parwez CR
    11. von Hein SM
    12. Clark HB
    13. Nguyen HP
    14. Förster E
    15. Beller A
    16. Jayadaev S
    17. Keene CD
    18. Bird TD
    19. Lucente D
    20. Vonsattel J-P
    21. Orr H
    22. Saft C
    23. Petrasch-Parwez E
    24. Wheeler VC

    (2020) Patterns of CAG repeat instability in the central nervous system and periphery in Huntington’s disease and in spinocerebellar ataxia type 1

    Human Molecular Genetics 29:2551–2567.

    https://doi.org/10.1093/hmg/ddaa139

    • Google Scholar
37. 1. Nakamori M
    2. Panigrahi GB
    3. Lanni S
    4. Gall-Duncan T
    5. Hayakawa H
    6. Tanaka H
    7. Luo J
    8. Otabe T
    9. Li J
    10. Sakata A
    11. Caron M-C
    12. Joshi N
    13. Prasolava T
    14. Chiang K
    15. Masson J-Y
    16. Wold MS
    17. Wang X
    18. Lee MYWT
    19. Huddleston J
    20. Munson KM
    21. Davidson S
    22. Layeghifard M
    23. Edward L-M
    24. Gallon R
    25. Santibanez-Koref M
    26. Murata A
    27. Takahashi MP
    28. Eichler EE
    29. Shlien A
    30. Nakatani K
    31. Mochizuki H
    32. Pearson CE

    (2020) A slipped-CAG DNA-binding small molecule induces trinucleotide-repeat contractions in vivo

    Nature Genetics 52:146–159.

    https://doi.org/10.1038/s41588-019-0575-8

    • Google Scholar
38. 1. Pinto RM
    2. Dragileva E
    3. Kirby A
    4. Lloret A
    5. Lopez E
    6. St Claire J
    7. Panigrahi GB
    8. Hou C
    9. Holloway K
    10. Gillis T
    11. Guide JR
    12. Cohen PE
    13. Li GM
    14. Pearson CE
    15. Daly MJ
    16. Wheeler VC

    (2013) Mismatch repair genes Mlh1 and Mlh3 modify CAG instability in Huntington's disease mice: genome-wide and candidate approaches

    PLOS Genetics 9:e1003930.

    https://doi.org/10.1371/journal.pgen.1003930

    • PubMed
    • Google Scholar
39. Software

    1. R Development Core Team

    (2019) R: A Language and Environment for Statistical Computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.r-project.org
40. 1. Rolfsmeier ML
    2. Dixon MJ
    3. Lahue RS

    (2000) Mismatch Repair Blocks Expansions of Interrupted Trinucleotide Repeats in Yeast

    Molecular Cell 6:1501–1507.

    https://doi.org/10.1016/S1097-2765(00)00146-5

    • Google Scholar
41. 1. Roze E
    2. Saudou F
    3. Caboche J

    (2008) Pathophysiology of Huntington’s disease: from huntingtin functions to potential treatments

    Current Opinion in Neurology 21:497–503.

    https://doi.org/10.1097/WCO.0b013e328304b692

    • PubMed
    • Google Scholar
42. 1. Rubinsztein DC
    2. Leggo J
    3. Chiano M
    4. Dodge A
    5. Norbury G
    6. Rosser E
    7. Craufurd D

    (1997) Genotypes at the GluR6 kainate receptor locus are associated with variation in the age of onset of Huntington disease

    PNAS 94:3872–3876.

    https://doi.org/10.1073/pnas.94.8.3872

    • Google Scholar
43. 1. Schwer B
    2. Wei P-C
    3. Chang AN
    4. Kao J
    5. Du Z
    6. Meyers RM
    7. Alt FW

    (2016) Transcription-associated processes cause DNA double-strand breaks and translocations in neural stem/progenitor cells

    PNAS 113:2258–2263.

    https://doi.org/10.1073/pnas.1525564113

    • Google Scholar
44. 1. Shelbourne PF
    2. Keller-McGandy C
    3. Bi WL
    4. Yoon S-R
    5. Dubeau L
    6. Veitch NJ
    7. Vonsattel JP
    8. Wexler NS
    9. Arnheim N
    10. Augood SJ

    (2007) Triplet repeat mutation length gains correlate with cell-type specific vulnerability in Huntington disease brain

    Human Molecular Genetics 16:1133–1142.

    https://doi.org/10.1093/hmg/ddm054

    • Google Scholar
45. 1. Squitieri F
    2. Ciarmiello A
    3. Di Donato S
    4. Frati L

    (2006) The search for cerebral biomarkers of Huntington's disease: a review of genetic models of age at onset prediction

    European Journal of Neurology 13:408–415.

    https://doi.org/10.1111/j.1468-1331.2006.01264.x

    • Google Scholar
46. 1. Sun Y-M
    2. Zhang Y-B
    3. Wu Z-Y
    4. Z-y W

    (2017) Huntington’s Disease: Relationship Between Phenotype and Genotype

    Molecular Neurobiology 54:342–348.

    https://doi.org/10.1007/s12035-015-9662-8

    • Google Scholar
47. 1. Swami M
    2. Hendricks AE
    3. Gillis T
    4. Massood T
    5. Mysore J
    6. Myers RH
    7. Wheeler VC

    (2009) Somatic expansion of the Huntington's disease CAG repeat in the brain is associated with an earlier age of disease onset

    Human Molecular Genetics 18:3039–3047.

    https://doi.org/10.1093/hmg/ddp242

    • Google Scholar
48. 1. Tabrizi SJ
    2. Cleeter MWJ
    3. Xuereb J
    4. Taanman J-W
    5. Cooper JM
    6. Schapira AHV

    (1999) Biochemical abnormalities and excitotoxicity in Huntington's disease brain

    Annals of Neurology 45:25–32.

    https://doi.org/10.1002/1531-8249(199901)45:1<25::AID-ART6>3.0.CO;2-E

    • Google Scholar
49. 1. Tabrizi SJ
    2. Flower MD
    3. Ross CA
    4. Wild EJ

    (2020) Huntington disease: new insights into molecular pathogenesis and therapeutic opportunities

    Nature Reviews Neurology 16:529–546.

    https://doi.org/10.1038/s41582-020-0389-4

    • Google Scholar
50. 1. Telenius H
    2. Kremer B
    3. Goldberg YP
    4. Theilmann J
    5. Andrew SE
    6. Zeisler J
    7. Adam S
    8. Greenberg C
    9. Ives EJ
    10. Clarke LA
    11. Hayden MR

    (1994) Somatic and gonadal mosaicism of the Huntington disease gene CAG repeat in brain and sperm

    Nature Genetics 6:409–414.

    https://doi.org/10.1038/ng0494-409

    • Google Scholar
51. 1. Tereshchenko AV
    2. Schultz JL
    3. Bruss JE
    4. Magnotta VA
    5. Epping EA
    6. Nopoulos PC

    (2020) Abnormal development of cerebellar-striatal circuitry in Huntington disease

    Neurology 94:e1908–e1915.

    https://doi.org/10.1212/WNL.0000000000009364

    • Google Scholar
52. 1. Tomé S
    2. Manley K
    3. Simard JP
    4. Clark GW
    5. Slean MM
    6. Swami M
    7. Shelbourne PF
    8. Tillier ERM
    9. Monckton DG
    10. Messer A
    11. Pearson CE

    (2013) MSH3 Polymorphisms and Protein Levels Affect CAG Repeat Instability in Huntington's Disease Mice

    PLOS Genetics 9:e1003280.

    https://doi.org/10.1371/journal.pgen.1003280

    • Google Scholar
53. 1. Wexler NS
    2. Lorimer J
    3. Porter J
    4. Gomez F
    5. Moskowitz C
    6. Shackell E
    7. Marder K
    8. Penchaszadeh G
    9. Roberts SA
    10. Gayán J
    11. Brocklebank D
    12. Cherny SS
    13. Cardon LR
    14. Gray J
    15. Dlouhy SR
    16. Wiktorski S
    17. Hodes ME
    18. Conneally PM
    19. Penney JB
    20. Gusella J
    21. Cha JH
    22. Irizarry M
    23. Rosas D
    24. Hersch S
    25. Hollingsworth Z
    26. MacDonald M
    27. Young AB
    28. Andresen JM
    29. Housman DE
    30. De Young MM
    31. Bonilla E
    32. Stillings T
    33. Negrette A
    34. Snodgrass SR
    35. Martinez-Jaurrieta MD
    36. Ramos-Arroyo MA
    37. Bickham J
    38. Ramos JS
    39. Marshall F
    40. Shoulson I
    41. Rey GJ
    42. Feigin A
    43. Arnheim N
    44. Acevedo-Cruz A
    45. Acosta L
    46. Alvir J
    47. Fischbeck K
    48. Thompson LM
    49. Young A
    50. Dure L
    51. O'Brien CJ
    52. Paulsen J
    53. Brickman A
    54. Krch D
    55. Peery S
    56. Hogarth P
    57. Higgins DS
    58. Landwehrmeyer B
    59. U.S.-Venezuela Collaborative Research Project

    (2004) Venezuelan kindreds reveal that genetic and environmental factors modulate Huntington’s disease age of onset

    PNAS 101:3498–3503.

    https://doi.org/10.1073/pnas.0308679101

    • PubMed
    • Google Scholar
54. Book

    1. Wickham H

    (2009) Ggplot2: Elegant Graphics for Data Analysis Use R!

    Springer-Verlag.

    https://doi.org/10.1007/978-0-387-98141-3

    • Google Scholar
55. 1. Wright GEB
    2. Collins JA
    3. Kay C
    4. McDonald C
    5. Dolzhenko E
    6. Xia Q
    7. Bečanović K
    8. Drögemöller BI
    9. Semaka A
    10. Nguyen CM
    11. Trost B
    12. Richards F
    13. Bijlsma EK
    14. Squitieri F
    15. Ross CJD
    16. Scherer SW
    17. Eberle MA
    18. Yuen RKC
    19. Hayden MR

    (2019) Length of Uninterrupted CAG, Independent of Polyglutamine Size, Results in Increased Somatic Instability, Hastening Onset of Huntington Disease

    The American Journal of Human Genetics 104:1116–1126.

    https://doi.org/10.1016/j.ajhg.2019.04.007

    • Google Scholar
56. 1. Xu P
    2. Pan F
    3. Roland C
    4. Sagui C
    5. Weninger K

    (2020) Dynamics of strand slippage in DNA hairpins formed by CAG repeats: roles of sequence parity and trinucleotide interrupts

    Nucleic Acids Research 48:2232–2245.

    https://doi.org/10.1093/nar/gkaa036

    • Google Scholar

Author details

1. Radhia Kacher

   1. Sorbonne Université, Paris Brain Institute (ICM Institut du Cerveau), AP-HP, INSERM, CNRS, University Hospital Pitié-Salpêtrière, Paris, France
   2. Univ. Grenoble Alpes, INSERM, U 1216, Grenoble Institut Neurosciences, Grenoble, France

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4679-7361

2. François-Xavier Lejeune

   1. Sorbonne Université, Paris Brain Institute (ICM Institut du Cerveau), AP-HP, INSERM, CNRS, University Hospital Pitié-Salpêtrière, Paris, France
   2. Paris Brain Institute’s Data and Analysis Core, University Hospital Pitié-Salpêtrière, Paris, France

   Contribution

   Software, Formal analysis, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

3. Sandrine Noël

   Neurogenetics Laboratory, Department of Genetics, Assistance Publique–Hôpitaux de Paris, University Hospital Pitié-Salpêtrière, Paris, France

   Contribution

   Resources, Software, Methodology

   Competing interests

   No competing interests declared

4. Cécile Cazeneuve

   Neurogenetics Laboratory, Department of Genetics, Assistance Publique–Hôpitaux de Paris, University Hospital Pitié-Salpêtrière, Paris, France

   Contribution

   Conceptualization, Resources, Methodology

   Competing interests

   No competing interests declared

5. Alexis Brice

   Sorbonne Université, Paris Brain Institute (ICM Institut du Cerveau), AP-HP, INSERM, CNRS, University Hospital Pitié-Salpêtrière, Paris, France

   Contribution

   Validation, Investigation, Writing - original draft

   Competing interests

   No competing interests declared

6. Sandrine Humbert

   Univ. Grenoble Alpes, INSERM, U 1216, Grenoble Institut Neurosciences, Grenoble, France

   Contribution

   Conceptualization, Supervision, Funding acquisition, Validation, Investigation, Writing - original draft, Writing - review and editing

   For correspondence

   sandrine.humbert@univ-grenoble-alpes.fr

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9501-2658

7. Alexandra Durr

   1. Sorbonne Université, Paris Brain Institute (ICM Institut du Cerveau), AP-HP, INSERM, CNRS, University Hospital Pitié-Salpêtrière, Paris, France
   2. Neurogenetics Laboratory, Department of Genetics, Assistance Publique–Hôpitaux de Paris, University Hospital Pitié-Salpêtrière, Paris, France

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   alexandra.durr@icm-institute.org

   Competing interests

   serves on the advisory boards of Triplet Therapeutics. and holds partly a Patent B 06291873.5, "Anaplerotic therapy of HD and other polyglutamine diseases."

   "This ORCID iD identifies the author of this article:" 0000-0002-8921-7104

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