Metabolic abnormalities have long been predicted in Huntington’s disease (HD) but remain poorly characterized. Chronobiological dysregulation has been described in HD and may include abnormalities in circadian-driven metabolism.
Objective:
Here we investigated metabolite profiles in the transgenic sheep model of HD (OVT73) at presymptomatic ages. Our goal was to understand changes to the metabolome as well as potential metabolite rhythm changes associated with HD.
Methods:
We used targeted liquid chromatography mass spectrometry (LC-MS) metabolomics to analyze metabolites in plasma samples taken from female HD transgenic and normal (control) sheep aged 5 and 7 years. Samples were taken hourly across a 27-h period. The resulting dataset was investigated by machine learning and chronobiological analysis.
Results:
The metabolic profiles of HD and control sheep were separable by machine learning at both ages. We found both absolute and rhythmic differences in metabolites in HD compared to control sheep at 5 years of age. An increase in both the number of disturbed metabolites and the magnitude of change of acrophase (the time at which the rhythms peak) was seen in samples from 7-year-old HD compared to control sheep. There were striking similarities between the dysregulated metabolites identified in HD sheep and human patients (notably of phosphatidylcholines, amino acids, urea, and threonine).
Conclusion:
This work provides the first integrated analysis of changes in metabolism and circadian rhythmicity of metabolites in a large animal model of presymptomatic HD.
Herzog-KrzywoszanskaR, KrzywoszanskiL. Sleep disorders in Huntington’s disease. Front Psychiatry. 2019;10:221. doi: 10.3389/fpsyt.2019.00221
4.
VoyseyZ, FazalSV, LazarAS, BarkerRA. The sleep and circadian problems of Huntington’s disease: when, why and their importance. J Neurol. 2021;268:2275–83.doi: 10.1007/s00415-020-10334-3
5.
OgilvieAC, NopoulosPC, SchultzJL. Sleep disturbances by disease type and stage in Huntington’s disease. Parkinsonism Relat Disord. 2021;91:13–8. doi: 10.1016/j.parkreldis.2021.08.011
6.
MortonAJ, WoodNI, HastingsMH, HurelbrinkC, BarkerRA, MaywoodES. Disintegration of the sleep-wake cycle and circadian timing in Huntington’s disease. J Neurosci. 2005;25:157–63. doi: 10.1523/JNEUROSCI.3842-04.2005
7.
KuljisD, KudoT, TaharaY, GhianiCA, ColwellCS. Pathophysiology in the suprachiasmatic nucleus in mouse models of Huntington’s disease. J Neurosci Res. 2018;96:1862–75. doi: 10.1002/jnr.24320
8.
HandleyRR, ReidSJ, PatassiniS, RudigerSR, ObolonkinV, McLaughlanCJ, et al. Metabolic disruption identified in the Huntington’s disease transgenic sheep model. Sci Rep. 2016;6:20681. doi: 10.1038/srep20681
9.
WangR, RossCA, CaiH, CongW-N, DaimonCM, CarlsonOD, et al. Metabolic and hormonal signatures in pre-manifest and manifest Huntington’s disease patients. Front Physiol. 2014;5.
10.
NielsenSMB, Vinther-JensenT, NielsenJE, N\ensuremathørremølle A, HasholtL, HjermindLE, et al. Liver function in Huntington’s disease assessed by blood biochemical analyses in a clinical setting. J Neurol Sci. 2016;362:326–32. doi: 10.1016/j.jns.2016.02.018
11.
LeoniV, MariottiC, TabriziSJ, ValenzaM, WildEJ, HenleySMD, et al. Plasma 24S-hydroxycholesterol and caudate MRI in pre-manifest and early Huntington’s disease. Brain. 2008;131:2851–9. doi: 10.1093/brain/awn212
12.
Świtońska-KurkowskaK, KristB, DelimataJ, FigielM. Juvenile Huntington’s disease and other opolyQ diseases, update on neurodevelopmental character and comparative bioinformatic review of transcriptomic and proteomic data. Front Cell Dev Biol. 2021;9. doi: 10.3389/fcell.2021.642773
13.
McGarryA, GaughanJ, HackmyerC, LovettJ, KhadeerM, ShaikhH, et al. Cross-sectional analysis of plasma and CSF metabolomic markers in Huntington’s disease for participants of varying functional disability: a pilot study. Sci Rep. 2020;10. doi: 10.1038/s41598-020-77526-9
14.
PatassiniS, BegleyP, XuJ, ChurchSJ, ReidSJ, KimEH, et al. Metabolite mapping reveals severe widespread perturbation of multiple metabolic processes in Huntington’s disease human brain. Biochim Biophys Acta. 2016;1862:1650–62. doi: 10.1016/j.bbadis.2016.06.002
15.
LiotG, ValetteJ, PépinJ, FlamentJ, BrouilletE. Energy defects in Huntington’s disease: Why “in vivo” evidence matters. Biochem Biophys Res Commun. 2017;483:1084–95. doi: 10.1016/j.bbrc.2016.09.065
16.
MastrokoliasA, PoolR, MinaE, HettneKM, van DuijnE, van der MastRC, et al. Integration of targeted metabolomics and transcriptomics identifies deregulation of phosphatidylcholine metabolism in Huntington’s disease peripheral blood samples. Metabolomics. 2016;12:137. doi: 10.1007/s11306-016-1084-8
17.
PouladiMA, MortonAJ, HaydenMR. Choosing an animal model for the study of Huntington’s disease. Nat Rev Neurosci. 2013;14:708–21. doi: 10.1038/nrn3570
18.
JacobsenJC, BawdenCS, RudigerSR, McLaughlanCJ, ReidSJ, WaldvogelHJ, et al. An ovine transgenic Huntington’s disease model. Hum Mol Genet. 2010;19:1873–82. doi: 10.1093/hmg/ddq063
19.
ReidSJ, PatassiniS, et al. Further molecular characterisation of the OVT73 transgenic sheep model of Huntington’s disease identifies cortical aggregates. J Huntingtons Dis. 2013;2(3):279–95. doi: 10.3233/jhd-130067
20.
MortonAJ, RudigerSR, WoodNI, SawiakSJ, BrownGC, MclaughlanCJ, et al. Early and progressive circadian abnormalities in Huntington’s disease sheep are unmasked by social environment. Hum Mol Genet. 2014;23:3375–83. doi: 10.1093/hmg/ddu047
21.
SchneiderWT, VasS, NicolAU, MortonAJ. Abnormally abrupt transitions from sleep-towake in Huntington’s disease sheep (Ovis aries) are revealed by automated analysis of sleep/wake transition dynamics. PLoS One. 2021;16:1–16. doi: 10.1371/journal.pone.0251767
22.
VasS, NicolAU, KalmarL, MilesJ, MortonAJ. Abnormal patterns of sleep and EEG power distribution during non-rapid eye movement sleep in the sheep model of Huntington’s disease. Neurobiol Dis. 2021;155:105367. doi: 10.1016/j.nbd.2021.105367
23.
MortonAJ, MiddletonB, RudigerS, BawdenCS, KuchelTR, SkeneDJ. Increased plasma melatonin in presymptomatic Huntington disease sheep (Ovis aries): Compensatory neuroprotection in a neurodegenerative disease?J Pineal Res. 2020;68:e12624. doi: https://doi.org/10.1111/jpi.12624.
EnglishJ, ArendtJ, PoultonA, SymonsAM. Short-term variations of circulating melatonin in the ewe. J Pineal Res. 1987;4:359–66. doi: 10.1111/j.1600-079X.1987.tb00875.x
26.
IsherwoodCM, Van der VeenDR, JohnstonJD, SkeneDJ. Twenty-four-hour rhythmicity of circulating metabolites: effect of body mass and type 2 diabetes. FASEB J.2017;31:5557–67. doi: 10.1096/fj.201700323R
27.
SlettenTL, RevellVL, MiddletonB, LederleKA, SkeneDJ. Age-related changes in acute and phase-advancing responses to monochromatic light. J Biol Rhythms. 2009;24:73–84. doi: 10.1177/0748730408328973
28.
LeeLC, LiongCY, JemainAA. Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: A review of contemporary practice strategies and knowledge gaps. Analyst. 2018;143:3526–39. doi: 10.1039/c8an00599k
29.
ChongJ, WishartDS, XiaJ. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr Protoc Bioinforma. 2019;68:e86. doi: https://doi.org/10.1002/cpbi.86.
30.
LiC-Y, SongH-T, WangX-X, WanY-Y, DingX-S, LiuS-J, et al. Urinary metabolomics reveals the therapeutic effect of HuangQi Injections in cisplatin-induced nephrotoxic rats. Sci Rep. 2017;7:3619. doi: 10.1038/s41598-017-03249-z
31.
CarlucciM, KrisciunasA, LiH, GibasP, KonceviciusK, PetronisA, et al. DiscoRhythm: An easy-to-use web application and R package for discovering rhythmicity. Bioinformatics. 2020;36:1952–4. doi: 10.1093/bioinformatics/btz834
32.
WuG, AnafiRC, HughesME, KornackerK, HogeneschJB. MetaCycle: An integrated R package to evaluate periodicity in large scale data. Bioinformatics. 2016;32:3351–3. doi: 10.1093/bioinformatics/btw405
33.
De Los SantosH, CollinsEJ, MannC, SaganAW, JankowskiMS, BennettKP, et al. ECHO: An application for detection and analysis of oscillators identifies metabolic regulation on genome-wide circadian output. Bioinformatics. 2020;36:773–81. doi: 10.1093/bioinformatics/btz617
34.
ThabenPF, WestermarkPO. Detecting rhythms in time series with rain. J Biol Rhythms. 2014;29:391–400. doi: 10.1177/0748730414553029
35.
AgostinelliF, CegliaN, ShahbabaB, Sassone-CorsiP, BaldiP, What time is it? Deep learning approaches for circadian rhythms. Bioinformatics. 2016;32:18–17. doi: 10.1093/bioinformatics/btw243
36.
SiskosAP, JainP, Römisch-MarglW, BennettM, AchaintreD, AsadY, et al. Interlaboratory reproducibility of a targeted metabolomics platform for analysis of human serum and plasma. Anal Chem. 2017;89:656–65. doi: 10.1021/acs.analchem.6b02930
37.
SumnerLW, AmbergA, BarrettD, BealeMH, BegerR, DaykinCA, et al. Proposed minimum reporting standards for chemical analysis: Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics. 2007;3:211–21. doi: 10.1007/s11306-007-0082-2
38.
ChengML, ChangKH, WuYR, ChenCM. Metabolic disturbances in plasma as biomarkers for Huntington’s disease. J Nutr Biochem. 2016;31:38–44. doi: 10.1016/j.jnutbio.2015.12.001
39.
KhachiyantsN, TrinkleD, SonSJ, KimKY. Sundown syndrome in persons with dementia: An update. Psychiatry Investig. 2011;8:275–87. doi: 10.4306/pi.2011.8.4.275
40.
McCaugheyWTE. The pathologic spectrum of huntington’s chorea. J Nerv Ment Dis. 1961;133:91–103. doi: 10.1097/00005053-196108000-00001
41.
BoltJMW, LewisGP. Huntington’s Chorea: a study of liver function and histology. QJM An Int J Med. 1973;42:151–74. doi: 10.1093/oxfordjournals.qjmed.a067327
42.
MaywoodES, FraenkelE, McAllisterCJ, WoodN, ReddyAB, HastingsMH, et al. Disruption of peripheral circadian timekeeping in a mouse model of Huntington’s disease and its restoration by temporally scheduled feeding. J Neurosci. 1019;30:9–204. doi: 10.1523/JNEUROSCI.1694-10.2010
43.
StüweSH, GoetzeO, LukasC, KlotzP, HoffmannR, BanaschM, et al. Hepatic mitochondrial dysfunction in manifest and premanifest Huntington disease. Neurology. 2013;80:743–6. doi: 10.1212/WNL.0b013e318282514e
44.
Acosta-RodríguezVA, MárquezS, SalvadorGA, PasquaréSJ, GornéLD, Garbarino-PicoE, et al. Daily rhythms of glycerophospholipid synthesis in fibroblast cultures involve differential enzyme contributions. J Lipid Res. 2013;54:1798–811. doi: 10.1194/jlr.M034264
45.
LawSH, ChanML, MaratheGK, ParveenF, ChenCH, KeLY. An updated review of lysophosphatidylcholine metabolism in human diseases. Int J Mol Sci. 2019;20. doi: 10.3390/ijms20051149
46.
CasellaC, LippI, RosserA, JonesDK, Metzler-BaddeleyC. A critical review of white matter changes in Huntington’s disease. Mov Disord. 2020;35:1302–11. doi: 10.1002/mds.28109
47.
HashimotoM, WatanabeK, MiyoshiK, KoyanagiY, TadanoJ, MiyawakiI. Multiplatform metabolomic analysis of the R6/2 mouse model of Huntington’s disease. FEBS Open Bio. 2020;11:2807–18. doi: 10.1002/2211-5463.13285
48.
PatassiniS, BegleyP, ReidSJ, XuJ, ChurchSJ, CurtisM, et al. Identification of elevated urea as a severe, ubiquitous metabolic defect in the brain of patients with Huntington’s disease. Biochem Biophys Res Commun. 2015;468:161–6. doi: 10.1016/j.bbrc.2015.10.140
49.
HandleyRR, ReidSJ, BrauningR, MacLeanP, MearsER, FourieI, et al. Brain urea increase is an early Huntington’s disease pathogenic event observed in a prodromal transgenic sheep model and HD cases. Proc Natl Acad Sci U S A. 2017;114:E11293–302. doi: 10.1073/pnas.1711243115
50.
ZilberterY, GubkinaO, IvanovAI. A unique array of neuroprotective effects of pyruvate in neuropathology. Front Neurosci. 2015;9. doi: 10.3389/fnins.2015.00017
51.
RyuJK, KimSU, McLarnonJG. Blockade of quinolinic acid-induced neurotoxicity by pyruvate is associated with inhibition of glial activation in a model of Huntington’s disease. Exp Neurol. 2004;187:150–9. doi: 10.1016/j.expneurol.2004.01.006
52.
KongG, EllulS, NarayanaVK, KanojiaK, HaHTT, LiS, et al. An integrated metagenomics and metabolomics approach implicates the microbiota-gut-brain axis in the pathogenesis of Huntington’s disease. Neurobiol Dis. 2022;148:105199. doi.org/10.1016/j.nbd.2020.105199
53.
WangN, LangfelderP, StricosM, RamanathanL, RichmanJB, VacaR, et al. Mapping brain gene coexpression in daytime transcriptomes unveils diurnal molecular networks and deciphers perturbation gene signatures. Neuron. 2022;110:(20)3318–3338.e9. doi.org/10.1016/j.neuron.2022.09.028
