Skip to main content
SpringerLink
Log in

Metabolomics in Huntington’s Disease

  • Chapter
  • First Online:
Biomarkers for Huntington's Disease

Part of the book series: Contemporary Clinical Neuroscience ((CCNE))

  • 239 Accesses

Abstract

Metabolomics approaches have been used to gain new insights and to identify potential biomarkers in Huntington’s disease (HD) for almost two decades. The present chapter contains an introduction to the field of metabolomics as well as a comprehensive summary of the literature describing metabolomics and lipidomics in HD to date, divided in two sections covering animal and cell model studies and human studies with more than 30 studies discussed in total. Metabolite set enrichment analyses of the metabolites altered in the published studies were performed, demonstrating considerable overlap between animal and cell model studies and human studies, e.g. in arginine biosynthesis and aminoacyl-tRNA biosynthesis. This is a promising indication of the suitability of HD animal and cell models for studying the HD metabolome. The potential, limitations, and ways forward for utilizing the HD metabolome in research and biomarker applications are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

CE:

Capillary electrophoresis

CE-MS:

Capillary electrophoresis - mass spectrometry

CSF:

Cerebrospinal fluid

GC:

Gas chromatography

GC-MS:

Gas chromatography-mass spectrometry

HD:

Huntington’s disease

HRMS:

High-resolution mass spectrometry

LC:

Liquid chromatography

LC-MS:

Liquid chromatography-mass spectrometry

MS:

Mass spectrometry

NMR:

Nuclear magnetic resonance

References

  • Alonso, A., Marsal, S., & Julià, A. (2015). Analytical methods in untargeted metabolomics: State of the art in 2015. Frontiers in Bioengineering and Biotechnology, 3, 23.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ashrafian, H., Sounderajah, V., Glen, R., Ebbels, T., Blaise, B. J., Kalra, D., et al. (2021). Metabolomics: The stethoscope for the twenty-first century. Medical Principles and Practice, 30(4), 301–310.

    Article  PubMed  Google Scholar 

  • Aziz, N. A., van der Burg, J. M. M., Landwehrmeyer, G. B., Brundin, P., Stijnen, T., EHDI Study Group, et al. (2008). Weight loss in Huntington disease increases with higher CAG repeat number. Neurology, 71(19), 1506–1513.

    Article  CAS  PubMed  Google Scholar 

  • Bates, G. P., & Hockly, E. (2003). Experimental therapeutics in Huntington’s disease: Are models useful for therapeutic trials? Current Opinion in Neurology, 16(4), 465–470.

    Article  PubMed  Google Scholar 

  • Bertrand, M., Decoville, M., Meudal, H., Birman, S., & Landon, C. (2020). Metabolomic nuclear magnetic resonance studies at presymptomatic and symptomatic stages of Huntington’s disease on a drosophila model. Journal of Proteome Research, 19(10), 4034–4045.

    Article  CAS  PubMed  Google Scholar 

  • Blaise, B. J., Correia, G., Tin, A., Young, J. H., Vergnaud, A. C., Lewis, M., et al. (2016). Power analysis and sample size determination in metabolic phenotyping. Analytical Chemistry, 88(10), 5179–5188.

    Article  CAS  PubMed  Google Scholar 

  • Carlsson, H., Abujrais, S., Herman, S., Khoonsari, P. E., Åkerfeldt, T., Svenningsson, A., et al. (2020). Targeted metabolomics of CSF in healthy individuals and patients with secondary progressive multiple sclerosis using high-resolution mass spectrometry. Metabolomics, 16(2), 26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Carlsson, H., Rollborn, N., Herman, S., Freyhult, E., Svenningsson, A., Burman, J., et al. (2021). Metabolomics of cerebrospinal fluid from healthy subjects reveal metabolites associated with ageing. Metabolites [Internet], 11(2). Available from: https://doi.org/10.3390/metabo11020126

  • Carlsson, H., Vaivade, A., Emami Khoonsari, P., Burman, J., & Kultima, K. (2022). Evaluation of polarity switching for untargeted lipidomics using liquid chromatography coupled to high resolution mass spectrometry. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 1195, 123200.

    Article  CAS  PubMed  Google Scholar 

  • Carroll, J. B., Deik, A., Fossale, E., Weston, R. M., Guide, J. R., Arjomand, J., et al. (2015). HdhQ111 mice exhibit tissue specific metabolite profiles that include striatal lipid accumulation. PLoS One, 10(8), e0134465.

    Article  PubMed  PubMed Central  Google Scholar 

  • Chang, K. L., New, L. S., Mal, M., Goh, C. W., Aw, C. C., Browne, E. R., et al. (2011). Metabolic profiling of 3-nitropropionic acid early-stage Huntington’s disease rat model using gas chromatography time-of-flight mass spectrometry. Journal of Proteome Research, 10(4), 2079–2087.

    Article  CAS  PubMed  Google Scholar 

  • Cheng, M. L., Chang, K. H., Wu, Y. R., & Chen, C. M. (2016). Metabolic disturbances in plasma as biomarkers for Huntington’s disease. The Journal of Nutritional Biochemistry, 31, 38–44.

    Article  CAS  PubMed  Google Scholar 

  • Chetwynd, A. J., Dunn, W. B., & Rodriguez-Blanco, G. (2017). Collection and preparation of clinical samples for metabolomics. In A. Sussulini (Ed.), Metabolomics: From fundamentals to clinical applications (pp. 19–44). Springer International Publishing.

    Chapter  Google Scholar 

  • Dettmer, K., Aronov, P. A., & Hammock, B. D. (2007). Mass spectrometry-based metabolomics. Mass Spectrometry Reviews, 26(1), 51–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Doherty, C. M., & Forbes, R. B. (2014). Diagnostic lumbar puncture. The Ulster Medical Journal, 83(2), 93–102.

    PubMed  PubMed Central  Google Scholar 

  • Emami Khoonsari, P., Moreno, P., Bergmann, S., Burman, J., Capuccini, M., Carone, M., et al. (2019). Interoperable and scalable data analysis with microservices: Applications in metabolomics. Bioinformatics, 35(19), 3752–3760.

    Google Scholar 

  • Emwas, A. H., Roy, R., McKay, R. T., Tenori, L., Saccenti, E., Gowda, G. A. N., et al. (2019). NMR spectroscopy for metabolomics research. Metabolites [Internet], 9(7). Available from: https://doi.org/10.3390/metabo9070123.

  • Faquih, T. O., Aziz, N. A., Gardiner, S. L., Li-Gao, R., de Mutsert, R., Milaneschi, Y., et al. (2023). Normal range CAG repat size variations in the HTT gene are associated with an adverse lipoprotein profile partially mediated by body mass index. Human Molecular Genetics[Internet]. Available from: https://doi.org/10.1093/hmg/ddad020

  • Farzana, F., McConville, M. J., Renoir, T., Li, S., Nie, S., Tran, H., et al. (2023). Longitudinal spatial mapping of lipid metabolites reveals pre-symptomatic changes in the hippocampi of Huntington’s disease transgenic mice. Neurobiology of Disease, 176, 105933.

    Article  CAS  PubMed  Google Scholar 

  • Fiehn, O. (2016). Metabolomics by gas chromatography-mass spectrometry: Combined targeted and untargeted profiling. Current Protocols in Molecular Biology, 114, 30.4.1–30.4.32.

    Article  PubMed  Google Scholar 

  • Galal, A., Talal, M., & Moustafa, A. (2022). Applications of machine learning in metabolomics: Disease modeling and classification. Frontiers in Genetics, 13, 1017340.

    Article  PubMed  PubMed Central  Google Scholar 

  • García-Campos, M. A., Espinal-Enríquez, J., & Hernández-Lemus, E. (2015). Pathway analysis: State of the art. Frontiers in Physiology, 6, 383.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ghazaleh, N., Houghton, R., Palermo, G., Schobel, S. A., Wijeratne, P. A., & Long, J. D. (2021). Ranking the predictive power of clinical and biological features associated with disease progression in Huntington’s disease. Frontiers in Neurology, 12, 678484.

    Article  PubMed  PubMed Central  Google Scholar 

  • Graham, S. F., Kumar, P., Bahado-Singh, R. O., Robinson, A., Mann, D., & Green, B. D. (2016a). Novel metabolite biomarkers of Huntington’s disease as detected by high-resolution mass spectrometry. Journal of Proteome Research, 15(5), 1592–1601.

    Article  CAS  PubMed  Google Scholar 

  • Graham, S. F., Kumar, P. K., Bjorndahl, T., Han, B., Yilmaz, A., Sherman, E., et al. (2016b). Metabolic signatures of Huntington’s disease (HD): 1H NMR analysis of the polar metabolome in post-mortem human brain. Biochim Biophys Acta (BBA), 1862(9), 1675–1684.

    Article  CAS  PubMed  Google Scholar 

  • Graham, S. F., Pan, X., Yilmaz, A., Macias, S., Robinson, A., Mann, D., et al. (2018). Targeted biochemical profiling of brain from Huntington’s disease patients reveals novel metabolic pathways of interest. Biochimica et Biophysica Acta - Molecular Basis of Disease, 1864(7), 2430–2437.

    Article  CAS  PubMed  Google Scholar 

  • Handley, R. R., Reid, S. J., Patassini, S., Rudiger, S. R., Obolonkin, V., McLaughlan, C. J., et al. (2016). Metabolic disruption identified in the Huntington’s disease transgenic sheep model. Scientific Reports, 6, 20681.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hashimoto, M., Watanabe, K., Miyoshi, K., Koyanagi, Y., Tadano, J., & Miyawaki, I. (2021). Multiplatform metabolomic analysis of the R6/2 mouse model of Huntington’s disease. FEBS Open Biology, 11(10), 2807–2818.

    Article  CAS  Google Scholar 

  • Herman, S., Khoonsari, P. E., Tolf, A., Steinmetz, J., Zetterberg, H., Akerfeldt, T., et al. (2018). Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis. Theranostics, 8(16), 4477–4490.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Herman, S., Åkerfeldt, T., Spjuth, O., Burman, J., Kultima, K. (2019a). Biochemical differences in cerebrospinal fluid between secondary progressive and relapsing−remitting multiple sclerosis. Cells [Internet], 8(2). Available from: https://doi.org/10.3390/cells8020084.

  • Herman, S., Niemelä, V., Emami Khoonsari, P., Sundblom, J., Burman, J., Landtblom, A. M., et al. (2019b). Alterations in the tyrosine and phenylalanine pathways revealed by biochemical profiling in cerebrospinal fluid of Huntington’s disease subjects. Scientific Reports, 9(1), 4129.

    Article  PubMed  PubMed Central  Google Scholar 

  • Herman, S., Arvidsson, M. S., Zjukovskaja, C., Emami, K. P., Svenningsson, A., Burman, J., et al. (2023). Disease phenotype prediction in multiple sclerosis. iScience, 26, 106906.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hölter, S. M., Stromberg, M., Kovalenko, M., Garrett, L., Glasl, L., Lopez, E., et al. (2013). A broad phenotypic screen identifies novel phenotypes driven by a single mutant allele in Huntington’s disease CAG knock-in mice. PLoS One, 8(11), e80923.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hosseinkhani, F., Huang, L., Dubbelman, A. C., Guled, F., Harms, A. C., & Hankemeier, T. (2022). Systematic evaluation of HILIC stationary phases for global metabolomics of human plasma. Metabolites [Internet], 12(2). Available from: https://doi.org/10.3390/metabo12020165.

  • Iuliano, M., Seeley, C., Sapp, E., Jones, E. L., Martin, C., Li, X., et al. (2021). Disposition of proteins and lipids in synaptic membrane compartments is altered in Q175/Q7 Huntington’s disease mouse striatum. Frontiers in Synaptic Neuroscience, 13, 618391.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jacobsen, J. C., Bawden, C. S., Rudiger, S. R., McLaughlan, C. J., Reid, S. J., Waldvogel, H. J., et al. (2010). An ovine transgenic Huntington’s disease model. Human Molecular Genetics, 19(10), 1873–1882.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Joyner, P. M., Matheke, R. M., Smith, L. M., & Cichewicz, R. H. (2010). Probing the metabolic aberrations underlying mutant huntingtin toxicity in yeast and assessing their degree of preservation in humans and mice. Journal of Proteome Research, 9(1), 404–412.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Klassen, A., Faccio, A. T., Canuto, G. A. B., da Cruz, P. L. R., Ribeiro, H. C., Tavares, M. F. M., et al. (2017). Metabolomics: Definitions and significance in systems biology. In A. Sussulini (Ed.), Metabolomics: From fundamentals to clinical applications (pp. 3–17). Springer International Publishing.

    Chapter  Google Scholar 

  • Kong, G., Ellul, S., Narayana, V. K., Kanojia, K., Ha, H. T. T., Li, S., et al. (2021). An integrated metagenomics and metabolomics approach implicates the microbiota-gut-brain axis in the pathogenesis of Huntington’s disease. Neurobiology of Disease, 148, 105199.

    Article  CAS  PubMed  Google Scholar 

  • Kumar, K. K., Goodwin, C. R., Uhouse, M. A., Bornhorst, J., Schwerdtle, T., Aschner, M., et al. (2015). Untargeted metabolic profiling identifies interactions between Huntington’s disease and neuronal manganese status†. Metallomics, 7(2), 363–370.

    Article  CAS  PubMed  Google Scholar 

  • Lamichhane, S., Sen, P., Dickens, A. M., Hyötyläinen, T., & Orešič, M. (2018). Chapter fourteen - an overview of metabolomics data analysis: Current tools and future perspectives. In J. Jaumot, C. Bedia, & R. Tauler (Eds.), Comprehensive analytical chemistry (pp. 387–413). Elsevier.

    Google Scholar 

  • Li, J. Y., Popovic, N., & Brundin, P. (2005). The use of the R6 transgenic mouse models of Huntington’s disease in attempts to develop novel therapeutic strategies. NeuroRx, 2(3), 447–464.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lu, W., Su, X., Klein, M. S., Lewis, I. A., Fiehn, O., & Rabinowitz, J. D. (2017). Metabolite measurement: Pitfalls to avoid and practices to follow. Annual Review of Biochemistry, 86, 277–304.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mangiarini, L., Sathasivam, K., Seller, M., Cozens, B., Harper, A., Hetherington, C., et al. (1996). Exon 1 of the HD gene with an expanded CAG repeat is sufficient to cause a progressive neurological phenotype in transgenic mice. Cell, 87(3), 493–506.

    Article  CAS  PubMed  Google Scholar 

  • Mastrokolias, A., Pool, R., Mina, E., Hettne, K. M., van Duijn, E., van der Mast, R. C., et al. (2016). Integration of targeted metabolomics and transcriptomics identifies deregulation of phosphatidylcholine metabolism in Huntington’s disease peripheral blood samples. Metabolomics, 12, 137.

    Article  PubMed  PubMed Central  Google Scholar 

  • McGarry, A., Gaughan, J., Hackmyer, C., Lovett, J., Khadeer, M., Shaikh, H., et al. (2020). Cross-sectional analysis of plasma and CSF metabolomic markers in Huntington’s disease for participants of varying functional disability: A pilot study. Scientific Reports, 10(1), 20490.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Menalled, L. B. (2005). Knock-in mouse models of Huntington’s disease. NeuroRx, 2(3), 465–470.

    Article  PubMed  PubMed Central  Google Scholar 

  • Newgard, C. B. (2017). Metabolomics and metabolic diseases: Where do we stand? Cell Metabolism, 25(1), 43–56.

    Article  CAS  PubMed  Google Scholar 

  • Nikas, J. B., & Low, W. C. (2011). Application of clustering analyses to the diagnosis of Huntington disease in mice and other diseases with well-defined group boundaries. Computer Methods and Programs in Biomedicine, 104(3), e133–e147.

    Article  PubMed  PubMed Central  Google Scholar 

  • Novella, J. A., Emami Khoonsari, P., Herman, S., Whitenack, D., Capuccini, M., Burman, J., et al. (2019). Container-based bioinformatics with pachyderm. Bioinformatics, 35(5), 839–846.

    Article  CAS  PubMed  Google Scholar 

  • Pang, Z., Chong, J., Zhou, G., de Lima Morais, D. A., Chang, L., Barrette, M., et al. (2021). MetaboAnalyst 5.0: Narrowing the gap between raw spectra and functional insights. Nucleic Acids Research, 49(W1), W388–W396.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Patassini, S., Begley, P., Reid, S. J., Xu, J., Church, S. J., Curtis, M., et al. (2015). Identification of elevated urea as a severe, ubiquitous metabolic defect in the brain of patients with Huntington’s disease. Biochemical and Biophysical Research Communications, 468(1–2), 161–166.

    Article  CAS  PubMed  Google Scholar 

  • Patassini, S., Begley, P., Xu, J., Church, S. J., Reid, S. J., Kim, E. H., et al. (2016). Metabolite mapping reveals severe widespread perturbation of multiple metabolic processes in Huntington’s disease human brain. Biochimica et Biophysica Acta, 1862(9), 1650–1662.

    Article  CAS  PubMed  Google Scholar 

  • Pautova, A., Burnakova, N., Revelsky, A. (2021). Metabolic profiling and quantitative analysis of cerebrospinal fluid using gas chromatography-mass spectrometry: Current methods and future perspectives. Molecules [Internet], 26(12). Available from: https://doi.org/10.3390/molecules26123597.

  • Perez de Souza, L., Alseekh, S., Scossa, F., & Fernie, A. R. (2021). Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research. Nature Methods, 18(7), 733–746.

    Article  CAS  PubMed  Google Scholar 

  • Peters, K., Bradbury, J., Bergmann, S., Capuccini, M., Cascante, M., de Atauri, P., et al. (2019). PhenoMeNal: Processing and analysis of metabolomics data in the cloud. Gigascience [Internet], 8(2). Available from: https://www.ncbi.nlm.nih.gov/pubmed/30535405

  • Peters, K., Herman, S., Khoonsari, P. E., Burman, J., Neumann, S., & Kultima, K. (2021). Metabolic drift in the aging nervous system is reflected in human cerebrospinal fluid. Scientific Reports, 11(1), 18822.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pirhaji, L., Milani, P., Leidl, M., Curran, T., Avila-Pacheco, J., Clish, C. B., et al. (2016). Revealing disease-associated pathways by network integration of untargeted metabolomics. Nature Methods, 13(9), 770–776.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pont, L., Benavente, F., Jaumot, J., Tauler, R., Alberch, J., Ginés, S., et al. (2016). Metabolic profiling for the identification of Huntington biomarkers by on-line solid-phase extraction capillary electrophoresis mass spectrometry combined with advanced data analysis tools. Electrophoresis, 37(5–6), 795–808.

    Article  CAS  PubMed  Google Scholar 

  • Pradhan, S. S., Thota, S. M., Rajaratnam, S., Bhagavatham, S. K. S., Pulukool, S. K., Rathnakumar, S., et al. (2022). Integrated multi-omics analysis of Huntington disease identifies pathways that modulate protein aggregation. Disease Models & Mechanisms [Internet], 15(10). Available from: https://doi.org/10.1242/dmm.049492

  • Ramautar, R. (2016). Chapter one - capillary electrophoresis–mass spectrometry for clinical metabolomics. In G. S. Makowski (Ed.), Advances in clinical chemistry (pp. 1–34). Elsevier.

    Google Scholar 

  • Rathahao-Paris, E., Alves, S., Junot, C., & Tabet, J. C. (2015). High resolution mass spectrometry for structural identification of metabolites in metabolomics. Metabolomics, 12(1), 10.

    Article  Google Scholar 

  • Rico, E., González, O., Blanco, M. E., & Alonso, R. M. (2014). Evaluation of human plasma sample preparation protocols for untargeted metabolic profiles analyzed by UHPLC-ESI-TOF-MS. Analytical and Bioanalytical Chemistry, 406(29), 7641–7652.

    Article  CAS  PubMed  Google Scholar 

  • Robertson, D. G. (2005). Metabonomics in toxicology: A review. Toxicological Sciences, 85(2), 809–822.

    Article  CAS  PubMed  Google Scholar 

  • Rosas, H. D., Doros, G., Bhasin, S., Thomas, B., Gevorkian, S., Malarick, K., et al. (2015). A systems-level “misunderstanding”: The plasma metabolome in Huntington’s disease. Annals of Clinical Translational Neurology, 2(7), 756–768.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sasaki, K., Sagawa, H., Suzuki, M., Yamamoto, H., Tomita, M., Soga, T., et al. (2019). Metabolomics platform with capillary electrophoresis coupled with high-resolution mass apectrometry for plasma analysis. Analytical Chemistry, 91(2), 1295–1301.

    Article  CAS  PubMed  Google Scholar 

  • Sayers, E. W., Bolton, E. E., Brister, J. R., Canese, K., Chan, J., Comeau, D. C., et al. (2022). Database resources of the national center for biotechnology information. Nucleic Acids Research, 50(D1), D20–D26.

    Article  CAS  PubMed  Google Scholar 

  • Segal, M. B. (1993). Extracellular and cerebrospinal fluids. Journal of Inherited Metabolic Disease, 16(4), 617–638.

    Article  CAS  PubMed  Google Scholar 

  • Singh, V., Sharma, R. K., Athilingam, T., Sinha, P., Sinha, N., & Thakur, A. K. (2017). NMR spectroscopy-based metabolomics of drosophila model of Huntington’s disease suggests altered cell energetics. Journal of Proteome Research, 16(10), 3863–3872.

    Article  CAS  PubMed  Google Scholar 

  • Skene, D. J., Middleton, B., Fraser, C. K., Pennings, J. L. A., Kuchel, T. R., Rudiger, S. R., et al. (2017). Metabolic profiling of presymptomatic Huntington’s dis202ease sheep reveals novel biomarkers. Scientific Reports, 7, 43030.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Smith, C. A., O’Maille, G., Want, E. J., Qin, C., Trauger, S. A., Brandon, T. R., et al. (2005). METLIN: A metabolite mass spectral database. Therapeutic Drug Monitoring, 27(6), 747–751.

    Article  CAS  PubMed  Google Scholar 

  • Sorolla, M. A., Rodríguez-Colman, M. J., Vall-Llaura, N., Vived, C., Fernández-Nogales, M., Lucas, J. J., et al. (2016). Impaired PLP-dependent metabolism in brain samples from Huntington disease patients and transgenic R6/1 mice. Metabolic Brain Disease, 31(3), 579–586.

    Article  CAS  PubMed  Google Scholar 

  • Spick, M., Hancox, T. P. M., Chowdhury, N. R., Middleton, B., Skene, D. J., Morton, A. J. (2023). Metabolomic Analysis of plasma in Huntington’s disease transgenic sheep (Ovis aries) reveals progressive circadian rhythm dysregulation. Journal of Huntington’s Disease [Internet]. Available from: https://doi.org/10.3233/JHD-220552

  • Trushina, E., Dutta, T., Persson, X. M. T., Mielke, M. M., & Petersen, R. C. (2013). Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer’s disease using metabolomics. PLoS One, 8(5), e63644.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tsang, T. M., Woodman, B., McLoughlin, G. A., Griffin, J. L., Tabrizi, S. J., Bates, G. P., et al. (2006). Metabolic characterization of the R6/2 transgenic mouse model of Huntington’s disease by high-resolution MAS 1H NMR spectroscopy. Journal of Proteome Research, 5(3), 483–492.

    Article  CAS  PubMed  Google Scholar 

  • Tsang, T. M., Haselden, J. N., & Holmes, E. (2009). Metabonomic characterization of the 3-nitropropionic acid rat model of Huntington’s disease. Neurochemical Research, 34(7), 1261–1271.

    Article  CAS  PubMed  Google Scholar 

  • Verwaest, K. A., Vu, T. N., Laukens, K., Clemens, L. E., Nguyen, H. P., Van Gasse, B., et al. (2011). 1H NMR based metabolomics of CSF and blood serum: A metabolic profile for a transgenic rat model of Huntington disease. Biochim Biophys Acta (BBA), 1812(11), 1371–1379.

    Article  CAS  PubMed  Google Scholar 

  • Vodicka, P., Mo, S., Tousley, A., Green, K. M., Sapp, E., Iuliano, M., et al. (2015). Mass spectrometry analysis of wild-type and knock-in Q140/Q140 Huntington’s disease mouse brains reveals changes in glycerophospholipids including alterations in phosphatidic acid and lyso-phosphatidic acid. Journal of Huntington’s Disease, 4(2), 187–201.

    Article  CAS  PubMed  Google Scholar 

  • Williams, B. B., Li, D., Wegrzynowicz, M., Vadodaria, B. K., Anderson, J. G., Kwakye, G. F., et al. (2010). Disease-toxicant screen reveals a neuroprotective interaction between Huntington’s disease and manganese exposure. Journal of Neurochemistry, 112(1), 227–237.

    Article  CAS  PubMed  Google Scholar 

  • Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., Young, N., et al. (2007). HMDB: The human metabolome database. Nucleic Acids Research, 35(Database issue), D521–D526.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wishart, D. S., Cheng, L. L., Copié, V., Edison, A. S., Eghbalnia, H. R., Hoch, J. C., et al. (2022a). NMR and metabolomics-a roadmap for the future. Metabolites [Internet], 12(8). Available from: https://doi.org/10.3390/metabo12080678

  • Wishart, D. S., Guo, A., Oler, E., Wang, F., Anjum, A., Peters, H., et al. (2022b). HMDB 5.0: The human metabolome database for 2022. Nucleic Acids Research, 50(D1), D622–D631.

    Article  CAS  PubMed  Google Scholar 

  • Züllig, T., Trötzmüller, M., & Köfeler, H. C. (2020). Lipidomics from sample preparation to data analysis: A primer. Analytical and Bioanalytical Chemistry, 412(10), 2191–2209.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by the Swedish Research Council (2021-02189), Åke Wiberg Foundation, NEURO Sweden, The Lars Hierta Memorial Foundation, and O. E. and Edla Johanssons Scientific Foundation. The authors also acknowledge Stephanie Herman, Aina Vaivade, and Jenny Jakobsson for their artwork, analysis, and assistance in preparing this chapter.

Author information

Authors and Affiliations

  1. Department of Medical Sciences, Uppsala University, Uppsala, Sweden

    Henrik Carlsson, Ida Erngren & Kim Kultima

Authors
  1. Henrik Carlsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ida Erngren
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kim Kultima
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kim Kultima .

Editor information

Editors and Affiliations

  1. Department of Neurobiology and Behavior, University of California, Irvine, CA, USA

    Elizabeth A. Thomas

  2. Department of Neurosciences, University of California, San Diego, CA, USA

    Georgia M. Parkin

1 Electronic Supplementary Material

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Carlsson, H., Erngren, I., Kultima, K. (2023). Metabolomics in Huntington’s Disease. In: Thomas, E.A., Parkin, G.M. (eds) Biomarkers for Huntington's Disease. Contemporary Clinical Neuroscience. Springer, Cham. https://doi.org/10.1007/978-3-031-32815-2_8

Download citation

Publish with us

Policies and ethics

Search

Navigation