Advertisement

Urinary and Plasma Metabolomics Identify the Distinct Metabolic Profile of Disease State in Chronic Mouse Model of Multiple Sclerosis

  • J. Singh
  • M. Cerghet
  • L. M. Poisson
  • I. Datta
  • K. Labuzek
  • H. Suhail
  • R. Rattan
  • Shailendra Giri
ORIGINAL ARTICLE

Abstract

Identification of non-invasive biomarkers of disease progression in multiple sclerosis (MS) is critically needed for monitoring the disease progression and for effective therapeutic interventions. Urine is an attractive source for non-invasive biomarkers because it is easily obtained in the clinic. In search of a urine metabolite signature of progression in chronic experimental autoimmune encephalomyelitis (EAE), we profiled urine at the chronic stage of the disease (day 45 post immunization) by global untargeted metabolomics. Using a combination of high-throughput liquid-and-gas chromatography with mass spectrometry, we found 105 metabolites (P < 0.05) significantly altered at the chronic stage, indicating a robust alteration in the urine metabolite profile during disease. Assessment of altered metabolites against the Kyoto Encyclopedia of Genes and Genomes revealed distinct non-overlapping metabolic pathways and revealed phenylalanine-tyrosine and associated metabolism being the most impacted. Combined with previously performed plasma profiling, eight common metabolites were significantly altered in both of the biofluids. Metaboanalyst analysis of these common metabolites revealed that phenylalanine metabolism and Valine, leucine, and isoleucine biosynthetic pathways are central metabolic pathways in both bio-fluids and could be analyzed further, either for the discovery of therapeutics or biomarker development. Overall, our study suggests that urine and plasma metabolomics may contribute to the identification of a distinct metabolic fingerprint of EAE disease discriminating from the healthy control which may aid in the development of an objective non-invasive monitoring method for progressive autoimmune diseases like MS.

Graphical Abstract

Untargeted urinary metabolomics of a chronic mouse model of multiple sclerosis identified Phenylalanine, tyrosine & tryptophan metabolism as the significantly altered metabolic pathway. Eight common metabolites were identified when we combined urinary and plasma metabolic signature, which revealed a perturbation of Phenylalanine metabolism and valine, leucine & isoleucine metabolic pathways, involved in CNS dysfunction during diseases. The identified eight metabolic signature of urine and plasma may be of clinical relevance as potential biomarkers and guide towards the identification of specific metabolic pathways as novel drug targets.

Keywords

Metabolomics EAE Multiple sclerosis Metabolomics Urine Biomarkers GC-MS/LC-MS Metabolic pathways 

Notes

Acknowledgements

The study was supported in part by funds from the Henry Ford Health System internal funding (A20020) and a research grant from the National Multiple Sclerosis Society (RG 4311A4/4) to SG.

Compliance with Ethical Standards

Conflict of Interest

The authors have no conflict of interest.

Supplementary material

11481_2018_9815_MOESM1_ESM.pdf (6 mb)
ESM 1 (PDF 6134 kb)

References

  1. Abreu SL (1982) Suppression of experimental allergic encephalomyelitis by interferon. Immunol Commun 11:1–7CrossRefGoogle Scholar
  2. Aittokallio T, Schwikowski B (2006) Graph-based methods for analysing networks in cell biology. Brief Bioinform 7:243–255CrossRefGoogle Scholar
  3. Bhargava P, Fitzgerald KC, Calabresi PA, Mowry EM (2017) Metabolic alterations in multiple sclerosis and the impact of vitamin D supplementation. JCI Insight 2Google Scholar
  4. Birkner K, Wasser B, Loos J, Plotnikov A, Seger R, Zipp F, Witsch E, Bittner S (2017) The role of ERK signaling in experimental autoimmune encephalomyelitis. Int J Mol Sci 18CrossRefGoogle Scholar
  5. Bjelobaba I, Begovic-Kupresanin V, Pekovic S, Lavrnja I (2018) Animal models of multiple sclerosis: focus on experimental autoimmune encephalomyelitis. J Neurosci Res 96:1021–1042CrossRefGoogle Scholar
  6. Bruhn H, Frahm J, Merboldt KD, Hanicke W, Hanefeld F, Christen HJ, Kruse B, Bauer HJ (1992) Multiple sclerosis in children: cerebral metabolic alterations monitored by localized proton magnetic resonance spectroscopy in vivo. Ann Neurol 32:140–150CrossRefGoogle Scholar
  7. Cao G, Edden RAE, Gao F, Li H, Gong T, Chen W, Liu X, Wang G, Zhao B (2018) Reduced GABA levels correlate with cognitive impairment in patients with relapsing-remitting multiple sclerosis. Eur Radiol 28:1140–1148CrossRefGoogle Scholar
  8. Cawley N, Solanky BS, Muhlert N, Tur C, Edden RA, Wheeler-Kingshott CA, Miller DH, Thompson AJ, Ciccarelli O (2015) Reduced gamma-aminobutyric acid concentration is associated with physical disability in progressive multiple sclerosis. Brain J Neurol 138:2584–2595CrossRefGoogle Scholar
  9. Daubner SC, Le T, Wang S (2011) Tyrosine hydroxylase and regulation of dopamine synthesis. Arch Biochem Biophys 508:1–12CrossRefGoogle Scholar
  10. Dickens AM, Larkin JR, Griffin JL, Cavey A, Matthews L, Turner MR, Wilcock GK, Davis BG, Claridge TD, Palace J, Anthony DC, Sibson NR (2014) A type 2 biomarker separates relapsing-remitting from secondary progressive multiple sclerosis. Neurology 83:1492–1499CrossRefGoogle Scholar
  11. Dunn WB, Broadhurst DI, Atherton HJ, Goodacre R, Griffin JL (2011) Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev 40:387–426CrossRefGoogle Scholar
  12. Gebregiworgis T, Massilamany C, Gangaplara A, Thulasingam S, Kolli V, Werth MT, Dodds ED, Steffen D, Reddy J, Powers R (2013) Potential of urinary metabolites for diagnosing multiple sclerosis. ACS Chem Biol 8:684–690CrossRefGoogle Scholar
  13. Gong X, Xie Z, Zuo H (2008) A new track for understanding the pathogenesis of multiple sclerosis: from the perspective of early developmental deficit caused by the potential 5-HT deficiency in individuals in high-latitude areas. Med Hypotheses 71:580–583CrossRefGoogle Scholar
  14. Johnson KP, Brooks BR, Cohen JA, Ford CC, Goldstein J, Lisak RP, Myers LW, Panitch HS, Rose JW, Schiffer RB (1995) Copolymer 1 reduces relapse rate and improves disability in relapsing-remitting multiple sclerosis: results of a phase III multicenter, double-blind placebo-controlled trial. The copolymer 1 multiple sclerosis study group. Neurology 45:1268–1276CrossRefGoogle Scholar
  15. Krementsov DN, Thornton TM, Teuscher C, Rincon M (2013) The emerging role of p38 mitogen-activated protein kinase in multiple sclerosis and its models. Mol Cell Biol 33:3728–3734CrossRefGoogle Scholar
  16. Lassmann H (2007) Experimental models of multiple sclerosis. Rev Neurol 163:651–655CrossRefGoogle Scholar
  17. Lim CK, Bilgin A, Lovejoy DB, Tan V, Bustamante S, Taylor BV, Bessede A, Brew BJ, Guillemin GJ (2017) Kynurenine pathway metabolomics predicts and provides mechanistic insight into multiple sclerosis progression. Sci Rep 7:41473CrossRefGoogle Scholar
  18. Loder C, Allawi J, Horrobin DF (2002) Treatment of multiple sclerosis with lofepramine, L-phenylalanine and vitamin B(12): mechanism of action and clinical importance: roles of the locus coeruleus and central noradrenergic systems. Med Hypotheses 59:594–602CrossRefGoogle Scholar
  19. Lovett-Racke AE (2017) Contribution of EAE to understanding and treating multiple sclerosis. J Neuroimmunol 304:40–42CrossRefGoogle Scholar
  20. Lutz NW, Viola A, Malikova I, Confort-Gouny S, Ranjeva JP, Pelletier J, Cozzone PJ (2007a) A branched-chain organic acid linked to multiple sclerosis: first identification by NMR spectroscopy of CSF. Biochem Biophys Res Commun 354:160–164CrossRefGoogle Scholar
  21. Lutz NW, Viola A, Malikova I, Confort-Gouny S, Audoin B, Ranjeva JP, Pelletier J, Cozzone PJ (2007b) Inflammatory multiple-sclerosis plaques generate characteristic metabolic profiles in cerebrospinal fluid. PLoS One 2:e595CrossRefGoogle Scholar
  22. Mangalam A, Poisson L, Nemutlu E, Datta I, Denic A, Dzeja P, Rodriguez M, Rattan R, Giri S (2013) Profile of circulatory metabolites in a relapsing-remitting animal model of multiple sclerosis using global metabolomics. J Clin Cell Immunol 4Google Scholar
  23. Mc Guire C, Prinz M, Beyaert R, van Loo G (2013) Nuclear factor kappa B (NF-kappaB) in multiple sclerosis pathology. Trends Mol Med 19:604–613CrossRefGoogle Scholar
  24. Melamud L, Golan D, Luboshitzky R, Lavi I, Miller A (2012) Melatonin dysregulation, sleep disturbances and fatigue in multiple sclerosis. J Neurol Sci 314:37–40CrossRefGoogle Scholar
  25. Mix E, Meyer-Rienecker H, Hartung HP, Zettl UK (2010) Animal models of multiple sclerosis--potentials and limitations. Prog Neurobiol 92:386–404CrossRefGoogle Scholar
  26. Nath N, Khan M, Rattan R, Mangalam A, Makkar RS, de Meester C, Bertrand L, Singh I, Chen Y, Viollet B, Giri S (2009) Loss of AMPK exacerbates experimental autoimmune encephalomyelitis disease severity. Biochem Biophys Res Commun 386:16–20CrossRefGoogle Scholar
  27. Noro T, Namekata K, Kimura A, Guo X, Azuchi Y, Harada C, Nakano T, Tsuneoka H, Harada T (2015) Spermidine promotes retinal ganglion cell survival and optic nerve regeneration in adult mice following optic nerve injury. Cell Death Dis 6:e1720CrossRefGoogle Scholar
  28. Paty DW, Li DK (1993) Interferon beta-1b is effective in relapsing-remitting multiple sclerosis. II. MRI analysis results of a multicenter, randomized, double-blind, placebo-controlled trial. UBC MS/MRI study group and the IFNB multiple sclerosis study group. Neurology 43:662–667CrossRefGoogle Scholar
  29. Poisson LM, Munkarah A, Madi H, Datta I, Hensley-Alford S, Tebbe C, Buekers T, Giri S, Rattan R (2015a) A metabolomic approach to identifying platinum resistance in ovarian cancer. J Ovarian Res 8:13CrossRefGoogle Scholar
  30. Poisson LM, Suhail H, Singh J, Datta I, Denic A, Labuzek K, Hoda MN, Shankar A, Kumar A, Cerghet M, Elias S, Mohney RP, Rodriguez M, Rattan R, Mangalam AK, Giri S (2015b) Untargeted plasma metabolomics identifies endogenous metabolite with drug-like properties in chronic animal model of multiple sclerosis. J Biol Chem 290:30697–30712CrossRefGoogle Scholar
  31. Polak PE, Kalinin S, Feinstein DL (2011) Locus coeruleus damage and noradrenaline reductions in multiple sclerosis and experimental autoimmune encephalomyelitis. Brain J Neurol 134:665–677CrossRefGoogle Scholar
  32. Polman CH, O'Connor PW, Havrdova E, Hutchinson M, Kappos L, Miller DH, Phillips JT, Lublin FD, Giovannoni G, Wajgt A, Toal M, Lynn F, Panzara MA, Sandrock AW, Investigators A (2006) A randomized, placebo-controlled trial of natalizumab for relapsing multiple sclerosis. N Engl J Med 354:899–910CrossRefGoogle Scholar
  33. Reinke SN, Broadhurst DL, Sykes BD, Baker GB, Catz I, Warren KG, Power C (2014) Metabolomic profiling in multiple sclerosis: insights into biomarkers and pathogenesis. Mult Scler 20:1396–1400CrossRefGoogle Scholar
  34. Sandyk R (1996) Tryptophan availability and the susceptibility to stress in multiple sclerosis: a hypothesis. Int J Neurosci 86:47–53CrossRefGoogle Scholar
  35. Steen G, Axelsson H, Bowallius M, Holthuis N, Molander BM (1985) Isoprenoid biosynthesis in multiple sclerosis. Acta Neurol Scand 72:328–335CrossRefGoogle Scholar
  36. Sumizu K (1962) Oxidation of hypotaurine in rat liver. Biochim Biophys Acta 63:210–212CrossRefGoogle Scholar
  37. Teitelbaum D, Meshorer A, Hirshfeld T, Arnon R, Sela M (1971) Suppression of experimental allergic encephalomyelitis by a synthetic polypeptide. Eur J Immunol 1:242–248CrossRefGoogle Scholar
  38. Tsuji A, Tamai I (1996) Sodium- and chloride-dependent transport of taurine at the blood-brain barrier. Adv Exp Med Biol 403:385–391CrossRefGoogle Scholar
  39. Urquhart N, Perry TL, Hansen S, Kennedy J (1974) Passage of taurine into adult mammalian brain. J Neurochem 22:871–872CrossRefGoogle Scholar
  40. Villoslada P, Alonso C, Agirrezabal I, Kotelnikova E, Zubizarreta I, Pulido-Valdeolivas I, Saiz A, Comabella M, Montalban X, Villar L, Alvarez-Cermeno JC, Fernandez O, Alvarez-Lafuente R, Arroyo R, Castro A (2017) Metabolomic signatures associated with disease severity in multiple sclerosis. Neurol Neuroimmunol Neuroinflamm 4:e321CrossRefGoogle Scholar
  41. Wade DT, Young CA, Chaudhuri KR, Davidson DL (2002) A randomised placebo controlled exploratory study of vitamin B-12, lofepramine, and L-phenylalanine (the "Cari Loder regime") in the treatment of multiple sclerosis. J Neurol Neurosurg Psychiatry 73:246–249CrossRefGoogle Scholar
  42. Wood PL (2014) Mass spectrometry strategies for clinical metabolomics and lipidomics in psychiatry, neurology, and neuro-oncology. Neuropsychopharmacology 39:24–33CrossRefGoogle Scholar
  43. Yang Q, Zheng C, Cao J, Cao G, Shou P, Lin L, Velletri T, Jiang M, Chen Q, Han Y, Li F, Wang Y, Cao W, Shi Y (2016) Spermidine alleviates experimental autoimmune encephalomyelitis through inducing inhibitory macrophages. Cell Death Differ 23:1850–1861CrossRefGoogle Scholar
  44. Yednock TA, Cannon C, Fritz LC, Sanchez-Madrid F, Steinman L, Karin N (1992) Prevention of experimental autoimmune encephalomyelitis by antibodies against alpha 4 beta 1 integrin. Nature 356:63–66CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of NeurologyHenry Ford Health SystemDetroitUSA
  2. 2.Center for BioinformaticsHenry Ford Health SystemDetroitUSA
  3. 3.Department of Public Health SciencesHenry Ford Health SystemDetroitUSA
  4. 4.Department of Internal Medicine and Clinical PharmacologyMedical University of SilesiaKatowicePoland
  5. 5.Division of Gynecology Oncology, Department of Women’s Health ServicesHenry Ford Health SystemDetroitUSA

Personalised recommendations