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Longitudinal Metabolite Profiling of Cerebrospinal Fluid in Normal Pressure Hydrocephalus Links Brain Metabolism with Exercise-Induced VEGF Production and Clinical Outcome

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Abstract

Idiopathic normal pressure hydrocephalus is a neurological disease caused by abnormal cerebrospinal fluid flow and presents with symptoms such as dementia. Current therapy involves the removal of excess cerebrospinal fluid by shunting. Not all patients respond to this therapy and biomarkers are needed that could facilitate the characterization of patients likely to benefit from this treatment. Here, we measure brain metabolism in normal pressure hydrocephalus patients by performing a novel longitudinal metabolomic profiling study of cerebrospinal fluid. We find that the levels of brain metabolites correlate with clinical parameters, the amount of vascular endothelial growth factor in the cerebrospinal fluid, and environmental stimuli such as exercise. Metabolomic analysis of normal pressure hydrocephalus patients provides insight into changes in brain metabolism that accompany cerebrospinal fluid disorders and may facilitate the development of new biomarkers for this condition.

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References

  1. Finney GR (2009) Normal pressure hydrocephalus. Int Rev Neurobiol 84:263–281

    Article  PubMed  Google Scholar 

  2. Meier U, Lemcke J, Al-Zain F (2008) Course of disease in patients with idiopathic normal pressure hydrocephalus (iNPH): a follow-up study 3, 4 and 5 years following shunt implantation. Acta Neurochir Suppl 102:125–127

    Article  CAS  PubMed  Google Scholar 

  3. Brinker T, Stopa E, Morrison J, Klinge P (2014) A new look at cerebrospinal fluid circulation. Fluids Barriers CNS 11:10

    Article  PubMed  PubMed Central  Google Scholar 

  4. Wright BL, Lai JT, Sinclair AJ (2012) Cerebrospinal fluid and lumbar puncture: a practical review. J Neurol 259:1530–1545

    Article  PubMed  Google Scholar 

  5. Mattsson N, Insel PS, Donohue M, Landau S, Jagust WJ, Shaw LM, Trojanowski JQ, Zetterberg H, Blennow K, Weiner MW (2014) Independent information from cerebrospinal fluid amyloid-beta and florbetapir imaging in Alzheimer’s disease. Brain 138:772–783

    Article  PubMed  PubMed Central  Google Scholar 

  6. Palsson E, Jakobsson J, Sodersten K, Fujita Y, Sellgren C, Ekman CJ, Agren H, Hashimoto K, Landen M (2015) Markers of glutamate signaling in cerebrospinal fluid and serum from patients with bipolar disorder and healthy controls. Eur Neuropsychopharmacol 25:133–140

    Article  PubMed  Google Scholar 

  7. Sosvorova L, Vcelak J, Mohapl M, Vitku J, Bicikova M, Hampl R (2014) Selected pro- and anti-inflammatory cytokines in cerebrospinal fluid in normal pressure hydrocephalus. Neuro Endocrinol Lett 35:586–593

    CAS  PubMed  Google Scholar 

  8. Pieragostino D, D’Alessandro M, di Ioia M, Rossi C, Zucchelli M, Urbani A, Di Ilio C, Lugaresi A, Sacchetta P, Del Boccio P (2015) An integrated metabolomics approach for the research of new cerebrospinal fluid biomarkers of multiple sclerosis. Mol BioSyst 11:1563–1572

    Article  CAS  PubMed  Google Scholar 

  9. Cassol E, Misra V, Dutta A, Morgello S, Gabuzda D (2014) Cerebrospinal fluid metabolomics reveals altered waste clearance and accelerated aging in HIV patients with neurocognitive impairment. AIDS (London, England) 28:1579–1591

    Article  CAS  Google Scholar 

  10. Trushina E, Dutta T, Persson XM, Mielke MM, Petersen RC (2013) Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer’s disease using metabolomics. PLoS ONE 8:e63644

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Mandal R, Guo AC, Chaudhary KK, Liu P, Yallou FS, Dong E, Aziat F, Wishart DS (2012) Multi-platform characterization of the human cerebrospinal fluid metabolome: a comprehensive and quantitative update. Genome Med 4:38

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Dombrowski SM, Deshpande A, Dingwall C, Leichliter A, Leibson Z, Luciano MG (2008) Chronic hydrocephalus-induced hypoxia: increased expression of VEGFR-2+ and blood vessel density in hippocampus. Neuroscience 152:346–359

    Article  CAS  PubMed  Google Scholar 

  13. Reeson P, Tennant KA, Gerrow K, Wang J, Weiser Novak S, Thompson K, Lockhart KL, Holmes A, Nahirney PC, Brown CE (2015) Delayed inhibition of VEGF signaling after stroke attenuates blood–brain barrier breakdown and improves functional recovery in a comorbidity-dependent manner. J Neurosci 35:5128–5143

    Article  CAS  PubMed  Google Scholar 

  14. Rosenstein JM, Krum JM, Ruhrberg C (2010) VEGF in the nervous system. Organogenesis 6:107–114

    Article  PubMed  PubMed Central  Google Scholar 

  15. Yang J, Shanahan K, Shriver LP, Luciano M (2015) Exercise-induced respondent changes of CSF vascular endothelial growth factor in adult chronic hydrocephalus patients. J Clin Neurosci 24:52–56

    Article  CAS  PubMed  Google Scholar 

  16. Yang J, Dombrowski SM, Deshpande A, Krajcir N, El-Khoury S, Krishnan C, Luciano MG (2011) Stability analysis of vascular endothelial growth factor in cerebrospinal fluid. Neurochem Res 36:1947–1954

    Article  CAS  PubMed  Google Scholar 

  17. Sangster T, Major H, Plumb R, Wilson AJ, Wilson ID (2006) A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis. Analyst 131:1075–1078

    Article  CAS  PubMed  Google Scholar 

  18. Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci USA 100:9440–9445

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Koo I, Yao S, Zhang X, Kim S (2014) Comparative analysis of false discovery rate methods in constructing metabolic association networks. J Bioinf Comput Biol 12:1450018

    Article  Google Scholar 

  20. Choi M, Chang CY, Clough T, Broudy D, Killeen T, MacLean B, Vitek O (2014) MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics (Oxford, England) 30:2524–2526

    Article  CAS  Google Scholar 

  21. Li S, Park Y, Duraisingham S, Strobel FH, Khan N, Soltow QA, Jones DP, Pulendran B (2013) Predicting network activity from high throughput metabolomics. PLoS Comput Biol 9:e1003123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Smith CA, O’Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, Custodio DE, Abagyan R, Siuzdak G (2005) METLIN: a metabolite mass spectral database. Ther Drug Monit 27:747–751

    Article  CAS  PubMed  Google Scholar 

  23. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J, Liu P, Yallou F, Bjorndahl T, Perez-Pineiro R, Eisner R, Allen F, Neveu V, Greiner R, Scalbert A (2013) HMDB 3.0–The Human Metabolome Database in 2013. Nucleic Acids Res 41:D801–D807

    Article  CAS  PubMed  Google Scholar 

  24. Petrossian TC, Clarke SG (2011) Uncovering the human methyltransferasome. Mol Cell Proteomics MCP 10(M110):000976

    PubMed  Google Scholar 

  25. Lu H, Liu X, Deng Y, Qing H (2013) DNA methylation, a hand behind neurodegenerative diseases. Front Aging Neurosci 5:85

    Article  PubMed  PubMed Central  Google Scholar 

  26. Spector R, Robert Snodgrass S, Johanson CE (2015) A balanced view of the cerebrospinal fluid composition and functions: focus on adult humans. Exp Neurol 273:57–68

    Article  CAS  PubMed  Google Scholar 

  27. Lourenco CF, Ledo A, Dias C, Barbosa RM, Laranjinha J (2015) Neurovascular and neurometabolic derailment in aging and Alzheimer’s disease. Front Aging Neurosci 7:103

    PubMed  PubMed Central  Google Scholar 

  28. Peake JM, Tan SJ, Markworth JF, Broadbent JA, Skinner TL, Cameron-Smith D (2014) Metabolic and hormonal responses to isoenergetic high-intensity interval exercise and continuous moderate-intensity exercise. Am J Physiol Endocrinol Metab 307:E539–E552

    Article  CAS  PubMed  Google Scholar 

  29. Yan B, Jiye A, Wang G, Lu H, Huang X, Liu Y, Zha W, Hao H, Zhang Y, Liu L, Gu S, Huang Q, Zheng Y, Sun J (2009) Metabolomic investigation into variation of endogenous metabolites in professional athletes subject to strength-endurance training. J Appl Physiol (Bethesda, Md : 1985) 106:531–538

    Article  CAS  Google Scholar 

  30. Brugnara L, Vinaixa M, Murillo S, Samino S, Rodriguez MA, Beltran A, Lerin C, Davison G, Correig X, Novials A (2012) Metabolomics approach for analyzing the effects of exercise in subjects with type 1 diabetes mellitus. PLoS ONE 7:e40600

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chouchani ET, Pell VR, Gaude E, Aksentijevic D, Sundier SY, Robb EL, Logan A, Nadtochiy SM, Ord EN, Smith AC, Eyassu F, Shirley R, Hu CH, Dare AJ, James AM, Rogatti S, Hartley RC, Eaton S, Costa AS, Brookes PS, Davidson SM, Duchen MR, Saeb-Parsy K, Shattock MJ, Robinson AJ, Work LM, Frezza C, Krieg T, Murphy MP (2014) Ischaemic accumulation of succinate controls reperfusion injury through mitochondrial ROS. Nature 515:431–435

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Hamel D, Sanchez M, Duhamel F, Roy O, Honore JC, Noueihed B, Zhou T, Nadeau-Vallee M, Hou X, Lavoie JC, Mitchell G, Mamer OA, Chemtob S (2014) G-protein-coupled receptor 91 and succinate are key contributors in neonatal postcerebral hypoxia-ischemia recovery. Arterioscler Thromb Vasc Biol 34:285–293

    Article  CAS  PubMed  Google Scholar 

  33. He W, Miao FJ, Lin DC, Schwandner RT, Wang Z, Gao J, Chen JL, Tian H, Ling L (2004) Citric acid cycle intermediates as ligands for orphan G-protein-coupled receptors. Nature 429:188–193

    Article  CAS  PubMed  Google Scholar 

  34. Li T, Hu J, Du S, Chen Y, Wang S, Wu Q (2014) ERK1/2/COX-2/PGE2 signaling pathway mediates GPR91-dependent VEGF release in streptozotocin-induced diabetes. Mol Vis 20:1109–1121

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Sanchez A, Tripathy D, Luo J, Yin X, Martinez J, Grammas P (2013) Neurovascular unit and the effects of dosage in VEGF toxicity: role for oxidative stress and thrombin. J Alzheimer’s Dis JAD 34:281–291

    CAS  PubMed  Google Scholar 

  36. Shea TB, Rogers E (2014) Lifetime requirement of the methionine cycle for neuronal development and maintenance. Curr Opin Psychiatry 27:138–142

    Article  PubMed  Google Scholar 

  37. Lam SM, Wang Y, Duan X, Wenk MR, Kalaria RN, Chen CP, Lai MK, Shui G (2014) Brain lipidomes of subcortical ischemic vascular dementia and mixed dementia. Neurobiol Aging 35:2369–2381

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Rodrigues GM Jr, Toffoli LV, Manfredo MH, Francis-Oliveira J, Silva AS, Raquel HA, Martins-Pinge MC, Moreira EG, Fernandes KB, Pelosi GG, Gomes MV (2015) Acute stress affects the global DNA methylation profile in rat brain: modulation by physical exercise. Behav Brain Res 279:123–128

    Article  CAS  PubMed  Google Scholar 

  39. Gomez-Pinilla F, Zhuang Y, Feng J, Ying Z, Fan G (2011) Exercise impacts brain-derived neurotrophic factor plasticity by engaging mechanisms of epigenetic regulation. Eur J Neurosci 33:383–390

    Article  CAS  PubMed  Google Scholar 

  40. McGirt MJ, Woodworth G, Coon AL, Thomas G, Williams MA, Rigamonti D (2008) Diagnosis, treatment, and analysis of long-term outcomes in idiopathic normal-pressure hydrocephalus. Neurosurgery 62(Suppl 2):670–677

    PubMed  Google Scholar 

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Acknowledgments

This work was supported by the University of Akron, Conquer Chiari Foundation, the AB Sciex Young Investigator Award (LPS), and Cleveland Clinic (ML).

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Author notes

  1. Mark Luciano

    Present address: Department of Neurology and Neurosurgery, Johns Hopkins, Baltimore, MD, 21287, USA

Authors and Affiliations

  1. Departments of Chemistry and Biology, University of Akron, Akron, OH, 44325, USA

    He Huang & Leah P. Shriver

  2. Department of Neurological Surgery, Section of Pediatric and Congenital Neurological Surgery, CSF Physiology Laboratory, Neurological Institute Cleveland Clinic, Cleveland, OH, 44106, USA

    Jun Yang & Mark Luciano

Authors
  1. He Huang
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  2. Jun Yang
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  3. Mark Luciano
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  4. Leah P. Shriver
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Corresponding authors

Correspondence to Mark Luciano or Leah P. Shriver.

Additional information

He Huang and Jun Yang have contributed equally to this work.

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Huang, H., Yang, J., Luciano, M. et al. Longitudinal Metabolite Profiling of Cerebrospinal Fluid in Normal Pressure Hydrocephalus Links Brain Metabolism with Exercise-Induced VEGF Production and Clinical Outcome. Neurochem Res 41, 1713–1722 (2016). https://doi.org/10.1007/s11064-016-1887-z

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  • DOI: https://doi.org/10.1007/s11064-016-1887-z

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