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Short-Term Cuprizone Feeding Verifies N-Acetylaspartate Quantification as a Marker of Neurodegeneration

  • Barbara Maria Krauspe
  • , W. Dreher
  • , Cordian Beyer
  • , Werner Baumgartner
  • , Bernd Denecke
  • , Katharina Janssen
  • , Claus-Dieter Langhans
  • , Tim Clarner
  • , Markus Kipp

Research output: Contribution to journalArticlepeer-review

Abstract

Proton magnetic resonance spectroscopy (1H-MRS) is a quantitative MR imaging technique often used to complement conventional MR imaging with specific metabolic information. A key metabolite is the amino acid derivative N-Acetylaspartate (NAA) which is an accepted marker to measure the extent of neurodegeneration in multiple sclerosis (MS) patients. NAA is catabolized by the enzyme aspartoacylase (ASPA) which is predominantly expressed in oligodendrocytes. Since the formation of MS lesions is paralleled by oligodendrocyte loss, NAA might accumulate in the brain, and therefore, the extent of neurodegeneration might be underestimated. In the present study, we used the well-characterized cuprizone model. There, the loss of oligodendrocytes is paralleled by a reduction in ASPA expression and activity as demonstrated by genome-wide gene expression analysis and enzymatic activity assays. Notably, brain levels of NAA were not increased as determined by gas chromatography-mass spectrometry and 1H-MRS. These important findings underpin the reliability of NAA quantification as a valid marker for the paraclinical determination of the extent of neurodegeneration, even under conditions of oligodendrocyte loss in which impaired metabolization of NAA is expected. Future studies have to reveal whether other enzymes are able to metabolize NAA or whether an excess of NAA is cleared by other mechanisms rather than enzymatic metabolism.
Original languageEnglish
Pages (from-to)733-748
Number of pages16
JournalJournal of Molecular Neuroscience
Volume55
Issue number3
DOIs
Publication statusPublished - Mar 2014

Fields of science

  • 206 Medical Engineering

JKU Focus areas

  • Mechatronics and Information Processing
  • Nano-, Bio- and Polymer-Systems: From Structure to Function

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