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Sci Rep. 2017 Feb 3;7:41473. doi: 10.1038/srep41473.

Kynurenine pathway metabolomics predicts and provides mechanistic insight into multiple sclerosis progression.

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Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, NSW 2109, Australia.
Department of Pharmacology, School of Medical Sciences, University of New South Wales, NSW 2052, Australia.
Department of Statistics, Faculty of Science and Engineering, Macquarie University, NSW 2109, Australia.
Bioanalytical Mass Spectrometry Facility, University of New South Wales, NSW 2052, Australia.
Menzies Research Institute Tasmania, University of Tasmania, TAS 7000, Australia.
ImmuSmol, Pessac, France.
Peter Duncan Neurosciences Research Unit, St Vincent's Centre for Applied Medical Research, Sydney, Australia.
Department of Neurology, St Vincent's Hospital, Sydney, Australia.


Activation of the kynurenine pathway (KP) of tryptophan metabolism results from chronic inflammation and is known to exacerbate progression of neurodegenerative disease. To gain insights into the links between inflammation, the KP and multiple sclerosis (MS) pathogenesis, we investigated the KP metabolomics profile of MS patients. Most significantly, we found aberrant levels of two key KP metabolites, kynurenic acid (KA) and quinolinic acid (QA). The balance between these metabolites is important as it determines overall excitotoxic activity at the N-methyl-D-Aspartate (NMDA) receptor. We also identified that serum KP metabolic signatures in patients can discriminate clinical MS subtypes with high sensitivity and specificity. A C5.0 Decision Tree classification model discriminated the clinical subtypes of MS with a sensitivity of 91%. After validation in another independent cohort, sensitivity was maintained at 85%. Collectively, our studies suggest that abnormalities in the KP may be associated with the switch from early-mild stage MS to debilitating progressive forms of MS and that analysis of KP metabolites in MS patient serum may have application as MS disease biomarkers.

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