Experimental Autoimmune Encephalomyelitis (EAE) is the most commonly used animal model for Multiple Sclerosis (MScl). CSF metabolomics in an acute EAE rat model was investigated using targetted LC–MS and GC–MS. Acute EAE in Lewis rats was induced by co-injection of Myelin Basic Protein with Complete Freund’s Adjuvant. CSF samples were collected at two time points: 10 days after inoculation, which was during the onset of the disease, and 14 days after inoculation, which was during the peak of the disease. The obtained metabolite profiles from the two time points of EAE development show profound differences between onset and the peak of the disease, suggesting significant changes in CNS metabolism over the course of MBP-induced neuroinflammation. Around the onset of EAE the metabolome profile shows significant decreases in arginine, alanine and branched amino acid levels, relative to controls. At the peak of the disease, significant increases in concentrations of multiple metabolites are observed, including glutamine, O-phosphoethanolamine, branched-chain amino acids and putrescine. Observed changes in metabolite levels suggest profound changes in CNS metabolism over the course of EAE. Affected pathways include nitric oxide synthesis, altered energy metabolism, polyamine synthesis and levels of endogenous antioxidants.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-011-0306-3) contains supplementary material, which is available to authorized users.

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BackgroundAnalysis of Cerebrospinal Fluid (CSF) samples holds great promise to diagnose neurological pathologies and gain insight into the molecular background of these pathologies. Proteomics and metabolomics methods provide invaluable information on the biomolecular content of CSF and thereby on the possible status of the central nervous system, including neurological pathologies. The combined information provides a more complete description of CSF content. Extracting the full combined information requires a combined analysis of different datasets i.e. fusion of the data.ResultsA novel fusion method is presented and applied to proteomics and metabolomics data from a pre-clinical model of multiple sclerosis: an Experimental Autoimmune Encephalomyelitis (EAE) model in rats. The method follows a mid-level fusion architecture. The relevant information is extracted per platform using extended canonical variates analysis. The results are subsequently merged in order to be analyzed jointly. We find that the combined proteome and metabolome data allow for the efficient and reliable discrimination between healthy, peripherally inflamed rats, and rats at the onset of the EAE. The predicted accuracy reaches 89% on a test set. The important variables (metabolites and proteins) in this model are known to be linked to EAE and/or multiple sclerosis.ConclusionsFusion of proteomics and metabolomics data is possible. The main issues of high-dimensionality and missing values are overcome. The outcome leads to higher accuracy in prediction and more exhaustive description of the disease profile. The biological interpretation of the involved variables validates our fusion approach.

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