BACKGROUND: Multiple sclerosis (MS) is a chronic immunemediated disease of the central nervous system with a highly variable clinical presentation and disease progression. In this study, we investigate the metabolomics profile of patients affected by relapsing-remitting MS (RRMS)and primary progressive MS (PPMS), in order to find potential biomarkers to distinguish between the two forms. METHODS: Cerebrospinal Fluid CSF and blood samples of 34 patients (RRMS n = 22, PPMS n = 12) were collected. Nuclear magnetic resonance (1H-NMR) and mass spectrometry (coupled with a gas chromatography and liquid chromatography) were used as analytical techniques. Subsequently, a multivariate statistical analysis was performed; the resulting significant variables underwent U-Mann-Whitney test and correction for multiple comparisons. Receiver Operating Characteristic ROC curves were built and the pathways analysis was conducted. RESULTS: The analysis of the serum and the CSF of the two classes, allowed the identification of several altered metabolites (lipids, biogenic amines, and amino acids). The pathways analysis indicated the following pathways were affected: Glutathione metabolism, nitrogen metabolism, glutamine-glutamate metabolism, arginine-ornithine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis etc. Conclusion: The analysis allowed the identification of a set of metabolites able to classify RRMS and PPMS patients, each of whom express different patterns of metabolites in the two biofluids.
BACKGROUND:Multiple sclerosis (MS) is a chronic immunemediated disease of the central nervous system with a highly variable clinical presentation and disease progression. In this study, we investigate the metabolomics profile of patients affected by relapsing-remitting MS (RRMS)and primary progressive MS (PPMS), in order to find potential biomarkers to distinguish between the two forms. METHODS: Cerebrospinal Fluid CSF and blood samples of 34 patients (RRMS n = 22, PPMS n = 12) were collected. Nuclear magnetic resonance (1H-NMR) and mass spectrometry (coupled with a gas chromatography and liquid chromatography) were used as analytical techniques. Subsequently, a multivariate statistical analysis was performed; the resulting significant variables underwent U-Mann-Whitney test and correction for multiple comparisons. Receiver Operating Characteristic ROC curves were built and the pathways analysis was conducted. RESULTS: The analysis of the serum and the CSF of the two classes, allowed the identification of several altered metabolites (lipids, biogenic amines, and amino acids). The pathways analysis indicated the following pathways were affected: Glutathione metabolism, nitrogen metabolism, glutamine-glutamate metabolism, arginine-ornithine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis etc. Conclusion: The analysis allowed the identification of a set of metabolites able to classify RRMS and PPMS patients, each of whom express different patterns of metabolites in the two biofluids.
Entities:
Keywords:
biomarkers; mass spectrometry; metabolomics; multiple sclerosis; nuclear magnetic resonance; pathways analysis
Authors: Anita Horvatić; Andrea Gelemanović; Boris Pirkić; Ozren Smolec; Blanka Beer Ljubić; Ivana Rubić; Peter David Eckersall; Vladimir Mrljak; Mark McLaughlin; Marko Samardžija; Marija Lipar Journal: Int J Mol Sci Date: 2021-10-28 Impact factor: 5.923
Authors: Marianna Gabriella Rispoli; Silvia Valentinuzzi; Giovanna De Luca; Piero Del Boccio; Luca Federici; Maria Di Ioia; Anna Digiovanni; Eleonora Agata Grasso; Valeria Pozzilli; Alessandro Villani; Antonio Maria Chiarelli; Marco Onofrj; Richard G Wise; Damiana Pieragostino; Valentina Tomassini Journal: Int J Mol Sci Date: 2021-10-15 Impact factor: 5.923
Authors: Federica Murgia; Luigi Atzori; Ezio Carboni; Maria Laura Santoru; Aran Hendren; Augusta Pisanu; Pierluigi Caboni; Laura Boi; Giuliana Fusco; Anna R Carta Journal: Int J Mol Sci Date: 2020-09-14 Impact factor: 5.923