Abstract

BackgroundMultiple sclerosis is an inflammatory and degenerative disease of the central nervous system (CNS) characterized by demyelination and concomitant axonal loss. The lack of a single specific test, and the similarity to other inflammatory diseases of the central nervous system, makes it difficult to have a clear diagnosis of multiple sclerosis. Therefore, laboratory tests that allows a clear and definite diagnosis, as well as to predict the different clinical courses of the disease are of utmost importance. Herein, we compared the cerebrospinal fluid (CSF) proteome of patients with multiple sclerosis (in the relapse–remitting phase of the disease) and other diseases of the CNS (inflammatory and non-inflammatory) aiming at identifying reliable biomarkers of multiple sclerosis.MethodsCSF samples from the discovery group were resolved by 2D-gel electrophoresis followed by identification of the protein spots by mass spectrometry. The results were analyzed using univariate (Student’s t test) and multivariate (Hierarchical Cluster Analysis, Principal Component Analysis, Linear Discriminant Analysis) statistical and numerical techniques, to identify a set of protein spots that were differentially expressed in CSF samples from patients with multiple sclerosis when compared with other two groups. Validation of the results was performed in samples from a different set of patients using quantitative (e.g., ELISA) and semi-quantitative (e.g., Western Blot) experimental approaches.ResultsAnalysis of the 2D-gels showed 13 protein spots that were differentially expressed in the three groups of patients: Alpha-1-antichymotrypsin, Prostaglandin-H2-isomerase, Retinol binding protein 4, Transthyretin (TTR), Apolipoprotein E, Gelsolin, Angiotensinogen, Agrin, Serum albumin, Myosin-15, Apolipoprotein B-100 and EF-hand calcium-binding domain—containing protein. ELISA experiments allowed validating part of the results obtained in the proteomics analysis and showed that some of the alterations in the CSF proteome are also mirrored in serum samples from multiple sclerosis patients. CSF of multiple sclerosis patients was characterized by TTR oligomerization, thus highlighting the importance of analyzing posttranslational modifications of the proteome in the identification of novel biomarkers of the disease.ConclusionsThe model built based on the results obtained upon analysis of the 2D-gels and in the validation phase attained an accuracy of about 80% in distinguishing multiple sclerosis patients and the other two groups.

Highlights

  • Multiple sclerosis is an inflammatory and degenerative disease of the central nervous system (CNS) characterized by demyelination and concomitant axonal loss

  • The model built based on the results obtained upon analysis of the 2D-gels and in the validation phase attained an accuracy of about 80% in distinguishing multiple sclerosis patients and the other two groups

  • In conclusion, Alpha-1-antichymotrypsin, PGDS, TTR, Apolipoprotein E (ApoE) and Gelsolin were identified as potential biomarkers for Relapse-Remitting Multiple Sclerosis (RRMS) when compared with other inflammatory diseases of CNS

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Summary

Introduction

Multiple sclerosis is an inflammatory and degenerative disease of the central nervous system (CNS) characterized by demyelination and concomitant axonal loss. The lack of a single specific test, and the similarity to other inflammatory diseases of the central nervous system, makes it difficult to have a clear diagnosis of multiple sclerosis. Laboratory tests that allows a clear and definite diagnosis, as well as to predict the different clinical courses of the disease are of utmost importance. The diagnosis of MS is difficult because of this heterogeneity and because its signs and symptoms can be similar to many other medical problems [3,4,5] These limitations in the clinical practice make studies aiming at identifying biological markers of MS extremely relevant. There is still an emerging need to further characterize the disease by simple and reliable laboratory tests, to describe clinical disease activity at a given timepoint, but ideally to be able to predict the future development, in response or not to treatment, of this disabling and partially asymptomatic disease [9]

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