Plasma Protein Levels Analysis in Multiple Sclerosis Sardinian Families Identified C9 and CYP24A1 as Candidate Biomarkers.

Andrea Nova,Ashley Beecham,Carlo Berzuini,Luisa Bernardinelli,Valeria Saddi,Jacob L Mccauley,Marialuisa Piras,Teresa Fazia

doi:10.3390/life12020151

Abstract

Here we investigate protein levels in 69 multiple sclerosis (MS) cases and 143 healthy controls (HC) from twenty Sardinian families to search for promising biomarkers in plasma. Using antibody suspension bead array technology, the plasma levels of 56 MS-related proteins were obtained. Differences between MS cases and HC were estimated using Linear Mixed Models or Linear Quantile Mixed Models. The proportion of proteins level variability, explained by a set of 119 MS-risk SNPs as to the literature, was also quantified. Higher plasma C9 and CYP24A1 levels were found in MS cases compared to HC (p < 0.05 after Holm multiple testing correction), with protein level differences estimated as, respectively, 0.53 (95% CI: 0.25, 0.81) and 0.42 (95% CI: 0.19, 0.65) times plasma level standard deviation measured in HC. Furthermore, C9 resulted in both statistically significantly higher relapsing-remitting MS (RRMS) and secondary-progressive MS (SPMS) compared to HC, with SPMS showing the highest differences. Instead, CYP24A1 was statistically significantly higher only in RRMS as compared to HC. Respectively, 26% (95% CI: 10%, 44%) and 16% (95% CI: 9%, 39%) of CYP24A1 and C9 plasma level variability was explained by known MS-risk SNPs. Our results highlight C9 and CYP24A1 as potential biomarkers in plasma for MS and allow us to gain insight into molecular disease mechanisms.

Highlights

Multiple sclerosis (MS) (OMIM 126200) is an inflammatory demyelinating disorder of the central nervous system (CNS) and the most frequent cause of chronic neurological disability [1,2,3]
The aim of this second analysis was to quantify the variability in protein levels in plasma, explained by well-known multiple sclerosis (MS)-risk genetic variants
MS is a heterogeneous disease, reliable biomarkers that can capture the different aspects of this heterogeneity are required to understand its mechanism and pathophysiology, especially to diagnose, predict prognosis and response to therapies, and not least to develop new therapeutic strategies [22]

Summary

Introduction

Multiple sclerosis (MS) (OMIM 126200) is an inflammatory demyelinating disorder of the central nervous system (CNS) and the most frequent cause of chronic neurological disability [1,2,3]. In the disease process several pathophysiological mechanisms, including inflammation, demyelination, axonal damage, and repair mechanisms are involved. These mechanisms are not uniformly represented across patient populations, contributing to the heterogeneity in phenotypic expression of the disease, its prognosis, and response to therapies [6]. The need for suitable biomarkers to assist in the treatment decisions and in the choice of effective therapy strategy is compelling. As defined in [7], “[a] biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biological process, pathogenic processes, or pharmacologic responses to a therapeutic intervention”

Methods

Results

Conclusion