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A Multiple N -Glucosylated Peptide Epitope Efficiently Detecting Antibodies in Multiple Sclerosis.

Authors :
Nuti F
Fernandez FR
Sabatino G
Peroni E
Mulinacci B
Paolini I
Pisa MD
Tiberi C
Lolli F
Petruzzo M
Lanzillo R
Morra VB
Rovero P
Papini AM
Source :
Brain sciences [Brain Sci] 2020 Jul 15; Vol. 10 (7). Date of Electronic Publication: 2020 Jul 15.
Publication Year :
2020

Abstract

Diagnostics of Multiple Sclerosis (MS) are essentially based on the gold standard magnetic resonance imaging. Few alternative simple assays are available to follow up disease activity. Considering that the disease can remain elusive for years, identification of antibodies fluctuating in biological fluids as relevant biomarkers of immune response is a challenge. In previous studies, we reported that anti- N -glucosylated ( N -Glc) peptide antibodies that can be easily detected in Solid-Phase Enzyme-Linked ImmunoSorbent Assays (SP-ELISA) on MS patients' sera preferentially recognize hyperglucosylated adhesin of non-typeable Haemophilus Influenzae . Since multivalency can be useful for diagnostic purposes to allow an efficient coating in ELISA, we report herein the development of a collection of Multiple N -glucosylated Peptide Epitopes ( N -Glc MEPs) to detect anti- N -Glc antibodies in MS. To this aim, a series of N -Glc peptide antigens to be represented in the N -GlcMEPs were tested in competitive ELISA. We confirmed that the epitope recognized by antibodies shall contain at least 5-mer sequences including the fundamental N -Glc moiety. Using a 4-branched dendrimeric lysine scaffold, we selected the N -Glc MEP 24 , carrying the minimal epitope Asn(Glc) anchored to a polyethylene glycol-based spacer (PEG) containing a 19-atoms chain, as an efficient multivalent probe to reveal specific and high affinity anti- N -Glc antibodies in MS.

Details

Language :
English
ISSN :
2076-3425
Volume :
10
Issue :
7
Database :
MEDLINE
Journal :
Brain sciences
Publication Type :
Academic Journal
Accession number :
32679694
Full Text :
https://doi.org/10.3390/brainsci10070453