Akademik Veri Yönetim Sistemi
Int. Proteomics Congress & 7th Turkish National Proteomics Congress, İstanbul, Türkiye, 18 - 19 Eylül 2025, (Özet Bildiri)
Background
Multiple sclerosis (MS) is a heterogeneous neuroinflammatory disease in which oligoclonal band (OCB) patterns provide valuable diagnostic information. Fc-linked IgG glycosylation modulates immune effector functions and has been associated with disease activity in autoimmune conditions. Translating glycan signatures into clinically useful biomarkers requires integration with routine clinical biochemistry parameters obtained from serum.
Objective
This study aims to establish the groundwork for developing glyco-biomarker panels capable of predicting MS disease course. We profiled serum IgG glycosylation from MS patients stratified by five distinct OCB profiles and will integrate these glycan signatures with selected patient-specific clinical biochemistry parameters to identify potential predictive markers.
Methods
IgG was purified from serum using Protein G affinity chromatography, followed by enzymatic release of N-linked glycans and MALDI-TOF MS analysis. Glycan structures were classified according to fucosylation, galactosylation, and sialylation features. In the planned next phase, these glycan datasets will be correlated with available biochemical parameters such as C-reactive protein (CRP), total cholesterol, HDL cholesterol, triglycerides, fasting glucose, creatinine, ALT, AST, albumin, vitamin D, ferritin, and homocysteine, depending on test availability for each patient.
Preliminary Results
OCB-defined patient groups exhibited distinct IgG glycosylation profiles, with significant differences observed in galactosylation, sialylation, and the abundance of bisected N-glycans. These preliminary findings suggest that specific glycan alterations may reflect underlying disease mechanisms and could serve as candidates for future biomarker development.
Future Directions
We will combine glycan profiles with selected biochemical markers to explore their predictive value in MS subtype classification and progression risk. This integrated approach may enable the development of clinically applicable biomarker panels to support patient stratification and personalized therapeutic strategies.