Glycoproteogenomics: A Frequent Gene Polymorphism Affects the Glycosylation Pattern of the Human Serum Fetuin/α-2-HS-Glycoprotein

Yu-Hsien Lin, Jing Zhu, Sander Meijer, Vojtech Franc, Albert J R Heck

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Fetuin, also known as α-2-HS-glycoprotein (gene name: AHSG), is one of the more abundant glycoproteins secreted into the bloodstream. There are two frequently occurring alleles of human AHSG, resulting in three genotypes (AHSG*1, AHSG*2, and heterozygous AHSG1/2). The backbone amino acid sequences of fetuin coded by the AHSG*1 and AHSG*2 genes differ in two amino acids including one known O-glycosylation site (aa position 256). Although fetuin levels have been extensively studied, the originating genotype is often ignored in such analysis. As fetuin has been suggested repeatedly as a potential biomarker for several disorders, the question whether the gene polymorphism affects the fetuin profile is of great interest. In this work, we describe detailed proteoform profiles of fetuin, isolated from serum of 10 healthy and 10 septic patient individuals and investigate potential glycoproteogenomics correlations, e.g. how gene polymorphisms affect glycosylation. We established an efficient method for fetuin purification from individuals' serum using ion-exchange chromatography. Subsequently, we performed hybrid mass spectrometric approaches integrating data from native mass spectra and peptide-centric MS analysis. Our data reveal a crucial effect of the gene polymorphism on the glycosylation pattern of fetuin. Moreover, we clearly observed increased fucosylation in the samples derived from the septic patients. Our serum proteoform analysis, targeted at one protein obtained from 20 individuals, exposes the wide variability in proteoform profiles, which should be taken into consideration when using fetuin as biomarker. Importantly, focusing on a single or few proteins, the quantitative proteoform profiles can provide, as shown here, already ample data to classify individuals by genotype and disease state.

Original languageEnglish
Pages (from-to)1479-1490
Number of pages12
JournalMolecular and Cellular Proteomics
Volume18
Issue number8
DOIs
Publication statusPublished - Aug 2019

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