Targeted Measurements of O- and N-Glycopeptides Show That Proteins in High Density Lipoprotein Particles Are Enriched with Specific Glycosylation Compared to Plasma

Dec 7, 2017·
Muchena J. Kailemia
,
Wanghui Wei
,
Khoa T. D. Nguyen
,
Elizabeth Beals
,
Lisa Sawrey-Kubicek
,
Christopher Rhodes
Chenghao Zhu
Chenghao Zhu
,
Romina Sacchi
,
Angela M. Zivkovic
,
Carlito B. Lebrilla
· 0 min read
Abstract
High density lipoprotein (HDL) particles are believed to be protective due to their inverse correlation with the prevalence of cardiovascular diseases. However, recent studies show that in some conditions such as heart disease and diabetes, HDL particles can become dysfunctional. Great attention has been directed toward HDL particle composition because the relative abundances of HDL constituents determine HDL’s functional properties. A key factor to consider when studying the structure and composition of plasma particles is the protein glycosylation. Here, we profile the O- and N-linked glycosylation of HDL associated-proteins including the truncated form of Apo CIII and their glycan heterogeneity in a site-specific manner. Apolipoprotein CIII, fetuin A, and alpha 1 antitrypsin are glycoproteins associated with lipoproteins and are implicated in many cardiovascular and other disease conditions. A targeted method (UHPLC-QQQ) was used to measure the glycoprotein concentrations and site-specific glycovariations of the proteins in human plasma and compared with HDL particles isolated from the same plasma samples. The proteins found in the plasma are differentially glycosylated compared to those isolated in HDL. The results of this study suggest that glycosylation may play a role in protein partitioning in the blood, with possible functional implications.
Type
Publication
Journal of Proteome Research
publications
Chenghao Zhu
Authors
Research Assistant Professor
Chenghao Zhu is a Research Assistant Professor in the NCI-designated Cancer Center at Sanford Burnham Prebys. His research focuses on developing computational methods and software for proteogenomics and applying proteogenomics to cancer diagnosis, prognosis, and clinico-epidemiologic questions.