Glycosylation profiling of selected proteins in cerebrospinal fluid from Alzheimer's disease and healthy subjects. Issue 26 (18th June 2019)

• Record Type: Journal Article
• Authors: Quaranta, Alessandro; Karlsson, Isabella; Ndreu, Lorena; Marini, Federico; Ingelsson, Martin; Thorsén, Gunnar
• Is Part Of: Analytical methods. Volume 11:Issue 26(2019)
• Page Start: 3331
• Page End: 3340
• Publication Date: 2019-06-18
• Subjects: Chemistry, Analytic -- Periodicals; Analytical biochemistry -- Periodicals; Chemical laboratories -- Standards -- Periodicals; 543.1905
• DOI: 10.1039/c9ay00381a
• Languages: English
• ISSNs: 1759-9660

Abstract

Abstract : Alteration of glycosylation has been observed in several diseases, such as cancer and neurodegenerative disorders. Abstract : Alteration of glycosylation has been observed in several diseases, such as cancer and neurodegenerative disorders. The study of changes in glycosylation could lead to a better understanding of mechanisms underlying these diseases and to the identification of new biomarkers. In this work the N-linked glycosylation of five target proteins in cerebrospinal fluid (CSF) from Alzheimer's disease (AD) patients and healthy controls have been analyzed for the first time. The investigated proteins, transferrin (TFN), α-1-antitrypsin (AAT), C1-inhibitor, immunoglobulin G (IgG), and α-1-acid glycoprotein (AGP), were selected based on the availability of VHH antibody fragments and their potential involvement in neurodegenerative and inflammation diseases. AD patients showed alterations in the glycosylation of low abundance proteins, such as C1-inhibitor and α-1-acid glycoprotein. These alterations would not have been detected if the glycosylation profile of the total CSF had been analyzed, due to the masking effect of the dominant profiles of high abundance glycoproteins, such as IgG. Information obtained from single proteins was not sufficient to correctly classify the two sample groups; however, by using an advanced multivariate technique a total non-error rate of 72 ± 3% was obtained. In fact, the corresponding model was able to correctly classify 71 ± 4% of the healthy subjects and 74 ± 7% of the AD patients. Even if the results were not conclusive for AD, this approach could be extremely useful for diseases in which glycosylation changes are reported to be more extensive, such as several types of cancer and autoimmune diseases.