Glycosylation is a process in which sugars are attached to proteins which occurs primarily in the ER and Golgi. This process is extremely complex and creates many different glycosylated forms of proteins, which carry important biological information. These sugars structures, called glycans, influence critical processes such as brain development, immune function and cell growth. Conversely, when glycosylation goes wrong, it can contribute to diseases such as immune disorders, cancer and liver disease, making glycans promising diagnostic markers.
Congenital Disorders of Glycosylation (CDGs) are rare inherited diseases caused by gene defects that disturb glycosylation. They serve as valuable models to study glycosylation abnormalities and broader understanding of glycosylation mechanisms. Because symptoms vary greatly between patients, even with the same gene defect, diagnosis often requires advanced molecular methods.
In this thesis, several omics technologies were used to improve CDG diagnostics and to better understand disease mechanisms. A new glycoproteomics workflow was developed to identify glycosylation changes in plasma proteins. This method was validated for routine use and enabled the diagnosis of cases that were previously missed by current diagnostic tests. A targeted LC-MS/MS method was also developed to detect a diagnostic sugar biomarker for MOGS-CDG, leading to the identification of new patients.
Furthermore, omics analysis in NANS-CDG uncovered unexpected pathway interactions, revising earlier assumptions about disease mechanisms and revealing potential therapeutic opportunities. Despite remaining challenges in data analysis and clinical translation, the use of glycomics, glycoproteomics and metabolomics is shown to be a powerful strategy for improving diagnosis and generating biological insight.
Overall, this thesis contributes new diagnostic tools, expands understanding of CDG pathophysiology and provides direction for future research and therapy development.