Elucidating the Fragmentation Pathways of Glycans for Clinical Glycomics

• Applicant: Niklas Geue, Ph.D.
• Subject Area: Analytical Chemistry ; Biochemistry ; Physical Chemistry of Molecules, Liquids and Interfaces, Biophysical Chemistry
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 559720072

Alterations in sugar composition are associated with diseases such as cystic fibrosis. Glycomics is a branch of research that aims to unravel the structures of these sugars (glycans) for diagnostic and prognostic biomarkers. This is usually achieved using a controlled collision-induced dissociation of larger oligosaccharides into fragments and measuring their mass in a mass spectrometer. However, due to the complexity of sugars, and in particular their unusual rearrangement reactions and formation of internal fragments, the underlying fragmentation mechanisms are poorly understood. Hence, making glycan fragmentation predictable is highly desirable and would transform glycomics from an expert technique into a widely applicable tool that can be used by non-specialists. This proposal addresses these problems using a range of state-of-the-art experimental and computational techniques. Structurally similar human milk oligosaccharides and O-glycans will be purified and modified with a range of chromatographic methods, solid-phase extraction and chemical and enzymatic modification approaches. Glycans are then subjected to collision-induced dissociation for fragmentation, whilst quantifying their stability, which will yield an initial glycan fragment database. To elucidate the fragments’ structures, a prototype ion mobility mass spectrometer will be used, which was modified with a cryogenic ion trap for gas-phase infrared spectroscopy experiments. The mass, charge, size and shape as well as gas-phase infrared spectra of the obtained glycan fragments will be measured, yielding highly diagnostic fingerprints. Candidate structures, generated with density functional theory, will be matched to the experimental data and the resulting structural assignments will reveal details of the underlying glycan dissociation mechanisms. A full experimental and theoretical database will be used to train a random forest machine learning algorithm, subsequently predicting the fragmentation pathways of similar, but unknown glycans. Experimental validation of the predicted fragment structures will demonstrate the robustness of the algorithm, opening the window to a more general application of this workflow for the highly relevant area of glycan fragmentation.

• DFG Programme: WBP Position