A glycomic workflow for LC-MS/MS analysis of urine glycosaminoglycan biomarkers in mucopolysaccharidoses.

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Nilsson J, Persson A, Vorontsov E, Nikpour M, Noborn F, Larson G, Blomqvist M

Glycoconj J 40 (5) 523-540 [2023-10-00; online 2023-07-18]

In recent years, several rational designed therapies have been developed for treatment of mucopolysaccharidoses (MPS), a group of inherited metabolic disorders in which glycosaminoglycans (GAGs) are accumulated in various tissues and organs. Thus, improved disease-specific biomarkers for diagnosis and monitoring treatment efficacy are of paramount importance. Specific non-reducing end GAG structures (GAG-NREs) have become promising biomarkers for MPS, as the compositions of the GAG-NREs depend on the nature of the lysosomal enzyme deficiency, thereby creating a specific pattern for each subgroup. However, there is yet no straightforward clinical laboratory platform which can assay all MPS-related GAG-NREs in one single analysis. Here, we developed and applied a GAG domain mapping approach for analyses of urine samples of ten MPS patients with various MPS diagnoses and corresponding aged-matched controls. We describe a nano-LC-MS/MS method of GAG-NRE profiling, utilizing 2-aminobenzamide reductive amination labeling to improve the sensitivity and the chromatographic resolution. Diagnostic urinary GAG-NREs were identified for MPS types IH/IS, II, IIIc, IVa and VI, corroborating GAG-NRE as biomarkers for these known enzyme deficiencies. Furthermore, a significant reduction of diagnostic urinary GAG-NREs in MPS IH (n = 2) and MPS VI (n = 1) patients under treatment was demonstrated. We argue that this straightforward glycomic workflow, designed for the clinical analysis of MPS-related GAG-NREs in one single analysis, will be of value for expanding the use of GAG-NREs as biomarkers for MPS diagnosis and treatment monitoring.

Glycoproteomics and MS Proteomics [Collaborative]

PubMed 37462780

DOI 10.1007/s10719-023-10128-5

Crossref 10.1007/s10719-023-10128-5

pmc: PMC10638189
pii: 10.1007/s10719-023-10128-5