Untargeted LC-HRMS metabolomics reveals candidate biomarkers for mucopolysaccharidoses.

Autor: Torres CL; Metabolomics Laboratory, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil., Scalco FB; Inborn Error of Metabolism Laboratory, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil., de Oliveira MLC; Inborn Error of Metabolism Laboratory, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil., Peake RWA; Department of Laboratory Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA., Garrett R; Metabolomics Laboratory, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Department of Laboratory Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: rafael_garrett@iq.ufrj.br.
Jazyk: angličtina
Zdroj: Clinica chimica acta; international journal of clinical chemistry [Clin Chim Acta] 2023 Feb 15; Vol. 541, pp. 117250. Date of Electronic Publication: 2023 Feb 09.
DOI: 10.1016/j.cca.2023.117250
Abstrakt: Background: Mucopolysaccharidoses (MPSs) are inherited genetic diseases caused by an absence or deficiency of lysosomal enzymes responsible for catabolizing glycosaminoglycans (GAGs). Undiagnosed patients, or those without adequate treatment in early life, can be severely and irreversibly affected by the disease. In this study, we applied liquid chromatography-high resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics to identify potential biomarkers for MPS disorders to better understand how MPS may affect the metabolome of patients.
Methods: Urine samples from 37 MPS patients (types I, II, III, IV, and VI; untreated and treated with enzyme replacement therapy (ERT)) and 38 controls were analyzed by LC-HRMS. Data were processed by an untargeted metabolomics workflow and submitted to multivariate statistical analyses to reveal significant differences between the MPS and control groups.
Results: A total of 12 increased metabolites common to all MPS types were identified. Dipeptides, amino acids and derivatives were increased in the MPS group compared to controls. N-acetylgalactosamines 4- or 6-sulfate, important constituents of GAGs, were also elevated in MPS patients, most prominently in those with MPS VI. Notably, treated patients exhibited lower levels of the aforementioned acylaminosugars than untreated patients in all MPS types.
Conclusions: Untargeted metabolomics has enabled the detection of metabolites that could improve our understanding of MPS physiopathology. These potential biomarkers can be utilized in screening methods to support diagnosis and ERT monitoring.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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