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Clin Chim Acta. 2017 Dec;475:7-14. doi: 10.1016/j.cca.2017.09.024. Epub 2017 Oct 2.

Urinary metabolic phenotyping of mucopolysaccharidosis type I combining untargeted and targeted strategies with data modeling.

Author information

1
Department of Metabolic Biochemistry, Rouen University Hospital, Rouen 76000, France; Normandie Univ, UNIROUEN, CHU Rouen, INSERM U1245, 76000 Rouen, France; Normandie Univ, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000 Rouen, France.
2
Normandie Univ, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000 Rouen, France.
3
Normandie Univ, UNIROUEN, CHU Rouen, INSERM U1245, 76000 Rouen, France; Department of Neonatal Pediatrics and Intensive Care, Rouen University Hospital, Rouen 76031, France.
4
Departement of Pediatric Neurology, Reference Center of Lysosomal Diseases, Trousseau Hospital, APHP, GRC ConCer-LD, Sorbonne Universities, UPMC University 06, Paris, France.
5
Service de Biochimie et Biologie Moléculaire Grand Est, Unité des Maladies Héréditaires du Métabolisme et Dépistage Néonatal, Centre de Biologie et de Pathologie Est CHU de Lyon, Lyon, France.
6
INSERM U1088, Laboratoire de Biochimie Métabolique, Centre de Biologie Humaine, CHU Sud, 80054 Amiens Cedex, France.
7
Reference Center of Inherited Metabolic Diseases, Imagine Institute, Hospital Necker Enfants Malades, APHP, University Paris Descartes, Paris, France.
8
Laboratoire de Biochimie et Biologie Moléculaire, Université de Lille et Pôle de Biologie Pathologie Génétique du CHRU de Lille, 59000 Lille, France.
9
Laboratory of Genetic Metabolic Diseases, Department of Clinical Chemistry and Pediatrics, Academic Medical Center, Amsterdam, The Netherlands.
10
Normandie Univ, UNIROUEN, CHU Rouen, INSERM U1245, 76000 Rouen, France.
11
Department of Metabolic Biochemistry, Rouen University Hospital, Rouen 76000, France; Normandie Univ, UNIROUEN, CHU Rouen, INSERM U1245, 76000 Rouen, France. Electronic address: soumeya.bekri@chu-rouen.fr.

Abstract

BACKGROUND:

Application of metabolic phenotyping could expand the pathophysiological knowledge of mucopolysaccharidoses (MPS) and may reveal the comprehensive metabolic impairments in MPS. However, few studies applied this approach to MPS.

METHODS:

We applied targeted and untargeted metabolic profiling in urine samples obtained from a French cohort comprising 19 MPS I and 15 MPS I treated patients along with 66 controls. For that purpose, we used ultra-high-performance liquid chromatography combined with ion mobility and high-resolution mass spectrometry following a protocol designed for large-scale metabolomics studies regarding robustness and reproducibility. Furthermore, 24 amino acids have been quantified using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Keratan sulfate, Heparan sulfate and Dermatan sulfate concentrations have also been measured using an LC-MS/MS method. Univariate and multivariate data analyses have been used to select discriminant metabolites. The mummichog algorithm has been used for pathway analysis.

RESULTS:

The studied groups yielded distinct biochemical phenotypes using multivariate data analysis. Univariate statistics also revealed metabolites that differentiated the groups. Specifically, metabolites related to the amino acid metabolism. Pathway analysis revealed that several major amino acid pathways were dysregulated in MPS. Comparison of targeted and untargeted metabolomics data with in silico results yielded arginine, proline and glutathione metabolisms being the most affected.

CONCLUSION:

This study is one of the first metabolic phenotyping studies of MPS I. The findings might help to generate new hypotheses about MPS pathophysiology and to develop further targeted studies of a smaller number of potentially key metabolites.

KEYWORDS:

Inborn errors of metabolism; Ion mobility; Lysosomal storage diseases; Mass spectrometry; Metabolomics; Mucopolysaccharidosis type I

PMID:
28982054
DOI:
10.1016/j.cca.2017.09.024
[Indexed for MEDLINE]
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