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Metabolomics Analysis Applied to Investigate MPS

By LabMedica International staff writers
Posted on 17 Oct 2017
Inborn errors of metabolism (IEM) represent a group of about 500 rare diseases with an overall estimated incidence of 1/2,500. The diversity of involved metabolisms explains the difficulties in establishing their diagnosis.

Lysosomal storage diseases (LSD) represent a group of about 50 inherited disorders due to lysosomal proteins deficiencies, which lead to a progressive accumulation of compounds within the lysosome. This metabolite storage causes various organ failures and premature death and Mucopolysaccharidoses (MPS) belong to the LSD group.

Image: The Synapt G2 high definition mass spectrometer (HDMS) (Photo courtesy of Waters).
Image: The Synapt G2 high definition mass spectrometer (HDMS) (Photo courtesy of Waters).

Clinical biochemists and their colleagues working with those at Rouen University Hospital (Rouen, France) applied targeted and untargeted metabolic profiling in urine samples obtained from a French cohort comprising 19 Mucopolysaccharidosis (MPS) I and 15 MPS I treated patients along with 66 controls. Random urine samples were collected from MPS patients in whom the diagnosis had been confirmed by demonstrating marked enzyme deficiency in leucocytes and/or by molecular analysis.

Ultra-high-performance liquid chromatography-ion mobility mass spectrometry and data-independent MS acquisitions with simultaneous analysis of molecular fragmentation (MSE) were performed on Synapt G2 HDMS mass spectrometer. All LC-IM/MS raw data files, data processing, peak detection and peak matching across samples using retention time correction and chromatographic alignment along with drift time and cross collision section (CCS) calculation were performed using Progenesis QI (Waters MS Technologies, Manchester, UK). The analysis of free amino acid profiles in urine was based on a liquid chromatography coupled to tandem mass spectrometry method and the aTRAQ reagent.

The scientists reported that 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.

The authors concluded that metabolic phenotyping enabled them to unveil profound metabolic impairments beyond the primary deficiency in MPS I. The understanding of disease pathophysiological bases may open new therapeutic strategies such as antioxidants adjuvants and diet intervention as complementary treatments for MPS and possibly for other LSDs. The study was published on October 2, 2017, in the journal Clinica Chemica Acta.

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