Unveiling metabolic remodeling in mucopolysaccharidosis type III through integrative metabolomics and pathway analysis

Abdellah Tebani,Bruno Gonzalez,Carlos Afonso,Farid Zerimech,Soumeya Bekri,Stéphane Marret,Lenaig Abily-Donval,Monique Piraud,Isabelle Schmitz-Afonso,Bénédicte Héron,Jérôme Ausseil

doi:10.1186/s12967-018-1625-1

Abstract

BackgroundMetabolomics represent a valuable tool to recover biological information using body fluids and may help to characterize pathophysiological mechanisms of the studied disease. This approach has not been widely used to explore inherited metabolic diseases. This study investigates mucopolysaccharidosis type III (MPS III). A thorough and holistic understanding of metabolic remodeling in MPS III may allow the development, improvement and personalization of patient care.MethodsWe applied both targeted and untargeted metabolomics to urine samples obtained from a French cohort of 49 patients, consisting of 13 MPS IIIA, 16 MPS IIIB, 13 MPS IIIC, and 7 MPS IIID, along with 66 controls. The analytical strategy is based on ultra-high-performance liquid chromatography combined with ion mobility and high-resolution mass spectrometry. Twenty-four amino acids have been assessed using tandem mass spectrometry combined with liquid chromatography. Multivariate data modeling has been used for discriminant metabolite selection. Pathway analysis has been performed to retrieve metabolic pathways impairments.ResultsData analysis revealed distinct biochemical profiles. These metabolic patterns, particularly those related to the amino acid metabolisms, allowed the different studied groups to be distinguished. Pathway analysis unveiled major amino acid pathways impairments in MPS III mainly arginine–proline metabolism and urea cycle metabolism.ConclusionThis represents one of the first metabolomics-based investigations of MPS III. These results may shed light on MPS III pathophysiology and could help to set more targeted studies to infer the biomarkers of the affected pathways, which is crucial for rare conditions such as MPS III.

Highlights

Metabolomics represent a valuable tool to recover biological information using body fluids and may help to characterize pathophysiological mechanisms of the studied disease
For MPSIIIC and control samples, the model has one predictive and three orthogonal components with R2 = 0.98, Q2 = 0.39 and CV-analysis of variance (ANOVA) p-value = 1.35 × 10−5 (Fig. 2f ). Another orthogonal partial leastsquares-discriminant analysis (OPLS-DA) model was built for MPSIIID and control samples with one predictive and two orthogonal components model with R2 = 0.95, Q2 = 0.36 and CV-ANOVA p-value = 1.83 × 10−5 (Fig. 2g)
MPS IIIA yielded 11 significantly changed amino acids compared to controls: arginine, aspartic acid, alanine, threonine, histidine, phenylalanine, glycine, proline, asparagine and tyrosine

Summary

Introduction

Metabolomics represent a valuable tool to recover biological information using body fluids and may help to characterize pathophysiological mechanisms of the studied disease This approach has not been widely used to explore inherited metabolic diseases. Lysosomal storage diseases (LSD) represent a group of about 50 inherited disorders related to deficient lysosomal proteins This impairment leads to a progressive accumulation of metabolites or macromolecules within the lysosomales. Mucopolysaccharidoses (MPS) are a subgroup of LSD They are related to impaired catabolism of glycosaminoglycans (GAGs), chondroitin sulfate (CS), dermatan sulfate (DS), heparan sulfate (HS), keratan sulfate (KS), and hyaluronan, leading to GAG accumulation in the lysosomes and extracellular matrix [7]. The goal of this work is to apply both targeted and untargeted metabolomics on MPS IIIA, MPS IIIB, MPS IIIC and MPS IIID patients, compared to controls, to investigate metabolic changes in these conditions

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