Combined targeted and untargeted high-resolution mass spectrometry analyses to investigate metabolic alterations in pompe disease,Metabolomics

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Combined targeted and untargeted high-resolution mass spectrometry analyses to investigate metabolic alterations in pompe disease
Metabolomics ( IF 3.5 ) Pub Date : 2023-03-29 , DOI: 10.1007/s11306-023-01989-w
Mariana B M de Moraes ₁ , Hygor M R de Souza _{1,

2} , Maria L C de Oliveira ₃ , Roy W A Peake ₄ , Fernanda B Scalco ₃ , Rafael Garrett _{1,

4}

Affiliation

Introduction

Pompe disease is a rare, lysosomal disorder, characterized by intra-lysosomal glycogen accumulation due to an impaired function of α-glucosidase enzyme. The laboratory testing for Pompe is usually performed by enzyme activity, genetic test, or urine glucose tetrasaccharide (Glc4) screening by HPLC. Despite being a good preliminary marker, the Glc4 is not specific for Pompe.

Objective

The purpose of the present study was to develop a simple methodology using liquid chromatography-high resolution mass spectrometry (LC-HRMS) for targeted quantitative analysis of Glc₄ combined with untargeted metabolic profiling in a single analytical run to search for complementary biomarkers in Pompe disease.

Methods

We collected 21 urine specimens from 13 Pompe disease patients and compared their metabolic signatures with 21 control specimens.

Results

Multivariate statistical analyses on the untargeted profiling data revealed Glc₄, creatine, sorbitol/mannitol, L-phenylalanine, N-acetyl-4-aminobutanal, N-acetyl-L-aspartic acid, and 2-aminobenzoic acid as significantly altered in Pompe disease. This panel of metabolites increased sample class prediction (Pompe disease versus control) compared with a single biomarker.

Conclusion

This study has demonstrated the potential of combined acquisition methods in LC-HRMS for Pompe disease investigation, allowing for routine determination of an established biomarker and discovery of complementary candidate biomarkers that may increase diagnostic accuracy, or improve the risk stratification of patients with disparate clinical phenotypes.

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