Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline

Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metab...

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Bibliographic Details
Published in:Metabolites 2021-08, Vol.11 (9), p.568
Main Authors: Hoegen, Brechtje, Zammit, Alan, Gerritsen, Albert, Engelke, Udo F. H., Castelein, Steven, van de Vorst, Maartje, Kluijtmans, Leo A. J., Huigen, Marleen C. D. G., Wevers, Ron A., van Gool, Alain J., Gilissen, Christian, Coene, Karlien L. M., Kulkarni, Purva
Format: Article
Language:English
Subjects:
Automation
Bioinformatics
bioinformatics pipeline
Body fluids
Case studies
Cofactors
Computer applications
data analysis
Data processing
Genotype & phenotype
Inborn errors of metabolism
inherited metabolic diseases
Laboratories
Mass spectrometry
Medical screening
Metabolism
Metabolites
Metabolomics
next-generation metabolic screening
Patients
Quality control
Reproducibility
Retention
Scientific imaging
Software
untargeted metabolomics
User interface
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Summary:Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metabolomics has evolved as a high throughput methodology offering a comprehensive readout of this metabolic fingerprint. This makes it a promising tool for diagnostic screening of IEM patients. However, the size and complexity of metabolomics data have posed a challenge in translating this avalanche of information into knowledge, particularly for clinical application. We have previously established next-generation metabolic screening (NGMS) as a metabolomics-based diagnostic tool for analyzing plasma of individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we present a computational pipeline to streamline the analysis of untargeted metabolomics data. This pipeline allows for time-efficient and reproducible data analysis, compatible with ISO:15189 accredited clinical diagnostics. The pipeline implements a combination of tools embedded in a workflow environment for large-scale clinical metabolomics data analysis. The accompanying graphical user interface aids end-users from a diagnostic laboratory for efficient data interpretation and reporting. We also demonstrate the application of this pipeline with a case study and discuss future prospects.
ISSN:2218-1989
2218-1989
DOI:10.3390/metabo11090568