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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MDPI AG
2021-08-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/11/9/568 |
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doaj-e09c0be30392434688b1e4c9754a14622021-09-26T00:40:41ZengMDPI AGMetabolites2218-19892021-08-011156856810.3390/metabo11090568Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational PipelineBrechtje Hoegen0Alan Zammit1Albert Gerritsen2Udo F. H. Engelke3Steven Castelein4Maartje van de Vorst5Leo A. J. Kluijtmans6Marleen C. D. G. Huigen7Ron A. Wevers8Alain J. van Gool9Christian Gilissen10Karlien L. M. Coene11Purva Kulkarni12Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsDepartment of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsDepartment of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsRadboud Institute of Molecular Life Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsDepartment of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsDepartment of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsTranslational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsTranslational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsRadboud Institute of Molecular Life Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsRadboud Institute of Molecular Life Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsDepartment of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsRadboud Institute of Molecular Life Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsRadboud Institute of Molecular Life Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The NetherlandsInborn 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.https://www.mdpi.com/2218-1989/11/9/568untargeted metabolomicsnext-generation metabolic screeninginherited metabolic diseasesdata analysismass spectrometrybioinformatics pipeline |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Brechtje Hoegen Alan Zammit Albert Gerritsen Udo F. H. Engelke Steven Castelein Maartje van de Vorst Leo A. J. Kluijtmans Marleen C. D. G. Huigen Ron A. Wevers Alain J. van Gool Christian Gilissen Karlien L. M. Coene Purva Kulkarni |
| spellingShingle |
Brechtje Hoegen Alan Zammit Albert Gerritsen Udo F. H. Engelke Steven Castelein Maartje van de Vorst Leo A. J. Kluijtmans Marleen C. D. G. Huigen Ron A. Wevers Alain J. van Gool Christian Gilissen Karlien L. M. Coene Purva Kulkarni Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline Metabolites untargeted metabolomics next-generation metabolic screening inherited metabolic diseases data analysis mass spectrometry bioinformatics pipeline |
| author_facet |
Brechtje Hoegen Alan Zammit Albert Gerritsen Udo F. H. Engelke Steven Castelein Maartje van de Vorst Leo A. J. Kluijtmans Marleen C. D. G. Huigen Ron A. Wevers Alain J. van Gool Christian Gilissen Karlien L. M. Coene Purva Kulkarni |
| author_sort |
Brechtje Hoegen |
| title |
Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline |
| title_short |
Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline |
| title_full |
Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline |
| title_fullStr |
Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline |
| title_full_unstemmed |
Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline |
| title_sort |
metabolomics-based screening of inborn errors of metabolism: enhancing clinical application with a robust computational pipeline |
| publisher |
MDPI AG |
| series |
Metabolites |
| issn |
2218-1989 |
| publishDate |
2021-08-01 |
| description |
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. |
| topic |
untargeted metabolomics next-generation metabolic screening inherited metabolic diseases data analysis mass spectrometry bioinformatics pipeline |
| url |
https://www.mdpi.com/2218-1989/11/9/568 |
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| _version_ |
1716870126925512704 |