Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics

Next-generation sequencing and next-generation metabolic screening are, independently, increasingly applied in clinical diagnostics of inborn errors of metabolism (IEM). Integrated into a single bioinformatic method, these two –omics technologies can potentially further improve the diagnostic yield...

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Main Authors: Marten H. P. M. Kerkhofs, Hanneke A. Haijes, A. Marcel Willemsen, Koen L. I. van Gassen, Maria van der Ham, Johan Gerrits, Monique G. M. de Sain-van der Velden, Hubertus C. M. T. Prinsen, Hanneke W. M. van Deutekom, Peter M. van Hasselt, Nanda M. Verhoeven-Duif, Judith J. M. Jans
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Metabolites
Subjects:
Online Access:https://www.mdpi.com/2218-1989/10/5/206
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spelling doaj-0f1989cf6b244a9cb2ff3dced237e8fd2020-11-25T02:26:34ZengMDPI AGMetabolites2218-19892020-05-011020620610.3390/metabo10050206Cross-Omics: Integrating Genomics with Metabolomics in Clinical DiagnosticsMarten H. P. M. Kerkhofs0Hanneke A. Haijes1A. Marcel Willemsen2Koen L. I. van Gassen3Maria van der Ham4Johan Gerrits5Monique G. M. de Sain-van der Velden6Hubertus C. M. T. Prinsen7Hanneke W. M. van Deutekom8Peter M. van Hasselt9Nanda M. Verhoeven-Duif10Judith J. M. Jans11Section Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Genomic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Genomic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diseases, Department of Child Health, Wilhelmina Children’s Hospital, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsSection Metabolic Diagnostics, Department of Genetics, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, 3584 EA Utrecht, The NetherlandsNext-generation sequencing and next-generation metabolic screening are, independently, increasingly applied in clinical diagnostics of inborn errors of metabolism (IEM). Integrated into a single bioinformatic method, these two –omics technologies can potentially further improve the diagnostic yield for IEM. Here, we present cross-omics: a method that uses untargeted metabolomics results of patient’s dried blood spots (DBSs), indicated by Z-scores and mapped onto human metabolic pathways, to prioritize potentially affected genes. We demonstrate the optimization of three parameters: (1) maximum distance to the primary reaction of the affected protein, (2) an extension stringency threshold reflecting in how many reactions a metabolite can participate, to be able to extend the metabolite set associated with a certain gene, and (3) a biochemical stringency threshold reflecting paired Z-score thresholds for untargeted metabolomics results. Patients with known IEMs were included. We performed untargeted metabolomics on 168 DBSs of 97 patients with 46 different disease-causing genes, and we simulated their whole-exome sequencing results in silico. We showed that for accurate prioritization of disease-causing genes in IEM, it is essential to take into account not only the primary reaction of the affected protein but a larger network of potentially affected metabolites, multiple steps away from the primary reaction.https://www.mdpi.com/2218-1989/10/5/206cross-omicsuntargeted metabolomicsgenomicsdiagnosticsdata integrationnext-generation sequencing
collection DOAJ
language English
format Article
sources DOAJ
author Marten H. P. M. Kerkhofs
Hanneke A. Haijes
A. Marcel Willemsen
Koen L. I. van Gassen
Maria van der Ham
Johan Gerrits
Monique G. M. de Sain-van der Velden
Hubertus C. M. T. Prinsen
Hanneke W. M. van Deutekom
Peter M. van Hasselt
Nanda M. Verhoeven-Duif
Judith J. M. Jans
spellingShingle Marten H. P. M. Kerkhofs
Hanneke A. Haijes
A. Marcel Willemsen
Koen L. I. van Gassen
Maria van der Ham
Johan Gerrits
Monique G. M. de Sain-van der Velden
Hubertus C. M. T. Prinsen
Hanneke W. M. van Deutekom
Peter M. van Hasselt
Nanda M. Verhoeven-Duif
Judith J. M. Jans
Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics
Metabolites
cross-omics
untargeted metabolomics
genomics
diagnostics
data integration
next-generation sequencing
author_facet Marten H. P. M. Kerkhofs
Hanneke A. Haijes
A. Marcel Willemsen
Koen L. I. van Gassen
Maria van der Ham
Johan Gerrits
Monique G. M. de Sain-van der Velden
Hubertus C. M. T. Prinsen
Hanneke W. M. van Deutekom
Peter M. van Hasselt
Nanda M. Verhoeven-Duif
Judith J. M. Jans
author_sort Marten H. P. M. Kerkhofs
title Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics
title_short Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics
title_full Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics
title_fullStr Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics
title_full_unstemmed Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics
title_sort cross-omics: integrating genomics with metabolomics in clinical diagnostics
publisher MDPI AG
series Metabolites
issn 2218-1989
publishDate 2020-05-01
description Next-generation sequencing and next-generation metabolic screening are, independently, increasingly applied in clinical diagnostics of inborn errors of metabolism (IEM). Integrated into a single bioinformatic method, these two –omics technologies can potentially further improve the diagnostic yield for IEM. Here, we present cross-omics: a method that uses untargeted metabolomics results of patient’s dried blood spots (DBSs), indicated by Z-scores and mapped onto human metabolic pathways, to prioritize potentially affected genes. We demonstrate the optimization of three parameters: (1) maximum distance to the primary reaction of the affected protein, (2) an extension stringency threshold reflecting in how many reactions a metabolite can participate, to be able to extend the metabolite set associated with a certain gene, and (3) a biochemical stringency threshold reflecting paired Z-score thresholds for untargeted metabolomics results. Patients with known IEMs were included. We performed untargeted metabolomics on 168 DBSs of 97 patients with 46 different disease-causing genes, and we simulated their whole-exome sequencing results in silico. We showed that for accurate prioritization of disease-causing genes in IEM, it is essential to take into account not only the primary reaction of the affected protein but a larger network of potentially affected metabolites, multiple steps away from the primary reaction.
topic cross-omics
untargeted metabolomics
genomics
diagnostics
data integration
next-generation sequencing
url https://www.mdpi.com/2218-1989/10/5/206
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