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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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 |
work_keys_str_mv |
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1724846239691833344 |