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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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Autores principales: Kerkhofs, Marten H. P. M., Haijes, Hanneke A., Willemsen, A. Marcel, van Gassen, Koen L. I., van der Ham, Maria, Gerrits, Johan, de Sain-van der Velden, Monique G. M., Prinsen, Hubertus C. M. T., van Deutekom, Hanneke W. M., van Hasselt, Peter M., Verhoeven-Duif, Nanda M., Jans, Judith J. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281020/
https://www.ncbi.nlm.nih.gov/pubmed/32443577
http://dx.doi.org/10.3390/metabo10050206
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author Kerkhofs, Marten H. P. M.
Haijes, Hanneke A.
Willemsen, A. Marcel
van Gassen, Koen L. I.
van der Ham, Maria
Gerrits, Johan
de Sain-van der Velden, Monique G. M.
Prinsen, Hubertus C. M. T.
van Deutekom, Hanneke W. M.
van Hasselt, Peter M.
Verhoeven-Duif, Nanda M.
Jans, Judith J. M.
author_facet Kerkhofs, Marten H. P. M.
Haijes, Hanneke A.
Willemsen, A. Marcel
van Gassen, Koen L. I.
van der Ham, Maria
Gerrits, Johan
de Sain-van der Velden, Monique G. M.
Prinsen, Hubertus C. M. T.
van Deutekom, Hanneke W. M.
van Hasselt, Peter M.
Verhoeven-Duif, Nanda M.
Jans, Judith J. M.
author_sort Kerkhofs, Marten H. P. M.
collection PubMed
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.
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spelling pubmed-72810202020-06-15 Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics Kerkhofs, Marten H. P. M. Haijes, Hanneke A. Willemsen, A. Marcel van Gassen, Koen L. I. van der Ham, Maria Gerrits, Johan de Sain-van der Velden, Monique G. M. Prinsen, Hubertus C. M. T. van Deutekom, Hanneke W. M. van Hasselt, Peter M. Verhoeven-Duif, Nanda M. Jans, Judith J. M. Metabolites Article 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. MDPI 2020-05-18 /pmc/articles/PMC7281020/ /pubmed/32443577 http://dx.doi.org/10.3390/metabo10050206 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kerkhofs, Marten H. P. M.
Haijes, Hanneke A.
Willemsen, A. Marcel
van Gassen, Koen L. I.
van der Ham, Maria
Gerrits, Johan
de Sain-van der Velden, Monique G. M.
Prinsen, Hubertus C. M. T.
van Deutekom, Hanneke W. M.
van Hasselt, Peter M.
Verhoeven-Duif, Nanda M.
Jans, Judith J. M.
Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics
title 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_short Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics
title_sort cross-omics: integrating genomics with metabolomics in clinical diagnostics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281020/
https://www.ncbi.nlm.nih.gov/pubmed/32443577
http://dx.doi.org/10.3390/metabo10050206
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