Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics
| Autor: | Judith J.M. Jans, Hanneke W. M. van Deutekom, Hanneke A. Haijes, Johan Gerrits, Marten H. P. M. Kerkhofs, Nanda M. Verhoeven-Duif, A. Marcel Willemsen, Koen L.I. van Gassen, Monique G.M. de Sain-van der Velden, Maria van der Ham, Peter M. van Hasselt, Hubertus C.M.T. Prinsen |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Endocrinology Diabetes and Metabolism Metabolite In silico lcsh:QR1-502 Genomics Computational biology 030105 genetics & heredity Biology Biochemistry lcsh:Microbiology DNA sequencing Article 03 medical and health sciences chemistry.chemical_compound Metabolomics genomics diagnostics Molecular Biology Gene data integration Omics next-generation metabolic screening cross-omics untargeted metabolomics Metabolic pathway 030104 developmental biology chemistry next-generation sequencing |
| Zdroj: | Metabolites Volume 10 Issue 5 Metabolites, Vol 10, Iss 206, p 206 (2020) |
| ISSN: | 2218-1989 |
| Popis: | 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 &ndash 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&rsquo 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. |
| Databáze: | OpenAIRE |
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