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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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Detalles Bibliográficos
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
Descripción
Sumario: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.