Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics

被引:22
作者
Kerkhofs, Marten H. P. M. [1 ]
Haijes, Hanneke A. [1 ,2 ]
Willemsen, A. Marcel [1 ]
van Gassen, Koen L. I. [3 ]
van der Ham, Maria [1 ]
Gerrits, Johan [1 ]
de Sain-van der Velden, Monique G. M. [1 ]
Prinsen, Hubertus C. M. T. [1 ]
van Deutekom, Hanneke W. M. [3 ]
van Hasselt, Peter M. [2 ]
Verhoeven-Duif, Nanda M. [1 ]
Jans, Judith J. M. [1 ]
机构
[1] Univ Utrecht, Univ Med Ctr Utrecht, Dept Genet, Sect Metab Diagnost, Lundlaan 6, NL-3584 EA Utrecht, Netherlands
[2] Univ Utrecht, Univ Med Ctr Utrecht, Wilhelmina Childrens Hosp, Sect Metab Dis,Dept Child Hlth, Lundlaan 6, NL-3584 EA Utrecht, Netherlands
[3] Univ Utrecht, Univ Med Ctr Utrecht, Dept Genet, Sect Genom Diagnost, Lundlaan 6, NL-3584 EA Utrecht, Netherlands
关键词
cross-omics; untargeted metabolomics; genomics; diagnostics; data integration; next-generation sequencing; next-generation metabolic screening; INBORN-ERRORS; VARIANTS; CHILDREN;
D O I
10.3390/metabo10050206
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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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页数:15
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