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Think big — think omics


Wevers, Ron A; Blau, Nenad (2018). Think big — think omics. Journal of Inherited Metabolic Disease, 41(3):281-283.

Abstract

Traditionally the laboratory diagnosis of inborn errors of metabolism largely relies on targeted hypothesis‐driven measurements of metabolites in body fluids. The hypothesis is usually based on the phenotypic characteristics of the patient, defined through deep phenotyping or phenomics. The biochemical phenotype, or rather the ‘metabolite profile’ reflects both endogenous factors such as genotype(s), gene expression, the different chemical reactions taking place in the body, as well as exogenous factors such as dietary habits, drug metabolism and the microbiome. The last decade has seen the emergence of untargeted, hypothesis‐free measurements. NMR spectroscopy and mass spectrometry in combination with different separation techniques (LC, GC or CE) pave the way to an important next step in our understanding of inborn errors of metabolism. The aim of untargeted metabolomics is to identify and measure as many metabolites as possible, including unknowns, to generate the metabolic fingerprint characteristic for a biological sample and indicative for genetic defects influencing human metabolism. Typically, untargeted metabolomics techniques show more than 10,000 “features” in a single body fluid sample. These are big data! Bioinformatic‐ and chemometric techniques are required to reveal the relevant diagnostic features, which in turn help identify the specific aetiologic condition in the individual patient. This approach is rather different from classical metabolomics studies that usually describe the comparison between a patient group and a control group. This issue of the journal illustrates that “next generation metabolic screening” (NGMS) techniques enable us to use untargeted metabolomics for diagnostics in a clinical setting (Coene et al 2018). Untargeted metabolomics techniques are approaching maturity. It is demonstrated in this special JIMD issue that untargeted metabolomics techniques can identify the vast majority of IEM‐diagnoses as reliably as the conventional techniques. In addition, many examples from NMR spectroscopy and untargeted MS analyses have already emerged that unravel the identity of hitherto unknown inborn errors of metabolism (van Karnebeek et al 2016; Pol et al 2018). These techniques also have the power to discover novel biomarkers even in well‐known and in‐depth studied inborn errors of metabolism (Abela et al 2016; Abela et al 2017).

Abstract

Traditionally the laboratory diagnosis of inborn errors of metabolism largely relies on targeted hypothesis‐driven measurements of metabolites in body fluids. The hypothesis is usually based on the phenotypic characteristics of the patient, defined through deep phenotyping or phenomics. The biochemical phenotype, or rather the ‘metabolite profile’ reflects both endogenous factors such as genotype(s), gene expression, the different chemical reactions taking place in the body, as well as exogenous factors such as dietary habits, drug metabolism and the microbiome. The last decade has seen the emergence of untargeted, hypothesis‐free measurements. NMR spectroscopy and mass spectrometry in combination with different separation techniques (LC, GC or CE) pave the way to an important next step in our understanding of inborn errors of metabolism. The aim of untargeted metabolomics is to identify and measure as many metabolites as possible, including unknowns, to generate the metabolic fingerprint characteristic for a biological sample and indicative for genetic defects influencing human metabolism. Typically, untargeted metabolomics techniques show more than 10,000 “features” in a single body fluid sample. These are big data! Bioinformatic‐ and chemometric techniques are required to reveal the relevant diagnostic features, which in turn help identify the specific aetiologic condition in the individual patient. This approach is rather different from classical metabolomics studies that usually describe the comparison between a patient group and a control group. This issue of the journal illustrates that “next generation metabolic screening” (NGMS) techniques enable us to use untargeted metabolomics for diagnostics in a clinical setting (Coene et al 2018). Untargeted metabolomics techniques are approaching maturity. It is demonstrated in this special JIMD issue that untargeted metabolomics techniques can identify the vast majority of IEM‐diagnoses as reliably as the conventional techniques. In addition, many examples from NMR spectroscopy and untargeted MS analyses have already emerged that unravel the identity of hitherto unknown inborn errors of metabolism (van Karnebeek et al 2016; Pol et al 2018). These techniques also have the power to discover novel biomarkers even in well‐known and in‐depth studied inborn errors of metabolism (Abela et al 2016; Abela et al 2017).

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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:04 Faculty of Medicine > University Children's Hospital Zurich > Medical Clinic
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Life Sciences > Genetics
Health Sciences > Genetics (clinical)
Language:English
Date:1 May 2018
Deposited On:31 Jan 2019 13:41
Last Modified:29 Jul 2020 09:06
Publisher:Springer
ISSN:0141-8955
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/s10545-018-0165-4

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