Think big — think omics

Ron A. Wevers, Nenad Blau
2018 Journal of Inherited Metabolic Disease  
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
more » ... 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 Bfeatures^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 Bnext 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). Although there is progress, it is at the same time clear that we are far from understanding the full complexity of the many metabolites that float in our body fluids. The data comprise many Bfeatures of unknown significance or identity^(FUS). Analogous to deciphering the significance of genetic variants of unknown significance, national-and international collaboration between metabolic groups will be pivotal to bring our understanding of human metabolism to the next level. Databases like the Human Metabolome Data Base (HMDB) curated by Professor David Wishart at The Metabolomics Innovation Centre (TMIC) in Edmonton Canada are a perfect vehicle to store our new metabolic knowledge and open it up to the IEM-and broader scientific community (Wishart et al 2018) . Input from groups that work on the microbiome and groups that study how food components are metabolised will be invaluable. Central IEM knowledge repositories like IEMbase map to both HMDB and human phenotype onthology (HPO) and offer a diagnostic aid for many genetic metabolic centres and clinical communities seeking support in the diagnosis of IEMs (Lee et al 2018). However, there is a new frontier still. For decades we have focused on the metabolites that escape the cell. The intracellular metabolome was left largely untouched for diagnostic purposes, not to mention the metabolome of the many cellular organelles. Samples, such as fibroblasts and blood cells, surely can shed new light on the forgotten intracellular metabolome. The metabolome of these cells will be a first step
doi:10.1007/s10545-018-0165-4 pmid:29541953 fatcat:6oi4qbi3ojdvlcjx4uuupszk24