Translational Metabolism: A multidisciplinary approach towards precision diagnosis of inborn errors of metabolism in the omics era

Wanders, Ronald J. A.; Vaz, Frédéric M.; Ferdinandusse, Sacha; Kuilenburg, André B. P.; Kemp, Stephan; Karnebeek, Clara D. van; Waterham, Hans R.; Houtkooper, Riekelt H.

doi:10.1002/jimd.12008

Cited by 19 publications

(17 citation statements)

References 97 publications

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“…As seen from the above examples of systems modeling for multi-omics integration, as well as through many other examples, most multi-omics systems models are based on some form of metabolic model or metabolic readout [11,20,83,89,90,91]. This is true even though they invariably include genomics, proteomics and/or transcriptomics data in the model.…”

Section: Multi-omics Data Integrationmentioning

confidence: 99%

Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

et al. 2019

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The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent ‘Australian and New Zealand Metabolomics Conference’ (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.

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Section: Multi-omics Data Integrationmentioning

confidence: 99%

Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

et al. 2019

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“…Based on a recent model by Wanders et al, Fig. 4 describes an approach for the laboratory investigation of three types of IEM presentation, the most challenging of which is patients with non-specific clinical findings with no immediate indication of an IEM 31 . For such individuals, WES is potentially the most effective front-line investigation 32 .…”

Section: Discussionmentioning

confidence: 99%

“…It is possible that such patients may benefit from GUM analysis when VUS are discovered by WES, requiring validation. In addition to metabolomics testing, functional characterization studies (such as enzyme activity measurement or flux studies) should be utilized, where appropriate 31 .…”

Section: Sample Collectionmentioning

confidence: 99%

Clinical Validation of Targeted and Untargeted Metabolomics Testing for Genetic Disorders: A 3 Year Comparative Study

Almontashiri

Zha

Kim

et al. 2020

Sci Rep

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Global untargeted metabolomics (GUM) has entered clinical diagnostics for genetic disorders. We compared the clinical utility of GUM with traditional targeted metabolomics (TM) as a screening tool in patients with established genetic disorders and determined the scope of GUM as a discovery tool in patients with no diagnosis under investigation. We compared TM and GUM data in 226 patients. The first cohort (n = 87) included patients with confirmed inborn errors of metabolism (IEM) and genetic syndromes; the second cohort (n = 139) included patients without diagnosis who were undergoing evaluation for a genetic disorder. In patients with known disorders (n = 87), GUM performed with a sensitivity of 86% (95% CI: 78-91) compared with TM for the detection of 51 diagnostic metabolites. The diagnostic yield of GUM in patients under evaluation with no established diagnosis (n = 139) was 0.7%. GUM successfully detected the majority of diagnostic compounds associated with known IEMs. The diagnostic yield of both targeted and untargeted metabolomics studies is low when assessing patients with non-specific, neurological phenotypes. GUM shows promise as a validation tool for variants of unknown significance in candidate genes in patients with non-specific phenotypes.

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“…To advance our knowledge in the understanding of the underlying mechanisms of IEM, as well as to identify therapeutic targets, an efficient model, that can be designed successfully for the various genes and pathways, is needed. Furthermore, to improve the translatability of whole exome/genome sequencing, the decisive advantages of the models must be determined along with careful interpretation of data to accurately correlate results [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Great Catch for Investigating Inborn Errors of Metabolism—Insights Obtained from Zebrafish

Breuer

Patten

2020

Biomolecules

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Inborn errors of metabolism cause abnormal synthesis, recycling, or breakdown of amino acids, neurotransmitters, and other various metabolites. This aberrant homeostasis commonly causes the accumulation of toxic compounds or depletion of vital metabolites, which has detrimental consequences for the patients. Efficient and rapid intervention is often key to survival. Therefore, it requires useful animal models to understand the pathomechanisms and identify promising therapeutic drug targets. Zebrafish are an effective tool to investigate developmental mechanisms and understanding the pathophysiology of disorders. In the past decades, zebrafish have proven their efficiency for studying genetic disorders owing to the high degree of conservation between human and zebrafish genes. Subsequently, several rare inherited metabolic disorders have been successfully investigated in zebrafish revealing underlying mechanisms and identifying novel therapeutic targets, including methylmalonic acidemia, Gaucher’s disease, maple urine disorder, hyperammonemia, TRAPPC11-CDGs, and others. This review summarizes the recent impact zebrafish have made in the field of inborn errors of metabolism.

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Translational Metabolism: A multidisciplinary approach towards precision diagnosis of inborn errors of metabolism in the omics era

Cited by 19 publications

References 97 publications

Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

Clinical Validation of Targeted and Untargeted Metabolomics Testing for Genetic Disorders: A 3 Year Comparative Study

A Great Catch for Investigating Inborn Errors of Metabolism—Insights Obtained from Zebrafish

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