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Bongaerts M, Bonte R, Demirdas S, Huidekoper HH, Langendonk J, Wilke M, de Valk W, Blom HJ, Reinders MJT, Ruijter GJG. Integration of metabolomics with genomics: Metabolic gene prioritization using metabolomics data and genomic variant (CADD) scores. Mol Genet Metab 2022; 136:199-218. [PMID: 35660124 DOI: 10.1016/j.ymgme.2022.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 04/06/2022] [Accepted: 05/17/2022] [Indexed: 11/30/2022]
Abstract
The integration of metabolomics data with sequencing data is a key step towards improving the diagnostic process for finding the disease-causing genetic variant(s) in patients suspected of having an inborn error of metabolism (IEM). The measured metabolite levels could provide additional phenotypical evidence to elucidate the degree of pathogenicity for variants found in genes associated with metabolic processes. We present a computational approach, called Reafect, that calculates for each reaction in a metabolic pathway a score indicating whether that reaction is deficient or not. When calculating this score, Reafect takes multiple factors into account: the magnitude and sign of alterations in the metabolite levels, the reaction distances between metabolites and reactions in the pathway, and the biochemical directionality of the reactions. We applied Reafect to untargeted metabolomics data of 72 patient samples with a known IEM and found that in 81% of the cases the correct deficient enzyme was ranked within the top 5% of all considered enzyme deficiencies. Next, we integrated Reafect with Combined Annotation Dependent Depletion (CADD) scores (a measure for gene variant deleteriousness) and ranked the metabolic genes of 27 IEM patients. We observed that this integrated approach significantly improved the prioritization of the genes containing the disease-causing variant when compared with the two approaches individually. For 15/27 IEM patients the correct affected gene was ranked within the top 0.25% of the set of potentially affected genes. Together, our findings suggest that metabolomics data improves the identification of affected genes in patients suffering from IEM.
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Affiliation(s)
- Michiel Bongaerts
- Department of Clinical Genetics, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015, GD, Rotterdam, the Netherlands.
| | - Ramon Bonte
- Department of Clinical Genetics, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015, GD, Rotterdam, the Netherlands
| | - Serwet Demirdas
- Department of Clinical Genetics, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015, GD, Rotterdam, the Netherlands
| | - Hidde H Huidekoper
- Department of Pediatrics, Center for Lysosomal and Metabolic Diseases, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015, GD, Rotterdam, the Netherlands
| | - Janneke Langendonk
- Department of Internal Medicine, Center for Lysosomal and Metabolic Diseases, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015, GD, Rotterdam, the Netherlands
| | - Martina Wilke
- Department of Clinical Genetics, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015, GD, Rotterdam, the Netherlands
| | - Walter de Valk
- Department of Clinical Genetics, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015, GD, Rotterdam, the Netherlands
| | - Henk J Blom
- Department of Clinical Genetics, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015, GD, Rotterdam, the Netherlands
| | - Marcel J T Reinders
- Faculty of Electrical Engineering, Mathematics and Computer Science, TU Delft, Van Mourik Broekmanweg 6, 2628, XE, Delft, the Netherlands
| | - George J G Ruijter
- Department of Clinical Genetics, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015, GD, Rotterdam, the Netherlands.
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Ford L, Mitchell M, Wulff J, Evans A, Kennedy A, Elsea S, Wittmann B, Toal D. Clinical metabolomics for inborn errors of metabolism. Adv Clin Chem 2022; 107:79-138. [PMID: 35337606 DOI: 10.1016/bs.acc.2021.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Metabolism is a highly regulated process that provides nutrients to cells and essential building blocks for the synthesis of protein, DNA and other macromolecules. In healthy biological systems, metabolism maintains a steady state in which the concentrations of metabolites are relatively constant yet are subject to metabolic demands and environmental stimuli. Rare genetic disorders, such as inborn errors of metabolism (IEM), cause defects in regulatory enzymes or proteins leading to metabolic pathway disruption and metabolite accumulation or deficiency. Traditionally, the laboratory diagnosis of IEMs has been limited to analytical methods that target specific metabolites such as amino acids and acyl carnitines. This approach is effective as a screening method for the most common IEM disorders but lacks the comprehensive coverage of metabolites that is necessary to identify rare disorders that present with nonspecific clinical symptoms. Fortunately, advancements in technology and data analytics has introduced a new field of study called metabolomics which has allowed scientists to perform comprehensive metabolite profiling of biological systems to provide insight into mechanism of action and gene function. Since metabolomics seeks to measure all small molecule metabolites in a biological specimen, it provides an innovative approach to evaluating disease in patients with rare genetic disorders. In this review we provide insight into the appropriate application of metabolomics in clinical settings. We discuss the advantages and limitations of the method and provide details related to the technology, data analytics and statistical modeling required for metabolomic profiling of patients with IEMs.
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Affiliation(s)
- Lisa Ford
- Metabolon, Inc., Morrisville, NC, United States
| | | | - Jacob Wulff
- Metabolon, Inc., Morrisville, NC, United States
| | - Annie Evans
- Metabolon, Inc., Morrisville, NC, United States
| | | | - Sarah Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | | | - Douglas Toal
- Metabolon, Inc., Morrisville, NC, United States.
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