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Oliva C, Arias A, Ruiz-Sala P, Garcia-Villoria J, Carling R, Bierau J, Ruijter GJG, Casado M, Ormazabal A, Artuch R. Targeted ultra performance liquid chromatography tandem mass spectrometry procedures for the diagnosis of inborn errors of metabolism: validation through ERNDIM external quality assessment schemes. Clin Chem Lab Med 2024; 0:cclm-2023-1291. [PMID: 38456798 DOI: 10.1515/cclm-2023-1291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 02/22/2024] [Indexed: 03/09/2024]
Abstract
OBJECTIVES Early diagnosis of inborn errors of metabolism (IEM) is crucial to ensure early detection of conditions which are treatable. This study reports on targeted metabolomic procedures for the diagnosis of IEM of amino acids, acylcarnitines, creatine/guanidinoacetate, purines/pyrimidines and oligosaccharides, and describes its validation through external quality assessment schemes (EQA). METHODS Analysis was performed on a Waters ACQUITY UPLC H-class system coupled to a Waters Xevo triple-quadrupole (TQD) mass spectrometer, operating in both positive and negative electrospray ionization mode. Chromatographic separation was performed on a CORTECS C18 column (2.1 × 150, 1.6 µm). Data were collected by multiple reaction monitoring. RESULTS The internal and EQA results were generally adequate, with a few exceptions. We calculated the relative measurement error (RME) and only a few metabolites displayed a RME higher than 30 % (asparagine and some acylcarnitine species). For oligosaccharides, semi-quantitative analysis of an educational panel clearly identified the 8 different diseases included. CONCLUSIONS Overall, we have validated our analytical system through an external quality control assessment. This validation will contribute to harmonization between laboratories, thus improving identification and management of patients with IEM.
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Affiliation(s)
- Clara Oliva
- Biochemistry and Molecular Genetics Department, 571524 Hospital Clínic de Barcelona , Barcelona, Spain
| | - Angela Arias
- Clinical Biochemistry Department, 16512 Institut de Recerca Sant Joan de Déu , Barcelona, Spain
| | - Pedro Ruiz-Sala
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- Centro de Diagnóstico de Enfermedades Moleculares, Universidad Autónoma de Madrid, IdIPAZ, Madrid, Spain
| | - Judit Garcia-Villoria
- Biochemistry and Molecular Genetics Department, 571524 Hospital Clínic de Barcelona , Barcelona, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Rachel Carling
- Department of Biochemical Sciences, 8945 Synnovis, Guy's & St Thomas' NHSFT , London, UK
| | - Jörgen Bierau
- Department of Clinical Genetics, 570888 Maastricht University Medical Center , Maastricht, The Netherlands
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - George J G Ruijter
- Center for Lysosomal and Metabolic Diseases, Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Mercedes Casado
- Clinical Biochemistry Department, 16512 Institut de Recerca Sant Joan de Déu , Barcelona, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Aida Ormazabal
- Clinical Biochemistry Department, 16512 Institut de Recerca Sant Joan de Déu , Barcelona, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Rafael Artuch
- Clinical Biochemistry Department, 16512 Institut de Recerca Sant Joan de Déu , Barcelona, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
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Moritz L, Schumann A, Pohl M, Köttgen A, Hannibal L, Spiekerkoetter U. A systematic review of metabolomic findings in adult and pediatric renal disease. Clin Biochem 2024; 123:110703. [PMID: 38097032 DOI: 10.1016/j.clinbiochem.2023.110703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 12/03/2023] [Accepted: 12/07/2023] [Indexed: 12/29/2023]
Abstract
Chronic kidney disease (CKD) affects over 0.5 billion people worldwide across their lifetimes. Despite a growingly ageing world population, an increase in all-age prevalence of kidney disease persists. Adult-onset forms of kidney disease often result from lifestyle-modifiable metabolic illnesses such as type 2 diabetes. Pediatric and adolescent forms of renal disease are primarily caused by morphological abnormalities of the kidney, as well as immunological, infectious and inherited metabolic disorders. Alterations in energy metabolism are observed in CKD of varying causes, albeit the molecular mechanisms underlying pathology are unclear. A systematic indexing of metabolites identified in plasma and urine of patients with kidney disease alongside disease enrichment analysis uncovered inborn errors of metabolism as a framework that links features of adult and pediatric kidney disease. The relationship of genetics and metabolism in kidney disease could be classified into three distinct landscapes: (i) Normal genotypes that develop renal damage because of lifestyle and / or comorbidities; (ii) Heterozygous genetic variants and polymorphisms that result in unique metabotypes that may predispose to the development of kidney disease via synergistic heterozygosity, and (iii) Homozygous genetic variants that cause renal impairment by perturbing metabolism, as found in children with monogenic inborn errors of metabolism. Interest in the identification of early biomarkers of onset and progression of CKD has grown steadily in the last years, though it has not translated into clinical routine yet. This systematic review indexes findings of differential concentration of metabolites and energy pathway dysregulation in kidney disease and appraises their potential use as biomarkers.
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Affiliation(s)
- Lennart Moritz
- Laboratory of Clinical Biochemistry and Metabolism, Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany; Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Anke Schumann
- Laboratory of Clinical Biochemistry and Metabolism, Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany; Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Martin Pohl
- Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany
| | - Luciana Hannibal
- Laboratory of Clinical Biochemistry and Metabolism, Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany.
| | - Ute Spiekerkoetter
- Department of General Pediatrics, Adolescent Medicine and Neonatology, Faculty of Medicine, Medical Center, University of Freiburg, 79106 Freiburg, Germany.
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Jeppesen MJ, Powers R. Multiplatform untargeted metabolomics. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:628-653. [PMID: 37005774 PMCID: PMC10948111 DOI: 10.1002/mrc.5350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 06/19/2023]
Abstract
Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.
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Affiliation(s)
- Micah J. Jeppesen
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
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4
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de Bruyn DP, Bongaerts M, Bonte R, Vaarwater J, Meester-Smoor MA, Verdijk RM, Paridaens D, Naus NC, de Klein A, Ruijter GJG, Kiliç E, Brosens E. Uveal Melanoma Patients Have a Distinct Metabolic Phenotype in Peripheral Blood. Int J Mol Sci 2023; 24:ijms24065077. [PMID: 36982149 PMCID: PMC10049075 DOI: 10.3390/ijms24065077] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/01/2023] [Accepted: 03/04/2023] [Indexed: 03/09/2023] Open
Abstract
Uveal melanomas (UM) are detected earlier. Consequently, tumors are smaller, allowing for novel eye-preserving treatments. This reduces tumor tissue available for genomic profiling. Additionally, these small tumors can be hard to differentiate from nevi, creating the need for minimally invasive detection and prognostication. Metabolites show promise as minimally invasive detection by resembling the biological phenotype. In this pilot study, we determined metabolite patterns in the peripheral blood of UM patients (n = 113) and controls (n = 46) using untargeted metabolomics. Using a random forest classifier (RFC) and leave-one-out cross-validation, we confirmed discriminatory metabolite patterns in UM patients compared to controls with an area under the curve of the receiver operating characteristic of 0.99 in both positive and negative ion modes. The RFC and leave-one-out cross-validation did not reveal discriminatory metabolite patterns in high-risk versus low-risk of metastasizing in UM patients. Ten-time repeated analyses of the RFC and LOOCV using 50% randomly distributed samples showed similar results for UM patients versus controls and prognostic groups. Pathway analysis using annotated metabolites indicated dysregulation of several processes associated with malignancies. Consequently, minimally invasive metabolomics could potentially allow for screening as it distinguishes metabolite patterns that are putatively associated with oncogenic processes in the peripheral blood plasma of UM patients from controls at the time of diagnosis.
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Affiliation(s)
- Daniël P. de Bruyn
- Department of Ophthalmology, Erasmus MC, 3000 CA Rotterdam, The Netherlands
- Department of Clinical Genetics, Erasmus MC, 3000 CA Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, 3000 CA Rotterdam, The Netherlands
| | - Michiel Bongaerts
- Department of Clinical Genetics, Erasmus MC, 3000 CA Rotterdam, The Netherlands
| | - Ramon Bonte
- Department of Clinical Genetics, Erasmus MC, 3000 CA Rotterdam, The Netherlands
| | - Jolanda Vaarwater
- Department of Ophthalmology, Erasmus MC, 3000 CA Rotterdam, The Netherlands
| | | | - Robert M. Verdijk
- The Rotterdam Eye Hospital, 3011 BH Rotterdam, The Netherlands
- Department of Pathology, Section Ophthalmic Pathology, Erasmus MC, 3000 CA Rotterdam, The Netherlands
- Department of Pathology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Dion Paridaens
- Department of Ophthalmology, Erasmus MC, 3000 CA Rotterdam, The Netherlands
- The Rotterdam Eye Hospital, 3011 BH Rotterdam, The Netherlands
| | - Nicole C. Naus
- Department of Ophthalmology, Erasmus MC, 3000 CA Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, 3000 CA Rotterdam, The Netherlands
| | - Annelies de Klein
- Department of Clinical Genetics, Erasmus MC, 3000 CA Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, 3000 CA Rotterdam, The Netherlands
| | | | - Emine Kiliç
- Department of Ophthalmology, Erasmus MC, 3000 CA Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, 3000 CA Rotterdam, The Netherlands
| | - Erwin Brosens
- Department of Clinical Genetics, Erasmus MC, 3000 CA Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, 3000 CA Rotterdam, The Netherlands
- Correspondence:
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Hagemeijer MC, van den Bosch JC, Bongaerts M, Jacobs EH, van den Hout JMP, Oussoren E, Ruijter GJG. Analysis of urinary oligosaccharide excretion patterns by UHPLC/HRAM mass spectrometry for screening of lysosomal storage disorders. J Inherit Metab Dis 2023; 46:206-219. [PMID: 36752951 DOI: 10.1002/jimd.12597] [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: 11/16/2022] [Revised: 01/20/2023] [Accepted: 02/06/2023] [Indexed: 02/09/2023]
Abstract
Oligosaccharidoses, sphingolipidoses and mucolipidoses are lysosomal storage disorders (LSDs) in which defective breakdown of glycan-side chains of glycosylated proteins and glycolipids leads to the accumulation of incompletely degraded oligosaccharides within lysosomes. In metabolic laboratories, these disorders are commonly diagnosed by thin-layer chromatography (TLC) but more recently also mass spectrometry-based approaches have been published. To expand the possibilities to screen for these diseases, we developed an ultra-high-performance liquid chromatography (UHPLC) with a high-resolution accurate mass (HRAM) mass spectrometry (MS) screening platform, together with an open-source iterative bioinformatics pipeline. This pipeline generates comprehensive biomarker profiles and allows for extensive quality control (QC) monitoring. Using this platform, we were able to identify α-mannosidosis, β-mannosidosis, α-N-acetylgalactosaminidase deficiency, sialidosis, galactosialidosis, fucosidosis, aspartylglucosaminuria, GM1 gangliosidosis, GM2 gangliosidosis (M. Sandhoff) and mucolipidosis II/III in patient samples. Aberrant urinary oligosaccharide excretions were also detected for other disorders, including NGLY1 congenital disorder of deglycosylation, sialic acid storage disease, MPS type IV B and GSD II (Pompe disease). For the latter disorder, we identified heptahexose (Hex7), as a potential urinary biomarker, in addition to glucose tetrasaccharide (Glc4), for the diagnosis and monitoring of young onset cases of Pompe disease. Occasionally, so-called "neonate" biomarker profiles were observed in young patients, which were probably due to nutrition. Our UHPLC/HRAM-MS screening platform can easily be adopted in biochemical laboratories and allows for simple and robust screening and straightforward interpretation of the screening results to detect disorders in which aberrant oligosaccharides accumulate.
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Affiliation(s)
- Marne C Hagemeijer
- Center for Lysosomal and Metabolic Diseases, Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Jeroen C van den Bosch
- Center for Lysosomal and Metabolic Diseases, Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Michiel Bongaerts
- Center for Lysosomal and Metabolic Diseases, Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Edwin H Jacobs
- Center for Lysosomal and Metabolic Diseases, Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Johanna M P van den Hout
- Center for Lysosomal and Metabolic Diseases, Department of Pediatrics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Esmee Oussoren
- Center for Lysosomal and Metabolic Diseases, Department of Pediatrics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - George J G Ruijter
- Center for Lysosomal and Metabolic Diseases, Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
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Benchmarking Outlier Detection Methods for Detecting IEM Patients in Untargeted Metabolomics Data. Metabolites 2023; 13:metabo13010097. [PMID: 36677022 PMCID: PMC9863797 DOI: 10.3390/metabo13010097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/19/2022] [Accepted: 01/03/2023] [Indexed: 01/11/2023] Open
Abstract
Untargeted metabolomics (UM) is increasingly being deployed as a strategy for screening patients that are suspected of having an inborn error of metabolism (IEM). In this study, we examined the potential of existing outlier detection methods to detect IEM patient profiles. We benchmarked 30 different outlier detection methods when applied to three untargeted metabolomics datasets. Our results show great differences in IEM detection performances across the various methods. The methods DeepSVDD and R-graph performed most consistently across the three metabolomics datasets. For datasets with a more balanced number of samples-to-features ratio, we found that AE reconstruction error, Mahalanobis and PCA reconstruction error also performed well. Furthermore, we demonstrated the importance of a PCA transform prior to applying an outlier detection method since we observed that this increases the performance of several outlier detection methods. For only one of the three metabolomics datasets, we observed clinically satisfying performances for some outlier detection methods, where we were able to detect 90% of the IEM patient samples while detecting no false positives. These results suggest that outlier detection methods have the potential to aid the clinical investigator in routine screening for IEM using untargeted metabolomics data, but also show that further improvements are needed to ensure clinically satisfying performances.
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7
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Hertzog A, Selvanathan A, Devanapalli B, Ho G, Bhattacharya K, Tolun AA. A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism. Transl Pediatr 2022; 11:1704-1716. [PMID: 36345452 PMCID: PMC9636448 DOI: 10.21037/tp-22-105] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 08/23/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND OBJECTIVE Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs. METHODS Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique. KEY CONTENT AND FINDINGS Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible. CONCLUSIONS When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.
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Affiliation(s)
- Ashley Hertzog
- NSW Biochemical Genetics Service, The Children's Hospital at Westmead, Westmead, NSW, Australia
| | - Arthavan Selvanathan
- Genetic Metabolic Disorders Service, The Children's Hospital at Westmead, Westmead, NSW, Australia
| | - Beena Devanapalli
- NSW Biochemical Genetics Service, The Children's Hospital at Westmead, Westmead, NSW, Australia
| | - Gladys Ho
- Sydney Genome Diagnostics, The Children's Hospital at Westmead, Westmead, NSW, Australia.,Specialty of Genomic Medicine, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Kaustuv Bhattacharya
- Genetic Metabolic Disorders Service, The Children's Hospital at Westmead, Westmead, NSW, Australia.,Specialty of Genomic Medicine, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Adviye Ayper Tolun
- NSW Biochemical Genetics Service, The Children's Hospital at Westmead, Westmead, NSW, Australia.,Specialty of Genomic Medicine, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
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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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Hoegen B, Hampstead JE, Engelke UF, Kulkarni P, Wevers RA, Brunner HG, Coene KLM, Gilissen C. Application of metabolite set enrichment analysis on untargeted metabolomics data prioritises relevant pathways and detects novel biomarkers for inherited metabolic disorders. J Inherit Metab Dis 2022; 45:682-695. [PMID: 35546254 PMCID: PMC9544878 DOI: 10.1002/jimd.12522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 11/19/2022]
Abstract
Untargeted metabolomics (UM) allows for the simultaneous measurement of hundreds of metabolites in a single analytical run. The sheer amount of data generated in UM hampers its use in patient diagnostics because manual interpretation of all features is not feasible. Here, we describe the application of a pathway-based metabolite set enrichment analysis method to prioritise relevant biological pathways in UM data. We validate our method on a set of 55 patients with a diagnosed inherited metabolic disorder (IMD) and show that it complements feature-based prioritisation of biomarkers by placing the features in a biological context. In addition, we find that by taking enriched pathways shared across different IMDs, we can identify common drugs and compounds that could otherwise obscure genuine disease biomarkers in an enrichment method. Finally, we demonstrate the potential of this method to identify novel candidate biomarkers for known IMDs. Our results show the added value of pathway-based interpretation of UM data in IMD diagnostics context.
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Affiliation(s)
- Brechtje Hoegen
- Department of Human Genetics, Radboud Institute for Molecular Life SciencesRadboud University Medical CenterNijmegenThe Netherlands
| | - Juliet E. Hampstead
- Department of Human Genetics, Radboud Institute for Molecular Life SciencesRadboud University Medical CenterNijmegenThe Netherlands
| | - Udo F.H. Engelke
- Department of Laboratory Medicine, Translational Metabolic Laboratory (TML)Radboud University Medical CenterNijmegenThe Netherlands
| | - Purva Kulkarni
- Department of Laboratory Medicine, Translational Metabolic Laboratory (TML)Radboud University Medical CenterNijmegenThe Netherlands
| | - Ron A. Wevers
- Department of Laboratory Medicine, Translational Metabolic Laboratory (TML)Radboud University Medical CenterNijmegenThe Netherlands
| | - Han G. Brunner
- Department of Human Genetics, Radboud Institute for Molecular Life SciencesRadboud University Medical CenterNijmegenThe Netherlands
- Department of Clinical Genetics, Maastricht University Medical Center, GROW School of Oncology and Development, MHENS School of NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Karlien L. M. Coene
- Department of Laboratory Medicine, Translational Metabolic Laboratory (TML)Radboud University Medical CenterNijmegenThe Netherlands
| | - Christian Gilissen
- Department of Human Genetics, Radboud Institute for Molecular Life SciencesRadboud University Medical CenterNijmegenThe Netherlands
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