Untargeted Metabolomics-Based Screening Method for Inborn Errors of Metabolism using Semi-Automatic Sample Preparation with an UHPLC- Orbitrap-MS Platform

Routine diagnostic screening of inborn errors of metabolism (IEM) is currently performed by different targeted analyses of known biomarkers. This approach is time-consuming, targets a limited number of biomarkers and will not identify new biomarkers. Untargeted metabolomics generates a global metabolic phenotype and has the potential to overcome these issues. We describe a novel, single platform, untargeted metabolomics method for screening IEM, combining semi-automatic sample preparation with pentafluorophenylpropyl phase (PFPP)-based UHPLC- Orbitrap-MS. We evaluated analytical performance and diagnostic capability of the method by analysing plasma samples of 260 controls and 53 patients with 33 distinct IEM. Analytical reproducibility was excellent, with peak area variation coefficients below 20% for the majority of the metabolites. We illustrate that PFPP-based chromatography enhances identification of isomeric compounds. Ranked z-score plots of metabolites annotated in IEM samples were reviewed by two laboratory specialists experienced in biochemical genetics, resulting in the correct diagnosis in 90% of cases. Thus, our untargeted metabolomics platform is robust and differentiates metabolite patterns of different IEMs from those of controls. We envision that the current approach to diagnose IEM, using numerous tests, will eventually be replaced by untargeted metabolomics methods, which also have the potential to discover novel biomarkers and assist in interpretation of genetic data.

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Using Out-of-Batch Reference Populations to Improve Untargeted Metabolomics for Screening Inborn Errors of Metabolism

Metabolites ◽

10.3390/metabo11010008 ◽

2020 ◽

Vol 11 (1) ◽

pp. 8

Author(s):

Michiel Bongaerts ◽

Ramon Bonte ◽

Serwet Demirdas ◽

Edwin H. Jacobs ◽

Esmee Oussoren ◽

...

Keyword(s):

Regression Model ◽

Inborn Errors Of Metabolism ◽

Untargeted Metabolomics ◽

Detection Accuracy ◽

Inborn Errors ◽

Mixed Effect ◽

Metabolomics Data ◽

Normalization Methods ◽

Metabolite Concentrations ◽

Reference Samples

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5–37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.

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Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline

Metabolites ◽

10.3390/metabo11090568 ◽

2021 ◽

Vol 11 (9) ◽

pp. 568

Author(s):

Brechtje Hoegen ◽

Alan Zammit ◽

Albert Gerritsen ◽

Udo F. H. Engelke ◽

Steven Castelein ◽

...

Keyword(s):

Data Analysis ◽

Clinical Application ◽

Inborn Errors Of Metabolism ◽

Large Scale ◽

Clinical Diagnostics ◽

Untargeted Metabolomics ◽

Computational Pipeline ◽

Inborn Errors ◽

Metabolomics Data ◽

Metabolic Fingerprint

Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metabolomics has evolved as a high throughput methodology offering a comprehensive readout of this metabolic fingerprint. This makes it a promising tool for diagnostic screening of IEM patients. However, the size and complexity of metabolomics data have posed a challenge in translating this avalanche of information into knowledge, particularly for clinical application. We have previously established next-generation metabolic screening (NGMS) as a metabolomics-based diagnostic tool for analyzing plasma of individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we present a computational pipeline to streamline the analysis of untargeted metabolomics data. This pipeline allows for time-efficient and reproducible data analysis, compatible with ISO:15189 accredited clinical diagnostics. The pipeline implements a combination of tools embedded in a workflow environment for large-scale clinical metabolomics data analysis. The accompanying graphical user interface aids end-users from a diagnostic laboratory for efficient data interpretation and reporting. We also demonstrate the application of this pipeline with a case study and discuss future prospects.

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Syndromic Inherited Retinal Diseases: Genetic, Clinical and Diagnostic Aspects

Diagnostics ◽

10.3390/diagnostics10100779 ◽

2020 ◽

Vol 10 (10) ◽

pp. 779

Author(s):

Yasmin Tatour ◽

Tamar Ben-Yosef

Keyword(s):

Inborn Errors Of Metabolism ◽

Phenotypic Variability ◽

Genetic Diseases ◽

Correct Diagnosis ◽

Clinical Findings ◽

Retinal Diseases ◽

Inborn Errors ◽

Diagnostic Aspects ◽

The Central Nervous System ◽

High Degree

Inherited retinal diseases (IRDs), which are among the most common genetic diseases in humans, define a clinically and genetically heterogeneous group of disorders. Over 80 forms of syndromic IRDs have been described. Approximately 200 genes are associated with these syndromes. The majority of syndromic IRDs are recessively inherited and rare. Many, although not all, syndromic IRDs can be classified into one of two major disease groups: inborn errors of metabolism and ciliopathies. Besides the retina, the systems and organs most commonly involved in syndromic IRDs are the central nervous system, ophthalmic extra-retinal tissues, ear, skeleton, kidney and the cardiovascular system. Due to the high degree of phenotypic variability and phenotypic overlap found in syndromic IRDs, correct diagnosis based on phenotypic features alone may be challenging and sometimes misleading. Therefore, genetic testing has become the benchmark for the diagnosis and management of patients with these conditions, as it complements the clinical findings and facilitates an accurate clinical diagnosis and treatment.

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The inborn errors of metabolism: General aspects

Oxford Textbook of Medicine ◽

10.1093/med/9780198746690.003.0225 ◽

2020 ◽

pp. 1929-1941

Author(s):

Timothy M. Cox ◽

Richard W.E. Watts

Keyword(s):

Inborn Errors Of Metabolism ◽

Quaternary Structure ◽

Correct Diagnosis ◽

Gene Replacement ◽

Inborn Errors ◽

Irreversible Damage ◽

Presymptomatic Stage ◽

General Aspects ◽

High Level

The inborn errors of metabolism are those inherited diseases in which the phenotype includes a characteristic constellation of biochemical abnormalities related to an alteration in the catalytic activity of a single specific enzyme, activator, or transport protein. Mechanism of diseases—mutations in the proteins giving rise to the inborn errors of metabolism affect primary, secondary, tertiary, or quaternary structure. This can lead to an enormous variety of consequences. Clinical presentation—the manifestations of metabolic disease are protean and may seem nondescript, especially in adults, hence a high level of suspicion may be required to make a correct diagnosis. Prevention and screening—there is a strong case for mass population screening for some inborn errors of metabolism at the presymptomatic stage to allow early detection and introduction of proven treatment before irreversible damage occurs. Management—definitive cure of the underlying abnormality is available for a few disorders, but precise characterization of the biochemical disturbance often permits rational treatment to be organized and provides the basis for further therapeutic endeavours. General approaches include (1) restriction of a substrate that cannot be metabolized including molecules derived from the diet; (2) replacement of a missing metabolic product; (3) removal of poisonous metabolites or rebalancing overproduction of toxic intermediates; (4) administering pharmacological doses of a cofactor, sometimes a vitamin, that may also stabilize a mutant enzyme; (5) replacement of a missing gene product, usually by enzymatic augmentation therapy or pharmacological chaperones, to prevent premature aggregation and denaturation; (6) repression of an overproduced protein or metabolite by stable RNA inhibition; (7) transplantation of cells or organs as a ‘gene replacement therapy’; and (8) activation of a poorly functioning protein.

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Erratum: Ismail, I.T.; et al. Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics. Metabolites 2019, 9, 242

Metabolites ◽

10.3390/metabo10010025 ◽

2020 ◽

Vol 10 (1) ◽

pp. 25

Author(s):

Israa T. Ismail ◽

Megan R. Showalter ◽

Oliver Fiehn

Keyword(s):

Inborn Errors Of Metabolism ◽

Untargeted Metabolomics ◽

Inborn Errors

The authors wish to make the following correction to this paper [...]

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Untargeted metabolomics as an unbiased approach to the diagnosis of inborn errors of metabolism of the non-oxidative branch of the pentose phosphate pathway

Molecular Genetics and Metabolism ◽

10.1016/j.ymgme.2020.07.013 ◽

2020 ◽

Vol 131 (1-2) ◽

pp. 147-154

Author(s):

Brian J. Shayota ◽

Taraka R. Donti ◽

Jing Xiao ◽

Charul Gijavanekar ◽

Adam D. Kennedy ◽

...

Keyword(s):

Pentose Phosphate Pathway ◽

Inborn Errors Of Metabolism ◽

Pentose Phosphate ◽

Untargeted Metabolomics ◽

Inborn Errors

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Untargeted Metabolomics for Metabolic Diagnostic Screening with Automated Data Interpretation Using a Knowledge-Based Algorithm

International Journal of Molecular Sciences ◽

10.3390/ijms21030979 ◽

2020 ◽

Vol 21 (3) ◽

pp. 979 ◽

Cited By ~ 3

Author(s):

Hanneke A. Haijes ◽

Maria van der Ham ◽

Hubertus C.M.T. Prinsen ◽

Melissa H. Broeks ◽

Peter M. van Hasselt ◽

...

Keyword(s):

High Resolution Mass Spectrometry ◽

Correct Diagnosis ◽

Diagnostic Algorithm ◽

Data Interpretation ◽

Dried Blood Spots ◽

Untargeted Metabolomics ◽

Plasma Samples ◽

Inborn Errors ◽

Diagnostic Screening ◽

Knowledge Based

Untargeted metabolomics may become a standard approach to address diagnostic requests, but, at present, data interpretation is very labor-intensive. To facilitate its implementation in metabolic diagnostic screening, we developed a method for automated data interpretation that preselects the most likely inborn errors of metabolism (IEM). The input parameters of the knowledge-based algorithm were (1) weight scores assigned to 268 unique metabolites for 119 different IEM based on literature and expert opinion, and (2) metabolite Z-scores and ranks based on direct-infusion high resolution mass spectrometry. The output was a ranked list of differential diagnoses (DD) per sample. The algorithm was first optimized using a training set of 110 dried blood spots (DBS) comprising 23 different IEM and 86 plasma samples comprising 21 different IEM. Further optimization was performed using a set of 96 DBS consisting of 53 different IEM. The diagnostic value was validated in a set of 115 plasma samples, which included 58 different IEM and resulted in the correct diagnosis being included in the DD of 72% of the samples, comprising 44 different IEM. The median length of the DD was 10 IEM, and the correct diagnosis ranked first in 37% of the samples. Here, we demonstrate the accuracy of the diagnostic algorithm in preselecting the most likely IEM, based on the untargeted metabolomics of a single sample. We show, as a proof of principle, that automated data interpretation has the potential to facilitate the implementation of untargeted metabolomics for metabolic diagnostic screening, and we provide suggestions for further optimization of the algorithm to improve diagnostic accuracy.

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Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics

Metabolites ◽

10.3390/metabo9100242 ◽

2019 ◽

Vol 9 (10) ◽

pp. 242 ◽

Cited By ~ 11

Author(s):

Ismail ◽

Showalter ◽

Fiehn

Keyword(s):

Metabolic Pathways ◽

Inborn Errors Of Metabolism ◽

False Negative ◽

Untargeted Metabolomics ◽

Genetic Mutations ◽

Screening Methods ◽

Inborn Errors ◽

Screening Programs ◽

Metabolic Screening

Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics.

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Screening blood spots for inborn errors of metabolism by electrospray tandem mass spectrometry with a microplate batch process and a computer algorithm for automated flagging of abnormal profiles

Clinical Chemistry ◽

10.1093/clinchem/43.7.1129 ◽

1997 ◽

Vol 43 (7) ◽

pp. 1129-1141 ◽

Cited By ~ 192

Author(s):

Mohamed S Rashed ◽

Martin P Bucknall ◽

Douglas Little ◽

Amin Awad ◽

Minnie Jacob ◽

...

Keyword(s):

Mass Spectrometry ◽

Sample Preparation ◽

Tandem Mass Spectrometry ◽

Metabolic Profiling ◽

Inborn Errors Of Metabolism ◽

Batch Process ◽

Tandem Mass ◽

Electrospray Tandem Mass Spectrometry ◽

Inborn Errors ◽

Diagnostic Parameters

Abstract Metabolic profiling of amino acids and acylcarnitines from blood spots by automated electrospray tandem mass spectrometry (ESI-MS/MS) is a powerful diagnostic tool for inborn errors of metabolism. New approaches to sample preparation and data interpretation have helped establish the methodology as a robust, high-throughput neonatal screening method. We introduce an efficient 96-well-microplate batch process for blood-spot sample preparation, with which we can obtain high-quality profiles from 500-1000 samples per day per instrument. A computer-assisted metabolic profiling algorithm automatically flags abnormal profiles. We selected diagnostic parameters for the algorithm by comparing profiles from patients with known metabolic disorders and those from normal newborns. Reference range and cutoff values for the diagnostic parameters were established by measuring either metabolite concentrations or peak ratios of certain metabolite pairs. Rigorous testing of the algorithm demonstrates its outstanding clinical sensitivity in flagging abnormal profiles and its high cumulative specificity.

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