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

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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Screening for inborn errors of metabolism using untargeted metabolomics and out-of-batch controls

10.1101/2020.04.14.040469 ◽

2020 ◽

Author(s):

Michiel Bongaerts ◽

Ramon Bonte ◽

Serwet Demirdas ◽

Ed H. Jacobs ◽

E. Oussoren ◽

...

Keyword(s):

Regression Model ◽

Inborn Errors Of Metabolism ◽

Untargeted Metabolomics ◽

Detection Accuracy ◽

Inborn Errors ◽

Mixed Effect ◽

Metabolomics Data ◽

Normalization Methods ◽

Metabolite Concentrations ◽

Control Samples

MotivationUntargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). In order to judge if metabolite levels are abnormal, analysis of a large number of reference samples is crucial to correct for variations in metabolite concentrations resulting from factors such as diet, age and gender. However, a large number of controls requires the use of out-of-batch controls, 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.Methods & resultsBased on six metrics, we compared existing normalization methods on their ability to reduce batch effects from eight independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method which uses 17 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 control samples obtained from all eight batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal as well as in the detection of 178 known biomarkers across 45 IEM patient samples and performed at least similar to an approach using 15 within-batch controls. Furthermore, our regression model indicates that 10-24% of the considered features showed significant age-dependent variations.ConclusionsOur comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch controls to establish clinically-relevant reference values for metabolite concentrations. These findings opens possibilities to use large scale out-of-batch control samples in a clinical setting, increasing throughput and detection accuracy.AvailabilityMetchalizer is available at https://github.com/mbongaerts/Metchalizer/

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

Metabolites ◽

10.3390/metabo9120289 ◽

2019 ◽

Vol 9 (12) ◽

pp. 289 ◽

Cited By ~ 5

Author(s):

Ramon Bonte ◽

Michiel Bongaerts ◽

Serwet Demirdas ◽

Janneke G. Langendonk ◽

Hidde H. Huidekoper ◽

...

Keyword(s):

Sample Preparation ◽

Inborn Errors Of Metabolism ◽

Correct Diagnosis ◽

Screening Method ◽

Current Approach ◽

Untargeted Metabolomics ◽

Metabolic Phenotype ◽

Inborn Errors ◽

New Biomarkers ◽

Orbitrap Ms

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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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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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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