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

Metabolites ◽  
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.

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

Metabolites ◽  
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.

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

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/

scholarly journals Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics

Metabolites ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 206 ◽  
Author(s):  
Marten H. P. M. Kerkhofs ◽  
Hanneke A. Haijes ◽  
A. Marcel Willemsen ◽  
Koen L. I. van Gassen ◽  
Maria van der Ham ◽  
...  
Keyword(s):  
Diagnostic Yield ◽  
Dried Blood Spots ◽  
Clinical Diagnostics ◽  
Untargeted Metabolomics ◽  
Next Generation ◽  
Primary Reaction ◽  
Inborn Errors ◽  
Whole Exome ◽  
Z Scores ◽  
Metabolic Screening

Next-generation sequencing and next-generation metabolic screening are, independently, increasingly applied in clinical diagnostics of inborn errors of metabolism (IEM). Integrated into a single bioinformatic method, these two –omics technologies can potentially further improve the diagnostic yield for IEM. Here, we present cross-omics: a method that uses untargeted metabolomics results of patient’s dried blood spots (DBSs), indicated by Z-scores and mapped onto human metabolic pathways, to prioritize potentially affected genes. We demonstrate the optimization of three parameters: (1) maximum distance to the primary reaction of the affected protein, (2) an extension stringency threshold reflecting in how many reactions a metabolite can participate, to be able to extend the metabolite set associated with a certain gene, and (3) a biochemical stringency threshold reflecting paired Z-score thresholds for untargeted metabolomics results. Patients with known IEMs were included. We performed untargeted metabolomics on 168 DBSs of 97 patients with 46 different disease-causing genes, and we simulated their whole-exome sequencing results in silico. We showed that for accurate prioritization of disease-causing genes in IEM, it is essential to take into account not only the primary reaction of the affected protein but a larger network of potentially affected metabolites, multiple steps away from the primary reaction.

scholarly journals Mass spectral similarity for untargeted metabolomics data analysis of complex mixtures

2015 ◽  
Vol 377 ◽  
pp. 719-727 ◽  
Author(s):  
Neha Garg ◽  
Clifford A. Kapono ◽  
Yan Wei Lim ◽  
Nobuhiro Koyama ◽  
Mark J.A. Vermeij ◽  
...  
Keyword(s):  
Data Analysis ◽  
Complex Mixtures ◽  
Untargeted Metabolomics ◽  
Metabolomics Data ◽  
Spectral Similarity ◽  
Mass Spectral

scholarly journals Inborn Errors of Metabolism Collaborative: large-scale collection of data on long-term follow-up for newborn-screened conditions

2016 ◽  
Vol 18 (12) ◽  
pp. 1276-1281 ◽  
Author(s):  
Susan A. Berry ◽  
◽  
Nancy D. Leslie ◽  
Mathew J. Edick ◽  
Sally Hiner ◽  
...  
Keyword(s):  
Inborn Errors Of Metabolism ◽  
Large Scale ◽  
Inborn Errors ◽  
Long Term Follow Up

scholarly journals Using Expert Driven Machine Learning to Enhance Dynamic Metabolomics Data Analysis

Metabolites ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 54 ◽  
Author(s):  
Charlie Beirnaert ◽  
Laura Peeters ◽  
Pieter Meysman ◽  
Wout Bittremieux ◽  
Kenn Foubert ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Time Series Data ◽  
Expert Knowledge ◽  
Ground Truth ◽  
Simulated Dataset ◽  
Untargeted Metabolomics ◽  
Series Data ◽  
Metabolomics Data ◽  
Shiny App

Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth. This simulated dataset was used to evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning. The results were compared to EDGE, a statistical method for time series data. This paper presents three novel outcomes. The first is a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs. control metabolomics data. Third, the tinderesting method is introduced to analyse more complex dynamic metabolomics experiments. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.

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

Metabolites ◽  
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 [...]

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

Metabolites ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 289 ◽  
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.

scholarly journals PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud

2018 ◽  
Author(s):  
Kristian Peters ◽  
James Bradbury ◽  
Sven Bergmann ◽  
Marco Capuccini ◽  
Marta Cascante ◽  
...  
Keyword(s):  
Data Analysis ◽  
User Interfaces ◽  
Large Scale ◽  
Open Data ◽  
Research Field ◽  
Metabolome Analysis ◽  
Data Repositories ◽  
Metabolomics Data ◽  
Web Interfaces ◽  
Data Formats

AbstractBackgroundMetabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism’s metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological and many other applied biological domains. Its computationally-intensive nature has driven requirements for open data formats, data repositories and data analysis tools. However, the rapid progress has resulted in a mosaic of independent – and sometimes incompatible – analysis methods that are difficult to connect into a useful and complete data analysis solution.FindingsThe PhenoMeNal (Phenome and Metabolome aNalysis) e-infrastructure provides a complete, workflow-oriented, interoperable metabolomics data analysis solution for a modern infrastructure-as-a-service (IaaS) cloud platform. PhenoMeNal seamlessly integrates a wide array of existing open source tools which are tested and packaged as Docker containers through the project’s continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi and Pachyderm.ConclusionsPhenoMeNal constitutes a keystone solution in cloud infrastructures available for metabolomics. It provides scientists with a ready-to-use, workflow-driven, reproducible and shareable data analysis platform harmonizing the software installation and configuration through user-friendly web interfaces. The deployed cloud environments can be dynamically scaled to enable large-scale analyses which are interfaced through standard data formats, versioned, and have been tested for reproducibility and interoperability. The flexible implementation of PhenoMeNal allows easy adaptation of the infrastructure to other application areas and ‘omics research domains.

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