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

Targeted ultra performance liquid chromatography tandem mass spectrometry procedures for the diagnosis of inborn errors of metabolism: validation through ERNDIM external quality assessment schemes

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.

Untargeted Metabolomic Analysis of Sjögren-Larsson Syndrome Reveals a Distinctive Pattern of Multiple Disrupted Biochemical Pathways

Sjögren-Larsson syndrome (SLS) is a rare inherited neurocutaneous disease characterized by ichthyosis, spastic diplegia or tetraplegia, intellectual disability and a distinctive retinopathy. SLS is caused by bi-allelic mutations in ALDH3A2, which codes for fatty aldehyde dehydrogenase (FALDH) and results in abnormal lipid metabolism. The biochemical abnormalities in SLS are not completely known, and the pathogenic mechanisms leading to symptoms are still unclear. To search for pathways that are perturbed in SLS, we performed untargeted metabolomic screening in 20 SLS subjects along with age- and sex-matched controls. Of 823 identified metabolites in plasma, 121 (14.7%) quantitatively differed in the overall SLS cohort from controls; 77 metabolites were decreased and 44 increased. Pathway analysis pointed to disrupted metabolism of sphingolipids, sterols, bile acids, glycogen, purines and certain amino acids such as tryptophan, aspartate and phenylalanine. Random forest analysis identified a unique metabolomic profile that had a predictive accuracy of 100% for discriminating SLS from controls. These results provide new insight into the abnormal biochemical pathways that likely contribute to disease in SLS and may constitute a biomarker panel for diagnosis and future therapeutic studies.

Ex vivo proton spectroscopy (1 H-NMR) analysis of inborn errors of metabolism: Automatic and computer-assisted analyses

There are about 1500 genetic metabolic diseases. A small number of treatable diseases are diagnosed by newborn screening programs, which are continually being developed. However, most diseases can only be diagnosed based on clinical symptoms or metabolic findings. The main biological fluids used are urine, plasma and, in special situations, cerebrospinal fluid. In contrast to commonly used methods such as gas chromatography and high performance liquid chromatography mass spectrometry, ex vivo proton spectroscopy (¹ H-NMR) is not yet used in routine clinical practice, although it has been recommended for more than 30 years. Automatic analysis and improved NMR technology have also expanded the applications used for the diagnosis of inborn errors of metabolism. We provide a mini-overview of typical applications, especially in urine but also in plasma, used to diagnose common but also rare genetic metabolic diseases with ¹ H-NMR. The use of computer-assisted diagnostic suggestions can facilitate interpretation of the profiles. In a proof of principle, to date, 182 reports of 59 different diseases and 500 reports of healthy children are stored. The percentage of correct automatic diagnoses was 74%. Using the same ¹ H-NMR profile-targeted analysis, it is possible to apply an untargeted approach that distinguishes profile differences from healthy individuals. Thus, additional conditions such as lysosomal storage diseases or drug interferences are detectable. Furthermore, because ¹ H-NMR is highly reproducible and can detect a variety of different substance categories, the metabolomic approach is suitable for monitoring patient treatment and revealing additional factors such as nutrition and microbiome metabolism. Besides the progress in analytical techniques, a multiomics approach is most effective to combine metabolomics with, for example, whole exome sequencing, to also diagnose patients with nondetectable metabolic abnormalities in biological fluids. In this mini review we also provide our own data to demonstrate the role of NMR in a multiomics platform in the field of inborn errors of metabolism.

Laboratory testing for mitochondrial diseases: biomarkers for diagnosis and follow-up

The currently available biomarkers generally lack the specificity and sensitivity needed for the diagnosis and follow-up of patients with mitochondrial diseases (MDs). In this group of rare genetic disorders (mutations in approximately 350 genes associated with MDs), all clinical presentations, ages of disease onset and inheritance types are possible. Blood, urine, and cerebrospinal fluid surrogates are well-established biomarkers that are used in clinical practice to assess MD. One of the main challenges is validating specific and sensitive biomarkers for the diagnosis of disease and prediction of disease progression. Profiling of lactate, amino acids, organic acids, and acylcarnitine species is routinely conducted to assess MD patients. New biomarkers, including some proteins and circulating cell-free mitochondrial DNA, with increased diagnostic specificity have been identified in the last decade and have been proposed as potentially useful in the assessment of clinical outcomes. Despite these advances, even these new biomarkers are not sufficiently specific and sensitive to assess MD progression, and new biomarkers that indicate MD progression are urgently needed to monitor the success of novel therapeutic strategies. In this report, we review the mitochondrial biomarkers that are currently analyzed in clinical laboratories, new biomarkers, an overview of the most common laboratory diagnostic techniques, and future directions regarding targeted versus untargeted metabolomic and genomic approaches in the clinical laboratory setting. Brief descriptions of the current methodologies are also provided.

Benchmarking Outlier Detection Methods for Detecting IEM Patients in Untargeted Metabolomics Data

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.

Diagnosing, discarding, or de-VUSsing: A practical guide to (un)targeted metabolomics as variant-transcending functional tests

PURPOSE

For patients with inherited metabolic disorders (IMDs), any diagnostic delay should be avoided because early initiation of personalized treatment could prevent irreversible health damage. To improve diagnostic interpretation of genetic data, gene function tests can be valuable assets. For IMDs, variant-transcending functional tests are readily available through (un)targeted metabolomics assays. To support the application of metabolomics for this purpose, we developed a gene-based guide to select functional tests to either confirm or exclude an IMD diagnosis.

METHODS

Using information from a diagnostic IMD exome panel, Kyoto Encyclopedia of Genes and Genomes, and Inborn Errors of Metabolism Knowledgebase, we compiled a guide for metabolomics-based gene function tests. From our practical experience with this guide, we retrospectively selected illustrative cases for whom combined metabolomic/genomic testing improved diagnostic success and evaluated the effect hereof on clinical management.

RESULTS

The guide contains 2047 metabolism-associated genes for which a validated or putative variant-transcending gene function test is available. We present 16 patients for whom metabolomic testing either confirmed or ruled out the presence of a second pathogenic variant, validated or ruled out pathogenicity of variants of uncertain significance, or identified a diagnosis initially missed by genetic analysis.

CONCLUSION

Metabolomics-based gene function tests provide additional value in the diagnostic trajectory of patients with suspected IMD by enhancing and accelerating diagnostic success.

A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism

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.

Application of metabolite set enrichment analysis on untargeted metabolomics data prioritises relevant pathways and detects novel biomarkers for inherited metabolic disorders

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.

A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research

Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.