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

Haijes, Hanneke A.; Ham, Maria van der; Prinsen, Hubertus C.M.T.; Broeks, Melissa H.; Hasselt, Peter M. van; Velden, Monique G.M. de Sain–van der; Verhoeven‐Duif, Nanda M.; Jans, Judith

doi:10.3390/ijms21030979

Cited by 17 publications

(20 citation statements)

References 13 publications

(38 reference statements)

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“…The most favorable biochemical stringency of the paired Z-score threshold turned out to be <−3.0 and >3.0. Interestingly, this paired Z-score threshold is more stringent than often used for NGMS [8,11,12,14], although for most diagnostic metabolites in IEMs, Z-scores are >3.0 [8,[10][11][12][13][14]. As is true for the maximum distance to the primary reaction, the most favorable paired Z-score threshold may also vary for the different IEMs [14], as some IEMs may present with more subtle metabolic aberrations than others [8,[10][11][12][13][14].…”

Section: Discussionmentioning

confidence: 99%

“…Implementation of NGS as a first-tier diagnostic tool has greatly increased the diagnostic yield and reduced diagnostic delay, which are both important benefits for patients [16][17][18]. Although the implementation of NGMS in clinical diagnostics is in its infancy when compared to NGS, several recent studies reported its potential applicability in clinical diagnostics of IEMs for individual patients [8][9][10][11][12][13][14]. Notwithstanding its added value, NGMS is-unlike NGS-not expected to become a stand-alone diagnostic tool, as genetic confirmation of the resulting diagnosis is often required.…”

Section: Discussionmentioning

confidence: 99%

“…If metabolomics studies are performed untargeted, a wide range of metabolites can be detected using only a single test in a hypothesis-free manner. Successful use of metabolomics approaches in clinical diagnostics of IEMs, referred to as next-generation metabolic screening (NGMS), has been reported by several groups [8][9][10][11][12][13][14]. Despite the potential of these approaches, independent confirmation of NGMS findings is still warranted.…”

Section: Introductionmentioning

confidence: 99%

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Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics

et al. 2020

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“…Previously, we have implemented an ultra‐high performance liquid chromatography‐quadrupole time‐of‐flight mass spectrometry (UHPLC‐QTOF‐MS) method for fast and accurate quantitative detection of 71 metabolites, replacing GC‐MS for the analysis of organic acids 4 . This was already promising in the light of metabolomics literature 5,6 since we obtained explicit biochemical signatures for 16 of the 18 organic acidurias included in the clinical validation.…”

Section: Introductionmentioning

confidence: 99%

Targeted urine metabolomics with a graphical reporting tool for rapid diagnosis of inborn errors of metabolism

Steinbusch

Wang

Waterval

et al. 2021

J of Inher Metab Disea

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The current diagnostic work‐up of inborn errors of metabolism (IEM) is rapidly moving toward integrative analytical approaches. We aimed to develop an innovative, targeted urine metabolomics (TUM) screening procedure to accelerate the diagnosis of patients with IEM. Urinary samples, spiked with three stable isotope‐labeled internal standards, were analyzed for 258 diagnostic metabolites with an ultra‐high performance liquid chromatography‐quadrupole time‐of‐flight mass spectrometry (UHPLC‐QTOF‐MS) configuration run in positive and negative ESI modes. The software automatically annotated peaks, corrected for peak overloading, and reported peak quality and shifting. Robustness and reproducibility were satisfactory for most metabolites. Z‐scores were calculated against four age‐group‐matched control cohorts. Disease phenotypes were scored based on database metabolite matching. Graphical reports comprised a needle plot, annotating abnormal metabolites, and a heatmap showing the prioritized disease phenotypes. In the clinical validation, we analyzed samples of 289 patients covering 78 OMIM phenotypes from 12 of the 15 society for the study of inborn errors of metabolism (SSIEM) disease groups. The disease groups include disorders in the metabolism of amino acids, fatty acids, ketones, purines and pyrimidines, carbohydrates, porphyrias, neurotransmitters, vitamins, cofactors, and creatine. The reporting tool easily and correctly diagnosed most samples. Even subtle aberrant metabolite patterns as seen in mild multiple acyl‐CoA dehydrogenase deficiency (GAII) and maple syrup urine disease (MSUD) were correctly called without difficulty. Others, like creatine transporter deficiency, are illustrative of IEM that remain difficult to diagnose. We present TUM as a powerful diagnostic screening tool that merges most urinary diagnostic assays expediting the diagnostics for patients suspected of an IEM.

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“…inborn errors of metabolism (IEM) (Kerkhofs, et al, 2020) (Alaimo, et al, 2020) (Linck, et al, 2020). Some strategies have already been developed for this purpose; Haijes et al applied expert knowledge to develop an algorithm that matches metabolic signatures obtained from metabolomics with expected metabolic signatures caused by each IEM, thereby ranking potential enzymatic deficiencies (Haijes, et al, 2020). Similarly, Baumgartner et al explored the use of classification algorithms to distinguish multiple IEM based on differences in metabolite levels (Baumgartner, et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Prioritizing disease-causing metabolic genes by integrating metabolomics with whole exome sequencing data

Bongaerts

Bonte

Demirdas

et al. 2021

Preprint

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The integration of metabolomics data with sequencing data is a key step towards improving the diagnostic process for finding the disease-causing gene(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 metabolic genes. We present a computational approach, called Reafect, that calculates for each reaction in a metabolic pathway a score indicating whether that reaction is being 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 80% of the cases the correct deficient enzyme was ranked within the top 5% of all considered enzyme deficiencies. Next, we integrated Reafect with CADD scores (a measure for variant deleteriousness) and ranked the potential disease-causing genes of 27 IEM patients. We observed that this integrated approach significantly improved the prioritization of the disease-causing genes when compared with the two approaches individually. For 15/27 IEM patients the correct disease-causing gene was ranked within the top 0.2% of the set of potential disease-causing genes. Together, our findings suggest that metabolomics data improves the identification of disease-causing genetic variants in patients suffering from IEM.

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

Cited by 17 publications

References 13 publications

Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics

Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics

Targeted urine metabolomics with a graphical reporting tool for rapid diagnosis of inborn errors of metabolism

Prioritizing disease-causing metabolic genes by integrating metabolomics with whole exome sequencing data

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