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Untargeted Metabolomics in Newborn Screening

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Clinical Metabolomics Applications in Genetic Diseases
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Abstract

Since its inception six decades ago, newborn screening has been lauded as a highly successful and cost-effective public health program by identifying disorders at the presymptomatic stage, enabling early disease-modifying intervention that otherwise invariably leads to death or permanent damage if treated at the symptomatic stage. The advent of multiplex high-throughput assays involving chromatography coupled with mass spectroscopy enabled the analysis of multiple disorders in a single run, vastly increasing the repertoire of screened disorders while keeping the cost nearly the same. Industrialized countries provide unified screening for more than 50 conditions, compared to about a dozen, a mere decade ago. Inevitably, we now screen, in essence, more than we know how to treat. Nonetheless, as a constant flow of new therapies breaks ground, providing accurate diagnostic data is vital for patient outcomes. Breaking the diagnostic barrier can mean new research, new drugs, and ultimately increased survival. In this chapter, we overview the concept of untargeted metabolomics as applied to newborn screening, how it fares compared to the well-standardized tests of the targeted screening, and its ability to screen for more disorders that are currently “unscreenable.”

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References

  1. Saudubray JM, Baumgartner MR, Walter JH. Inborn metabolic diseases: diagnosis and treatment. Berlin, Heidelberg: Springer; 2016.

    Book  Google Scholar 

  2. Lee B, Scaglia F. Inborn errors of metabolism : from neonatal screening to metabolic pathways. New York, NY: Oxford University Press; 2015.

    Google Scholar 

  3. Wright Muelas M, Roberts I, Mughal F, O'Hagan S, Day PJ, Kell DB. An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines. Metabolomics. 2020;16(10):107.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Coene KLM, Kluijtmans LAJ, van der Heeft E, Engelke UFH, de Boer S, Hoegen B, et al. Next-generation metabolic screening: targeted and untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients. J Inherit Metab Dis. 2018;41(3):337–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bonte R, Bongaerts M, Demirdas S, Langendonk JG, Huidekoper HH, Williams M, et al. Untargeted metabolomics-based screening method for inborn errors of metabolism using semi-automatic sample preparation with an UHPLC-Orbitrap-MS Platform. Metabolites. 2019;9(12):289.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Miller MJ, Kennedy AD, Eckhart AD, Burrage LC, Wulff JE, Miller LA, et al. Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism. J Inherit Metab Dis. 2015;38(6):1029–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Haijes HA, Willemsen M, Van der Ham M, Gerrits J, Pras-Raves ML, Prinsen H, et al. Direct infusion based metabolomics identifies metabolic disease in patients’ dried blood spots and plasma. Metabolites. 2019;9(1):12.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Körver-Keularts I, Wang P, Waterval H, Kluijtmans LAJ, Wevers RA, Langhans CD, et al. Fast and accurate quantitative organic acid analysis with LC-QTOF/MS facilitates screening of patients for inborn errors of metabolism. J Inherit Metab Dis. 2018;41(3):415–24.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kennedy AD, Miller MJ, Beebe K, Wulff JE, Evans AM, Miller LA, et al. Metabolomic profiling of human urine as a screen for multiple inborn errors of metabolism. Genet Test Mol Biomarkers. 2016;20(9):485–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Jakubowski H, Boers GH, Strauss KA. Mutations in cystathionine beta-synthase or methylenetetrahydrofolate reductase gene increase N-homocysteinylated protein levels in humans. FASEB J. 2008;22(12):4071–6.

    Article  CAS  PubMed  Google Scholar 

  11. Liu N, Xiao J, Gijavanekar C, Pappan KL, Glinton KE, Shayota BJ, et al. Comparison of untargeted metabolomic profiling vs traditional metabolic screening to identify inborn errors of metabolism. JAMA Netw Open. 2021;4(7):e2114155.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Almontashiri NAM, Zha L, Young K, Law T, Kellogg MD, Bodamer OA, et al. Clinical validation of targeted and untargeted metabolomics testing for genetic disorders: a 3 year comparative study. Sci Rep. 2020;10(1):9382.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Mills PB, Footitt EJ, Ceyhan S, Waters PJ, Jakobs C, Clayton PT, et al. Urinary AASA excretion is elevated in patients with molybdenum cofactor deficiency and isolated sulphite oxidase deficiency. J Inherit Metab Dis. 2012;35(6):1031–6.

    Article  CAS  PubMed  Google Scholar 

  14. Schwahn BC, Van Spronsen FJ, Belaidi AA, Bowhay S, Christodoulou J, Derks TG, et al. Efficacy and safety of cyclic pyranopterin monophosphate substitution in severe molybdenum cofactor deficiency type a: a prospective cohort study. Lancet. 2015;386(10007):1955–63.

    Article  CAS  PubMed  Google Scholar 

  15. Shefer S, Dayal B, Tint GS, Salen G, Mosbach EH. Identification of pentahydroxy bile alcohols in cerebrotendinous xanthomatosis: characterization of 5beta-cholestane-3alpha, 7alpha, 12alpha, 24xi, 25-pentol and 5beta-cholestane-3alpha, 7alpha, 12alpha, 23xi, 25-pentol. J Lipid Res. 1975;16(4):280–6.

    Article  CAS  PubMed  Google Scholar 

  16. Jalal K, Carter RL, Barczykowski A, Tomatsu S, Langan TJ. A roadmap for potential improvement of newborn screening for inherited metabolic diseases following recent developments and successful applications of bivariate Normal limits for pre-symptomatic detection of MPS I, Pompe disease, and Krabbe disease. Int J Neonatal Screen. 2022;8(4):61.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Bax BE. Mitochondrial neurogastrointestinal encephalomyopathy: approaches to diagnosis and treatment. J Transl Genet Genom. 2020;4:1–16.

    PubMed  PubMed Central  Google Scholar 

  18. Danjou M, Guardia D, Geoffroy PA, Seguy D, Cottencin O. Mitochondrial neuro-gastro-intestinal encephalopathy (MNGIE): when and how to suspect it in front of an atypical anorexia nervosa? Encéphale. 2016;42(6):574–9.

    Article  CAS  PubMed  Google Scholar 

  19. Tai CH, Lee NC, Chien YH, Byrne BJ, Muramatsu SI, Tseng SH, et al. Long-term efficacy and safety of eladocageneexuparvovec in patients with AADC deficiency. Mol Ther. 2021;30:509.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Merola A, Kobayashi N, Romagnolo A, Wright BA, Artusi CA, Imbalzano G, et al. Gene therapy in movement disorders: a systematic review of ongoing and completed clinical trials. Front Neurol. 2021;12:648532.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Routes J, Verbsky J. Newborn screening for severe combined immunodeficiency. Curr Allergy Asthma Rep. 2018;18(6):34.

    Article  PubMed  Google Scholar 

  22. Chetnik K, Petrick L, Pandey G. MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data. Metabolomics. 2020;16(11):117.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hoegen B, Zammit A, Gerritsen A, Engelke UFH, Castelein S, van de Vorst M, et al. Metabolomics-based screening of inborn errors of metabolism: enhancing clinical application with a robust computational pipeline. Metabolites. 2021;11(9):568.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Heinemann J. Machine learning in untargeted metabolomics experiments. Methods Mol Biol. 2019;1859:287–99.

    Article  CAS  PubMed  Google Scholar 

  25. García-Pérez P, Zhang L, Miras-Moreno B, Lozano-Milo E, Landin M, Lucini L, et al. The combination of untargeted metabolomics and machine learning predicts the biosynthesis of phenolic compounds in Bryophyllum medicinal plants (genus Kalanchoe). Plants (Basel). 2021;10(11):2430.

    Article  PubMed  Google Scholar 

  26. Odom JD, Sutton VR. Metabolomics in clinical practice: improving diagnosis and informing management. Clin Chem. 2021;67(12):1606–17.

    Article  PubMed  Google Scholar 

  27. Alaimo JT, Glinton KE, Liu N, Xiao J, Yang Y, Reid Sutton V, et al. Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis. Genet Med. 2020;22(9):1560–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Shayota BJ, Donti TR, Xiao J, Gijavanekar C, Kennedy AD, Hubert L, et al. Untargeted metabolomics as an unbiased approach to the diagnosis of inborn errors of metabolism of the non-oxidative branch of the pentose phosphate pathway. Mol Genet Metab. 2020;131(1–2):147–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Heacock AM, Adams E. Formation and excretion of pyrrole-2-carboxylate in man. J Clin Invest. 1974;54(4):810–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wajner M, Wannmacher CM, Purkiss P. High urinary excretion of N-(pyrrole-2-carboxyl) glycine in type II hyperprolinemia. Clin Genet. 1990;37(6):485–9.

    Article  CAS  PubMed  Google Scholar 

  31. Atwal PS, Donti TR, Cardon AL, Bacino CA, Sun Q, Emrick L, et al. Aromatic L-amino acid decarboxylase deficiency diagnosed by clinical metabolomic profiling of plasma. Mol Genet Metab. 2015;115(2–3):91–4.

    Article  CAS  PubMed  Google Scholar 

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Correspondence to Sarah H. Elsea .

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Glossary

Dried blood spot (DBS)

A method of whole blood sample collection, in which a small amount of fresh blood is blotted onto an absorbent filter paper, followed by drying. This method provides a convenient storage and shipment platform and is widely used for newborn screening. Typically, a small punch from the DBS paper is eluted with phosphate-buffered saline, availing the sample for testing.

Multiplex assay

An assay measuring simultaneously multiple analytes in a single testing. These tests are becoming more popular in the metabolic sciences where several similar analytes are tested for alterations from the normal range, e.g., urine polyols for evaluation of the pentose phosphate pathway, urine glycosaminoglycans for the diagnosis of mucopolysaccharidoses, or carbohydrate moieties for congenital disorders of glycosylation.

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Manor, J., Elsea, S.H. (2023). Untargeted Metabolomics in Newborn Screening. In: Abdel Rahman, A.M. (eds) Clinical Metabolomics Applications in Genetic Diseases. Springer, Singapore. https://doi.org/10.1007/978-981-99-5162-8_5

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  • DOI: https://doi.org/10.1007/978-981-99-5162-8_5

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