Skip to main content
SpringerLink
Log in

A Multiplatform Metabolomics Approach to Characterize Plasma Levels of Phenylalanine and Tyrosine in Phenylketonuria

  • Research Report
  • Chapter
  • First Online:
JIMD Reports, Volume 32

Part of the book series: JIMD Reports ((JIMD,volume 32))

Abstract

Background: Different pathophysiological mechanisms have been described in phenylketonuria (PKU) but the indirect metabolic consequences of metabolic disorders caused by elevated Phe or low Tyr concentrations remain partially unknown. We used a multiplatform metabolomics approach to evaluate the metabolic signature associated with Phe and Tyr.

Material and methods: We prospectively included 10 PKU adult patients and matched controls. We analysed the metabolome profile using GC-MS (urine), amino-acid analyzer (urine and plasma) and nuclear magnetic resonance spectroscopy (urine). We performed a multivariate analysis from the metabolome (after exclusion of Phe, Tyr and directly derived metabolites) to explain plasma Phe and Tyr concentrations, and the clinical status. Finally, we performed a univariate analysis of the most discriminant metabolites and we identified the associated metabolic pathways.

Results: We obtained a metabolic pattern from 118 metabolites and we built excellent multivariate models to explain Phe, Tyr concentrations and PKU diagnosis. Common metabolites of these models were identified: Gln, Arg, succinate and alpha aminobutyric acid. Univariate analysis showed an inverse correlation between Arg, alpha aminobutyric acid and Phe and a positive correlation between Arg, succinate, Gln and Tyr (p < 0.0003). Thus, we highlighted the following pathways: Arg and Pro, Ala, Asp and Glu metabolism.

Discussion: We obtain a specific metabolic signature related to Tyr and Phe concentrations. We confirmed the involvement of different pathophysiological mechanisms previously described in PKU such as protein synthesis, energetic metabolism and oxidative stress. The metabolomics approach is relevant to explore PKU pathogenesis.

Competing interests: None declared

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

1H NMR:

Nuclear magnetic resonance

CV ANOVA:

ANalysis Of VAriance testing of cross validated predictive residuals, used to evaluate the robustness of multivariate model

GC-MS:

Gas chromatography coupled with mass spectrometry

HCA:

Hierarchical cluster analysis

KEGG:

Pathway database

LC:

Liquid chromatography

METPA:

A web metabolomics tool to analyse metabolic pathways

OPLS-DA:

Orthogonal partial least-squares discriminant analysis

PLS:

Partial least square

Q 2 :

Parameter to estimate of the predictive ability of the model, used to evaluate the robustness of multivariate model

R 2 :

Parameter defined as a fraction of the variance explained by a component, used to evaluate the robustness of multivariate model

ROC:

Receiver-operating characteristics

UV Scaling:

UV scaling is defined by a variable that is centred and scaled to “Unit Variance”, i.e. the base weight is computed as 1/SD, where SD is the standard deviation of variable computed around the mean.

VIP:

Variable importance parameters

References

  • Bilder DA, Burton BK, Coon H et al (2013) Psychiatric symptoms in adults with phenylketonuria. Mol Genet Metab 108:155–160

    Article  CAS  PubMed  Google Scholar 

  • Blasco H, Nadal-Desbarats L, Pradat PF et al (2014) Untargeted 1H-NMR metabolomics in CSF: toward a diagnostic biomarker for motor neuron disease. Neurology 82:1167–1174

    Article  CAS  PubMed  Google Scholar 

  • Chiarla C, Giovannini I, Siegel JH (2011) Characterization of alpha-amino-n-butyric acid correlations in sepsis. Transl Res 158:328–333

    Article  CAS  PubMed  Google Scholar 

  • Christ SE, Huijbregts SC, de Sonneville LM, White DA (2010) Executive function in early-treated phenylketonuria: profile and underlying mechanisms. Mol Genet Metab 99(Suppl 1):S22–S32

    Article  CAS  PubMed  Google Scholar 

  • Christ SE, Moffitt AJ, Peck D, White DA, Hilgard J (2012) Decreased functional brain connectivity in individuals with early-treated phenylketonuria: evidence from resting state fMRI. J Inherit Metab Dis 35:807–816

    Article  PubMed  Google Scholar 

  • Curtius HC, Baerlocher K, Vollmin JA (1972) Pathogenesis of phenylketonuria: inhibition of DOPA and catecholamine synthesis in patients with phenylketonuria. Clin Chim Acta 42:235–239

    Article  CAS  PubMed  Google Scholar 

  • de Groot MJ, Hoeksma M, Blau N, Reijngoud DJ, van Spronsen FJ (2010) Pathogenesis of cognitive dysfunction in phenylketonuria: review of hypotheses. Mol Genet Metab 99(Suppl 1):S86–S89

    Article  PubMed  Google Scholar 

  • de Groot MJ, Sijens PE, Reijngoud DJ, Paans AM, van Spronsen FJ (2015) Phenylketonuria: brain phenylalanine concentrations relate inversely to cerebral protein synthesis. J Cereb Blood Flow Metab 35:200–205

    Article  PubMed  Google Scholar 

  • Denes J, Szabo E, Robinette SL et al (2012) Metabonomics of newborn screening dried blood spot samples: a novel approach in the screening and diagnostics of inborn errors of metabolism. Anal Chem 84:10113–10120

    Article  CAS  PubMed  Google Scholar 

  • Giovannini M, Verduci E, Salvatici E, Paci S, Riva E (2012) Phenylketonuria: nutritional advances and challenges. Nutr Metab (Lond) 9:7

    Article  CAS  Google Scholar 

  • Harding CO, Winn SR, Gibson KM, Arning E, Bottiglieri T, Grompe M (2014) Pharmacologic inhibition of l-tyrosine degradation ameliorates cerebral dopamine deficiency in murine phenylketonuria (PKU). J Inherit Metab Dis 37:735–743

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hargreaves IP, Heales SJ, Briddon A, Land JM, Lee PJ (2002) Blood mononuclear cell coenzyme Q10 concentration and mitochondrial respiratory chain succinate cytochrome-c reductase activity in phenylketonuric patients. J Inherit Metab Dis 25:673–679

    Article  CAS  PubMed  Google Scholar 

  • Hennermann JB, Querfeld U (2013) Unknown pathomechanisms of renal impairment in PKU. J Inherit Metab Dis 36:1087–1088

    Article  PubMed  Google Scholar 

  • Jahja R, Huijbregts SC, de Sonneville LM, van der Meere JJ, van Spronsen FJ (2014) Neurocognitive evidence for revision of treatment targets and guidelines for phenylketonuria. J Pediatr 164(895–899), e892

    Google Scholar 

  • Janeckova H, Hron K, Wojtowicz P et al (2012) Targeted metabolomic analysis of plasma samples for the diagnosis of inherited metabolic disorders. J Chromatogr A 1226:11–17

    Article  CAS  PubMed  Google Scholar 

  • Kaufman S (1976) Phenylketonuria: biochemical mechanisms. Adv Neurochem 2:1–32

    Google Scholar 

  • Kemsley EK, Le Gall G, Dainty JR et al (2007) Multivariate techniques and their application in nutrition: a metabolomics case study. Br J Nutr 98:1–14

    Article  CAS  PubMed  Google Scholar 

  • Knudsen GM, Hasselbalch S, Toft PB, Christensen E, Paulson OB, Lou H (1995) Blood-brain barrier transport of amino acids in healthy controls and in patients with phenylketonuria. J Inherit Metab Dis 18:653–664

    Article  CAS  PubMed  Google Scholar 

  • Krug S, Kastenmuller G, Stuckler F et al (2012) The dynamic range of the human metabolome revealed by challenges. FASEB J 26:2607–2619

    Article  CAS  PubMed  Google Scholar 

  • Kyprianou N, Murphy E, Lee P, Hargreaves I (2009) Assessment of mitochondrial respiratory chain function in hyperphenylalaninaemia. J Inherit Metab Dis 32:289–296

    Article  CAS  PubMed  Google Scholar 

  • Longo N, Arnold GL, Pridjian G et al (2015) Long-term safety and efficacy of sapropterin: the PKUDOS registry experience. Mol Genet Metab 114:557–563

    Article  CAS  PubMed  Google Scholar 

  • Madsen R, Lundstedt T, Trygg J (2010) Chemometrics in metabolomics—a review in human disease diagnosis. Anal Chim Acta 659:23–33

    Article  CAS  PubMed  Google Scholar 

  • Martynyuk AE, van Spronsen FJ, Van der Zee EA (2010) Animal models of brain dysfunction in phenylketonuria. Mol Genet Metab 99(Suppl 1):S100–S105

    Article  CAS  PubMed  Google Scholar 

  • McKean CM (1972) The effects of high phenylalanine concentrations on serotonin and catecholamine metabolism in the human brain. Brain Res 47:469–476

    Article  CAS  PubMed  Google Scholar 

  • Merimee TJ, Lillicrap DA, Rabinowitz D (1965) Effect of arginine on serum-levels of human growth-hormone. Lancet 2:668–670

    Article  CAS  PubMed  Google Scholar 

  • Mutze U, Beblo S, Kortz L et al (2012) Metabolomics of dietary fatty acid restriction in patients with phenylketonuria. PLoS One 7, e43021

    Article  PubMed  PubMed Central  Google Scholar 

  • Piatti PM, Monti LD, Valsecchi G et al (2001) Long-term oral l-arginine administration improves peripheral and hepatic insulin sensitivity in type 2 diabetic patients. Diabetes Care 24:875–880

    Article  CAS  PubMed  Google Scholar 

  • Rath M, Muller I, Kropf P, Closs EI, Munder M (2014) Metabolism via arginase or nitric oxide synthase: two competing arginine pathways in macrophages. Front Immunol 5:532

    Article  PubMed  PubMed Central  Google Scholar 

  • Rech VC, Feksa LR, Dutra-Filho CS, Wyse AT, Wajner M, Wannmacher CM (2002) Inhibition of the mitochondrial respiratory chain by phenylalanine in rat cerebral cortex. Neurochem Res 27:353–357

    Article  CAS  PubMed  Google Scholar 

  • Ribas GS, Sitta A, Wajner M, Vargas CR (2011) Oxidative stress in phenylketonuria: what is the evidence? Cell Mol Neurobiol 31:653–662

    Article  CAS  PubMed  Google Scholar 

  • Rocha JC, van Spronsen FJ, Almeida MF, Ramos E, Guimaraes JT, Borges N (2013) Early dietary treated patients with phenylketonuria can achieve normal growth and body composition. Mol Genet Metab 110(Suppl):S40–S43

    Article  CAS  PubMed  Google Scholar 

  • Rosa AP, Jacques CE, Moraes TB, Wannmacher CM, Dutra Ade M, Dutra-Filho CS (2012) Phenylpyruvic acid decreases glucose-6-phosphate dehydrogenase activity in rat brain. Cell Mol Neurobiol 32:1113–1118

    Article  CAS  PubMed  Google Scholar 

  • Sanayama Y, Nagasaka H, Takayanagi M et al (2011) Experimental evidence that phenylalanine is strongly associated to oxidative stress in adolescents and adults with phenylketonuria. Mol Genet Metab 103:220–225

    Article  CAS  PubMed  Google Scholar 

  • Schuck PF, Malgarin F, Cararo JH, Cardoso F, Streck EL, Ferreira GC (2015) Phenylketonuria pathophysiology: on the role of metabolic alterations. Aging Dis 6:390–399

    Article  PubMed  PubMed Central  Google Scholar 

  • Schumacher U, Lukacs Z, Kaltschmidt C et al (2008) High concentrations of phenylalanine stimulate peroxisome proliferator-activated receptor gamma: implications for the pathophysiology of phenylketonuria. Neurobiol Dis 32:385–390

    Article  CAS  PubMed  Google Scholar 

  • Thalhammer O, Pollak A, Lubec G, Konigshofer H (1980) Intracellular concentrations of phenylalanine, tyrosine and alpha-aminobutyric acid in 13 homozygotes and 19 heterozygotes for phenylketonuria (PKU) compared with 26 normals. Hum Genet 54:213–216

    Article  CAS  PubMed  Google Scholar 

  • Thevenot EA, Roux A, Xu Y, Ezan E, Junot C (2015) Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. J Proteome Res 14:3322–3335

    Article  CAS  PubMed  Google Scholar 

  • Turki A, Murthy G, Ueda K et al (2015) Minimally invasive (13)C-breath test to examine phenylalanine metabolism in children with phenylketonuria. Mol Genet Metab 115:78–83

    Article  CAS  PubMed  Google Scholar 

  • van Spronsen FJ, Hoeksma M, Reijngoud DJ (2009) Brain dysfunction in phenylketonuria: is phenylalanine toxicity the only possible cause? J Inherit Metab Dis 32:46–51

    Article  PubMed  Google Scholar 

  • van Spronsen FJ, Huijbregts SC, Bosch AM, Leuzzi V (2011) Cognitive, neurophysiological, neurological and psychosocial outcomes in early-treated PKU-patients: a start toward standardized outcome measurement across development. Mol Genet Metab 104(Suppl):S45–S51

    Article  PubMed  Google Scholar 

  • Weckwerth W (2008) Integration of metabolomics and proteomics in molecular plant physiology—coping with the complexity by data-dimensionality reduction. Physiol Plant 132:176–189

    Article  CAS  PubMed  Google Scholar 

  • Westerhuis JA, van Velzen EJ, Hoefsloot HC, Smilde AK (2010) Multivariate paired data analysis: multilevel PLSDA versus OPLSDA. Metabolomics 6:119–128

    Article  CAS  PubMed  Google Scholar 

  • Williams JZ, Abumrad N, Barbul A (2002) Effect of a specialized amino acid mixture on human collagen deposition. Ann Surg 236:369–374, discussion 374–365

    Article  PubMed  PubMed Central  Google Scholar 

  • Xiong X, Sheng X, Liu D, Zeng T, Peng Y, Wang Y (2015) A GC/MS-based metabolomic approach for reliable diagnosis of phenylketonuria. Anal Bioanal Chem 407:8825–8833

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgement

The authors would like to thank Hervé Meudal (Centre de Biophysique Moléculaire Orleans) for technical assistance with NMR spectrometer, and Colette Faideau, Stéphanie Premeau, Ghislaine Bruneau and Laurence Saison for their technical help.

This study was funded by the Hospital of Tours.

Author information

Authors and Affiliations

  1. Laboratoire de biochimie et biologie moléculaire, Hôpital Bretonneau, CHRU de Tours, 2, Bd Tonnellé, 37044, Tours cedex 1, France

    H. Blasco, C. Veyrat-Durebex, F. Patin, C. R. Andres & C. Antar

  2. INSERM U930, Université François Rabelais Tours, Tours cedex 1, France

    H. Blasco, C. Veyrat-Durebex, F. Patin, P. Emond, C. R. Andres, C. Antar & L. Nadal-Desbarats

  3. Centre de Biophysique Moléculaire, CNRS UPR4301, Orléans, France

    M. Bertrand & C. Landon

  4. Service de Pédiatrie, CHRU de Tours, Tours cedex 1, France

    F. Labarthe

  5. INSERM U1069, Université François Rabelais Tours, Tours cedex 1, France

    F. Labarthe & F. Maillot

  6. Service de médecine interne, CHRU de Tours, Tours cedex 1, France

    H. Henique & F. Maillot

Authors
  1. H. Blasco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Veyrat-Durebex
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Bertrand
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. F. Patin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. F. Labarthe
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. H. Henique
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. P. Emond
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. C. R. Andres
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. C. Antar
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. C. Landon
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. L. Nadal-Desbarats
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. F. Maillot
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to H. Blasco .

Editor information

Editors and Affiliations

  1. Tulane University Medical School, New Orleans, Louisiana, USA

    Eva Morava (Editor-in-Chief) (Editor-in-Chief)

  2. Division of Metabolism and Children’s Research Centre, University Children’s Hospital Zurich, Zurich, Switzerland

    Matthias Baumgartner

  3. Division of Child and Adolescent Neurology, Mayo Clinic, Rochester, Minnesota, USA

    Marc Patterson

  4. Clinical and Molecular Genetics Unit, UCL Institute of Child Health, London, UK

    Shamima Rahman

  5. Division of Human Genetics, Medical University Innsbruck, Innsbruck, Austria

    Johannes Zschocke

  6. Center for Child and Adolescent Medicine, Heidelberg University Hospital, Heidelberg, Germany

    Verena Peters (Managing Editor) (Managing Editor)

Additional information

Communicated by: Nenad Blau, PhD

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Additional Material

(DOC 35 kb)

Fig. S1

Distribution of values of A) Phe and B) Tyr in PKU patients (1 to 10), and healthy controls (11 to 20). The histogram shows the percentage of patients having the values of Phe and Tyr concentrations comprised in the ranges of concentrations presented on the X axis. The vertical bars represent the standard deviation for each range of concentration. Above the histogram, a horizontal boxplot is shown to visualize the median concentrations, the quartiles and the confidence interval (red) (TIF 58 kb)

Fig. S2

Loading plot corresponding to the Partial Least Square (PLS) model explaining the concentrations of Phe. Variables near each other are positively correlated; variables opposite to each other are negatively correlated. Variables with the largest absolute loading values dominate the projection and are correlated with Phe concentrations (TIF 94 kb)

Fig. S3

Loading plot corresponding to the Partial Least Square (PLS) model explaining the concentrations of Tyr. Variables near each other are positively correlated; variables opposite to each other are negatively correlated. Variables with the largest absolute loading values dominate the projection and are correlated with Tyr concentrations (TIF 68 kb)

Fig. S4

Dendrogram obtained from Hierarchical Cluster Analysis (HCA) based on the 13 relevant metabolites used in the Partial Least Square (PLS) model to explain Tyr concentrations, and showing 5 subgroups of subjects, the X axis represents the patients and the Y axis the distance between the clusters; B) Score plot characterized by the same colours as identified in the dendrogram. To note, the control 11 (*) is classified with the PKU group (TIF 69 kb)

Fig. S5

Scatter plot of Orthogonal partial least-squares discriminant analysis (OPLS-DA) scores from 12 metabolites. R2X(cum): 0.719, R2Y(cum): 0.837, Q2(cum): 0.761, CV ANOVA: 0.0003 (TIF 251 kb)

Table S1

List of the identified metabolites obtained from Gas Chromatography coupled with Mass Spectrometry (GC-MS), amino acid analyzer and Nuclear Magnetic Resonance (NMR). The metabolites marked with * were identified in urine and blood, and the other were measured only in urine. We noted a list of 76 molecules because 26 amino acids are found both in urine and plasma and 16 are not yet identified (XLSX 12 kb)

Appendices

Synopsis

The metabolomics approach based on a multiplatform strategy is promising to improve the knowledge of PKU pathogenesis.

Compliance with Ethics Guidelines

Conflict of Interest Statements

Blasco H, Veyrat-Durebex C, Bertrand M, Patin F, Labarthe F, Henique H, Emond P, Andres CR, Antar C, Landon C, Nadal-Desbarats L and Maillot F declare no conflict of interest

All the authors confirm independence from the sponsors; the content of the article has not been influenced by the sponsor

Informed Consent

All procedures followed were in accordance with the ethical standards of the responsible committee and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients for being included in the study.

Contribution of Authors

Hélène Blasco performed the statistical analysis, interpreted the data and wrote a part of the manuscript

Charlotte Veyrat-Durebex: acquired data

Franck Patin: performed the statistical analysis, interpreted the data and wrote a part of manuscript

Labarthe F: critically revised the manuscript for important intellectual content

Bertrand M: acquired NMR data

Hélène Hénique: acquired clinical data

Patrick Emond: acquired GC-MS data

Christian R Andres: critically revised the manuscript for important intellectual content

Catherine Antar: pre-treated and integrated NMR data

Céline Landon: acquired NMR data

Lydie Nadal-Desbararats: pre-treated, integrated NMR data and identified metabolites

François Maillot: recruited patients, interpreted data and critically revised the manuscript for important intellectual content

Rights and permissions

Reprints and permissions

Copyright information

© 2016 SSIEM and Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Blasco, H. et al. (2016). A Multiplatform Metabolomics Approach to Characterize Plasma Levels of Phenylalanine and Tyrosine in Phenylketonuria. In: Morava, E., Baumgartner, M., Patterson, M., Rahman, S., Zschocke, J., Peters, V. (eds) JIMD Reports, Volume 32. JIMD Reports, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/8904_2016_568

Download citation

  • DOI: https://doi.org/10.1007/8904_2016_568

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-54384-9

  • Online ISBN: 978-3-662-54385-6

  • eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)

Publish with us

Policies and ethics

Search

Navigation