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

  • H. BlascoEmail author
  • C. Veyrat-Durebex
  • M. Bertrand
  • F. Patin
  • F. Labarthe
  • H. Henique
  • P. Emond
  • C. R. Andres
  • C. Antar
  • C. Landon
  • L. Nadal-Desbarats
  • F. Maillot
Research Report
Part of the JIMD Reports book series (JIMD, volume 32)


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.


Biomarkers Metabolomics NMR Phenylketonuria 



Nuclear magnetic resonance


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


Gas chromatography coupled with mass spectrometry


Hierarchical cluster analysis


Pathway database


Liquid chromatography


A web metabolomics tool to analyse metabolic pathways


Orthogonal partial least-squares discriminant analysis


Partial least square


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


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


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.


Variable importance parameters



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.

Supplementary material

978-3-662-54385-6_568_MOESM1_ESM.docx (320 kb)
Additional Material (DOC 35 kb)
978-3-662-54385-6_568_MOESM2_ESM.tif (58 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)
978-3-662-54385-6_568_MOESM3_ESM.tif (94 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)
978-3-662-54385-6_568_MOESM4_ESM.tif (68 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)
978-3-662-54385-6_568_MOESM5_ESM.tif (70 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)
978-3-662-54385-6_568_MOESM6_ESM.tif (251 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)
978-3-662-54385-6_568_MOESM7_ESM.xlsx (13 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)


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Copyright information

© SSIEM and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • H. Blasco
    • 1
    • 2
    Email author
  • C. Veyrat-Durebex
    • 1
    • 2
  • M. Bertrand
    • 3
  • F. Patin
    • 1
    • 2
  • F. Labarthe
    • 4
    • 5
  • H. Henique
    • 6
  • P. Emond
    • 2
  • C. R. Andres
    • 1
    • 2
  • C. Antar
    • 1
    • 2
  • C. Landon
    • 3
  • L. Nadal-Desbarats
    • 2
  • F. Maillot
    • 5
    • 6
  1. 1.Laboratoire de biochimie et biologie moléculaire, Hôpital Bretonneau, CHRU de ToursTours cedex 1France
  2. 2.INSERM U930, Université François Rabelais ToursTours cedex 1France
  3. 3.Centre de Biophysique Moléculaire, CNRS UPR4301OrléansFrance
  4. 4.Service de Pédiatrie, CHRU de ToursTours cedex 1France
  5. 5.INSERM U1069, Université François Rabelais ToursTours cedex 1France
  6. 6.Service de médecine interne, CHRU de ToursTours cedex 1France

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