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

Abstract

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. 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. 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. 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.