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Phenylketonuria (PKU) is a protein metabolism disorder characterized by high blood levels of the amino acid phenylalanine. This results from a mutation in the gene responsible for phenylalanine hydroxylase, an enzyme that converts phenylalanine into tyrosine. When this enzyme is deficient, phenylalanine builds up in the blood, leading to symptoms such as vomiting, rashes, seizures, growth deficiency, and severe mental retardation. An early diagnosis and a diet restricting phenylalanine intake...
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Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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Linking genotypes database with locus-specific database and genotype-phenotype correlation in phenylketonuria.

Sarah Wettstein1, Jarl Underhaug2, Belen Perez3

  • 1Division of Metabolism, University Children's Hospital, Zürich, Switzerland.

European Journal of Human Genetics : EJHG
|June 19, 2014
PubMed
Summary
This summary is machine-generated.

Predicting phenylketonuria (PKU) patient outcomes is improved by analyzing phenylalanine hydroxylase (PAH) gene variants. Computational tools accurately forecast enzyme activity, patient phenotype, and treatment response, aiding personalized PKU management.

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Area of Science:

  • Biochemistry
  • Genetics
  • Computational Biology

Background:

  • Phenylketonuria (PKU) exhibits diverse metabolic phenotypes due to variable phenylalanine hydroxylase (PAH) enzyme dysfunction.
  • Numerous genetic variants contribute to the spectrum of PKU severity and treatment response.

Purpose of the Study:

  • To assess the predictive power of computational algorithms for PAH enzyme function, PKU patient phenotypes, and tetrahydrobiopterin (BH4) responsiveness.
  • To establish correlations between PAH protein stability, enzyme activity, and clinical outcomes in a large PKU cohort.

Main Methods:

  • Utilized FoldX, SIFT Blink, Polyphen-2, and SNPs3D algorithms to analyze 834 PAH variants from PAHvdb.
  • Correlated computational predictions with residual enzyme activity, patient phenotypes, and BH4 responsiveness using data from 4181 PKU patients in the BIOPKU database.
  • Developed an interactive viewer for visualizing missense variant structures in PAHvdb.

Main Results:

  • Found significant quantitative relationships between PAH protein stability, enzyme activity, and allelic phenotype (r(s) = 0.479, -0.458, and 0.799 respectively).
  • Enzyme stability and activity algorithms, along with allelic phenotype, were powerful predictors of patient phenotype and BH4 response.
  • Achieved high prediction accuracy for patient phenotype (up to ≈100% for deleterious genotypes) and 71.0% accuracy for BH4 response.

Conclusions:

  • Computational analysis of PAH variants offers robust prediction of PKU patient phenotypes and treatment responsiveness.
  • This study represents the largest analysis of its kind, integrating locus-specific and genotype databases for enhanced PKU patient stratification.
  • Findings support the use of predictive algorithms for personalized medicine approaches in managing phenylketonuria.