Personalized Genotype-Based Approach for Treatment of Phenylketonuria

Abstract

Extensive studies have examined the clinical manifestations, pathogenic mechanisms, and genetic variations of phenylketonuria (PKU) across different populations, resulting in a substantial collection of molecular genetic data on the phenylalanine hydroxylase (PAH) gene and its variants. However, many genotypes are associated with a range of clinical phenotypes, as well as variable responsiveness to sapropterin, presenting ongoing challenges for effective treatment. To address this, we enhanced the PAH activity landscapes method by incorporating high-throughput techniques, including automated pipetting, integrated data processing via Gaussian modeling of 3D surfaces, and bioinformatics analyses with robust quality control. Using PAH activity landscapes, we visualized PAH enzymatic function across 99 common PAH genotypes under varying metabolic and therapeutic conditions. This deep functional phenotyping approach enabled us to identify distinct genotype subpopulations by using consensus clustering, correlate them with clinical phenotypes, and propose subpopulation-specific treatment protocols. Our findings suggest that clinical phenotypes can be predicted and treatment regimens can be adjusted based on residual PAH function profiles. To further support personalized treatment strategies, we revised our publicly accessible PAH genotype & activity landscapes database to share the latest insights into PAH function and patient phenotypes—namely residual enzyme activity and responsiveness to sapropterin as conveyed by two alleles. This resource underscores the translational significance of functional research in PKU and offers a practical tool to support personalized treatment in clinical settings.