Gundorova Polina, Yousefi Behnam, Woidy Mathias, Rose-Heine Malcolm Summer, Khatri Robin, Kasten Viviane, Bonn Stefan, Muntau Ania Carolina, Gersting Soeren Waldemar
University Children's Research, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
German Center for Child and Adolescent Health (DZKJ), Partner Site Hamburg, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
J Inherit Metab Dis. 2025 Sep;48(5):e70067. doi: 10.1002/jimd.70067.
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.
广泛的研究已经考察了不同人群中苯丙酮尿症（PKU）的临床表现、致病机制和基因变异，从而积累了大量关于苯丙氨酸羟化酶（PAH）基因及其变异的分子遗传学数据。然而，许多基因型与一系列临床表型相关，并且对四氢生物蝶呤的反应性也各不相同，这给有效治疗带来了持续的挑战。为了解决这一问题，我们通过纳入高通量技术增强了PAH活性图谱方法，这些技术包括自动移液、通过三维表面的高斯建模进行综合数据处理以及具有强大质量控制的生物信息学分析。利用PAH活性图谱，我们可视化了99种常见PAH基因型在不同代谢和治疗条件下的PAH酶功能。这种深度功能表型分析方法使我们能够通过一致性聚类识别不同的基因型亚群，将它们与临床表型相关联，并提出亚群特异性的治疗方案。我们的研究结果表明，可以根据PAH残余功能谱预测临床表型并调整治疗方案。为了进一步支持个性化治疗策略，我们修订了可公开访问的PAH基因型和活性图谱数据库，以分享对PAH功能和患者表型的最新见解，即由两个等位基因所传达的残余酶活性和对四氢生物蝶呤的反应性。这一资源强调了PKU功能研究的转化意义，并提供了一个实用工具来支持临床环境中的个性化治疗。
