Ruifang Wang1, Nan Shen2, Jun Ye1, Lianshu Han1, Wenjuan Qiu1, Huiwen Zhang1, Lili Liang1, Yu Sun1, Yanjie Fan1, Lili Wang1, Yu Wang1, Zhuwen Gong1, Huili Liu1, Jianguo Wang1, Hui Yan1, Nenad Blau3, Xuefan Gu4, Yongguo Yu5. 1. Department of Pediatric Endocrinology and Genetic Metabolism, Shanghai Institute for Pediatric Research, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200092, China. 2. Department of Rehabilitation Medicine, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200092, China. 3. Dietmar-Hopp Metabolic Center, University Children's Hospital, 69120 Heidelberg, Germany. Electronic address: nenad.blau@med.uni-heidelberg.de. 4. Department of Pediatric Endocrinology and Genetic Metabolism, Shanghai Institute for Pediatric Research, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200092, China. Electronic address: guxuefan@xinhuamed.com.cn. 5. Department of Pediatric Endocrinology and Genetic Metabolism, Shanghai Institute for Pediatric Research, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200092, China. Electronic address: yuyongguo@shsmu.edu.cn.
Abstract
BACKGROUND: Hyperphenylalaninemia (HPA) is an inherited metabolic disorder that is caused by a deficiency of phenylalanine hydroxylase (PAH) or tetrahydrobiopterin. The prevalence of HPA varies widely around the world. METHODS: A spectrum of HPA candidate genes in 1020 Chinese HPA patients was reported. Sanger sequencing, next generation sequencing (NGS), multiplex ligation-dependent probe amplification (MLPA) and quantitative real-time PCR (qRT-PCR) were applied to precisely molecular diagnose HPA patients. The allelic phenotype values (APV) and genotypic phenotype values (GPV) were calculated in PAH-deficient patients based on a recently developed formula. RESULTS: Apart from genetic diagnoses confirmed in 915 HPA patients (89.7%) by Sanger sequencing, pathogenic variants were discovered in another 57 patients (5.6%) through deep detections (NGS, MLPA and qRT-PCR). We identified 196, 42, 10 and 2 variants in PAH, PTS, QDPR and GCH1, respectively. And a total of 47 novel variants were found in these genes. Through the APV and GPV calculations, it was found that the new GPV system was well correlated with metabolic phenotypes in most PAH-deficient patients. CONCLUSIONS: More HPA candidate variants were identified using new molecular diagnostic methods. The new APV and GPV system is likely to be highly beneficial for predicting clinical phenotypes for PAH-deficient patients.
BACKGROUND:Hyperphenylalaninemia (HPA) is an inherited metabolic disorder that is caused by a deficiency of phenylalanine hydroxylase (PAH) or tetrahydrobiopterin. The prevalence of HPA varies widely around the world. METHODS: A spectrum of HPA candidate genes in 1020 Chinese HPApatients was reported. Sanger sequencing, next generation sequencing (NGS), multiplex ligation-dependent probe amplification (MLPA) and quantitative real-time PCR (qRT-PCR) were applied to precisely molecular diagnose HPApatients. The allelic phenotype values (APV) and genotypic phenotype values (GPV) were calculated in PAH-deficientpatients based on a recently developed formula. RESULTS: Apart from genetic diagnoses confirmed in 915 HPApatients (89.7%) by Sanger sequencing, pathogenic variants were discovered in another 57 patients (5.6%) through deep detections (NGS, MLPA and qRT-PCR). We identified 196, 42, 10 and 2 variants in PAH, PTS, QDPR and GCH1, respectively. And a total of 47 novel variants were found in these genes. Through the APV and GPV calculations, it was found that the new GPV system was well correlated with metabolic phenotypes in most PAH-deficientpatients. CONCLUSIONS: More HPA candidate variants were identified using new molecular diagnostic methods. The new APV and GPV system is likely to be highly beneficial for predicting clinical phenotypes for PAH-deficientpatients.
Authors: Ting Wang; Jun Ma; Qin Zhang; Ang Gao; Qi Wang; Hong Li; Jingjing Xiang; Benjing Wang Journal: Front Genet Date: 2019-10-29 Impact factor: 4.599