Chemical treatment enhances skipping of a mutated exon in the dystrophin gene

Duchenne muscular dystrophy (DMD) is a fatal muscle wasting disease caused by a loss of the dystrophin protein. Control of dystrophin mRNA splicing to convert severe DMD to a milder phenotype is attracting much attention. Here we report a dystrophinopathy patient who has a point mutation in exon 31 of the dystrophin gene. Although the mutation generates a stop codon, a small amount of internally deleted, but functional, dystrophin protein is produced in the patient cells. An analysis of the mRNA reveals that the mutation promotes exon skipping and restores the open reading frame of dystrophin. Presumably, the mutation disrupts an exonic splicing enhancer and creates an exonic splicing silencer. Therefore, we searched for small chemicals that enhance exon skipping, and found that TG003 promotes the skipping of exon 31 in the endogenous dystrophin gene in a dose-dependent manner and increases the production of the dystrophin protein in the patient's cells

The Genetic Landscape and Epidemiology of Phenylketonuria

Phenylketonuria (PKU), caused by variants in the phenylalanine hydroxylase (PAH) gene, is the most common autosomal-recessive Mendelian phenotype of amino acid metabolism. We estimated that globally 0.45 million individuals have PKU, with global prevalence 1:23,930 live births (range 1:4,500 [Italy]-1:125,000 [Japan]). Comparing genotypes and metabolic phenotypes from 16,092 affected subjects revealed differences in disease severity in 51 countries from 17 world regions, with the global phenotype distribution of 62% classic PKU, 22% mild PKU, and 16% mild hyperphenylalaninemia. A gradient in genotype and phenotype distribution exists across Europe, from classic PKU in the east to mild PKU in the southwest and mild hyperphenylalaninemia in the south. The c.1241A gt G (p.Tyr414Cys)-associated genotype can be traced from Northern to Western Europe, from Sweden via Norway, to Denmark, to the Netherlands. The frequency of classic PKU increases from Europe (56%) via Middle East (71%) to Australia (80%). Of 758 PAH variants, c.1222C gt T (p.Arg408Trp) (22.2%), c.1066-11G gt A (IVS10-11G gt A) (6.4%), and c.782G gt A (p.Arg261Gln) (5.5%) were most common and responsible for two prevalent genotypes: p.[Arg408Trp];[Arg408Trp] (11.4%) and c.[1066-11G gt A];[1066-11G gt A] (2.6%). Most genotypes (73%) were compound heterozygous, 27% were homozygous, and 55% of 3,659 different genotypes occurred in only a single individual. PAH variants were scored using an allelic phenotype value and correlated with pre-treatment blood phenylalanine concentrations (n = 6,115) and tetrahydrobiopterin loading test results (n = 4,381), enabling prediction of both a genotype-based phenotype (88%) and tetrahydrobiopterin responsiveness (83%). This study shows that large genotype databases enable accurate phenotype prediction, allowing appropriate targeting of therapies to optimize clinical outcome

The Genetic Landscape and Epidemiology of Phenylketonuria

Phenylketonuria (PKU), caused by variants in the phenylalanine hydroxylase (PAH) gene, is the most common autosomal-recessive Mendelian phenotype of amino acid metabolism. We estimated that globally 0.45 million individuals have PKU, with global prevalence 1:23,930 live births (range 1:4,500 [Italy]–1:125,000 [Japan]). Comparing genotypes and metabolic phenotypes from 16,092 affected subjects revealed differences in disease severity in 51 countries from 17 world regions, with the global phenotype distribution of 62% classic PKU, 22% mild PKU, and 16% mild hyperphenylalaninemia. A gradient in genotype and phenotype distribution exists across Europe, from classic PKU in the east to mild PKU in the southwest and mild hyperphenylalaninemia in the south. The c.1241A>G (p.Tyr414Cys)-associated genotype can be traced from Northern to Western Europe, from Sweden via Norway, to Denmark, to the Netherlands. The frequency of classic PKU increases from Europe (56%) via Middle East (71%) to Australia (80%). Of 758 PAH variants, c.1222C>T (p.Arg408Trp) (22.2%), c.1066−11G>A (IVS10−11G>A) (6.4%), and c.782G>A (p.Arg261Gln) (5.5%) were most common and responsible for two prevalent genotypes: p.[Arg408Trp];[Arg408Trp] (11.4%) and c.[1066−11G>A];[1066−11G>A] (2.6%). Most genotypes (73%) were compound heterozygous, 27% were homozygous, and 55% of 3,659 different genotypes occurred in only a single individual. PAH variants were scored using an allelic phenotype value and correlated with pre-treatment blood phenylalanine concentrations (n = 6,115) and tetrahydrobiopterin loading test results (n = 4,381), enabling prediction of both a genotype-based phenotype (88%) and tetrahydrobiopterin responsiveness (83%). This study shows that large genotype databases enable accurate phenotype prediction, allowing appropriate targeting of therapies to optimize clinical outcome.Fil: Hillert, Alicia. No especifíca;Fil: Anikster, Yair. No especifíca;Fil: Belanger Quintana, Amaya. No especifíca;Fil: Burlina, Alberto. No especifíca;Fil: Burton, Barbara K.. No especifíca;Fil: Carducci, Carla. No especifíca;Fil: Chiesa, Ana Elena. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones Endocrinológicas "Dr. César Bergada". Gobierno de la Ciudad de Buenos Aires. Centro de Investigaciones Endocrinológicas "Dr. César Bergada". Fundación de Endocrinología Infantil. Centro de Investigaciones Endocrinológicas "Dr. César Bergada"; ArgentinaFil: Christodoulou, John. No especifíca;Fil: Dordevic, Maja. No especifíca;Fil: Desviat, Lourdes R.. No especifíca;Fil: Eliyahu, Aviva. No especifíca;Fil: Evers, Roeland A.F.. No especifíca;Fil: Fajkusova, Lena. No especifíca;Fil: Feillet, Francois. No especifíca;Fil: Bonfim Freitas, Pedro E.. No especifíca;Fil: Gizewska, María. No especifíca;Fil: Gundorova, Polina. No especifíca;Fil: Karall, Daniela. No especifíca;Fil: Kneller, Katya. No especifíca;Fil: Kutsev, Sergey I.. No especifíca;Fil: Leuzzi, Vincenzo. No especifíca;Fil: Levy, Harvey L.. No especifíca;Fil: Lichter Koneck, Uta. No especifíca;Fil: Muntau, Ania C.. No especifíca;Fil: Namour, Fares. No especifíca;Fil: Oltarzewsk, Mariusz. No especifíca;Fil: Paras, Andrea. No especifíca;Fil: Perez, Belén. No especifíca;Fil: Polak, Emil. No especifíca;Fil: Polyakov, Alexander V.. No especifíca;Fil: Porta, Francesco. No especifíca;Fil: Rohrbach, Marianne. No especifíca;Fil: Scholl Bürgi, Sabine. No especifíca;Fil: Spécola, Norma. No especifíca;Fil: Stojiljkovic, Maja. No especifíca;Fil: Shen, Nan. No especifíca;Fil: Santana da Silva, Luiz C.. No especifíca;Fil: Skouma, Anastasia. No especifíca;Fil: van Spronsen, Francjan. No especifíca;Fil: Stoppioni, Vera. No especifíca;Fil: Thöny, Beat. No especifíca;Fil: Trefz, Friedrich K.. No especifíca;Fil: Vockley, Jerry. No especifíca;Fil: Yu, Youngguo. No especifíca;Fil: Zschocke, Johannes. No especifíca;Fil: Hoffmann, Georg F.. No especifíca;Fil: Garbade, Sven F.. No especifíca;Fil: Blau, Nenad. No especifíca

Improved Criteria for the Classification of Titin Variants in Inherited Skeletal Myopathies

28siBACKGROUND: Extensive genetic screening results in the identification of thousands of rare variants that are difficult to interpret. Because of its sheer size, rare variants in the titin gene (TTN) are detected frequently in any individual. Unambiguous interpretation of molecular findings is almost impossible in many patients with myopathies or cardiomyopathies. OBJECTIVE: To refine the current classification framework for TTN-associated skeletal muscle disorders and standardize the interpretation of TTN variants. METHODS: We used the guidelines issued by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) to re-analyze TTN genetic findings from our patient cohort. RESULTS: We identified in the classification guidelines three rules that are not applicable to titin-related skeletal muscle disorders; six rules that require disease-/gene-specific adjustments and four rules requiring quantitative thresholds for a proper use. In three cases, the rule strength need to be modified. CONCLUSIONS: We suggest adjustments are made to the guidelines. We provide frequency thresholds to facilitate filtering of candidate causative variants and guidance for the use and interpretation of functional data and co-segregation evidence. We expect that the variant classification framework for TTN-related skeletal muscle disorders will be further improved along with a better understanding of these diseases.nonenoneSavarese M.; Johari M.; Johnson K.; Arumilli M.; Torella A.; Topf A.; Rubegni A.; Kuhn M.; Giugliano T.; Glaser D.; Fattori F.; Thompson R.; Penttila S.; Lehtinen S.; Gibertini S.; Ruggieri A.; Mora M.; Maver A.; Peterlin B.; Mankodi A.; Lochmuller H.; Santorelli F.M.; Schoser B.; Fajkusova L.; Straub V.; Nigro V.; Hackman P.; Udd B.Savarese, M.; Johari, M.; Johnson, K.; Arumilli, M.; Torella, A.; Topf, A.; Rubegni, A.; Kuhn, M.; Giugliano, T.; Glaser, D.; Fattori, F.; Thompson, R.; Penttila, S.; Lehtinen, S.; Gibertini, S.; Ruggieri, A.; Mora, M.; Maver, A.; Peterlin, B.; Mankodi, A.; Lochmuller, H.; Santorelli, F. M.; Schoser, B.; Fajkusova, L.; Straub, V.; Nigro, V.; Hackman, P.; Udd, B

Improved Criteria for the Classification of Titin Variants in Inherited Skeletal Myopathies

BACKGROUND: Extensive genetic screening results in the identification of thousands of rare variants that are difficult to interpret. Because of its sheer size, rare variants in the titin gene (TTN) are detected frequently in any individual. Unambiguous interpretation of molecular findings is almost impossible in many patients with myopathies or cardiomyopathies. OBJECTIVE: To refine the current classification framework for TTN-associated skeletal muscle disorders and standardize the interpretation of TTN variants. METHODS: We used the guidelines issued by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) to re-analyze TTN genetic findings from our patient cohort. RESULTS: We identified in the classification guidelines three rules that are not applicable to titin-related skeletal muscle disorders; six rules that require disease-/gene-specific adjustments and four rules requiring quantitative thresholds for a proper use. In three cases, the rule strength need to be modified. CONCLUSIONS: We suggest adjustments are made to the guidelines. We provide frequency thresholds to facilitate filtering of candidate causative variants and guidance for the use and interpretation of functional data and co-segregation evidence. We expect that the variant classification framework for TTN-related skeletal muscle disorders will be further improved along with a better understanding of these diseases