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1.
Elife ; 4: e09406, 2015 Oct 16.
Article in English | MEDLINE | ID: mdl-26473617

ABSTRACT

Truncating mutations in the giant sarcomeric protein Titin result in dilated cardiomyopathy and skeletal myopathy. The most severely affected dilated cardiomyopathy patients harbor Titin truncations in the C-terminal two-thirds of the protein, suggesting that mutation position might influence disease mechanism. Using CRISPR/Cas9 technology, we generated six zebrafish lines with Titin truncations in the N-terminal and C-terminal regions. Although all exons were constitutive, C-terminal mutations caused severe myopathy whereas N-terminal mutations demonstrated mild phenotypes. Surprisingly, neither mutation type acted as a dominant negative. Instead, we found a conserved internal promoter at the precise position where divergence in disease severity occurs, with the resulting protein product partially rescuing N-terminal truncations. In addition to its clinical implications, our work may shed light on a long-standing mystery regarding the architecture of the sarcomere.


Subject(s)
Cardiomyopathy, Dilated/pathology , Connectin/genetics , Muscular Diseases/pathology , Promoter Regions, Genetic , Sequence Deletion , Animals , Connectin/metabolism , Disease Models, Animal , Humans , Mutant Proteins/genetics , Mutant Proteins/metabolism , Zebrafish
2.
Genome Biol ; 15(12): 534, 2014 Dec 03.
Article in English | MEDLINE | ID: mdl-25633252

ABSTRACT

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in the developed world. Human genetic studies, including genome-wide sequencing and SNP-array approaches, promise to reveal disease genes and mechanisms representing new therapeutic targets. In practice, however, identification of the actual genes contributing to disease pathogenesis has lagged behind identification of associated loci, thus limiting the clinical benefits. RESULTS: To aid in localizing causal genes, we develop a machine learning approach, Objective Prioritization for Enhanced Novelty (OPEN), which quantitatively prioritizes gene-disease associations based on a diverse group of genomic features. This approach uses only unbiased predictive features and thus is not hampered by a preference towards previously well-characterized genes. We demonstrate success in identifying genetic determinants for CVD-related traits, including cholesterol levels, blood pressure, and conduction system and cardiomyopathy phenotypes. Using OPEN, we prioritize genes, including FLNC, for association with increased left ventricular diameter, which is a defining feature of a prevalent cardiovascular disorder, dilated cardiomyopathy or DCM. Using a zebrafish model, we experimentally validate FLNC and identify a novel FLNC splice-site mutation in a patient with severe DCM. CONCLUSION: Our approach stands to assist interpretation of large-scale genetic studies without compromising their fundamentally unbiased nature.


Subject(s)
Artificial Intelligence , Filamins/genetics , Genomics/methods , Hypertrophy, Left Ventricular/genetics , Hypertrophy, Left Ventricular/pathology , Algorithms , Animals , Cardiovascular Diseases/genetics , Cardiovascular Diseases/pathology , Disease Models, Animal , Genetic Predisposition to Disease , Genome-Wide Association Study , Humans , Mice , Molecular Sequence Data , Mutation , Zebrafish
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