Allele-Specific Silencing of MutantMyh6Transcripts in Mice Suppresses Hypertrophic Cardiomyopathy

Dominant mutations in sarcomere proteins such as the myosin heavy chains (MHC) are the leading genetic causes of human hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy. We found that expression of the HCM-causing cardiac MHC gene (Myh6) R403Q mutation in mice can be selectively silenced by an RNA interference (RNAi) cassette delivered by an adeno-associated virus vector. RNAi-transduced MHC403/+mice developed neither hypertrophy nor myocardial fibrosis, the pathologic manifestations of HCM, for at least 6 months. Because inhibition of HCM was achieved by only a 25% reduction in the levels of the mutant transcripts, we suggest that the variable clinical phenotype in HCM patients reflects allele-specific expression and that partial silencing of mutant transcripts may have therapeutic benefit.

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Mice Expressing Mutant Myosin Heavy Chains Are a Model for Familial Hypertrophic Cardiomyopathy

Molecular Medicine ◽

10.1007/bf03401640 ◽

1996 ◽

Vol 2 (5) ◽

pp. 556-567 ◽

Cited By ~ 100

Author(s):

Karen L. Vikstrom ◽

Stephen M. Factor ◽

Leslie A. Leinwand

Keyword(s):

Hypertrophic Cardiomyopathy ◽

Familial Hypertrophic Cardiomyopathy ◽

Myosin Heavy Chains ◽

Heavy Chains

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Correlation between the clinical phenotype of MYH9 -related disease and tissue distribution of class II nonmuscle myosin heavy chains

Genomics ◽

10.1016/j.ygeno.2003.12.012 ◽

2004 ◽

Vol 83 (6) ◽

pp. 1125-1133 ◽

Cited By ~ 49

Author(s):

Valeria Marigo ◽

Alessandra Nigro ◽

Alessandro Pecci ◽

Donatella Montanaro ◽

Mariateresa Di Stazio ◽

...

Keyword(s):

Tissue Distribution ◽

Clinical Phenotype ◽

Class Ii ◽

Myosin Heavy Chains ◽

Nonmuscle Myosin ◽

Heavy Chains ◽

Related Disease

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Cardiac and skeletal muscle expression of mutant β-myosin heavy chains, degree of functional impairment and phenotypic heterogeneity in hypertrophic cardiomyopathy

Journal of Cellular Physiology ◽

10.1002/jcp.24047 ◽

2012 ◽

Vol 227 (10) ◽

pp. 3471-3476 ◽

Cited By ~ 10

Author(s):

Marina Di Domenico ◽

Rita Casadonte ◽

Pietroantonio Ricci ◽

Mario Santini ◽

Giacomo Frati ◽

...

Keyword(s):

Skeletal Muscle ◽

Hypertrophic Cardiomyopathy ◽

Functional Impairment ◽

Phenotypic Heterogeneity ◽

Myosin Heavy Chains ◽

Heavy Chains

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The Molecular Biology and Pathophysiology of Hypertrophic Cardiomyopathy Due to Mutations in the Beta Myosin Heavy Chains and the Essential and Regulatory Light Chains

Advances in Experimental Medicine and Biology - Mechanisms of Work Production and Work Absorption in Muscle ◽

10.1007/978-1-4684-6039-1_13 ◽

1998 ◽

pp. 105-115 ◽

Cited By ~ 21

Author(s):

Neal D. Epstein

Keyword(s):

Hypertrophic Cardiomyopathy ◽

Molecular Biology ◽

Light Chains ◽

Myosin Heavy Chains ◽

Regulatory Light Chains ◽

Heavy Chains

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Investigation of allele specific expression in various tissues of broiler chickens using the detection tool VADT

Scientific Reports ◽

10.1038/s41598-021-83459-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

M. Joseph Tomlinson ◽

Shawn W. Polson ◽

Jing Qiu ◽

Juniper A. Lake ◽

William Lee ◽

...

Keyword(s):

Broiler Chickens ◽

Nucleotide Polymorphisms ◽

Rna Seq ◽

Specific Expression ◽

Single Nucleotide ◽

Allele Specific Expression ◽

Detection Tool ◽

Commercial Broiler ◽

Significant Phenomenon ◽

Allele Specific

AbstractDifferential abundance of allelic transcripts in a diploid organism, commonly referred to as allele specific expression (ASE), is a biologically significant phenomenon and can be examined using single nucleotide polymorphisms (SNPs) from RNA-seq. Quantifying ASE aids in our ability to identify and understand cis-regulatory mechanisms that influence gene expression, and thereby assist in identifying causal mutations. This study examines ASE in breast muscle, abdominal fat, and liver of commercial broiler chickens using variants called from a large sub-set of the samples (n = 68). ASE analysis was performed using a custom software called VCF ASE Detection Tool (VADT), which detects ASE of biallelic SNPs using a binomial test. On average ~ 174,000 SNPs in each tissue passed our filtering criteria and were considered informative, of which ~ 24,000 (~ 14%) showed ASE. Of all ASE SNPs, only 3.7% exhibited ASE in all three tissues, with ~ 83% showing ASE specific to a single tissue. When ASE genes (genes containing ASE SNPs) were compared between tissues, the overlap among all three tissues increased to 20.1%. Our results indicate that ASE genes show tissue-specific enrichment patterns, but all three tissues showed enrichment for pathways involved in translation.

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Replicate sequencing libraries are important for quantification of allelic imbalance

Nature Communications ◽

10.1038/s41467-021-23544-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Asia Mendelevich ◽

Svetlana Vinogradova ◽

Saumya Gupta ◽

Andrey A. Mironov ◽

Shamil R. Sunyaev ◽

...

Keyword(s):

Allelic Imbalance ◽

False Positive Rate ◽

Error Rates ◽

Differential Analysis ◽

Rna Seq ◽

Specific Expression ◽

Technical Noise ◽

Specific Analysis ◽

Positive Rate ◽

Allele Specific

AbstractA sensitive approach to quantitative analysis of transcriptional regulation in diploid organisms is analysis of allelic imbalance (AI) in RNA sequencing (RNA-seq) data. A near-universal practice in such studies is to prepare and sequence only one library per RNA sample. We present theoretical and experimental evidence that data from a single RNA-seq library is insufficient for reliable quantification of the contribution of technical noise to the observed AI signal; consequently, reliance on one-replicate experimental design can lead to unaccounted-for variation in error rates in allele-specific analysis. We develop a computational approach, Qllelic, that accurately accounts for technical noise by making use of replicate RNA-seq libraries. Testing on new and existing datasets shows that application of Qllelic greatly decreases false positive rate in allele-specific analysis while conserving appropriate signal, and thus greatly improves reproducibility of AI estimates. We explore sources of technical overdispersion in observed AI signal and conclude by discussing design of RNA-seq studies addressing two biologically important questions: quantification of transcriptome-wide AI in one sample, and differential analysis of allele-specific expression between samples.

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