Silencing of CCR4-NOT complex subunits affects heart structure and function.
Lisa Elmén1, Claudia B Volpato2, Anaïs Kervadec1
1Development Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N Torrey Pines Rd, La Jolla, CA 92037, USA.
Genetic variants in the CNOT1 gene promoter are linked to longer QT intervals. The CCR4-NOT complex, including CNOT1 and CNOT7, plays a vital role in heart development and function, impacting cardiomyocyte proliferation and cardiac performance.
Area of Science:
- Cardiovascular Genetics
- Molecular Biology
- Gene Regulation
Background:
- Genome-wide association studies (GWAS) identify genetic variants associated with cardiovascular traits like QT interval length.
- The CCR4-NOT complex, a conserved regulator of gene expression, includes CNOT1, a gene with variants linked to QT interval length.
Purpose of the Study:
- To investigate the functional impact of genetic variants in the CNOT1 promoter on QT interval length.
- To explore the role of CCR4-NOT complex components in cardiac development and function using cellular and in vivo models.
Main Methods:
- Analysis of CNOT1 promoter variants in a cardiac cell line using reporter assays.
- siRNA-mediated knockdown of CCR4-NOT genes in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs).
- Cardiac-specific knockdown of CCR4-NOT orthologs in Drosophila models.
Main Results:
- A CNOT1 promoter haplotype associated with longer QT intervals reduced reporter gene expression in cardiac cells.
- Knockdown of CNOT1 and other CCR4-NOT genes in hiPSC-CMs impaired proliferative capacity.
- In vivo knockdown of CNOT1 and CNOT7/8/Pop2 in Drosophila led to lethality or severe cardiac defects, including dilated cardiomyopathy and arrhythmias.
Conclusions:
- Genetic variants in the CNOT1 promoter may contribute to abnormal QT intervals through reduced CNOT1 expression.
- The CCR4-NOT complex is essential for cardiomyocyte proliferation, cardiac development, and overall heart function.
- This study highlights the utility of combining cell-based and in vivo models for investigating GWAS-identified disease-associated genes.
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