Scaled multidimensional assays of variant effect identify sequence-function relationships in hypertrophic
Yuta Yamamoto1, Kaiser Chua1, Alexis Ferrasse1
1Stanford Center for Inherited Cardiovascular Disease, Division of Cardiovascular Medicine, Department of Medicine, Stanford School of Medicine, Palo Alto, CA.
Insights
Genetic variants in MYBPC3 cause hypertrophic cardiomyopathy (HCM). This study developed a new method to analyze variant effects in heart cells, improving diagnosis and revealing disease mechanisms for better therapies.
Area of Science:
- Genetics
- Cardiology
- Molecular Biology
Background:
- Hypertrophic cardiomyopathy (HCM) affects 1 in 500 people, with genetic diagnosis aiding risk identification and therapy.
- Mutations in the myosin binding protein C3 (MYBPC3) gene are a common cause of HCM.
- Many MYBPC3 variants are of uncertain significance (VUS), hindering clinical decisions and disease mechanism understanding.
Purpose of the Study:
- To develop a scalable, multidimensional mapping strategy for evaluating the functional impact of MYBPC3 variants.
- To analyze variant effects on HCM-relevant phenotypes in human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs).
- To improve variant interpretation and uncover novel disease mechanisms for potential therapeutic strategies.
Main Methods:
- Developed a scaled multidimensional mapping strategy using saturation base editing at the native MYBPC3 locus.
- Employed long-read RNA sequencing to assess variant splice effects.
- Measured HCM-relevant phenotypes including MYBPC3 abundance, hypertrophic signaling, and ubiquitin-proteasome function in iPSC-CMs.
Main Results:
- High-resolution functional analysis of MYBPC3 variants in iPSC-CMs was achieved.
- Identified novel splice-disrupting variants and revealed decreased MYBPC3 abundance as a key driver of HCM phenotypes.
- Observed compensatory downregulation of protein degradation and identified novel disease mechanisms for missense variants.
Conclusions:
- The developed platform enables genome engineering in iPSCs for multiplexed variant effect assays.
- Enhanced understanding of variant pathogenicity and uncovered novel biological mechanisms.
- Provides a foundation for informing therapeutic strategies for HCM.
Background:
An estimated 1 in 500 people live with hypertrophic cardiomyopathy (HCM), a disease for which genetic diagnosis can identify family members at risk, and increasingly guide therapy. Mutations in the myosin binding protein C3 (MYBPC3) gene account for a significant proportion of HCM cases. However, many of these variants are classified as variants of uncertain significance (VUS), complicating clinical decision-making. Scalable methods for variant interpretation in disease-specific cell types are crucial for understanding variant impact and uncovering disease mechanisms.
Methods:
We developed a scaled multidimensional mapping strategy to evaluate the functional impact of variants across a critical domain of MYBPC3. We incorporate saturation base editing at the native MYBPC3 locus, a long-read RNA sequencing-enabled assay of variant splice effects, and measurements of HCM-relevant phenotypes, including MYBPC3 abundance, hypertrophic signaling, and ubiquitin-proteasome function in human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs).
Results:
Our multidimensional mapping strategy enabled high-resolution functional analysis of MYBPC3 variants in iPSC-CMs. Targeted transient base editing generated a comprehensive variant library at the native locus, capturing diverse variant effects on cellular HCM-relevant phenotypes. Our massively parallel splicing assay identified novel splice-disrupting variants. Integration of functional assays revealed that decreased MYBPC3 abundance is a key driver of HCM-related phenotypes. In parallel, downregulation of protein degradation was observed as a compensatory response to MYBPC3 loss of function, and novel disease mechanisms were identified for missense variants near a critical binding domain, underscoring their contribution to pathogenesis. Bayesian estimates of variant effects enable the reclassification of clinical variants.
Conclusions:
This work provides a platform for extending genome engineering in iPSCs to multiplexed assays of variant effects across diverse disease-relevant cellular phenotypes, enhancing the understanding of variant pathogenicity and uncovering novel biological mechanisms that could inform therapeutic strategies.
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