Biological Basis of Genetic cMyBP-C Cardiomyopathies

ABSTRACT This collaborative project integrates fundamental molecular biology, cell level biophysics, animal-level physiology, and computer modeling to advance understanding of molecular mechanisms by which inherited mutations in cardiac myosin binding protein C (cMyBP-C) cause disease. Some individuals who inherit mutations in this protein are at increased risk of developing hypertrophic cardiomyopathy but clinicians know that not all mutations lead to significant disease. Linking genotype to phenotype is particularly challenging for missense mutations as these often cause cMyBP-C molecules with abnormal function to be expressed in a patient’s heart. More than 1000 missense mutations have already been identified but there is rarely enough clinical information to determine the severity and/or best treatments for a given variant. Therefore, most are still characterized as variants of unknown significance. Further, the field’s understanding of the basic mechanisms by which missense mutations in cMyBP-C cause disease is limited because cMyBP-C exhibits complex behaviors and it’s N-terminal and central domains can impact contractile function in diverse ways by interacting with both myosin and actin. While it seems likely that the mutation’s location on the molecule determines its impact on contractile function, mechanistic analyses of the region-specific molecular underpinnings of cMyBP-C missense variants has not yet been performed. The large number of variants makes it impractical to create animal or cell-based models for each missense mutation. This project advances the field by combining strategically selected biological experiments with computer modeling to develop a data-driven pipeline that can ultimately be used to identify which missense mutations currently classified as variants of unknown significance pose the greatest risk to patients and the best way to treat each variant. The plan is highly innovative and makes intelligent use of the skills and resources of four leading investigators. The team are committed to developing shared resources and will publish their computer code as open-source projects as well as sharing their cell and animal-level data as freely-accessible databases to accelerate future research.