CALMODULIN MUTATION (CALM1-E141G) ASSOCIATED WITH LONG QT SYNDROME DISRUPTS CALMODULIN CALCIUM BINDING AND IMPAIRS L-TYPE CA CHANNEL INACTIVATION

Calmodulin Mutations in Human Disease

Calcium ions (Ca²⁺) are the basis of a unique and potent array of cellular responses. Calmodulin (CaM) is a small but vital protein that is able to rapidly transmit information about changes in Ca²⁺ concentrations to its regulatory targets. CaM plays a critical role in cellular Ca²⁺ signaling, and interacts with a myriad of target proteins. Ca²⁺-dependent modulation by CaM is a major component of a diverse array of processes, ranging from gene expression in neurons to the shaping of the cardiac action potential in heart cells. Furthermore, the protein sequence of CaM is highly evolutionarily conserved, and identical CaM proteins are encoded by three independent genes (CALM1-3) in humans. Mutations within any of these three genes may lead to severe cardiac deficits including severe long QT syndrome (LQTS) and/or catecholaminergic polymorphic ventricular tachycardia (CPVT). Research into disease-associated CaM variants has identified several proteins modulated by CaM that are likely to underlie the pathogenesis of these calmodulinopathies, including the cardiac L-type Ca²⁺ channel (LTCC) Ca_V1.2, and the sarcoplasmic reticulum Ca²⁺ release channel, ryanodine receptor 2 (RyR2). Here, we review the research that has been done to identify calmodulinopathic CaM mutations and evaluate the mechanisms underlying their role in disease.

Understanding Calmodulin Variants Affecting Calcium-Dependent Inactivation of L-Type Calcium Channels through Whole-Cell Simulation of the Cardiac Ventricular Myocyte

Mutations in the calcium-sensing protein calmodulin (CaM) have been linked to two cardiac arrhythmia diseases, Long QT Syndrome 14 (LQT14) and Catecholaminergic Polymorphic Ventricular Tachycardia Type 4 (CPVT4), with varying degrees of severity. Functional characterization of the CaM mutants most strongly associated with LQT14 show a clear disruption of the calcium-dependent inactivation (CDI) of the L-Type calcium channel (LCC). CPVT4 mutants on the other hand are associated with changes in their affinity to the ryanodine receptor. In clinical studies, some variants have been associated with both CPVT4 and LQT15. This study uses simulations in a model for excitation-contraction coupling in the rat ventricular myocytes to understand how LQT14 variant might give the functional phenotype similar to CPVT4. Changing the CaM-dependent transition rate by a factor of 0.75 corresponding to the D96V variant and by a factor of 0.90 corresponding to the F142L or N98S variants, in a physiologically based stochastic model of the LCC prolonger, the action potential duration changed by a small amount in a cardiac myocyte but did not disrupt CICR at 1, 2, and 4 Hz. Under beta-adrenergic simulation abnormal excitation-contraction coupling was observed above 2 Hz pacing for the mutant CaM. The same conditions applied under beta-adrenergic stimulation led to the rapid onset of arrhythmia in the mutant CaM simulations. Simulations with the LQT14 mutations under the conditions of rapid pacing with beta-adrenergic stimulation drives the cardiac myocyte toward an arrhythmic state known as Ca2+ overload. These simulations provide a mechanistic link to a disease state for LQT14-associated mutations in CaM to yield a CPVT4 phenotype. The results show that small changes to the CaM-regulated inactivation of LCC promote arrhythmia and underscore the significance of CDI in proper heart function.

Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations

Distinct missense mutations in a specific gene have been associated with different diseases as well as differing severity of a disease. Current computational methods predict the potential pathogenicity of a missense variant but fail to differentiate between separate disease or severity phenotypes. We have developed a method to overcome this limitation by applying machine learning to features extracted from molecular dynamics simulations, creating a way to predict the effect of novel genetic variants in causing a disease, drug resistance, or another specific trait. As an example, we have applied this novel approach to variants in calmodulin associated with two distinct arrhythmias as well as two different neurodegenerative diseases caused by variants in amyloid-β peptide. The new method successfully predicts the specific disease caused by a gene variant and ranks its severity with more accuracy than existing methods. We call this method molecular dynamics phenotype prediction model.