Woodbury, L. | Search bioRxiv

bioRxiv2023

Arrhythmia-associated Calmodulin Variants Interact with KCNQ1 to Confer Aberrant Membrane Trafficking and Function

RationaleMissense variants in calmodulin (CaM) predispose patients to arrhythmias associated with high mortality rates. As CaM regulates several key cardiac ion channels, a mechanistic understanding of CaM variant-associated arrhythmias requires elucidating individual CaM variant effect on distinct channels. One key CaM regulatory target is the KCNQ1 (KV7.1) voltage-gated potassium channel that underlie the IKs current. Yet, relatively little is known as to how CaM variants interact with KCNQ1 or affect its function. ObjectiveTo observe how arrhythmia-associated CaM variants affect binding to KCNQ1, channel membrane trafficking, and KCNQ1 function. Methods and ResultsWe combine a live-cell FRET binding assay, fluorescence trafficking assay, and functional electrophysiology to characterize >10 arrhythmia-associated CaM variants effect on KCNQ1. We identify one variant (G114W) that exhibits severely weakened binding to KCNQ1 but find that most other CaM variants interact with similar binding affinity to KCNQ1 when compared to CaM wild-type over physiological Ca2+ ranges. We further identify several CaM variants that affect KCNQ1 and IKs membrane trafficking and/or baseline current activation kinetics, thereby contextualizing KCNQ1 dysfunction in calmodulinopathy. Lastly, we delineate CaM variants with no effect on KCNQ1 function. ConclusionsThis study provides comprehensive functional data that reveal how CaM variants contribute to creating a pro-arrhythmic substrate by causing abnormal KCNQ1 membrane trafficking and current conduction. We find that CaM variant regulation of KCNQ1 is not uniform with effects varying from benign to significant loss of function. This study provides a new approach to collecting details of CaM binding that are key for understanding how CaM variants predispose patients to arrhythmia via the dysregulation of multiple cardiac ion channels.

biophysics↗

bioRxiv2021

Deep Learning-based Subcellular Phenotyping of Protrusion Dynamics Reveals Fine Differential Drug Responses at Subcellular and Single-Cell Levels

Live cell imaging provides unparallel insights into dynamic cellular processes across spatiotemporal scales. Despite its potential, the inherent spatiotemporal heterogeneity within live cell imaging data often obscures critical mechanical details underlying cellular dynamics. Uncovering fine-grained phenotypes of live cell dynamics is pivotal for precise understandings of the heterogeneity of physiological and pathological processes. However, this endeavor introduces formidable technical challenges to unsupervised machine learning, demanding the extraction of features that can faithfully preserve heterogeneity, effectively discriminate between different molecularly perturbed states, and provide interpretability. While deep learning shows promise in extracting useful features from large datasets, it often falls short in producing such high-fidelity features, especially in unsupervised learning settings. To tackle these challenges, we present DeepHACX (Deep phenotyping of Heterogeneous Activities of Cellular dynamics with eXplanations), a self-training deep learning framework designed for fine-grained and interpretable phenotyping. This framework seamlessly integrates an unsupervised teacher model with interpretable features to facilitate feature learning in a student deep neural network (DNN). Significantly, it incorporates an autoencoder-based regularizer, termed SENSER (SENSitivity-enhancing autoEncoding Regularizer), designed to prompt the student DNN to maximize the heterogeneity associated with molecular perturbations. This approach enables the acquisition of features that not only discriminate between different molecularly perturbed states but also faithfully preserve the heterogeneity linked to these perturbations. In our study, DeepHACX successfully delineated fine-grained phenotypes within the heterogeneous protrusion dynamics of migrating epithelial cells, uncovering specific responses to pharmacological perturbations. Remarkably, DeepHACX adeptly captured a minimal number of highly interpretable features uniquely linked to these fine-grained phenotypes, each corresponding to specific temporal intervals crucial for their manifestation. This unique capability positions DeepHACX as a valuable tool for investigating diverse cellular dynamics and comprehensively studying their heterogeneity.

bioinformatics↗