Disease-linked supertrafficking of a potassium channel

Integrative analysis of KCNQ1 variants reveals molecular mechanisms of type 1 long QT syndrome pathogenesis

Loss-of-function (LOF) pathogenic variants in KCNQ1 encoding a cardiac potassium channel predispose to sudden cardiac death in type 1 congenital long QT syndrome (LQT1). To determine the spectrum of molecular mechanisms responsible for this life-threatening condition, we used an integrative approach to determine the biophysical, functional, and trafficking properties of 61 KCNQ1 variants distributed throughout all domains of the channel. Impaired trafficking to the plasma membrane was the most common cause of LOF across all channel domains, often but not always coinciding with protein instability. However, many LOF variants, particularly in transmembrane domains, trafficked normally, but when coexpressed with KCNE1 exhibited impaired conductance, altered voltage dependence, or abnormal gating kinetics, highlighting diverse pathogenic mechanisms. This indicates a need for personalized treatment approaches for LQT1. Use of our data to benchmark variant pathogenicity prediction methods demonstrated that prediction accuracy depends on the exact mechanism of pathogenicity associated with a given variant.

Evidence that the cold- and menthol-sensing functions of the human TRPM8 channel evolved separately

Transient receptor potential melastatin 8 (TRPM8) is a temperature- and menthol-sensitive ion channel that contributes to diverse physiological roles, including cold sensing and pain perception. Clinical trials targeting TRPM8 have faced repeated setbacks predominantly due to the knowledge gap in unraveling the molecular underpinnings governing polymodal activation. A better understanding of the molecular foundations between the TRPM8 activation modes may aid the development of mode-specific, thermal-neutral therapies. Ancestral sequence reconstruction was used to explore the origins of TRPM8 activation modes. By resurrecting key TRPM8 nodes along the human evolutionary trajectory, we gained valuable insights into the trafficking, stability, and function of these ancestral forms. Notably, this approach unveiled the differential emergence of cold and menthol sensitivity over evolutionary time, providing a fresh perspective on complex polymodal behavior. These studies provide a paradigm for understanding polymodal behavior in TRPM8 and other proteins with the potential to enhance our understanding of sensory receptor biology and pave the way for innovative therapeutic interventions.

Predicting the functional impact of KCNQ1 variants with artificial neural networks

Recent advances in experimental and computational protein structure determination have provided access to high-quality structures for most human proteins and mutants thereof. However, linking changes in structure in protein mutants to functional impact remains an active area of method development. If successful, such methods can ultimately assist physicians in taking appropriate treatment decisions. This work presents three artificial neural network (ANN)-based predictive models that classify four key functional parameters of KCNQ1 variants as normal or dysfunctional using PSSM-based evolutionary and/or biophysical descriptors. Recent advances in predicting protein structure and variant properties with artificial intelligence (AI) rely heavily on the availability of evolutionary features and thus fail to directly assess the biophysical underpinnings of a change in structure and/or function. The central goal of this work was to develop an ANN model based on structure and physiochemical properties of KCNQ1 potassium channels that performs comparably or better than algorithms using only on PSSM-based evolutionary features. These biophysical features highlight the structure-function relationships that govern protein stability, function, and regulation. The input sensitivity algorithm incorporates the roles of hydrophobicity, polarizability, and functional densities on key functional parameters of the KCNQ1 channel. Inclusion of the biophysical features outperforms exclusive use of PSSM-based evolutionary features in predicting activation voltage dependence and deactivation time. As AI is increasingly applied to problems in biology, biophysical understanding will be critical with respect to 'explainable AI', i.e., understanding the relation of sequence, structure, and function of proteins. Our model is available at www.kcnq1predict.org.

Remodeling of Ion Channel Trafficking and Cardiac Arrhythmias

Both inherited and acquired cardiac arrhythmias are often associated with the abnormal functional expression of ion channels at the cellular level. The complex machinery that continuously traffics, anchors, organizes, and recycles ion channels at the plasma membrane of a cardiomyocyte appears to be a major source of channel dysfunction during cardiac arrhythmias. This has been well established with the discovery of mutations in the genes encoding several ion channels and ion channel partners during inherited cardiac arrhythmias. Fibrosis, altered myocyte contacts, and post-transcriptional protein changes are common factors that disorganize normal channel trafficking during acquired cardiac arrhythmias. Channel availability, described notably for hERG and K_V1.5 channels, could be another potent arrhythmogenic mechanism. From this molecular knowledge on cardiac arrhythmias will emerge novel antiarrhythmic strategies.