µ-Theraphotoxin-Pn3a inhibition of Cav3.3 channels reveals a novel isoform-selective drug binding site

Low voltage-activated calcium currents are mediated by T-type calcium channels CaV3.1, CaV3.2, and CaV3.3, which modulate a variety of physiological processes including sleep, cardiac pace-making, pain, and epilepsy. CaV3 isoforms’ biophysical properties, overlapping expression and lack of subtype-selective pharmacology hinder the determination of their specific physiological roles in health and disease. Notably, CaV3.3’s contribution to normal and pathophysiological function has remained largely unexplored. We have identified Pn3a as the first subtype-selective spider venom peptide inhibitor of CaV3.3, with >100-fold lower potency against the other T-type isoforms. Pn3a modifies CaV3.3 gating through a depolarizing shift in the voltage dependence of activation thus decreasing CaV3.3-mediated currents in the normal range of activation potentials. Paddle chimeras of KV1.7 channels bearing voltage sensor sequences from all four CaV3.3 domains revealed preferential binding of Pn3a to the S3-S4 region of domain II (CaV3.3DII). This novel T-type channel pharmacological site was explored through computational docking simulations of Pn3a into all T-type channel isoforms highlighting it as subtype-specific pharmacophore with therapeutic potential. This research expands our understanding of T-type calcium channel pharmacology and supports the suitability of Pn3a as a molecular tool in the study of the physiological roles of CaV3.3 channels.

CryoEM structure of a prokaryotic cyclic nucleotide-gated ion channel

Cyclic nucleotide-gated (CNG) and hyperpolarization-activated cyclic nucleotide-regulated (HCN) ion channels play crucial physiological roles in phototransduction, olfaction, and cardiac pace making. These channels are characterized by the presence of a carboxyl-terminal cyclic nucleotide-binding domain (CNBD) that connects to the channel pore via a C-linker domain. Although cyclic nucleotide binding has been shown to promote CNG and HCN channel opening, the precise mechanism underlying gating remains poorly understood. Here we used cryoEM to determine the structure of the intact LliK CNG channel isolated from Leptospira licerasiae—which shares sequence similarity to eukaryotic CNG and HCN channels—in the presence of a saturating concentration of cAMP. A short S4–S5 linker connects nearby voltage-sensing and pore domains to produce a non–domain-swapped transmembrane architecture, which appears to be a hallmark of this channel family. We also observe major conformational changes of the LliK C-linkers and CNBDs relative to the crystal structures of isolated C-linker/CNBD fragments and the cryoEM structures of related CNG, HCN, and KCNH channels. The conformation of our LliK structure may represent a functional state of this channel family not captured in previous studies.

Intelligent Classifier for Atrial Fibrillation (ECG)

This chapter is focused on the analysis and classification of arrhythmias. An arrhythmia is any cardiac pace that is not the typical sinusoidal one due to alterations in the formation and/or transportation of the impulses. In pathological conditions, the depolarization process can be initiated outside the sinoatrial (SA) node and several kinds of extra-systolic or ectopic beatings can appear. Besides, electrical impulses can be blocked, accelerated, deviated by alternate trajectories and can change its origin from one heart beat to the other, thus originating several types of blockings and anomalous connections. In both situations, changes in the signal morphology or in the duration of its waves and intervals can be produced on the ECG, as well as a lack of one of the waves. This work is focused on the development of intelligent classifiers in the area of biomedicine, focusing on the problem of diagnosing cardiac diseases based on the electrocardiogram (ECG), or more precisely on the differentiation of the types of atrial fibrillations. First of all we will study the ECG, and the treatment of the ECG in order to work with it, with this specific pathology. In order to achieve this we will study different ways of elimination, in the best possible way, of any activity that is not caused by the auriculars. We will study and imitate the ECG treatment methodologies and the characteristics extracted from the electrocardiograms that were used by the researchers that obtained the best results in the Physionet Challenge, where the classification of ECG recordings according to the type of Atrial Fibrillation (AF) that they showed, was realised. We will extract a great amount of characteristics, partly those used by these researchers and additional characteristics that we consider to be important for the distinction mentioned before. A new method based on evolutionary algorithms will be used to realise a selection of the most relevant characteristics and to obtain a classifier that will be capable of distinguishing the different types of this pathology.