Genome Editing and Cardiac Arrhythmias.

This article reviews progress in the field of cardiac genome editing, in particular, its potential utility in treating cardiac arrhythmias. First, we discuss genome editing methods by which DNA can be disrupted, inserted, deleted, or corrected in cardiomyocytes. Second, we provide an overview of in vivo genome editing in preclinical models of heritable and acquired arrhythmias. Third, we discuss recent advancements in cardiac gene transfer, including delivery methods, gene expression optimization, and potential adverse effects associated with therapeutic somatic genome editing. While genome editing for cardiac arrhythmias is still in its infancy, this approach holds great promise, especially for inherited arrhythmia syndromes with a defined genetic defect.

Role of Ca2+ in healthy and pathologic cardiac function: from normal excitation-contraction coupling to mutations that cause inherited arrhythmia.

Calcium (Ca2+) ions are a key second messenger involved in the rhythmic excitation and contraction of cardiomyocytes throughout the heart. Proper function of Ca2+-handling proteins is required for healthy cardiac function, whereas disruption in any of these can cause cardiac arrhythmias. This comprehensive review provides a broad overview of the roles of Ca2+-handling proteins and their regulators in healthy cardiac function and the mechanisms by which mutations in these proteins contribute to inherited arrhythmias. Major Ca2+ channels and Ca2+-sensitive regulatory proteins involved in cardiac excitation-contraction coupling are discussed, with special emphasis on the function of the RyR2 macromolecular complex. Inherited arrhythmia disorders including catecholaminergic polymorphic ventricular tachycardia, long QT syndrome, Brugada syndrome, short QT syndrome, and arrhythmogenic right-ventricular cardiomyopathy are discussed with particular emphasis on subtypes caused by mutations in Ca2+-handling proteins.

Limitations of alignment-free tools in total RNA-seq quantification.

BACKGROUND: Alignment-free RNA quantification tools have significantly increased the speed of RNA-seq analysis. However, it is unclear whether these state-of-the-art RNA-seq analysis pipelines can quantify small RNAs as accurately as they do with long RNAs in the context of total RNA quantification. RESULT: We comprehensively tested and compared four RNA-seq pipelines for accuracy of gene quantification and fold-change estimation. We used a novel total RNA benchmarking dataset in which small non-coding RNAs are highly represented along with other long RNAs. The four RNA-seq pipelines consisted of two commonly-used alignment-free pipelines and two variants of alignment-based pipelines. We found that all pipelines showed high accuracy for quantifying the expression of long and highly-abundant genes. However, alignment-free pipelines showed systematically poorer performance in quantifying lowly-abundant and small RNAs. CONCLUSION: We have shown that alignment-free and traditional alignment-based quantification methods perform similarly for common gene targets, such as protein-coding genes. However, we have identified a potential pitfall in analyzing and quantifying lowly-expressed genes and small RNAs with alignment-free pipelines, especially when these small RNAs contain biological variations.