TBX5-encoded T-box transcription factor 5 variant T223M is associated with long QT syndrome and pediatric sudden cardiac death.

Long QT syndrome (LQTS) is a genetic disease resulting in a prolonged QT interval on a resting electrocardiogram, predisposing affected individuals to polymorphic ventricular tachycardia and sudden death. Although a number of genes have been implicated in this disease, nearly one in four individuals exhibiting the LQTS phenotype are genotype-negative. Whole-exome sequencing identified a missense T223M variant in TBX5 that cosegregates with prolonged QT interval in a family with otherwise genotype-negative LQTS and sudden death. The TBX5-T223M variant was absent among large ostensibly healthy populations (gnomAD) and predicted to be pathogenic by in silico modeling based on Panther, PolyPhen-2, Provean, SIFT, SNAP2, and PredictSNP prediction tools. The variant was located in a highly conserved region of TBX5 predicted to be part of the DNA-binding interface. A luciferase assay identified a 57.5% reduction in the ability of TBX5-T223M to drive expression at the atrial natriuretic factor promotor compared to wildtype TBX5 in vitro. We conclude that the variant is pathogenic in this family, and we put TBX5 forward as a disease susceptibility allele for genotype-negative LQTS. The identification of this familial variant may serve as a basis for the identification of previously unknown mechanisms of LQTS with broader implications for cardiac electrophysiology.

Decontaminate Traces From Fluorescence Calcium Imaging Videos Using Targeted Non-negative Matrix Factorization.

Fluorescence microscopy and genetically encoded calcium indicators help understand brain function by recording large-scale in vivo videos in assorted animal models. Extracting the fluorescent transients that represent active periods of individual neurons is a key step when analyzing imaging videos. Non-specific calcium sources and background adjacent to segmented neurons contaminate the neurons' temporal traces with false transients. We developed and characterized a novel method, temporal unmixing of calcium traces (TUnCaT), to quickly and accurately unmix the calcium signals of neighboring neurons and background. Our algorithm used background subtraction to remove the false transients caused by background fluctuations, and then applied targeted non-negative matrix factorization to remove the false transients caused by neighboring calcium sources. TUnCaT was more accurate than existing algorithms when processing multiple experimental and simulated datasets. TUnCaT's speed was faster than or comparable to existing algorithms.