TECRL, a new life‐threatening inherited arrhythmia gene associated with overlapping clinical features of both LQTS and CPVT

Genetic causes of many familial arrhythmia syndromes remain elusive. In this study, whole-exome sequencing (WES) was carried out on patients from three different families that presented with life-threatening arrhythmias and high risk of sudden cardiac death (SCD). Two French Canadian probands carried identical homozygous rare variant in TECRL gene (p.Arg196Gln), which encodes the trans-2,3-enoyl-CoA reductase-like protein. Both patients had cardiac arrest, stress-induced atrial and ventricular tachycardia, and QT prolongation on adrenergic stimulation. A third patient from a consanguineous Sudanese family diagnosed with catecholaminergic polymorphic ventricular tachycardia (CPVT) had a homozygous splice site mutation (c.331+1G>A) in TECRL Analysis of intracellular calcium ([Ca(2+)]i) dynamics in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) generated from this individual (TECRLHom-hiPSCs), his heterozygous but clinically asymptomatic father (TECRLHet-hiPSCs), and a healthy individual (CTRL-hiPSCs) from the same Sudanese family, revealed smaller [Ca(2+)]i transient amplitudes as well as elevated diastolic [Ca(2+)]i in TECRLHom-hiPSC-CMs compared with CTRL-hiPSC-CMs. The [Ca(2+)]i transient also rose markedly slower and contained lower sarcoplasmic reticulum (SR) calcium stores, evidenced by the decreased magnitude of caffeine-induced [Ca(2+)]i transients. In addition, the decay phase of the [Ca(2+)]i transient was slower in TECRLHom-hiPSC-CMs due to decreased SERCA and NCX activities. Furthermore, TECRLHom-hiPSC-CMs showed prolonged action potentials (APs) compared with CTRL-hiPSC-CMs. TECRL knockdown in control human embryonic stem cell-derived CMs (hESC-CMs) also resulted in significantly longer APs. Moreover, stimulation by noradrenaline (NA) significantly increased the propensity for triggered activity based on delayed afterdepolarizations (DADs) in TECRLHom-hiPSC-CMs and treatment with flecainide, a class Ic antiarrhythmic drug, significantly reduced the triggered activity in these cells. In summary, we report that mutations in TECRL are associated with inherited arrhythmias characterized by clinical features of both LQTS and CPVT Patient-specific hiPSC-CMs recapitulated salient features of the clinical phenotype and provide a platform for drug screening evidenced by initial identification of flecainide as a potential therapeutic. These findings have implications for diagnosis and treatment of inherited cardiac arrhythmias

Inferring microRNA and transcription factor regulatory networks in heterogeneous data

Background: Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA. Results: We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then, in order to produce more meaningful results for further biological experimentation and research, the method searches the learnt network to identify the interplay between miRNAs and TFs and applies a network motif finding algorithm to further infer the network. We apply the proposed framework to the data sets of epithelial-to-mesenchymal transition (EMT). The results elucidate the complex gene regulatory mechanism for EMT which involves both TFs and miRNAs. Several discovered interactions and molecular functions have been confirmed by literature. In addition, many other discovered interactions and bio-markers are of high statistical significance and thus can be good candidates for validation by experiments. Moreover, the results generated by our method are compact, involving a small number of interactions which have been proved highly relevant to EMT. Conclusions: We have designed a framework to infer gene regulatory networks involving both TFs and miRNAs from multiple sources of data, including gene expression data, target information, and sample categories. Results on the EMT data sets have shown that the proposed approach is able to produce compact and meaningful gene regulatory networks that are highly relevant to the biological conditions of the data sets. This framework has the potential for application to other heterogeneous datasets to reveal the complex gene regulatory relationships.Thuc D Le, Lin Liu, Bing Liu, Anna Tsykin, Gregory J Goodall, Kenji Satou and Jiuyong L

Time-resolved analysis of transcriptional events during SNAI1-triggered epithelial to mesenchymal transition

The transcription regulator SNAI1 triggers a transcriptional program leading to epithelial to mesenchymal transition (EMT), providing epithelial cells with mesenchymal features and invasive properties during embryonic development and tumor progression. To identify early transcriptional changes occurring during SNAI1-induced EMT, we performed a time-resolved genome-scale study using human breast carcinoma cells conditionally expressing SNAI1. The approach we developed for microarray data analysis, allowed identifying three distinct EMT stages and the temporal classification of genes. Importantly, we identified unexpected, biphasic expression profiles of EMT-associated genes, supporting their pivotal role during this process. Finally, we established early EMT gene networks by identifying transcription factors and their potential targets which may orchestrate early events of EMT. Collectively, our work provides a framework for the identification and future systematic analysis of novel genes which contribute to SNAI1-triggered EMT