Multiscale computational analysis of the bioelectric consequences of myocardial ischaemia and infarction

[EN] Ischaemic heart disease is considered as the single most frequent cause of death, provoking more than 7 000 000 deaths every year worldwide. A high percentage of patients experience sudden cardiac death, caused in most cases by tachyarrhythmic mechanisms associated to myocardial ischaemia and infarction. These diseases are difficult to study using solely experimental means due to their complex dynamics and unstable nature. In the past decades, integrative computational simulation techniques have become a powerful tool to complement experimental and clinical research when trying to elucidate the intimate mechanisms of ischaemic electrophysiological processes and to aid the clinician in the improvement and optimization of therapeutic procedures. The purpose of this paper is to briefly review some of the multiscale computational models of myocardial ischaemia and infarction developed in the past 20 years, ranging from the cellular level to whole-heart simulations.This work was partially supported by the 'VI Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica' from the Ministerio de Economia y Competitividad of Spain (grant number TIN2012-37546-C03-01) and the European Commission (European Regional Development Funds-ERDF-FEDER), and by the Direccion General de Politica Cientifica de la Generalitat Valenciana (grant number GV/2013/119).Ferrero De Loma-Osorio, JM.; Trénor Gomis, BA.; Romero Pérez, L. (2014). Multiscale computational analysis of the bioelectric consequences of myocardial ischaemia and infarction. EP-Europace. 16(3):405-415. https://doi.org/10.1093/europace/eut405S40541516

Genetic regulation of circular RNA expression in human aortic smooth muscle cells and vascular traits

Summary: Circular RNAs (circRNAs) are a class of non-coding RNAs that have cell-type-specific expression and are relevant in cardiovascular disease. Aortic smooth muscle cells (SMCs) play a crucial role in cardiovascular disease. In this study, we employed a systems genetics approach to identify SMC circRNA transcripts and their relevance in cardiovascular traits across the genome. We quantified circRNA expression across 151 quiescent and proliferative human aortic SMCs from donors of various genetic ancestries. We identified 1,589 expressed circRNAs. Between quiescent and proliferative SMCs, we identified 173 differentially expressed circRNAs. To characterize the genetic regulation of circRNA expression, we associated the genotypes of 6.3 million single nucleotide polymorphisms (SNPs) with circRNA abundance and found 96 circRNAs that were associated with genetic loci. Three SNPs were associated with circRNA expression in proliferative SMCs but not quiescent SMCs. We identified six SNPs that had distinct association directions with circRNA isoforms from the same gene. Lastly, to identify the relevance of circRNAs in cardiovascular disease, we overlapped genetic loci associated with circRNA expression with vascular disease-related genome-wide association studies loci. We identified 14 blood pressure, one myocardial infarction, and three coronary artery disease loci, which were associated with a circRNA transcript but not an mRNA transcript. Overall, our results provide insight into the genetic basis of vascular disease traits mediated by circRNA expression

Genetic Regulation of SMC Gene Expression and Splicing Predict Causal CAD Genes

Background: Coronary artery disease (CAD) is the leading cause of death worldwide. Recent meta-analyses of genome-wide association studies have identified over 175 loci associated with CAD. The majority of these loci are in noncoding regions and are predicted to regulate gene expression. Given that vascular smooth muscle cells (SMCs) play critical roles in the development and progression of CAD, we aimed to identify the subset of the CAD loci associated with the regulation of transcription in distinct SMC phenotypes. Methods: We measured gene expression in SMCs isolated from the ascending aortas of 151 heart transplant donors of various genetic ancestries in quiescent or proliferative conditions and calculated the association of their expression and splicing with ∼6.3 million imputed single-nucleotide polymorphism markers across the genome. Results: We identified 4910 expression and 4412 splicing quantitative trait loci (sQTLs) representing regions of the genome associated with transcript abundance and splicing. A total of 3660 expression quantitative trait loci (eQTLs) had not been observed in the publicly available Genotype-Tissue Expression dataset. Further, 29 and 880 eQTLs were SMC-specific and sex-biased, respectively. We made these results available for public query on a user-friendly website. To identify the effector transcript(s) regulated by CAD loci, we used 4 distinct colocalization approaches. We identified 84 eQTL and 164 sQTL that colocalized with CAD loci, highlighting the importance of genetic regulation of mRNA splicing as a molecular mechanism for CAD genetic risk. Notably, 20% and 35% of the eQTLs were unique to quiescent or proliferative SMCs, respectively. One CAD locus colocalized with a sex-specific eQTL (TERF2IP), and another locus colocalized with SMC-specific eQTL (ALKBH8). The most significantly associated CAD locus, 9p21, was an sQTL for the long noncoding RNA CDKN2B-AS1, also known as ANRIL, in proliferative SMCs. Conclusions: Collectively, our results provide evidence for the molecular mechanisms of genetic susceptibility to CAD in distinct SMC phenotypes