Systems analysis of dilated cardiomyopathy in the next generation sequencing era

Abstract

Dilated cardiomyopathy (DCM) is a form of severe failure of cardiac muscle caused by a long list of etiologies ranging from myocardial infarction, DNA mutations in cardiac genes, to toxics. Systems analysis integrating next-generation sequencing (NGS)-based omics approaches, such as the sequencing of DNA, RNA, and chromatin, provide valuable insights into DCM mechanisms. The outcome and interpretation of NGS methods can be affected by the localization of cardiac biopsy, level of tissue degradation, and variable ratios of different cell populations, especially in the presence of fibrosis. Heart tissue composition may even differ between sexes, or siblings carrying the same disease causing mutation. Therefore, before planning any experiments, it is important to fully appreciate the complexities of DCM, and the selection of samples suitable for given research question should be an interdisciplinary effort involving clinicians and biologists. The list of NGS omics datasets in DCM to date is short. More studies have to be performed to contribute to public data repositories and facilitate systems analysis. In addition, proper data integration is a difficult task requiring complex computational approaches. Despite these complications, there are multiple promising implications of systems analysis in DCM. By combining various types of datasets, for example, RNA-seq, ChIP-seq, or 4C, deep insights into cardiac biology, and possible biomarkers and treatment targets, can be gained. Systems analysis can also facilitate the annotation of noncoding mutations in cardiac-specific DNA regulatory regions that play a substantial role in maintaining the tissue- and cell-specific transcriptional programs in the heart. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Laboratory Methods and Technologies > Genetic/Genomic Methods Laboratory Methods and Technologies > RNA Methods.

RNA sequencing discloses the genome‑wide profile of long noncoding RNAs in dilated cardiomyopathy

“…Dilated cardiomyopathy (DCM) is a common type of non-ischemic cardiomyopathy, which leads to progressive heart failure and sudden cardiac-associated mortality (1–3). DCM is characterized by ventricular chamber enlargement and systolic dysfunction, and has strong associations with cardiac development, organ morphology, myocardial energy metabolism and abnormal gene expression (4,5). In patients with DCM, the possible causes of the condition are often ascribed to coronary artery disease, hypertension, viral infections and alcohol abuse (6).…”

H3K27ac acetylome signatures reveal the epigenomic reorganization in remodeled non-failing human hearts

“…These findings also demonstrate the value of genome-wide chromatin analysis in understanding the molecular regulation and etiology of myocardial remodeling and cardiovascular disease in general. With the development of single-cell sequencing techniques in biobanked patient material and the accumulating evidence of cell-to-cell heterogeneity within the very same cell population in response to myocardial remodeling [ 29 ], we might gain knowledge on the chromatin profile at the single-cell resolution. This will allow us to examine gene expression, protein level, and the crosstalk between multiple cell types and to investigate the cell-to-cell variations after chromatin regulatory activity changes and structural reorganization, further contributing to the understanding of DNA mutations in regions with regulatory function and new drug target identification for future therapies.…”

“…Additionally, studies have shown that H3K27ac exhibits the best correlation with both active promoters and enhancers when compared with other marks [ 26 , 27 ]. Despite the rapid increase of ChIP-seq data over the last decade [ 28 , 29 ], the H3K27ac landscape that defines myocardial remodeling has been only scarcely investigated [ 17 ], mainly due to the lack of proper samples and controls.…”

Applications of Deep Learning for Drug Discovery Systems with BigData

“…In addition to technological innovations related to data collection (such as nextgeneration sequencers) and dramatic improvements in data-processing technology (such as computer specifications), omics analysis, which comprehensively measures tens of thousands of biomolecules (such as transcripts and proteins) in a single experiment, is now essential in the field of life science research [87][88][89][90][91]. Against this backdrop, the economic cost of omics analysis is decreasing, and multi-omics analysis, in which multiple types of omics analyses are performed under certain experimental conditions, is becoming increasingly popular as an attempt to systematically understand life phenomena.…”