Systems analysis of dilated cardiomyopathy in the next generation sequencing era

Genetic and functional variants of the TBX20 gene promoter in dilated cardiomyopathy

BACKGROUND

Dilated cardiomyopathy (DCM) is a major cause of heart failure and sudden cardiac death. As DCM is a genetically heterogeneous disease, genetic variants of cardiac transcription factor genes may play an important role. Transcription factor TBX20, an indispensable factor in normal heart development, is involved in the regulation of cardiac structure and function. Although the TBX20 gene is associated with the occurrence and development of DCM, the influence of genetic variants of the TBX20 gene promoter region on DCM has not been reported.

METHODS

We conducted a case-control study consisting of 107 DCM patients and 210 healthy controls. Genetic variants within TBX20 gene promoter region were identified using sequencing techniques and were functionally analyzed by dual-luciferase reporting assay. Electrophoretic mobility shift assay (EMSA) was used to investigate DNA-protein interactions.

RESULTS

In this study cohort (n = 317), we identified eight variants within TBX20 gene promoter. One novel DNA sequence variants (DSV) (g.4275G>T) and four single-nucleotide polymorphisms (SNPs) [g.4169G>A (rs1263874255), g.4949C>T (rs1191745927), g.5114G>A (rs112076877), g.5252C>T (rs1356932911)] were identified in DCM patients, but in none of controls. Among them, the DSV (g.4275G>T) and three SNPs [g.4949C>T (rs1191745927), g.5114G>A (rs112076877) and g.5252C>T (rs1356932911)] significantly altered the transcription activity of TBX20 gene promoter by dual-luciferase reporting assay (p < 0.05). Further, EMSA assay indicated that the DSV (g.4275G>T) and three SNPs [g.4949C>T (rs1191745927), g.5114G>A (rs112076877) and g.5252C>T (rs1356932911)] affected the binding of transcription factors.

CONCLUSIONS

These data indicate that the DSV (g.4275G>T) and three SNPs [g.4949C>T (rs1191745927), g.5114G>A (rs112076877) and g.5252C>T (rs1356932911)] increase transcription activity of TBX20 gene promoter in both HEK-293 and neonatal rat cardiomyocytes (NRCMs) cell lines by affecting the binding of transcription factors. But the mechanism remains to be verified in vivo.

Transcriptome studies of inherited dilated cardiomyopathies

Dilated cardiomyopathy (DCM) is a group of heart muscle diseases that often lead to heart failure, with more than 50 causative genes have being linked to DCM. The heterogenous nature of the inherited DCMs suggest the need of precision medicine. Consistent with this emerging concept, transcriptome studies in human patients with DCM indicated distinct molecular signature for DCMs of different genetic etiology. To facilitate this line of research, we reviewed the status of transcriptome studies of inherited DCMs by focusing on three predominant DCM causative genes, TTN, LMNA, and BAG3. Besides studies in human patients, we summarized transcriptomic analysis of these inherited DCMs in a variety of model systems ranging from iPSCs to rodents and zebrafish. We concluded that the RNA-seq technology is a powerful genomic tool that has already led to the discovery of new modifying genes, signaling pathways, and related therapeutic avenues. We also pointed out that both temporal (different pathological stages) and spatial (different cell types) information need to be considered for future transcriptome studies. While an important bottle neck is the low throughput in experimentally testing differentially expressed genes, new technologies in efficient animal models such as zebrafish starts to be developed. It is anticipated that the RNA-seq technology will continue to uncover both unique and common pathological events, aiding the development of precision medicine for inherited DCMs.

Chromatin Immunoprecipitation Sequencing (ChIP-seq) Protocol for Small Amounts of Frozen Biobanked Cardiac Tissue

Chromatin immunoprecipitation and sequencing (ChIP-seq) is a well-established method to study the epigenetic profile at the genome-wide scale, including histone modifications and DNA-protein interactions. It provides valuable insights to better understand disease mechanisms. Here we present an optimized ChIP-seq protocol suitable for human cardiac tissues, especially the frozen biobanked small biopsy samples.

Protein Aggregation Is an Early Manifestation of Phospholamban p.(Arg14del)-Related Cardiomyopathy: Development of PLN-R14del-Related Cardiomyopathy

BACKGROUND

The p.(Arg14del) pathogenic variant (R14del) of the PLN (phospholamban) gene is a prevalent cause of cardiomyopathy with heart failure. The exact underlying pathophysiology is unknown, and a suitable therapy is unavailable. We aim to identify molecular perturbations underlying this cardiomyopathy in a clinically relevant PLN-R14del mouse model.

METHODS

We investigated the progression of cardiomyopathy in PLN-R14^Δ/Δ mice using echocardiography, ECG, and histological tissue analysis. RNA sequencing and mass spectrometry were performed on cardiac tissues at 3 (before the onset of disease), 5 (mild cardiomyopathy), and 8 (end stage) weeks of age. Data were compared with cardiac expression levels of mice that underwent myocardial ischemia-reperfusion or myocardial infarction surgery, in an effort to identify alterations that are specific to PLN-R14del-related cardiomyopathy.

RESULTS

At 3 weeks of age, PLN-R14^Δ/Δ mice had normal cardiac function, but from the age of 4 weeks, we observed increased myocardial fibrosis and impaired global longitudinal strain. From 5 weeks onward, ventricular dilatation, decreased contractility, and diminished ECG voltages were observed. PLN protein aggregation was present before onset of functional deficits. Transcriptomics and proteomics revealed differential regulation of processes involved in remodeling, inflammation, and metabolic dysfunction, in part, similar to ischemic heart disease. Altered protein homeostasis pathways were identified exclusively in PLN-R14^Δ/Δ mice, even before disease onset, in concert with aggregate formation.

CONCLUSIONS

We mapped the development of PLN-R14del-related cardiomyopathy and identified alterations in proteostasis and PLN protein aggregation among the first manifestations of this disease, which could possibly be a novel target for therapy.

Integrated regulatory network based on lncRNA-miRNA-mRNA-TF reveals key genes and sub-networks associated with dilated cardiomyopathy

Dilated Cardiomyopathy (DCM) is a multifactorial condition often leading to heart failure in many clinical cases. Due to the high number of DCMincidence reported as familial, a gene level network based study was conducted utilizing high throughput next generation sequencing data. We exploited the exome and transcriptome sequencing data in NCBI-SRA database to construct a high confidence scale-free regulatory network consisting of lncRNA, miRNA, mRNA and Transcription Factors (TFs). Analysis of RNA-Seq data revealed 477 differentially expressed coding transcripts and 77 lncRNAs. 268 miRNAs regulated either lncRNAs or mRNAs. Out of the 477 coding transcripts that are deregulated, 82 were TFs. We identified three major hub nodeslncRNA (XIST), miRNA (hsa-miR-195-5p) and mRNA (NOVA1) from the network. We also found putative disease associations of DCM with diabetes and DCM with hypoventillation syndrome. Five highly connected modules were also identified from the network. The hubs showed significant connectivity with the modules.Through this study we were able to gain insights into the underlying lncRNA-miRNA-mRNA-TF network. From a high throughput dataset we have isolated a handful of probable targets that may be utilized for studying the mechanisms of DCM development and progression to heart failure.

Consensus Transcriptional Landscape of Human End-Stage Heart Failure

Background Transcriptomic studies have contributed to fundamental knowledge of myocardial remodeling in human heart failure (HF). However, the key HF genes reported are often inconsistent between studies, and systematic efforts to integrate evidence from multiple patient cohorts are lacking. Here, we aimed to provide a framework for comprehensive comparison and analysis of publicly available data sets resulting in an unbiased consensus transcriptional signature of human end-stage HF. Methods and Results We curated and uniformly processed 16 public transcriptomic studies of left ventricular samples from 263 healthy and 653 failing human hearts. First, we evaluated the degree of consistency between studies by using linear classifiers and overrepresentation analysis. Then, we meta-analyzed the deregulation of 14 041 genes to extract a consensus signature of HF. Finally, to functionally characterize this signature, we estimated the activities of 343 transcription factors, 14 signaling pathways, and 182 micro RNAs, as well as the enrichment of 5998 biological processes. Machine learning approaches revealed conserved disease patterns across all studies independent of technical differences. These consistent molecular changes were prioritized with a meta-analysis, functionally characterized and validated on external data. We provide all results in a free public resource (https://saezlab.shinyapps.io/reheat/) and exemplified usage by deciphering fetal gene reprogramming and tracing the potential myocardial origin of the plasma proteome markers in patients with HF. Conclusions Even though technical and sampling variability confound the identification of differentially expressed genes in individual studies, we demonstrated that coordinated molecular responses during end-stage HF are conserved. The presented resource is crucial to complement findings in independent studies and decipher fundamental changes in failing myocardium.

Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics

Dilated cardiomyopathy (DCM) is a leading cause of heart failure and life-threatening ventricular arrhythmias (LTVA). Work-up and risk stratification of DCM is clinically challenging, as there is great heterogeneity in phenotype and genotype. Throughout the last decade, improved genetic testing of patients has identified genotype-phenotype associations and enhanced evaluation of at-risk relatives leading to better patient prognosis. The field is now ripe to explore opportunities to improve personalised risk assessments. Multivariable risk models presented as "risk calculators" can incorporate a multitude of clinical variables and predict outcome (such as heart failure hospitalisations or LTVA). In addition, genetic risk scores derived from genome/exome-wide association studies can estimate an individual's lifetime genetic risk of developing DCM. The use of clinically granular investigations, such as late gadolinium enhancement on cardiac magnetic resonance imaging, is warranted in order to increase predictive performance. To this end, constructing big data infrastructures improves accessibility of data by using electronic health records, existing research databases, and disease registries. By applying methods such as machine and deep learning, we can model complex interactions, identify new phenotype clusters, and perform prognostic modelling. This review aims to provide an overview of the evolution of DCM definitions as well as its clinical work-up and considerations in the era of genomics. In addition, we present exciting examples in the field of big data infrastructures, personalised prognostic assessment, and artificial intelligence.

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

BACKGROUND

H3K27ac histone acetylome changes contribute to the phenotypic response in heart diseases, particularly in end-stage heart failure. However, such epigenetic alterations have not been systematically investigated in remodeled non-failing human hearts. Therefore, valuable insight into cardiac dysfunction in early remodeling is lacking. This study aimed to reveal the acetylation changes of chromatin regions in response to myocardial remodeling and their correlations to transcriptional changes of neighboring genes.

RESULTS

We detected chromatin regions with differential acetylation activity (DARs; Padj. < 0.05) between remodeled non-failing patient hearts and healthy donor hearts. The acetylation level of the chromatin region correlated with its RNA polymerase II occupancy level and the mRNA expression level of its adjacent gene per sample. Annotated genes from DARs were enriched in disease-related pathways, including fibrosis and cell metabolism regulation. DARs that change in the same direction have a tendency to cluster together, suggesting the well-reorganized chromatin architecture that facilitates the interactions of regulatory domains in response to myocardial remodeling. We further show the differences between the acetylation level and the mRNA expression level of cell-type-specific markers for cardiomyocytes and 11 non-myocyte cell types. Notably, we identified transcriptome factor (TF) binding motifs that were enriched in DARs and defined TFs that were predicted to bind to these motifs. We further showed 64 genes coding for these TFs that were differentially expressed in remodeled myocardium when compared with controls.

CONCLUSIONS

Our study reveals extensive novel insight on myocardial remodeling at the DNA regulatory level. Differences between the acetylation level and the transcriptional level of cell-type-specific markers suggest additional mechanism(s) between acetylome and transcriptome. By integrating these two layers of epigenetic profiles, we further provide promising TF-encoding genes that could serve as master regulators of myocardial remodeling. Combined, our findings highlight the important role of chromatin regulatory signatures in understanding disease etiology.

Multiomic approaches to delineate the pathogenesis of cardiac disease

PURPOSE OF REVIEW

Comprehensive analyses of the genome, transcriptome, proteome and metabolome are instrumental in identifying biomarkers of disease, to gain insight into mechanisms underlying the development of cardiovascular disease, and show promise for better stratifying patients according to disease subtypes. This review highlights recent 'omics' studies, including integration of multiple 'omics' that have advanced mechanistic understanding and diagnosis in humans and animal models.

RECENT FINDINGS

Transcriptome-based discovery continues to be a primary method to obtain data for hypothesis generation and the understanding of disease pathogenesis has been enhanced by single cell-based methods capable of revealing heterogeneity in cellular responses. Advances in proteome coverage and quantitation of individual protein species, together with enhanced methods for detecting posttranslational modifications, have improved discovery of protein-based biomarkers.

SUMMARY

High-throughput assays capable of quantitating the vast majority of any particular type of biomolecule within a tissue sample, isolated cells or plasma are now available. In order to make best use of the large amount of data that can be generated on given molecule types, as well as their interrelationships in disease, continued development of pattern-recognition algorithms ('machine learning') will be required and the subclassification of disease that is made possible by such algorithms will be likely to inform clinical practice, and vice versa.

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, of which the underlying mechanisms have not yet been fully elucidated. Long noncoding RNAs (lncRNAs) have been reported to serve crucial physiological roles in various cardiac diseases. However, the genome‑wide expression profile of lncRNAs remains to be elucidated in DCM. In the present study, a case‑control study was performed to identify expression deviations in circulating lncRNAs between patients with DCM and controls by RNA sequencing. Partial dysregulated lncRNAs were validated by reverse transcription‑polymerase chain reaction. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and lncRNA‑messenger RNA (mRNA) co‑expression network analyses were employed to probe potential functions of these dysregulated lncRNAs in DCM. Comparison between 8 DCM and 8 control samples demonstrated that there were alterations in the expression levels of 988 lncRNAs and 1,418 mRNAs in total. The dysregulated lncRNAs were found to be mainly associated with system development, organ morphogenesis and metabolic regulation in terms of 'biological processes'. Furthermore, the analysis revealed that the gap junction pathway, phagosome, and dilated and hypertrophic cardiomyopathy pathways may serve crucial roles in the development of DCM. The lncRNA‑mRNA co‑expression network also suggested that the target genes of the lncRNAs were different in patients with DCM as compared with those in the controls. In conclusion, the present study revealed the genome‑wide profile of circulating lncRNAs in DCM by RNA sequencing, and explored the potential functions of these lncRNAs in DCM using bioinformatics analysis. These findings provide a theoretical foundation for future studies of lncRNAs in DCM.