Heart failure: Pilot transcriptomic analysis of cardiac tissue by RNA-sequencing

RNA quality score evaluation: A preliminary study of RNA integrity number (RIN) and RNA integrity and quality number (RNA IQ)

In the past several years, with the in-depth development of RNA-related research, exploring the application of transcriptome and corresponding RNA biomarkers has become one of the research hotspots in the field of forensic science. High-quality RNA is essential for successful downstream workflows, especially in the steps of screening biomarkers by microarray or RNA sequencing (RNA-seq). Thus, accurately evaluating the quality of RNA samples is a critical step in obtaining meaningful expression data. The RNA integrity number (RIN) generated from the Agilent Bioanalyzer system has been widely used for RNA quality control in the past two decades. Recently, Thermo Fisher Scientific launched a ratiometric fluorescence-based method to quickly check whether an RNA sample has degraded, and the results are presented as RNA integrity and quality number (RNA IQ). Both quality score systems determine RNA quality using a numerical system based on a scale of 1-10, with 1 denoting significantly degraded specimens and 10 representing high-quality, intact RNA samples. In this preliminary study, we evaluated the consistency, reproducibility and linearity of two quality scores in RNA quality determination by analyzing heat- and RNase- artificially degraded samples. Meanwhile, the expression levels of three microRNAs (hsa-let-7 g-5p, hsa-miR-93-5p and hsa-miR-191-5p) in intact and severely degraded RNA samples were estimated by TaqMan-qPCR and droplet digital PCR. Overall, both quality scores showed good repeatability and reproducibility in their respective tests. In the samples subjected to thermal degradation, RIN showed a trend corresponding to heating time, while RNA IQ value showed almost no change on the time gradient. However, in RNase A mediated degradation, RNA IQ value observed better linearity. Furthermore, the expression levels of three microRNAs in the severely degraded samples did not show significant changes compared to the intact RNA samples. RNA degradation is a very complex and highly variable process, which is difficult to comprehensively evaluate through any one index and cannot directly compare these two parameters. Nevertheless, combined with previous research results and the expression levels of three microRNAs in this study, analyzing RNA biomarkers with stable regions or small sizes in challenged samples may be a conservative and reliable approach.

Integrative analysis of bioinformatics and machine learning to identify cuprotosis-related biomarkers and immunological characteristics in heart failure

Backgrounds

Cuprotosis is a newly discovered programmed cell death by modulating tricarboxylic acid cycle. Emerging evidence showed that cuprotosis-related genes (CRGs) are implicated in the occurrence and progression of multiple diseases. However, the mechanism of cuprotosis in heart failure (HF) has not been investigated yet.

Methods

The HF microarray datasets GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed CRGs between HF patients and nonfailing donors (NFDs). Four machine learning models were used to identify key CRGs features for HF diagnosis. The expression profiles of key CRGs were further validated in a merged GEO external validation dataset and human samples through quantitative reverse-transcription polymerase chain reaction (qRT-PCR). In addition, Gene Ontology (GO) function enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and immune infiltration analysis were used to investigate potential biological functions of key CRGs.

Results

We discovered nine differentially expressed CRGs in heart tissues from HF patients and NFDs. With the aid of four machine learning algorithms, we identified three indicators of cuprotosis (DLAT, SLC31A1, and DLST) in HF, which showed good diagnostic properties. In addition, their differential expression between HF patients and NFDs was confirmed through qRT-PCR. Moreover, the results of enrichment analyses and immune infiltration exhibited that these diagnostic markers of CRGs were strongly correlated to energy metabolism and immune activity.

Conclusions

Our study discovered that cuprotosis was strongly related to the pathogenesis of HF, probably by regulating energy metabolism-associated and immune-associated signaling pathways.

Familial Dilated Cardiomyopathy: A Novel MED9 Short Isoform Identification

Familial dilated cardiomyopathy (DCM) is among the leading indications for heart transplantation. DCM alters the transcriptomic profile. The alteration or activation/silencing of physiologically operating transcripts may explain the onset and progression of this pathological state. The mediator complex (MED) plays a fundamental role in the transcription process. The aim of this study is to investigate the MED subunits, which are altered in DCM, to identify target crossroads genes. RNA sequencing allowed us to identify specific MED subunits that are altered during familial DCM, transforming into human myocardial samples. N = 13 MED subunits were upregulated and n = 7 downregulated. MED9 alone was significantly reduced in patients compared to healthy subjects (HS) (FC = -1.257; p < 0.05). Interestingly, we found a short MED9 isoform (MED9s) (ENSG00000141026.6), which was upregulated when compared to the full-transcript isoform (MED9f). Motif identification analysis yielded several significant matches (p < 0.05), such as GATA4, which is downregulated in CHD. Moreover, although the protein-protein interaction network showed FOG2/ZFPM2, FOS and ID2 proteins to be the key interacting partners of GATA4, only FOG2/ZFPM2 overexpression showed an interaction score of "high confidence" ≥ 0.84. A significant change in the MED was observed during HF. For the first time, the MED9 subunit was significantly reduced between familial DCM and HS (p < 0.05), showing an increased MED9s isoform in DCM patients with respect to its full-length transcript. MED9 and GATA4 shared the same sequence motif and were involved in a network with FOG2/ZFPM2, FOS, and ID2, proteins already implicated in cardiac development.

A Set of Possible Markers for Monitoring Heart Failure and Cognitive Impairment Associated: A Review of Literature from the Past 5 Years

BACKGROUND

Heart failure is an epidemiologically relevant disease because of the aging population and widespread lifestyles that promote it. In addition to the acute event, it is possible for the disease to become chronic with periodic flare-ups. It is essential to study pathology from a diagnostic and prognostic point of view and to identify parameters for effective monitoring. In addition, heart failure is associated with multiple comorbidities, including cognitive impairment, which is monitored clinically but not through specific biomarkers in these patients. The purpose of this review is to gather the most recent scientific evidence on a few possible biomarkers previously identified for monitoring heart failure and associated cognitive impairment.

METHODS

We surveyed studies inherent to a set of previously identified markers, evaluating English-language articles from the past five years conducted in adult heart failure patient populations. We used the databases PubMed, Web of Sciences, and Cochrane Library for search studies, and we considered articles published in journals with an impact factor greater than five in the publication year.

RESULTS

Among the biomarkers evaluated, a concordant indication for serial measurements for heart failure monitoring emerged only for interleukin-6. For the other markers, there is still little evidence available, which is interesting but sometimes conflicting. Interesting studies have also emerged for biomarkers of cognitive decline assessed in patients with heart failure, confirming the hypotheses of the increasingly studied heart-brain correlation.

CONCLUSION

Certainly, further studies in large populations are needed to identify effective biomarkers for monitoring heart failure and associated cognitive impairment.

Meta-Analysis of Mechano-Sensitive Ion Channels in Human Hearts: Chamber- and Disease-Preferential mRNA Expression

The cardiac cell mechanical environment changes on a beat-by-beat basis as well as in the course of various cardiac diseases. Cells sense and respond to mechanical cues via specialized mechano-sensors initiating adaptive signaling cascades. With the aim of revealing new candidates underlying mechano-transduction relevant to cardiac diseases, we investigated mechano-sensitive ion channels (MSC) in human hearts for their chamber- and disease-preferential mRNA expression. Based on a meta-analysis of RNA sequencing studies, we compared the mRNA expression levels of MSC in human atrial and ventricular tissue samples from transplant donor hearts (no cardiac disease), and from patients in sinus rhythm (underlying diseases: heart failure, coronary artery disease, heart valve disease) or with atrial fibrillation. Our results suggest that a number of MSC genes are expressed chamber preferentially, e.g., CHRNE in the atria (compared to the ventricles), TRPV4 in the right atrium (compared to the left atrium), CACNA1B and KCNMB1 in the left atrium (compared to the right atrium), as well as KCNK2 and KCNJ2 in ventricles (compared to the atria). Furthermore, 15 MSC genes are differentially expressed in cardiac disease, out of which SCN9A (lower expressed in heart failure compared to donor tissue) and KCNQ5 (lower expressed in atrial fibrillation compared to sinus rhythm) show a more than twofold difference, indicative of possible functional relevance. Thus, we provide an overview of cardiac MSC mRNA expression in the four cardiac chambers from patients with different cardiac diseases. We suggest that the observed differences in MSC mRNA expression may identify candidates involved in altered mechano-transduction in the respective diseases.

The Current Therapeutic Role of Chromatin Remodeling for the Prognosis and Treatment of Heart Failure

Cardiovascular diseases are a major cause of death globally, with no cure to date. Many interventions have been studied and suggested, of which epigenetics and chromatin remodeling have been the most promising. Over the last decade, major advancements have been made in the field of chromatin remodeling, particularly for the treatment of heart failure, because of innovations in bioinformatics and gene therapy. Specifically, understanding changes to the chromatin architecture have been shown to alter cardiac disease progression via variations in genomic sequencing, targeting cardiac genes, using RNA molecules, and utilizing chromatin remodeler complexes. By understanding these chromatin remodeling mechanisms in an injured heart, treatments for heart failure have been suggested through individualized pharmaceutical interventions as well as biomarkers for major disease states. By understanding the current roles of chromatin remodeling in heart failure, a potential therapeutic approach may be discovered in the future.

Big Data in cardiac surgery: real world and perspectives

Big Data, and the derived analysis techniques, such as artificial intelligence and machine learning, have been considered a revolution in the modern practice of medicine. Big Data comes from multiple sources, encompassing electronic health records, clinical studies, imaging data, registries, administrative databases, patient-reported outcomes and OMICS profiles. The main objective of such analyses is to unveil hidden associations and patterns. In cardiac surgery, the main targets for the use of Big Data are the construction of predictive models to recognize patterns or associations better representing the individual risk or prognosis compared to classical surgical risk scores. The results of these studies contributed to kindle the interest for personalized medicine and contributed to recognize the limitations of randomized controlled trials in representing the real world. However, the main sources of evidence for guidelines and recommendations remain RCTs and meta-analysis. The extent of the revolution of Big Data and new analytical models in cardiac surgery is yet to be determined.

Biomarker screening in preeclampsia: an RNA-sequencing approach based on data from multiple studies

OBJECTIVE

Biomarkers have become important in the prognosis and diagnosis of various diseases. High-throughput methods, such as RNA sequencing facilitate the detection of differentially expressed genes (DEGs), hence potential biomarker candidates. Individual studies suggest long lists of DEGs, hampering the identification of clinically relevant ones. Concerning preeclampsia - a major obstetric burden with high risk for adverse maternal and/or neonatal outcomes - limitations in diagnosis and prediction are still important issues. We, therefore, developed a workflow to facilitate the screening for biomarkers.

METHODS

On the basis of the tool DESeq2, a comprehensive workflow for identifying DEGs was established, analyzing data from several publicly available RNA-sequencing studies. We applied it to four RNA-sequencing datasets (one blood, three placenta) analyzing patients with preeclampsia and normotensive controls. We compared our results with other published approaches and evaluated their performance.

RESULTS

We identified 110 genes that are dysregulated in preeclampsia, observed in at least three of the studies analyzed, six even in all four studies. These included FLT-1, TREM-1, and FN1, which either represent established biomarkers at protein level, or promising candidates based on recent studies. For comparison, using a published meta-analysis approach, 5240 DEGs were obtained.

CONCLUSION

This study presents a data analysis workflow for preeclampsia biomarker screening, capable of identifying promising biomarker candidates, while drastically reducing the numbers of candidates. Moreover, we were also able to confirm its performance for heart failure. This approach can be applied to additional diseases for biomarker identification, and the set of DEGs identified in preeclampsia represents a resource for further studies.

PGC-1α4 Interacts with REST to Upregulate Neuronal Genes and Augment Energy Consumption in Developing Cardiomyocytes

Transcriptional coactivator PGC-1α is a main regulator of cardiac energy metabolism. In addition to canonical PGC-1α1, other PGC-1α isoforms have been found to exert specific biological functions in a variety of tissues. We investigated the expression patterns and the biological effects of the non-canonical isoforms in the heart. We used RNA sequencing data to identify the expression patterns of PGC-1α isoforms in the heart. To evaluate the biological effects of the alternative isoform expression, we generated a transgenic mouse with cardiac-specific overexpression of PGC-1α4 and analysed the cardiac phenotype with a wide spectrum of physiological and biophysical tools. Our results show that non-canonical isoforms are expressed in the heart, and that the main variant PGC-1α4 is induced by β-adrenergic signalling in adult cardiomyocytes. Cardiomyocyte specific PGC-1α4 overexpression in mice relieves the RE1-Silencing Transcription factor (REST)-mediated suppression of neuronal genes during foetal heart development. The resulting de-repression of REST target genes induces a cardiac phenotype with increased cellular energy consumption, resulting in postnatal dilated cardiomyopathy. These results propose a new concept for actions of the PGC-1α protein family where activation of the Pgc-1α gene, through its isoforms, induces a phenotype with concurrent supply and demand for cellular energy. These data highlight the biological roles of the different PGC-1α isoforms, which should be considered when future therapies are developed.

Integrated Bioinformatics Identifies FREM1 as a Diagnostic Gene Signature for Heart Failure

Objective

This study is aimed at integrating bioinformatics and machine learning to determine novel diagnostic gene signals in the progression of heart failure disease.

Methods

The heart failure microarray datasets and RNA-seq datasets have been downloaded from the public database. Differentially expressed genes (DE genes) are screened out, and then, we analyze their biological functions and pathways. Integrating three machine learning methods, the least absolute shrinkage and selection operator (LASSO) algorithm, random forest (RF) algorithm, and support vector machine recursive feature elimination (SVM-RFE) are used to determine candidate diagnostic gene signals. Then, external independent RNA-seq datasets evaluate the diagnostic value of gene signals. Finally, the convolution tool CIBERSORT estimated the composition pattern of immune cell subtypes in heart failure and carried out a correlation analysis combined with gene signals.

Results

Under the set threshold, we obtained 47 DE genes with the most significant differences. Enrichment analysis shows that most of them are related to hypertrophy, matrix structural constituent, protein binding, inflammatory immune pathway, cardiovascular disease, and inflammatory disease. Three machine learning methods assisted in determining the potential characteristic signals Fras1-related extracellular matrix 1 (FREM1) and meiosis-specific nuclear structural 1 (MNS1). Validation of external datasets confirms that FREM1 is a diagnostic gene signal for heart failure. Immune cell subtypes of tissue specimens found T cell CD8, mast cell resting, T cell CD4 memory resting, T cell regulation (Tregs), monocytes, macrophages M2, T cell CD4 naive, macrophages M0, and neutrophils are associated with HF.

Conclusion

The gene signal FREM1 may be a potential molecular target in the development of HF and is related to the difference in immune infiltration of HF tissue.

Transcriptomal Insights of Heart Failure from Normality to Recovery

Current management of heart failure (HF) is centred on modulating the progression of symptoms and severity of left ventricular dysfunction. However, specific understandings of genetic and molecular targets are needed for more precise treatments. To attain a clearer picture of this, we studied transcriptome changes in a chronic progressive HF model. Fifteen sheep (Ovis aries) underwent supracoronary aortic banding using an inflatable cuff. Controlled and progressive induction of pressure overload in the LV was monitored by echocardiography. Endomyocardial biopsies were collected throughout the development of LV failure (LVF) and during the stage of recovery. RNA-seq data were analysed using the PANTHER database, Metascape, and DisGeNET to annotate the gene expression for functional ontologies. Echocardiography revealed distinct clinical differences between the progressive stages of hypertrophy, dilatation, and failure. A unique set of transcript expressions in each stage was identified, despite an overlap of gene expression. The removal of pressure overload allowed the LV to recover functionally. Compared to the control stage, there were a total of 256 genes significantly changed in their expression in failure, 210 genes in hypertrophy, and 73 genes in dilatation. Gene expression in the recovery stage was comparable with the control stage with a well-noted improvement in LV function. RNA-seq revealed the expression of genes in each stage that are not reported in cardiovascular pathology. We identified genes that may be potentially involved in the aetiology of progressive stages of HF, and that may provide future targets for its management.

FGF21 (Fibroblast Growth Factor 21) Defines a Potential Cardiohepatic Signaling Circuit in End-Stage Heart Failure

BACKGROUND

Extrinsic control of cardiomyocyte metabolism is poorly understood in heart failure (HF). FGF21 (Fibroblast growth factor 21), a hormonal regulator of metabolism produced mainly in the liver and adipose tissue, is a prime candidate for such signaling.

METHODS

To investigate this further, we examined blood and tissue obtained from human subjects with end-stage HF with reduced ejection fraction at the time of left ventricular assist device implantation and correlated serum FGF21 levels with cardiac gene expression, immunohistochemistry, and clinical parameters.

RESULTS

Circulating FGF21 levels were substantially elevated in HF with reduced ejection fraction, compared with healthy subjects (HF with reduced ejection fraction: 834.4 [95% CI, 628.4-1040.3] pg/mL, n=40; controls: 146.0 [86.3-205.7] pg/mL, n=20, P=1.9×10-5). There was clear FGF21 staining in diseased cardiomyocytes, and circulating FGF21 levels negatively correlated with the expression of cardiac genes involved in ketone metabolism, consistent with cardiac FGF21 signaling. FGF21 gene expression was very low in failing and nonfailing hearts, suggesting extracardiac production of the circulating hormone. Circulating FGF21 levels were correlated with BNP (B-type natriuretic peptide) and total bilirubin, markers of chronic cardiac and hepatic congestion.

CONCLUSIONS

Circulating FGF21 levels are elevated in HF with reduced ejection fraction and appear to bind to the heart. The liver is likely the main extracardiac source. This supports a model of hepatic FGF21 communication to diseased cardiomyocytes, defining a potential cardiohepatic signaling circuit in human HF.

Recent Multiomics Approaches in Endometrial Cancer

Endometrial cancer is the most common gynecological cancers in developed countries. Many of the mechanisms involved in its initiation and progression remain unclear. Analysis providing comprehensive data on the genome, transcriptome, proteome, and epigenome could help in selecting molecular markers and targets in endometrial cancer. Multiomics approaches can reveal disturbances in multiple biological systems, giving a broader picture of the problem. However, they provide a large amount of data that require processing and further integration prior to analysis. There are several repositories of multiomics datasets, including endometrial cancer data, as well as portals allowing multiomics data analysis and visualization, including Oncomine, UALCAN, LinkedOmics, and miRDB. Multiomics approaches have also been applied in endometrial cancer research in order to identify novel molecular markers and therapeutic targets. This review describes in detail the latest findings on multiomics approaches in endometrial cancer.

Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery

Objectives: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) approach with protein-protein interaction network. Methods: The transcriptomic profiles of human myocardial tissues were investigated integrating an original computational approach, based on the Custom Decision Tree algorithm, in a differential expression bioinformatic framework. Validation was performed by quantitative real-time PCR. Results: Our preliminary study, using samples from transplanted tissues, allowed the discovery of specific DCM-related genes, including MYH6, NPPA, MT-RNR1 and NEAT1, already known to be involved in cardiomyopathies Interestingly, a combination of these expression profiles with clinical characteristics showed a significant association between NEAT1 and left ventricular end-diastolic diameter (LVEDD) (Rho = 0.73, p = 0.05), according to severity classification (NYHA-class III). Conclusions: The use of the ML approach was useful to discover preliminary specific genes that could lead to a rapid selection of molecular targets correlated with DCM clinical parameters. For the first time, NEAT1 under-expression was significantly associated with LVEDD in the human heart.

Alternative Splicing Mediated by RNA-Binding Protein RBM24 Facilitates Cardiac Myofibrillogenesis in a Differentiation Stage-Specific Manner

Background: Mutations in genes encoding sarcomeric proteins lead to failures in sarcomere assembly, the building blocks of contracting muscles, resulting in cardiomyopathies that are a leading cause of morbidity and mortality worldwide. Splicing variants of sarcomeric proteins are crucial at different stages of myofibrillogenesis, accounting for sarcomeric structural integrity. RNA-binding motif protein 24 (RBM24) is known as a tissue-specific splicing regulator that plays an essential role in cardiogenesis. However, it had been unclear if the developmental stage-specific alternative splicing facilitated by RBM24 contributes to sarcomere assembly and cardiogenesis. Our aim isto study the molecular mechanism by which RBM24 regulates cardiogenesis and sarcomere assembly in a temporal-dependent manner. Methods: We ablated RBM24 from human embryonic stem cells (hESCs) using CRISPR/Cas9 techniques. Results: Although RBM24^-/- hESCs still differentiated into sarcomere-hosting cardiomyocytes, they exhibited disrupted sarcomeric structures with punctate Z-lines due to impaired myosin replacement during early myofibrillogenesis. Transcriptomics revealed >4000 genes regulated by RBM24. Among them, core myofibrillogenesis proteins (e.g. ACTN2, TTN, and MYH10) were misspliced. Consequently, MYH6 cannot replace non-muscle myosin MYH10, leading to myofibrillogenesis arrest at the early premyofibril stage and causing disrupted sarcomeres. Intriguingly, we found that the actin-binding domain (ABD; encoded by exon 6) of the Z-line anchor protein ACTN2 is predominantly excluded from early cardiac differentiation, whereas it is consistently included in human adult heart. CRISPR/Cas9-mediated deletion of exon 6 from ACTN2 in hESCs, as well as forced expression of full-length ACTN2 in RBM24^-/- hESCs, further corroborated that inclusion of exon 6 is critical for sarcomere assembly. Overall, we have demonstrated that RBM24-facilitated inclusion of exon 6 in ACTN2 at distinct stages of cardiac differentiation is evolutionarily conserved and crucial to sarcomere assembly and integrity. Conclusions: RBM24 acts as a master regulator to modulate the temporal dynamics of core myofibrillogenesis genes and thereby orchestrates sarcomere organization.

The Emerging Role of Epigenetics in Therapeutic Targeting of Cardiomyopathies

Cardiomyopathies (CMPs) are a heterogeneous group of myocardial diseases accountable for the majority of cases of heart failure (HF) and/or sudden cardiac death (SCD) worldwide. With the recent advances in genomics, the original classification of CMPs on the basis of morphological and functional criteria (dilated (DCM), hypertrophic (HCM), restrictive (RCM), and arrhythmogenic ventricular cardiomyopathy (AVC)) was further refined into genetic (inherited or familial) and acquired (non-inherited or secondary) forms. Despite substantial progress in the identification of novel CMP-associated genetic variations, as well as improved clinical recognition diagnoses, the functional consequences of these mutations and the exact details of the signaling pathways leading to hypertrophy, dilation, and/or contractile impairment remain elusive. To date, global research has mainly focused on the genetic factors underlying CMP pathogenesis. However, growing evidence shows that alterations in molecular mediators associated with the diagnosis of CMPs are not always correlated with genetic mutations, suggesting that additional mechanisms, such as epigenetics, may play a role in the onset or progression of CMPs. This review summarizes published findings of inherited CMPs with a specific focus on the potential role of epigenetic mechanisms in regulating these cardiac disorders.

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

INTRODUCTION

Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules.

METHODS

The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed.

RESULTS

A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene-miRNA regulatory network and target gene-TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed.

CONCLUSION

The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies

The early identification of pathogenic mechanisms is essential to predict the incidence and progression of cardiomyopathies and to plan appropriate preventive interventions. Noninvasive cardiac imaging such as cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging plays an important role in diagnosis and management of cardiomyopathies and provides useful prognostic information. Most molecular factors exert their functions by interacting with other cellular components, thus many diseases reflect perturbations of intracellular networks. Indeed, complex diseases and traits such as cardiomyopathies are caused by perturbations of biological networks. The network medicine approach, by integrating systems biology, aims to identify pathological interacting genes and proteins, revolutionizing the way to know cardiomyopathies and shifting the understanding of their pathogenic phenomena from a reductionist to a holistic approach. In addition, artificial intelligence tools, applied to morphological and functional imaging, could allow imaging scans to be automatically analyzed to extract new parameters and features for cardiomyopathy evaluation. The aim of this review is to discuss the tools of network medicine in cardiomyopathies that could reveal new candidate genes and artificial intelligence imaging-based features with the aim to translate into clinical practice as diagnostic, prognostic, and predictive biomarkers and shed new light on the clinical setting of cardiomyopathies. The integration and elaboration of clinical habits, molecular big data, and imaging into machine learning models could provide better disease phenotyping, outcome prediction, and novel drug targets, thus opening a new scenario for the implementation of precision medicine for cardiomyopathies.

Exploration of Potential Genetic Biomarkers for Heart Failure: A Systematic Review

Patients with heart failure (HF) often present with signs and symptoms that are often nonspecific and with a wide differential diagnosis, making diagnosis and prognosis of HF by clinical presentation alone challenging. Our knowledge on genetic diversity is rapidly evolving with high-throughput DNA sequencing technology, which makes a great potential for genetic biomarker development. The present review attempts to provide a comprehensive review on the modification of major genetic components in HF patients and to explore the potential application of these components as clinical biomarkers in the diagnosis and in monitoring the progress of HF. The literature search was conducted using six databases, resulting in the inclusion of eighteen studies in the review. The findings of these studies were summarized narratively. An appraisal of the reporting quality of the included studies was conducted using a twelve-item checklist adapted from the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist. The findings showed that changes in genetic components in patients with HF compared to healthy controls could be noninvasive diagnostic or prognostic tools for HF with higher specificity and sensitivity in comparison with the traditional biomarkers. This review provided evidence for the potential of developing genetic biomarkers of HF.

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.

Identifying functional metabolic shifts in heart failure with the integration of omics data and a heart-specific, genome-scale model

In diseased states, the heart can shift to use different carbon substrates, measured through changes in uptake of metabolites by imaging methods or blood metabolomics. However, it is not known whether these measured changes are a result of transcriptional changes or external factors. Here, we explore transcriptional changes in late-stage heart failure using publicly available data integrated with a model of heart metabolism. First, we present a heart-specific genome-scale metabolic network reconstruction (GENRE), iCardio. Next, we demonstrate the utility of iCardio in interpreting heart failure gene expression data by identifying tasks inferred from differential expression (TIDEs), which represent metabolic functions associated with changes in gene expression. We identify decreased gene expression for nitric oxide (NO) and N-acetylneuraminic acid (Neu5Ac) synthesis as common metabolic markers of heart failure. The methods presented here for constructing a tissue-specific model and identifying TIDEs can be extended to multiple tissues and diseases of interest.

Forensic transcriptome analysis using massively parallel sequencing

The application of transcriptome analyses in forensic genetics has experienced tremendous growth and development in the past decade. The earliest studies and main applications were body fluid and tissue identification, using targeted RNA transcripts and a reverse transcription endpoint PCR method. A number of markers have been identified for the forensically most relevant body fluids and tissues and the method has been successfully used in casework. The introduction of Massively Parallel Sequencing (MPS) opened up new perspectives and opportunities to advance the field. Contrary to genomic DNA where two copies of an autosomal DNA segment are present in a cell, abundant RNA species are expressed in high copy numbers. Even whole transcriptome sequencing (RNA-Seq) of forensically relevant body fluids and of postmortem material was shown to be possible. This review gives an overview on forensic transcriptome analyses and applications. The methods cover whole transcriptome as well as targeted MPS approaches. High resolution forensic transcriptome analyses using MPS are being applied to body fluid/ tissue identification, determination of the age of stains and the age of the donor, the estimation of the post-mortem interval and to post mortem death investigations.

Knockdown of miR-1275 protects against cardiomyocytes injury through promoting neuromedin U type 1 receptor

The present study aimed to assess the role of miR-1275 in cardiac ischemia reperfusion injury. H9 human embryonic stem cell (hESC)-derived cardiomyocytes stimulated by oxygen-glucose deprivation/reoxygenation (OGD/R) were used to simulate myocardial injury in vitro. miR-1275 expression levels in cells were measured by RT-qPCR. The release of lactate dehydrogenase (LDH) and creatine kinase (CK) was examined through LDH and CK ELISA kits. Cell apoptosis was detected through flow cytometry. A Fura-2 Calcium Flux Assay Kit and a Fluo-4 assay kit were used to determine the Ca²⁺ concentration. Expression levels of proteins were tested by Western blotting. The binding effect of miR-1275 and neuromedin U type 1 receptor (NMUR1) was detected by dual-luciferase activity assay. The results showed that miR-1275 was upregulated in OGD/R-stimulated cardiomyocytes. Inhibition of miR-1275 suppressed the increased activity of LDH and CK, cell apoptosis, reactive oxygen species (ROS) production, intracellular Ca²⁺ concentration and sarcoplasmic reticulum (SR) Ca²⁺ leak induced by OGD/R treatment in cardiomyocytes. miR-1275 directly targets 3'UTR of NMUR1 and negatively regulates NMUR1 expression. Silence of NMUR1 abolished the protecting effect of the miR-1275 antagomir on myocardial OGD/R injury. Our study indicated that the miR-1275 antagomir protects cardiomyocytes from OGD/R injury through the promotion of NMUR1.

Fibrosis Distinguishes Critical Limb Ischemia Patients from Claudicants in a Transcriptomic and Histologic Analysis

Most patients with critical limb ischemia (CLI) from peripheral arterial disease (PAD) do not have antecedent intermittent claudication (IC). We hypothesized that transcriptomic analysis would identify CLI-specific pathways, particularly in regards to fibrosis. Derivation cohort data from muscle biopsies in PAD and non-PAD (controls) was obtained from the Gene Expression Omnibus (GSE120642). Transcriptomic analysis indicated CLI patients (N = 16) had a unique gene expression profile, when compared with non-PAD controls (N = 15) and IC (N = 20). Ninety-eight genes differed between controls and IC, 2489 genes differed between CLI and controls, and 2783 genes differed between CLI and IC patients. Pathway enrichment analysis showed that pathways associated with TGFβ, collagen deposition, and VEGF signaling were enriched in CLI but not IC. Receiver operating curve (ROC) analysis of nine fibrosis core gene expression revealed the areas under the ROC (AUC) were all >0.75 for CLI. Furthermore, the fibrosis area (AUC = 0.81) and % fibrosis (AUC = 0.87) in validation cohort validated the fibrosis discrimination CLI from IC and control (all n = 12). In conclusion, transcriptomic analysis identified fibrosis pathways, including those involving TGFβ, as a novel gene expression feature for CLI but not IC. Fibrosis is an important characteristic of CLI, which we confirmed histologically, and may be a target for novel therapies in PAD.

DNA methylation profiling of CD04+/CD08+ T cells reveals pathogenic mechanisms in increasing hyperglycemia: PIRAMIDE pilot study

Background

DNA methylation can play a pathogenic role in the early stages of hyperglycemia linking homeostasis imbalance and vascular damage.

Material and methods

We investigated DNA methylome by RRBS in CD04⁺ and CD08⁺ T cells from healthy subjects (HS) to pre-diabetics (Pre-Diab) and type 2 diabetic (T2D) patients to identify early biomarkers of glucose impairment and vascular damage. Our cross-sectional study enrolled 14 individuals from HS state to increasing hyperglycemia (pilot study, PIRAMIDE trial, NCT03792607).

Results

Globally, differentially methylated regions (DMRs) were mostly annotated to promoter regions. Hypermethylated DMRs were greater than hypomethylated in CD04⁺ T cells whereas CD08⁺ T showed an opposite trend. Moreover, DMRs overlapping between Pre-Diab and T2D patients were mostly hypermethylated in both T cells. Interestingly, SPARC was the most hypomethylated gene in Pre-Diab and its methylation level gradually decreased in T2D patients. Besides, SPARC showed a significant positive correlation with DBP (+0.76), HDL (+0.54), Creatinine (+0.83), LVDd (+0.98), LVSD (+0.98), LAD (+0.98), LVPWd (+0.84), AODd (+0.81), HR (+0.72), Triglycerides (+0.83), LAD (+0.69) and AODd (+0.52) whereas a negative correlation with Cholesterol (−0.52) and LDL (−0.71) in T2D.

Conclusion

SPARC hypomethylation in CD08⁺ T cells may be a useful biomarker of vascular complications in Pre-Diab with a possible role for primary prevention warranting further multicenter clinical trials to validate our findings.

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We conducted the first methylome analysis by RRBS platform in circulating CD04⁺ and CD08⁺ T cells in increasing hyperglycemia.

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This approach has revealed possible biomarkers for cardiovascular and kidney complications in prediabetes.

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SPARC hypomethylation may underly a pro-fibrotic endophenotype to be validated in larger multicenter trials.

Epigenetic-sensitive liquid biomarkers and personalised therapy in advanced heart failure: a focus on cell-free DNA and microRNAs

Dilated cardiomyopathy (DCM) represents a common genetic cause of mechanical and/or electrical dysfunction leading to heart failure (HF) onset for which truncating variants in titin (TTN) gene result in the most frequent mutations. Moreover, myocyte and endothelial cell apoptosis is a key endophenotype underlying cardiac remodelling. Therefore, a deeper knowledge about molecular networks leading to acute injury and apoptosis may reveal novel circulating biomarkers useful to better discriminate HF phenotypes, patients at risk of heart transplant as well as graft reject in order to improve personalised therapy. Remarkably, increased plasma levels of cell-free DNA (cfDNA) may reflect the extent of cellular damage, whereas circulating mitochondrial DNA (mtDNA) may be a promising biomarker of poor prognosis in patients with HF. Furthermore, some panels of circulating miRNAs may improve the stratification of natural history of disease. For example, a combination of miR-558, miR-122* and miR-520d-5p, as well as miR-125a-5p, miR-550a-5p, miR-638 and miR-190a, may aid to discriminate different phenotypes of HF ranging from preserved to reduced ejection fraction. We give update on the most relevant genetic determinants involved in DCM and discuss the putative role of non-invasive biomarkers to overcome current limitations of the reductionist approach in HF management.

Precision medicine in distinct heart failure phenotypes: Focus on clinical epigenetics

Heart failure (HF) management is challenging due to high clinical heterogeneity of this disease which makes patients responding differently to evidence-based standard therapy established by the current reductionist approach. Better understanding of the genetic and epigenetic interactions may clarify molecular signatures underlying maladaptive responses in HF, including metabolic shift, myocardial injury, fibrosis, and mitochondrial dysfunction. DNA methylation, histone modifications and micro-RNA (miRNAs) may be major epigenetic players in the pathogenesis of HF. DNA hypermethylation of the kruppel-like factor 15 (KLF15) gene plays a key role in switching the failing heart from oxidative to glycolytic metabolism. Moreover, hypomethylation at H3K9 promoter level of atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) genes also leads to reactivation of fetal genes in man. The role of miRNAs has been investigated in HF patients undergoing heart transplantation, for whom miR-10a, miR-155, miR-31, and miR-92 may be putative useful prognostic biomarkers. Recently, higher RNA methylation levels have been observed in ischemic human hearts, opening the era of "epitranscriptome" in the pathogenesis of HF. Currently, hydralazine, statins, apabetalone, and omega-3 polyunsatured fatty acids (PUFA) are being tested in clinical trials to provide epigenetic-driven therapeutic interventions. Moreover, network-oriented analysis could advance current medical practice by focusing on protein-protein interactions (PPIs) perturbing the "cardiac" interactome. In this review, we provide an epigenetic map of maladaptive responses in HF patients. Furthermore, we propose the "EPi-transgeneratIonal network mOdeling for STratificatiOn of heaRt Morbidity" (EPIKO-STORM), a clinical research strategy offering novel opportunities to stratify the natural history of HF.

Biomarkers in heart failure: the past, current and future

Despite the enhanced knowledge of the pathophysiology of heart failure (HF), it still remains a serious syndrome with substantial morbidity, mortality, and frequent hospitalizations. These are due to the current improvements in other cardiovascular diseases (like myocardial infarction), the aging population, and growing prevalence of comorbidities. Biomarker-guided management has brought a new dimension in prognostication, diagnosis, and therapy options. Following the recommendation of natriuretic peptides (B-type natriuretic peptide and N-terminal-proBNP), many other biomarkers have been thoroughly studied to reflect different pathophysiological processes (such as fibrosis, inflammation, myocardial injury, and remodeling) in HF and some of them (like cardiac troponins, soluble suppression of tumorigenesis-2, and galectin 3) have subsequently been recommended to aid in the diagnosis and prognostication in HF. Consequently, multi-marker approach has also been approved owing to the varied nature of HF syndrome. In this review, we discussed the guidelines available for HF biomarkers, procedures for evaluating novel markers, and the utilities of both emerging and established biomarkers for risk stratification, diagnosis, and management of HF in the clinics. We later looked at how the rapidly emerging field-OMICs, can help transform HF biomarkers discoveries and establishment.

Epigenetic-sensitive pathways in personalized therapy of major cardiovascular diseases

The complex pathobiology underlying cardiovascular diseases (CVDs) has yet to be explained. Aberrant epigenetic changes may result from alterations in enzymatic activities, which are responsible for putting in and/or out the covalent groups, altering the epigenome and then modulating gene expression. The identification of novel individual epigenetic-sensitive trajectories at single cell level might provide additional opportunities to establish predictive, diagnostic and prognostic biomarkers as well as drug targets in CVDs. To date, most of studies investigated DNA methylation mechanism and miRNA regulation as epigenetics marks. During atherogenesis, big epigenetic changes in DNA methylation and different ncRNAs, such as miR-93, miR-340, miR-433, miR-765, CHROME, were identified into endothelial cells, smooth muscle cells, and macrophages. During man development, lipid metabolism, inflammation and homocysteine homeostasis, alter vascular transcriptional mechanism of fundamental genes such as ABCA1, SREBP2, NOS, HIF1. At histone level, increased HDAC9 was associated with matrix metalloproteinase 1 (MMP1) and MMP2 expression in pro-inflammatory macrophages of human carotid plaque other than to have a positive effect on toll like receptor signaling and innate immunity. HDAC9 deficiency promoted inflammation resolution and reverse cholesterol transport, which might block atherosclerosis progression and promote lesion regression. Here, we describe main human epigenetic mechanisms involved in atherosclerosis, coronary heart disease, ischemic stroke, peripheral artery disease; cardiomyopathy and heart failure. Different epigenetics mechanisms are activated, such as regulation by circular RNAs, as MICRA, and epitranscriptomics at RNA level. Moreover, in order to open new frontiers for precision medicine and personalized therapy, we offer a panoramic view on the most innovative bioinformatic tools designed to identify putative genes and molecular networks underlying CVDs in man.

Meta-Analysis of Dilated Cardiomyopathy Using Cardiac RNA-Seq Transcriptomic Datasets

Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Several studies have used RNA-sequencing (RNA-seq) to profile differentially expressed genes (DEGs) associated with DCM. In this study, we aimed to profile gene expression signatures and identify novel genes associated with DCM through a quantitative meta-analysis of three publicly available RNA-seq studies using human left ventricle tissues from 41 DCM cases and 21 control samples. Our meta-analysis identified 789 DEGs including 581 downregulated and 208 upregulated genes. Several DCM-related genes previously reported, including MYH6, CKM, NKX2-5 and ATP2A2, were among the top 50 DEGs. Our meta-analysis also identified 39 new DEGs that were not detected using those individual RNA-seq datasets. Some of those genes, including PTH1R, ADAM15 and S100A4, confirmed previous reports of associations with cardiovascular functions. Using DEGs from this meta-analysis, the Ingenuity Pathway Analysis (IPA) identified five activated toxicity pathways, including failure of heart as the most significant pathway. Among the upstream regulators, SMARCA4 was downregulated and prioritized by IPA as the top affected upstream regulator for several DCM-related genes. To our knowledge, this study is the first to perform a transcriptomic meta-analysis for clinical DCM using RNA-seq datasets. Overall, our meta-analysis successfully identified a core set of genes associated with DCM.

Reconstruction and Analysis of the lncRNA-miRNA-mRNA Network Based on Competitive Endogenous RNA Reveal Functional lncRNAs in Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) is an important cause of sudden death and heart failure with an unknown etiology. Recent studies have suggested that long non-coding RNA (lncRNA) can interact with microRNA (miRNA) and indirectly interact with mRNA through competitive endogenous RNA (ceRNA) activities. However, the mechanism of ceRNA in DCM remains unclear. In this study, a miRNA array was first performed using heart samples from DCM patients and healthy controls. For further validation, we conducted real-time quantitative reverse transcription (RT)-PCR using samples from DCM patients and a doxorubicin-induced rodent model of cardiomyopathy, revealing that miR-144-3p and miR-451a were down-regulated, and miR-21-5p was up-regulated. Based on the ceRNA theory, we constructed a global triple network using data from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) and our miRNA array. The lncRNA-miRNA-mRNA network comprised 22 lncRNA nodes, 32 mRNA nodes, and 11 miRNA nodes. Hub nodes and the number of relationship pairs were then analyzed, and the results showed that two lncRNAs (NONHSAT001691 and NONHSAT006358) targeting miR-144/451 were highly related to DCM. Then, cluster module and random walk with restart for the ceRNA network were analyzed and identified four lncRNAs (NONHSAT026953/NONHSAT006250/NONHSAT133928/NONHSAT041662) targeting miR-21 that were significantly related to DCM. This study provides a new strategy for research on DCM or other diseases. Furthermore, lncRNA-miRNA pairs may be regarded as candidate diagnostic biomarkers or potential therapeutic targets of DCM.

Distinct Circulating Expression Profiles of Long Noncoding RNAs in Heart Failure Patients With Ischemic and Nonischemic Dilated Cardiomyopathy

Ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM), with distinct long-term prognosis and responses to treatment, are two major problems that lead to heart failure (HF) ultimately. In this study, we investigated the long noncoding RNA (lncRNA) and messenger RNA (mRNA) expressions in the plasma of patients with DCM and ICM and analyzed the different lncRNA profile between the two groups. The microarray analysis identified 3,222 and 1,911 significantly differentially expressed lncRNAs and mRNAs between DCM and ICM group. The most enriched upregulated functional terms included positive regulation of I-kappaB kinase/nuclear factor-kappaB signaling and regulation of cellular localization, while the top 10 downregulated genes mainly consisted of acid secretion and myosin heavy chain binding. Furthermore, the Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that the differentially expressed lncRNA-coexpressed mRNAs between DCM and ICM group were significantly enriched in the natural killer cell mediated cytotoxicity and ras signaling pathway respectively. Quantitative real-time PCR confirmed 8 of 12 lncRNAs were upregulated in DCM group compared to ICM group which was consistent with the initial microarray results. The lncRNA/mRNA coexpression network indicated the possible functions of the validated lncRNAs. These findings revealed for the first time the specific expression pattern of both protein-coding RNAs and lncRNAs in plasma of HF patients due to DCM and ICM which may provide some important evidence to conveniently identify the etiology of myocardial dysfunctions and help to explore a better strategy for future HF prognosis evaluation.

Fluid-based assays and precision medicine of cardiovascular diseases: the ‘hope’ for Pandora’s box?

Progresses in liquid-based assays may provide novel useful non-invasive indicators of cardiovascular (CV) diseases. By analysing circulating cells or their products in blood, saliva and urine samples, we can investigate molecular changes present at specific time points in each patient allowing sequential monitoring of disease evolution. For example, an increased number of circulating endothelial cells may be a diagnostic biomarker for diabetic nephropathy and heart failure with preserved ejection fraction. The assessment of circulating cell-free DNA (cfDNA) levels may be useful to predict severity of acute myocardial infarction, as well as diagnose heart graft rejection. Remarkably, circulating epigenetic biomarkers, including DNA methylation, histone modifications and non-coding RNAs are key pathogenic determinants of CV diseases representing putative useful biomarkers and drug targets. For example, the unmethylated FAM101A gene may specifically trace cfDNA derived from cardiomyocyte death providing a powerful diagnostic biomarker of apoptosis during ischaemia. Moreover, changes in plasma levels of circulating miR-92 may predict acute coronary syndrome onset in patients with diabetes. Now, network medicine provides a framework to analyse a huge amount of big data by describing a CV disease as a result of a chain of molecular perturbations rather than a single defect (reductionism). We outline advantages and challenges of liquid biopsy with respect to traditional tissue biopsy and summarise the main completed and ongoing clinical trials in CV diseases. Furthermore, we discuss the importance of combining fluid-based assays, big data and network medicine to improve precision medicine and personalised therapy in this field.

Epigenetic Modifications in Cardiovascular Aging and Diseases

Aging is associated with a progressive decline in cardiovascular structure and function. Accumulating evidence links cardiovascular aging to epigenetic alterations encompassing a complex interplay of DNA methylation, histone posttranslational modifications, and dynamic nucleosome occupancy governed by numerous epigenetic factors. Advances in genomics technology have led to a profound understanding of chromatin reorganization in both cardiovascular aging and diseases. This review summarizes recent discoveries in epigenetic mechanisms involved in cardiovascular aging and diseases and discusses potential therapeutic strategies to retard cardiovascular aging and conquer related diseases through the rejuvenation of epigenetic signatures to a young state.

Noncoding RNAs in Cardiac Hypertrophy

Cardiac hypertrophy is classified as pathological and physiological hypertrophy. Pathological hypertrophy typically precedes the onset of heart failure, one of the largest contributors to disease burden and deaths worldwide. In contrast, physiological hypertrophy is an adaptive response and protects against adverse cardiac remodeling. Noncoding RNAs (ncRNAs) have drawn significant attention over the last couple of decades, and their dysregulation is increasingly being linked to cardiac hypertrophy and cardiovascular diseases. In this review, we will summarize the profiling, function, and molecular mechanism of microRNAs, long noncoding RNAs, and circular RNAs in pathological cardiac hypertrophy. Additionally, we also review microRNAs responsible for physiological hypertrophy. With better understanding of ncRNAs in cardiac hypertrophy, manipulation of the important ncRNAs will offer exciting avenues for the prevention and therapy of heart failure.

Uncovering novel landscape of cardiovascular diseases and therapeutic targets for cardioprotection via long noncoding RNA–miRNA–mRNA axes

Protein coding sequences account for around 3% of the human genome, the rest are noncoding RNA (ncRNA) including long ncRNA (lncRNA) and miRNA. Accumulating evidence indicates that lncRNAs and miRNAs are candidate biomarkers for diagnosis, prognosis and therapy of cardiovascular diseases. The lncRNAs act as sponge-like effects on numerous miRNAs, subsequently regulating miRNAs and their targets, mRNA functions. The role of lncRNA–miRNA–mRNA axis in pathogenesis of cardiovascular diseases has been recently reported and highlighted. Herein, this review discusses emerging roles of lncRNA–miRNA–mRNA axis in cardiovascular pathophysiology and regulation, with a novel focus on cardioprotective network activities of the two subgroup ncRNAs.

The role of long non-coding RNAs in cardiac development and disease

Cells display a set of RNA molecules at one time point, reflecting thus the cellular transcriptional steady state, configuring therefore its transcriptome. It is basically composed of two different classes of RNA molecules; protein-coding RNAs (cRNAs) and protein non-coding RNAs (ncRNAs). Sequencing of the human genome and subsequently the ENCODE project identified that more than 80% of the genome is transcribed in some type of RNA. Importantly, only 3% of these transcripts correspond to protein-coding RNAs, pointing that ncRNAs are as important or even more as cRNAs. ncRNAs have pivotal roles in development, differentiation and disease. Non-coding RNAs can be classified into two distinct classes according to their length; i.e., small (<200 nt) and long (>200 nt) noncoding RNAs. The structure, biogenesis and functional roles of small non-coding RNA have been widely studied, particularly for microRNAs (miRNAs). In contrast to microRNAs, our current understanding of long non-coding RNAs (lncRNAs) is limited. In this manuscript, we provide state-of-the art review of the functional roles of long non-coding RNAs during cardiac development as well as an overview of the emerging role of these ncRNAs in distinct cardiac diseases.

Next generation sequencing applications for cardiovascular disease

The Human Genome Project (HGP), as the primary sequencing of the human genome, lasted more than one decade to be completed using the traditional Sanger's method. At present, next-generation sequencing (NGS) technology could provide the genome sequence data in hours. NGS has also decreased the expense of sequencing; therefore, nowadays it is possible to carry out both whole-genome (WGS) and whole-exome sequencing (WES) for the variations detection in patients with rare genetic diseases as well as complex disorders such as common cardiovascular diseases (CVDs). Finding new variants may contribute to establishing a risk profile for the pathology process of diseases. Here, recent applications of NGS in cardiovascular medicine are discussed; both Mendelian disorders of the cardiovascular system and complex genetic CVDs including inherited cardiomyopathy, channelopathies, stroke, coronary artery disease (CAD) and are considered. We also state some future use of NGS in clinical practice for increasing our information about the CVDs genetics and the limitations of this new technology. Key messages Traditional Sanger's method was the mainstay for Human Genome Project (HGP); Sanger sequencing has high fidelity but is slow and costly as compared to next generation methods. Within cardiovascular medicine, NGS has been shown to be successful in identifying novel causative mutations and in the diagnosis of Mendelian diseases which are caused by a single variant in a single gene. NGS has provided the opportunity to perform parallel analysis of a great number of genes in an unbiased approach (i.e. without knowing the underlying biological mechanism) which probably contribute to advance our knowledge regarding the pathology of complex diseases such as CVD.