Transcriptome analysis in heart failure

Comparative Analysis of Non-Coding RNA Transcriptomics in Heart Failure

Heart failure constitutes a clinical complex syndrome with different symptomatic characteristics depending on age, sex, race and ethnicity, among others, which has become a major public health issue with an increasing prevalence. One of the most interesting tools seeking to improve prevention, diagnosis, treatment and prognosis of this pathology has focused on finding new molecular biomarkers since heart failure relies on deficient cardiac homeostasis, which is regulated by a strict gene expression. Therefore, currently, analyses of non-coding RNA transcriptomics have been oriented towards human samples. The present review develops a comparative study emphasizing the relevance of microRNAs, long non-coding RNAs and circular RNAs as potential biomarkers in heart failure. Significantly, further studies in this field of research are fundamental to supporting their widespread clinical use. In this sense, the various methodologies used by the authors should be standardized, including larger cohorts, homogeneity of the samples and uniformity of the bioinformatic pipelines used to reach stratification and statistical significance of the results. These basic adjustments could provide promising steps to designing novel strategies for clinical management of patients with heart failure.

Dynamic regulation of HIF-1 signaling in the rhesus monkey heart after ischemic injury

Background

Hypoxia inducible factor-1 (HIF-1) plays a key role in modulating post-infarct healing after myocardial ischemic injury through transcriptional regulation of hundreds of genes involved in diverse cardiac remodeling processes. However, the dynamic changes in HIF-1 target gene expression in the ischemic heart after myocardial infarction (MI) have not been well characterized.

Methods

We employed a rhesus monkey model of MI induced by left anterior descending artery ligation and examined the expression pattern of HIF-1 target genes in the ischemic heart at 1, 7, and 28 days after injury by bulk RNA-sequencing analysis.

Results

Myocardial transcriptomic analysis demonstrated a temporal-specific regulation of genes associated with the inflammatory response, cell proliferation, fibrosis and mitochondrial metabolism during the pathological progression of MI. HIF-1 target genes involved in processes related to glycolysis, angiogenesis, and extracellular matrix (ECM) remodeling also exhibited distinct expression patterns during MI progression. Copper concentrations were gradually decreased in the heart after ischemic injury, which was positively correlated with the expression of HIF-1-mediated angiogenic and glycolytic genes but negatively correlated with the expression of HIF-1-mediated ECM remodeling genes. Moreover, genes related to intracellular copper trafficking and storage were suppressed along with the loss of myocardial copper in the ischemic heart.

Conclusions

This study demonstrated a dynamic, functional-specific regulation of HIF-1 target gene expression during the progression of MI. The fine-tuning of HIF-1 signaling in the ischemic heart may be relate to the alteration in myocardial copper homeostasis. These findings provide transcriptomic insights into the distinct roles of HIF-1 signaling in the heart after ischemic injury, which will help determine the beneficial cutoff point for HIF-1 targeted therapy in ischemic heart diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02841-0.

Full-Length Transcriptome Sequencing: An Insight Into the Dog Model of Heart Failure

Heart failure (HF) leads to a progressive increase in morbidity and mortality rates. This study aimed to explore the transcriptional landscape during HF and identify differentially expressed transcripts (DETs) and alternative splicing events associated with HF. We generated a dog model of HF (n = 3) using right ventricular pacemaker implantation. We performed full-length transcriptome sequencing (based on nanopore platform) on the myocardial tissues and analyzed the transcripts using differential expression analysis and functional annotation methods [Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses]. Additionally, we estimated the expression of the selected genes by quantitative real-time PCR (qRT-PCR) and detected the proportion of immune cells using flow cytometry. We found that increased B-type natriuretic peptide reduced ejection fraction, and apparent clinical signs were observed in the dog model of HF. We identified 67,458 transcripts using full-length transcriptome sequencing. A total of 785 DETs were obtained from the HF and control groups. These DETs were mainly enriched in the immune responses, especially Th1, Th2, and Th17 cell differentiation processes. Furthermore, flow cytometry results revealed that the proportion of Th1 and Th17 cells increased in patients with HF compared to controls, while the proportion of Th2 cells decreased. Differentially expressed genes in the HF and control groups associated with Th1, Th2, and Th17 cell differentiation were quantified using qRT-PCR. We also identified variable splicing events of sarcomere genes (e.g., MYBPC3, TNNT2, TTN, FLNC, and TTNI3). In addition, we detected 4,892 transcription factors and 406 lncRNAs associated with HF. Our analysis based on full-length transcript sequencing provided an analysis perspective in a dog model of HF, which is valuable for molecular research in an increasingly relevant large animal model of HF.

Omics technologies in personalized combination therapy for cardiovascular diseases: challenges and opportunities

The primary purpose of 'omics' technologies is to understand the intricacy of genomics, proteomics, metabolomics and other molecular mechanisms to reveal the complex traits of human diseases. The significant use of omics technologies and their applications in medicine gear up the study of the pathogenesis of several disorders. The detection of biomarkers in the early onset of diseases is challenging; still, omics can discover novel molecular mechanisms and biomarkers. In this review, the different types of omics and their technologies are explicated and aimed to provide their emerging applications in cardiovascular precision medicine. These technologies significantly impact optimizing medical treatment for individuals to reach a higher level in precision medicine.

Systematic analysis of molecular mechanisms of heart failure through the pathway and network-based approach

AIMS

The molecular networks and pathways involved in heart failure (HF) are still largely unknown. The present study aimed to systematically investigate the genes associated with HF, comprehensively explore their interactions and functions, and identify possible regulatory networks involved in HF.

MAIN METHODS

The weighted gene coexpression network analysis (WGCNA), crosstalk analysis, and Pivot analysis were used to identify gene connections, interaction networks, and molecular regulatory mechanisms. Functional analysis and protein-protein interaction (PPI) were performed using DAVID and STRING databases. Gene set variation analysis (GSVA) and receiver operating characteristic (ROC) curve analysis were also performed to evaluate the relationship of the hub genes with HF.

KEY FINDINGS

A total of 5968 HF-related genes were obtained to construct the co-expression networks, and 18 relatively independent and closely linked modules were identified. Pivot analysis suggested that four transcription factors and five noncoding RNAs were involved in regulating the process of HF. The genes in the module with the highest positive correlation to HF was mainly enriched in cardiac remodeling and response to stress. Five upregulated hub genes (ASPN, FMOD, NT5E, LUM, and OGN) were identified and validated. Furthermore, the GSVA scores of the five hub genes for HF had a relatively high areas under the curve (AUC).

SIGNIFICANCE

The results of this study revealed specific molecular networks and their potential regulatory mechanisms involved in HF. These may provide new insight into understanding the mechanisms underlying HF and help to identify more effective therapeutic targets for HF.

A three-tiered integrative analysis of transcriptional data reveals the shared pathways related to heart failure from different aetiologies

Heart failure (HF) is the end stage of most heart disease cases and can be initiated from multiple aetiologies. However, whether the molecular basis of HF has a commonality between different aetiologies has not been elucidated. To address this lack, we performed a three‐tiered analysis by integrating transcriptional data and pathway information to explore the commonalities of HF from different aetiologies. First, through differential expression analysis, we obtained 111 genes that were frequently differentially expressed in HF from 11 different aetiologies. Several genes, such as NPPA and NPPB, are early and accurate biomarkers for HF. We also provided candidates for further experimental verification, such as SERPINA3 and STAT4. Then, using gene set enrichment analysis, we successfully identified 19 frequently dysregulated pathways. In particular, we found that pathways related to immune system signalling, the extracellular matrix and metabolism were critical in the development of HF. Finally, we successfully acquired 241 regulatory relationships between 64 transcriptional factors (TFs) and 17 frequently dysregulated pathways by integrating a regulatory network, and some of the identified TFs have already been proven to play important roles in HF. Taken together, the three‐tiered analysis of HF provided a systems biology perspective on HF and emphasized the molecular commonality of HF from different aetiologies.

Novel insights into cardiac regeneration based on differential fetal and adult ovine heart transcriptomic analysis

The adult mammalian cardiomyocyte has a very limited capacity to reenter the cell cycle and advance into mitosis. Therefore, diseases characterized by lost contractile tissue usually evolve into myocardial remodeling and heart failure. Analyzing the cardiac transcriptome at different developmental stages in a large mammal closer to the human than laboratory rodents may serve to disclose positive and negative cardiomyocyte cell cycle regulators potentially targetable to induce cardiac regeneration in the clinical setting. Thus we aimed at characterizing the transcriptomic profiles of the early fetal, late fetal, and adult sheep heart by employing RNA-seq technique and bioinformatic analysis to detect protein-encoding genes that in some of the stages were turned off, turned on, or differentially expressed. Genes earlier proposed as positive cell cycle regulators such as cyclin A, cdk2, meis2, meis3, and PCNA showed higher expression in fetal hearts and lower in AH, as expected. In contrast, genes previously proposed as cell cycle inhibitors, such as meis1, p16, and sav1, tended to be higher in fetal than in adult hearts, suggesting that these genes are involved in cell processes other than cell cycle regulation. Additionally, we described Gene Ontology (GO) enrichment of different sets of genes. GO analysis revealed that differentially expressed gene sets were mainly associated with metabolic and cellular processes. The cell cycle-related genes fam64a, cdc20, and cdk1, and the metabolism-related genes pitx and adipoq showed strong differential expression between fetal and adult hearts, thus being potent candidates to be targeted in human cardiac regeneration strategies.NEW & NOTEWORTHY We characterized the transcriptomic profiles of the fetal and adult sheep hearts employing RNAseq technique and bioinformatic analyses to provide sets of transcripts whose variation in expression level may link them to a specific role in cell cycle regulation. It is important to remark that this study was performed in a large mammal closer to humans than laboratory rodents. In consequence, the results can be used for further translational studies in cardiac regeneration.

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.