Transcriptional changes associated with advancing stages of heart failure underlie atrial and ventricular arrhythmogenesis

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.

Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure

BACKGROUND

Left-sided heart failure (HF) is documented as a key prognostic factor in HF. However, the relative molecular mechanisms underlying left-sided HF is unknown. The purpose of this study is to unearth significant modules, pivotal genes and candidate regulatory components governing the progression of left-sided HF by bioinformatical analysis.

METHODS

A total of 319 samples in GSE57345 dataset were used for weighted gene correlation network analysis (WGCNA). ClusterProfiler package in R was used to conduct functional enrichment for genes uncovered from the modules of interest. Regulatory networks of genes were built using Cytoscape while Enrichr database was used for identification of transcription factors (TFs). The MCODE plugin was used for identifying hub genes in the modules of interest and their validation was performed based on GSE1869 dataset.

RESULTS

A total of six significant modules were identified. Notably, the blue module was confirmed as the most crucially associated with left-sided HF, ischemic heart disease (ISCH) and dilated cardiomyopathy (CMP). Functional enrichment conveyed that genes belonging to this module were mainly those driving the extracellular matrix-associated processes such as extracellular matrix structural constituent and collagen binding. A total of seven transcriptional factors, including Suppressor of Zeste 12 Protein Homolog (SUZ12) and nuclear factor erythroid 2 like 2 (NFE2L2), adrenergic receptor (AR), were identified as possible regulators of coexpression genes identified in the blue module. A total of three key genes (OGN, HTRA1 and MXRA5) were retained after validation of their prognostic value in left-sided HF. The results of functional enrichment confirmed that these key genes were primarily involved in response to transforming growth factor beta and extracellular matrix.

CONCLUSION

We uncovered a candidate gene signature correlated with HF, ISCH and CMP in the left ventricle, which may help provide better prognosis and therapeutic decisions and in HF, ISCH and CMP patients.