Identification of Potential miRNA-mRNA Regulatory Network Contributing to Hypertrophic Cardiomyopathy (HCM)

Feline hypertrophic cardiomyopathy: Does the microRNA-mRNA regulatory network contribute to heart sarcomeric protein remodelling?

Feline primary hypertrophic cardiomyopathy (HCM) is an intrinsic myocardial disease characterized by concentric hypertrophy of the left ventricle. In the present study, we investigated the microRNA-mRNA regulatory network in feline myocardial tissue affected by primary (HCMI) and secondary HCM (HCMII). MRNA expression levels of sarcomeric genes, including, TNNT2, TNNI3, MYH7, MYBPC3, TPM1 and ACTC1 were assessed in the FFPE myocardial tissues. FFPE tissues from healthy cats were sequenced by the NGS, to explore, in the entire non-deposited miRNome, the expression level of microRNAs targeting the complementary sequences of selected sarcomeric mRNAs. The sarcomeric genes TNNT2, MYH7, MYBPC3 and TPM1 showed a statistically significant upregulation in HCMI compared to HCMII (p < .01), except ACTC1 which was downregulated (p < .01); TNNI3 showed no statistically significant difference. In HCMII miR-122-5p, miR-338-3p, miR-484, miR-370-3p, miR-92b-3p, miR-375 and miR-370-3p showed a significant upregulation (p < .01) compared to control. The exception was miR-30a-5p which showed downregulation. Worthy of note is the 4-fold higher expression of miR-370-3p, a key regulator of MYBPC3, in HMCI compared to HMCII. This research does not solve the aetiological mystery of HCM, but it may help to find a way to help diagnose and define the prognosis of HCM in cats.

MicroRNA-452-5p regulates fibrogenesis via targeting TGF-β/SMAD4 axis in SCN5A-knockdown human cardiac fibroblasts

The mutated SCN5A gene encoding defective Nav1.5 protein causes arrhythmic ailments and is associated with enhanced cardiac fibrosis. This study investigated whether SCN5A mutation directly affects cardiac fibroblasts and explored how defective SCN5A relates to cardiac fibrosis. SCN5A knockdown (SCN5AKD) human cardiac fibroblasts (HCF) had higher collagen, α-SMA, and fibronectin expressions. Micro-RNA deep sequencing and qPCR analysis revealed the downregulation of miR-452-5p and bioinformatic analysis divulged maladaptive upregulation of transforming growth factor β (TGF-β) signaling in SCN5AKD HCF. Luciferase reporter assays validated miR-452-5p targets SMAD4 in SCN5AKD HCF. Moreover, miR-452-5p mimic transfection in SCN5AKD HCF or AAV9-mediated miR-452-5p delivery in isoproterenol-induced heart failure (HF) rats, resulted in the attenuation of TGF-β signaling and fibrogenesis. The exogenous miR-452-5p significantly improved the poor cardiac function in HF rats. In conclusion, miR-452-5p regulates cardiac fibrosis progression by targeting the TGF-β/SMAD4 axis under the loss of the SCN5A gene.

Genetic Mutations and Mitochondrial Redox Signaling as Modulating Factors in Hypertrophic Cardiomyopathy: A Scoping Review

Hypertrophic cardiomyopathy (HCM) is a heart condition characterized by cellular and metabolic dysfunction, with mitochondrial dysfunction playing a crucial role. Although the direct relationship between genetic mutations and mitochondrial dysfunction remains unclear, targeting mitochondrial dysfunction presents promising opportunities for treatment, as there are currently no effective treatments available for HCM. This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews guidelines. Searches were conducted in databases such as PubMed, Embase, and Scopus up to September 2023 using "MESH terms". Bibliographic references from pertinent articles were also included. Hypertrophic cardiomyopathy (HCM) is influenced by ionic homeostasis, cardiac tissue remodeling, metabolic balance, genetic mutations, reactive oxygen species regulation, and mitochondrial dysfunction. The latter is a common factor regardless of the cause and is linked to intracellular calcium handling, energetic and oxidative stress, and HCM-induced hypertrophy. Hypertrophic cardiomyopathy treatments focus on symptom management and complication prevention. Targeted therapeutic approaches, such as improving mitochondrial bioenergetics, are being explored. This includes coenzyme Q and elamipretide therapies and metabolic strategies like therapeutic ketosis. Understanding the biomolecular, genetic, and mitochondrial mechanisms underlying HCM is crucial for developing new therapeutic modalities.

Histone Modifications and miRNA Perturbations Contribute to Transcriptional Dysregulation of Hypertrophy in Obstructive Hypertrophic Cardiomyopathy

Background

Recently, we demonstrated transcriptional downregulation of hypertrophy pathways in myectomy tissue derived from patients with obstructive hypertrophic cardiomyopathy (HCM) despite translational activation of hypertrophy pathways. The mechanisms and modifiers of this transcriptional dysregulation in HCM remain unexplored. We hypothesized that miRNA and post-translational modifications of histones contribute to transcriptional dysregulation in HCM.

Methods

First, miRNA-sequencing and chromatin immunoprecipitation sequencing (ChIP-seq) were performed on HCM myectomy tissue and control donor hearts to characterize miRNA and differential histone marks across the genome. Next, the differential miRNA and histone marks were integrated with RNA-sequencing (RNA-seq) data. Finally, the effects of miRNA and histones were removed in silico to determine their necessity for transcriptional dysregulation of pathways.

Results

miRNA-analysis identified 19 differentially expressed miRNA. ChIP-seq analysis identified 2,912 (7%) differential H3K4me3 peaks, 23,339 (21%) differential H3K9ac peaks, 33 (0.05%) differential H3K9me3 peaks, 58,837 (42%) differential H3K27ac peaks, and 853 (3%) differential H3K27me3 peaks. Univariate analysis of concordance between H3K9ac with RNA-seq data showed activation of cardiac hypertrophy signaling, while H3K27me showed downregulation of cardiac hypertrophy signaling. Similarly, miRNAs were predicted to result in downregulation of cardiac hypertrophy signaling. In silico knock-out that effects either miRNA or histones attenuated transcriptional downregulation while knocking out both abolished downregulation of hypertrophy pathways completely.

Conclusion

Myectomy tissue from patients with obstructive HCM shows transcriptional dysregulation, including transcriptional downregulation of hypertrophy pathways mediated by miRNA and post-translational modifications of histones. Cardiac hypertrophy loci showed activation via changes in H3K9ac and a mix of activation and repression via H3K27ac.

Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning

OBJECTIVES

Hypertrophic cardiomyopathy (HCM), a leading cause of heart failure and sudden death, requires early diagnosis and treatment. This study investigated the underlying pathogenesis and explored potential diagnostic gene biomarkers for HCM.

METHODS

Transcriptional profiles of myocardial tissues from patients with HCM (dataset GSE36961) were downloaded from the Gene Expression Omnibus database and subjected to bioinformatics analyses, including differentially expressed gene (DEG) identification, enrichment analyses, and protein-protein interaction (PPI) network analysis. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination were performed to identify candidate diagnostic gene biomarkers. mRNA expression levels of candidate biomarkers were tested in an external dataset (GSE141910); area under the receiver operating characteristic curve (AUC) values were obtained to validate diagnostic efficacy.

RESULTS

Overall, 156 DEGs (109 downregulated, 47 upregulated) were identified. Enrichment and PPI network analyses indicated that the DEGs were involved in biological functions and molecular pathways including inflammatory response, platelet activity, complement and coagulation cascades, extracellular matrix organization, phagosome, apoptosis, and VEGFA-VEGFR2 signaling. RASD1, CDC42EP4, MYH6, and FCN3 were identified as diagnostic biomarkers for HCM.

CONCLUSIONS

RASD1, CDC42EP4, MYH6, and FCN3 might be diagnostic gene biomarkers for HCM and can provide insights concerning HCM pathogenesis.

Feline myocardial transcriptome in health and in hypertrophic cardiomyopathy-A translational animal model for human disease

Hypertrophic cardiomyopathy (HCM) is the most common heart disease in cats, characterized by primary left ventricular hypertrophy. Feline HCM closely resembles human HCM and is suggested as translational animal model for the human disease. A genetic cause is established in humans and suspected for cats, but little is known about the gene expression and pathways involved in the pathogenesis of HCM. To investigate the myocardial transcriptome changes in HCM, RNA sequencing was conducted on left ventricle (LV) and left atrium (LA) samples of healthy cats and cats with HCM (each n = 5; 20 samples). Ingenuity Pathway Analysis was used to determine functional pathways, regulators, and networks. Distinct gene expression profiles were identified in the LV and LA of the feline healthy and HCM myocardium. Analysis of differentially expressed mRNAs (>2 fold; FDR < 0.01) found chamber-specific (LV vs. LA) expression in both healthy and HCM groups, with higher transcriptional activity in the LA. Genes that contribute to the distinct structure and function of each chamber in health and HCM were identified in the regional comparison. The gene expression profiles of HCM compared to healthy hearts revealed disease related genes, including THBS4 and KLHL33 (LV), FAM177B and THRSP (LA), the latter 3 have not been reported for the myocardium so far, as the top differently expressed genes in the HCM heart. Differently expressed genes and functional pathways found in the HCM heart are associated with cardiac remodeling and fibrosis, inflammation, microvascular changes, calcium signaling and cardiac metabolism, with some regional differences. RhoGDI-RhoGTPase signaling, integrin and ILK signaling pathways, the LXR/RXR pathway in the LA, and the PPARα/RXRα, HIF1α and CXCR4 pathways in the LV might be of particular importance in the HCM disease process. This study identified region-specific myocardial gene transcription patterns as well as novel genes and pathways associated with HCM.

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris

MicroRNAs (miRNAs) are important types of noncoding RNAs, and there is a lack of holistic and systematic understanding of the functions they play in disease. We proposed a research strategy, including two parts network analysis and network modelling, to analyze, model, and predict the regulatory network of miRNAs from a network perspective, using unstable angina pectoris as an example. In the network analysis section, we proposed the WGCNA & SimCluster method using both correlation and similarity to find hub miRNAs, and validation on two datasets showed better results than the methods using correlation or similarity alone. In the network modelling section, we used six knowledge graph or graph neural network models for link prediction of three types of edges and multilabel classification of two types of nodes. Comparative experiments showed that the RotatE model was a good model for link prediction, while the RGCN model was the best model for multilabel classification. Potential target genes were predicted for hub miRNAs and validation of hub miRNA-target gene interactions, target genes as biomarkers and target gene functions were performed using a three-step validation approach. In conclusion, our study provides a new strategy to analyze and model miRNA regulatory networks.

Identification of Potential miRNA-mRNA Regulatory Network Associated with Pig Growth Performance in the Pituitaries of Bama Minipigs and Landrace Pigs

Pig growth performance is one of the criteria for judging pork production and is influenced by genotype and external environmental factors such as feeding conditions. The growth performance of miniature pigs, such as Bama minipigs, differs considerably from that of the larger body size pigs, such as Landrace pigs, and can be regarded as good models in pig growth studies. In this research, we identified differentially expressed genes in the pituitary gland of Bama minipigs and Landrace pigs. Through the pathway enrichment analysis, we screened the growth-related pathways and the genes enriched in the pathways and established the protein-protein interaction network. The RNAHybrid algorithm was used to predict the interaction between differentially expressed microRNAs and differentially expressed mRNAs. Four regulatory pathways (Y-82-ULK1/CDKN1A, miR-4334-5p-STAT3/PIK3R1/RPS6KA3/CAB39L, miR-4331-SCR/BCL2L1, and miR-133a-3p-BCL2L1) were identified via quantitative real-time PCR to detect the expression and correlation of candidate miRNAs and mRNAs. In conclusion, we revealed potential miRNA-mRNA regulatory networks associated with pig growth performance in the pituitary glands of Bama minipigs and Landrace pigs, which may help to elucidate the underlying molecular mechanisms of growth differences in pigs of different body sizes.

Screening of Lipid Metabolism-Related Gene Diagnostic Signature for Patients With Dilated Cardiomyopathy

Background

Dilated cardiomyopathy (DCM) is characterized by enlarged ventricular dimensions and systolic dysfunction and poor prognosis. Myocardial lipid metabolism appears abnormal in DCM. However, the mechanism of lipid metabolism disorders in DCM remains unclear.

Methods

A gene set variation analysis (GSVA) were performed to estimate pathway activity related to DCM progression. Three datasets and clinical data downloaded from the Gene Expression Omnibus (GEO), including dilated cardiomyopathy and donor hearts, were integrated to obtain gene expression profiles and identify differentially expressed genes related to lipid metabolism. GO enrichment analyses of differentially expressed lipid metabolism-related genes (DELs) were performed. The clinical information used in this study were obtained from GSE21610 dataset. Data from the EGAS00001003263 were used for external validation and our hospital samples were also tested the expression levels of these genes through RT-PCR. Subsequently, logistic regression model with the LASSO method for DCM prediction was established basing on the 7 DELs.

Results

GSVA analysis showed that the fatty acid metabolism was closely related to DCM progression. The integrated dataset identified 19 DELs, including 8 up-regulated and 11 down-regulated genes. A total of 7 DELs were identified by further external validation of the data from the EGAS00001003263 and verified by RT-PCR. By using the LASSO model, 6 genes, including CYP2J2, FGF1, ETNPPL, PLIN2, LPCAT3, and DGKG, were identified to construct a logistic regression model. The area under curve (AUC) values over 0.8 suggested the good performance of the model.

Conclusion

Integrated bioinformatic analysis of gene expression in DCM and the effective logistic regression model construct in our study may contribute to the early diagnosis and prevention of DCM in people with high risk of the disease.

A Review on the Evolving Roles of MiRNA-Based Technologies in Diagnosing and Treating Heart Failure

MiRNA-regulated processes are pivotal in cardiovascular homeostasis and disease. These short non-coding RNAs have ideal properties that could be utilized as potential biomarkers; moreover, their functions as post-transcriptional regulators of mRNA make them interesting therapeutic targets. In this review, we summarize the current state of miRNA-based biomarkers in a variety of diseases leading to heart failure, as well as provide an outlook on developing miRNA-based therapies in the heart failure field.

Identification of potential genes related to breast cancer brain metastasis in breast cancer patients

Brain metastases (BMs) usually develop in breast cancer (BC) patients. Thus, the molecular mechanisms of breast cancer brain metastasis (BCBM) are of great importance in designing therapeutic strategies to treat or prevent BCBM. The present study attempted to identify novel diagnostic and prognostic biomarkers of BCBM. Two datasets (GSE125989 and GSE100534) were obtained from the Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs) in cases of BC with and without brain metastasis (BM). A total of 146 overlapping DEGs, including 103 up-regulated and 43 down-regulated genes, were identified. Functional enrichment analysis showed that these DEGs were mainly enriched for functions including extracellular matrix (ECM) organization and collagen catabolic fibril organization. Using protein-protein interaction (PPI) and principal component analysis (PCA) analysis, we identified ten key genes, including LAMA4, COL1A1, COL5A2, COL3A1, COL4A1, COL5A1, COL5A3, COL6A3, COL6A2, and COL6A1. Additionally, COL5A1, COL4A1, COL1A1, COL6A1, COL6A2, and COL6A3 were significantly associated with the overall survival of BC patients. Furthermore, COL6A3, COL5A1, and COL4A1 were potentially correlated with BCBM in human epidermal growth factor 2 (HER2) expression. Additionally, the miR-29 family might participate in the process of metastasis by modulating the cancer microenvironment. Based on datasets in the GEO database, several DEGs have been identified as playing potentially important roles in BCBM in BC patients.