Myocardial transcriptome analysis of human arrhythmogenic right ventricular cardiomyopathy

Targeting ERBB2 and PIK3R1 as a therapeutic strategy for dilated cardiomyopathy: A single-cell sequencing and mendelian randomization analysis

Background

Dilated cardiomyopathy (DCM) is widely recognized as a significant contributor to heart failure. Nevertheless, the absence of pharmaceutical interventions capable of reversing disease progression and improving prognosis underscores the imperative for additional research in this area.

Methods

First, we identified and evaluated three gene sets, namely "SC-DCM", "EP-DCM" and "Drug", using big data and multiple bioinformatics analysis methods. Accordingly, drug-treatable ("Hub") genes in DCM were identified. Following this, four microarray expression profile datasets were employed to authenticate the expression levels and discriminatory efficacy of "Hub" genes. Additionally, mendelian randomization analysis was conducted to ascertain the causal association between the "Hub genes" and heart failure. Finally, the "DGIdb" was applied to identify "Hub" genes-targeted drugs. The "ssGSEA" algorithm assessed the level of immune cell infiltration in DCM.

Results

Enrichment analysis showed that the "SC-DCM" and "EP-DCM" gene sets were closely associated with DCM. PIK3R1 and ERBB2 were identified as drug-treatable genes in DCM. Additional analysis using MR supported a causal relationship between ERBB2 and heart failure, but not PIK3R1. Moreover, PIK3R1 was positively correlated with immune activation, while ERBB2 was negatively correlated. We found that everolimus was a pharmacological inhibitor for both PIK3R1 and ERBB2. However, no pharmacological agonist was found for ERBB2.

Conclusion

PIK3R1 and ERBB2 are drug-treatable genes in DCM. ERBB2 has a causal effect on heart failure, and its normal expression may play a role in preventing the progression of DCM to heart failure. In addition, there is a cross-expression of PIK3R1 and ERBB2 genes in both DCM and tumors. The adaptive immune system and PIK3R1 may be involved in DCM disease progression, while ERBB2 exerts a protective effect against DCM.

Gender-specific genetic and epigenetic signatures in cardiovascular disease

Cardiac sex differences represent a pertinent focus in pursuit of the long-awaited goal of personalized medicine. Despite evident disparities in the onset and progression of cardiac pathology between sexes, historical oversight has led to the neglect of gender-specific considerations in the treatment of patients. This oversight is attributed to a predominant focus on male samples and a lack of sex-based segregation in patient studies. Recognizing these sex differences is not only relevant to the treatment of cisgender individuals; it also holds paramount importance in addressing the healthcare needs of transgender patients, a demographic that is increasingly prominent in contemporary society. In response to these challenges, various agencies, including the National Institutes of Health, have actively directed their efforts toward advancing our comprehension of this phenomenon. Epigenetics has proven to play a crucial role in understanding sex differences in both healthy and disease states within the heart. This review presents a comprehensive overview of the physiological distinctions between males and females during the development of various cardiac pathologies, specifically focusing on unraveling the genetic and epigenetic mechanisms at play. Current findings related to distinct sex-chromosome compositions, the emergence of gender-biased genetic variations, and variations in hormonal profiles between sexes are highlighted. Additionally, the roles of DNA methylation, histone marks, and chromatin structure in mediating pathological sex differences are explored. To inspire further investigation into this crucial subject, we have conducted global analyses of various epigenetic features, leveraging data previously generated by the ENCODE project.

Integrated analysis of differentially expressed genes and miRNA expression profiles in dilated cardiomyopathy

Background

Although dilated cardiomyopathy (DCM) is a prevalent form of cardiomyopathy, the molecular mechanisms underlying its pathogenesis and progression remain poorly understood. It is possible to identify and validate DCM-associated genes, pathways, and miRNAs using bioinformatics analysis coupled with clinical validation methods.

Methods

Our analysis was performed using 3 mRNA datasets and 1 miRNA database. We employed several approaches, including gene ontology (GO) analysis, KEGG pathway enrichment analysis, protein-protein interaction networks analysis, and analysis of hub genes to identify critical genes and pathways linked to DCM. We constructed a regulatory network for DCM that involves interactions between miRNAs and mRNAs. We also validated the differently expressed miRNAs in clinical samples (87 DCM ，83 Normal) using qRT-PCR.The miRNAs' clinical value was evaluated by receiver operating characteristic curves (ROCs).

Results

78 differentially expressed genes (DEGs) and 170 differentially expressed miRNAs (DEMs) were associated with DCM. The top five GO annotations were collagen-containing extracellular matrix, cell substrate adhesion, negative regulation of cell differentiation, and inflammatory response. The most enriched KEGG pathways were the Neurotrophin signaling pathway, Thyroid hormone signaling pathway, Wnt signaling pathway, and Axon guidance. In the PPI network, we identified 10 hub genes, and in the miRNA-mRNA regulatory network, we identified 8 hub genes and 15 miRNAs. In the clinical validation, we found 13 miRNAs with an AUC value greater than 0.9.

Conclusion

Our research offers novel insights into the underlying mechanisms of DCM and has implications for identifying potential targets for diagnosis and treatment of this condition.

Identification of Signature Genes of Dilated Cardiomyopathy Using Integrated Bioinformatics Analysis

Dilated cardiomyopathy (DCM) is characterized by left ventricular or biventricular enlargement with systolic dysfunction. To date, the underlying molecular mechanisms of dilated cardiomyopathy pathogenesis have not been fully elucidated, although some insights have been presented. In this study, we combined public database resources and a doxorubicin-induced DCM mouse model to explore the significant genes of DCM in full depth. We first retrieved six DCM-related microarray datasets from the GEO database using several keywords. Then we used the "LIMMA" (linear model for microarray data) R package to filter each microarray for differentially expressed genes (DEGs). Robust rank aggregation (RRA), an extremely robust rank aggregation method based on sequential statistics, was then used to integrate the results of the six microarray datasets to filter out the reliable differential genes. To further improve the reliability of our results, we established a doxorubicin-induced DCM model in C57BL/6N mice, using the "DESeq2" software package to identify DEGs in the sequencing data. We cross-validated the results of RRA analysis with those of animal experiments by taking intersections and identified three key differential genes (including BEX1, RGCC and VSIG4) associated with DCM as well as many important biological processes (extracellular matrix organisation, extracellular structural organisation, sulphur compound binding, and extracellular matrix structural components) and a signalling pathway (HIF-1 signalling pathway). In addition, we confirmed the significant effect of these three genes in DCM using binary logistic regression analysis. These findings will help us to better understand the pathogenesis of DCM and may be key targets for future clinical management.

Modeling incomplete penetrance in arrhythmogenic cardiomyopathy by human induced pluripotent stem cell derived cardiomyocytes

Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) are commonly used to model arrhythmogenic cardiomyopathy (ACM), a heritable cardiac disease characterized by severe ventricular arrhythmias, fibrofatty myocardial replacement and progressive ventricular dysfunction. Although ACM is inherited as an autosomal dominant disease, incomplete penetrance and variable expressivity are extremely common, resulting in different clinical manifestations. Here, we propose hiPSC-CMs as a powerful in vitro model to study incomplete penetrance in ACM. Six hiPSC lines were generated from blood samples of three ACM patients carrying a heterozygous deletion of exon 4 in the PKP2 gene, two asymptomatic (ASY) carriers of the same mutation and one healthy control (CTR), all belonging to the same family. Whole exome sequencing was performed in all family members and hiPSC-CMs were examined by ddPCR, western blot, Wes™ immunoassay system, patch clamp, immunofluorescence and RNASeq. Our results show molecular and functional differences between ACM and ASY hiPSC-CMs, including a higher amount of mutated PKP2 mRNA, a lower expression of the connexin-43 protein, a lower overall density of sodium current, a higher intracellular lipid accumulation and sarcomere disorganization in ACM compared to ASY hiPSC-CMs. Differentially expressed genes were also found, supporting a predisposition for a fatty phenotype in ACM hiPSC-CMs. These data indicate that hiPSC-CMs are a suitable model to study incomplete penetrance in ACM.

Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in dilated cardiomyopathy with heart failure

The etiologies and pathogenesis of dilated cardiomyopathy (DCM) with heart failure (HF) remain to be defined. Thus, exploring specific diagnosis biomarkers and mechanisms is urgently needed to improve this situation. In this study, three gene expression profiling datasets (GSE29819, GSE21610, GSE17800) and one single-cell RNA sequencing dataset (GSE95140) were obtained from the Gene Expression Omnibus (GEO) database. GSE29819 and GSE21610 were combined into the training group, while GSE17800 was the test group. We used the weighted gene co-expression network analysis (WGCNA) and identified fifteen driver genes highly associated with DCM with HF in the module. We performed the least absolute shrinkage and selection operator (LASSO) on the driver genes and then constructed five machine learning classifiers (random forest, gradient boosting machine, neural network, eXtreme gradient boosting, and support vector machine). Random forest was the best-performing classifier established on five Lasso-selected genes, which was utilized to select out NPPA, OMD, and PRELP for diagnosing DCM with HF. Moreover, we observed the up-regulation mRNA levels and robust diagnostic accuracies of NPPA, OMD, and PRELP in the training group and test group. Single-cell RNA-seq analysis further demonstrated their stable up-regulation expression patterns in various cardiomyocytes of DCM patients. Besides, through gene set enrichment analysis (GSEA), we found TGF-β signaling pathway, correlated with NPPA, OMD, and PRELP, was the underlying mechanism of DCM with HF. Overall, our study revealed NPPA, OMD, and PRELP serving as diagnostic biomarkers for DCM with HF, deepening the understanding of its pathogenesis.

Defective Desmosomal Adhesion Causes Arrhythmogenic Cardiomyopathy by Involving an Integrin-αVβ6/TGF-β Signaling Cascade

BACKGROUND

Arrhythmogenic cardiomyopathy (ACM) is characterized by progressive loss of cardiomyocytes with fibrofatty tissue replacement, systolic dysfunction, and life-threatening arrhythmias. A substantial proportion of ACM is caused by mutations in genes of the desmosomal cell-cell adhesion complex, but the underlying mechanisms are not well understood. In the current study, we investigated the relevance of defective desmosomal adhesion for ACM development and progression.

METHODS

We mutated the binding site of DSG2 (desmoglein-2), a crucial desmosomal adhesion molecule in cardiomyocytes. This DSG2-W2A mutation abrogates the tryptophan swap, a central interaction mechanism of DSG2 on the basis of structural data. Impaired adhesive function of DSG2-W2A was confirmed by cell-cell dissociation assays and force spectroscopy measurements by atomic force microscopy. The DSG2-W2A knock-in mouse model was analyzed by echocardiography, ECG, and histologic and biomolecular techniques including RNA sequencing and transmission electron and superresolution microscopy. The results were compared with ACM patient samples, and their relevance was confirmed in vivo and in cardiac slice cultures by inhibitor studies applying the small molecule EMD527040 or an inhibitory integrin-αVβ6 antibody.

RESULTS

The DSG2-W2A mutation impaired binding on molecular level and compromised intercellular adhesive function. Mice bearing this mutation develop a severe cardiac phenotype recalling the characteristics of ACM, including cardiac fibrosis, impaired systolic function, and arrhythmia. A comparison of the transcriptome of mutant mice with ACM patient data suggested deregulated integrin-αVβ6 and subsequent transforming growth factor-β signaling as driver of cardiac fibrosis. Blocking integrin-αVβ6 led to reduced expression of profibrotic markers and reduced fibrosis formation in mutant animals in vivo.

CONCLUSIONS

We show that disruption of desmosomal adhesion is sufficient to induce a phenotype that fulfils the clinical criteria to establish the diagnosis of ACM, confirming the dysfunctional adhesion hypothesis. Deregulation of integrin-αVβ6 and transforming growth factor-β signaling was identified as a central step toward fibrosis. A pilot in vivo drug test revealed this pathway as a promising target to ameliorate fibrosis. This highlights the value of this model to discern mechanisms of cardiac fibrosis and to identify and test novel treatment options for ACM.

Dysregulation and imbalance of innate and adaptive immunity are involved in the cardiomyopathy progression

Background

Cardiomyopathy is known to be a heterogeneous disease with numerous etiologies. They all have varying degrees and types of myocardial pathological changes, resulting in impaired contractility, ventricle relaxation, and heart failure. The purpose of this study was to determine the pathogenesis, immune-related pathways and important biomarkers engaged in the progression of cardiomyopathy from various etiologies.

Methods

We downloaded the gene microarray data from the Gene Expression Omnibus (GEO). The hub genes between cardiomyopathy and non-cardiomyopathy control groups were identified using differential expression analysis, least absolute shrinkage and selection operator (LASSO) regression and weighted gene co-expression network analysis (WGCNA). To assess the diagnostic precision of hub genes, receiver-operating characteristic (ROC) curves as well as the area under the ROC curve (AUC) were utilized. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis and Gene Ontology (GO) analysis were conducted on the obtained differential genes. Finally, single-sample GSEA (ssGSEA) and Gene Set Enrichment Analysis (GSEA) were utilized to analyze the infiltration level of 28 immune cells and their relationship with hub genes based on gene expression profile data and all differential gene files.

Results

A total of 82 differentially expressed genes (DEGs) were screened after the training datasets were merged and intersected. The WGCNA analysis clustered the expression profile data into four co-expression modules, The turquoise module exhibited the strongest relationship with clinical traits, and nine candidate key genes were obtained from the module. Then we intersected DEGs with nine candidate genes. LASSO regression analysis identified the last three hub genes as promising biomarkers to distinguish the cardiomyopathy group from the non-cardiomyopathy control group. ROC curve analysis in the validation dataset revealed the sensitivity and accuracy of three hub genes as marker genes. The majority of the functional enrichment analysis results were concentrated on immunological and inflammatory pathways. Immune infiltration analysis revealed a significant correlation between regulatory T cells, type I helper T cells, macrophages, myeloid-derived suppressor cells, natural killer cells, activated dendritic cells and the abundance of immune infiltration in hub genes.

Conclusion

The hub genes (CD14, CCL2, and SERPINA3) can be used as markers to distinguish cardiomyopathy from non-cardiomyopathy individuals. Among them, SERPINA3 has the best diagnostic performance. T cell immunity (adaptive immune response) is closely linked to cardiomyopathy progression. Hub genes may protect the myocardium from injury through myeloid-derived suppressor cells, regulatory T cells, helper T cells, monocytes/macrophages, natural killer cells and activated dendritic cells. The innate immune response is crucial to this process. Dysregulation and imbalance of innate immune cells or activation of adaptive immune responses are involved in cardiomyopathy disease progression in patients.

Dominant Myocardial Fibrosis and Complex Immune Microenvironment Jointly Shape the Pathogenesis of Arrhythmogenic Right Ventricular Cardiomyopathy

Background

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable life-threatening myocardial disease characterized by ventricular arrhythmias and sudden cardiac death. Few studies used RNA-sequencing (RNA-seq) technology to analyze gene expression profiles, hub genes, dominant pathogenic processes, immune microenvironment in ARVC. This study aimed to explore these questions via integrated bioinformatics analysis.

Methods

RNA-sequencing datasets of GSE107475, GSE107311, GSE107156, and GSE107125 were obtained from the Gene Expression Omnibus database, including right and left ventricular myocardium from ARVC patients and normal controls. Weighted gene co-expression network analysis identified the ARVC hub modules and genes. Functional enrichment and protein-protein interaction analysis were performed by Metascape and STRING. Single-sample gene-set enrichment analysis (ssGSEA) was applied to assess immune cell infiltration. Transcription regulator (TF) analysis was performed by TRRUST.

Results

Three ARVC hub modules with 25 hub genes were identified. Functional enrichment analysis of the hub genes indicated that myocardial fibrosis was the dominant pathogenic process. Higher myocardial fibrosis activity existed in ARVC than in normal controls. A complex immune microenvironment was discovered that type 2 T helper cell, type 1 T helper cell, regulatory T cell, plasmacytoid dendritic cell, neutrophil, mast cell, central memory CD4 T cell, macrophage, CD56dim natural killer cell, myeloid-derived suppressor cell, memory B cell, natural killer T cell, and activated CD8 T cell were highly infiltrated in ARVC myocardium. The immune-related hub module was enriched in immune processes and inflammatory disease pathways, with hub genes including CD74, HLA-DRA, ITGAM, CTSS, CYBB, and IRF8. A positive linear correlation existed between immune cell infiltration and fibrosis activity in ARVC. NFKB1 and RELA were the shared TFs of ARVC hub genes and immune-related hub module genes, indicating the critical role of NFκB signaling in both mechanisms. Finally, the potential lncRNA-miRNA-mRNA interaction network for ARVC hub genes was constructed.

Conclusion

Myocardial fibrosis is the dominant pathogenic process in end-stage ARVC patients. A complex immune microenvironment exists in the diseased myocardium of ARVC, in which T cell subsets are the primary category. A tight relationship exists between myocardial fibrosis activity and immune cell infiltration. NFκB signaling pathway possibly contributes to both mechanisms.

Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology

A major limitation of gene expression biomarker studies is that they are not reproducible as they simply do not generalize to larger, real-world, heterogeneous populations. Frequentist multi-cohort gene expression meta-analysis has been frequently used as a solution to this problem to identify biomarkers that are truly differentially expressed. However, the frequentist meta-analysis framework has its limitations-it needs at least 4-5 datasets with hundreds of samples, is prone to confounding from outliers and relies on multiple-hypothesis corrected p-values. To address these shortcomings, we have created a Bayesian meta-analysis framework for the analysis of gene expression data. Using real-world data from three different diseases, we show that the Bayesian method is more robust to outliers, creates more informative estimates of between-study heterogeneity, reduces the number of false positive and false negative biomarkers and selects more generalizable biomarkers with less data. We have compared the Bayesian framework to a previously published frequentist framework and have developed a publicly available R package for use.

Identification and Verification of Feature Biomarkers Associated With Immune Cells in Dilated Cardiomyopathy by Bioinformatics Analysis

Objective: To explore immune-related feature genes in patients with dilated cardiomyopathy (DCM).

Methods: Expression profiles from three datasets (GSE1145, GSE21610 and GSE21819) of human cardiac tissues of DCM and healthy controls were downloaded from the GEO database. After data preprocessing, differentially expressed genes (DEGs) were identified by the ‘limma’ package in R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were then performed to identify biological functions of the DEGs. The compositional patterns of stromal and immune cells were estimated using xCell. Hub genes and functional modules were identified based on protein-protein interaction (PPI) network analysis by STRING webtool and Cytoscape application. Correlation analysis was performed between immune cell subtypes and hub genes. Hub genes with |correlation coefficient| > 0.5 and p value <0.05 were selected as feature biomarkers. A logistic regression model was constructed based on the selected biomarkers and validated in datasets GSE5406 and GSE57338.

Results: A total of 1,005 DEGs were identified. Functional enrichment analyses indicated that extracellular matrix remodeling and immune and inflammation disorder played important roles in the pathogenesis of DCM. Immune cells, including CD8⁺ T-cells, macrophages M1 and Th1 cells, were proved to be significantly changed in DCM patients by immune cell infiltration analysis. In the PPI network analysis, STAT3, IL6, CCL2, PIK3R1, ESR1, CCL5, IL17A, TLR2, BUB1B and MYC were identified as hub genes, among which CCL2, CCL5 and TLR2 were further screened as feature biomarkers by using hub genes and immune cells correlation analysis. A diagnosis model was successfully constructed by using the three biomarkers with area under the curve (AUC) scores 0.981, 0.867 and 0.946 in merged dataset, GSE5406 and GSE57338, respectively.

Conclusion: The present study identified three immune-related genes as diagnostic biomarkers for DCM, providing a novel perspective of immune and inflammatory response for the exploration of DCM molecular mechanisms.

Identification of Immune Markers in Dilated Cardiomyopathies with Heart Failure by Integrated Weighted Gene Coexpression Network Analysis

Dilated cardiomyopathy (DCM), a heterogeneous cardiomyopathy, is a major cause of heart failure and heart transplant. Currently, immunotherapy is believed to be an effective treatment method for DCM. However, individual differences are so obvious that the clinical effect is not satisfactory. In order to find immune-related biomarkers of DCM to guide treatment and improve clinical efficacy, we downloaded a GSE120895 dataset from the Gene Expression Omnibus (GEO) database using CIBERSORT and WGCNA algorithms in RStudio and visualizing the protein–protein interaction (PPI) network for key modules by Cytoscape, and finally obtained six hub genes. A GSE17800 dataset was downloaded from the GEO dataset to verify the diagnostic values of hub genes, MYG1, FLOT1, and ATG13, which were excellent. Our study revealed unpublished potential immune mechanisms, biomarkers, and therapeutic targets of DCM.

Deciphering the Role of Wnt and Rho Signaling Pathway in iPSC-Derived ARVC Cardiomyocytes by In Silico Mathematical Modeling

Arrhythmogenic Right Ventricular cardiomyopathy (ARVC) is an inherited cardiac muscle disease linked to genetic deficiency in components of the desmosomes. The disease is characterized by progressive fibro-fatty replacement of the right ventricle, which acts as a substrate for arrhythmias and sudden cardiac death. The molecular mechanisms underpinning ARVC are largely unknown. Here we propose a mathematical model for investigating the molecular dynamics underlying heart remodeling and the loss of cardiac myocytes identity during ARVC. Our methodology is based on three computational models: firstly, in the context of the Wnt pathway, we examined two different competition mechanisms between β-catenin and Plakoglobin (PG) and their role in the expression of adipogenic program. Secondly, we investigated the role of RhoA-ROCK pathway in ARVC pathogenesis, and thirdly we analyzed the interplay between Wnt and RhoA-ROCK pathways in the context of the ARVC phenotype. We conclude with the following remark: both Wnt/β-catenin and RhoA-ROCK pathways must be inactive for a significant increase of PPARγ expression, suggesting that a crosstalk mechanism might be responsible for mediating ARVC pathogenesis.

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.

Distinct Myocardial Transcriptomic Profiles of Cardiomyopathies Stratified by the Mutant Genes

Cardiovascular diseases are the number one cause of morbidity and mortality worldwide, but the underlying molecular mechanisms remain not well understood. Cardiomyopathies are primary diseases of the heart muscle and contribute to high rates of heart failure and sudden cardiac deaths. Here, we distinguished four different genetic cardiomyopathies based on gene expression signatures. In this study, RNA-Sequencing was used to identify gene expression signatures in myocardial tissue of cardiomyopathy patients in comparison to non-failing human hearts. Therefore, expression differences between patients with specific affected genes, namely LMNA (lamin A/C), RBM20 (RNA binding motif protein 20), TTN (titin) and PKP2 (plakophilin 2) were investigated. We identified genotype-specific differences in regulated pathways, Gene Ontology (GO) terms as well as gene groups like secreted or regulatory proteins and potential candidate drug targets revealing specific molecular pathomechanisms for the four subtypes of genetic cardiomyopathies. Some regulated pathways are common between patients with mutations in RBM20 and TTN as the splice factor RBM20 targets amongst other genes TTN, leading to a similar response on pathway level, even though many differentially expressed genes (DEGs) still differ between both sample types. The myocardium of patients with mutations in LMNA is widely associated with upregulated genes/pathways involved in immune response, whereas mutations in PKP2 lead to a downregulation of genes of the extracellular matrix. Our results contribute to further understanding of the underlying molecular pathomechanisms aiming for novel and better treatment of genetic cardiomyopathies.

Noncanonical WNT Activation in Human Right Ventricular Heart Failure

Background: No medical therapies exist to treat right ventricular (RV) remodeling and RV failure (RVF), in large part because molecular pathways that are specifically activated in pathologic human RV remodeling remain poorly defined. Murine models have suggested involvement of Wnt signaling, but this has not been well-defined in human RVF.

Methods: Using a candidate gene approach, we sought to identify genes specifically expressed in human pathologic RV remodeling by assessing the expression of 28 WNT-related genes in the RVs of three groups: explanted nonfailing donors (NF, n = 29), explanted dilated and ischemic cardiomyopathy, obtained at the time of cardiac transplantation, either with preserved RV function (pRV, n = 78) or with RVF (n = 35).

Results: We identified the noncanonical WNT receptor ROR2 as transcriptionally strongly upregulated in RVF compared to pRV and NF (Benjamini-Hochberg adjusted P < 0.05). ROR2 protein expression correlated linearly to mRNA expression (R² = 0.41, P = 8.1 × 10⁻¹⁸) among all RVs, and to higher right atrial to pulmonary capillary wedge ratio in RVF (R² = 0.40, P = 3.0 × 10⁻⁵). Utilizing Masson's trichrome and ROR2 immunohistochemistry, we identified preferential ROR2 protein expression in fibrotic regions by both cardiomyocytes and noncardiomyocytes. We compared RVF with high and low ROR2 expression, and found that high ROR2 expression was associated with increased expression of the WNT5A/ROR2/Ca²⁺ responsive protease calpain-μ, cleavage of its target FLNA, and FLNA phosphorylation, another marker of activation downstream of ROR2. ROR2 protein expression as a continuous variable, correlated strongly to expression of calpain-μ (R² = 0.25), total FLNA (R² = 0.67), calpain cleaved FLNA (R² = 0.32) and FLNA phosphorylation (R² = 0.62, P < 0.05 for all).

Conclusion: We demonstrate robust reactivation of a fetal WNT gene program, specifically its noncanonical arm, in human RVF characterized by activation of ROR2/calpain mediated cytoskeleton protein cleavage.

Bioinformatics analysis of sex differences in arrhythmogenic right ventricular cardiomyopathy

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited disease that exhibits sex differences on clinical presentation. The present study aimed to investigate the sex differences associated with ARVC by conducting an integrated bioinformatics analysis. The GSE29819 gene expression dataset was downloaded from the Gene Expression Omnibus database. The online analytical tool GEO2R was then used to screen for differentially expressed genes (DEGs), which were subsequently processed using enrichment analysis and protein‑protein interaction (PPI) network construction. Functional annotation of the DEGs was determined using ClueGO. The PPI network was constructed with Search Tool for the Retrieval of Interacting Genes, and was visualized with Cytoscape to identify the modules and hub genes. Compared with the female group, a total of 1,188 DEGs, of which 915 were upregulated and 273 were downregulated, were identified in the male group. The enrichment analysis revealed that in KEGG pathways, the upregulated DEGs were substantially enriched in the 'nicotine addiction' pathways, whereas the downregulated DEGS were mainly enriched in the 'ECM‑receptor interaction' and 'protein digestion and absorption' pathways. The PPI network contained 899 nodes and 1,627 edges, among which four significant modules were identified. In addition, kininogen 1, lysophosphatidic acid receptor 5, formyl peptide receptor (FPR) 2, adenylate cyclase 2, γ‑aminobutyric acid type B receptor subunit 2, FPR1, hydroxycarboxylic acid receptor 1, prostaglandin E receptor 3, cannabinoid receptor 1 and proenkephalin were identified as the top 10 hub genes. The key genes and related pathways identified in this study provide genetic insight into the diversity in phenotypes between female and male patients with ARVC, and may facilitate therapeutic individualization.

Mechanosensitive Gene Regulation by Myocardin-Related Transcription Factors Is Required for Cardiomyocyte Integrity in Load-Induced Ventricular Hypertrophy

BACKGROUND

Hypertrophic cardiomyocyte growth and dysfunction accompany various forms of heart disease. The mechanisms responsible for transcriptional changes that affect cardiac physiology and the transition to heart failure are not well understood. The intercalated disc (ID) is a specialized intercellular junction coupling cardiomyocyte force transmission and propagation of electrical activity. The ID is gaining attention as a mechanosensitive signaling hub and hotspot for causative mutations in cardiomyopathy.

METHODS

Transmission electron microscopy, confocal microscopy, and single-molecule localization microscopy were used to examine changes in ID structure and protein localization in the murine and human heart. We conducted detailed cardiac functional assessment and transcriptional profiling of mice lacking myocardin-related transcription factor (MRTF)-A and MRTF-B specifically in adult cardiomyocytes to evaluate the role of mechanosensitive regulation of gene expression in load-induced ventricular remodeling.

RESULTS

We found that MRTFs localize to IDs in the healthy human heart and accumulate in the nucleus in heart failure. Although mice lacking MRTFs in adult cardiomyocytes display normal cardiac physiology at baseline, pressure overload leads to rapid heart failure characterized by sarcomere disarray, ID disintegration, chamber dilation and wall thinning, cardiac functional decline, and partially penetrant acute lethality. Transcriptional profiling reveals a program of actin cytoskeleton and cardiomyocyte adhesion genes driven by MRTFs during pressure overload. Indeed, conspicuous remodeling of gap junctions at IDs identified by single-molecule localization microscopy may partially stem from a reduction in Mapre1 expression, which we show is a direct mechanosensitive MRTF target.

CONCLUSIONS

Our study describes a novel paradigm in which MRTFs control an acute mechanosensitive signaling circuit that coordinates cross-talk between the actin and microtubule cytoskeleton and maintains ID integrity and cardiomyocyte homeostasis in heart disease.

The arrhythmogenic cardiomyopathy-specific coding and non-coding transcriptome in human cardiac stromal cells

Background

Arrhythmogenic cardiomyopathy (ACM) is a genetic autosomal disease characterized by abnormal cell-cell adhesion, cardiomyocyte death, progressive fibro-adipose replacement of the myocardium, arrhythmias and sudden death. Several different cell types contribute to the pathogenesis of ACM, including, as recently described, cardiac stromal cells (CStCs). In the present study, we aim to identify ACM-specific expression profiles of human CStCs derived from endomyocardial biopsies of ACM patients and healthy individuals employing TaqMan Low Density Arrays for miRNA expression profiling, and high throughput sequencing for gene expression quantification.

Results

We identified 3 miRNAs and 272 genes as significantly differentially expressed at a 5% false discovery rate. Both the differentially expressed genes as well as the target genes of the ACM-specific miRNAs were found to be enriched in cell adhesion-related biological processes. Functional similarity and protein interaction-based network analyses performed on the identified deregulated genes, miRNA targets and known ACM-causative genes revealed clusters of highly related genes involved in cell adhesion, extracellular matrix organization, lipid transport and ephrin receptor signaling.

Conclusions

We determined for the first time the coding and non-coding transcriptome characteristic of ACM cardiac stromal cells, finding evidence for a potential contribution of miRNAs, specifically miR-29b-3p, to ACM pathogenesis or phenotype maintenance.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4876-6) contains supplementary material, which is available to authorized users.

Comprehensive Myocardial Proteogenomics Profiling Reveals C/EBPα as the Key Factor in the Lipid Storage of ARVC

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is hereditary cardiomyopathy characterized by the fibro-fatty replacement of the myocardium. A small number of noncomprehensive profiling studies based on human cardiac tissues have been conducted and reported; consequently, ARVC's gene expression pattern characteristics remain largely undocumented. Our study applies large-scaled, quantitative proteomics based on TMT-labeled LC-MS/MS to analyze the left and right ventricular myocardium of four ARVC and four DCM explanted hearts to compare them with normal hearts. Our objective is to reveal the characteristic proteome pattern in ARVC compared with DCM as well as nondiseased heart. We also conducted the RNA sequencing of 10 right ventricles from ARVC hearts paired with four nondiseased donor hearts to validate the proteome results. In a manner similar to that of the well-defined DCM heart failure model, the ARVC model demonstrates the downregulation of mitochondrial function proteins and the effects of many heart failure regulators such as TGFB, RICTOR, and KDM5A. In addition, the inflammatory signaling, especially the complement system, was activated much more severely in ARVC than in DCM. Our most significant discovery was the lipid metabolism reprogramming of both ARVC ventricles in accordance with the upregulation of lipogenesis factors such as FABP4 and FASN. We identified the key upstream regulator of lipogenesis as C/EBPα. Transcriptome profiling verified the consistency with proteome alterations. This comprehensive proteogenomics profiling study reveals that an activation of C/EBPα, along with the upregulation of its lipogenesis targets, accounts for lipid storage and acts as a hallmark of ARVC.

Sexual dimorphisms of mRNA and miRNA in human/murine heart disease

BACKGROUND

Sexual dimorphisms are well recognized in various cardiac diseases such as ischemic cardiomyopathy (ICM), hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM). Thorough understanding of the underlying genetic programs is crucial to optimize treatment strategies specified for each gender. By performing meta-analysis and microarray analysis, we sought to comprehensively characterize the sexual dimorphisms in the healthy and diseased heart at the level of both mRNA and miRNA transcriptome.

RESULTS

Existing mRNA microarray data of both mouse and human heart were integrated, identifying dozens/ hundreds of sexually dimorphic genes in healthy heart, ICM, HCM, and DCM. These sexually dimorphic genes overrepresented gene ontologies (GOs) important for cardiac homeostasis. Further, microarray of miRNA, isolated from mouse sham left ventricle (LV) (n = 6 & n = 5 for male & female) and chronic MI LV (n = 19 & n = 19) and from human normal LV (n = 6 & n = 6) and ICM LV (n = 4 & n = 5), was conducted. This revealed that 13 mouse miRNAs are sexually dimorphic in MI and 6 in normal heart. In human, 3 miRNAs were sexually dimorphic in ICM and 15 in normal heart. These data revealed miRNA-mRNA networks that operate in a sexually-biased fashion.

CONCLUSIONS

mRNA and miRNA transcriptome of normal and disease heart show significant sex differences, which might impact the cardiac homeostasis. Together this study provides the first comprehensive picture of the genome-wide program underlying the heart sexual dimorphisms, laying the foundation for gender specific treatment strategies.

Cardiomyocyte Hypertrophy in Arrhythmogenic Cardiomyopathy

Arrhythmogenic cardiomyopathy (AC) is a hereditary disease leading to sudden cardiac death or heart failure. AC pathology is characterized by cardiomyocyte loss and replacement fibrosis. Our goal was to determine whether cardiomyocytes respond to AC progression by pathological hypertrophy. To this end, we examined tissue samples from AC patients with end-stage heart failure and tissue samples that were collected at different disease stages from desmoglein 2-mutant mice, a well characterized AC model. We find that cardiomyocyte diameters are significantly increased in right ventricles of AC patients. Increased mRNA expression of the cardiac stress marker natriuretic peptide B is also observed in the right ventricle of AC patients. Elevated myosin heavy chain 7 mRNA expression is detected in left ventricles. In desmoglein 2-mutant mice, cardiomyocyte diameters are normal during the concealed disease phase but increase significantly after acute disease onset on cardiomyocyte death and fibrotic myocardial remodeling. Hypertrophy progresses further during the chronic disease stage. In parallel, mRNA expression of myosin heavy chain 7 and natriuretic peptide B is up-regulated in both ventricles with right ventricular preference. Calcineurin/nuclear factor of activated T cells (Nfat) signaling, which is linked to pathological hypertrophy, is observed during AC progression, as evidenced by Nfatc2 and Nfatc3 mRNA in cardiomyocytes and increased mRNA of the Nfat target regulator of calcineurin 1. Taken together, we demonstrate that pathological hypertrophy occurs in AC and is secondary to cardiomyocyte loss and cardiac remodeling.

Methods to increase reproducibility in differential gene expression via meta-analysis

Findings from clinical and biological studies are often not reproducible when tested in independent cohorts. Due to the testing of a large number of hypotheses and relatively small sample sizes, results from whole-genome expression studies in particular are often not reproducible. Compared to single-study analysis, gene expression meta-analysis can improve reproducibility by integrating data from multiple studies. However, there are multiple choices in designing and carrying out a meta-analysis. Yet, clear guidelines on best practices are scarce. Here, we hypothesized that studying subsets of very large meta-analyses would allow for systematic identification of best practices to improve reproducibility. We therefore constructed three very large gene expression meta-analyses from clinical samples, and then examined meta-analyses of subsets of the datasets (all combinations of datasets with up to N/2 samples and K/2 datasets) compared to a ‘silver standard’ of differentially expressed genes found in the entire cohort. We tested three random-effects meta-analysis models using this procedure. We showed relatively greater reproducibility with more-stringent effect size thresholds with relaxed significance thresholds; relatively lower reproducibility when imposing extraneous constraints on residual heterogeneity; and an underestimation of actual false positive rate by Benjamini–Hochberg correction. In addition, multivariate regression showed that the accuracy of a meta-analysis increased significantly with more included datasets even when controlling for sample size.

The Discovery of Novel Genomic, Transcriptomic, and Proteomic Biomarkers in Cardiovascular and Peripheral Vascular Disease: The State of the Art

Cardiovascular disease (CD) and peripheral vascular disease (PVD) are leading causes of mortality and morbidity in western countries and also responsible of a huge burden in terms of disability, functional decline, and healthcare costs. Biomarkers are measurable biological elements that reflect particular physiological or pathological states or predisposition towards diseases and they are currently widely studied in medicine and especially in CD. In this context, biomarkers can also be used to assess the severity or the evolution of several diseases, as well as the effectiveness of particular therapies. Genomics, transcriptomics, and proteomics have opened new windows on disease phenomena and may permit in the next future an effective development of novel diagnostic and prognostic medicine in order to better prevent or treat CD. This review will consider the current evidence of novel biomarkers with clear implications in the improvement of risk assessment, prevention strategies, and medical decision making in the field of CD.

Loss of plakoglobin immunoreactivity in intercalated discs in arrhythmogenic right ventricular cardiomyopathy: protein mislocalization versus epitope masking

AIMS

To examine the relevance and cause of reduced plakoglobin IF in intercalated discs for arrhythmogenic right ventricular cardiomyopathy (ARVC) and ARVC-like disease in mouse and human.

METHODS AND RESULTS

Normalized semi-quantitative IF measurements were performed in a standardized format in desmoglein 2-mutant mice with an ARVC-like phenotype (n = 6) and in cardiac biopsies from humans with ARVC and non-ARVC heart disease (n = 10). Reduced plakoglobin staining was detectable in ARVC only with one antibody directed against a defined epitope but not with three other antibodies reacting with different epitopes of plakoglobin.

CONCLUSIONS

Reduced plakoglobin staining in intercalated discs of heart tissue from human ARVC patients and in a murine ARVC model is caused by alterations in epitope accessibility and not by protein relocalization.

Rho-Kinase Inhibition During Early Cardiac Development Causes Arrhythmogenic Right Ventricular Cardiomyopathy in Mice

OBJECTIVE

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is characterized by fibrofatty changes of the right ventricle, ventricular arrhythmias, and sudden death. Though ARVC is currently regarded as a disease of the desmosome, desmosomal gene mutations have been identified only in half of ARVC patients, suggesting the involvement of other associated mechanisms. Rho-kinase signaling is involved in the regulation of intracellular transport and organizes cytoskeletal filaments, which supports desmosomal protein complex at the myocardial cell-cell junctions. Here, we explored whether inhibition of Rho-kinase signaling is involved in the pathogenesis of ARVC.

APPROACH AND RESULTS

Using 2 novel mouse models with SM22α- or αMHC-restricted overexpression of dominant-negative Rho-kinase, we show that mice with Rho-kinase inhibition in the developing heart (SM22α-restricted) spontaneously develop cardiac dilatation and dysfunction, myocardial fibrofatty changes, and ventricular arrhythmias, resulting in premature sudden death, phenotypes fulfilling the criteria of ARVC in humans. Rho-kinase inhibition in the developing heart results in the development of ARVC phenotypes in dominant-negative Rho-kinase mice through 3 mechanisms: (1) reduction of cardiac cell proliferation and ventricular wall thickness, (2) stimulation of the expression of the proadipogenic noncanonical Wnt ligand, Wnt5b, and the major adipogenic transcription factor, PPARγ (peroxisome proliferator activated receptor-γ), and inhibition of Wnt/β-catenin signaling, and (3) development of desmosomal abnormalities. These mechanisms lead to the development of cardiac dilatation and dysfunction, myocardial fibrofatty changes, and ventricular arrhythmias, ultimately resulting in sudden premature death in this ARVC mouse model.

CONCLUSIONS

This study demonstrates a novel crucial role of Rho-kinase inhibition during cardiac development in the pathogenesis of ARVC in mice.

Cardiac protein kinases: the cardiomyocyte kinome and differential kinase expression in human failing hearts

AIMS

Protein kinases are potential therapeutic targets for heart failure, but most studies of cardiac protein kinases derive from other systems, an approach that fails to account for specific kinases expressed in the heart and the contractile cardiomyocytes. We aimed to define the cardiomyocyte kinome (i.e. the protein kinases expressed in cardiomyocytes) and identify kinases with altered expression in human failing hearts.

METHODS AND RESULTS

Expression profiling (Affymetrix microarrays) detected >400 protein kinase mRNAs in rat neonatal ventricular myocytes (NVMs) and/or adult ventricular myocytes (AVMs), 32 and 93 of which were significantly up-regulated or down-regulated (greater than two-fold), respectively, in AVMs. Data for AGC family members were validated by qPCR. Proteomics analysis identified >180 cardiomyocyte protein kinases, with high relative expression of mitogen-activated protein kinase cascades and other known cardiomyocyte kinases (e.g. CAMKs, cAMP-dependent protein kinase). Other kinases are poorly investigated (e.g. Slk, Stk24, Oxsr1). Expression of Akt1/2/3, BRaf, ERK1/2, Map2k1, Map3k8, Map4k4, MST1/3, p38-MAPK, PKCδ, Pkn2, Ripk1/2, Tnni3k, and Zak was confirmed by immunoblotting. Relative to total protein, Map3k8 and Tnni3k were up-regulated in AVMs vs. NVMs. Microarray data for human hearts demonstrated variation in kinome expression that may influence responses to kinase inhibitor therapies. Furthermore, some kinases were up-regulated (e.g. NRK, JAK2, STK38L) or down-regulated (e.g. MAP2K1, IRAK1, STK40) in human failing hearts.

CONCLUSION

This characterization of the spectrum of kinases expressed in cardiomyocytes and the heart (cardiomyocyte and cardiac kinomes) identified novel kinases, some of which are differentially expressed in failing human hearts and could serve as potential therapeutic targets.

A novel transmission-based test of association for multivariate phenotypes: an application to systolic and diastolic blood pressure levels

Unlike case-control studies, family-based tests for association are protected against population stratification. Complex genetic traits are often governed by quantitative precursors and it has been argued that it may be a more powerful strategy to analyze these quantitative precursors instead of the clinical end point trait. Although methods have been developed for family-based association tests for single quantitative traits, it is of interest to develop such methods for multivariate phenotypes. We propose a novel transmission-based approach based on a trio design using a simple logistic regression to test for association with a multivariate phenotype. We use our proposed method to analyze data on systolic and diastolic blood pressure levels provided in Genetic Analysis Workshop 18. However, we find that the bivariate analysis of the two phenotypes did not provide more promising results compared to univariate analyses, suggesting a possibility of a different set of major genetic variants modulating the two phenotypes.

Association mapping of blood pressure levels in a longitudinal framework using binomial regression

Heritable quantitative characters underline complex genetic traits. However, a single quantitative phenotype may not be a suitably good surrogate for a clinical end point trait.

It may be more optimal to use a multivariate phenotype vector correlated with the end point trait to carry out an association analysis. Existing methods, such as variance components and principal components, suffer from inherent limitations, such as lack of robustness or difficulty in biological interpretation of association findings. In an effort to circumvent these limitations, we propose a novel regression approach based on a conditional binomial model to detect association between a single-nucleotide polymorphism and a multivariate phenotype vector. We use our proposed method to analyze data on systolic and diastolic blood pressure levels provided in Genetic Analysis Workshop 18. We find that the bivariate analysis of the two phenotypes yields more promising results in terms of lower p-values compared to univariate analyses.

Array data extractor (ADE): a LabVIEW program to extract and merge gene array data

Background

Large data sets from gene expression array studies are publicly available offering information highly valuable for research across many disciplines ranging from fundamental to clinical research. Highly advanced bioinformatics tools have been made available to researchers, but a demand for user-friendly software allowing researchers to quickly extract expression information for multiple genes from multiple studies persists.

Findings

Here, we present a user-friendly LabVIEW program to automatically extract gene expression data for a list of genes from multiple normalized microarray datasets. Functionality was tested for 288 class A G protein-coupled receptors (GPCRs) and expression data from 12 studies comparing normal and diseased human hearts. Results confirmed known regulation of a beta 1 adrenergic receptor and further indicate novel research targets.

Conclusions

Although existing software allows for complex data analyses, the LabVIEW based program presented here, “Array Data Extractor (ADE)”, provides users with a tool to retrieve meaningful information from multiple normalized gene expression datasets in a fast and easy way. Further, the graphical programming language used in LabVIEW allows applying changes to the program without the need of advanced programming knowledge.

Desmosomal cadherins are decreased in explanted arrhythmogenic right ventricular dysplasia/cardiomyopathy patient hearts

Aims

Arrhythmogenic right ventricular Dysplasia/cardiomyopathy (ARVD/C) is an autosomal dominant inherited cardiomyopathy associated with ventricular arrhythmia, heart failure and sudden death. Genetic studies have demonstrated the central role of desmosomal proteins in this disease, where 50% of patients harbor a mutation in a desmosmal gene. However, clinical diagnosis of the disease remains difficult and molecular mechanisms appears heterogeneous and poorly understood. The aim of this study was to characterize the expression profile of desmosomal proteins in explanted ARVD/C heart samples, in order to identify common features of the disease.

Methods and Results

We examined plakophilin-2, desmoglein-2, desmocollin-2, plakoglobin and β-catenin protein expression levels from seven independent ARVD/C heart samples compared to two ischemic, five dilated cardiomyopathy and one healthy heart sample as controls. Ventricular and septum sections were examined by immunoblot analysis of total heart protein extracts and by immunostaining.

Immunoblots indicated significant decreases in desmoglein-2 and desmocollin-2, independent of any known underlying mutations, whereas immune-histochemical analysis showed normal localization of all desmosomal proteins. Quantitative RT-PCR revealed normal DSG2 and DSC2 mRNA transcript levels, suggesting increased protein turn-over rather than transcriptional down regulation.

Conclusion

Reduced cardiac desmoglein-2 and desmocollin-2 levels appear to be specifically associated with ARVD/C, independent of underlying mutations. These findings highlight a key role of desmosomal cadherins in the pathophysiology of ARVD/C. Whether these reductions could be considered as specific markers for ARVD/C requires replication analysis.

BAY 87-2243, a highly potent and selective inhibitor of hypoxia-induced gene activation has antitumor activities by inhibition of mitochondrial complex I

The activation of the transcription factor hypoxia-inducible factor-1 (HIF-1) plays an essential role in tumor development, tumor progression, and resistance to chemo- and radiotherapy. In order to identify compounds targeting the HIF pathway, a small molecule library was screened using a luciferase-driven HIF-1 reporter cell line under hypoxia. The high-throughput screening led to the identification of a class of aminoalkyl-substituted compounds that inhibited hypoxia-induced HIF-1 target gene expression in human lung cancer cell lines at low nanomolar concentrations. Lead structure BAY 87-2243 was found to inhibit HIF-1α and HIF-2α protein accumulation under hypoxic conditions in non-small cell lung cancer (NSCLC) cell line H460 but had no effect on HIF-1α protein levels induced by the hypoxia mimetics desferrioxamine or cobalt chloride. BAY 87-2243 had no effect on HIF target gene expression levels in RCC4 cells lacking Von Hippel–Lindau (VHL) activity nor did the compound affect the activity of HIF prolyl hydroxylase-2. Antitumor activity of BAY 87-2243, suppression of HIF-1α protein levels, and reduction of HIF-1 target gene expression in vivo were demonstrated in a H460 xenograft model. BAY 87-2243 did not inhibit cell proliferation under standard conditions. However under glucose depletion, a condition favoring mitochondrial ATP generation as energy source, BAY 87-2243 inhibited cell proliferation in the nanomolar range. Further experiments revealed that BAY 87-2243 inhibits mitochondrial complex I activity but has no effect on complex III activity. Interference with mitochondrial function to reduce hypoxia-induced HIF-1 activity in tumors might be an interesting therapeutic approach to overcome chemo- and radiotherapy-resistance of hypoxic tumors.

In vitro functional analyses of arrhythmogenic right ventricular cardiomyopathy-associated desmoglein-2-missense variations

Background

Although numerous sequence variants in desmoglein-2 (DSG2) have been associated with arrhythmogenic right ventricular cardiomyopathy (ARVC), the functional impact of new sequence variations is difficult to estimate.

Methodology/Principal Findings

To test the functional consequences of DSG2-variants, we established an expression system for the extracellular domain and the full-length DSG2 using the human cell line HT1080. We established new tools to investigate ARVC-associated DSG2 variations and compared wild-type proteins and proteins with one of the five selected variations (DSG2-p.R46Q, -p.D154E, -p.D187G, -p.K294E, -p.V392I) with respect to prodomain cleavage, adhesion properties and cellular localisation.

Conclusions/Significance

The ARVC-associated DSG2-p.R46Q variation was predicted to be probably damaging by bioinformatics tools and to concern a conserved proprotein convertase cleavage site. In this study an impaired prodomain cleavage and an influence on the DSG2-properties could be demonstrated for the R46Q-variant leading to the classification of the variant as a potential gain-of-function mutant. In contrast, the variants DSG2-p.K294E and -p.V392I, which have an arguable impact on ARVC pathogenesis and are predicted to be benign, did not show functional differences to the wild-type protein in our study. Notably, the variants DSG2-p.D154E and -p.D187G, which were predicted to be damaging by bioinformatics tools, had no detectable effects on the DSG2 protein properties in our study.