A personalized, multiomics approach identifies genes involved in cardiac hypertrophy and heart failure

KDM3A knockdown regulates COMP, LOX, COL8A1 and ACOT1 genes in myocardial fibrosis

Cardiovascular disease (CVD) is one of the main causes of death in Saudi Arabia. Cardiac remodeling plays a critical role in the pathophysiology of heart failure. Major focus of our study was to identify crucial genes involved in the pathological remodeling of the heart caused by pressure overload. We utilized various in-silico tools to analyze and interpret microarray data obtained from the Gene Expression Omnibus (GEO) database (GSE120739), including GEO2R analysis, Metascape analysis, WebGestalt analysis, and IPA (Ingenuity pathway analysis). Our findings indicate that certain genes, including Cartilage Oligomeric Matrix Protein (COMP), collagen type VIII alpha 1 chain (COL8A1) and Lysyl Oxidase (LOX) under the influence caused by knockdown of KDM3A, were down regulated by the extracellular matrix pathway. Moreover, genes, such as Acyl-CoA Thioesterase 1 (ACOT1) were up regulated by the fatty acid metabolism pathway. Overexpression of lysine-specific demethylase 3A (KDM3A) leads to the up regulation of fibrosis-related genes COMP, COL8A1, and LOX and the down regulation of ACOT1, result in enhanced fibrosis and heart failure. Our results suggest that COMP, COL8A1, LOX, and ACOT1 warrant further investigation in the development of cardiac fibrosis and as potential biomarkers for causing heart failure.

E3 ubiquitin ligase rififylin has yin and yang effects on rabbit cardiac transient outward potassium currents (Ito) and corresponding channel proteins

Genome-wide association studies have reported a correlation between a SNP of the RING finger E3 ubiquitin protein ligase rififylin (RFFL) and QT interval variability in humans (Newton-Cheh et al., 2009). Previously, we have shown that RFFL downregulates expression and function of the human-like ether-a-go-go-related gene potassium channel and corresponding rapidly activating delayed rectifier potassium current (IKr) in adult rabbit ventricular cardiomyocytes. Here, we report that RFFL also affects the transient outward current (Ito), but in a peculiar way. RFFL overexpression in adult rabbit ventricular cardiomyocytes significantly decreases the contribution of its fast component (Ito,f) from 35% to 21% and increases the contribution of its slow component (Ito,s) from 65% to 79%. Since Ito,f in rabbits is mainly conducted by Kv4.3, we investigated the effect of RFFL on Kv4.3 expressed in HEK293A cells. We found that RFFL overexpression reduced Kv4.3 expression and corresponding Ito,f in a RING domain-dependent manner in the presence or absence of its accessory subunit Kv channel-interacting protein 2. On the other hand, RFFL overexpression in Kv1.4-expressing HEK cells leads to an increase in both Kv1.4 expression level and Ito,s, similarly in a RING domain-dependent manner. Our physiologically detailed rabbit ventricular myocyte computational model shows that these yin and yang effects of RFFL overexpression on Ito,f, and Ito,s affect phase 1 of the action potential waveform and slightly decrease its duration in addition to suppressing IKr. Thus, RFFL modifies cardiac repolarization reserve via ubiquitination of multiple proteins that differently affect various potassium channels and cardiac action potential duration.

Cardiac Development at a Single-Cell Resolution

Mammalian cardiac development is a complex, multistage process. Though traditional lineage tracing studies have characterized the broad trajectories of cardiac progenitors, the advent and rapid optimization of single-cell RNA sequencing methods have yielded an ever-expanding toolkit for characterizing heterogeneous cell populations in the developing heart. Importantly, they have allowed for a robust profiling of the spatiotemporal transcriptomic landscape of the human and mouse heart, revealing the diversity of cardiac cells-myocyte and non-myocyte-over the course of development. These studies have yielded insights into novel cardiac progenitor populations, chamber-specific developmental signatures, the gene regulatory networks governing cardiac development, and, thus, the etiologies of congenital heart diseases. Furthermore, single-cell RNA sequencing has allowed for the exquisite characterization of distinct cardiac populations such as the hard-to-capture cardiac conduction system and the intracardiac immune population. Therefore, single-cell profiling has also resulted in new insights into the regulation of cardiac regeneration and injury repair. Single-cell multiomics approaches combining transcriptomics, genomics, and epigenomics may uncover an even more comprehensive atlas of human cardiac biology. Single-cell analyses of the developing and adult mammalian heart offer an unprecedented look into the fundamental mechanisms of cardiac development and the complex diseases that may arise from it.

The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases

Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.

Network-based response module comprised of gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis

This cross-cohort study aimed to (1) determine a network-based molecular signature that predicts the likelihood of inadequate response to the tumor necrosis factor-ɑ inhibitor (TNFi) therapy, infliximab, in ulcerative colitis (UC) patients, and (2) address biomarker irreproducibility across different cohort studies. Whole-transcriptome microarray data were derived from biopsies of affected colon tissue from 2 cohorts of infliximab-treated UC patients (training N = 24 and validation N = 22). Response was defined as endoscopic and histologic healing at 4-6 weeks and 8 weeks, respectively. From the training cohort, genes with RNA expression that significantly correlated with clinical response outcomes were mapped onto the Human Interactome network map of protein-protein interactions to identify a largest connected component (LCC) of proteins indicative of infliximab response status in UC. Expression levels of transcripts within the LCC were fed into a probabilistic neural network model to generate a classifier that predicts inadequate response to infliximab. A classifier predictive of inadequate response to infliximab was generated and tested in a cross-cohort, blinded fashion; the AUC was 0.83 and inadequate response was predicted with a 100% positive predictive value and 64% sensitivity. Genes separately identified from the 2 cohorts that correlated with response to infliximab appeared distinct but mapped onto the same network region of the Human Interactome, reflecting a common underlying biology of response among UC patients. Cross-cohort validation of a classifier predictive of infliximab response status in UC patients indicates that a molecular signature of non-response to TNFi therapies is present in patients' baseline gene expression data. The goal is to develop a diagnostic test that predicts which patients will have an inadequate response to targeted therapies and define new targets and pathways for therapeutic development.

New strategies and therapies for the prevention of heart failure in high-risk patients

Despite declines in total cardiovascular mortality rates in the United States, heart failure (HF) mortality rates as well as hospitalizations and readmissions have increased in the past decade. Increases have been relatively higher among young and middle-aged adults (<65 years). Therefore, identification of individuals HF at-risk (Stage A) or with pre-HF (Stage B) before the onset of overt clinical signs and symptoms (Stage C) is urgently needed. Multivariate risk models (e.g., Pooled Cohort Equations to Prevent Heart Failure [PCP-HF]) have been externally validated in diverse populations and endorsed by the 2022 HF Guidelines to apply a risk-based framework for the prevention of HF. However, traditional risk factors included in the PCP-HF model only account for half of an individual's lifetime risk of HF; novel risk factors (e.g., adverse pregnancy outcomes, impaired lung health, COVID-19) are emerging as important risk-enhancing factors that need to be accounted for in personalized approaches to prevention. In addition to determining the role of novel risk-enhancing factors, integration of social determinants of health (SDoH) in identifying and addressing HF risk is needed to transform the current clinical paradigm for the prevention of HF. Comprehensive strategies to prevent the progression of HF must incorporate pharmacotherapies (e.g., sodium glucose co-transporter-2 inhibitors that have also been termed the "statins" of HF prevention), intensive blood pressure lowering, and heart-healthy behaviors. Future directions include investigation of novel prediction models leveraging machine learning, integration of risk-enhancing factors and SDoH, and equitable approaches to interventions for risk-based prevention of HF.

RETRACTED: Network-based response module comprised of gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the authors after consulting with the Editors. During a follow-up study, the authors regretfully discovered that the microarray probe-to-gene mapping was incorrect. Although the methodology and primary findings remain the same, the identity of the biomarker genes are incorrect as a result of this honest mistake. The extent of the changes to correct this information necessitated the publication of a corrected version of this article: https://doi.org/10.1016/j.trsl.2022.03.006.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Integrative Analysis of Multi-Omics and Genetic Approaches-A New Level in Atherosclerotic Cardiovascular Risk Prediction

Genetics and environmental and lifestyle factors deeply affect cardiovascular diseases, with atherosclerosis as the etiopathological factor (ACVD) and their early recognition can significantly contribute to an efficient prevention and treatment of the disease. Due to the vast number of these factors, only the novel "omic" approaches are surmised. In addition to genomics, which extended the effective therapeutic potential for complex and rarer diseases, the use of "omics" presents a step-forward that can be harnessed for more accurate ACVD prediction and risk assessment in larger populations. The analysis of these data by artificial intelligence (AI)/machine learning (ML) strategies makes is possible to decipher the large amount of data that derives from such techniques, in order to provide an unbiased assessment of pathophysiological correlations and to develop a better understanding of the molecular background of ACVD. The predictive models implementing data from these "omics", are based on consolidated AI best practices for classical ML and deep learning paradigms that employ methods (e.g., Integrative Network Fusion method, using an AI/ML supervised strategy and cross-validation) to validate the reproducibility of the results. Here, we highlight the proposed integrated approach for the prediction and diagnosis of ACVD with the presentation of the key elements of a joint scientific project of the University of Milan and the Almazov National Medical Research Centre.

Stearoyl-CoA Desaturase (SCD) Induces Cardiac Dysfunction with Cardiac Lipid Overload and Angiotensin II AT1 Receptor Protein Up-Regulation

Heart failure is a major cause of death worldwide with insufficient treatment options. In the search for pathomechanisms, we found up-regulation of an enzyme, stearoyl-CoA desaturase 1 (Scd1), in different experimental models of heart failure induced by advanced atherosclerosis, chronic pressure overload, and/or volume overload. Because the pathophysiological role of Scd1/SCD in heart failure is not clear, we investigated the impact of cardiac SCD upregulation through the generation of C57BL/6-Tg(MHCSCD)Sjaa mice with myocardium-specific expression of SCD. Echocardiographic examination showed that 4.9-fold-increased SCD levels triggered cardiac hypertrophy and symptoms of heart failure at an age of eight months. Tg-SCD mice had a significantly reduced left ventricular cardiac ejection fraction of 25.7 ± 2.9% compared to 54.3 ± 4.5% of non-transgenic B6 control mice. Whole-genome gene expression profiling identified up-regulated heart-failure-related genes such as resistin, adiponectin, and fatty acid synthase, and type 1 and 3 collagens. Tg-SCD mice were characterized by cardiac lipid accumulation with 1.6- and 1.7-fold-increased cardiac contents of saturated lipids, palmitate, and stearate, respectively. In contrast, unsaturated lipids were not changed. Together with saturated lipids, apoptosis-enhancing p53 protein contents were elevated. Imaging by autoradiography revealed that the heart-failure-promoting and membrane-spanning angiotensin II AT1 receptor protein of Tg-SCD hearts was significantly up-regulated. In transfected HEK cells, the expression of SCD increased the number of cell-surface angiotensin II AT1 receptor binding sites. In addition, increased AT1 receptor protein levels were detected by fluorescence spectroscopy of fluorescent protein-labeled AT1 receptor-Cerulean. Taken together, we found that SCD promotes cardiac dysfunction with overload of cardiotoxic saturated lipids and up-regulation of the heart-failure-promoting AT1 receptor protein.

Multi-omics approaches for revealing the complexity of cardiovascular disease

The development and progression of cardiovascular disease (CVD) can mainly be attributed to the narrowing of blood vessels caused by atherosclerosis and thrombosis, which induces organ damage that will result in end-organ dysfunction characterized by events such as myocardial infarction or stroke. It is also essential to consider other contributory factors to CVD, including cardiac remodelling caused by cardiomyopathies and co-morbidities with other diseases such as chronic kidney disease. Besides, there is a growing amount of evidence linking the gut microbiota to CVD through several metabolic pathways. Hence, it is of utmost importance to decipher the underlying molecular mechanisms associated with these disease states to elucidate the development and progression of CVD. A wide array of systems biology approaches incorporating multi-omics data have emerged as an invaluable tool in establishing alterations in specific cell types and identifying modifications in signalling events that promote disease development. Here, we review recent studies that apply multi-omics approaches to further understand the underlying causes of CVD and provide possible treatment strategies by identifying novel drug targets and biomarkers. We also discuss very recent advances in gut microbiota research with an emphasis on how diet and microbial composition can impact the development of CVD. Finally, we present various biological network analyses and other independent studies that have been employed for providing mechanistic explanation and developing treatment strategies for end-stage CVD, namely myocardial infarction and stroke.

Multiomics, virtual reality and artificial intelligence in heart failure

Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography-mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85-0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10^-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.

Cardioinformatics: the nexus of bioinformatics and precision cardiology

Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as 'cardioinformatics'.

Sex differences in human adipose tissue gene expression and genetic regulation involve adipogenesis

Sex differences in adipose tissue distribution and function are associated with sex differences in cardiometabolic disease. While many studies have revealed sex differences in adipocyte cell signaling and physiology, there is a relative dearth of information regarding sex differences in transcript abundance and regulation. We investigated sex differences in subcutaneous adipose tissue transcriptional regulation using omic-scale data from ∼3000 geographically and ethnically diverse human samples. We identified 162 genes with robust sex differences in expression. Differentially expressed genes were implicated in oxidative phosphorylation and adipogenesis. We further determined that sex differences in gene expression levels could be related to sex differences in the genetics of gene expression regulation. Our analyses revealed sex-specific genetic associations, and this finding was replicated in a study of 98 inbred mouse strains. The genes under genetic regulation in human and mouse were enriched for oxidative phosphorylation and adipogenesis. Enrichment analysis showed that the associated genetic loci resided within binding motifs for adipogenic transcription factors (e.g., PPARG and EGR1). We demonstrated that sex differences in gene expression could be influenced by sex differences in genetic regulation for six genes (e.g., FADS1 and MAP1B). These genes exhibited dynamic expression patterns during adipogenesis and robust expression in mature human adipocytes. Our results support a role for adipogenesis-related genes in subcutaneous adipose tissue sex differences in the genetic and environmental regulation of gene expression.

Protein arginine methyltransferase 6 mediates cardiac hypertrophy by differential regulation of histone H3 arginine methylation

Heart failure remains a major cause of hospitalization and death worldwide. Heart failure can be caused by abnormalities in the epigenome resulting from dysregulation of histone-modifying enzymes. While chromatin enzymes catalyzing lysine acetylation and methylation of histones have been the topic of many investigations, the role of arginine methyltransferases has been overlooked. In an effort to understand regulatory mechanisms implicated in cardiac hypertrophy and heart failure, we assessed the expression of protein arginine methyltransferases (PRMTs) in the left ventricle of failing human hearts and control hearts. Our results show a significant up-regulation of protein arginine methyltransferase 6 (PRMT6) in failing human hearts compared to control hearts, which also occurs in the early phase of cardiac hypertrophy in mouse hearts subjected to pressure overload hypertrophy induced by trans-aortic constriction (TAC), and in neonatal rat ventricular myocytes (NRVM) stimulated with the hypertrophic agonist phenylephrine (PE). These changes are associated with a significant increase in arginine 2 asymmetric methylation of histone H3 (H3R2Me2a) and reduced lysine 4 tri-methylation of H3 (H3K4Me3) observed both in NRVM and in vivo. Importantly, forced expression of PRMT6 in NRVM enhances the expression of the hypertrophic marker, atrial natriuretic peptide (ANP). Conversely, specific silencing of PRMT6 reduces ANP protein expression and cell size, indicating that PRMT6 is critical for the PE-mediated hypertrophic response. Silencing of PRMT6 reduces H3R2Me2a, a mark normally associated with transcriptional repression. Furthermore, evaluation of cardiac contractility and global ion channel activity in live NRVM shows a striking reduction of spontaneous beating rates and prolongation of extra-cellular field potentials in cells expressing low-level PRMT6. Altogether, our results indicate that PRMT6 is a critical regulator of cardiac hypertrophy, implicating H3R2Me2a as an important histone modification. This study identifies PRMT6 as a new epigenetic regulator and suggests a new point of control in chromatin to inhibit pathological cardiac remodeling.

Human cardiac organoids for the modelling of myocardial infarction and drug cardiotoxicity

Environmental factors are the largest contributors to cardiovascular disease. Here we show that cardiac organoids that incorporate an oxygen-diffusion gradient and that are stimulated with the neurotransmitter noradrenaline model the structure of the human heart after myocardial infarction (by mimicking the infarcted, border and remote zones), and recapitulate hallmarks of myocardial infarction (in particular, pathological metabolic shifts, fibrosis and calcium handling) at the transcriptomic, structural and functional levels. We also show that the organoids can model hypoxia-enhanced doxorubicin cardiotoxicity. Human organoids that model diseases with non-genetic pathological factors could help with drug screening and development.

Relevance of Multi-Omics Studies in Cardiovascular Diseases

Cardiovascular diseases are the leading cause of death around the world. Despite the larger number of genes and loci identified, the precise mechanisms by which these genes influence risk of cardiovascular disease is not well understood. Recent advances in the development and optimization of high-throughput technologies for the generation of “omics data” have provided a deeper understanding of the processes and dynamic interactions involved in human diseases. However, the integrative analysis of “omics” data is not straightforward and represents several logistic and computational challenges. In spite of these difficulties, several studies have successfully applied integrative genomics approaches for the investigation of novel mechanisms and plasma biomarkers involved in cardiovascular diseases. In this review, we summarized recent studies aimed to understand the molecular framework of these diseases using multi-omics data from mice and humans. We discuss examples of omics studies for cardiovascular diseases focused on the integration of genomics, epigenomics, transcriptomics, and proteomics. This review also describes current gaps in the study of complex diseases using systems genetics approaches as well as potential limitations and future directions of this emerging field.

Multi omics analysis of fibrotic kidneys in two mouse models

Kidney fibrosis represents an urgent unmet clinical need due to the lack of effective therapies and an inadequate understanding of the molecular pathogenesis. We have generated a comprehensive and combined multi-omics dataset (proteomics, mRNA and small RNA transcriptomics) of fibrotic kidneys that is searchable through a user-friendly web application: http://hbcreports.med.harvard.edu/fmm/. Two commonly used mouse models were utilized: a reversible chemical-induced injury model (folic acid (FA) induced nephropathy) and an irreversible surgically-induced fibrosis model (unilateral ureteral obstruction (UUO)). mRNA and small RNA sequencing, as well as 10-plex tandem mass tag (TMT) proteomics were performed with kidney samples from different time points over the course of fibrosis development. The bioinformatics workflow used to process, technically validate, and combine the single omics data will be described. In summary, we present temporal multi-omics data from fibrotic mouse kidneys that are accessible through an interrogation tool (Mouse Kidney Fibromics browser) to provide a searchable transcriptome and proteome for kidney fibrosis researchers.

Design Type(s)	transcription profiling design • proteomic profiling design • stimulus or stress design
Measurement Type(s)	transcription profiling assay • protein expression profiling assay
Technology Type(s)	RNA sequencing • mass spectrometry
Factor Type(s)	experimental condition • temporal_instant • biological replicate
Sample Characteristic(s)	Mus musculus • kidney

Machine-accessible metadata file describing the reported data (ISA-Tab format)

The Ca2+ transient as a feedback sensor controlling cardiomyocyte ionic conductances in mouse populations

Conductances of ion channels and transporters controlling cardiac excitation may vary in a population of subjects with different cardiac gene expression patterns. However, the amount of variability and its origin are not quantitatively known. We propose a new conceptual approach to predict this variability that consists of finding combinations of conductances generating a normal intracellular Ca²⁺ transient without any constraint on the action potential. Furthermore, we validate experimentally its predictions using the Hybrid Mouse Diversity Panel, a model system of genetically diverse mouse strains that allows us to quantify inter-subject versus intra-subject variability. The method predicts that conductances of inward Ca²⁺ and outward K⁺ currents compensate each other to generate a normal Ca²⁺ transient in good quantitative agreement with current measurements in ventricular myocytes from hearts of different isogenic strains. Our results suggest that a feedback mechanism sensing the aggregate Ca²⁺ transient of the heart suffices to regulate ionic conductances.

Sfrp1 attenuates TAC-induced cardiac dysfunction by inhibiting Wnt signaling pathway- mediated myocardial apoptosis in mice

Background

Hemodynamic overload causes cardiac hypertrophy leading to heart failure. Wnt signaling pathway was reported activated in heart failure. Secreted frizzled related protein 1 (Sfrp1) is a suppressor of Wnt signaling activation. The aim of the present study was to investigate the protective effect of Sfrp1 on hemodynamic overload- induced cardiac dysfunction.

Methods

A mice transverse aortic constriction (TAC)- induced heart failure model was established. A recombinant adeno-associated virus 9 (AAV9) vector was used to deliver Sfrp1 gene into myocardium. Fluorescence and immunohistochemistry staining was used to evaluate the effectiveness of viral vector delivery. Invasive hemodynamic examination was used to evaluate cardiac systolic and diastolic functions. Myocardium apoptosis was detected by TUNEL assay. The expression levels of Sfrp1, β-catenin, caspase3, Bax, Bcl-2 and c-Myc were measured by Western blotting.

Results

Increased mean arterial pressure and impaired cardiac function confirmed the establishment of TAC model. Sfrp1 protein expression was effectively increased in myocardium of mice treated with AAV9-Sfrp1 viral vector. The viral vector administration improved both systolic and diastolic cardiac functions by reducing myocardial apoptosis in TAC mice. The expression levels of β-catenin, caspase3 and Bax were significantly reduced while the expression levels of Bcl-2 and c-Myc were dramatically increased in myocardium by the viral vector treatment in TAC mice.

Conclusions

AAV9 viral vector delivered sfrp1 expression gene into myocardium, which attenuated TAC-induced cardiac dysfunction by inhibiting Wnt signaling pathway activation- mediated apoptosis.