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Background: Ventricular tachyarrhythmias and sudden cardiac death show a circadian pattern of occurrence in patients with heart failure. In the rodent ventricle, a significant portion of genes, including some ion channels, shows a circadian pattern of expression. However, genes that define electrophysiological properties in failing human heart ventricles have not been examined for a circadian expression pattern., Methods: Ventricular tissue samples were collected from patients at the time of cardiac transplantation. Two sets of samples (n=37 and 46, one set with a greater arrhythmic history) were selected to generate pseudo-time series according to their collection time. A third set (n=27) of samples was acquired from the nonfailing ventricles of brain-dead donors. The expression of 5 known circadian clock genes and 19 additional ion channel genes plausibly important to electrophysiological properties were analyzed by real-time polymerase chain reaction and then analyzed for the percentage of expression variation attributed to a 24-hour circadian pattern., Results: The 5 known circadian clock gene transcripts showed a strong circadian expression pattern. Compared with rodent hearts, the human circadian clock gene transcripts showed a similar temporal order of acrophases but with a ≈7.6 hours phase shift. Five of the ion channel genes also showed strong circadian expression. Comparable studies of circadian clock gene expression in samples recovered from nonheart failure brain-dead donors showed acrophase shifts, or weak or complete loss of circadian rhythmicity, suggesting alterations in circadian gene expression., Conclusions: Ventricular tissue from failing human hearts display a circadian pattern of circadian clock gene expression but phase-shifted relative to rodent hearts. At least 5 ion channels show a circadian expression pattern in the ventricles of failing human hearts, which may underlie a circadian pattern of ventricular tachyarrhythmia/sudden cardiac death. Nonfailing hearts from brain-dead donors show marked differences in circadian clock gene expression patterns, suggesting fundamental deviations from circadian expression.

Perspectives on whether the functions of MAS, a G protein-coupled receptor, are beneficial or deleterious in the heart remain controversial. MAS gene knockout reduces coronary vasodilatation leading to ischemic injury. G protein signaling activated by MAS has been implicated in progression of adaptive cardiac hypertrophy to heart failure and fibrosis. In the present study, we observed increased expression of MAS, connective tissue growth factor (CTGF) and collagen genes in failing (HF) human heart samples when compared to non-failing (NF). Expression levels of MAS are correlated with CTGF in HF and NF leading to our hypothesis that MAS controls CTGF production and the ensuing expression of collagen genes. In support of this hypothesis we show that the non-peptide MAS agonist AR234960 increases both mRNA and protein levels of CTGF via ERK1/2 signaling in HEK293-MAS cells and adult human cardiac fibroblasts. MAS-mediated CTGF expression can be specifically blocked by MAS inverse agonist AR244555 and also by MEK1 inhibition. Expression of CTGF gene was essential for MAS-mediated up-regulation of different collagen subtype genes in HEK293-MAS cells and human cardiac fibroblasts. Knockdown of CTGF by RNAi disrupted collagen gene regulation by the MAS-agonist. Our data indicate that CTGF mediates the profibrotic effects of MAS in cardiac fibroblasts. Blocking MAS-CTGF-collagen pathway should be considered for pharmacological intervention for HF.

The standard expression quantitative trait loci (eQTL) detects polymorphisms associated with gene expression without revealing causality. We introduce a coupled Bayesian regression approach--eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combination of regulatory single-nucleotide polymorphisms (SNPs) that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance but also predicts gene expression more accurately than other methods. Based on realistic simulated data, we demonstrate that eQTeL accurately detects causal regulatory SNPs, including those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal.

Atrial Fibrillation (AF), the most common sustained arrhythmia, has a strong genetic component, but the mechanism by which common genetic variants lead to increased AF susceptibility is unknown. Genome-wide association studies (GWAS) have identified that the single nucleotide polymorphisms (SNPs) most strongly associated with AF are located on chromosome 4q25 in an intergenic region distal to the PITX2 gene. Our objective was to determine whether the AF-associated SNPs on chromosome 4q25 were associated with PITX2c expression in adult human left atrial appendages. Analysis of a lone AF GWAS identified four independent AF risk SNPs at chromosome 4q25. Human adult left atrial appendage tissue was obtained from 239 subjects of European Ancestry and used for SNP analysis of genomic DNA and determination of PITX2c RNA expression levels by quantitative PCR. Subjects were divided into three groups based on their history of AF and pre-operative rhythm. AF rhythm subjects had higher PITX2c expression than those with history of AF but in sinus rhythm. PITX2c expression was not associated with the AF risk SNPs in human adult left atrial appendages in all subjects combined or in each of the three subgroups. However, we identified seven SNPs modestly associated with PITX2c expression located in the introns of the ENPEP gene, ∼54 kb proximal to PITX2. PITX2c expression in human adult left atrial appendages is not associated with the chromosome 4q25 AF risk SNPs; thus, the mechanism by which these SNPs are associated with AF remains enigmatic.

The need to assess heart failure at an early stage highlights the importance of accurate microarray analysis using small tissue samples. To test our ability to obtain high quality RNA from biopsy-sized cardiac specimens, amplification was performed on RNA from biopsy-sized samples of left ventricle (LV) tissue from one explanted failing human heart and one non-failing heart. Two methods were used: one-cycle (1C) amplification of 1.6 microg of RNA, and two-cycle (2C) amplification of 50 ng of RNA. The resulting cRNA was hybridized to Affymetrix GeneChip arrays. Over 65% of all differentially expressed genes for failing vs non-failing hearts were concordant between 1C and 2C RNA amplification. Differentially expressed genes between 1C and 2C RNA amplification in our study were highly correlated (R(2) = 0.957 and changes in gene expression agreed with prior studies on genes and heart failure; e.g., decreased alpha-myosin heavy chain and alpha-tropomyosin, as well as increased expression of insulin-like growth factor). Two cycles of amplification from cardiac biopsies will permit accurate transcription profiling of heart failure at pre-symptomatic stages. Ability to measure gene expression from nanogram amounts of RNA will provide new opportunities to predict progression to symptomatic heart failure, and to identify potential targets for therapy.

It is well established that the gene expression patterns are substantially altered in cardiac hypertrophy and heart failure, however, less is known about the reasons behind such global differences. MicroRNAs (miRNAs) are short non-coding RNAs that can target multiple molecules to regulate wide array of proteins in diverse pathways. The goal of the study was to profile alterations in miRNA expression using end-stage human heart failure samples with an aim to build signaling network pathways using predicted targets for the altered miRNA and to determine nodal molecules regulating individual networks. Profiling of miRNAs using custom designed microarray and validation with an independent set of samples identified eight miRNAs that are altered in human heart failure including one novel miRNA yet to be implicated in cardiac pathology. To gain an unbiased perspective on global regulation by top eight altered miRNAs, functional relationship of predicted targets for these eight miRNAs were examined by network analysis. Ingenuity Pathways Analysis network algorithm was used to build global signaling networks based on the targets of altered miRNAs which allowed us to identify participating networks and nodal molecules that could contribute to cardiac pathophysiology. Majority of the nodal molecules identified in our analysis are targets of altered miRNAs and known regulators of cardiovascular signaling. Cardio-genomics heart failure gene expression public data base was used to analyze trends in expression pattern for target nodal molecules and indeed changes in expression of nodal molecules inversely correlated to miRNA alterations. We have used NF kappa B network as an example to show that targeting other molecules in the network could alter the nodal NF kappa B despite not being a miRNA target suggesting an integrated network response. Thus, using network analysis we show that altering key functional target proteins may regulate expression of the myriad signaling pathways underlying the cardiac pathology. [ABSTRACT FROM AUTHOR]

Background: Genome-wide studies reveal that genetic variants at chromosome 4q25 constitute the strongest locus associated with atrial fibrillation, the most frequent arrhythmia. However, the mechanisms underlying this association are unknown. Our goal is to find and characterize left atrial-expressed transcripts in the chromosome 4q25 atrial fibrillation risk locus that may play a role in atrial fibrillation pathogenesis.Methods and Results: RNA sequencing performed on human left/right pairs identified an intergenic long noncoding RNA adjacent to the PITX2 gene, which we have named PANCR (PITX2 adjacent noncoding RNA). In a human tissue screen, PANCR was expressed specifically in the left atria and eye and in no other chambers of the heart. The levels of PANCR and PITX2c RNAs were highly correlated in 233 human left atrial appendage samples. PANCR levels were not associated with either atrial rhythm status or the genotypes of the chromosome 4q25 atrial fibrillation risk variants. Both PANCR and PITX2c RNAs were induced early during differentiation of human embryonic stem cells into cardiomyocytes. Because long noncoding RNAs often control gene expression, we performed siRNA-mediated knockdown of PANCR, and this treatment repressed PITX2c expression and mimicked the effects of PITX2c knockdown on global mRNA and miRNA expression. Cell fractionation studies demonstrate that PANCR is primarily localized in the cytoplasm.Conclusions: PANCR and PITX2c are coordinately expressed early during cardiomyocyte differentiation from stem cells. PANCR knockdown decreased PITX2c expression in differentiated cardiomyocytes, altering the transcriptome in a manner similar to PITX2c knockdown. [ABSTRACT FROM AUTHOR]

Background: RNA sequencing (RNA-Seq) allows an unbiased survey of the entire transcriptome in a high-throughput manner. A major application of RNA-Seq is to detect differential isoform expression across experimental conditions, which is of great biological interest due to its direct relevance to protein function and disease pathogenesis. Detection of differential isoform expression is challenging because of uncertainty in isoform expression estimation owing to ambiguous reads and variability in precision of the estimates across samples. It is desirable to have a method that can account for these issues and is flexible enough to allow adjustment for covariates. Results: In this paper, we present MetaDiff, a random-effects meta-regression model that naturally fits for the above purposes. Through extensive simulations and analysis of an RNA-Seq dataset on human heart failure, we show that the random-effects meta-regression approach is computationally fast, reliable, and can improve the power of differential expression analysis while controlling for false positives due to the effect of covariates or confounding variables. In contrast, several existing methods either fail to control false discovery rate or have reduced power in the presence of covariates or confounding variables. The source code, compiled JAR package and documentation of MetaDiff are freely available at https://github.com/jiach/MetaDiff. Conclusion: Our results indicate that random-effects meta-regression offers a flexible framework for differential expression analysis of isoforms, particularly when gene expression is influenced by other variables. [ABSTRACT FROM AUTHOR]

Failure of the human heart to maintain sufficient output of blood for the demands of the body, heart failure, is a common condition with high mortality even with modern therapeutic alternatives. To identify molecular determinants of mortality in patients with new-onset heart failure, we performed a meta-analysis of genome-wide association studies and follow-up genotyping in independent populations. We identified and replicated an association for a genetic variant on chromosome 5q22 with 36% increased risk of death in subjects with heart failure (rs9885413, P = 2.7x10-9). We provide evidence from reporter gene assays, computational predictions and epigenomic marks that this polymorphism increases activity of an enhancer region active in multiple human tissues. The polymorphism was further reproducibly associated with a DNA methylation signature in whole blood (P = 4.5x10-40) that also associated with allergic sensitization and expression in blood of the cytokine TSLP (P = 1.1x10-4). Knockdown of the transcription factor predicted to bind the enhancer region (NHLH1) in a human cell line (HEK293) expressing NHLH1 resulted in lower TSLP expression. In addition, we observed evidence of recent positive selection acting on the risk allele in populations of African descent. Our findings provide novel genetic leads to factors that influence mortality in patients with heart failure.

Genome-wide association studies have identified a coronary artery disease (CAD) risk locus in a non-coding region at 9p21.3, the nearest genes being CDKN2A and CDKN2B. To understand the pathways by which this locus might influence CAD susceptibility, we investigated associations between the 9p21.3 risk genotype and global gene expression in heart tissue from donors with no diagnosed heart disease (n = 108, predominant cause of death, cerebral vascular accident) and in carotid plaque (n = 106), aorta (n = 104) and mammary artery (n = 88) tissues from heart valve and carotid endarterectomy patients. Genotyping was performed with Taqman assays and Illumina arrays, and gene expression profiles generated with Affymetrix microarrays. Associations were analyzed with an additive genetic model. In heart tissue, 46 genes were putatively altered in association with the 9p21.3 risk allele (70% down-regulated, fold-change >1.1 per allele, p<0.05 adjusted for age, gender, ethnicity, cause of death). These genes were enriched for biomarkers of myocardial infarction (p = 1.53×10-9), response to wounding (p = 2.65×10-10) and inflammatory processes (p<1.97×10-7). Among the top 10 most downregulated genes, 7 genes shared a set of transcription factor binding sites within conserved promoter regions (p<1.14×10-5), suggesting they may be co-regulated. Canonical pathway modelling of the most differentially expressed transcripts across all tissues (154 genes, 60% down-regulated, fold-change >1.1 per allele, p<0.01) showed that 75% of the genes could be transcriptionally regulated through the cell cycle G1 phase progression pathway (p<1.08×10-258), in which CDKN2A and CDKN2B play a regulatory role. These data suggest that the cell cycle G1 phase progression pathway is activated in individuals with the 9p21.3 risk allele. This may contribute to a proliferative phenotype that promotes adverse cardiac hypertrophy and vascular remodeling, leading to an increased CAD risk. [ABSTRACT FROM AUTHOR]

Identifies a gene expression fingerprint for heart failure. Statistical method to establish comprehensive cutoff point for identification of differentially-expressed genes; Differentiation of etiologies of heart failure by gene expression fingerprint; Gene clusters that show coordinated up- or downregulation in human heart failure.

Background: We have previously shown that several metabolic genes are downregulated in the failing human heart. We now tested the hypothesis that mechanical unloading might reverse this process. Methods: Clinical data and myocardial tissue were collected from 14 failing hearts paired for the time of implantation and explantation of a left ventricular assist device (LVAD) and compared to 10 non-failing hearts. Transcript levels for key regulators of energy metabolism and for atrial natriuretic factor (ANF) were measured by real-time quantitative RT-PCR. Results: The expression of the glucose transporter 1 and 4 (GLUT1, GLUT4), muscle carnitine palmitoyl transferase-1 (mCPT-1), and uncoupling protein 3 (UCP3) were downregulated by up to 80% in the failing heart. Although LVAD treatment improved clinical markers of heart failure (decrease of left ventricular diastolic dimension and normalization of serum sodium), only UCP3 expression reversed to non-failing transcript levels following mechanical unloading. Conclusions: LVAD treatment only partially reverses depressed metabolic gene expression in the failing human heart. Reversal of depressed UCP3 expression may be an important mechanism for reducing the formation of oxygen-derived free radicals. Further studies are necessary to define the effects of mechanical unloading on post-transcriptional mechanisms.Copyright © 2002 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Acetylation and methylation of histones alter the chromatin structure and accessibility that affect transcriptional regulators binding to enhancers and promoters. The binding of transcriptional regulators enables the interaction between enhancers and promoters, thus affecting gene expression. However, our knowledge of these epigenetic alternations in patients with heart failure remains limited. From the comprehensive analysis of major histone modifications, 3-dimensional chromatin interactions, and transcriptome in left ventricular (LV) tissues from dilated cardiomyopathy (DCM) patients and non-heart failure (NF) donors, differential active enhancer and promoter regions were identified between NF and DCM. Moreover, the genome-wide average promoter signal is significantly lower in DCM than in NF. Super-enhancer (SE) analysis revealed that fewer SEs were found in DCM LVs than in NF ones, and three unique SE-associated genes between NF and DCM were identified. Moreover, SEs are enriched within the genomic region associated with long-range chromatin interactions. The differential enhancer-promoter interactions were observed in the known heart failure gene loci and are correlated with the gene expression levels. Motif analysis identified known cardiac factors and possible novel players for DCM. We have established the cistrome of four histone modifications and chromatin interactome for enhancers and promoters in NF and DCM tissues. Differential histone modifications and enhancer-promoter interactions were found in DCM, which were associated with gene expression levels of a subset of disease-associated genes in human heart failure. Unlabelled Image • We establish the genomic cistrome and chromatin interactome in NF and DCM. • Global H3K4me3 enrichment is reduced in DCM heart. • Super-enhancers are enriched in genomic regions associated with long-range chromatin interaction. [ABSTRACT FROM AUTHOR]

It is well established that gene expression patterns are substantially altered in cardiac hypertrophy and heart failure, but the reasons for such differences are not clear. MicroRNAs (miRNAs) are short noncoding RNAs that provide a novel mechanism for gene regulation. The goal of this study was to comprehensively test for alterations in miRNA expression using human heart failure samples with an aim to build signaling pathway networks using predicted targets for the miRNAs and to identify nodal molecules that control these networks. Genome-wide profiling of miRNAs was performed using custom-designed miRNA microarray followed by validation on an independent set of samples. Eight miRNAs are significantly altered in heart failure of which we have identified two novel miRNAs that are yet to be implicated in cardiac pathophysiology. To gain an unbiased global perspective on regulation by altered miRNAs, predicted targets of eight miRNAs were analyzed using the Ingenuity Pathways Analysis network algorithm to build signaling networks and identify nodal molecules. The majority of nodal molecules identified in our analysis are targets of altered miRNAs and are known regulators of cardiovascular signaling. A heart failure gene expression data base was used to analyze changes in expression patterns for these target nodal molecules. Indeed, expression of nodal molecules was altered in heart failure and inversely correlated to miRNA changes validating our analysis. Importantly, using network analysis we have identified a limited number of key functional targets that may regulate expression of the myriad proteins in heart failure and could be potential therapeutic targets. [ABSTRACT FROM AUTHOR]