Sylvia Merk

Reprogramming of the Human Atrial Transcriptome in Permanent Atrial Fibrillation. Expression of a Ventricular-Like Genomic Signature

Atrial fibrillation is associated with increased expression of ventricular myosin isoforms in atrial myocardium, regarded as part of a dedifferentiation process. Whether reexpression of ventricular isoforms in atrial fibrillation is restricted to transcripts encoding for contractile proteins is unknown. Therefore, this study compares atrial mRNA expression in patients with permanent atrial fibrillation to atrial mRNA expression in patients with sinus rhythm and to ventricular gene expression using Affymetrix U133 arrays. In atrial myocardium, we identified 1434 genes deregulated in atrial fibrillation, the majority of which, including key elements of calcium-dependent signaling pathways, displayed downregulation. Functional classification based on Gene Ontology provided the specific gene sets of the interdependent processes of structural, contractile, and electrophysiological remodeling. In addition, we demonstrate for the first time a prominent upregulation of transcripts involved in metabolic activities, suggesting an adaptive response to increased metabolic demand in fibrillating atrial myocardium. Ventricular-predominant genes were 5 times more likely to be upregulated in atrial fibrillation (174 genes upregulated, 35 genes downregulated), whereas atrial-specific transcripts were predominantly downregulated (56 genes upregulated, 564 genes downregulated). Overall, in fibrillating atrial myocardium, functional classes of genes characteristic of ventricular myocardium were found to be upregulated (eg, metabolic processes), whereas functional classes predominantly expressed in atrial myocardium were downregulated (eg, signal transduction and cell communication). Therefore, dedifferentiation with adoption of a ventricular-like signature is a general feature of the fibrillating atrium.

Global gene expression in human myocardium-oligonucleotide microarray analysis of regional diversity and transcriptional regulation in heart failure.

To obtain region- and disease-specific transcription profiles of human myocardial tissue, we explored mRNA expression from all four chambers of eight explanted failing [idiopathic dilated cardiomyopathy (DCM), n=5; ischemic cardiomyopathy (ICM), n=3], and five non-failing hearts using high-density oligonucleotide arrays (Affymetrix U95Av2). We performed pair-wise comparisons of gene expression in the categories (1) atria versus ventricles, (2) disease-regulated genes in atria and (3) disease-regulated genes in ventricles. In the 51 heart samples examined, 549 genes showed divergent distribution between atria and ventricles (272 genes with higher expression in atria, 277 genes with higher expression in ventricles). Two hundred and eighty-eight genes were differentially expressed in failing myocardium compared to non-failing hearts (19 genes regulated in atria and ventricles, 172 regulated in atria only, 97 genes regulated in ventricles only). For disease-regulated genes, down-regulation was 4.5-times more common than up-regulation. Functional classification according to Gene Ontology identified specific biological patterns for differentially expressed genes. Eleven genes were validated by RT-PCR showing a good correlation with the microarray data. Our goal was to determine a gene expression fingerprint of the heart, accounting for region- and disease-specific aspects. Recognizing common gene expression patterns in heart failure will significantly contribute to the understanding of heart failure and may eventually lead to the development of pathway-specific therapies.

Functional profiling of human atrial and ventricular gene expression.

The purpose of our investigation was to identify the transcriptional basis for ultrastructural and functional specialization of human atria and ventricles. Using exploratory microarray analysis (Affymetrix U133A+B), we detected 11,740 transcripts expressed in human heart, representing the most comprehensive report of the human myocardial transcriptome to date. Variation in gene expression between atria and ventricles accounted for the largest differences in this data set, as 3.300 and 2.974 transcripts showed higher expression in atria and ventricles, respectively. Functional classification based on Gene Ontology identified chamber-specific patterns of gene expression and provided molecular insights into the regional specialization of cardiomyocytes, correlating important functional pathways to transcriptional activity: Ventricular myocytes preferentially express genes satisfying contractile and energetic requirements, while atrial myocytes exhibit specific transcriptional activities related to neurohumoral function. In addition, several pro-fibrotic and apoptotic pathways were concentrated in atrial myocardium, substantiating the higher susceptibility of atria to programmed cell death and extracellular matrix remodelling observed in human and experimental animal models of heart failure. Differences in transcriptional profiles of atrial and ventricular myocardium thus provide molecular insights into myocardial cell diversity and distinct region-specific adaptations to physiological and pathophysiological conditions. Moreover, as major functional classes of atrial- and ventricular-specific transcripts were common to human and murine myocardium, an evolutionarily conserved chamber-specific expression pattern in mammalian myocardium is suggested.

mdclust---exploratory microarray analysis by multidimensional clustering

MOTIVATION Unsupervised clustering of microarray data may detect potentially important, but not obvious characteristics of samples, for instance subgroups of diagnoses with distinct gene profiles or systematic errors in experimentation. RESULTS Multidimensional clustering (mdclust) is a method, which identifies sets of sample clusters and associated genes. It applies iteratively two-means clustering and score-based gene selection. For any phenotype variable best matching sets of clusters can be selected. This provides a method to identify gene-phenotype associations, suited even for settings with a large number of phenotype variables. An optional model based discriminant step may reduce further the number of selected genes.