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identification of genomic regions that control tissue-specific gene expression is currently problematic. ChIP and high-throughput sequencing (ChlP-seq) of enhancer-associated proteins such as p300 identifies some but not all enhancers active in a tissue. Here we show that co-occupancy of a chromatin region by multiple transcription factors (TFs) identifies a distinct set of enhancers. GATAbinding protein 4 (GATA4), NK2 transcription factor-related, locus 5 (NKX2-5), T-box 5 (TBX5), serum response factor (SRF), and myocyte-enhancer factor 2A (MEF2A), here referred to as "cardiac TF5," have been hypothesized to collaborate to direct cardiac gene expression. Using a modified ChIP-seq procedure, we defined chromatin occupancy by these TF5 and p300 genome wide and provided unbiased support for this hypothesis. We used this principle to show that co-occupancy of a chromatin region by multiple TF5 can be used to identify cardiac enhancers. Of 13 such regions tested in transient transgenic embryos, seven (54%) drove cardiac gene expression. Among these regions were three cardiac-specific enhancers of Gata4, 5,1, and switch/sucrose nonfermentablerelated, matrix-associated, actin-dependent regulator of chromatin, subfamily d, member 3 (Smarcd3), an epigenetic regulator of cardiac gene expression. Multiple cardiac TF5 and p300-bound regions were associated with cardiac-enriched genes and with functional annotations related to heart development. Importantly, the large majority (1,375/1,71 5) of loci bound by multiple cardiac TFs did not overlap loci bound by p300. Our data identify thousands of prospective cardiac regulatory sequences and indicate that multiple TF co-occupancy of a genomic region identifies developmentally relevant enhancers that are largely distinct from p300-associated enhancers. [ABSTRACT FROM AUTHOR]

An important event in the pathogenesis of heart failure is the development of pathological cardiac hypertrophy. In cultured cardiomyocytes, the transcription factor Gata4 is required for agonist-induced hypertrophy. We hypothesized that, in the intact organism, Gata4 is an important regulator of postnatal heart function and of the hypertrophic response of the heart to pathological stress. To test this hypothesis, we studied mice heterozygous for deletion of the second exon of Gata4 (G4D). At baseline, G4D mice had mild systolic and diastolic dysfunction associated with reduced heart weight and decreased cardiomyocyte number. After transverse aortic constriction (TAC), G4D mice developed overt heart failure and eccentric cardiac hypertrophy, associated with significantly increased fibrosis and cardiomyocyte apoptosis. Inhibition of apoptosis by overexpression of the insulin-like growth factor 1 receptor prevented TAC-induced heart failure in G4D mice. Unlike WT-TAC controls, G4D-TAC cardiomyocytes hypertrophied by increasing in length more than width. Gene expression profiling revealed up-regulation of genes associated with apoptosis and fibrosis, including members of the TGF-β pathway. Our data demonstrate that Gata4 is essential for cardiac function in the postnatal heart. After pressure overload, Gata4 regulates the pattern of cardiomyocyte hypertrophy and protects the heart from load-induced failure. [ABSTRACT FROM AUTHOR]

Accurate and rapid identification of perturbed pathways through the analysis of genome-wide expression profiles facilitates the generation of biological hypotheses. We propose a statistical framework for determining whether a specified group of genes for a pathway has a coordinated association with a phenotype of interest. Several issues on proper hypothesis-testing procedures are clarified. In particular, it is shown that the differences in the correlation structure of each set of genes can lead to a biased comparison among gene sets unless a normalization procedure is applied. We propose statistical tests for two important but different aspects of association for each group of genes. This approach has more statistical power than currently available methods and can result in the discovery of statistically significant pathways that are not detected by other methods. This method is applied to data sets involving diabetes, inflammatory myopathies. and Alzheimer's disease, using gene sets we compiled from various public databases. ln the case of inflammatory myopathies, we have correctly identified the known cytotoxic T lymphocyte-mediated autoimmunity in inclusion body myositis. Furthermore, we predicted the presence of dendritic cells in inclusion body myositis and of an IFN-α/β response in dermatomyositis, neither of which was previously described. These predictions have been subsequently corroborated by immunohistochemistry. [ABSTRACT FROM AUTHOR]