54 research outputs found

    Discovery of Small Molecules Targeting the Synergy of Cardiac Transcription Factors GATA4 and NKX2-5

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    Transcription factors are pivotal regulators of gene transcription, and many diseases are associated with the deregulation of transcriptional networks. In the heart, the transcription factors GATA4 and NKX2-5 are required for cardiogenesis. GATA4 and NKX2-5 interact physically, and the activation of GATA4, in cooperation with NKX2-5, is essential for stretch-induced cardiomyocyte hypertrophy. Here, we report the identification of four small molecule families that either inhibit or enhance the GATA4-NKX2-5 transcriptional synergy. A fragment-based screening, reporter gene assay, and pharmacophore search were utilized for the small molecule screening, identification, and optimization. The compounds modulated the hypertrophic agonist-induced cardiac gene expression. The most potent hit compound, N-[4-(diethylamino)phenyl]-5-methyl-3-phenylisoxazole-4-carboxamide (3, IC50 = 3 mu M), exhibited no activity on the protein kinases involved in the regulation of GATA4 phosphorylation. The identified and chemically and biologically characterized active compound, and its derivatives may provide a novel class of small molecules for modulating heart regeneration.Peer reviewe

    Specification of Drosophila Corpora Cardiaca Neuroendocrine Cells from Mesoderm Is Regulated by Notch Signaling

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    Drosophila neuroendocrine cells comprising the corpora cardiaca (CC) are essential for systemic glucose regulation and represent functional orthologues of vertebrate pancreatic Ξ±-cells. Although Drosophila CC cells have been regarded as developmental orthologues of pituitary gland, the genetic regulation of CC development is poorly understood. From a genetic screen, we identified multiple novel regulators of CC development, including Notch signaling factors. Our studies demonstrate that the disruption of Notch signaling can lead to the expansion of CC cells. Live imaging demonstrates localized emergence of extra precursor cells as the basis of CC expansion in Notch mutants. Contrary to a recent report, we unexpectedly found that CC cells originate from head mesoderm. We show that Tinman expression in head mesoderm is regulated by Notch signaling and that the combination of Daughterless and Tinman is sufficient for ectopic CC specification in mesoderm. Understanding the cellular, genetic, signaling, and transcriptional basis of CC cell specification and expansion should accelerate discovery of molecular mechanisms regulating ontogeny of organs that control metabolism

    The Mitochondrial Fusion-Promoting Factor Mitofusin Is a Substrate of the PINK1/Parkin Pathway

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    Loss-of-function mutations in the PINK1 or parkin genes result in recessive heritable forms of parkinsonism. Genetic studies of Drosophila orthologs of PINK1 and parkin indicate that PINK1, a mitochondrially targeted serine/threonine kinase, acts upstream of Parkin, a cytosolic ubiquitin-protein ligase, to promote mitochondrial fragmentation, although the molecular mechanisms by which the PINK1/Parkin pathway promotes mitochondrial fragmentation are unknown. We tested the hypothesis that PINK1 and Parkin promote mitochondrial fragmentation by targeting core components of the mitochondrial morphogenesis machinery for ubiquitination. We report that the steady-state abundance of the mitochondrial fusion-promoting factor Mitofusin (dMfn) is inversely correlated with the activity of PINK1 and Parkin in Drosophila. We further report that dMfn is ubiquitinated in a PINK1- and Parkin-dependent fashion and that dMfn co-immunoprecipitates with Parkin. By contrast, perturbations of PINK1 or Parkin did not influence the steady-state abundance of the mitochondrial fission-promoting factor Drp1 or the mitochondrial fusion-promoting factor Opa1, or the subcellular distribution of Drp1. Our findings suggest that dMfn is a direct substrate of the PINK1/Parkin pathway and that the mitochondrial morphological alterations and tissue degeneration phenotypes that derive from mutations in PINK1 and parkin result at least in part from reduced ubiquitin-mediated turnover of dMfn

    A Motor Function for the DEAD-Box RNA Helicase, Gemin3, in Drosophila

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    The survival motor neuron (SMN) protein, the determining factor for spinal muscular atrophy (SMA), is complexed with a group of proteins in human cells. Gemin3 is the only RNA helicase in the SMN complex. Here, we report the identification of Drosophila melanogaster Gemin3 and investigate its function in vivo. Like in vertebrates, Gemin3 physically interacts with SMN in Drosophila. Loss of function of gemin3 results in lethality at larval and/or prepupal stages. Before they die, gemin3 mutant larvae exhibit declined mobility and expanded neuromuscular junctions. Expression of a dominant-negative transgene and knockdown of Gemin3 in mesoderm cause lethality. A less severe Gemin3 disruption in developing muscles leads to flightless adults and flight muscle degeneration. Our findings suggest that Drosophila Gemin3 is required for larval development and motor function

    Drosophila Araucan and Caupolican Integrate Intrinsic and Signalling Inputs for the Acquisition by Muscle Progenitors of the Lateral Transverse Fate

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    A central issue of myogenesis is the acquisition of identity by individual muscles. In Drosophila, at the time muscle progenitors are singled out, they already express unique combinations of muscle identity genes. This muscle code results from the integration of positional and temporal signalling inputs. Here we identify, by means of loss-of-function and ectopic expression approaches, the Iroquois Complex homeobox genes araucan and caupolican as novel muscle identity genes that confer lateral transverse muscle identity. The acquisition of this fate requires that Araucan/Caupolican repress other muscle identity genes such as slouch and vestigial. In addition, we show that Caupolican-dependent slouch expression depends on the activation state of the Ras/Mitogen Activated Protein Kinase cascade. This provides a comprehensive insight into the way Iroquois genes integrate in muscle progenitors, signalling inputs that modulate gene expression and protein activity

    A Machine Learning Approach for Identifying Novel Cell Type–Specific Transcriptional Regulators of Myogenesis

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    Transcriptional enhancers integrate the contributions of multiple classes of transcription factors (TFs) to orchestrate the myriad spatio-temporal gene expression programs that occur during development. A molecular understanding of enhancers with similar activities requires the identification of both their unique and their shared sequence features. To address this problem, we combined phylogenetic profiling with a DNA–based enhancer sequence classifier that analyzes the TF binding sites (TFBSs) governing the transcription of a co-expressed gene set. We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types. Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species. Functional assays revealed that the diverged enhancer orthologs were active in largely similar patterns as their D. melanogaster counterparts, although there was extensive evolutionary shuffling of known TFBSs. We then built and trained a classifier using this enhancer set and identified additional related enhancers based on the presence or absence of known and putative TFBSs. Predicted FC enhancers were over-represented in proximity to known FC genes; and many of the TFBSs learned by the classifier were found to be critical for enhancer activity, including POU homeodomain, Myb, Ets, Forkhead, and T-box motifs. Empirical testing also revealed that the T-box TF encoded by org-1 is a previously uncharacterized regulator of muscle cell identity. Finally, we found extensive diversity in the composition of TFBSs within known FC enhancers, suggesting that motif combinatorics plays an essential role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type–specific developmental gene expression patterns
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