160,089 research outputs found

    The homeodomain protein PAL-1 specifies a lineage-specific regulatory network in the C. elegans embryo

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    Maternal and zygotic activities of the homeodomain protein PAL-1 specify the identity and maintain the development of the multipotent C blastomere lineage in the C. elegans embryo. To identify PAL-1 regulatory target genes, we used microarrays to compare transcript abundance in wild-type embryos with mutant embryos lacking a C blastomere and to mutant embryos with extra C blastomeres. pal-1-dependent C-lineage expression was verified for select candidate target genes by reporter gene analysis, though many of the target genes are expressed in additional lineages as well. The set of validated target genes includes 12 transcription factors, an uncharacterized wingless ligand and five uncharacterized genes. Phenotypic analysis demonstrates that the identified PAL-1 target genes affect specification, differentiation and morphogenesis of C-lineage cells. In particular, we show that cell fate-specific genes (or tissue identity genes) and a posterior HOX gene are activated in lineage-specific fashion. Transcription of targets is initiated in four temporal phases, which together with their spatial expression patterns leads to a model of the regulatory network specified by PAL-1

    Identification of transcription factor binding sites in promoter databases

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    Transcription factors (TFs) are the proteins which regulates the expression of their target genes either in a positive or negative manner. TFs realize this task by binding to a specific DNA sequence contained in promoter regions, via their DNA binding motifs. Among ETS family TFs, Pea3 proteins are involved in the regulation of expression of genes, which are important for cell growth, development, differentiation, oncogenic transformation and apoptosis. In silico studies should be done to find out the novel target genes for this TF. Even though a few bioinformatics tools are available for this purpose, the user needs to go back and forth between different tools, and to repeat these steps for each of their candidate gene. Here we combined these tools and constituted a new tool which examines the affinity of any TF towards the selected target genesÔÇÖ promoter sequences. The tool is tested on several genes, which are predicted to be regulated by Pea3 TF

    Elevating microRNA-122 in blood improves outcomes after temporary middle cerebral artery occlusion in rats.

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    Because our recent studies have demonstrated that miR-122 decreased in whole blood of patients and in whole blood of rats following ischemic stroke, we tested whether elevating blood miR-122 would improve stroke outcomes in rats. Young adult rats were subjected to a temporary middle cerebral artery occlusion (MCAO) or sham operation. A polyethylene glycol-liposome-based transfection system was used to administer a miR-122 mimic after MCAO. Neurological deficits, brain infarction, brain vessel integrity, adhesion molecule expression and expression of miR-122 target and indirect-target genes were examined in blood at 24ÔÇëh after MCAO with or without miR-122 treatment. miR-122 decreased in blood after MCAO, whereas miR-122 mimic elevated miR-122 in blood 24ÔÇëh after MCAO. Intravenous but not intracerebroventricular injection of miR-122 mimic decreased neurological deficits and brain infarction, attenuated ICAM-1 expression, and maintained vessel integrity after MCAO. The miR-122 mimic also down-regulated direct target genes (e.g. Vcam1, Nos2, Pla2g2a) and indirect target genes (e.g. Alox5, Itga2b, Timp3, Il1b, Il2, Mmp8) in blood after MCAO which are predicted to affect cell adhesion, diapedesis, leukocyte extravasation, eicosanoid and atherosclerosis signaling. The data show that elevating miR-122 improves stroke outcomes and we postulate this occurs via downregulating miR-122 target genes in blood leukocytes

    Assessment of RNAi-induced silencing in banana (Musa spp.)

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    In plants, RNA- based gene silencing mediated by small RNAs functions at the transcriptional or post-transcriptional level to negatively regulate target genes, repetitive sequences, viral RNAs and/or transposon elements. Post-transcriptional gene silencing (PTGS) or the RNA interference (RNAi) approach has been achieved in a wide range of plant species for inhibiting the expression of target genes by generating double-stranded RNA (dsRNA). However, to our knowledge, successful RNAi-application to knock-down endogenous genes has not been reported in the important staple food crop banana

    Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53

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    Background: The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets. Results: This work developed a novel approach, which is based on a nonlinear mathematical model, to infer genetic regulation from microarray gene expression data. By using the p53 network as a test system, we used the nonlinear model to estimate the activities of transcription factor (TF) p53 from the expression levels of its target genes, and to identify the activation/inhibition status of p53 to its target genes. The predicted top 317 putative p53 target genes were supported by DNA sequence analysis. A comparison between our prediction and the other published predictions of p53 targets suggests that most of putative p53 targets may share a common depleted or enriched sequence signal on their upstream non-coding region. Conclusions: The proposed quantitative model can not only be used to infer the regulatory relationship between TF and its down-stream genes, but also be applied to estimate the protein activities of TF from the expression levels of its target genes

    Xenobiotic-induced activation of human aryl hydrocarbon receptor target genes in Drosophila is mediated by the epigenetic chromatin modifiers

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    Aryl hydrocarbon receptor (AHR) is the key transcription factor that controls animal development and various adaptive processes. The AHR\u27s target genes are involved in biodegradation of endogenous and exogenous toxins, regulation of immune response, organogenesis, and neurogenesis. Ligand binding is important for the activation of the AHR signaling pathway. Invertebrate AHR homologs are activated by endogenous ligands whereas vertebrate AHR can be activated by both endogenous and exogenous ligands (xenobiotics). Several studies using mammalian cultured cells have demonstrated that transcription of the AHR target genes can be activated by exogenous AHR ligands, but little is known about the effects of AHR in a living organism. Here, we examined the effects of human AHR and its ligands using transgenic Drosophila lines with an inducible human AhR gene. We found that exogenous AHR ligands can increase as well as decrease the transcription levels of the AHR target genes, including genes that control proliferation, motility, polarization, and programmed cell death. This suggests that AHR activation may affect the expression of gene networks that could be critical for cancer progression and metastasis. Importantly, we found that AHR target genes are also controlled by the enzymes that modify chromatin structure, in particular components of the epigenetic Polycomb Repressive complexes 1 and 2. Since exogenous AHR ligands (alternatively - xenobiotics) and small molecule inhibitors of epigenetic modifiers are often used as pharmaceutical anticancer drugs, our findings may have significant implications in designing new combinations of therapeutic treatments for oncological diseases. ┬ę Akishina et al

    Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

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    Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks
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