Conservation and function of the transcriptional regulatory protein Runt.
Ontology highlight
ABSTRACT:
SUBMITTER: Pepling ME
PROVIDER: S-EPMC40929 | biostudies-other | 1995 Sep
REPOSITORIES: biostudies-other
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Project description:The CCAAT-binding protein NF-Y is involved in the regulation of a variety of eukaryotic genes and is formed in higher eukaryotes by three subunits NF-YA/B/C. We have characterized NF-Y of the trematode parasite Schistosoma mansoni and studied the structure and the function of the SMNF-YA subunit. In this work, we present the cloning and sequence analysis of the B subunit of the parasite factor. SMNF-YB contains the conserved HAP-3 homology domain but the remaining part of the protein was found to be highly divergent from all other species. We demonstrated by transfections of GAL4 fusion constructs, that mouse NF-YB does not contain activation domains while the C-terminal part of SMNF-YB has transcriptional activation potential. On the other hand, the N-terminal parts of SMNF-YA and mouse NF-YA were shown to mediate transactivation; the integrity of a large 160 amino acid glutamine-rich domain of NF-YA was required for this function and an adjacent serine- and threonine-rich domain was necessary for full activity in HepG2, but redundant in other cell types. Transactivation domains identified in SMNF-YB are also rich in serine and threonine residues. Our results indicate that serine/threonine-richsequences from helminth parasites potentiate trans-cription and that such structures have diverged during evolution within the same transcription factor.
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Project description:BackgroundIncreasing evidence shows that whole genomes of eukaryotes are almost entirely transcribed into both protein coding genes and an enormous number of non-protein-coding RNAs (ncRNAs). Therefore, revealing the underlying regulatory mechanisms of transcripts becomes imperative. However, for a complete understanding of transcriptional regulatory mechanisms, we need to identify the regions in which they are found. We will call these transcriptional regulation regions, or TRRs, which can be considered functional regions containing a cluster of regulatory elements that cooperatively recruit transcriptional factors for binding and then regulating the expression of transcripts.ResultsWe constructed a hierarchical stochastic language (HSL) model for the identification of core TRRs in yeast based on regulatory cooperation among TRR elements. The HSL model trained based on yeast achieved comparable accuracy in predicting TRRs in other species, e.g., fruit fly, human, and rice, thus demonstrating the conservation of TRRs across species. The HSL model was also used to identify the TRRs of genes, such as p53 or OsALYL1, as well as microRNAs. In addition, the ENCODE regions were examined by HSL, and TRRs were found to pervasively locate in the genomes.ConclusionOur findings indicate that 1) the HSL model can be used to accurately predict core TRRs of transcripts across species and 2) identified core TRRs by HSL are proper candidates for the further scrutiny of specific regulatory elements and mechanisms. Meanwhile, the regulatory activity taking place in the abundant numbers of ncRNAs might account for the ubiquitous presence of TRRs across the genome. In addition, we also found that the TRRs of protein coding genes and ncRNAs are similar in structure, with the latter being more conserved than the former.
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Project description:BackgroundThe murine leukemia virus (MLV) has been a powerful model of pathogenesis for the discovery of genes involved in cancer. Its splice donor (SD')-associated retroelement (SDARE) is important for infectivity and tumorigenesis, but the mechanism remains poorly characterized. Here, we show for the first time that P50 protein, which is produced from SDARE, acts as an accessory protein that transregulates transcription and induces cell transformation.ResultsBy infecting cells with MLV particles containing SDARE transcript alone (lacking genomic RNA), we show that SDARE can spread to neighbouring cells as shown by the presence of P50 in infected cells. Furthermore, a role for P50 in cell transformation was demonstrated by CCK8, TUNEL and anchorage-independent growth assays. We identified the integrase domain of P50 as being responsible for transregulation of the MLV promoter using luciferase assay and RTqPCR with P50 deleted mutants. Transcriptomic analysis furthermore revealed that the expression of hundreds of cellular RNAs involved in cancerogenesis were deregulated in the presence of P50, suggesting that P50 induces carcinogenic processes via its transcriptional regulatory function.ConclusionWe propose a novel SDARE-mediated mode of propagation of the P50 accessory protein in surrounding cells. Moreover, due to its transforming properties, P50 expression could lead to a cellular and tissue microenvironment that is conducive to cancer development.
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Project description:BackgroundCell and circadian cycles control a large fraction of cell and organismal physiology by regulating large periodic transcriptional programs that encompass anywhere from 15 to 80% of the genome despite performing distinct functions. In each case, these large periodic transcriptional programs are controlled by gene regulatory networks (GRNs), and it has been shown through genetics and chromosome mapping approaches in model systems that at the core of these GRNs are small sets of genes that drive the transcript dynamics of the GRNs. However, it is unlikely that we have identified all of these core genes, even in model organisms. Moreover, large periodic transcriptional programs controlling a variety of processes certainly exist in important non-model organisms where genetic approaches to identifying networks are expensive, time-consuming, or intractable. Ideally, the core network components could be identified using data-driven approaches on the transcriptome dynamics data already available.ResultsThis study shows that a unified set of quantified dynamic features of high-throughput time series gene expression data are more prominent in the core transcriptional regulators of cell and circadian cycles than in their outputs, in multiple organism, even in the presence of external periodic stimuli. Additionally, we observe that the power to discriminate between core and non-core genes is largely insensitive to the particular choice of quantification of these features.ConclusionsThere are practical applications of the approach presented in this study for network inference, since the result is a ranking of genes that is enriched for core regulatory elements driving a periodic phenotype. In this way, the method provides a prioritization of follow-up genetic experiments. Furthermore, these findings reveal something unexpected-that there are shared dynamic features of the transcript abundance of core components of unrelated GRNs that control disparate periodic phenotypes.
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Project description:BackgroundCis-regulatory modules (CRMs) are distinct, genomic regions surrounding the target gene that can independently activate the promoter to drive transcription. The activation of a CRM is controlled by the binding of a certain combination of transcription factors (TFs). It would be of great benefit if the transcriptional output mediated by a specific CRM could be predicted. Of equal benefit would be identifying in silico a specific CRM as the driver of the expression in a specific tissue or situation. We extend a recently developed biochemical modeling approach to manage both prediction tasks. Given a set of TFs, their protein concentrations, and the positions and binding strengths of each of the TFs in a putative CRM, the model predicts the transcriptional output of the gene. Our approach predicts the location of the regulating CRM by using predicted TF binding sites in regions near the gene as input to the model and searching for the region that yields a predicted transcription rate most closely matching the known rate.ResultsHere we show the ability of the model on the example of one of the CRMs regulating the eve gene, MSE2. A model trained on the MSE2 in D. melanogaster was applied to the surrounding sequence of the eve gene in seven other Drosophila species. The model successfully predicts the correct MSE2 location and output in six out of eight Drosophila species we examine.ConclusionThe model is able to generalize from D. melanogaster to other Drosophila species and accurately predicts the location and transcriptional output of MSE2 in those species. However, we also show that the current model is not specific enough to function as a genome-wide CRM scanner, because it incorrectly predicts other genomic regions to be MSE2s.
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Project description:MotivationProteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species.ResultsTo improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms.
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Project description:Runx proteins are essential for a number of developmental processes and are aberrantly expressed in many human cancers. Runx factors bind DNA and co-factors to activate or repress genes crucial for bone formation, hematopoiesis, and neuronal development. Co-activator activator (CoAA) is a nuclear protein that regulates gene expression, RNA splicing and is overexpressed in many human tumors. In this study, we identified CoAA as a Runx2 binding protein. CoAA repressed Runx factor-dependent activation of reporter genes in a histone deacetylase-independent manner. CoAA also blocked Runx2-mediated repression of the Axin2 promoter, a novel Runx target gene. The carboxy-terminus of CoAA is essential for binding the Runt domains of Runx1 and Runx2. In electophoretic mobility shift assays, CoAA inhibited Runx2 interactions with DNA. These data indicate that CoAA is an inhibitor of Runx factors and can negate Runx factor regulation of gene expression. CoAA is expressed at high levels in human fetal osteoblasts and osteosarcoma cell lines. Suppression of CoAA expression by RNA interference reduced osteosarcoma cell viability in vitro, suggesting that it contributes to the proliferation and/or survival of osteoblast lineage cells.
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Project description:Transcription factors (TFs) combine with co-factors to form transcriptional regulatory modules (TRMs) that regulate gene expression programs with spatiotemporal specificity. Here we present a novel and generic method (rTRM) for the reconstruction of TRMs that integrates genomic information from TF binding, cell type-specific gene expression and protein-protein interactions. rTRM was applied to reconstruct the TRMs specific for embryonic stem cells (ESC) and hematopoietic stem cells (HSC), neural progenitor cells, trophoblast stem cells and distinct types of terminally differentiated CD4(+) T cells. The ESC and HSC TRM predictions were highly precise, yielding 77 and 96 proteins, of which ?75% have been independently shown to be involved in the regulation of these cell types. Furthermore, rTRM successfully identified a large number of bridging proteins with known roles in ESCs and HSCs, which could not have been identified using genomic approaches alone, as they lack the ability to bind specific DNA sequences. This highlights the advantage of rTRM over other methods that ignore PPI information, as proteins need to interact with other proteins to form complexes and perform specific functions. The prediction and experimental validation of the co-factors that endow master regulatory TFs with the capacity to select specific genomic sites, modulate the local epigenetic profile and integrate multiple signals will provide important mechanistic insights not only into how such TFs operate, but also into abnormal transcriptional states leading to disease.
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Project description:The coordination of the synthesis of functionally-related proteins can be achieved at the post-transcriptional level by the action of common regulatory molecules, such as RNA-binding proteins (RBPs). Despite advances in the genome-wide identification of RBPs and their binding transcripts, the protein-RNA interaction space is still largely unexplored, thus hindering a broader understanding of the extent of the post-transcriptional regulation of related coding RNAs. Here, we propose a computational approach that combines protein-mRNA interaction networks and statistical analyses to provide an inferred regulatory landscape for more than 800 human RBPs and identify the cellular processes that can be regulated at the post-transcriptional level. We show that 10% of the tested sets of functionally-related mRNAs can be post-transcriptionally regulated. Moreover, we propose a classification of (i) the RBPs and (ii) the functionally-related mRNAs, based on their distinct behaviors in the functional landscape, hinting towards mechanistic regulatory hypotheses. In addition, we demonstrate the usefulness of the inferred functional landscape to investigate the cellular role of both well-characterized and novel RBPs in the context of human diseases.
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Project description:BACKGROUND: Sox10, a member of the Sry-related HMG-Box gene family, is a critical transcription factor for several important cell lineages, most notably the neural crest stem cells and the derivative peripheral glial cells and melanocytes. Thus far, only a handful of direct target genes are known for this transcription factor limiting our understanding of the biological network it governs. RESULTS: We describe identification of multiple direct regulatory target genes of Sox10 through a procedure based on function and conservation. By combining RNA interference technique and DNA microarray technology, we have identified a set of genes that show significant down-regulation upon introduction of Sox10 specific siRNA into Schwannoma cells. Subsequent comparative genomics analyses led to potential binding sites for Sox10 protein conserved across several mammalian species within the genomic region proximal to these genes. Multiple sites belonging to 4 different genes (proteolipid protein, Sox10, extracellular superoxide dismutase, and pleiotrophin) were shown to directly interact with Sox10 by chromatin immunoprecipitation assay. We further confirmed the direct regulation through the identified cis-element for one of the genes, extracellular superoxide dismutase, using electrophoretic mobility shift assay and reporter assay. CONCLUSION: In sum, the process of combining differential expression profiling and comparative genomics successfully led to further defining the role of Sox10, a critical transcription factor for the development of peripheral glia. Our strategy utilizing relatively accessible techniques and tools should be applicable to studying the function of other transcription factors.
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