[Show abstract][Hide abstract] ABSTRACT: The p53 homologs, p63 and p73, share approximately 85% amino acid identity in their DNA-binding domains, but they have distinct biological functions.
Using chromatin immunoprecipitation and high-resolution tiling arrays covering the human genome, we identify p73 DNA binding sites on a genome-wide level in ME180 human cervical carcinoma cells. Strikingly, the p73 binding profile is indistinguishable from the previously described binding profile for p63 in the same cells. Moreover, the p73:p63 binding ratio is similar at all genomic loci tested, suggesting that there are few, if any, targets that are specific for one of these factors. As assayed by sequential chromatin immunoprecipitation, p63 and p73 co-occupy DNA target sites in vivo, suggesting that p63 and p73 bind primarily as heterotetrameric complexes in ME180 cells.
The observation that p63 and p73 associate with the same genomic targets suggest that their distinct biological functions are due to cell-type specific expression and/or protein domains that involve functions other than DNA binding.
[Show abstract][Hide abstract] ABSTRACT: Meiosis in the female germ line of mammals is distinguished by a prolonged arrest in prophase of meiosis I between homologous chromosome recombination and ovulation. How DNA damage is detected in these arrested oocytes is poorly understood, but it is variably thought to involve p53, a central tumour suppressor in mammals. While the function of p53 in monitoring the genome of somatic cells is clear, a consensus for the importance of p53 for germ line integrity has yet to emerge. Here we show that the p53 homologue p63 (refs 5, 6), and specifically the TAp63 isoform, is constitutively expressed in female germ cells during meiotic arrest and is essential in a process of DNA damage-induced oocyte death not involving p53. We also show that DNA damage induces both the phosphorylation of p63 and its binding to p53 cognate DNA sites and that these events are linked to oocyte death. Our data support a model whereby p63 is the primordial member of the p53 family and acts in a conserved process of monitoring the integrity of the female germ line, whereas the functions of p53 are restricted to vertebrate somatic cells for tumour suppression. These findings have implications for understanding female germ line fidelity, the regulation of fertility and the evolution of tumour suppressor mechanisms.
[Show abstract][Hide abstract] ABSTRACT: Using tiled microarrays covering the entire human genome, we identify approximately 5800 target sites for p63, a p53 homolog essential for stratified epithelial development. p63 targets are enriched for genes involved in cell adhesion, proliferation, death, and signaling pathways. The quality of the derived DNA sequence motif for p63 targets correlates with binding strength binding in vivo, but only a small minority of motifs in the genome is bound by p63. Conversely, many p63 targets have motif scores expected for random genomic regions. Thus, p63 binding in vivo is highly selective and often requires additional factors beyond the simple protein-DNA interaction. There is a significant, but complex, relationship between p63 target sites and p63-responsive genes, with DeltaNp63 isoforms being linked to transcriptional activation. Many p63 binding regions are evolutionarily conserved and/or associated with sequence motifs for other transcription factors, suggesting that a substantial portion of p63 sites is biologically relevant.
[Show abstract][Hide abstract] ABSTRACT: With the completion of full genome sequences and advancement in high-throughput technologies, in silico methods have been successfully used to integrate diverse data sources toward unraveling the combinatorial nature of transcriptional regulation. So far, almost all of these studies are restricted to lower eukaryotes such as budding yeast. We describe here a computational search for functional transcription-factor (TF) combinations using phylogenetically conserved sequences and microarray-based expression data. Taking into account both orientational and positional constraints, we investigated the overrepresentation of binding sites in the vicinity of one another and whether these combinations result in more coherent expression profiles. Without any prior biological knowledge, the search led to the discovery of several experimentally established TF associations, as well as some novel ones. In particular, we identified a regulatory module controlling cell cycle-dependent transcription of G2-M genes and expanded its functional generality. We also detected many homotypic combinations, supporting the importance of binding-site density in transcriptional regulation of higher eukaryotes.
[Show abstract][Hide abstract] ABSTRACT: The prediction of regulatory elements is a problem where computational methods offer great hope. Over the past few years, numerous tools have become available for this task. The purpose of the current assessment is twofold: to provide some guidance to users regarding the accuracy of currently available tools in various settings, and to provide a benchmark of data sets for assessing future tools.
Full-text · Article · Feb 2005 · Nature Biotechnology
[Show abstract][Hide abstract] ABSTRACT: addition, we also made de novo predictions for some unknown TF binding sites. q 2002 Elsevier Science Ltd. All rights reserved Keywords: computational biology; transcription factor; clustering; DNA regulatory motif; expression profile *Corresponding author Introduction Understanding how the expression levels of thousands of genes are regulated at all times in the life of a cell remains one of the greatest challenges of molecular biology. A major component of gene regulation occurs at the level of transcription. Central to this mechanism are transcription factors (TFs), proteins that typically bind to specific, short DNA sequence motifs of ,5 -- 25 bp in the cis-regulatory region (promoter, enhancer) of a gene and activate or repress its transcription. The identification of relevant TFs and their binding sites is an important step in elucidating the mechanism of transcriptional regulation of a particular gene. Traditionally, TF binding sites have been characterized by a variety of
[Show abstract][Hide abstract] ABSTRACT: While microarray-based expression profiling has facilitated the use of computational methods to find potential cis-regulatory promoter elements, few current in silico approaches explicitly link regulatory motifs with the transcription factors that bind them. We have thus developed a TF-centric clustering (TFCC) algorithm that may provide such missing information through incorporation of biological knowledge about TFs. TFCC is a semi-supervised clustering algorithm which relies on the assumption that the expression profiles of some TFs may be related to those of the genes under their control. We examined this premise and found the vicinities of TFs in expression space are often enriched with the genes they regulate. So, instead of clustering genes based on the mutual similarity of their expression profiles to each other, we used TFs as seeds to group together genes whose expression patterns correlate with that of a particular TF. Then a Gibbs sampling algorithm was applied to search for shared cis-regulatory elements in promoters of clustered genes. Our working hypothesis was that if a TF-centric cluster indeed contains many targets of the seeding TF, at least one of the discovered motifs would be the site bound by the very same TF. We tested the TFCC approach on eight cell cycle and sporulation regulating TFs whose binding sites have been previously characterized in Saccharomyces cerevisiae, and correctly identified binding site motifs for half of them. In addition, we also made de novo predictions for some unknown TF binding sites.
Full-text · Article · May 2002 · Journal of Molecular Biology