Plant Transcription Factors @ uni-potsdam.de

We present the Plant Transcription Factor Database (PlnTFDB), and the putative complete set of TFs in the algae _Chlamydomonas reinhardtii_, _Ostreococcus tauri_ and the vascular plants _Oryza sativa_ and _Arabidopsis thaliana_

In vivo delivery of transcription factors with multifunctional oligonucleotides.

Therapeutics based on transcription factors have the potential to revolutionize medicine but have had limited clinical success as a consequence of delivery problems. The delivery of transcription factors is challenging because it requires the development of a delivery vehicle that can complex transcription factors, target cells and stimulate endosomal disruption, with minimal toxicity. Here, we present a multifunctional oligonucleotide, termed DARTs (DNA assembled recombinant transcription factors), which can deliver transcription factors with high efficiency in vivo. DARTs are composed of an oligonucleotide that contains a transcription-factor-binding sequence and hydrophobic membrane-disruptive chains that are masked by acid-cleavable galactose residues. DARTs have a unique molecular architecture, which allows them to bind transcription factors, trigger endocytosis in hepatocytes, and stimulate endosomal disruption. The DARTs have enhanced uptake in hepatocytes as a result of their galactose residues and can disrupt endosomes efficiently with minimal toxicity, because unmasking of their hydrophobic domains selectively occurs in the acidic environment of the endosome. We show that DARTs can deliver the transcription factor nuclear erythroid 2-related factor 2 (Nrf2) to the liver, catalyse the transcription of Nrf2 downstream genes, and rescue mice from acetaminophen-induced liver injury

A compendium of Caenorhabditis elegans regulatory transcription factors: a resource for mapping transcription regulatory networks

Background Transcription regulatory networks are composed of interactions between transcription factors and their target genes. Whereas unicellular networks have been studied extensively, metazoan transcription regulatory networks remain largely unexplored. Caenorhabditis elegans provides a powerful model to study such metazoan networks because its genome is completely sequenced and many functional genomic tools are available. While C. elegans gene predictions have undergone continuous refinement, this is not true for the annotation of functional transcription factors. The comprehensive identification of transcription factors is essential for the systematic mapping of transcription regulatory networks because it enables the creation of physical transcription factor resources that can be used in assays to map interactions between transcription factors and their target genes. Results By computational searches and extensive manual curation, we have identified a compendium of 934 transcription factor genes (referred to as wTF2.0). We find that manual curation drastically reduces the number of both false positive and false negative transcription factor predictions. We discuss how transcription factor splice variants and dimer formation may affect the total number of functional transcription factors. In contrast to mouse transcription factor genes, we find that C. elegans transcription factor genes do not undergo significantly more splicing than other genes. This difference may contribute to differences in organism complexity. We identify candidate redundant worm transcription factor genes and orthologous worm and human transcription factor pairs. Finally, we discuss how wTF2.0 can be used together with physical transcription factor clone resources to facilitate the systematic mapping of C. elegans transcription regulatory networks. Conclusion wTF2.0 provides a starting point to decipher the transcription regulatory networks that control metazoan development and function

Coding limits on the number of transcription factors

Transcription factor proteins bind specific DNA sequences to control the expression of genes. They contain DNA binding domains which belong to several super-families, each with a specific mechanism of DNA binding. The total number of transcription factors encoded in a genome increases with the number of genes in the genome. Here, we examined the number of transcription factors from each super-family in diverse organisms. We find that the number of transcription factors from most super-families appears to be bounded. For example, the number of winged helix factors does not generally exceed 300, even in very large genomes. The magnitude of the maximal number of transcription factors from each super-family seems to correlate with the number of DNA bases effectively recognized by the binding mechanism of that super-family. Coding theory predicts that such upper bounds on the number of transcription factors should exist, in order to minimize cross-binding errors between transcription factors. This theory further predicts that factors with similar binding sequences should tend to have similar biological effect, so that errors based on mis-recognition are minimal. We present evidence that transcription factors with similar binding sequences tend to regulate genes with similar biological functions, supporting this prediction. The present study suggests limits on the transcription factor repertoire of cells, and suggests coding constraints that might apply more generally to the mapping between binding sites and biological function.Comment: http://www.weizmann.ac.il/complex/tlusty/papers/BMCGenomics2006.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1590034/ http://www.biomedcentral.com/1471-2164/7/23

Nucleosome-mediated cooperativity between transcription factors

Cooperative binding of transcription factors (TFs) to cis-regulatory regions (CRRs) is essential for precision of gene expression in development and other processes. The classical model of cooperativity requires direct interactions between TFs, thus constraining the arrangement of TFs sites in a CRR. On the contrary, genomic and functional studies demonstrate a great deal of flexibility in such arrangements with variable distances, numbers of sites, and identities of the involved TFs. Such flexibility is inconsistent with the cooperativity by direct interactions between TFs. Here we demonstrate that strong cooperativity among non-interacting TFs can be achieved by their competition with nucleosomes. We find that the mechanism of nucleosome-mediated cooperativity is mathematically identical to the Monod-Wyman-Changeux (MWC) model of cooperativity in hemoglobin. This surprising parallel provides deep insights, with parallels between heterotropic regulation of hemoglobin (e.g. Bohr effect) and roles of nucleosome-positioning sequences and chromatin modifications in gene regulation. Characterized mechanism is consistent with numerous experimental results, allows substantial flexibility in and modularity of CRRs, and provides a rationale for a broad range of genomic and evolutionary observations. Striking parallels between cooperativity in hemoglobin and in transcription regulation point at a new design principle that may be used in range of biological systems

Cis-regulatory module detection using constraint programming

We propose a method for finding CRMs in a set of co-regulated genes. Each CRM consists of a set of binding sites of transcription factors. We wish to find CRMs involving the same transcription factors in multiple sequences. Finding such a combination of transcription factors is inherently a combinatorial problem. We solve this problem by combining the principles of itemset mining and constraint programming. The constraints involve the putative binding sites of transcription factors, the number of sequences in which they co-occur and the proximity of the binding sites. Genomic background sequences are used to assess the significance of the modules. We experimentally validate our approach and compare it with state-of-the-art techniques

Dynamic multifactor hubs interact transiently with sites of active transcription in Drosophila embryos.

The regulation of transcription requires the coordination of numerous activities on DNA, yet how transcription factors mediate these activities remains poorly understood. Here, we use lattice light-sheet microscopy to integrate single-molecule and high-speed 4D imaging in developing Drosophila embryos to study the nuclear organization and interactions of the key transcription factors Zelda and Bicoid. In contrast to previous studies suggesting stable, cooperative binding, we show that both factors interact with DNA with surprisingly high off-rates. We find that both factors form dynamic subnuclear hubs, and that Bicoid binding is enriched within Zelda hubs. Remarkably, these hubs are both short lived and interact only transiently with sites of active Bicoid-dependent transcription. Based on our observations, we hypothesize that, beyond simply forming bridges between DNA and the transcription machinery, transcription factors can organize other proteins into hubs that transiently drive multiple activities at their gene targets.Editorial noteThis article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter)

In Vitro Analysis of the Thyroid Hormone Receptor in Mitochondrial Transcription

The central dogma theory relates how DNA is transcribed into messenger RNA (mRNAs) and then translated into proteins. Since the nucleus contains the majority of the DNA in cells, research related to transcription and translation focuses on these processes within the nucleus and cytosol; however, these processes are also taking place within the mitochondrial organelle. Mitochondria are most widely known for their essential role in producing energy for the cell, but the organelle also contains its own small, circular genome. Transcription of mitochondrial DNA (mtDNA) follows similar mechanisms as does transcription of nuclear DNA. During this essential process, specific mitochondrial transcription factors, such as TFAM and TFB2M, regulate the attachment of the mitochondrial RNA polymerase (POLRMT) to the promoter and initiation of transcription. With a fully functioning mitochondrial RNA polymerase, transcription is properly conducted, and transcripts can be translated to protein by the mitochondrial ribosome. Mitochondrial transcription is a major regulatory process within the organelle, and determining transcription factors involved in this control point is important for understanding mitochondrial function and many diseases relating to mitochondrial dysfunction. Numerous transcription factors are found both in the nucleus as well as in the mitochondria where their function is not well understood. One such transcription factor is the thyroid hormone receptor. Previous research suggests that when the hormone triiodothyronine (T3) is present and taken up in cells, mitochondrial transcription increases. The mechanism behind the T3 stimulation of transcription is thought to be a coordinated effect by interacting with both the mitochondrial and nuclear thyroid hormone receptor. Our aim is to analyze the level of interaction that the mitochondrial thyroid hormone receptor (mt-TRalpha1) has with the mitochondrial DNA and other core mitochondrial transcription factors in the presence and absence of the T3 hormone. With this information, we further understand another component of mitochondrial transcription that could have implications in mitochondrial dysfunction and disease

Walking, hopping and jumping: a model of transcription factor dynamics on DNA

We present a model of how transcription factors scan DNA to find their specific binding sites. Following the classical work of Winter et al. (1981), our model assumes two modes of transcription factor dynamics. Adjacent moves, where the proteins make a single step movement to one side, or short walks where the transcription factors slide along the DNA several binding sites at a time. The purpose of this article is twofold. Firstly, we discuss how such a system can be efficiently modeled computationally. Secondly, we analyse how the mean first binding times of transcription factors to their specific time depends on key parameters of the system