Genomic insights into the different layers of gene regulation in yeast (original) (raw)
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The YEASTRACT (http://www.yeastract.com) information system is a tool for the analysis and prediction of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in June 2013, this database contains over 200 000 regulatory associations between transcription factors (TFs) and target genes, including 326 DNA binding sites for 113 TFs. All regulatory associations stored in YEASTRACT were revisited and new information was added on the experimental conditions in which those associations take place and on whether the TF is acting on its target genes as activator or repressor. Based on this information, new queries were developed allowing the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. This release further offers tools to rank the TFs controlling a gene or genome-wide response by their relative importance, based on (i) the percentage of target genes in the data set; (ii) the enrichment of the TF regulon in the data set when compared with the genome; or (iii) the score computed using the TFRank system, which selects and prioritizes the relevant TFs by walking through the yeast regulatory network. We expect that with the new data and services made available, the system will continue to be instrumental for yeast biologists and systems biology researchers.
Genome-wide analysis of nascent transcription in Saccharomyces cerevisiae
G3: Genes, Genomes, Genetics, 2011
The assessment of transcriptional regulation requires a genome-wide survey of active RNA polymerases. Thus, we combined the nuclear run-on assay, which labels and captures nascent transcripts, with high-throughput DNA sequencing to examine transcriptional activity in exponentially growing Saccharomyces cerevisiae. Sequence read data from these nuclear run-on libraries revealed that transcriptional regulation in yeast occurs not only at the level of RNA polymerase recruitment to promoters but also at postrecruitment steps. Nascent synthesis signals are strongly enriched at TSS throughout the yeast genome, particularly at histone loci. Nascent transcripts reveal antisense transcription for more than 300 genes, with the read data providing support for the activity of distinct promoters driving transcription in opposite directions rather than bidirectional transcription from single promoters. By monitoring total RNA in parallel, we found that transcriptional activity accounts for 80% of the variance in transcript abundance. We computed RNA stabilities from nascent and steady-state transcripts for each gene and found that the most stable and unstable transcripts encode proteins whose functional roles are consistent with these stabilities. We also surveyed transcriptional activity after heat shock and found that most, but not all, heat shockinducible genes increase their abundance by increasing their RNA synthesis. In summary, this study provides a genome-wide view of RNA polymerase activity in yeast, identifies regulatory steps in the synthesis of transcripts, and analyzes transcript stabilities.
Deciphering principles of transcription regulation in eukaryotic genomes
Molecular Systems Biology, 2006
Transcription regulation has been responsible for organismal complexity and diversity in the course of biological evolution and adaptation, and it is determined largely by the context-dependent behavior of cis-regulatory elements (CREs). Therefore, understanding principles underlying CRE behavior in regulating transcription constitutes a fundamental objective of quantitative biology, yet these remain poorly understood. Here we present a deterministic mathematical strategy, the motif expression decomposition (MED) method, for deriving principles of transcription regulation at the single-gene resolution level. MED operates on all genes in a genome without requiring any a priori knowledge of gene cluster membership, or manual tuning of parameters. Applying MED to Saccharomyces cerevisiae transcriptional networks, we identified four functions describing four different ways that CREs can quantitatively affect gene expression levels. These functions, three of which have extrema in different positions in the gene promoter (short-, mid-, and long-range) whereas the other depends on the motif orientation, are validated by expression data. We illustrate how nature could use these principles as an additional dimension to amplify the combinatorial power of a small set of CREs in regulating transcription.
The topology of the transcription regulatory network in the yeast, Saccharomyces cerevisiae
Physica A: Statistical Mechanics and its Applications, 2003
2 Motivation: A central goal of postgenomic biology is the elucidation of the regulatory relationships among all cellular constituents that together comprise the 'genetic network' of a cell or microorganism. Experimental manipulation of gene activity coupled with the assessment of perturbed transcriptome (i. e., global mRNA expression) patterns represents one approach toward this goal, and may provide a backbone into which other measurements can be later integrated.
Assessing regulatory features of the current transcriptional network of Saccharomyces cerevisiae
Scientific Reports, 2020
The capacity of living cells to adapt to different environmental, sometimes adverse, conditions is achieved through differential gene expression, which in turn is controlled by a highly complex transcriptional network. We recovered the full network of transcriptional regulatory associations currently known for Saccharomyces cerevisiae, as gathered in the latest release of the YEASTRACT database. We assessed topological features of this network filtered by the kind of supporting evidence and of previously published networks. It appears that in-degree distribution, as well as motif enrichment evolve as the yeast transcriptional network is being completed. Overall, our analyses challenged some results previously published and confirmed others. These analyses further pointed towards the paucity of experimental evidence to support theories and, more generally, towards the partial knowledge of the complete network.
Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae
BMC Bioinformatics, 2006
Background: Gene expression and transcription factor (TF) binding data have been used to reveal gene transcriptional regulatory networks. Existing knowledge of gene regulation can be presented using gene connectivity networks. However, these composite connectivity networks do not specify the range of biological conditions of the activity of each link in the network.
Nucleic Acids Research, 2007
The Yeast search for transcriptional regulators and consensus tracking (YEASTRACT) information system (www.yeastract.com) was developed to support the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in September 2007, this database contains over 30 990 regulatory associations between Transcription Factors (TFs) and target genes and includes 284 specific DNA binding sites for 108 characterized TFs. Computational tools are also provided to facilitate the exploitation of the gathered data when solving a number of biological questions, in particular the ones that involve the analysis of global gene expression results. In this new release, YEASTRACT includes DISCOVERER, a set of computational tools that can be used to identify complex motifs over-represented in the promoter regions of co-regulated genes. The motifs identified are then clustered in families, represented by a position weight matrix and are automatically compared with the known transcription factor binding sites described in YEASTRACT. Additionally, in this new release, it is possible to generate graphic depictions of transcriptional regulatory networks for documented or potential regulatory associations between TFs and target genes. The visual display of these networks of interactions is instrumental in functional studies. Tutorials are available on the system to exemplify the use of all the available tools.
Transcriptional Regulatory Networks in Saccharomyces cerevisiae
Science, 2002
transport (32). The model was also used for tracer forecasts during the MINOS campaign. 40. E. Roeckner et al. "The atmospheric general circulation model ECHAM-4: Model description and simulation of present-day climate," MPI-Report 218 (Max Planckcles 15, 909 (2001). The SO 2 emission estimates are inherently uncertain. The SO 2 decrease after 1980 is nevertheless consistent with sulfate concentration and deposition data (27-31, 45, 46). 55.