Transcriptional Regulation In Yeast Research Articles

Transcriptional Regulation in Yeast during Diauxic Shift and Stationary Phase

The preferred source of carbon and energy for yeast cells is glucose. When yeast cells are grown in liquid cultures, they metabolize glucose predominantly by glycolysis, releasing ethanol in the medium. When glucose becomes limiting, the cells enter diauxic shift characterized by decreased growth rate and by switching metabolism from glycolysis to aerobic utilization of ethanol. When ethanol is depleted from the medium, cells enter quiescent or stationary phase G(0). Cells in diauxic shift and stationary phase are stressed by the lack of nutrients and by accumulation of toxic metabolites, primarily from the oxidative metabolism, and are differentiated in ways that allow them to maintain viability for extended periods of time. The transition of yeast cells from exponential phase to quiescence is regulated by protein kinase A, TOR, Snf1p, and Rim15p pathways that signal changes in availability of nutrients, converge on transcriptional factors Msn2p, Msn4p, and Gis1p, and elicit extensive reprogramming of the transcription machinery. However, the events in transcriptional regulation during diauxic shift and quiescence are incompletely understood. Because cells from multicellular eukaryotic organisms spend most of their life in G(0) phase, understanding transcriptional regulation in quiescence will inform other fields, such as cancer, development, and aging.

A lysine-rich region in Dot1p is crucial for direct interaction with H2B ubiquitylation and high level methylation of H3K79

Dot1p is involved in maintenance of the heterochromatin boundary, the DNA damage response, and transcriptional regulation in yeast and animals. Dot1p is a histone H3 lysine 79 (H3K79) methyltransferase, but H3K79 trimethylation (H3K79me3) by Dot1p requires histone H2B monoubiquitylation (H2Bub) as a pre-requisite. The underlying mechanism for H2Bub requirement has not been well elucidated. In this work, we found that nucleosomes containing H2Bub stimulate the yeast Dot1p to produce H3K79me3. A pulldown assay showed that the yeast Dot1p directly binds to ubiquitin. In addition, we demonstrate that a lysine-rich region (aa 101–140) in the first half of DNA binding domain of the Dot1p is critical in interaction with ubiquitin as well as binding to nucleosome core. Consistent with this, either deletion or point mutation of the lysine-rich region resulted in defect in global H3K79me3 accumulation and subtelomeric gene silencing in vivo. Taken together, our results indicate that a direct interaction between the lysine-rich region of Dot1p and the ubiquitin of H2Bub is required for H2Bub-mediated trans-tail regulation.

Understanding Gene Sequence Variation in the Context of Transcription Regulation in Yeast

DNA sequence polymorphism in a regulatory protein can have a widespread transcriptional effect. Here we present a computational approach for analyzing modules of genes with a common regulation that are affected by specific DNA polymorphisms. We identify such regulatory-linkage modules by integrating genotypic and expression data for individuals in a segregating population with complementary expression data of strains mutated in a variety of regulatory proteins. Our procedure searches simultaneously for groups of co-expressed genes, for their common underlying linkage interval, and for their shared regulatory proteins. We applied the method to a cross between laboratory and wild strains of S. cerevisiae, demonstrating its ability to correctly suggest modules and to outperform extant approaches. Our results suggest that middle sporulation genes are under the control of polymorphism in the sporulation-specific tertiary complex Sum1p/Rfm1p/Hst1p. In another example, our analysis reveals novel inter-relations between Swi3 and two mitochondrial inner membrane proteins underlying variation in a module of aerobic cellular respiration genes. Overall, our findings demonstrate that this approach provides a useful framework for the systematic mapping of quantitative trait loci and their role in gene expression variation.

Identification of yeast transcriptional regulation networks using multivariate random forests.

The recent availability of whole-genome scale data sets that investigate complementary and diverse aspects of transcriptional regulation has spawned an increased need for new and effective computational approaches to analyze and integrate these large scale assays. Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays) to sequence features residing in gene promoters (as derived from DNA motif data) and transcription factor binding to gene promoters (as derived from tiling microarrays). We extend the random forest approach to model a multivariate response as represented, for example, by time-course gene expression measures. An analysis of the multivariate random forest output reveals complex regulatory networks, which consist of cohesive, condition-dependent regulatory cliques. Each regulatory clique features homogeneous gene expression profiles and common motifs or synergistic motif groups. We apply our method to several yeast physiological processes: cell cycle, sporulation, and various stress conditions. Our technique displays excellent performance with regard to identifying known regulatory motifs, including high order interactions. In addition, we present evidence of the existence of an alternative MCB-binding pathway, which we confirm using data from two independent cell cycle studies and two other physioloigical processes. Finally, we have uncovered elaborate transcription regulation refinement mechanisms involving PAC and mRRPE motifs that govern essential rRNA processing. These include intriguing instances of differing motif dosages and differing combinatorial motif control that promote regulatory specificity in rRNA metabolism under differing physiological processes.

Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p

Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of protein–protein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental 13 C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, this correlation improved through incorporating a network-based model of amino acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional 13 C-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications.

A Simple Model of the Modular Structure of Transcriptional Regulation in Yeast

Resolving the general organizational principles that govern the interactions during transcriptional gene regulation has great relevance for understanding disease progression, biofabrication, and biological systems in general. The available genome-level monitoring technologies and the best understood biological work on gene regulation are together providing us with unprecedented amounts of data and universal modeling frameworks in which to reason about regulatory systems on a computational level. Gene regulatory systems exhibit modularity in their regulatory sequences as well as in the corresponding gene expression. This modularity has a nontrivial, general combinatorial structure that can be studied and generalized to model classes of regulatory systems. Here, we study computationally the combinatorial nature of transcriptional regulation by assuming a one-to-one relationship between shared patterns in genome-wide gene-expression and cis-region modules. In our combinatorial framework, the DNA binding events are complementary to their expression counterparts, and together let us approximate the underlying regulation structure. Our model maps regulatory systems onto hierarchical structures which can be approximated by conflating existing large scale gene expression and ChIP-chip data. We have developed methods for building regulatory hierarchies and identifying the basic functional units, or modules, of transcriptional regulation. We validate our model using yeast data by showing agreement of our predictions with experimental data, and using the hierarchies to resolve a finer structure of co-regulation.

Human Immunodeficiency Virus Type 1 Tat Protein Inhibits the SIRT1 Deacetylase and Induces T Cell Hyperactivation

Symptoms of T cell hyperactivation shape the course and outcome of HIV-1 infection, but the mechanism(s) underlying this chronic immune activation are not well understood. We find that the viral transactivator Tat promotes hyperactivation of T cells by blocking the nicotinamide adenine dinucleotide (NAD(+))-dependent deacetylase SIRT1. Tat directly interacts with the deacetylase domain of SIRT1 and blocks the ability of SIRT1 to deacetylate lysine 310 in the p65 subunit of NF-kappaB. Because acetylated p65 is more active as a transcription factor, Tat hyperactivates the expression of NF-kappaB-responsive genes, a function lost in SIRT1-/- cells. These results support a model where the normal function of SIRT1 as a negative regulator of T cell activation is suppressed by Tat during HIV infection. These events likely contribute to the state of immune cell hyperactivation found in HIV-infected individuals.

Blue light diminishes interaction of PAS/LOV proteins, putative blue light receptors in Arabidopsis thaliana, with their interacting partners

The light, oxygen, or voltage (LOV) domain that belongs to the Per-ARNT-Sim (PAS) domain superfamily is a blue light sensory module. The Arabidopsis thaliana PAS/LOV PROTEIN (PLP) gene encodes three putative blue light receptor proteins, PLPA, PLPB, and PLPC, because of its mRNA splicing variation. PLPA and PLPB each contain one PAS domain at the N-terminal region and one LOV domain at the C-terminal region, while the LOV domain is truncated in PLPC. RNA gel blot analysis showed that PLP mRNA was markedly expressed after exposure to salt or dehydration stress. Yeast two-hybrid screening led to the isolation of VITAMIN C DEFECTIVE 2 (VTC2), VTC2-LIKE (VTC2L), and BEL1-LIKE HOMEODOMAIN 10 proteins (BLH10A and BLH10B) as PLP-interacting proteins. The molecular interaction of PLPA with VTC2L, BLH10A or BLH10B, and that of PLPB with VTC2L were diminished when yeasts were grown under blue light illumination. Furthermore, the possible binding of flavin chromophore to PLPA and PLPB was demonstrated. These results imply that the LOV domain of PLPA and PLPB functions as a blue light sensor, and suggest the applicability of these interactions to blue light-dependent switching in transcriptional regulation in yeast or other organisms.

Acclimation ofSaccharomyces cerevisiaeto Low Temperature: A Chemostat-based Transcriptome Analysis

Effects of suboptimal temperatures on transcriptional regulation in yeast have been extensively studied in batch cultures. To eliminate indirect effects of specific growth rates that are inherent to batch-cultivation studies, genome-wide transcriptional responses to low temperatures were analyzed in steady-state chemostats, grown at a fixed specific growth rate (0.03 h(-1)). Although in vivo metabolic fluxes were essentially the same in cultures grown at 12 and at 30 degrees C, concentrations of the growth-limiting nutrients (glucose or ammonia) were higher at 12 degrees C. This difference was reflected by transcript levels of genes that encode transporters for the growth-limiting nutrients. Several transcriptional responses to low temperature occurred under both nutrient-limitation regimes. Increased transcription of ribosome-biogenesis genes emphasized the importance of adapting protein-synthesis capacity to low temperature. In contrast to observations in cold-shock and batch-culture studies, transcript levels of environmental stress response genes were reduced at 12 degrees C. Transcription of trehalose-biosynthesis genes and intracellular trehalose levels indicated that, in contrast to its role in cold-shock adaptation, trehalose is not involved in steady-state low-temperature adaptation. Comparison of the chemostat-based transcriptome data with literature data revealed large differences between transcriptional reprogramming during long-term low-temperature acclimation and the transcriptional responses to a rapid transition to low temperature.

Regulation of the HAP1 gene involves positive actions of histone deacetylases

The yeast transcriptional regulator Hap1 promotes both transcriptional activation and repression. Previous studies have shown that Hap1 binds to the promoter of its own gene and represses its transcription. In this report, we identified the DNA site that allows Hap1-binding with high affinity. This Hap1-binding site contains only one CGG triplet and is distinct from the typical Hap1-binding upstream activation sequences (UASs) mediating transcriptional activation. Furthermore, at the HAP1 promoter, Ssa is bound to DNA with Hap1, whereas Hsp90 is not bound. Intriguingly, we found that histone deacetylases, including Rpd3, Hda1, Sin3 and Hos1, are not required for the repression of the HAP1 gene by Hap1. Rather, they are required for transcriptional activation of the HAP1 promoter, and this requirement is dependent on the HAP1 basal promoter. These results reveal a complex mechanism of transcriptional regulation at the HAP1 promoter, involving multiple DNA elements and regulatory proteins.

Alternative routes and mutational robustness in complex regulatory networks

Alternative pathways through a gene regulation network connect a regulatory molecule to its (indirect) regulatory target via different intermediate regulators. We here show for two large transcriptional regulation networks, and for 15 different signal transduction networks, that multiple alternative pathways between regulator and target pairs are the rule rather than the exception. We find that in the yeast transcriptional regulation network intermediate regulators that are part of many alternative pathways between a regulator and target pair evolve at faster rates. This variation is not solely explicable by higher expression levels of such regulators, nor is it solely explicable by their variable usage in different physiological or environmental conditions, as indicated by their variable expression. This suggests that such pathways can continue to function despite amino acid changes that may impair one intermediate regulator. Our results underscore the importance of systems biology approaches to understand functional and evolutionary constraints on genes and proteins.

Novel Regulatory Properties of Saccharomyces cerevisiae Arp4

ARP4, an essential gene of Saccharomyces cerevisiae, codes for a nuclear actin-related protein. Arp4 is a subunit of several chromatin-modifying complexes and is known to be involved in the transcriptional regulation in yeast. We used a mutant strain with a single amino acid substitution (G161D) in the conserved actin fold domain to investigate the influence of Arp4 on stress and nitrogen catabolite repression genes. The deficiency of functional Arp4 caused a highly increased sensitivity towards nitrogen starvation and to the macrolide antibiotic rapamycin. We show the changes of mRNA levels of selected genes under these conditions. The upregulation of stress genes as a consequence of treatment with rapamycin was largely Msn2p/Msn4p-dependent. The sensitivity towards rapamycin indicates a participation of Arp4 in the regulation of the TOR pathway. Consistently, arp4G161D cells exhibited an affected cell cycle. Long-term cultivation, which leads to a G1 arrest in wild-type cells, provoked arrest in G2/M (more than 60%) in the mutant strain. The same effect was observed upon treatment with rapamycin, indicating an unexpected relationship of Arp4 to TOR-mediated cell cycle arrest.

Oxidant-specific Folding of Yap1p Regulates Both Transcriptional Activation and Nuclear Localization

The yeast transcriptional regulator Yap1p is a key determinant in oxidative stress resistance. This protein is found in the cytoplasm under non-stressed conditions but rapidly accumulates in the nucleus following oxidant exposure. There it activates transcription of genes encoding antioxidants that return the redox balance of the cell to an acceptable range. Yap1p localization to the nucleus requires the oxidant-specific formation of disulfide bonds in the N-terminal cysteine-rich domain (N-CRD) and/or the C-terminal cysteine-rich domain (C-CRD). H(2)O(2) exposure triggers the formation of two interdomain disulfide bonds between the N-and C-CRDs. This dually disulfide-bonded structure has been argued to mask the nuclear export signal in the C-CRD that would otherwise prevent Yap1p nuclear accumulation. The C-CRD is required for wild-type H(2)O(2) tolerance but dispensable for resistance to diamide. The Saccharomyces cerevisiae TRX2 gene, encoding a thioredoxin protein, cannot be induced by H(2)O(2) in the presence of various mutant forms of Yap1p lacking the normally functioning C-CRD. In this work, we demonstrate that the proper folding of Yap1p in the presence of H(2)O(2) is required for recruitment of the mediator component Rox3p to the TRX2 promoter in addition to the nuclear accumulation of Yap1p during stress by this oxidant. These data demonstrate that the dually disulfide-bonded Yap1p N- and C-CRDs form a bifunctional protein domain controlling both nuclear localization and transcriptional activation.

Transcription regulation: a genomic network

The transcriptional regulatory system plays a central role in directing gene expression changes in response to internal and external stimuli. In this talk, I will present our group's computational studies on transcription regulation in yeast, ranging from large-scale experimental studies to computational analyses of regulatory networks. In the first half I will introduce results from ChIp-chip experiments that identify genome-wide DNA-binding sites of transcription factors, particularly focusing on the cell cycle regulatory system. In the second half I will discuss how these observations fit in with the idea of a genomic regulatory network, and examine the effects of such networks on gene expression levels. Finally, I will introduce the concept of dynamic network usage in the context of transcription regulation, and how specific regulatory pathways are employed to bring about these transitions.

CAGER: classification analysis of gene expression regulation using multiple information sources

BackgroundMany classification approaches have been applied to analyzing transcriptional regulation of gene expressions. These methods build models that can explain a gene's expression level from the regulatory elements (features) on its promoter sequence. Different types of features, such as experimentally verified binding motifs, motifs discovered by computer programs, or transcription factor binding data measured with Chromatin Immunoprecipitation (ChIP) assays, have been used towards this goal. Each type of features has been shown successful in modeling gene transcriptional regulation under certain conditions. However, no comparison has been made to evaluate the relative merit of these features. Furthermore, most publicly available classification tools were not designed specifically for modeling transcriptional regulation, and do not allow the user to combine different types of features.ResultsIn this study, we use a specific classification method, decision trees, to model transcriptional regulation in yeast with features based on predefined motifs, automatically identified motifs, ChlP-chip data, or their combinations. We compare the accuracies and stability of these models, and analyze their capabilities in identifying functionally related genes. Furthermore, we design and implement a user-friendly web server called CAGER (Classification Analysis of Gene Expression Regulation) that integrates several software components for automated analysis of transcriptional regulation using decision trees. Finally, we use CAGER to study the transcriptional regulation of Arabidopsis genes in response to abscisic acid, and report some interesting new results.ConclusionModels built with ChlP-chip data suffer from low accuracies when the condition under which gene expressions are measured is significantly different from the condition under which the ChIP experiment is conducted. Models built with automatically identified motifs can sometimes discover new features, but their modeling accuracies may have been over-estimated in previous studies. Furthermore, models built with automatically identified motifs are not stable with respect to noises. A combination of ChlP-chip data and predefined motifs can substantially improve modeling accuracies, and is effective in identifying true regulons. The CAGER web server, which is freely available at , allows the user to select combinations of different feature types for building decision trees, and interact with the models graphically. We believe that it will be a useful tool to facilitate the discovery of gene transcriptional regulatory networks.

Transcriptional regulatory code of a eukaryotic genome.

DNA-binding transcriptional regulators interpret the genome's regulatory code by binding to specific sequences to induce or repress gene expression. Comparative genomics has recently been used to identify potential cis-regulatory sequences within the yeast genome on the basis of phylogenetic conservation, but this information alone does not reveal if or when transcriptional regulators occupy these binding sites. We have constructed an initial map of yeast's transcriptional regulatory code by identifying the sequence elements that are bound by regulators under various conditions and that are conserved among Saccharomyces species. The organization of regulatory elements in promoters and the environment-dependent use of these elements by regulators are discussed. We find that environment-specific use of regulatory elements predicts mechanistic models for the function of a large population of yeast's transcriptional regulators.

Molecular evolution in the yeast transcriptional regulation network.

We analyze the structure of the yeast transcriptional regulation network, as revealed by chromatin immunoprecipitation experiments, and characterize the molecular evolution of both its transcriptional regulators and their target (regulated) genes. We test the hypothesis that highly connected genes are more important to the function of gene networks. Three lines of evidence-the rate of molecular evolution of network genes, the rate at which network genes undergo gene duplication, and the effects of synthetic null mutation in network genes-provide no strong support for this hypothesis. In addition, we ask how network genes diverge in their transcriptional regulation after duplication. Both loss (subfunctionalization) and gain (neofunctionalization) of transcription factor binding play a role in this divergence, which is often rapid. On the one hand, gene duplicates experience a net loss in the number of transcription factors binding to them, indicating the importance of losing transcription factor binding sites after gene duplication. On the other hand, the number of transcription factors that bind to highly diverged duplicates is significantly greater than would be expected if loss of binding played the only role in the divergence of duplicate genes.

Identification and Distinct Regulation of Yeast TATA Box-Containing Genes

Despite being one of the first eukaryotic transcriptional regulatory elements identified, the sequence of a native TATA box and its significance remain elusive. Applying criteria associated with TATA boxes we queried several Saccharomyces genomes and arrived at the consensus TATA(A/T)A(A/T)(A/G). Approximately 20% of yeast genes contain a TATA box. Strikingly, TATA box-containing genes are associated with responses to stress, are highly regulated, and preferentially utilize SAGA rather than TFIID when compared to TATA-less promoters. Transcriptional regulation in yeast appears to be mechanistically bipolar, possibly reflecting a need to balance inducible stress-related responses with constitutive housekeeping functions.

Molecular evolution in the yeast transcriptional regulation network

Molecular evolution in the yeast transcriptional regulation network

Histone Acetylation and Cancer

Acta Oncologica Vol. 42, No. 7, p. 792, 2003Received 7 March 2003Accepted 14 April 2003To the Editor:The organization of chromatin is crucial for the regulation of geneexpression. In particular, both the positioning and properties ofnucleosomes influence promoter-specific transcription in responseto extracellular or intracellular signals (1). There is a growing bodyof evidence to suggest that the acetylation and deacetylation ofhistones play significant roles in transcriptional regulation ofeukaryotic cells. To date, several papers have been published abouttheir role in gene expression (2). They are catalysed by specificenzyme families, histone acetyltransferases (HATs) and deacety-lases (HDACs). HATs have been identified as transcriptionalcoactivators and HDACs as yeast transcriptional regulators. Thereare at least four groups of proteins with intrinsic HAT activity,while eight HDACs belonging to three distinct classes have beendescribed in mammalian cells (1).The balance between acetylation and deacetylation is animportant factor in regulating gene expression and is thus linkedto the control of cell fate. Disruption of HAT or HDAC activity ispossibly associated with cancer development (3). Genes that encodeHAT enzymes are either translocated, or amplified, or over-expressed, or mutated in different types of cancers. Nucleosomescontaining highly charged hypoacetylated histones bind tightly tothe phosphate backbone of DNA, inhibiting transcription, pre-sumably because transcription factors, regulatory complexes andRNA polymerase do not have access to DNA. Acetylationneutralizes the charge of the histones and generates a more openDNA conformation. From molecular analyses of human diseases itis suggested that changes in acetylation may play a role in theuncontrolled cell growth of cancer. Since aberrant histone acetyla-tion has been linked to malignant diseases in some cases, HDACinhibitors have potential as new drugs due to their ability tomodulate transcription and to induce differentiation and apoptosis.Today’s treatment strategies are mostly limited to general HDACinhibitors, some of which have pleiotropic and/or unknown effectsin addition to deacetylase repression. Several drugs that are able toinhibit HDACs are at present undergoing clinical trials (4). Theseinclude short chain fatty acids (such as 4-phenylbutyrate andvalporic acid), hydroxamic acids (such as suberoylanilide hydro-xamic acid or SAHA, pyroxamide, trichostatin A or TSA,oxamflatin and CHAPs), cyclic tetrapeptides (such as trapoxin,apicidin and depsipeptide*