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Transient responses and adaptation to steady state: Gene regulation from a population perspective Erez Braun and Naama Brenner Depts. Of Physics and Chemical Engineering Technion, Israel Institute of Technology, Haifa, 32000, ISRAEL Genetic regulatory networks in yeast are currently at the center of interest. Most recent studies focus on structural, static or short-term properties of these networks from a cellular genomewide perspective. However, genetic regulatory networks are dynamical systems, and studying their long-term dynamics under controlled conditions is necessary for their complete characterization. Since gene expression is a relatively slow process, its dynamics displays behavior with time scales comparable to and longer than the cell cycle. This suggests the relevant “systems” level for such problems as that of the cell population. Our research focuses on the dynamics of regulatory networks in yeast, emphasizing the perspective of clonal populations. This perspective entails following the dynamics for many generations under controlled condition; carefully distinguishing between transient responses and steady state; measuring gene expression at single-cell resolution to account for cell variability; taking into account population factors such as protein inheritance; and more. Using our specially designed experimental system, we have demonstrated novel adaptive dynamics in the well-known GAL system in yeast. This is a classic model for a eukaryotic genetic switch, induced by galactose and repressed by glucose. We followed the expression of a reporter gfp under a GAL promoter at single-cell resolution in large populations of yeast cells. Experiments were conducted for long time scales, several generations, while controlling the environment in continuous culture. This combination enabled us, for the first time, to distinguish between transient responses and steady state. We have found that both galactose induction and glucose repression are only transient responses. Over several generations, the system converges to a single robust steady state, independent of external conditions; this adaptation is physiological and does not involve random genetic mutations. Thus, at steady state the GAL network loses its hallmark functionality as a sensitive carbon source rheostat. This result suggests that, while short-term dynamics are determined by specific modular responses, over long time scales inter-modular interactions take over and shape a robust steady-state response of the regulatory system. [1] To shed light on the functional significance of this adaptation, we next placed an essential gene (HIS3) under GAL regulation. This construction mimics the process of gene recruitment, where one gene is placed under a foreign regulation system, which is a major driving force in evolution. Growing the cells in glucose and using a competitive inhibitor, we were able to load the GAL system and apply variable degrees of selection pressure on the population. The results show that the population utilizes the adaptation from glucose repression to overcome the pressure and survives otherwise lethal conditions. This experimental technique enables to study the adaptive capabilities of the regulatory system in a yeast population. [Unpublished]. [1] E. Braun and N. Brenner, Phys. Biol. 1, 67 (2004). Mass Isotope Ratio Analysis of Intracellular Metabolites by LC-MS/MS: New Roads towards Accurate Steady State and Dynamic Fluxome Analysis W.A. van Winden, L. Wu, M.R. Mashego, W.M. van Gulik, J.C. van Dam, C. Ras, A.M. Pröll, J.L. Vinke, J.J. Heijnen* *Dept. Of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC, Delft, The Netherlands, tel. :+31-15-2785307, fax : +31-15-2782355. ([email protected]) Rational metabolic engineering of micro-organisms consists of an iterative sequence of three stages: culturing and quantitative analysis of micro-organisms, redesign of the microorganism and, finally, synthesis of a new genotype that has improved phenotypical features. The quantitative data cover four levels of the phenotype: fluxome, metabolome, proteome, and transcriptome. In case of highly interconnected metabolic and regulatory networks that are to be redesigned, quantitative analysis will have to include a mathematical modelling step. Our group focuses on the development and application of tools for the quantitative analysis and modelling of the intracellular metabolome (the size of the metabolite pools) and fluxome (the rates of the metabolic reactions) in the central carbon metabolism of Saccharomyces cerevisiae. Liquid chromatography coupled to tandem mass spectrometry forms the heart of our quantitative analysis. We can routinely analyse most intermediates of the glycolysis and TCA cycle, plus some intermediates of the pentose phosphate pathway. Recently, we developed a method for the analysis of the mono, di and triphosphate forms of four nucleotides. Despite the use of optimized protocols and equipment for rapid sampling, quenching and extraction of the biomass [1], quantitative recovery of the metabolites remains challenging due to the low concentrations and fast intracellular turnover rates of the metabolites and due to the multiple processing steps of the sample. Complete recovery is not required when measuring the mass isotopomer fractions of metabolites in biomass that is grown on partially 13C-labeled substrates, since the differentially 13C-labeled metabolites are co-processed, co-eluted in the LC and co-ionised in the MS-inlet and only separated in the final stage of the analysis. This application of LCMS/MS in our laboratory adds to the established 13C-NMR and GC-MS methods for 13Clabeling analysis, which are increasingly used for steady state fluxome analysis [2]. We also used the above advantage of differentially 13C-labeled metabolites in a new method for metabolome quantification following a substrate pulse to the fermentor. To do so, we cultured 100% uniformly 13C-labeled biomass and processed the biomass to obtain a mix of fully 13C-labeled intracellular metabolites. Fixed amounts of this mix are added to samples of an unlabeled culture of which the metabolome is to be analysed. The 13C-labeled metabolite extract then serves as a mix of ideal internal standards for LC-MS/MS analysis [3]. The mix has been show to contain 13C-labeled equivalents of all the components we currently analyse. An additional advantage of the use of the approach is that the 13C-labeled intermediates serve as an identity check of compounds that are not commercially available: they should co-elute with their unlabeled equivalent and should have the correct number of carbon atoms in the mother molecule and daughter fragments in the MS/MS. References [1] Lange, H.C. et al. Biotechnol. Bioeng., 75, 406 – 415, 2001 [2] Sauer, U. Curr. Opinion Biotechnol., 15, 1 - 6, 2004 [3] Mashego, M.R. et al. Biotechnol. Bioeng., 85, 620 – 628, 2004 Keywords: yeast, LC-MS/MS, mass isotope distribution, fluxome, metabolome Session: New tools/ technology for physiological studies Dynamics of the yeast cell cycle interactome - an integrative systems biology approach Ramus Wernersson, Ulrik de Lichtenberg, Lars Juhl Jensen, Søren Brunak & Peer Bork Center for Biological Sequence Analysis, BioCentrum-DTU, The Technical University of Denmark, Building 208, DK-2800 Lyngby, Denmark In recent years, a large number of high-throughput experimental techniques have been developed and applied in particular to the yeast Saccharomycs cerevisiae. These include DNA microarrays, protein-protein interaction screens, subcellular localization experiments and chromatin IP investigations of protein-DNA interactions. This has made yeast the most widely used platform for large-scale analysis such as studies of biological networks. Most of these analysis, however, have dealt with just one data type and mainly focussed on global proporties of the transcriptome or interactome. Typical examples include the identification of subsets of genes regulated in response to particular conditions or static topological proporties of interactions networks. In this work, we construct an interaction network for the yeast cell cycle and analyze the dynamics of this network by integrating protein-protein interaction data with information on gene expression, protein subcellular localization and post-translational modifications. The resulting time-dependent interaction network not only reveals novel components and modules, it also shows that only two thirds of the subunits of the central cell cycle machineries are periodically transcribed. Yet, all modules but one are subject to transcriptional control through at least one periodic component. This appears to be a general design principle which provides temporal control of complex assembly and function. Our work also reveals that the proteins that are periodically expressed are often also subject to additional regulation, through phosphorylation by the cyclin dependent kinase Cdc28p (Cdk1). Increasing fermentative capacity: an integrative approach Sergio Rossell*, Coen C. van der Weijden, Alexander Lindenbergh, Arthur L. Kruckeberg, Barbara M. Bakker & Hans V. Westerhoff. BioCentrum Amsterdam, Faculty of Earth and Life Sciences, Department of Molecular Cell Physiology. De Boelelaan 1085 NL-1081 HV Amsterdam. The Netherlands. * Corresponding author: Tel: +31 20 4446966 Fax: +31 20 4447229 [email protected] Fermentative capacity (FC) is defined as the specific rate of carbon dioxide production under anaerobic conditions with excess of sugar. It is a well-defined physiological trait and a key quality parameter in the industrial production of the yeast Saccharomyces cerevisiae. Nutrient starvation, which is also an industrially relevant phenomenon, has been reported to result in a decreased fermentative capacity. However, it has not been possible to correlate this decrease with the activity of any single enzyme activity. Here we use an integrative approach to address the problem of understanding and then perhaps engineering a physiological trait such as FC. We have evaluated the activities of all the enzymes involved, the fluxes through the pathway, and those that branch from it. In contrast to earlier attempts we have included the determination of the glucose transport activity and of the branching fluxes. These measurements were performed for three different conditions: unstarved (exponentially growing), nitrogen-starved, and carbon-starved (deprived of a nitrogen or carbon source, respectively, for 24 h); and for two different strains: a wild type (CENPK 113-7D) and a hxk2∆ deletion mutant (KY116) that lacks hexokinase II. In the wild type, both types of nutrient starvation led to a decrease in FC, which coincided with a decrease in the glucose transport capacity. In the case of nitrogen starvation, glucose transport activity quantitatively accounted for the reduction in glucose consumption. A surplus ethanol production was observed, which could be explained by the degradation of storage carbohydrates. In carbon-starved cells 40 % of the decrease of glucose consumption could be explained by a decrease of glucose transport capacity, implying that the other 60 % is regulated metabolically. Surprisingly, no other glycolytic enzyme activities were reduced and no detectable amounts of storage carbohydrates were synthesized. We anticipate that changes outside glycolysis (e.g. ATP utilization) contribute to the decreased FC. In the hxk2∆ mutant strain, nutrient starvation did not lead to a significant decrease of the FC, although it did result in a decrease of some enzyme activities including glucose transport. Metabolic modeling and regulation analysis are applied to understand the relative contribution of each of the enzymes to the overall fermentative capacity. Keywords: nutrient starvation, glucose transport, glycolysis, metabolic modeling Impact of Cyclic AMP Signaling on Cell Cycle Progression and Energy Metabolism in Saccharomyces cerevisiae D. Müller, L. Aguilera-Vázquez, H. Diaz-Cuervo, E. Guerrero-Martín, J. O. Marquetand, P.K. Murugan, A. Niebel, and M. Reuss Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31,D-70569 Stuttgart, Germany The pursuit of a systems-level understanding of biological processes constitutes one of the central goals of systems biology. We have used a combination of experimental techniques and mathematical modeling to analyze the impact of signal transduction via cyclic AMP (cAMP) and protein kinase A (PKA) on the coordination of energy metabolism and cell cycle progression in Saccharomyces cerevisiae. The aim is to develop a single cell model which provides a combined description of metabolic processes and signaling events in conjunction with a dynamic representation of the cell cycle. Experiments in synchronous cultures and in chemostat cultures have demonstrated distinct dynamics of cAMP and the downstream targets of PKA in energy metabolism during the cell cycle. However, the upstream signal responsible for the differential activation of this signaling pathway under these conditions is elusive. We have obtained evidence suggesting that intracellular nucleotide concentrations may play a role in linking cAMP signaling and hence cell cycle progression to the energetic state of the cell, at least under glucose-limited conditions. A modular mathematical model has been developed, which aims at capturing not only the dynamics of cAMP-PKA signal transduction, but also that of central carbon metabolism and the cell cycle machinery itself. The signaling module comprises the dynamics of cAMP synthesis and degradation as well as the resulting PKA activation. Simulation studies demonstrate that the model is able to reproduce, e.g., the adaptive response of the signaling pathway to a persistent stimulus of extracellular glucose observed in experiments. The metabolic module is based on a previously established model of glycolysis and the pentose phosphate pathway, which has been extended to include the dynamics of the storage carbohydrates trehalose and glycogen along with their associated regulation by PKA. Cell growth is described based on the output from the metabolic module. A kinetic model of the yeast cell cycle machinery [1,2] constitutes the basis of the cell cycle module, which has been amended to account for PKA influence on cell cycle progression. Population distributions of cell cycle stages were quantified by fluorescence microscopy and were subsequently employed for parameter estimation. Upon integration of the developed modules, the resulting single-cell model will yield a dynamic description of the cAMP-dependent regulation of metabolism and cell cycle progression during the different cell cycle phases. The chosen modular approach is potentially applicable to systems of medical importance where the link between signal transduction, energy metabolism, and the cell cycle is crucial, e.g. when modeling tumor cell behavior. Moreover, the model can also serve as a basis for a segregated description of heterogeneous cell populations, an issue of major importance in the operation of large-scale bioprocesses. References [1] Chen KC, Csikász-Nagy A, Györffy B, Val J, Novák B, Tyson JJ (2000) Mol Cell Biol 11:369-391. [2] Cross, FR (2003) Dev Cell 4(5):741-52. Vertical Genomics: from gene expression to function, … and back J. Bouwman, R.J.M. van Spanning, H.V. Westerhoff, and B.M. Bakker* Molecular cell physiology, Molecular Cell Biology (IMC), Vrije Universiteit, Amsterdam Functional behavior of cells largely occurs at the level of 'fluxes', i.e. rates of processes such as product formation, protein production and gene expression. How these processes are interconnected has not been studied quantitatively. Thanks to genomics, it should be possible to evaluate the implications of processes at the level of transcription for functional fluxes. Glycolysis in yeast is a good model system to test this relation, for it is one of the few pathways for which the kinetic properties of the enzymes are known sufficiently to calculate the flux from the enzyme activities and yeast can be brought under the well-defined steady-state and transient conditions. To measure alterations in the expression of glycolytic genes a new method will be used for quantitative and rapid monitoring mRNA levels. This method, MLPA (Multiplex Ligation-dependent Probe Amplification) uses amplifiable probes of a certain length. These are made by a ligation reaction dependent on the amount of cDNA present in the sample. A mixture is used of different MLPA-probes that are amplified with the same primers and in which each probe is specific for a certain mRNA. In addition protein levels, metabolite levels and fluxes will be measured. Ultimately these data and bioinformatics shall then deduce precisely how altered gene expression leads to altered function. Jildau Bouwman Department of Molecular Cell Physiology Molecular Cell Biology (IMC) Faculty of Biology Vrije Universiteit De Boelelaan 1085 1081 HV Amsterdam (The Netherlands) Tel: +31 20 4446966 Fax: +31 20 4447229 Email: [email protected] Rob J.M. van Spanning Email: [email protected] Barbara M. Bakker* Email: [email protected] H.V. Westerhoff Email: [email protected] Discovering Activated Regulatory Networks in the DNA Damage Response Pathways of Yeast Christopher Workman, Scott A. McCuine, Craig Mak, Trey Ideker UC San Diego, La Jolla, USA; Jean-Bosco Tagne, Richard A. Young Whitehead Institute, Cambridge, USA To further elucidate DNA alkylation damage response in yeast, we have applied an integrative approach to study the regulatory pathways responding to the DNA damaging agent methyl methanesulfonate (MMS). Models were constructed using data derived from classical genomic and microarray approaches, and were refined using computational systems biology approaches. After a phenotypic screening was used to identify a set of transcription factors important for DNA damage response, the implicated regulatory pathways were systematically interrogated using chromatin immunoprecipitation (chIP-chip) and DNA microarrays to monitor protein-DNA interactions and genome-wide expression patterns in single geneknockout strains. These data were integrated and modeled using tools for comparison of networks across multiple conditions, for statistical identification of expression-activated network regions (ActiveModules)1, and visualized using software we have developed for operating on network models (Cytoscape)2. Using this systems approach, we have generated new hypotheses revealing the complex web of interactions, cellular factors, and mechanisms involved in DNA damage response. 1. Ideker, T., Ozier, O., Schwikowski, B. & Siegel, A.F. Discovering regulatory and signaling circuits in molecular interaction networks. Bioinformatics 18 Suppl 1, S233-40 (2002). 2. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13, 2498-504 (2003).