Crosstalk regulation among group 2- Sigma factors in Synechocystis PCC6803

BACKGROUND: The cyanobacterium Synechocystis PCC6803 contains one group 1 (sigA) and four group 2 (sigB, sigC, sigD and sigE) sigma factors. The activity of these multiple sigma factors determines the transcriptional program of this bacterium. We wanted to study the role of the group 2 sigma factors in Synechocystis. We have therefore constructed mutants of each of the group 2 sigma factors and investigated their crosstalk. RESULTS: We used quantitative RT-PCR analysis to measure the relative abundance of the sig mRNAs in the four sigma mutants. Our data indicate that a network of mutual transcriptional regulation links the expression of the sigma genes. Accordingly, an environmental stress acting on only one of the sigma factors will indirectly modify the expression of most of the other sigma factors. This was confirmed by the transcriptional analysis of the sig mRNAs as a function of nitrogen starvation. CONCLUSION: Taken together, our observations suggest that the crosstalk regulation between all group 1 and group 2 genes could be important for the adaptation of the bacterium to different environmental and physiological conditions

Inferring the connectivity of a regulatory network from mRNA quantification in Synechocystis PCC6803

A major task of contemporary biology is to understand and predict the functioning of regulatory networks. We use expression data to deduce the regulation network connecting the sigma factors of Synechocystis PCC6803, the most global regulators in bacteria. Synechocystis contains one group 1 (SigA) and four group 2 (SigB, SigC, SigD and SigE) sigma factors. From the relative abundance of the sig mRNA measured in the wild-type and the four group 2 sigma mutants, we derive a network of the influences of each sigma factor on the transcription of all other sigma factors. Internal or external stimuli acting on only one of the sigma factors will thus indirectly modify the expression of most of the others. From this model, we predict the control points through which the circadian time modulates the expression of the sigma factors. Our results show that the cross regulation between the group 1 and group 2 sigma factors is very important for the adaptation of the bacterium to different environmental and physiological conditions

Réseaux de régulation chez Escherichia coli

L'adaptation d'une bactérie aux changements de son environnement est contrôlée par un réseau de régulation large et complexe, faisant intervenir de nombreux acteurs et modules différents. Dans ce travail, nous avons étudiés un module de régulation spécifique, contrôlant l'adaptation de la bactérie Escherichia coli à un changement de sources de carbone. Dans un milieu contenant du glucose et de l'acétate, la croissance est divisée en deux phases : les bactéries utilisent préférentiellement le glucose et commencent à métaboliser l'acétate qu'après l'épuisement du glucose. En effet, la présence du glucose réprime la transcription d'un gène nécessaire à la croissance sur acétate, le gène acs (codant pour l'acétyl-CoA synthétase). Le mécanisme régulateur fait intervenir le facteur de transcription Crp-AMPc et le système de transfert de phosphate (PTS), qui permet l'import du glucose. Plusieurs modèles décrivent en détail la cascade de réactions moléculaires à l'origine de cette répression catabolique . Cependant, certaines de nos observations expérimentales ne sont pas correctement prédites par les modèles actuels. Ces modèles doivent être révisés ou complétés. L'outil majeur que nous employons pour les expériences est la fusion transcriptionnelle : une région promotrice fusionnée en amont d'un gène rapporteur (GFP, luciferase). Avec ces constructions, nous mesurons la dynamique de l'expression génique dans différentes souches (mutants) et différentes conditions environnementales. Les observations à l'échelle de la population sont corroborées par des mesures similaires à l'échelle de la cellule unique. Nous utilisons cette même technologie pour construire de petits systèmes synthétiques qui sondent davantage le phénomène de répression catabolique. Nous avons ainsi créé un interrupteur génétique dont le fonctionnement est contrôlé par le flux glycolytique et nous avons construit un petit système de communication intercellulaire basé sur la molécule AMPc. Enfin, nous proposons une manière originale de mesurer l'état métabolique des cellules en utilisant la dépendance énergétique de la luciferase.The adaptation of bacteria to changes in their environment is controlled by a large and complex regulatory network involving many different actors and modules. In this work, we have studied a specific module controlling the adaptation of Escherichia coli to a change in carbon sources. In a medium containing glucose and acetate, growth is divided into two phases : the bacteria preferentially use glucose and start to metabolize acetate only after glucose exhaustion. Indeed, the presence of glucose represses the transcription of a gene needed for growth on acetate : the acs gene (coding for acetyl-CoA synthetase). The regulatory mechanism involves the Crp-cAMP regulator and the phosphate transfer system (PTS), which is responsible for glucose import. Several models describe the cascade of molecular reactions responsible for this catabolite repression . However, our work shows that many of our experimental observations are incorrectly predicted by current models. These models have to be amended.We use transcriptional fusion, i.e., the fusion of a promoter region upstream of a reporter gene (GFP, luciferase), to measure the dynamics of gene expression in different genetic backgrounds and environmental conditions. Observations at the population level are corroborated by similar measurements at the single cell level. We use this same technology to construct small synthetic systems that probe further aspects of the phenomenon of catabolite repression. We have thus created a genetic toggle switch controlled by the glycolytic flux and we have built an inter-cellular communication system mediated by cAMP. Finally, we propose a novel way to measure the metabolic state of cells by using the energy dependence of the luciferase enzyme.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Symbolic Reachability Analysis of Genetic Regulatory Networks using Qualitative Abstractions

The switch-like character of gene regulation has motivated the use of hybrid, discrete-continuous models of genetic regulatory networks. While powerful techniques for the analysis, verification, and control of hybrid systems have been developed, the specificities of the biological application domain pose a number of challenges, notably the absence of quantitative information on parameter values and the size and complexity of networks of biological interest. We introduce a method for the analysis of reachability properties of genetic regulatory networks that is based on a class of discontinuous piecewise-affine (PA) differential equations well-adapted to the above constraints. More specifically, we introduce a hyperrectangular partition of the state space that forms the basis for a discrete abstraction preserving the sign of the derivatives of the state variables. The resulting discrete transition system provides a conservative approximation of the qualitative dynamics of the network and can be efficiently computed in a symbolic manner from inequality constraints on the parameters. The method has been implemented in the computer tool Genetic Network Analyzer (GNA), which has been applied to the analysis of a regulatory system whose functioning is not well-understood by biologists, the nutritional stress response in the bacterium Escherichia coli

Dynamic optimisation of resource allocation in microorganisms: Extended abstract

International audienceBacterial growth is a fundamental process in which cells sustain and reproduce themselves from available matter and energy. Optimisation principles have been widely used to explain and predict the growth behaviour of microor-ganisms, assumed to be optimized by evolution. This has given rise, among other things, to bacterial growth laws describing how the abundance of components of the gene expression machinery increases with the growth rate. These studies have mainly focused on the situation where the system is in balanced growth, a steady state in which all cell components grow at the same rate. Balanced-growth conditions, however, are far from natural growth conditions in which the environment is continually changing. We focus on the optimal allocation of resources between the gene expression machinery and other subsystems during growth-phase transitions. We describe an abstract model of the biochemical reaction processes occur-ring in the cell, based on first principles and articulated around two subsystems: the gene expression machinery and the uptake of nutrients from the environment. Using this so-called self-replicator model, we investigate the optimal dynamic reallocation of resources following a rapid change in the environment. We formulate our question as an optimal control problem that can be solved using Pontryagin's maximum principle. Preliminary results have shown the predominance of bang-singular control of resource allocation following abrupt environmental transitions

Experimental and computational validation of models of fluorescent and luminescent reporter genes in bacteria

<p>Abstract</p> <p>Background</p> <p>Fluorescent and luminescent reporter genes have become popular tools for the real-time monitoring of gene expression in living cells. However, mathematical models are necessary for extracting biologically meaningful quantities from the primary data.</p> <p>Results</p> <p>We present a rigorous method for deriving relative protein synthesis rates (mRNA concentrations) and protein concentrations by means of kinetic models of gene expression. We experimentally and computationally validate this approach in the case of the protein Fis, a global regulator of transcription in <it>Escherichia coli</it>. We show that the mRNA and protein concentration profiles predicted from the models agree quite well with direct measurements obtained by Northern and Western blots, respectively. Moreover, we present computational procedures for taking into account systematic biases like the folding time of the fluorescent reporter protein and differences in the half-lives of reporter and host gene products. The results show that large differences in protein half-lives, more than mRNA half-lives, may be critical for the interpretation of reporter gene data in the analysis of the dynamics of regulatory systems.</p> <p>Conclusions</p> <p>The paper contributes to the development of sound methods for the interpretation of reporter gene data, notably in the context of the reconstruction and validation of models of regulatory networks. The results have wide applicability for the analysis of gene expression in bacteria and may be extended to higher organisms.</p

Dynamical allocation of cellular resources as an optimal control problem: Novel insights into microbial growth strategies

International audienceMicrobial physiology exhibits growth laws that relate the macromolecular composition of the cell to the growth rate. Recent work has shown that these empirical regularities can be derived from coarse-grained models of resource allocation. While these studies focus on steady-state growth, such conditions are rarely found in natural habitats, where microorganisms are continually challenged by environmental fluctuations. The aim of this paper is to extend the study of microbial growth strategies to dynamical environments, using a self-replicator model. We formulate dynamical growth maximization as an optimal control problem that can be solved using Pontryagin's Maximum Principle. We compare this theoretical gold standard with different possible implementations of growth control in bacterial cells. We find that simple control strategies enabling growth-rate maximization at steady state are suboptimal for transitions from one growth regime to another, for example when shifting bacterial cells to a medium supporting a higher growth rate. A near-optimal control strategy in dynamical conditions is shown to require information on several, rather than a single physiological variable. Interestingly, this strategy has structural analogies with the regulation of ribosomal protein synthesis by ppGpp in the enterobacterium Escherichia coli. It involves sensing a mismatch between precursor and ribosome concentrations, as well as the adjustment of ribosome synthesis in a switch-like manner. Our results show how the capability of regulatory systems to integrate information about several physiological variables is critical for optimizing growth in a changing environment

Qualitative Simulation of Genetic Regulatory Networks Using Piecewise-Linear Models

In order to cope with the large amounts of data that have become available in genomics, mathematical tools for the analysis of networks of interactions between genes, proteins, and other molecules are indispensable. We present a method for the qualitative simulation of genetic regulatory networks, based on a class of piecewise-linear (PL) differential equations that has been well-studied in mathematical biology. The simulation method is well-adapted to state-of-the-art measurement techniques in genomics which often provide qualitative and coarse-grained descriptions of genetic regulatory networks. The method is able to deal with nontrivial mathematical problems induced by the discontinuous right-hand sides of the differential equations. Furthermore, it guarantees that the simulation covers all possible solutions of quantitative PL models corresponding to the qualitative PL model used by the method. The qualitative simulation method has been implemented in Java

Genetic Network Analyzer: A Tool for the Qualitative Simulation of Genetic Regulatory Networks

The study of genetic regulatory networks has received a major impetus from the recent development of experimental techniques allowing the measuremen- t of spatiotemporal patterns of gene expression in a massively parallel way. This progress of experimental methods calls for the development of appropriate computer tools for the modeling and simulation of gene regulation processes. These tools should be able to deal with two major difficulties hampering modeling and simulation studies, viz. incomplete knowledge of the biochemical reaction mechanisms and the absence of quantitative informatio- n on kinetic parameters and molecular concentrations. We present a computer tool for the modeling and simulation of genetic regulatory networks, called Genetic Network Analyzer (GNA). The tool is based on a qualitative simulation method that employs coarse-grained models of regulatory networks. The use of GNA is illustrated in a study of the network of genes and interactions regulating the initiation of sporulation in Bacillus subtilis

Importance of metabolic coupling for the dynamics of gene expression following a diauxic shift in E. coli

Gene regulatory networks consist of direct interactions, but also include indirect interactions mediated by metabolism. We investigate to which extent these indirect interactions arising from metabolic coupling influence the dynamics of the system. To this end, we build a qualitative model of the gene regulatory network controlling carbon assimilation in E. coli, and use this model to study the changes in gene expression following a diauxic shift from glucose to acetate. In particular, we compare the steady-state concentrations of enzymes and transcription regulators during growth on glucose and acetate, as well as the dynamic response of gene expression to the exhaustion of glucose and the subsequent assimilation of acetate. We find significant differences between the dynamics of the system in the absence and presence of metabolic coupling. This shows that interactions arising from metabolic coupling cannot be ignored when studying the dynamics of gene regulatory networks.Les réseaux de régulation géniques sont composés d'interactions directes, mais incluent aussi des interactions indirectes dues au couplage avec le métabolisme. Nous étudions dans quelle mesure ces interactions indirectes influencent la dynamique du système. Dans ce but, nous avons construit un modèle qualitatif du réseau de régulation génique contrôlant l'assimilation du carbone chez E. coli et nous utilisons ce modèle pour étudier la réponse génique lors d'une diauxie glucose-acetate. Plus précisément, nous comparons les concentrations à l'état stationnaire des enzymes et des régulateurs globaux lors d'une croissance sur glucose et sur acetate, ainsi que la dynamique de l'expression de gènes suite à l'épuisement du glucose. Nous trouvons des différences significatives entre la dynamique prédite en absence et en présence des interactions indirectes. Nos résultats montrent que les interactions dues au couplage avec le métabolisme doivent être prises en compte quand on s'intéresse à la dynamique de réseaux de régulation géniques