Modeling and stochastic simulation of the Ras/cAMP/PKA pathway in the yeast Saccharomyces cerevisiae evidences a key regulatory function for intracellular guanine nucleotides pools

The Ras small GTPase RSR1 regulates cellulase production in Trichoderma reesei

BACKGROUND

Lignocellulose is the most abundant renewable resource in the world and has attracted widespread attention. It can be hydrolyzed into sugars with the help of cellulases and hemicellulases that are secreted by filamentous fungi. Several studies have revealed that the Ras small GTPase superfamily regulates important cellular physiological processes, including synthesis of metabolites, sporulation, and cell growth and differentiation. However, it remains unknown how and to what extent Ras small GTPases participate in cellulase production.

RESULTS

In this study, we found that the putative Ras small GTPase RSR1 negatively regulated the expression of cellulases and xylanases. Deletion of rsr1 (∆rsr1) significantly increased cellulase production and decreased the expression levels of ACY1-cAMP-protein kinase A (PKA) signaling pathway genes and the concentration of intracellular cyclic adenosine monophosphate (cAMP). Loss of acy1 based on ∆rsr1 (∆rsr1∆acy1) could further increase cellulase production and the expression levels of cellulase genes, while overexpression of acy1 based on ∆rsr1 (∆rsr1-OEacy1) significantly reduced cellulase production and transcriptional levels of cellulase genes. In addition, our results revealed that RSR1 negatively controlled cellulase production via the ACY1-cAMP-PKA pathway. Transcriptome analysis revealed significantly increased expression of three G-protein coupled receptors (GPCRs; tre62462, tre58767, and tre53238) and approximately two-fold higher expression of ACE3 and XYR1, which transcriptionally activated cellulases with the loss of rsr1. ∆rsr1∆ tre62462 exhibited a decrease in cellulase activity compared to ∆rsr1, while that of ∆rsr1∆tre58767 and ∆rsr1∆tre53238 showed a remarkable improvement compared to ∆rsr1. These findings revealed that GPCRs on the membrane may sense extracellular signals and transmit them to rsr1 and then to ACY1-cAMP-PKA, thereby negatively controlling the expression of the cellulase activators ACE3 and XYR1. These data indicate the crucial role of Ras small GTPases in regulating cellulase gene expression.

CONCLUSIONS

Here, we demonstrate that some GPCRs and Ras small GTPases play key roles in the regulation of cellulase genes in Trichoderma reesei. Understanding the roles of these components in the regulation of cellulase gene transcription and the signaling processes in T. reesei can lay the groundwork for understanding and transforming other filamentous fungi.

Modeling and analysis of the macronutrient signaling network in budding yeast

Adaptive modulation of the global cellular growth state of unicellular organisms is crucial for their survival in fluctuating nutrient environments. Because these organisms must be able to respond reliably to ever varying and unpredictable nutritional conditions, their nutrient signaling networks must have a certain inbuilt robustness. In eukaryotes, such as the budding yeast Saccharomyces cerevisiae, distinct nutrient signals are relayed by specific plasma membrane receptors to signal transduction pathways that are interconnected in complex information-processing networks, which have been well characterized. However, the complexity of the signaling network confounds the interpretation of the overall regulatory "logic" of the control system. Here, we propose a literature-curated molecular mechanism of the integrated nutrient signaling network in budding yeast, focusing on early temporal responses to carbon and nitrogen signaling. We build a computational model of this network to reconcile literature-curated quantitative experimental data with our proposed molecular mechanism. We evaluate the robustness of our estimates of the model's kinetic parameter values. We test the model by comparing predictions made in mutant strains with qualitative experimental observations made in the same strains. Finally, we use the model to predict nutrient-responsive transcription factor activities in a number of mutant strains undergoing complex nutrient shifts.

High-energy guanine nucleotides as a signal capable of linking growth to cellular energy status via the control of gene transcription

This mini-review considers the idea that guanylate nucleotide energy charge acts as an integrative signal for the regulation of gene expression in eukaryotic cells and discusses possible routes for that signal's transduction. Gene expression is intimately linked with cell nutrition and diverse signaling systems serve to coordinate the synthesis of proteins required for growth and proliferation with the prevailing cellular nutritional status. Using short pathways for the inducible and futile consumption of ATP or GTP in engineered cells of Saccharomyces cerevisiae, we have recently shown that GTP levels can also play a role in determining how genes act to respond to changes in cellular energy supply. This review aims to interpret the importance of GTP as an integrative signal in the context of an increasing body of evidence indicating the spatio-temporal complexity of cellular de novo purine nucleotide biosynthesis.

Determination of the Global Pattern of Gene Expression in Yeast Cells by Intracellular Levels of Guanine Nucleotides

This paper investigates whether, independently of the supply of any specific nutrient, gene transcription responds to the energy status of the cell by monitoring ATP and GTP levels. Short pathways for the inducible and futile consumption of ATP or GTP were engineered into the yeast Saccharomyces cerevisiae, and the effect of an increased demand for these purine nucleotides on gene transcription was analyzed. The resulting changes in transcription were most consistently associated with changes in GTP and GEC levels, although the reprogramming in gene expression during glucose repression is sensitive to adenine nucleotide levels. The results show that GTP levels play a central role in determining how genes act to respond to changes in energy supply and that any comprehensive understanding of the control of eukaryotic gene expression requires the elucidation of how changes in guanine nucleotide abundance are sensed and transduced to alter the global pattern of transcription.

Correlations between gene transcription and the abundance of high-energy purine nucleotides in Saccharomyces cerevisiae have often been noted. However, there has been no systematic investigation of this phenomenon in the absence of confounding factors such as nutrient status and growth rate, and there is little hard evidence for a causal relationship. Whether transcription is fundamentally responsive to prevailing cellular energetic conditions via sensing of intracellular purine nucleotides, independently of specific nutrition, remains an important question. The controlled nutritional environment of chemostat culture revealed a strong correlation between ATP and GTP abundance and the transcription of genes required for growth. Short pathways for the inducible and futile consumption of ATP or GTP were engineered into S. cerevisiae, permitting analysis of the transcriptional effect of an increased demand for these nucleotides. During steady-state growth using the fermentable carbon source glucose, the futile consumption of ATP led to a decrease in intracellular ATP concentration but an increase in GTP and the guanylate energy charge (GEC). Expression of transcripts encoding proteins involved in ribosome biogenesis, and those controlled by promoters subject to SWI/SNF-dependent chromatin remodelling, was correlated with these nucleotide pool changes. Similar nucleotide abundance changes were observed using a nonfermentable carbon source, but an effect on the growth-associated transcriptional programme was absent. Induction of the GTP-cycling pathway had only marginal effects on nucleotide abundance and gene transcription. The transcriptional response of respiring cells to glucose was dampened in chemostats induced for ATP cycling, but not GTP cycling, and this was primarily associated with altered adenine nucleotide levels.

LASSIE: simulating large-scale models of biochemical systems on GPUs

BACKGROUND

Mathematical modeling and in silico analysis are widely acknowledged as complementary tools to biological laboratory methods, to achieve a thorough understanding of emergent behaviors of cellular processes in both physiological and perturbed conditions. Though, the simulation of large-scale models-consisting in hundreds or thousands of reactions and molecular species-can rapidly overtake the capabilities of Central Processing Units (CPUs). The purpose of this work is to exploit alternative high-performance computing solutions, such as Graphics Processing Units (GPUs), to allow the investigation of these models at reduced computational costs.

RESULTS

LASSIE is a "black-box" GPU-accelerated deterministic simulator, specifically designed for large-scale models and not requiring any expertise in mathematical modeling, simulation algorithms or GPU programming. Given a reaction-based model of a cellular process, LASSIE automatically generates the corresponding system of Ordinary Differential Equations (ODEs), assuming mass-action kinetics. The numerical solution of the ODEs is obtained by automatically switching between the Runge-Kutta-Fehlberg method in the absence of stiffness, and the Backward Differentiation Formulae of first order in presence of stiffness. The computational performance of LASSIE are assessed using a set of randomly generated synthetic reaction-based models of increasing size, ranging from 64 to 8192 reactions and species, and compared to a CPU-implementation of the LSODA numerical integration algorithm.

CONCLUSIONS

LASSIE adopts a novel fine-grained parallelization strategy to distribute on the GPU cores all the calculations required to solve the system of ODEs. By virtue of this implementation, LASSIE achieves up to 92× speed-up with respect to LSODA, therefore reducing the running time from approximately 1 month down to 8 h to simulate models consisting in, for instance, four thousands of reactions and species. Notably, thanks to its smaller memory footprint, LASSIE is able to perform fast simulations of even larger models, whereby the tested CPU-implementation of LSODA failed to reach termination. LASSIE is therefore expected to make an important breakthrough in Systems Biology applications, for the execution of faster and in-depth computational analyses of large-scale models of complex biological systems.

Model-guided optogenetic study of PKA signaling in budding yeast

Optogenetic activation of the adenylate cyclase enzyme in Saccharomyces cerevisiae, paired with computational modeling, enables study of the dynamic quantitative properties of the cAMP/PKA signaling network. The ability to deliver such precise perturbation reveals fundamental dynamical features of PKA signaling, including the time scales of feedback.

In eukaryotes, protein kinase A (PKA) is a master regulator of cell proliferation and survival. The activity of PKA is subject to elaborate control and exhibits complex time dynamics. To probe the quantitative attributes of PKA dynamics in the yeast Saccharomyces cerevisiae, we developed an optogenetic strategy that uses a photoactivatable adenylate cyclase to achieve real-time regulation of cAMP and the PKA pathway. We capitalize on the precise and rapid control afforded by this optogenetic tool, together with quantitative computational modeling, to study the properties of feedback in the PKA signaling network and dissect the nonintuitive dynamic effects that ensue from perturbing its components. Our analyses reveal that negative feedback channeled through the Ras1/2 GTPase is delayed, pinpointing its time scale and its contribution to the dynamic features of the cAMP/PKA signaling network.

NACE: A web-based tool for prediction of intercompartmental efficiency of human molecular genetic networks

Molecular genetic processes generally involve proteins from distinct intracellular localisations. Reactions that follow the same process are distributed among various compartments within the cell. In this regard, the reaction rate and the efficiency of biological processes can depend on the subcellular localisation of proteins. Previously, the authors proposed a method of evaluating the efficiency of biological processes based on the analysis of the distribution of protein subcellular localisation (Popik et al., 2014). Here, NACE is presented, which is an open access web-oriented program that implements this method and allows the user to evaluate the intercompartmental efficiency of human molecular genetic networks. The method has been extended by a new feature that provides the evaluation of the tissue-specific efficiency of networks for more than 2800 anatomical structures. Such assessments are important in cases when molecular genetic pathways in different tissues proceed with the participation of various proteins with a number of intracellular localisations. For example, an analysis of KEGG pathways, conducted using the developed program, showed that the efficiencies of many KEGG pathways are tissue-specific. Analysis of efficiencies of regulatory pathways in the liver, linking proteins of the hepatitis C virus with human proteins involved in the KEGG apoptosis pathway, showed that intercompartmental efficiency might play an important role in host-pathogen interactions. Thus, the developed tool can be useful in the study of the effectiveness of functioning of various molecular genetic networks, including metabolic, regulatory, host-pathogen interactions and others taking into account tissue-specific gene expression. The tool is available via the following link: http://www-bionet.sscc.ru/nace/.

Complex regulation of Hsf1-Skn7 activities by the catalytic subunits of PKA in Saccharomyces cerevisiae: experimental and computational evidences

Background

The cAMP-dependent protein kinase regulatory network (PKA-RN) regulates metabolism, memory, learning, development, and response to stress. Previous models of this network considered the catalytic subunits (CS) as a single entity, overlooking their functional individualities. Furthermore, PKA-RN dynamics are often measured through cAMP levels in nutrient-depleted cells shortly after being fed with glucose, dismissing downstream physiological processes.

Results

Here we show that temperature stress, along with deletion of PKA-RN genes, significantly affected HSE-dependent gene expression and the dynamics of the PKA-RN in cells growing in exponential phase. Our genetic analysis revealed complex regulatory interactions between the CS that influenced the inhibition of Hsf1/Skn7 transcription factors. Accordingly, we found new roles in growth control and stress response for Hsf1/Skn7 when PKA activity was low (cdc25Δ cells). Experimental results were used to propose an interaction scheme for the PKA-RN and to build an extension of a classic synchronous discrete modeling framework. Our computational model reproduced the experimental data and predicted complex interactions between the CS and the existence of a repressor of Hsf1/Skn7 that is activated by the CS. Additional genetic analysis identified Ssa1 and Ssa2 chaperones as such repressors. Further modeling of the new data foresaw a third repressor of Hsf1/Skn7, active only in theabsence of Tpk2. By averaging the network state over all its attractors, a good quantitative agreement between computational and experimental results was obtained, as the averages reflected more accurately the population measurements.

Conclusions

The assumption of PKA being one molecular entity has hindered the study of a wide range of behaviors. Additionally, the dynamics of HSE-dependent gene expression cannot be simulated accurately by considering the activity of single PKA-RN components (i.e., cAMP, individual CS, Bcy1, etc.). We show that the differential roles of the CS are essential to understand the dynamics of the PKA-RN and its targets. Our systems level approach, which combined experimental results with theoretical modeling, unveils the relevance of the interaction scheme for the CS and offers quantitative predictions for several scenarios (WT vs. mutants in PKA-RN genes and growth at optimal temperature vs. heat shock).

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0185-8) contains supplementary material, which is available to authorized users.

Analysis of signaling networks distributed over intracellular compartments based on protein-protein interactions

Background

Biological processes are usually distributed over various intracellular compartments. Proteins from diverse cellular compartments are often involved in similar signaling networks. However, the difference in the reaction rates between similar proteins among different compartments is usually quite high. We suggest that the estimation of frequency of intracompartmental as well as intercompartmental protein-protein interactions is an appropriate approach to predict the efficiency of a pathway.

Results

Using data from the databases STRING, ANDSystem, IntAct and UniProt, a PPI frequency matrix of intra/inter-compartmental interactions efficiencies was constructed. This matrix included 15 human-specific cellular compartments. An approach for estimating pathway efficiency using the matrix of intra/inter-compartmental PPI frequency, based on analysis of reactions efficiencies distribution was suggested. An investigation of KEGG pathway efficiencies was conducted using the developed method. The clusterization and the ranking of KEGG pathways based on their efficiency were performed. "Amino acid metabolism" and "Genetic information processing" revealed the highest efficiencies among other functional classes of KEGG pathways. "Nervous system" and "Signaling molecules interaction" contained the most inefficient pathways. Statistically significant differences were found between efficiencies of KEGG and randomly-generated pathways. Based on these observations, the validity of this approach was discussed.

Conclusion

The estimation of efficiency of signaling networks is a complicated task because of the need for the data on the kinetic reactions. However, the proposed method does not require such data and can be used for preliminary analysis of different protein networks.

Kinetic models in industrial biotechnology - Improving cell factory performance

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.

cuTauLeaping: a GPU-powered tau-leaping stochastic simulator for massive parallel analyses of biological systems

Tau-leaping is a stochastic simulation algorithm that efficiently reconstructs the temporal evolution of biological systems, modeled according to the stochastic formulation of chemical kinetics. The analysis of dynamical properties of these systems in physiological and perturbed conditions usually requires the execution of a large number of simulations, leading to high computational costs. Since each simulation can be executed independently from the others, a massive parallelization of tau-leaping can bring to relevant reductions of the overall running time. The emerging field of General Purpose Graphic Processing Units (GPGPU) provides power-efficient high-performance computing at a relatively low cost. In this work we introduce cuTauLeaping, a stochastic simulator of biological systems that makes use of GPGPU computing to execute multiple parallel tau-leaping simulations, by fully exploiting the Nvidia's Fermi GPU architecture. We show how a considerable computational speedup is achieved on GPU by partitioning the execution of tau-leaping into multiple separated phases, and we describe how to avoid some implementation pitfalls related to the scarcity of memory resources on the GPU streaming multiprocessors. Our results show that cuTauLeaping largely outperforms the CPU-based tau-leaping implementation when the number of parallel simulations increases, with a break-even directly depending on the size of the biological system and on the complexity of its emergent dynamics. In particular, cuTauLeaping is exploited to investigate the probability distribution of bistable states in the Schlögl model, and to carry out a bidimensional parameter sweep analysis to study the oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae.

Yeast as a model for Ras signalling

For centuries yeast species have been popular hosts for classical biotechnology processes, such as baking, brewing, and wine making, and more recently for recombinant proteins production, thanks to the advantages of unicellular organisms (i.e., ease of genetic manipulation and rapid growth) together with the ability to perform eukaryotic posttranslational modifications. Moreover, yeast cells have been used for few decades as a tool for identifying the genes and pathways involved in basic cellular processes such as the cell cycle, aging, and stress response. In the budding yeast S. cerevisiae the Ras/cAMP/PKA pathway is directly involved in the regulation of metabolism, cell growth, stress resistance, and proliferation in response to the availability of nutrients and in the adaptation to glucose, controlling cytosolic cAMP levels and consequently the cAMP-dependent protein kinase (PKA) activity. Moreover, Ras signalling has been identified in several pathogenic yeasts as a key controller for virulence, due to its involvement in yeast morphogenesis. Nowadays, yeasts are still useful for Ras-like proteins investigation, both as model organisms and as a test tube to study variants of heterologous Ras-like proteins.

Modeling mutant phenotypes and oscillatory dynamics in the Saccharomyces cerevisiae cAMP-PKA pathway

Background The cyclic AMP-Protein Kinase A (cAMP-PKA) pathway is an evolutionarily conserved signal transduction mechanism that regulates cellular growth and differentiation in animals and fungi. We present a mathematical model that recapitulates the short-term and long-term dynamics of this pathway in the budding yeast, Saccharomyces cerevisiae. Our model is aimed at recapitulating the dynamics of cAMP signaling for wild-type cells as well as single (pde1Δ and pde2Δ) and double (pde1Δpde2Δ) phosphodiesterase mutants.

Results Our model focuses on PKA-mediated negative feedback on the activity of phosphodiesterases and the Ras branch of the cAMP-PKA pathway. We show that both of these types of negative feedback are required to reproduce the wild-type signaling behavior that occurs on both short and long time scales, as well as the the observed responses of phosphodiesterase mutants. A novel feature of our model is that, for a wide range of parameters, it predicts that intracellular cAMP concentrations should exhibit decaying oscillatory dynamics in their approach to steady state following glucose stimulation. Experimental measurements of cAMP levels in two genetic backgrounds of S. cerevisiae confirmed the presence of decaying cAMP oscillations as predicted by the model.

Conclusions Our model of the cAMP-PKA pathway provides new insights into how yeast respond to alterations in their nutrient environment. Because the model has both predictive and explanatory power it will serve as a foundation for future mathematical and experimental studies of this important signaling network.

The role of feedback control mechanisms on the establishment of oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae

In the yeast Saccharomyces cerevisiae, the Ras/cAMP/PKA pathway is involved in the regulation of cell growth and proliferation in response to nutritional sensing and stress conditions. The pathway is tightly regulated by multiple feedback loops, exerted by the protein kinase A (PKA) on a few pivotal components of the pathway. In this article, we investigate the dynamics of the second messenger cAMP by performing stochastic simulations and parameter sweep analysis of a mechanistic model of the Ras/cAMP/PKA pathway, to determine the effects that the modulation of these feedback mechanisms has on the establishment of stable oscillatory regimes. In particular, we start by studying the role of phosphodiesterases, the enzymes that catalyze the degradation of cAMP, which represent the major negative feedback in this pathway. Then, we show the results on cAMP oscillations when perturbing the amount of protein Cdc25 coupled with the alteration of the intracellular ratio of the guanine nucleotides (GTP/GDP), which are known to regulate the switch of the GTPase Ras protein. This multi-level regulation of the amplitude and frequency of oscillations in the Ras/cAMP/PKA pathway might act as a fine tuning mechanism for the downstream targets of PKA, as also recently evidenced by some experimental investigations on the nucleocytoplasmic shuttling of the transcription factor Msn2 in yeast cells.

Toward a multiscale description of microvascular flow regulation: o(2)-dependent release of ATP from human erythrocytes and the distribution of ATP in capillary networks

Integration of the numerous mechanisms that have been suggested to contribute to optimization of O₂ supply to meet O₂ need in skeletal muscle requires a systems biology approach which permits quantification of these physiological processes over a wide range of length scales. Here we describe two individual computational models based on in vivo and in vitro studies which, when incorporated into a single robust multiscale model, will provide information on the role of erythrocyte-released ATP in perfusion distribution in skeletal muscle under both physiological and pathophysiological conditions. Healthy human erythrocytes exposed to low O₂ tension release ATP via a well characterized signaling pathway requiring activation of the G-protein, Gi, and adenylyl cyclase leading to increases in cAMP. This cAMP then activates PKA and subsequently CFTR culminating in ATP release via pannexin 1. A critical control point in this pathway is the level of cAMP which is regulated by pathway-specific phosphodiesterases. Using time constants (~100 ms) that are consistent with measured erythrocyte ATP release, we have constructed a dynamic model of this pathway. The model predicts levels of ATP release consistent with measurements obtained over a wide range of hemoglobin O₂ saturations (sO₂). The model further predicts how insulin, at concentrations found in pre-diabetes, enhances the activity of PDE3 and reduces intracellular cAMP levels leading to decreased low O₂-induced ATP release from erythrocytes. The second model, which couples O₂ and ATP transport in capillary networks, shows how intravascular ATP and the resulting conducted vasodilation are affected by local sO₂, convection and ATP degradation. This model also predicts network-level effects of decreased ATP release resulting from elevated insulin levels. Taken together, these models lay the groundwork for investigating the systems biology of the regulation of microvascular perfusion distribution by erythrocyte-derived ATP.

Simulation of the Ras/cAMP/PKA pathway in budding yeast highlights the establishment of stable oscillatory states

In the yeast Saccharomyces cerevisiae, the Ras/cAMP/PKA pathway plays a major role in the regulation of metabolism, stress resistance and cell cycle progression. We extend here a mechanistic model of the Ras/cAMP/PKA pathway that we previously defined by describing the molecular interactions and post-translational modifications of proteins, and perform a computational analysis to investigate the dynamical behaviors of the components of this pathway, regulated by different control mechanisms. We carry out stochastic simulations to consider, in particular, the effect of the negative feedback loops on the activity of both Ira2 (a Ras-GAP) and Cdc25 (a Ras-GEF) proteins. Our results show that stable oscillatory regimes for the dynamics of cAMP can be obtained only through the activation of these feedback mechanisms, and when the amount of Cdc25 is within a specific range. In addition, we highlight that the levels of guanine nucleotides pools are able to regulate the pathway, by influencing the transition between stable steady states and oscillatory regimes.

Life in the midst of scarcity: adaptations to nutrient availability in Saccharomyces cerevisiae

Cells of all living organisms contain complex signal transduction networks to ensure that a wide range of physiological properties are properly adapted to the environmental conditions. The fundamental concepts and individual building blocks of these signalling networks are generally well-conserved from yeast to man; yet, the central role that growth factors and hormones play in the regulation of signalling cascades in higher eukaryotes is executed by nutrients in yeast. Several nutrient-controlled pathways, which regulate cell growth and proliferation, metabolism and stress resistance, have been defined in yeast. These pathways are integrated into a signalling network, which ensures that yeast cells enter a quiescent, resting phase (G0) to survive periods of nutrient scarceness and that they rapidly resume growth and cell proliferation when nutrient conditions become favourable again. A series of well-conserved nutrient-sensory protein kinases perform key roles in this signalling network: i.e. Snf1, PKA, Tor1 and Tor2, Sch9 and Pho85-Pho80. In this review, we provide a comprehensive overview on the current understanding of the signalling processes mediated via these kinases with a particular focus on how these individual pathways converge to signalling networks that ultimately ensure the dynamic translation of extracellular nutrient signals into appropriate physiological responses.

Deterministic mathematical models of the cAMP pathway in Saccharomyces cerevisiae

BACKGROUND

Cyclic adenosine monophosphate (cAMP) has a key signaling role in all eukaryotic organisms. In Saccharomyces cerevisiae, it is the second messenger in the Ras/PKA pathway which regulates nutrient sensing, stress responses, growth, cell cycle progression, morphogenesis, and cell wall biosynthesis. A stochastic model of the pathway has been reported.

RESULTS

We have created deterministic mathematical models of the PKA module of the pathway, as well as the complete cAMP pathway. First, a simplified conceptual model was created which reproduced the dynamics of changes in cAMP levels in response to glucose addition in wild-type as well as cAMP phosphodiesterase deletion mutants. This model was used to investigate the role of the regulatory Krh proteins that had not been included previously. The Krh-containing conceptual model reproduced very well the experimental evidence supporting the role of Krh as a direct inhibitor of PKA. These results were used to develop the Complete cAMP Model. Upon simulation it illustrated several important features of the yeast cAMP pathway: Pde1p is more important than is Pde2p for controlling the cAMP levels following glucose pulses; the proportion of active PKA is not directly proportional to the cAMP level, allowing PKA to exert negative feedback; negative feedback mechanisms include activating Pde1p and deactivating Ras2 via phosphorylation of Cdc25. The Complete cAMP model is easier to simulate, and although significantly simpler than the existing stochastic one, it recreates cAMP levels and patterns of changes in cAMP levels observed experimentally in vivo in response to glucose addition in wild-type as well as representative mutant strains such as pde1Delta, pde2Delta, cyr1Delta, and others. The complete model is made available in SBML format.

CONCLUSION

We suggest that the lower number of reactions and parameters makes these models suitable for integrating them with models of metabolism or of the cell cycle in S. cerevisiae. Similar models could be also useful for studies in the human pathogen Candida albicans as well as other less well-characterized fungal species.

Enzymatic characteristics of a Ser/Thr protein kinase, SpkA, from Myxococcus xanthus

Two Ser/Thr protein kinases, SpkA and SpkB, selected from Myxococcus xanthus based on amino acid sequence similarities with the catalytic subunits of cAMP-dependent protein kinases (PKA) were synthesized using a cell-free protein synthesis system. In various protein kinase assays, purified StkA and StkB showed their highest protein kinase activities in a PKA assay using the selective PKA substrate Kemptide and in a protein kinase C (PKC) assay using the selective PKC substrate neurogranin((28-43)), respectively. SpkA had apparent K(m) values of 45 microM and 37 microM for Kemptide and ATP, respectively. Phosphorylation of Kemptide was inhibited by a specific PKA inhibitor peptide, PKI(5-24), and the IC(50) and K(i) values for inhibition of the SpkA activity were 117 nM and 36 nM, respectively.