AKAP-Lbc enhances cyclic AMP control of the ERK1/2 cascade

Mitogen-activated protein kinase (MAPK) cascades propagate a variety of cellular activities. Processive relay of signals through RAF-MEK-ERK modulates cell growth and proliferation. Signalling through this ERK cascade is frequently amplified in cancers, and drugs such as sorafenib (which is prescribed to treat renal and hepatic carcinomas) and PLX4720 (which targets melanomas) inhibit RAF kinases. Natural factors that influence ERK1/2 signalling include the second messenger cyclic AMP. However, the mechanisms underlying this cascade have been difficult to elucidate. We demonstrate that the A-kinase-anchoring protein AKAP-Lbc and the scaffolding protein kinase suppressor of Ras (KSR-1) form the core of a signalling network that efficiently relay signals from RAF, through MEK, and on to ERK1/2. AKAP-Lbc functions as an enhancer of ERK signalling by securing RAF in the vicinity of MEK1 and synchronizing protein kinase A (PKA)-mediated phosphorylation of Ser 838 on KSR-1. This offers mechanistic insight into cAMP-responsive control of ERK signalling events.     

Analysis of a kinetic model for melanin biosynthesis pathway.

The kinetic behavior of the melanin biosynthesis pathway from L-tyrosine up to dopachrome has been studied from experimental and simulation assays. The reaction mechanism proposed is based on a single active site of tyrosinase. The diphenolase and monophenolase activities of tyrosinase involve one single (oxidase) and two overlapped (hydroxylase and oxidase) catalytic cycles, respectively. The stoichiometry of the pathway implies that one molecule of tyrosinase must accomplish two turnovers in the hydroxylase cycle for each one in the oxidase cycle. Furthermore, the steady-state rates of dopachrome production and O2 consumption from tyrosine and L-dopa, also fulfill the stoichiometry of the pathway: VO2T/VDCT = 1.5 and VO2T/VDCD = 1.0, where T represents L-tyrosine, DC represents dopachrome, and D represents L-dopa. It has been ascertained by high performance liquid chromatography that in the steady-state, a quantity of dopa is accumulated ([D]ss) which fulfills the constant ratio [D]ss = R[T]0. Taking this ratio into account, an analytical expression has been deduced for the monophenolase activity of tyrosinase. In this expression kcatT congruent to (2/3)k3(K1/K2)R, revealing that kcatT is not a true catalytic constant, since it also depends on equilibrium constants and on the experimental R = 0.057. This low value explains the lower catalytic efficiency of tyrosinase on tyrosine than on dopa, (VmaxT/KmT)/(VmaxD/KmD) congruent to (2/3)R, since a significant portion of tyrosinase is scavenged from the catalytic turnover as dead-end complex EmetT in the steady-state of the monophenolase activity of tyrosinase.     

A multi-step kinetic model for substrate assimilation and bacterial growth: application to benzene biodegradation

A multi-step kinetic model based on the concept of synthesizing unit (SU) was developed for describing benzene biodegradation in Pseudomonas putida F1. The model herein presented considered substrate arrival rates to the SU rather than concentrations, and provided a reasonable good fit of the dynamics of both catechol and biomass concentrations experimentally determined. It was based on very general assumptions and could be applied to any process accumulating metabolic intermediates. Conventional growth models considering a single step can be regarded as a particular case of this multi-step model. Despite the merits of this model, its applicability strongly depends on the knowledge of the complex induction-repression and inhibition mechanisms governing the different catabolic steps of the degradation pathway, which in most cases are difficult to elucidate experimentally and/or to model mathematically. In this particular case repression of benzene oxidation by catechol and self-inhibition of catechol transformation were experimentally confirmed and considered in the simulation, resulting in a good fit (relative average error of 6%) of the experimental data.     

A kinetic model incorporating energy spilling for substrate removal in substrate-sufficient batch culture of activated sludge

Batch assays are currently used to study the kinetic behavior of microbial growth. However, it has been shown that the outcome of batch experiments is greatly influenced by the initial ratio of substrate concentration (S _o) to biomass concentration (X _o). Substrate-sufficient batch culture is known to have mechanisms of spilling energy that lead to significant nongrowth-associated substrate consumption, and the Monod equation is no longer appropriate. By incorporating substrate consumption associated with energy spilling into the balance of the substrate oxidation reaction, a kinetic model for the observed specific substrate consumption rate was developed for substrate-sufficient batch culture of activated sludge, and was further verified by experimental data. It was demonstrated that the specific substrate consumption rate increased with the increase of the S _o/X _o ratio, and the majority of substrate was consumed through energy spilling at high S _o/X _o ratios. It appears that the S _o/X _o ratio is a key parameter in regulating metabolic pathways of microorganisms. Received: 18 January 1999 / Received revision: 7 May 1999 / Accepted: 28 May 1999     

A model for short-term control of the bacterioplankton by substrate and grazing

Substrate supply and grazing are the factors with the greatest potential for short-term control of planktonic bacterial density and productivity. A model was developed based on Monod kinetics, where growth rates are limited by food supply in a saturation type equation. In the model, substrate, bacteria, heterotrophic flagellates and zooplankton are state variables linked by trophic transfer and expressed as carbon. The steady state assumption allows calculation of equations indicating the following: (l) bacterial density is determined primarily by the ratio of substrate input to grazing rate; (2) bacterial production is balanced by a combination of losses due to maintenance, death and grazing, and occurs at a rate determined by the rate of substrate input and the growth yield; (3) ambient substrate concentration is directly related to grazing rate. Sensitivity analysis of the model on a computer demonstrates some differences between grazer-controlled and substrate-controlled bacterial systems, and predictions of the model are listed for possible validation in natural systems. The model is potentially useful in evaluating the ‘link vs. sink’ question, as it provides a framework for investigating energy flow through the microbial food web as a function of controlling factors.     

A kinetic completion of the cyclic photosynthetic electron pathway of Rhodopseudomonas sphaeroides: cytochrome b-cytochrome c2 oxidation-reduction.

In Rhodopseudomonas sphaeroides, following a single-turnover flash of light, cytochrome c2 is oxidized by reaction center bacteriochlorophyll, and a cytochrome b is reduced by the primary electron acceptor, probably via ubiquinone. In this report we show that, in the uncoupled state, the rate of re-oxidation of the cytochrome b is identical to the rate of reduction of the cytochrome c2, a kinetic completion of the cyclic photosynthetic electron transport system.     

A kinetic model describing Shewanella oneidensis MR-1 growth, substrate consumption, and product secretion

Aerobic growth of Shewanella oneidensis MR-1 in minimal lactate medium was studied in batch cultivation. Acetate production was observed in the middle of the exponential growth phase and was enhanced when the dissolved oxygen (DO) concentration was low. Once the lactate was nearly exhausted, S. oneidensis MR-1 used the acetate produced during growth on lactate with a similar biomass yield as lactate. A two-substrate Monod model, with competitive and uncompetitive substrate inhibition, was devised to describe the dependence of biomass growth on lactate, acetate, and oxygen and the acetate growth inhibition across a broad range of concentrations. The parameters estimated for this model indicate interesting growth kinetics: lactate is converted to acetate stoichiometrically regardless of the DO concentration; cells grow well even at low DO levels, presumably due to a very low K(m) for oxygen; cells metabolize acetate (maximum specific growth rate, micro(max,A) of 0.28 h(-1)) as a single carbon source slower than they metabolize lactate (micro(max,L) of 0.47 h(-1)); and growth on acetate is self-inhibiting at a concentration greater than 10 mM. After estimating model parameters to describe growth and metabolism under six different nutrient conditions, the model was able to successfully estimate growth, oxygen and lactate consumption, and acetate production and consumption under entirely different growth conditions.     

A kinetic model of RNA folding

The RNA folding process is represented as a Markov process with states corresponding to RNA secondary structures and transition probabilities corresponding to transformations of a secondary structure caused by formation or disintegration of a helix. Transition probabilities (kinetic constants) are determined. A notion of a group of structures is introduced, and it allows to reduce the state space. Energetic and kinetic parameters of pseudoknots are estimated. Algorithms for computation of a kinetic ensemble for structures and groups of structures are presented, as well as their modifications that take into account pseudoknots. The described algorithms are implemented as a procedure for prediction of RNA secondary structure that is included in the package DNA-SUN.     

Computational model of VEGFR2 pathway to ERK activation and modulation through receptor trafficking

Vascular Endothelial Growth Factor (VEGF) signal transduction is central to angiogenesis in development and in pathological conditions such as cancer, retinopathy and ischemic diseases. We constructed and validated a computational model of VEGFR2 trafficking and signaling, to study the role of receptor trafficking kinetics in modulating ERK phosphorylation in VEGF-stimulated endothelial cells. Trafficking parameters were optimized and validated against four previously published in vitro experiments. Based on these parameters, model simulations demonstrated interesting behaviors that may be highly relevant to understanding VEGF signaling in endothelial cells. First, at moderate VEGF doses, VEGFR2 phosphorylation and ERK phosphorylation are related in a log-linear fashion, with a stable duration of ERK activation; but with higher VEGF stimulation, phosphoERK becomes saturated, and its duration increases. Second, a large endosomal fraction of VEGFR2 makes the ERK activation reaction network less sensitive to perturbations in VEGF dosage. Third, extracellular-matrix-bound VEGF binds and activates VEGFR2, but by internalizing at a slower rate, matrix-bound VEGF-induced intracellular ERK phosphorylation is predicted to be greater in magnitude and more sustained, in agreement with experimental evidence. Fourth, different endothelial cell types appear to have different trafficking rates, which result in different levels of endosomal receptor localization and different ERK response profiles.     

A cyclic GMP-dependent signalling pathway regulates bacterial phytopathogenesis

Cyclic guanosine 3′,5′‐monophosphate (cyclic GMP) is a second messenger whose role in bacterial signalling is poorly understood. A genetic screen in the plant pathogen Xanthomonas campestris (Xcc) identified that XC_0250, which encodes a protein with a class III nucleotidyl cyclase domain, is required for cyclic GMP synthesis. Purified XC_0250 was active in cyclic GMP synthesis in vitro. The linked gene XC_0249 encodes a protein with a cyclic mononucleotide‐binding (cNMP) domain and a GGDEF diguanylate cyclase domain. The activity of XC_0249 in cyclic di‐GMP synthesis was enhanced by addition of cyclic GMP. The isolated cNMP domain of XC_0249 bound cyclic GMP and a structure–function analysis, directed by determination of the crystal structure of the holo‐complex, demonstrated the site of cyclic GMP binding that modulates cyclic di‐GMP synthesis. Mutation of either XC_0250 or XC_0249 led to a reduced virulence to plants and reduced biofilm formation in vitro. These findings describe a regulatory pathway in which cyclic GMP regulates virulence and biofilm formation through interaction with a novel effector that directly links cyclic GMP and cyclic di‐GMP signalling.     

A kinetic model for plasmid curing

A simple mathematical model of drug-induced plasmid elimination (curing) considering density-dependent growth rates and plasmid transfers is presented. It describes nonlinear population dynamics of conjugative plasmids during in vitro curing experiments in batch culture. The model was tested on kinetics of acridine orange curing of F'lac plasmid. Effects of density dependence, plasmid elimination, selection for plasmidless segregants, conjugation, initial and maximal population density, and postsegregational killing on curing kinetics are simulated and discussed.     

A kinetic model for calcium distribution

Models for the distribution of minerals in the body are of interest as they allow researchers to trace the effect of a dose on mineral levels in plasma, storage and other compartments. Limited models are available in the literature for tracing the distribution of a calcium dose through a short time period. We propose a more general kinetic model which includes both limited absorption through the gut and loss of calcium via excretion. This new method has the advantages of giving reasonable results over moderate time periods, and allowing the extrapolation of calcium levels in extracellular fluid and storage. We fit the model to published data in order to obtain typical parameter values. These values are then used to analyze the implications of the model regarding the effect of calcium dose on calcium levels in various compartments.     

A model for oscillatory kinetic systems

A kinetic model is proposed for oscillatory kinetic phenomena. The exact analytic solution is exhibited and shown to account for several features exhibited by oscillatory chemical and biological systems.     

A kinetic model for glutamate dehydrogenase

A model for the glutamate dehydrogenase reaction has been obtained that contains the reported intermediates suggested by binding and equilibrium isotope exchange methods. Calculated steady state-initial velocity rates using this model are quantitatively consistent with a wide range of nonlinear experimental data in both directions.     

A kinetic model for cell agglutination

A model of concanavalin A (ConA) mediated cell agglutination kinetics is proposed, in which the binding of the lectin, the agglutination of cells and the disintegration of cell clumps are discussed. This resulted in a differential equation, which is solved in terms of the average number of cells per cell clump as a function of time.     

Identification of substrate recognition determinants for human ERK1 and ERK2 protein kinases

Two epidermal growth factor-stimulated protein kinases that correspond to ERK1 and ERK2 have been purified from human epidermoid carcinoma cells (Northwood, I. C., Gonzalez, F. A., Wartmann, M., Raden, D. L., and Davis, R. J. (1991) J. Biol. Chem. 266, 15266-15276). A consensus primary sequence for substrates of ERK1 has been identified as -Pro-Leu-Ser/Thr-Pro- (Alvarez, E., Northwood, I. C., Gonzalez, F. A., Latour, D. A., Seth, A., Abate, C., Curran, T., and Davis, R. J. (1991) J. Biol. Chem. 266, 15277-15285). However, the structural determinants for substrate recognition are not understood. We performed a systematic analysis of the effect of point mutations in the primary sequence of peptide substrates on the rate of phosphorylation by ERK1 and ERK2. The results of this investigation demonstrate that the substrate specificities of the ERK1 and ERK2 protein kinases are very similar. We propose that the primary sequence of substrates for ERK1 and ERK2 protein kinases can be generalized as -Pro-Xaan-Ser/Thr-Pro- (where Xaa is a neutral or basic amino acid and n = 1 or 2).