Aggregation of membrane proteins by cytosolic cross-linkers: theory and simulation of the LAT-Grb2-SOS1 system

Spatio-temporal signaling in mast cells

This chapter summarizes the evidence for localized signaling domains in mast cells and basophils, with a particular focus on the high affinity IgE receptor, FcεRI and its crosstalk with other membrane proteins. It is noteworthy that a literature spanning 30 years established the FcεRI as a model receptor for studying activation-induced changes in receptor diffusion and lipid raft association. Now a combination of high resolution microscopy methods, including immunoelectron microscopy and sophisticated fluorescence-based techniques, provide new insight into the nanoscale spatial and temporal aspects of receptor topography on the mast cell plasma membrane. Physical crosslinking of FcεRI with multivalent ligands leads to formation of IgE receptor clusters, termed "signaling patches," that recruit downstream signaling molecules. However, classes of receptors that engage solely withmono valent ligands can also form distinctive signaling patches. The dynamic relationships between receptor diffusion, aggregation state, clustering, signal initiation and signal strength are discussed in the context of these recent findings.

RuleMonkey: software for stochastic simulation of rule-based models

Background

The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems.

Results

Here, we present a software tool called RuleMonkey, which implements a network-free method for simulation of rule-based models that is similar to Gillespie's method. The method is suitable for rule-based models that can be encoded in BNGL, including models with rules that have global application conditions, such as rules for intramolecular association reactions. In addition, the method is rejection free, unlike other network-free methods that introduce null events, i.e., steps in the simulation procedure that do not change the state of the reaction system being simulated. We verify that RuleMonkey produces correct simulation results, and we compare its performance against DYNSTOC, another BNGL-compliant tool for network-free simulation of rule-based models. We also compare RuleMonkey against problem-specific codes implementing network-free simulation methods.

Conclusions

RuleMonkey enables the simulation of rule-based models for which the underlying reaction networks are large. It is typically faster than DYNSTOC for benchmark problems that we have examined. RuleMonkey is freely available as a stand-alone application http://public.tgen.org/rulemonkey. It is also available as a simulation engine within GetBonNie, a web-based environment for building, analyzing and sharing rule-based models.

Cooperation of adapter molecules in proximal signaling cascades during allergic inflammation

Activation of mast cells through their high-affinity immunoglobulin E receptor (FcepsilonRI) plays an important role in allergic disorders. Other mast cell-activating stimuli, such as Toll-like receptor (TLR) ligands, synergize with FcepsilonRI to enhance allergic inflammation. Thus, there is much interest in understanding how signaling occurs downstream of these receptors. One key event for FcepsilonRI-mediated mast cell activation is the inducible formation of multimolecular proximal signaling complexes. These complexes are nucleated by adapter proteins, scaffolds that localize various signaling molecules through their multiple molecule-binding domains. Here we review recent findings in proximal signaling cascades with an emphasis on how adapter molecules cooperate with each other to generate an optimal signal in mast cells, and we discuss how signals crosstalk between FcepsilonRI and TLRs in enhancing mast cell activation. Deciphering the molecular mechanisms leading to mast cell activation will hopefully bring new ideas for the development of novel therapeutics to control allergic diseases.

Modeling multivalent ligand-receptor interactions with steric constraints on configurations of cell-surface receptor aggregates

We use flow cytometry to characterize equilibrium binding of a fluorophore-labeled trivalent model antigen to bivalent IgE-FcepsilonRI complexes on RBL cells. We find that flow cytometric measurements are consistent with an equilibrium model for ligand-receptor binding in which binding sites are assumed to be equivalent and ligand-induced receptor aggregates are assumed to be acyclic. However, this model predicts extensive receptor aggregation at antigen concentrations that yield strong cellular secretory responses, which is inconsistent with the expectation that large receptor aggregates should inhibit such responses. To investigate possible explanations for this discrepancy, we evaluate four rule-based models for interaction of a trivalent ligand with a bivalent cell-surface receptor that relax simplifying assumptions of the equilibrium model. These models are simulated using a rule-based kinetic Monte Carlo approach to investigate the kinetics of ligand-induced receptor aggregation and to study how the kinetics and equilibria of ligand-receptor interaction are affected by steric constraints on receptor aggregate configurations and by the formation of cyclic receptor aggregates. The results suggest that formation of linear chains of cyclic receptor dimers may be important for generating secretory signals. Steric effects that limit receptor aggregation and transient formation of small receptor aggregates may also be important.

Differential regulation of type I interferon and epidermal growth factor pathways by a human Respirovirus virulence factor

A number of paramyxoviruses are responsible for acute respiratory infections in children, elderly and immuno-compromised individuals, resulting in airway inflammation and exacerbation of chronic diseases like asthma. To understand the molecular pathogenesis of these infections, we searched for cellular targets of the virulence protein C of human parainfluenza virus type 3 (hPIV3-C). We found that hPIV3-C interacts directly through its C-terminal domain with STAT1 and GRB2, whereas C proteins from measles or Nipah viruses failed to do so. Binding to STAT1 explains the previously reported capacity of hPIV3-C to block type I interferon signaling, but the interaction with GRB2 was unexpected. This adaptor protein bridges Epidermal Growth Factor (EGF) receptor to MAPK/ERK pathway, a signaling cascade recently found to be involved in airway inflammatory response. We report that either hPIV3 infection or transient expression of hPIV3-C both increase cellular response to EGF, as assessed by Elk1 transactivation and phosphorylation levels of ERK1/2, 40S ribosomal subunit protein S6 and translation initiation factor 4E (eIF4E). Furthermore, inhibition of MAPK/ERK pathway with U0126 prevented viral protein expression in infected cells. Altogether, our data provide molecular basis to explain the role of hPIV3-C as a virulence factor and determinant of pathogenesis and demonstrate that Paramyxoviridae have evolved a single virulence factor to block type I interferon signaling and to boost simultaneous cellular response to growth factors.

Respiroviruses are important pathogens responsible for acute respiratory tract infections associated with severe airway inflammation in children, elderly and immuno-compromised individuals. Their RNA genome encodes for structural proteins that compose viral particles, but also for virulence factors that alter cell biology to enhance virus replication and spreading. Among them, the C protein plays a critical role by blocking cellular response to type I interferons, the main antiviral cytokines secreted during virus infections. To provide molecular basis to this activity, we found that the C protein of human parainfluenza virus type 3 (hPIV3-C), the most frequent human Respirovirus, interacts with STAT1, a key component of type I interferon receptor complex. But hPIV3-C was also found to interact with GRB2, an adaptor molecule involved in cellular response to Epidermal Growth Factor (EGF), and to enhance cell response to this cytokine. This pathway increases protein translation, promotes cell survival and contributes to airway inflammation and mucus secretion. Thus, our findings show that hPIV3-C not only inhibits the antiviral response but also stimulates cellular response to EGF, which benefits virus replication and induces an excessive inflammation of airways during infection.