The complexity of complexes in signal transduction

Many activities of cells are controlled by cell-surface receptors, which in response to ligands, trigger intracellular signaling reactions that elicit cellular responses. A hallmark of these signaling reactions is the reversible nucleation of multicomponent complexes, which typically begin to assemble when ligand-receptor binding allows an enzyme, often a kinase, to create docking sites for signaling molecules through chemical modifications, such as tyrosine phosphorylation. One function of such docking sites is the co-localization of enzymes with their substrates, which can enhance both enzyme activity and specificity. The directed assembly of complexes can also influence the sensitivity of cellular responses to ligand-receptor binding kinetics and determine whether a cellular response is up- or downregulated in response to a ligand stimulus. The full functional implications of ligand-stimulated complex formation are difficult to discern intuitively. Complex formation is governed by conditional interactions among multivalent signaling molecules and influenced by quantitative properties of both the components in a system and the system itself. Even a simple list of the complexes that can potentially form in response to a ligand stimulus is problematic because of the number of ways signaling molecules can be modified and combined. Here, we review the role of multicomponent complexes in signal transduction and advocate the use of mathematical models that incorporate detail at the level of molecular domains to study this important aspect of cellular signaling.     

Interaction of a monoclonal IgE-specific antibody with cell-surface IgE-Fc epsilon RI: characterization of equilibrium binding and secretory response

Aggregation of FcepsilonRI, the high-affinity cell-surface receptor for IgE antibody, is required for degranulation of basophils and mast cells, but not all receptor aggregates elicit this cellular response. The stereochemical constraints on clusters of FcepsilonRI that are able to signal cellular responses, such as degranulation, have yet to be fully defined. To improve our understanding of the properties of FcepsilonRI aggregates that influence receptor signaling, we have studied the interaction of 23G3, a rat IgG(1)(kappa) IgE-specific monoclonal antibody, with IgE-FcepsilonRI complexes on rat mucosal-type mast cells (RBL-2H3 line). We find that 23G3 is a potent secretagogue. This property and the structural features of 23G3 (two symmetrically arrayed IgE-specific binding sites) make 23G3 a potentially valuable reagent for investigating the relationship between FcepsilonRI clustering and FcepsilonRI-mediated signaling events. To develop a mathematical model of 23G3-induced aggregation of FcepsilonRI, we used fluorimetry and flow cytometry to quantitatively monitor equilibrium binding of FITC-labeled 23G3 intact Ab and its Fab' fragment to cell-surface IgE. The results indicate that IgE bound to FcepsilonRI expresses two epitopes for 23G3 binding; that 23G3 binds IgE resident on the cell surface with negative cooperativity; and that 23G3 appears to induce mostly but not exclusively noncyclic dimeric aggregates of FcepsilonRI. There is no simple relationship between receptor aggregation at equilibrium and the degranulation response. Further studies are needed to establish how 23G3-induced aggregation of IgE-FcepsilonRI correlates with cellular responses.     

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.     

The Brucella TIR-like protein TcpB interacts with the death domain of MyD88

The pathogen Brucella melitensis secretes a Toll/interleukin-1 receptor (TIR) domain containing protein that abrogates host innate immune responses. In this study, we have characterized the biochemical interactions of Brucella TIR-like protein TcpB with host innate immune adaptor proteins. Using protein-fragment complementation assays based on Gaussia luciferase and green fluorescent protein, we find that TcpB interacts directly with MyD88 and that this interaction is significantly stronger than the interaction of TcpB with TIRAP, the only other adaptor protein that detectably interacts with TcpB. Surprisingly, the TcpB-MyD88 interaction depends on the death domain (DD) of MyD88, and TcpB does not interact with the isolated TIR domain of MyD88. TcpB disrupts MyD88(DD)-MyD88(DD), MyD88(DD)-MyD88(TIR) and MyD88(DD)-MyD88 interactions but not MyD88-MyD88 or MyD88(TIR)-MyD88(TIR) interactions. Structural models consistent with these results suggest how TcpB might inhibit TLR signaling by targeting MyD88 via a DD-TIR domain interface.     

Changes inClostridium acetobutylicum during acetone-butanol fermentation detected by measurement of electrophoretic mobility

Summary Changes inClostridium acetobutylicum during acetone-butanol fermentation have been detected by measurement of electrophoretic mobility using a laser doppler particle electrophoresis instrument. These results are shown to correspond to the two stages of the fermentation and to agree with other observations of flocculation and changes in morphology of the microorganisms.