Shaping the response: the role of FcεRI and Syk expression levels in mast cell signaling

Modulating the Human Basophil Phenotype During Its Development and Maturation: Basophils Derived from In Vitro Cultures of CD34+ Progenitor Cells

Historically, the human basophil that is studied experimentally comes from peripheral blood. But there is evidence that only a short portion of the basophil life cycle related to IgE-mediated function occurs in the blood. The same evidence suggests that IgE-mediated functionality is present for 5-7 days in the bone marrow (or other tissues) during which the cell modulates its phenotype according to local conditions. It is suggested that to properly understand the nature of basophil behavior, a better understanding of its biology during maturation would be helpful. For example, one highly suggestive line of evidence for the relevance of understanding the maturation period is related to the change in basophil phenotype that occurs during treatment of patients with omalizumab. During this treatment, the intrinsic reactivity or sensitivity of the basophils is significantly increased despite, or perhaps because of, the dramatic reduction in FcεRI expression that accompanies this treatment. One of the critical signaling enzymes to increase expression selectively in basophils during treatment is SYK, which is one of the earliest signaling tyrosine kinases involved in translating the aggregation of FcεRI into secretion from the cell. Treatment with omalizumab increases SYK expression, and this observation focuses some attention of how SYK expression is regulated. It is possible that the key regulation occurs during maturation of the basophil. Regardless of the mechanisms operative in this particular treatment, understanding the process of maturation and the extrinsic factors that influence it may lead to better understanding of disease processes. Therefore, this chapter will discuss and present techniques to work with maturing human basophils.

Dependence of Optimal Histamine Release on Cell Surface IgE Density on Human Basophils: Nature of the Stimulus

BACKGROUND

The characteristics of the aggregation reaction that follows allergen binding to cell surface IgE on basophils and mast cells depend on a variety of factors that include the density of IgE and the affinity of the allergen for IgE. For simple bivalent stimuli, one prediction is that the location of the optimum for aggregation is not dependent on IgE density, only the affinity for IgE. However, this behavior does not occur for stimulation with an anti-IgE antibody (Ab) during the treatment of patients with omalizumab.

METHODS

This study re-examined the stability of the optimum for histamine release, relative to cell surface IgE density, using the simple bivalent penicillin hapten (BPO2) or a bivalent monoclonal anti-IgE Ab.

RESULTS

The results validated one prediction for one bivalent hapten, BPO2. Across a range of BPO-specific IgE density of 270-23,500/cell, optimal histamine release remained constant (10 nM BPO2). In contrast, across a range of approximately 6,000-110,000/cell, optimal histamine release shifted 8- to 30-fold for anti-IgE Ab. The distinguishing characteristic between the 2 bivalent stimuli was the difference in their crosslink re-equilibration. Recent modeling of histamine release suggested that the SYK-to-receptor ratio could determine the position of histamine release optimum. The study showed that there were significant shifts in the SYK-to-receptor ratio (from 1: 6 to 5: 1) but the basophil's ability to sense this ratio was restricted to transient crosslinks, as occurred with anti-IgE Ab.

CONCLUSIONS

The results suggest that ligand crosslinking dynamics couple with SYK and receptor expression levels to determine qualitative characteristics of the dose response curve for secretion.

Generalizing Gillespie's Direct Method to Enable Network-Free Simulations

Gillespie's direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie's direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termed network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie's direct method for network-free simulation. Finally, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.

Modeling biomolecular site dynamics in immunoreceptor signaling systems

The immune system plays a central role in human health. The activities of immune cells, whether defending an organism from disease or triggering a pathological condition such as autoimmunity, are driven by the molecular machinery of cellular signaling systems. Decades of experimentation have elucidated many of the biomolecules and interactions involved in immune signaling and regulation, and recently developed technologies have led to new types of quantitative, systems-level data. To integrate such information and develop nontrivial insights into the immune system, computational modeling is needed, and it is essential for modeling methods to keep pace with experimental advances. In this chapter, we focus on the dynamic, site-specific, and context-dependent nature of interactions in immunoreceptor signaling (i.e., the biomolecular site dynamics of immunoreceptor signaling), the challenges associated with capturing these details in computational models, and how these challenges have been met through use of rule-based modeling approaches.

An Interaction Library for the FcεRI Signaling Network

Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein–protein and protein–lipid interactions. This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP₃ at the plasma membrane and the soluble second messenger IP₃. We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.

Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and posttranslational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation).

Computational modeling of the main signaling pathways involved in mast cell activation

A global and rigorous understanding of the signaling pathways and cross-regulatory processes involved in mast cell activation requires the integration of published information with novel functional datasets into a comprehensive computational model. Based on an exhaustive curation of the existing literature and using the software CellDesigner, we have built and annotated a comprehensive molecular map for the FcεRI signaling network. This map can be used to visualize and interpret high-throughput expression data. Furthermore, leaning on this map and using the logical modeling software GINsim, we have derived a qualitative dynamical model, which recapitulates the most salient features of mast cell activation. The resulting logical model can be used to explore the dynamical properties of the system and its responses to different stimuli, in normal or mutant conditions.

Omalizumab increases the intrinsic sensitivity of human basophils to IgE-mediated stimulation

BACKGROUND

Treatment of allergic patients with omalizumab results in a paradoxical increase in their basophil histamine release (HR) response ex vivo to cross-linking anti-IgE antibody. It is not known whether this change in response is associated with an increase in intrinsic cellular sensitivity, which would be a paradoxical response.

OBJECTIVE

We sought to determine whether the increase in response to anti-IgE antibody is a reflection of an increased cellular sensitivity expressed as molecules of antigen-specific IgE per basophil required to produce 50% of the maximal response.

METHODS

Patients were treated with omalizumab or placebo for 12 weeks (NCT01003301 at ClinicalTrials.gov), and the metric of basophil sensitivity was assessed at 4 time points: baseline, 6 to 8 weeks, 12 weeks (after which treatment stopped), and 24 weeks (12 weeks after the end of treatment).

RESULTS

As observed previously, treatment with omalizumab resulted in a marked increase in the maximal HR induced by cross-linking anti-IgE antibody. This change was accompanied by a marked shift in intrinsic basophil sensitivity, ranging from 2.5- to 125-fold, with an average of 6-fold at the midpoint of the treatment to 12-fold after 12 weeks. The magnitude of the increase in cellular sensitivity was inversely related to the starting sensitivity or the starting maximum HR. The increased cellular sensitivity also occurred when using leukotriene C4 secretion as a metric of the basophil response. Twelve weeks after the end of treatment, cellular sensitivity was found to shift toward the baseline value, although the return to baseline was not yet complete at this time point.

CONCLUSIONS

Treatment with omalizumab results in a markedly increased sensitivity of basophils to IgE-mediated stimulation in terms of the number of IgE molecules required to produce a given response. These results provide a better quantitative sense of the phenotypic change that occurs in basophils during omalizumab treatment, which has both mechanistic and clinical implications.

Guidelines for visualizing and annotating rule-based models

Rule-based modeling provides a means to represent cell signaling systems in a way that captures site-specific details of molecular interactions. For rule-based models to be more widely understood and (re)used, conventions for model visualization and annotation are needed. We have developed the concepts of an extended contact map and a model guide for illustrating and annotating rule-based models. An extended contact map represents the scope of a model by providing an illustration of each molecule, molecular component, direct physical interaction, post-translational modification, and enzyme-substrate relationship considered in a model. A map can also illustrate allosteric effects, structural relationships among molecular components, and compartmental locations of molecules. A model guide associates elements of a contact map with annotation and elements of an underlying model, which may be fully or partially specified. A guide can also serve to document the biological knowledge upon which a model is based. We provide examples of a map and guide for a published rule-based model that characterizes early events in IgE receptor (FcεRI) signaling. We also provide examples of how to visualize a variety of processes that are common in cell signaling systems but not considered in the example model, such as ubiquitination. An extended contact map and an associated guide can document knowledge of a cell signaling system in a form that is visual as well as executable. As a tool for model annotation, a map and guide can communicate the content of a model clearly and with precision, even for large models.

Scaffold-mediated nucleation of protein signaling complexes: elementary principles

Proteins with multiple binding sites play important roles in cell signaling systems by nucleating protein complexes in which, for example, enzymes and substrates are co-localized. Proteins that specialize in this function are called by a variety names, including adapter, linker and scaffold. Scaffold-mediated nucleation of protein complexes can be either constitutive or induced. Induced nucleation is commonly mediated by a docking site on a scaffold that is activated by phosphorylation. Here, by considering minimalist mathematical models, which recapitulate scaffold effects seen in more mechanistically detailed models, we obtain analytical and numerical results that provide insights into scaffold function. These results elucidate how recruitment of a pair of ligands to a scaffold depends on the concentrations of the ligands, on the binding constants for ligand-scaffold interactions, on binding cooperativity, and on the milieu of the scaffold, as ligand recruitment is affected by competitive ligands and decoy receptors. For the case of a bivalent scaffold, we obtain an expression for the unique scaffold concentration that maximally recruits a pair of monovalent ligands. Through simulations, we demonstrate that a bivalent scaffold can nucleate distinct sets of ligands to equivalent extents when the scaffold is present at different concentrations. Thus, the function of a scaffold can potentially change qualitatively with a change in copy number. We also demonstrate how a scaffold can change the catalytic efficiency of an enzyme and the sensitivity of the rate of reaction to substrate concentration. The results presented here should be useful for understanding scaffold function and for engineering scaffolds to have desired properties.