Timescale Separation of Positive and Negative Signaling Creates History-Dependent Responses to IgE Receptor Stimulation

Multistep IgE Mast Cell Desensitization Is a Dose- and Time-Dependent Process Partially Regulated by SHIP-1

Multistep mast cell desensitization blocks the release of mediators following IgE crosslinking with increasing doses of Ag. Although its in vivo application has led to the safe reintroduction of drugs and foods in IgE-sensitized patients at risk for anaphylaxis, the mechanisms of the inhibitory process have remained elusive. We sought to investigate the kinetics, membrane, and cytoskeletal changes and to identify molecular targets. IgE-sensitized wild-type murine (WT) and FcεRIα humanized (h) bone marrow mast cells were activated and desensitized with DNP, nitrophenyl, dust mites, and peanut Ags. The movements of membrane receptors, FcεRI/IgE/Ag, actin, and tubulin and the phosphorylation of Syk, Lyn, P38-MAPK, and SHIP-1 were assessed. Silencing SHIP-1 protein was used to dissect the SHIP-1 role. Multistep IgE desensitization of WT and transgenic human bone marrow mast cells blocked the release of β-hexosaminidase in an Ag-specific fashion and prevented actin and tubulin movements. Desensitization was regulated by the initial Ag dose, number of doses, and time between doses. FcεRI, IgE, Ags, and surface receptors were not internalized during desensitization. Phosphorylation of Syk, Lyn, p38 MAPK, and SHIP-1 increased in a dose-response manner during activation; in contrast, only SHIP-1 phosphorylation increased in early desensitization. SHIP-1 phosphatase function had no impact on desensitization, but silencing SHIP-1 increased β-hexoxaminidase release, preventing desensitization. Multistep IgE mast cell desensitization is a dose- and time-regulated process that blocks β-hexosaminidase, impacting membrane and cytoskeletal movements. Signal transduction is uncoupled, favoring early phosphorylation of SHIP-1. Silencing SHIP-1 impairs desensitization without implicating its phosphatase function.

Erroneous detection of desensitization doses in the prevention of hypersensitivity reactions

BACKGROUND

Desensitization protocols have empirically established their efficacy and safety in eliminating most of the hypersensitivity reactions to drugs and other allergens. Without such procedures, the offending drugs can otherwise be lethal, for some patients, when singularly administered at therapeutic doses. These binding events and the subsequent signaling cascades have been extensively modulated by different desensitization methods, without any clear explanation as to why it is necessary to use increasing allergen doses.

PURPOSE

To use a novel theoretical approach in order to model the desensitization algorithms currently in practice, that seeks to shed light on the mechanism behind their clinical efficacy.

METHOD

An approach using signal processing concepts is applied in this work to introduce aliasing as the erroneous detection of higher drug doses responsible for the efficacy of desensitization procedures.

RESULTS

Available experimental data is modeled and correct predictions as to the efficacy of the drug treatment procedures are produced.

CONCLUSIONS

Desensitization algorithms may benefit from using concepts from signal processing theory in order to avoid hypersensitivity reactions.

Computational Methods for the Design of Recombinase Logic Circuits with Adaptable Circuit Specifications

Synthetic biology aims at engineering new biological systems and functions that can be used to provide new technological solutions to worldwide challenges. Detection and processing of multiple signals are crucial for many synthetic biology applications. A variety of logic circuits operating in living cells have been implemented. One particular class of logic circuits uses site-specific recombinases mediating specific DNA inversion or excision. Recombinase logic offers many interesting features, including single-layer architectures, memory, low metabolic footprint, and portability in many species. Here, we present two automated design strategies for both Boolean and history-dependent recombinase-based logic circuits. One approach is based on the distribution of computation within multicellular consortia, and the other is a single-cell design. Both are complementary and adapted for non-expert users via a web design interface, called CALIN and RECOMBINATOR, for multicellular and single-cell design strategies, respectively. In this book chapter, we are guiding the reader step by step through recombinase logic circuit design, from selecting the design strategy fitting to their final system of interest to obtaining the final design using one of our design web interfaces.

New Mechanistic Advances in FcεRI-Mast Cell-Mediated Allergic Signaling

Mast cells originate from the CD34⁺/CD117⁺ hematopoietic progenitors in the bone marrow, migrate into circulation, and ultimately mature and reside in peripheral tissues. Microbiota/metabolites and certain immune cells (e.g., Treg cells) play a key role in maintaining immune tolerance. Cross-linking of allergen-specific IgE on mast cells activates the high-affinity membrane-bound receptor FcεRI, thereby initiating an intracellular signal cascade, leading to degranulation and release of pro-inflammatory mediators. The intracellular signal transduction is intricately regulated by various kinases, transcription factors, and cytokines. Importantly, multiple signal components in the FcεRI-mast cell–mediated allergic cascade can be targeted for therapeutic purposes. Pharmacological interventions that include therapeutic antibodies against IgE, FcεRI, and cytokines as well as inhibitors/activators of several key intracellular signaling molecues have been used to inhibit allergic reactions. Other factors that are not part of the signal pathway but can enhance an individual’s susceptibility to allergen stimulation are referred to as cofactors. Herein, we provide a mechanistic overview of the FcεRI-mast cell–mediated allergic signaling. This will broaden our scope and visions on specific preventive and therapeutic strategies for the clinical management of mast cell–associated hypersensitivity reactions.

Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit

SUMMARY

Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty.

AVAILABILITY AND IMPLEMENTATION

PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

FcεRI: A Master Regulator of Mast Cell Functions

Mast cells (MCs) perform multiple functions thought to underlie different manifestations of allergies. Various aspects of antigens (Ags) and their interactions with immunoglobulin E (IgE) cause diverse responses in MCs. FcεRI, a high-affinity IgE receptor, deciphers the Ag–IgE interaction and drives allergic responses. FcεRI clustering is essential for signal transduction and, therefore, determines the quality of MC responses. Ag properties precisely regulate FcεRI dynamics, which consequently initiates differential outcomes by switching the intracellular-signaling pathway, suggesting that Ag properties can control MC responses, both qualitatively and quantitatively. Thus, the therapeutic benefits of FcεRI-targeting strategies have long been examined. Disrupting IgE–FcεRI interactions is a potential therapeutic strategy because the binding affinity between IgE and FcεRI is extremely high. Specifically, FcεRI desensitization, due to internalization, is also a potential therapeutic target that is involved in the mechanisms of allergen-specific immunotherapy. Several recent findings have suggested that silent internalization is strongly associated with FcεRI dynamics. A comprehensive understanding of the role of FcεRI may lead to the development of novel therapies for allergies. Here, we review the qualitatively diverse responses of MCs that impact the attenuation/development of allergies with a focus on the role of FcεRI toward Ag exposure.

Temporal Modulation of Drug Desensitization Procedures

Drug hypersensitivity reactions are an unavoidable clinical consequence of the presence of new therapeutic agents. These adverse reactions concern patients afflicted with infectious diseases (e.g., hypersensitivity to antibiotics), and with non-infectious chronic diseases, such as in cancers, diabetes or cystic fibrosis treatments, and may occur at the first drug administration or after repeated exposures. Here we revise recent key studies on the mechanisms underlying the desensitization protocols, and propose an additional temporal regulation layer that is based on the circadian control of the signaling pathway involved and on the modulation of the memory effects established by the desensitization procedures.

Recent advance of spleen tyrosine kinase in diseases and drugs

Spleen tyrosine kinase (Syk) is a non-receptor protein tyrosine kinase, also known as p72Syk. It is important for downstream signaling from cell surface receptors, such as Fc receptors, complement receptors and integrin. Syk plays the critical role in triggering immune and allergic reactions, the signaling pathway of Syk has become the research focus on drugs for allergic disease and human malignancies. This review summarized the characteristics of Syk, its mechanism in related reactions, and mainly discussed the signal transduction pathway mediated by Syk. With the development of industry and the aggravation of environmental pollution, the incidence of allergic diseases is increasing, it has become a global priority disease. In this process, Syk participates in IgE/FcεRI signaling pathway plays a critical role in triggering allergic reactions. This review described the characteristics and the interaction mechanism of Syk and its binding proteins in disease, and summarized the research status of targeted Syk inhibitors.

Rational programming of history-dependent logic in cellular populations

Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with rich signal processing abilities. Here we implement robust, scalable history-dependent programs by distributing the computational labor across a cellular population. Our design is based on standardized recombinase-driven DNA scaffolds expressing different genes according to the order of occurrence of inputs. These multicellular computing systems are highly modular, do not require cell-cell communication channels, and any program can be built by differential composition of strains containing well-characterized logic scaffolds. We developed automated workflows that researchers can use to streamline program design and optimization. We anticipate that the history-dependent programs presented here will support many applications using cellular populations for material engineering, biomanufacturing and healthcare.

Bayesian inference using qualitative observations of underlying continuous variables

MOTIVATION

Recent work has demonstrated the feasibility of using non-numerical, qualitative data to parameterize mathematical models. However, uncertainty quantification (UQ) of such parameterized models has remained challenging because of a lack of a statistical interpretation of the objective functions used in optimization.

RESULTS

We formulated likelihood functions suitable for performing Bayesian UQ using qualitative observations of underlying continuous variables or a combination of qualitative and quantitative data. To demonstrate the resulting UQ capabilities, we analyzed a published model for immunoglobulin E (IgE) receptor signaling using synthetic qualitative and quantitative datasets. Remarkably, estimates of parameter values derived from the qualitative data were nearly as consistent with the assumed ground-truth parameter values as estimates derived from the lower throughput quantitative data. These results provide further motivation for leveraging qualitative data in biological modeling.

AVAILABILITY AND IMPLEMENTATION

The likelihood functions presented here are implemented in a new release of PyBioNetFit, an open-source application for analyzing Systems Biology Markup Language- and BioNetGen Language-formatted models, available online at www.github.com/lanl/PyBNF.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Parameter Estimation and Uncertainty Quantification for Systems Biology Models

Mathematical models can provide quantitative insights into immunoreceptor signaling, and other biological processes, but require parameterization and uncertainty quantification before reliable predictions become possible. We review currently available methods and software tools to address these problems. We consider gradient-based and gradient-free methods for point estimation of parameter values, and methods of profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification. We consider recent and potential future applications of these methods to systems-level modeling of immune-related phenomena.

PyBioNetFit and the Biological Property Specification Language

In systems biology modeling, important steps include model parameterization, uncertainty quantification, and evaluation of agreement with experimental observations. To help modelers perform these steps, we developed the software PyBioNetFit, which in addition supports checking models against known system properties and solving design problems. PyBioNetFit introduces Biological Property Specification Language (BPSL) for the formal declaration of system properties. BPSL allows qualitative data to be used alone or in combination with quantitative data. PyBioNetFit performs parameterization with parallelized metaheuristic optimization algorithms that work directly with existing model definition standards: BioNetGen Language (BNGL) and Systems Biology Markup Language (SBML). We demonstrate PyBioNetFit's capabilities by solving various example problems, including the challenging problem of parameterizing a 153-parameter model of cell cycle control in yeast based on both quantitative and qualitative data. We demonstrate the model checking and design applications of PyBioNetFit and BPSL by analyzing a model of targeted drug interventions in autophagy signaling.

A Step-by-Step Guide to Using BioNetFit

BioNetFit is a software tool designed for solving parameter identification problems that arise in the development of rule-based models. It solves these problems through curve fitting (i.e., nonlinear regression). BioNetFit is compatible with deterministic and stochastic simulators that accept BioNetGen language (BNGL)-formatted files as inputs, such as those available within the BioNetGen framework. BioNetFit can be used on a laptop or stand-alone multicore workstation as well as on many Linux clusters, such as those that use the Slurm Workload Manager to schedule jobs. BioNetFit implements a metaheuristic population-based global optimization procedure, an evolutionary algorithm (EA), to minimize a user-defined objective function, such as a residual sum of squares (RSS) function. BioNetFit also implements a bootstrapping procedure for determining confidence intervals for parameter estimates. Here, we provide step-by-step instructions for using BioNetFit to estimate the values of parameters of a BNGL-encoded model and to define bootstrap confidence intervals. The process entails the use of several plain-text files, which are processed by BioNetFit and BioNetGen. In general, these files include (1) one or more EXP files, which each contains (experimental) data to be used in parameter identification/bootstrapping; (2) a BNGL file containing a model section, which defines a (rule-based) model, and an actions section, which defines simulation protocols that generate GDAT and/or SCAN files with model predictions corresponding to the data in the EXP file(s); and (3) a CONF file that configures the fitting/bootstrapping job and that defines algorithmic parameter settings.

The Autoimmune Skin Disease Bullous Pemphigoid: The Role of Mast Cells in Autoantibody-Induced Tissue Injury

Bullous pemphigoid (BP) is an autoimmune and inflammatory skin disease associated with subepidermal blistering and autoantibodies directed against the hemidesmosomal components BP180 and BP230. Animal models of BP were developed by passively transferring anti-BP180 IgG into mice, which recapitulates the key features of human BP. By using these in vivo model systems, key cellular and molecular events leading to the BP disease phenotype are identified, including binding of pathogenic IgG to its target, complement activation of the classical pathway, mast cell degranulation, and infiltration and activation of neutrophils. Proteinases released by infiltrating neutrophils cleave BP180 and other hemidesmosome-associated proteins, causing DEJ separation. Mast cells and mast cell-derived mediators including inflammatory cytokines and proteases are increased in lesional skin and blister fluids of BP. BP animal model evidence also implicates mast cells in the pathogenesis of BP. However, recent studies questioned the pathogenic role of mast cells in autoimmune diseases such as multiple sclerosis, rheumatoid arthritis, and epidermolysis bullosa acquisita. This review highlights the current knowledge on BP pathophysiology with a focus on a potential role for mast cells in BP and mast cell-related critical issues needing to be addressed in the future.