A population dynamics analysis of the interaction between adaptive regulatory T cells and antigen presenting cells

Controlling Cell Interactions with DNA Directed Assembly

The creation of complex cellular environments is critical to mimicking tissue environments that will play a critical role in next-generation tissue engineering, stem cell programming, and therapeutic screening. To address this growing need, techniques capable of manipulating cell-cell and cell-material interactions are required that span single-cell to 3D tissue architectures. DNA programmed assembly and placement of cells present a powerful technique for the bottom-up synthesis of living microtissues for probing key questions in cell-cell and cell-material-driven behaviors through its refined control over placement and architecture. This review examines the current state of the art in the programming of cellular interactions with DNA and its applications spanning tissue model building, fundamental cellular biology, and cell manipulation for measurements across a host of applications.

Light-Controlled Cell-Cell Assembly Using Photocaged Oligonucleotides

The interactions between heterogeneous cell populations play important roles in dictating various cell behaviors. Cell-cell contact mediates communication through the exchange of signaling molecules, electrical coupling, and direct membrane-linked ligand-receptor interactions. In vitro culturing of multiple cell types with control over their specific arrangement is difficult, especially in three-dimensional (3D) systems. While techniques that allow one to control the arrangement of cells and direct contact between different cell types have been developed that expand upon simple co-culture methods, specific control over heterojunctions that form between cells is not easily accomplished with current methods, such as 3D cell-printing. In this article, DNA-mediated cell interactions are combined with cell-compatible photolithographic approaches to control cell assembly. Specifically, cells are coated with oligonucleotides containing DNA nucleobases that are protected with photocleavable moieties; this coating facilitated light-controlled cell assembly when these cells were mixed with cells coated with complementary oligonucleotides. By combining this technology with digital micromirror devices mounted on a microscope, selective activation of specific cell populations for interactions with other cells was achieved. Importantly, this technique is rapid and uses non-UV light sources. Taken together, this technique opens new pathways for on-demand programming of complex cell structures.

Modeling the Potential of Treg-Based Therapies for Transplant Rejection: Effect of Dose, Timing, and Accumulation Site

Introduction: The adoptive transfer of regulatory T cells (Tregs) has emerged as a method to promote graft tolerance. Clinical trials have demonstrated the safety of adoptive transfer and are now assessing their therapeutic efficacy. Strategies that generate large numbers of antigen specific Tregs are even more efficacious. However, the combinations of factors that influence the outcome of adoptive transfer are too numerous to be tested experimentally. Here, mathematical modeling is used to predict the most impactful treatment scenarios. Methods: We adapted our mathematical model of murine heart transplant rejection to simulate Treg adoptive transfer and to correlate therapeutic efficacy with Treg dose and timing, frequency of administration, and distribution of injected cells. Results: The model predicts that Tregs directly accumulating to the graft are more protective than Tregs localizing to draining lymph nodes. Inhibiting antigen-presenting cell maturation and effector functions at the graft site was more effective at modulating rejection than inhibition of T cell activation in lymphoid tissues. These complex dynamics define non-intuitive relationships between graft survival and timing and frequency of adoptive transfer. Conclusion: This work provides the framework for better understanding the impact of Treg adoptive transfer and will guide experimental design to improve interventions.

Effects of immunosuppressants on T-cell dynamics: Understanding from a generic coarse-grained immune network model

Long-term immunosuppressive therapy is a drug regimen often used to lower aggressive immune responses in various chronic inflammatory diseases. However, such long-term therapy leading to immune suppression may trigger other adverse reactions in the immune system. The rising concern regarding the optimal dose and duration of such treatment has motivated us to understand non-classical immunomodulatory responses induced by various immunosuppressive steroid and secosteroid drugs such as glucocorticoid and vitamin D supplements. The immunomodulatory actions of such immunosuppressants (that govern the adaptive immune response) are often mediated through their characteristic control over CD4+ T-cells involving pro- and antiinflammatory T-cells. Several early studies attempted to decode temporal and dose-dependent behaviors of such pro- and anti-inflammatory T-cells using the chemical dynamics approach. We first summarize these early works. Then, we develop a minimal coarse-grained kinetic network model to capture the commonality in their immunomodulatory functions. This generic model successfully reproduces the characteristic dynamical features, including the clinical latency period in long-term T-cell dynamics. The temporal behavior of T-cells is found to be sensitive to specific rate parameters and doses of immunosuppressants. The steady-state analysis reflects the transition from an early classified weakly regulated (autoimmune-prone) immune state to a strongly regulated state (immunocompromised state), separated by an intervening state of moderate/balanced regulation. An optimal dose and duration are essential in rescuing balanced immune regulation. This review elucidates how developing a simple generic coarse-grained immune network model may provide immense information that helps diagnose inefficacy in adaptive immune function before and after administering immunosuppressants such as glucocorticoid or vitamin D.

Immune phase transition under steroid treatment

The steroid hormone glucocorticoid (GC) is a well-known immunosuppressant that controls T-cell-mediated adaptive immune response. In this work, we have developed a minimal kinetic network model of T-cell regulation connecting relevant experimental and clinical studies to quantitatively understand the long-term effects of GC on pro-inflammatory T-cell (T_{pro}) and anti-inflammatory T-cell (T_{anti}) dynamics. Due to the antagonistic relation between these two types of T cells, their long-term steady-state population ratio helps us to characterize three classified immune regulations: (i) weak ([T_{pro}]>[T_{anti}]), (ii) strong ([T_{pro}]<[T_{anti}]), and (iii) moderate ([T_{pro}]∼[T_{anti}]), holding the characteristic bistability. In addition to the differences in their long-term steady-state outcome, each immune regulation shows distinct dynamical phases. In the presteady state, a characteristic intermediate stationary phase is observed to develop only in the moderate regulation regime. In the medicinal field, the resting time in this stationary phase is distinguished as a clinical latent period. GC dose-dependent steady-state analysis shows an optimal level of GC to drive a phase transition from the weak or autoimmune prone to the moderate regulation regime. Subsequently, the presteady state clinical latent period tends to diverge near that optimal GC level where [T_{pro}]:[T_{anti}] is highly balanced. The GC-optimized elongated stationary phase explains the rationale behind the requirement of long-term immune diagnostics, especially when long-term GC-based chemotherapeutics and other immunosuppressive drugs are administrated. Moreover, our study reveals GC sensitivity of clinical latent period, which might serve as an early warning signal in diagnosing different immune phases and determining immune phasewise steroid treatment.

Fluctuation theory of immune response: A statistical mechanical approach to understand pathogen induced T-cell population dynamics

In this period of intense interest in human immunity, we attempt here to quantify the immune response against pathogen invasion through T-cell population dynamics. Borrowing concepts from equilibrium statistical mechanics, we introduce a new description of the immune response function (IMRF) in terms of fluctuations in the population number of relevant biological cells (effector and regulatory T-cells). We use a coarse-grained chemical reaction network model (CG-CRNM) to calculate the number fluctuations and show that the response function derived as such can, indeed, capture the crossover observed in a T-cell driven immune response. We employ the network model to learn the effect of vitamin-D as an immunomodulator. We solve our CG-CRNM using a stochastic Gillespie algorithm. Depending on the effector T-cell concentration, we can classify immune regulation regimes into three categories: weak, strong, and moderate. The IMRF is found to behave differently in these three regimes. A damped cross-regulatory behavior found in the dynamics of effector and regulatory T-cell concentration in the diseased states correlates well with the same found in a cohort of patients with specific malignancies and autoimmune diseases. Importantly, the crossover from the weakly regulated steady state to the other (the strongly regulated) is accompanied by a divergence-like growth in the fluctuation of both the effector and the regulatory T-cell concentration, characteristic of a dynamic phase transition. We believe such steady-state IMRF analyses could help not only to phase-separate different immune stages but also aid in the valuable connection between autoimmunity, optimal vitamin-D, and consequences of immunosuppressive stress and malignancy.

QSP-IO: A Quantitative Systems Pharmacology Toolbox for Mechanistic Multiscale Modeling for Immuno-Oncology Applications

Immunotherapy has shown great potential in the treatment of cancer; however, only a fraction of patients respond to treatment, and many experience autoimmune‐related side effects. The pharmaceutical industry has relied on mathematical models to study the behavior of candidate drugs and more recently, complex, whole‐body, quantitative systems pharmacology (QSP) models have become increasingly popular for discovery and development. QSP modeling has the potential to discover novel predictive biomarkers as well as test the efficacy of treatment plans and combination therapies through virtual clinical trials. In this work, we present a QSP modeling platform for immuno‐oncology (IO) that incorporates detailed mechanisms for important immune interactions. This modular platform allows for the construction of QSP models of IO with varying degrees of complexity based on the research questions. Finally, we demonstrate the use of the platform through two example applications of immune checkpoint therapy.

Higher parental occupational social contact is associated with a reduced risk of incident pediatric type 1 diabetes: Mediation through molecular enteroviral indices

We aimed to examine the association between parental occupational social contact and hygiene factors on type 1 diabetes (T1D) risk and possible mediation of these effects through child enteroviral infection. We interviewed 333 incident T1D cases and 660 controls from 2008–2011 in Melbourne, Australia. Enteroviral indices (ribonucleic acid by reverse transcription polymerase chain reaction and Coxsackie B virus antibody levels) in peripheral blood were measured in nested case control samples. Parent occupational social contact was assessed by the number of well or sick children, adults or animals contacted daily through work. Higher parental occupational social contact was strongly associated with reduced T1D risk with evidence of dose response (contact with the well or sick score, Adjusted odds ratio (AOR) per category: 0.73 (95% Confidence Interval (CI): 0.66, 0.81); P<0.001 or AOR 0.63 (95% CI: 0.53, 0.75); P<0.001) respectively). Nine of the ten parental social contact indices, were significant mediated through one or more enteroviral indices. The strength of association between enterovirus presence and T1D onset increased with child age (1.2 fold increase per year; P = 0.05). Lower child hand hygiene enhanced the adverse effect of low parental occupational contact with the sick; Synergy Index 5.16 (95% CI: 3.61, 7.36). The interaction between hand washing and parental occupational contact is more consistent with protection against parental enteroviral shedding than the sharing of a protective infectious agent or microbiome.

A stochastic chemical dynamic approach to correlate autoimmunity and optimal vitamin-D range

Motivated by several recent experimental observations that vitamin-D could interact with antigen presenting cells (APCs) and T-lymphocyte cells (T-cells) to promote and to regulate different stages of immune response, we developed a coarse grained but general kinetic model in an attempt to capture the role of vitamin-D in immunomodulatory responses. Our kinetic model, developed using the ideas of chemical network theory, leads to a system of nine coupled equations that we solve both by direct and by stochastic (Gillespie) methods. Both the analyses consistently provide detail information on the dependence of immune response to the variation of critical rate parameters. We find that although vitamin-D plays a negligible role in the initial immune response, it exerts a profound influence in the long term, especially in helping the system to achieve a new, stable steady state. The study explores the role of vitamin-D in preserving an observed bistability in the phase diagram (spanned by system parameters) of immune regulation, thus allowing the response to tolerate a wide range of pathogenic stimulation which could help in resisting autoimmune diseases. We also study how vitamin-D affects the time dependent population of dendritic cells that connect between innate and adaptive immune responses. Variations in dose dependent response of anti-inflammatory and pro-inflammatory T-cell populations to vitamin-D correlate well with recent experimental results. Our kinetic model allows for an estimation of the range of optimum level of vitamin-D required for smooth functioning of the immune system and for control of both hyper-regulation and inflammation. Most importantly, the present study reveals that an overdose or toxic level of vitamin-D or any steroid analogue could give rise to too large a tolerant response, leading to an inefficacy in adaptive immune function.

A mathematical perspective on CD4(+) T cell quorum-sensing

We analyse a mathematical model of the peripheral CD4(+) T cell population, based on a quorum-sensing mechanism, by which an optimum number of regulatory T cells can be established and maintained. We divide the population of a single T cell receptor specificity into four pools: naive, IL-2 producing, IL-2 non-producing, and regulatory CD4(+) T cells. Proliferation, death and differentiation of cells are introduced as transition probabilities of a stochastic Markov model, with the assumption that the amount of IL-2 available to CD4(+) T cells is proportional to the size of the population of IL-2 producing CD4(+) T cells. We explore the population dynamics both in the absence and in the presence of specific antigen. We study the establishment of the peripheral CD4(+) T cell pool from thymic output in the absence of antigen, and its return to homeostasis after an immune challenge, by steady state analysis of the deterministic approximation. The number of regulatory T cells at steady state is greater in the presence of antigen than in its absence. We also consider the stochastic dynamics of the model after an immune challenge, in particular the behaviour leading to ultimate extinction of the IL-2 producing and regulatory T cell populations.

Mechanisms Underlying CD4+ Treg Immune Regulation in the Adult: From Experiments to Models

To maintain immunological balance the organism has to be tolerant to self while remaining competent to mount an effective immune response against third-party antigens. An important mechanism of this immune regulation involves the action of regulatory T-cell (Tregs). In this mini-review, we discuss some of the known and proposed mechanisms by which Tregs exert their influence in the context of immune regulation, and the contribution of mathematical modeling for these mechanistic studies. These models explore the mechanisms of action of regulatory T cells, and include hypotheses of multiple signals, delivered through simultaneous antigen-presenting cell (APC) conjugation; interaction of feedback loops between APC, Tregs, and effector cells; or production of specific cytokines that act on effector cells. As the field matures, and competing models are winnowed out, it is likely that we will be able to quantify how tolerance-inducing strategies, such as CD4-blockade, affect T-cell dynamics and what mechanisms explain the observed behavior of T-cell based tolerance.

Dynamic cross-regulation of antigen-specific effector and regulatory T cell subpopulations and microglia in brain autoimmunity

Background

Multiple Sclerosis (MS) is considered a T-cell-mediated autoimmune disease with a prototypical oscillatory behavior, as evidenced by the presence of clinical relapses. Understanding the dynamics of immune cells governing the course of MS, therefore, has many implications for immunotherapy. Here, we used flow cytometry to analyze the time-dependent behavior of antigen-specific effector (T_eff) and regulatory (T_reg) T cells and microglia in mice model of MS, Experimental Autoimmune Encephalomyelitis (EAE), and compared the observations with a mathematical cross-regulation model of T-cell dynamics in autoimmune disease.

Results

We found that T_eff and T_reg cells specific to myelin olygodendrocyte glycoprotein (MOG) developed coupled oscillatory dynamics with a 4- to 5-day period and decreasing amplitude that was always higher for the T_eff populations, in agreement with the mathematical model. Microglia activation followed the oscillations of MOG-specific T_eff cells in the secondary lymphoid organs, but they were activated before MOG-specific T-cell peaks in the CNS. Finally, we assessed the role of B-cell depletion induced by anti-CD20 therapy in the dynamics of T cells in an EAE model with more severe disease after therapy. We observed that B-cell depletion decreases T_eff expansion, although its oscillatory behavior persists. However, the effect of B cell depletion was more significant in the T_reg population within the CNS, which matched with activation of microglia and worsening of the disease. Mathematical modeling of T-cell cross-regulation after anti-CD20 therapy suggests that B-cell depletion may influence the dynamics of T cells by fine-tuning their activation.

Conclusions

The oscillatory dynamics of T-cells have an intrinsic origin in the physiological regulation of the adaptive immune response, which influences both disease phenotype and response to immunotherapy.

ENteric Immunity SImulator: a tool for in silico study of gastroenteric infections

Clinical symptoms of microbial infection of the gastrointestinal (GI) tract are often exacerbated by inflammation induced pathology. Identifying novel avenues for treating and preventing such pathologies is necessary and complicated by the complexity of interacting immune pathways in the gut, where effector and inflammatory immune cells are regulated by anti-inflammatory or regulatory cells. Here we present new advances in the development of the ENteric Immunity SImulator (ENISI), a simulator of GI immune mechanisms in response to resident commensal bacteria as well as invading pathogens and the effect on the development of intestinal lesions. ENISI is a tool for identifying potential treatment strategies that reduce inflammation-induced damage and, at the same time, ensure pathogen removal by allowing one to test plausibility of in vitro observed behavior as explanations for observations in vivo, propose behaviors not yet tested in vitro that could explain these tissue-level observations, and conduct low-cost, preliminary experiments of proposed interventions/treatments. An example of such application is shown in which we simulate dysentery resulting from Brachyispira hyodysenteriae infection and identify aspects of the host immune pathways that lead to continued inflammation-induced tissue damage even after pathogen elimination.

T cell state transition produces an emergent change detector

We model the stages of a T cell response from initial activation to T cell expansion and contraction using a system of ordinary differential equations. Results of this modeling suggest that state transitions enable the T cell population to detect change and respond effectively to changes in antigen stimulation levels, rather than simply the presence or absence of antigen. A key component of the system that gives rise to this emergent change detector is initial activation of naïve T cells. The activation step creates a barrier that separates the long-term, slow dynamics of naïve T cells from the short-term, fast dynamics of effector T cells. This separation allows the T cell population to compare current, up-to-date changes in antigen levels to long-term, steady state levels. As a result, the T cell population responds very effectively to sudden shifts in antigen levels, even if the antigen were already present prior to the change. This feature provides a mechanism for T cells to react to rapidly expanding sources of antigen stimulation, such as viruses, while maintaining tolerance to constant or slowly fluctuating sources of stimulation, such as healthy tissue during growth. In addition to modeling T cell activation, we also formulate a model of the proliferation of effector T cells in response to the consumption of positive growth signal, secreted throughout the T cell response. We discuss how the interaction between T cells and growth signal generates an emergent threshold detector that responds preferentially to large changes in antigen stimulation while ignoring small ones. As a final step, we discuss how the de novo generation of adaptive regulatory T cells during the latter phase of the T cell response creates a negative feedback loop that controls the duration and magnitude of the T cell response. Hence, the immune network continually adjusts to a shifting baseline of (self and non-self) antigens, and responds primarily to abrupt changes in these antigens rather than merely their presence or absence.

Lifelong dynamics of human CD4+CD25+ regulatory T cells: insights from in vivo data and mathematical modeling

Despite their limited proliferation capacity, regulatory T cells (T(regs)) constitute a population maintained over the entire lifetime of a human organism. The means by which T(regs) sustain a stable pool in vivo are controversial. Using a mathematical model, we address this issue by evaluating several biological scenarios of the origins and the proliferation capacity of two subsets of T(regs): precursor CD4(+)CD25(+)CD45RO(-) and mature CD4(+)CD25(+)CD45RO(+) cells. The lifelong dynamics of T(regs) are described by a set of ordinary differential equations, driven by a stochastic process representing the major immune reactions involving these cells. The model dynamics are validated using data from human donors of different ages. Analysis of the data led to the identification of two properties of the dynamics: (1) the equilibrium in the CD4(+)CD25(+)FoxP3(+)T(regs) population is maintained over both precursor and mature T(regs) pools together, and (2) the ratio between precursor and mature T(regs) is inverted in the early years of adulthood. Then, using the model, we identified three biologically relevant scenarios that have the above properties: (1) the unique source of mature T(regs) is the antigen-driven differentiation of precursors that acquire the mature profile in the periphery and the proliferation of T(regs) is essential for the development and the maintenance of the pool; there exist other sources of mature T(regs), such as (2) a homeostatic density-dependent regulation or (3) thymus- or effector-derived T(regs), and in both cases, antigen-induced proliferation is not necessary for the development of a stable pool of T(regs). This is the first time that a mathematical model built to describe the in vivo dynamics of regulatory T cells is validated using human data. The application of this model provides an invaluable tool in estimating the amount of regulatory T cells as a function of time in the blood of patients that received a solid organ transplant or are suffering from an autoimmune disease.

Emergent group dynamics governed by regulatory cells produce a robust primary T cell response

The currently accepted paradigm for the primary T cell response is that effector T cells commit to autonomous developmental programs. This concept is based on several experiments that have demonstrated that the dynamics of a T cell response is largely determined shortly after antigen exposure and that T cell dynamics do not depend on the level and duration of antigen stimulation. Another experimental study has also shown that T cell responses are robust to variations in antigen-specific precursor frequency. Various mathematical models have corroborated the first result that programmed T cell responses are insensitive to the level of antigen stimulation. However, this paper proposes that programmed responses do not entirely explain the robustness of T cell dynamics to variations in precursor frequency. This work studies the hypothesis that the dynamics of a T cell response may also be governed by a feedback loop involving adaptive regulatory cells rather than by intrinsic developmental programs. We formulate two mathematical models based on T cell developmental programs. In one model, effector cells undergo a fixed number of divisions before dying. In the second model, effector cells live for a fixed time during which they may divide. The study of these models suggests that developmental programs are not sufficiently robust as they produce an immune response that directly scales with precursor frequencies. Consequently, we derive a third model based on the principle that adaptive regulatory T cells develop in the course of an immune response and suppress effector cells. Our simulations show that this feedback mechanism responds robustly over a range of at least four orders of magnitude of precursor frequencies. We conclude that the proliferation program paradigm does not entirely capture the observed robustness of T cell responses to variations in precursor frequency. We propose an alternative mechanism by which the primary T cell response is governed by an emergent group dynamic and not by individual T cell programs.