Th1 or Th2: How an Appropriate T Helper Response can be Made

Modeling Macrophage Polarization and Its Effect on Cancer Treatment Success

Positive feedback loops drive immune cell polarization toward a pro-tumor phenotype that accentuates immunosuppression and tumor angiogenesis. This phenotypic switch leads to the escape of cancer cells from immune destruction. These positive feedback loops are generated by cytokines such as TGF-β, Interleukin-10 and Interleukin-4, which are responsible for the polarization of monocytes and M1 macrophages into pro-tumor M2 macrophages, and the polarization of naive helper T cells intopro-tumor Th2 cells. In this article, we present a deterministic ordinary differential equation (ODE) model that includes key cellular interactions and cytokine signaling pathways that lead to immune cell polarization in the tumor microenvironment. The model was used to simulate various cancer treatments in silico. We identified combination therapies that consist of M1 macrophages or Th1 helper cells, coupled with an anti-angiogenic treatment, that are robust with respect to immune response strength, initial tumor size and treatment resistance. We also identified IL-4 and IL-10 as the targets that should be neutralized in order to make these combination treatments robust with respect to immune cell polarization. The model simulations confirmed a hypothesis based on published experimental evidence that a polarization into the M1 and Th1 phenotypes to increase the M1-to-M2 and Th1-to-Th2 ratios plays a significant role in treatment success. Our results highlight the importance of immune cell reprogramming as a viable strategy to eradicate a highly vascularized tumor when the strength of the immune response is characteristically weak and cell polarization to the pro-tumor phenotype has occurred.

Computational modeling of heterogeneity and function of CD4+ T cells

The immune system is composed of many different cell types and hundreds of intersecting molecular pathways and signals. This large biological complexity requires coordination between distinct pro-inflammatory and regulatory cell subsets to respond to infection while maintaining tissue homeostasis. CD4+ T cells play a central role in orchestrating immune responses and in maintaining a balance between pro- and anti- inflammatory responses. This tight balance between regulatory and effector reactions depends on the ability of CD4+ T cells to modulate distinct pathways within large molecular networks, since dysregulated CD4+ T cell responses may result in chronic inflammatory and autoimmune diseases. The CD4+ T cell differentiation process comprises an intricate interplay between cytokines, their receptors, adaptor molecules, signaling cascades and transcription factors that help delineate cell fate and function. Computational modeling can help to describe, simulate, analyze, and predict some of the behaviors in this complicated differentiation network. This review provides a comprehensive overview of existing computational immunology methods as well as novel strategies used to model immune responses with a particular focus on CD4+ T cell differentiation.

Asynchronous stochastic Boolean networks as gene network models

Logical models have widely been used to gain insights into the biological behavior of gene regulatory networks (GRNs). Most logical models assume a synchronous update of the genes' states in a GRN. However, this may not be appropriate, because each gene may require a different period of time for changing its state. In this article, asynchronous stochastic Boolean networks (ASBNs) are proposed for investigating various asynchronous state-updating strategies in a GRN. As in stochastic computation, ASBNs use randomly permutated stochastic sequences to encode probability. Investigated by several stochasticity models, a GRN is considered to be subject to noise and external perturbation. Hence, both stochasticity and asynchronicity are considered in the state evolution of a GRN. As a case study, ASBNs are utilized to investigate the dynamic behavior of a T helper network. It is shown that ASBNs are efficient in evaluating the steady-state distributions (SSDs) of the network with random gene perturbation. The SSDs found by using ASBNs show the robustness of the attractors of the T helper network, when various stochasticity and asynchronicity models are considered to investigate its dynamic behavior.

Competition for antigen between Th1 and Th2 responses determines the timing of the immune response switch during Mycobaterium avium subspecies paratuberulosis infection in ruminants

Johne's disease (JD), a persistent and slow progressing infection of ruminants such as cows and sheep, is caused by slow replicating bacilli Mycobacterium avium subspecies paratuberculosis (MAP) infecting macrophages in the gut. Infected animals initially mount a cell-mediated CD4 T cell response against MAP which is characterized by the production of interferon (Th1 response). Over time, Th1 response diminishes in most animals and antibody response to MAP antigens becomes dominant (Th2 response). The switch from Th1 to Th2 response occurs concomitantly with disease progression and shedding of the bacteria in feces. Mechanisms controlling this Th1/Th2 switch remain poorly understood. Because Th1 and Th2 responses are known to cross-inhibit each other, it is unclear why initially strong Th1 response is lost over time. Using a novel mathematical model of the immune response to MAP infection we show that the ability of extracellular bacteria to persist outside of macrophages naturally leads to switch of the cellular response to antibody production. Several additional mechanisms may also contribute to the timing of the Th1/Th2 switch including the rate of proliferation of Th1/Th2 responses at the site of infection, efficiency at which immune responses cross-inhibit each other, and the rate at which Th1 response becomes exhausted over time. Our basic model reasonably well explains four different kinetic patterns of the Th1/Th2 responses in MAP-infected sheep by variability in the initial bacterial dose and the efficiency of the MAP-specific T cell responses. Taken together, our novel mathematical model identifies factors of bacterial and host origin that drive kinetics of the immune response to MAP and provides the basis for testing the impact of vaccination or early treatment on the duration of infection.

Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of Johne's disease, a chronic enteric disease of ruminants such as sheep and cows. Due to early culling and reduction in milk production of affected animals, MAP inflicts high economic cost to diary farms. MAP infection has a long incubation period of several years, and during the asymptomatic stage a strong cellular (T helper 1) immune response is thought to control MAP replication. Over time, Th1 response is lost and ineffective antibody response driven by Th2 cells becomes predominant. We develop the first mathematical model of helper T cell response to MAP infection to understand impact of various mechanisms on the dynamics of the switch from Th1 to Th2 response. Our results suggest that in contrast to the generally held belief, Th1/Th2 switch may be driven by the accumulation of long-lived extracellular bacteria, and therefore, may be the consequence of the disease progression of MAP-infected animals and not its cause. Our model highlights limitations of our current understanding of regulation of helper T cell responses during MAP infection and identifies areas for future experimental research.

Molecular adjuvant interleukin-33 enhances the antifertility effect of Lagurus lagurus zona pellucida 3 DNA vaccine administered by the mucosal route

It has been shown that cytokines can act as molecular adjuvant to enhance the immune response induced by DNA vaccines, but it is unknown whether interleukin 33 (IL-33) can enhance the immunocontraceptive effect induced by DNA vaccines. In the present study, we explored the effects of murine IL-33 on infertility induced by Lagurus lagurus zona pellucida 3 (Lzp3) contraceptive DNA vaccine administered by the mucosal route. Plasmid pcD-Lzp3 and plasmid pcD-mIL-33 were encapsulated with chitosan to generate the nanoparticle chi-(pcD-Lzp3+pcD-mIL-33) as the DNA vaccine. Sixty female ICR mice, divided into 5 groups (n=12/group), were intranasally immunized on days 0, 14, 28, and 42. After intranasal immunization, the anti-LZP3-specific IgG in serum and IgA in vaginal secretions and feces were determined by ELISA. The results showed that chi-(pcD-Lzp3+pcD-mIL-33) co-immunization induced the highest levels of serum IgG, secreted mucosal IgA, and T cell proliferation. Importantly, mice co-immunized with chi-(pcD-Lzp3+pcD-mIL-33) had the lowest birth rate and mean litter size, which correlated with high levels of antibodies. Ovaries from infertile female mice co-immunized with chi-(pcD-Lzp3+pcD-mIL-33) showed abnormal development of ovarian follicles, indicated by atretic follicles and loss of oocytes. Our results demonstrated that intranasal delivery of the molecular adjuvant mIL-33 with chi-pcD-Lzp3 significantly increased infertility by enhancing both systemic and mucosal immune responses. Therefore, chi-(pcD-Lzp3+pcD-mIL-33) co-immunization could be a strategy for controlling the population of wild animal pests.

Cellular and population plasticity of helper CD4(+) T cell responses

Vertebrates are constantly exposed to pathogens, and the adaptive immunity has most likely evolved to control and clear such infectious agents. CD4⁺ T cells are the major players in the adaptive immune response to pathogens. Following recognition of pathogen-derived antigens naïve CD4⁺ T cells differentiate into effectors which then control pathogen replication either directly by killing pathogen-infected cells or by assisting with generation of cytotoxic T lymphocytes (CTLs) or pathogen-specific antibodies. Pathogen-specific effector CD4⁺ T cells are highly heterogeneous in terms of cytokines they produce. Three major subtypes of effector CD4⁺ T cells have been identified: T-helper 1 (Th1) cells producing IFN-γ and TNF-α, Th2 cells producing IL-4 and IL-10, and Th17 cells producing IL-17. How this heterogeneity is maintained and what regulates changes in effector T cell composition during chronic infections remains poorly understood. In this review we discuss recent advances in our understanding of CD4⁺ T cell differentiation in response to microbial infections. We propose that a change in the phenotype of pathogen-specific effector CD4⁺ T cells during chronic infections, for example, from Th1 to Th2 response as observed in Mycobactrium avium ssp. paratuberculosis (MAP) infection of ruminants, can be achieved by conversion of T cells from one effector subset to another (cellular plasticity) or due to differences in kinetics (differentiation, proliferation, death) of different effector T cell subsets (population plasticity). We also shortly review mathematical models aimed at describing CD4⁺ T cell differentiation and outline areas for future experimental and theoretical research.

Implicit methods for qualitative modeling of gene regulatory networks

Advancements in high-throughput technologies to measure increasingly complex biological phenomena at the genomic level are rapidly changing the face of biological research from the single-gene single-protein experimental approach to studying the behavior of a gene in the context of the entire genome (and proteome). This shift in research methodologies has resulted in a new field of network biology that deals with modeling cellular behavior in terms of network structures such as signaling pathways and gene regulatory networks. In these networks, different biological entities such as genes, proteins, and metabolites interact with each other, giving rise to a dynamical system. Even though there exists a mature field of dynamical systems theory to model such network structures, some technical challenges are unique to biology such as the inability to measure precise kinetic information on gene-gene or gene-protein interactions and the need to model increasingly large networks comprising thousands of nodes. These challenges have renewed interest in developing new computational techniques for modeling complex biological systems. This chapter presents a modeling framework based on Boolean algebra and finite-state machines that are reminiscent of the approach used for digital circuit synthesis and simulation in the field of very-large-scale integration (VLSI). The proposed formalism enables a common mathematical framework to develop computational techniques for modeling different aspects of the regulatory networks such as steady-state behavior, stochasticity, and gene perturbation experiments.

A MATHEMATICAL MODEL OF IMMUNE RESPONSE IN CUTANEOUS LEISHMANIASIS

The TH1/TH2 paradigm has been largely used in the interpretation of several diseases, particularly in leishmaniasis. As far as we know there is no mathematical description of this model related to leishmaniasis. We have extended and modified a previous published set of equations1in order to adapt it to leishmanial disease particularities. The main modifications were: (1) the analysis of logistic and exponential parasite growth curves, (2) the assumption of the TH2 arm of the immune response having a positive action on parasite growth. The set of three simultaneous differential equations describing the TH1 arm, TH2 arm and parasite growth were analyzed for conditions of existence and stability of the solutions.

Stable solutions valid for the logistic and exponential parasite growth models, with its possible clinical correlations, were obtained in the following situations: (1) parasite and TH2 extinction [TH1 cure], (2) parasite extinction and TH1/TH2 co-existence [TH1/TH2 cure], (3) TH1 and parasite co-existence, TH2 extinction [stable TH1 infection], and (4) TH1, TH2 and parasite co-existence [stable TH1/TH2 infection]. TH2 and parasite co-existence associated to TH1 extinction [stable TH2 infection] was obtained only with the logistic growth model. The model also provides an alternative hypothesis for TH1 bias in resistant mice and emphazises the importance of natural immunity for the existence of chronic states.

Stochastic approximation to the T cell mediated specific response of the immune system

We develop a stochastic model to study the specific response of the immune system. The model is based on the dynamical interaction between Regulatory and Effector CD4+ T cells in the presence of Antigen Presenting Cells inside a lymphatic node. At a mean field level the model predicts the existence of different regimes where active, tolerant, or cyclic immune responses are possible. To study the model beyond mean field and to understand the specific responses of the immune system we use the Linear Noise Approximation and show that fluctuations due to finite size effects may strongly alter the mean field scenario. Moreover, it was found that the existence of a certain characteristic frequency for the fluctuations. All the analytical predictions were compared with simulations using Gillespie's algorithm.

Dynamics of intracellular information decoding

A variety of cellular functions are robust even to substantial intrinsic and extrinsic noise in intracellular reactions and the environment that could be strong enough to impair or limit them. In particular, of substantial importance is cellular decision-making in which a cell chooses a fate or behavior on the basis of information conveyed in noisy external signals. For robust decoding, the crucial step is filtering out the noise inevitably added during information transmission. As a minimal and optimal implementation of such an information decoding process, the autocatalytic phosphorylation and autocatalytic dephosphorylation (aPadP) cycle was recently proposed. Here, we analyze the dynamical properties of the aPadP cycle in detail. We describe the dynamical roles of the stationary and short-term responses in determining the efficiency of information decoding and clarify the optimality of the threshold value of the stationary response and its information-theoretical meaning. Furthermore, we investigate the robustness of the aPadP cycle against the receptor inactivation time and intrinsic noise. Finally, we discuss the relationship among information decoding with information-dependent actions, bet-hedging and network modularity.

A control theoretic approach to venom immunotherapy with state jumps

We investigate a model-based control method to boost the immune response. We apply this control method to select the appropriate immune response between the Th1 and Th2 responses. The idea of state jump is discussed using hybrid models notation. To implement the control idea we propose physically available methods.

Diversity and plasticity of Th cell types predicted from regulatory network modelling

Alternative cell differentiation pathways are believed to arise from the concerted action of signalling pathways and transcriptional regulatory networks. However, the prediction of mammalian cell differentiation from the knowledge of the presence of specific signals and transcriptional factors is still a daunting challenge. In this respect, the vertebrate hematopoietic system, with its many branching differentiation pathways and cell types, is a compelling case study. In this paper, we propose an integrated, comprehensive model of the regulatory network and signalling pathways controlling Th cell differentiation. As most available data are qualitative, we rely on a logical formalism to perform extensive dynamical analyses. To cope with the size and complexity of the resulting network, we use an original model reduction approach together with a stable state identification algorithm. To assess the effects of heterogeneous environments on Th cell differentiation, we have performed a systematic series of simulations considering various prototypic environments. Consequently, we have identified stable states corresponding to canonical Th1, Th2, Th17 and Treg subtypes, but these were found to coexist with other transient hybrid cell types that co-express combinations of Th1, Th2, Treg and Th17 markers in an environment-dependent fashion. In the process, our logical analysis highlights the nature of these cell types and their relationships with canonical Th subtypes. Finally, our logical model can be used to explore novel differentiation pathways in silico.

T lymphocytes play a key role in the regulation of the immune response in mammals. Various T-helper subtypes (Th1, Th2, Th17, Treg,…) have been identified over the years, characterised by the expression of specific transcription factors and cytokines, which have a critical influence on the selection of different immune responses, driving pro-inflammatory or allergic responses, promoting alternative antibody classes, or preventing (auto)immunity by inhibiting the activation and proliferation of other cells. To gain insight into the heterogeneity and the plasticity of late T-helper lineages, we have built an integrated model of the regulatory network and signalling pathways controlling Th cell differentiation. Relying on a logical modelling framework, we have performed a systematic series of simulations to assess the effects of heterogeneous environments on Th cell differentiation. We have identified stable states corresponding to canonical Th1, Th2, Th17 and Treg subtypes, but also to hybrid cell types co-expressing combinations of Th1, Th2, Treg and Th17 markers in an environment-dependent fashion. Our analysis highlights the nature of these cell types and their relationships with canonical Th subtypes.

Implementation of dynamic Bayesian decision making by intracellular kinetics

Decision making in a noisy and dynamically changing environment is a fundamental task for a cell. To choose appropriate decisions over time, a cell must be equipped with intracellular kinetics that can conduct dynamic and efficient decision making. By using the theory of sequential inference, I demonstrate that dynamic Bayesian decision making can be implemented by an intracellular kinetics with a dual positive feedback structure. I also show that the combination of linear instantaneous and nonlinear stationary sensitivities to the input dominantly contributes to decision making efficiency, and that the state-dependent sensitivity change further suppresses noisy response. The statistical principles underlying these two factors are further clarified to be a log-likelihood-dependent quantification of the input information and uncertainty-dependent sensitivity control.

Modeling stochasticity and robustness in gene regulatory networks

Motivation: Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations.

Results: In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs.

Availability: Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/∼garg/genysis.html.

Contact:abhishek.garg@epfl.ch

Inflammatory cytokines modulate chemokine production patterns of HepG2 cells toward initially inclined direction

AIM

Human hepatocytes are known to express an array of inflammatory cytokines and chemokines. In this study, we examined the potential roles of hepatocytes in regulating immune responses in the liver, by assessing the induction of Th1- or Th2-specific chemokines in HepG2 cells after various inflammatory stimulations.

METHODS

HepG2 cells were stimulated with IL-1alpha, IFN-gamma, IL-4, IL-10, and/or CCL2, harvested at several time points, and served for the analyses of cytokine/chemokine mRNA expressions by semi-quantitative RT-PCR.

RESULTS

(i) IL-1alpha up-regulated mRNA levels of CXCL8, CXCL10, and CCL2. IFN-gamma increased those of CXCL9, CXCL10, and CCL5, while IL-4 or IL-10 had no effect. (ii) Addition of IL-4 to the culture of IFN-gamma-stimulated cells, down-regulated CXCL9 and CXCL10 mRNA levels. (iii) Addition of IFN-gamma to the culture of IL-1alpha-stimulated cells, further up-regulated CXCL9 and CXCL10 mRNA levels. Addition of IL-4 decreased CXCL8 and CXCL10 levels, and increased CCL2 level in IL-1alpha-stimulated cells. (iv) CCL2 induced IL-4 mRNA expression.

CONCLUSIONS

IFN-gamma augmented mRNA expression of Th1-specific chemokines (CXCL9 and CXCL10) in HepG2 cells. IL-4 had no effect on those of Th2-spesific chemokines (CCL17 and CCL22); however, it was supposed to augment Th2 response indirectly through the induction of CCL2 under the inflammatory condition. The findings suggest that hepatocytes have ability to promote immune responses in the liver toward the direction, initially determined by the cytokine balances in the local inflammatory region.

Synchronous versus asynchronous modeling of gene regulatory networks

Motivation:In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene–gene, protein–protein and gene–protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes.

Results: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software.

Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1–Th2 cellular differentiation process.

Availability: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Contact:abhishek.garg@epfl.ch

3D computation modelling of the influence of cytokine secretion on Th‐cell development suggests that negative selection (inhibition of Th1 cells) is more effective than positive selection by IL‐4 for Th2 cell dominance

Th-cell development has been suggested to include selective mechanisms in which certain cytokines select either Th1 or Th2 cells to proliferate and grow. The selective theory is based on the observation that Th2 cells secrete IL-4, a cytokine that promotes Th2 development, whereas Th1 cells secrete interferon-gamma (IFN-gamma) that favours Th1 development, and both positive and negative selective influences have been suggested to operate. In this study, we investigate the role of autocrine secretion and utilization of IL-4 by Th2 cells and address the question of whether an activated Th2 cell can be positively selected by IL-4 secreted from other Th2 cells. We present a spatial three dimensional (3D) modelling approach to simulate the interaction between the IL-4 ligand and its IL-4 receptors expressed on discrete IL-4 secreting cells. The simulations, based on existing experimental data on the IL-4 receptor-ligand system, illustrate how Th-cell development is highly dependent on the distance between cells that are communicating. The model suggests that a single Th2 cell is likely to communicate with possible target cells within a range of approximately 100 microm and that an activated Th2 cell manages to fill most of its own IL-4 receptors, even at a low secretion rate. The predictions made by the model suggest that negative selection against Th1 cells is more effective than positive selection by IL-4 for promoting Th2 dominance.

Ecological and evolutionary principles in immunology

Experimental immunology has given rise to detailed insights into how immune cells react to infectious agents and fight pathogens. At the same time, however, the interplay between infectious agents and immune responses can be viewed as an ecological system in vivo. This is characterized by complex interactions between species of immune cells and populations of pathogens. This review discusses how an understanding of the immune system can be aided by the application of ecological and evolutionary principles: competition, predation, and the evolution of viruses in vivo. These concepts can shed light onto important immunological concepts such as the correlates of efficient virus control, immunodominance, the relationship between viral evolution and the development of pathology, as well as the ability of the immune system to control immunosuppressive infections.

In silico simulations suggest that Th‐cell development is regulated by both selective and instructive mechanisms

Th-cell differentiation is highly influenced by the local cytokine environment. Although cytokines such as IL-12 and IL-4 are known to polarize the Th-cell response towards Th1 or Th2, respectively, it is not known whether these cytokines instruct the developmental fate of uncommitted Th cells or select cells that have already been committed through a stochastic process. We present an individual based model that accommodates both stochastic and deterministic processes to simulate the dynamic behaviour of selective versus instructive Th-cell development. The predictions made by each model show distinct behaviours, which are compared with experimental observations. The simulations show that the instructive model generates an exclusive Th1 or Th2 response in the absence of an external cytokine source, whereas the selective model favours coexistence of the phenotypes. A hybrid model, including both instructive and selective development, shows behaviour similar to either the selective or the instructive model dependent on the strength of activation. The hybrid model shows the closest qualitative agreement with a number of well-established experimental observations. The predictions by each model suggest that neither pure selective nor instructive Th development is likely to be functional as exclusive mechanisms in Th1/Th2 development.

A network model for the control of the differentiation process in Th cells

T helper cells differentiate from a precursor type, Th0, to either the Th1 or Th2 phenotypes. While a number of molecules are known to participate in this process, it is not completely understood how they regulate each other to ensure differentiation. This article presents the core regulatory network controlling the differentiation of Th cells, reconstructed from published molecular data. This network encompasses 17 nodes, namely IFN-gamma, IL-4, IL-12, IL-18, IFN-beta, IFN-gammaR, IL-4R, IL-12R, IL-18R, IFN-betaR, STAT-1, STAT-6, STAT-4, IRAK, SOCS-1, GATA-3, and T-bet, as well as their cross-regulatory interactions. The reconstructed network was modeled as a discrete dynamical system, and analyzed in terms of its constituent feedback loops. The stable steady states of the Th network model are consistent with the stable molecular patterns of activation observed in wild type and mutant Th0, Th1 and Th2 cells.

How do adaptive immune systems control pathogens while avoiding autoimmunity?

Immune systems face a daunting control challenge. On the one hand, they need to minimize damage from pathogens, without wasting energy and resources, but on the other must avoid initiating or perpetuating autoimmune responses. Finally, because pathogens interfere with immune function, immune systems must be robust against sabotage. We describe here how these challenges are met by two immune systems, the intracellular RNA interference system and the vertebrate CD8 T-cell response. We extrapolate from these two systems to propose principles for strategically robust control.

Expression modulation of multiple cytokines in vivo by cyanobacteria blooms extract from taihu lake, China

Cyanobacterial blooms that generate microcystins (MCs) are being increasingly recognized as a potent health hazard in aquatic ecosystems. However, immunomodulation induced by cyanotoxins has not been well documented. This paper reports the in vivo data on the immune disorder caused by crude microcystin (MC) extract of cyanobacteria blooms collected from Taihu Lake, China, with respect to cytokine mRNA levels. Using reverse-transcriptional polymerase chain reaction (RT-PCR), the expression of multiple cytokines, including proinflammatory (IL-1beta, TNF-alpha, and IL-6) and Th1/Th2-related cytokines (IL-2, IL-4 and IL-10), was evaluated following the cyanobacteria blooms extract containing MCs (CBE) exposure at four doses of 23, 38, 77, 115 mg lyophilized algae cells/kg body weight. The results showed that the mRNA levels of TNF-alpha, IL-1beta, IL-2 and IL-4 decreased significantly following injection of all doses as compared to the control (LPS or ConA only), while the IL-6 level was unaffected. Contrast to this decrease, the level of IL-10 mRNA was, however, transiently up regulated following injection of the lowest dose of CBE. The distinct patterns of expression of these cytokines suggested a modulation of cytokine network, the essential component of the host immune system. We further developed a mathematical model to simulate the interaction of T helper cell subsets and related cytokines, which proved to be a good approach to study the kinetics of the interaction of cells and cytokines in microcystin immunosuppression.

GATA-3 transcriptional imprinting in Th2 lymphocytes: a mathematical model

Immunological memory involves the fast recall of cytokine expression by T helper (Th) lymphocytes. Two distinct profiles of cytokine expression, Th1 and Th2, can be induced by antigen and polarizing signals during activation of naive Th cells and can subsequently be reexpressed on stimulation by antigen alone. The transcription factor GATA-3 induces Th2 development. GATA-3 is activated by the Th2-polarizing stimulus, IL-4, and has recently been observed to autoactivate its transcription. Based on these experimental data, we developed a mathematical model of GATA-3 expression that assumes independent activation of GATA-3 transcription by IL-4 and by GATA-3. Cooperativity of GATA-3 transcriptional activation is shown to create a threshold for autoactivation, resulting in the coexistence of two distinct GATA-3 expression states: a state of basal expression and a state of high expression sustained by autoactivation. Suprathreshold IL-4 signals induce a transition from basal to high GATA-3 expression. Thus, GATA-3 autoactivation creates a bistable system that can memorize a transient inductive signal. The model further predicts conditions under which the state of high GATA-3 expression can be abolished, which may extinguish the Th2 cytokine memory.

The immunological synapse

The immunological synapse plays a central role in organising the immune system. Through their synaptic activity both T and B cells usually, but not always, acquire the information that critically determines the level and nature of the responses that they make. For T cells much of that information comes from epicrine and paracrine cell-cell interactions in the cluster that forms around a dendritic cell. These interactions are being dissected by experiments in which two populations of TCR-transgenic T cells are combined in vivo. Another important aspect of synaptic activity is the way in which different levels of expression of MHC class II molecules influence Th1/Th2 balance. In exploring this form of control we are learning something of general importance about cis-regulation.

Memorizing innate instructions requires a sufficiently specific adaptive immune system

During its primary encounter with a pathogen, the immune system has to decide which type of immune response is most appropriate. Based on signals from the innate immune system and the immunological context in which the pathogen is presented, responding lymphocytes will adopt a particular phenotype, e.g. secrete a particular profile of cytokines. Once stimulated, lymphocytes store the appropriate type of response by differentiating from a naive to a memory phenotype. This allows the appropriate type of immune reaction to be regenerated upon re-stimulation of those memory clones. We developed a computer simulation model in which cross-reacting effector/memory clones contribute to the immunological context of pathogens. If a pathogen is recognized by both naive clones and pre-existing effector/memory clones, the naive lymphocytes adopt the effector mechanism of the memory clone. The adaptive immune system thereby stores immunological decisions and somatically learns to induce the right type of immune response to pathogens sharing epitopes. The influence of effector/memory lymphocytes may be detrimental when they cross-react to new pathogens that require a different kind of immune response. Here, we show that the immune system needs to be sufficiently specific to avoid such mistakes and to profit from the information that is stored in effector/memory lymphocytes. Repertoire diversity is required to reconcile this specificity with reactivity against many pathogens.