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

Targeted Demethylation of FOXP3-TSDR Enhances the Suppressive Capacity of STAT6-deficient Inducible T Regulatory Cells

In vitro induced T regulatory cells (iTregs) are promising for addressing inflammation-driven diseases. However, current protocols for the generation and expansion of iTregs fail to induce extensive demethylation of the Treg-specific demethylated region (TSDR) within the FOXP3 gene, recognized as the master regulator for regulatory T cells (Tregs). This deficiency results in the rapid loss of Foxp3 expression and an unstable regulatory phenotype. Nevertheless, inhibition of STAT6 signaling effectively stabilizes Foxp3 expression in iTregs. Thus, this study aimed to develop a protocol combining epigenetic editing with STAT6 deficiency to improve iTregs' ability to maintain stable suppressive function and a functional phenotype. Our findings demonstrate that the combination of STAT6 deficiency (STAT6-/-) with targeted demethylation of the TSDR using a CRISPR-TET1 tool leads to extensive demethylation of FOXP3-TSDR. Demethylation in STAT6-/- iTregs was associated with enhanced expression of Foxp3 and suppressive markers such as CTLA-4, PD-1, IL-10, and TGF-β. Furthermore, the edited STAT6-/- iTregs exhibited an increased capacity to suppress CD8+ and CD4+ lymphocytes and could more efficiently impair Th1-signature gene expression compared to conventional iTregs. In conclusion, the deactivation of STAT6 and TSDR-targeted demethylation via CRISPR-TET1 is sufficient to induce iTregs with heightened stability and increased suppressive capacity, offering potential applications against inflammatory and autoimmune diseases.

Plasticity and lineage commitment of individual TH1 cells are determined by stable T-bet expression quantities

T helper 1 (TH1) cell identity is defined by the expression of the lineage-specifying transcription factor T-bet. Here, we examine the influence of T-bet expression heterogeneity on subset plasticity by leveraging cell sorting of distinct in vivo-differentiated TH1 cells based on their quantitative expression of T-bet and interferon-γ. Heterogeneous T-bet expression states were regulated by virus-induced type I interferons and were stably maintained even after secondary viral infection. Exposed to alternative differentiation signals, the sorted subpopulations exhibited graded levels of plasticity, particularly toward the TH2 lineage: T-bet quantities were inversely correlated with the ability to express the TH2 lineage-specifying transcription factor GATA-3 and TH2 cytokines. Reprogramed TH1 cells acquired graded mixed TH1 + TH2 phenotypes with a hybrid epigenetic landscape. Continuous presence of T-bet in differentiated TH1 cells was essential to ensure TH1 cell stability. Thus, innate cytokine signals regulate TH1 cell plasticity via an individual cell-intrinsic rheostat to enable T cell subset adaptation to subsequent challenges.

Network switches and their role in circadian clocks

Circadian rhythms are generated by complex interactions among genes and proteins. Self-sustained ∼24 h oscillations require negative feedback loops and sufficiently strong nonlinearities that are the product of molecular and network switches. Here, we review common mechanisms to obtain switch-like behavior, including cooperativity, antagonistic enzymes, multisite phosphorylation, positive feedback, and sequestration. We discuss how network switches play a crucial role as essential components in cellular circadian clocks, serving as integral parts of transcription-translation feedback loops that form the basis of circadian rhythm generation. The design principles of network switches and circadian clocks are illustrated by representative mathematical models that include bistable systems and negative feedback loops combined with Hill functions. This work underscores the importance of negative feedback loops and network switches as essential design principles for biological oscillations, emphasizing how an understanding of theoretical concepts can provide insights into the mechanisms generating biological rhythms.

Tectorigenin inhibits oxidative stress by activating the Keap1/Nrf2/HO-1 signaling pathway in Th2-mediated allergic asthmatic mice

Asthma is a chronic obstructive airway condition and one of the most common non-communicable illnesses worldwide. Tectorigenin (Tec) is an isoflavonoid found in plants that possesses significant antioxidative and anti-inflammatory abilities. Nevertheless, the antioxidative properties of Tec have not yet been documented in allergic asthma. In this study, we created an asthmatic BALB/c mouse model induced by ovalbumin (OVA) and used it to assess the efficacy of Tec as a possible therapy agent. Tec decreased the serum OVA-specific immunoglobulin (Ig) E and IgG1 secretion levels. The total number of cells and the distribution of inflammatory cells decreased significantly in bronchoalveolar lavage fluid (BALF), with weakened inflammatory reaction in pulmonary tissues. Additionally, Tec regulated the T helper 1(Th1)/Th2 balance by increasing the expression of Th1- related factors (interleukin (IL)-12 and T-bet) and decreasing the expression of Th2-related factors (IL-4, IL-5, IL-13, and GATA binding protein 3. In addition, the pro-inflammatory cytokines such as IL-6, tumor necrosis factor-alpha, and IL-1β were also inhibited by Tec. Tec also dramatically increased antioxidant (catalase and superoxide dismutase) concentrations while lowering the intensity of the indicators of oxidative stress such as reactive oxygen species and malondialdehyde in BALF. Finally, Tec effectively activated the Keap1/Nrf2/HO-1 signaling pathway and prevented the epithelial-mesenchymal transition. The results of the current study show that Tec may be useful in relieving the inflammatory and oxidative stress responses associated with asthma.

MicroRNA governs bistable cell differentiation and lineage segregation via a noncanonical feedback

Positive feedback driven by transcriptional regulation has long been considered a key mechanism underlying cell lineage segregation during embryogenesis. Using the developing spinal cord as a paradigm, we found that canonical, transcription-driven feedback cannot explain robust lineage segregation of motor neuron subtypes marked by two cardinal factors, Hoxa5 and Hoxc8. We propose a feedback mechanism involving elementary microRNA-mRNA reaction circuits that differ from known feedback loop-like structures. Strikingly, we show that a wide range of biologically plausible post-transcriptional regulatory parameters are sufficient to generate bistable switches, a hallmark of positive feedback. Through mathematical analysis, we explain intuitively the hidden source of this feedback. Using embryonic stem cell differentiation and mouse genetics, we corroborate that microRNA-mRNA circuits govern tissue boundaries and hysteresis upon motor neuron differentiation with respect to transient morphogen signals. Our findings reveal a previously underappreciated feedback mechanism that may have widespread functions in cell fate decisions and tissue patterning.

GATA3 Mediates a Fast, Irreversible Commitment to BMP4-Driven Differentiation in Human Embryonic Stem Cells

During early development, extrinsic triggers prompt pluripotent cells to begin the process of differentiation. When and how human embryonic stem cells (hESCs) irreversibly commit to differentiation is a fundamental yet unanswered question. By combining single-cell imaging, genomic approaches, and mathematical modeling, we find that hESCs commit to exiting pluripotency unexpectedly early. We show that bone morphogenetic protein 4 (BMP4), an important differentiation trigger, induces a subset of early genes to mirror the sustained, bistable dynamics of upstream signaling. Induction of one of these genes, GATA3, drives differentiation in the absence of BMP4. Conversely, GATA3 knockout delays differentiation and prevents fast commitment to differentiation. We show that positive feedback at the level of the GATA3-BMP4 axis induces fast, irreversible commitment to differentiation. We propose that early commitment may be a feature of BMP-driven fate choices and that interlinked feedback is the molecular basis for an irreversible transition from pluripotency to differentiation.

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Irreversible commitment to BMP4-driven hESC differentiation is fast

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SMAD activation is sustained, bistable, and irreversible due to positive feedback

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GATA3 mirrors SMAD dynamics and mediates fast commitment to differentiation

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GATA3 is an early commitment gene

Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges

Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent "omic"-level findings, and examine how these findings have been systematically integrated to generate further insight.Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or "endotypes" that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.

Modeling Procedures

Being concerned by the understanding of the mechanism underlying chronic degenerative diseases , we presented in the previous chapter the medical systems biology conceptual framework that we present for that purpose in this volume. More specifically, we argued there the clear advantages offered by a state-space perspective when applied to the systems-level description of the biomolecular machinery that regulates complex degenerative diseases. We also discussed the importance of the dynamical interplay between the risk factors and the network of interdependencies that characterizes the biochemical, cellular, and tissue-level biomolecular reactions that underlie the physiological processes in health and disease. As we pointed out in the previous chapter, the understanding of this interplay (articulated around cellular phenotypic plasticity properties, regulated by specific kinds of gene regulatory networks) is necessary if prevention is chosen as the human-health improvement strategy (potentially involving the modulation of the patient's lifestyle). In this chapter we provide the medical systems biology mathematical and computational modeling tools required for this task.

Exploring intermediate cell states through the lens of single cells

As our catalog of cell states expands, appropriate characterization of these states and the transitions between them is crucial. Here we discuss the roles of intermediate cell states (ICSs) in this growing collection. We begin with definitions and discuss evidence for the existence of ICSs and their relevance in various tissues. We then provide a list of possible functions for ICSs with examples. Finally, we describe means by which ICSs and their functional roles can be identified from single-cell data or predicted from models.

Interferon-Gamma at the Crossroads of Tumor Immune Surveillance or Evasion

Interferon-gamma (IFN-γ) is a pleiotropic molecule with associated antiproliferative, pro-apoptotic and antitumor mechanisms. This effector cytokine, often considered as a major effector of immunity, has been used in the treatment of several diseases, despite its adverse effects. Although broad evidence implicating IFN-γ in tumor immune surveillance, IFN-γ-based therapies undergoing clinical trials have been of limited success. In fact, recent reports suggested that it may also play a protumorigenic role, namely, through IFN-γ signaling insensitivity, downregulation of major histocompatibility complexes, and upregulation of indoleamine 2,3-dioxygenase and of checkpoint inhibitors, as programmed cell-death ligand 1. However, the IFN-γ-mediated responses are still positively associated with patient's survival in several cancers. Consequently, major research efforts are required to understand the immune contexture in which IFN-γ induces its intricate and highly regulated effects in the tumor microenvironment. This review discusses the current knowledge on the pro- and antitumorigenic effects of IFN-γ as part of the complex immune response to cancer, highlighting the relevance to identify IFN-γ responsive patients for the improvement of therapies that exploit associated signaling pathways.

Immunological memories of the bone marrow

Memory for antigens once encountered is a hallmark of the immune system of vertebrates, providing us with an immunity adapted to pathogens of our environment. Despite its fundamental relevance, the cells and genes representing immunological memory are still poorly understood. Here we discuss the concept of a circulating, proliferating, and ubiquitous population of effector lymphocytes vs concepts of resting and dormant populations of dedicated memory lymphocytes, distinct from effector lymphocytes and residing in defined tissues, particularly in barrier tissues and in the bone marrow. The lifestyle of memory plasma cells of the bone marrow may serve as a paradigm, showing that persistence of memory lymphocytes is not defined by intrinsic "half-lives", but rather conditional on distinct survival signals provided by dedicated niches. These niches are organized by individual mesenchymal stromal cells. They define the capacity of immunological memory and regulate its homeostasis.

Indoleamine-2,3-dioxygenase and Interleukin-6 associated with tumor response to neoadjuvant chemotherapy in breast cancer

Purpose

Indoleamine-2,3-dioxygenase (IDO) and Interleukin-6 (IL-6) contribute to poor therapeutic effects, tumor relapse and aggressive tumor growth. IDO and IL-6 incorporate a positive feedback signal loop to maintain IDO and IL-6 constitutive expression and facilitate tumor progression.

Results

IDO expression was associated with IL-6 expression and plasma IL-6 level (P<0.05). Concentrating on clinicopathological features prior neoadjuvant chemotherapy, both IDO expression and plasma IL-6 level were associated with clinical T stage and N stage (P<0.05). IL-6 expression was associated with clinical T stage (P=0.016). The co-expression of IDO/IL-6 was correlated with clinical T, N stage and estrogen receptor (ER) status (P<0.05). IDO, IL-6 expression, clinical T stage, pathological T stage, ER status and Luminal type were correlated with clinical response to neoadjuvant chemotherapy (P<0.05). Multivariate analysis showed that IDO expression were correlated with clinical response to neoadjuvant chemotherapy (P=0.034). IL-6 expression and pathological T stage were correlated with pCR (P<0.05). In the multivariate analysis, postoperative pathological T stage associated with pCR (P=0.041). In the prognostic analysis, only clinical T stage was significant correlated with overall survival (P=0.003).

Materials and Methods

46 breast cancer patients received neoadjuvant chemotherapy enrolled in this study. Immunohistochemistry was applied for evaluating IDO and IL-6 expression in biopsy tissues prior neoadjuvant chemotherapy. Immunofluorescence was applied to observe the co-localization of IDO and IL-6. Serum IL-6 level was examined via ELISA. The associations between IDO, IL-6, Serum IL-6 level and clinicopathological features, response to neoadjuvant chemotherapy were analyzed.

Conclusion

IDO and IL-6 expression associated with advanced breast cancer and poor response to neoadjuvant chemotherapy.

Potential contribution of tandem circadian enhancers to nonlinear oscillations in clock gene expression

Limit-cycle oscillations require the presence of nonlinear processes. Although mathematical studies have long suggested that multiple nonlinear processes are required for autonomous circadian oscillation in clock gene expression, the underlying mechanism remains controversial. Here we show experimentally that cell-autonomous circadian transcription of a mammalian clock gene requires a functionally interdependent tandem E-box motif; the lack of either of the two E-boxes results in arrhythmic transcription. Although previous studies indicated the role of the tandem motifs in increasing circadian amplitude, enhancing amplitude does not explain the mechanism for limit-cycle oscillations in transcription. In this study, mathematical analysis suggests that the interdependent behavior of enhancer elements including not only E-boxes but also ROR response elements might contribute to limit-cycle oscillations by increasing transcriptional nonlinearity. As expected, introduction of the interdependence of circadian enhancer elements into mathematical models resulted in autonomous transcriptional oscillation with low Hill coefficients. Together these findings suggest that interdependent tandem enhancer motifs on multiple clock genes might cooperatively enhance nonlinearity in the whole circadian feedback system, which would lead to limit-cycle oscillations in clock gene expression.

Diverse continuum of CD4+ T-cell states is determined by hierarchical additive integration of cytokine signals

During cell differentiation, progenitor cells integrate signals from their environment that guide their development into specialized phenotypes. The ways by which cells respond to complex signal combinations remain difficult to analyze and model. To gain additional insight into signal integration, we systematically mapped the response of CD4⁺ T cells to a large number of input cytokine combinations that drive their differentiation. We find that, in response to varied input combinations, cells differentiate into a continuum of cell fates as opposed to a limited number of discrete phenotypes. Input cytokines hierarchically influence the cell population, with TGFβ being most dominant followed by IL-6 and IL-4. Mathematical modeling explains these results using additive signal integration within hierarchical groups of input cytokine combinations and correctly predicts cell population response to new input conditions. These findings suggest that complex cellular responses can be effectively described using a segmented linear approach, providing a framework for prediction of cellular responses to new cytokine combinations and doses, with implications to fine-tuned immunotherapies.

Association between asthma and female sex hormones

CONTEXT AND OBJECTIVE:

The relationship between sex hormones and asthma has been evaluated in several studies. The aim of this review article was to investigate the association between asthma and female sex hormones, under different conditions (premenstrual asthma, use of oral contraceptives, menopause, hormone replacement therapy and pregnancy).

DESIGN AND SETTING:

Narrative review of the medical literature, Universidade Federal do Tocantins (UFT) and Universidade Federal de São Paulo (Unifesp).

METHODS:

We searched the CAPES journal portal, a Brazilian platform that provides access to articles in the MEDLINE, PubMed, SciELO, and LILACS databases. The following keywords were used based on Medical Subject Headings: asthma, sex hormones, women and use of oral contraceptives.

RESULTS:

The associations between sex hormones and asthma remain obscure. In adults, asthma is more common in women than in men. In addition, mortality due to asthma is significantly higher among females. The immune system is influenced by sex hormones: either because progesterone stimulates progesterone-induced blocking factor and Th2 cytokines or because contraceptives derived from progesterone and estrogen stimulate the transcription factor GATA-3.

CONCLUSIONS:

The associations between asthma and female sex hormones remain obscure. We speculate that estrogen fluctuations are responsible for asthma exacerbations that occur in women. Because of the anti-inflammatory action of estrogen, it decreases TNF-α production, interferon-γ expression and NK cell activity. We suggest that further studies that highlight the underlying physiopathological mechanisms contributing towards these interactions should be conducted.

Demystifying the cytokine network: Mathematical models point the way

Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.

Linear models of activation cascades: analytical solutions and coarse-graining of delayed signal transduction

Cellular signal transduction usually involves activation cascades, the sequential activation of a series of proteins following the reception of an input signal. Here, we study the classic model of weakly activated cascades and obtain analytical solutions for a variety of inputs. We show that in the special but important case of optimal gain cascades (i.e. when the deactivation rates are identical) the downstream output of the cascade can be represented exactly as a lumped nonlinear module containing an incomplete gamma function with real parameters that depend on the rates and length of the cascade, as well as parameters of the input signal. The expressions obtained can be applied to the non-identical case when the deactivation rates are random to capture the variability in the cascade outputs. We also show that cascades can be rearranged so that blocks with similar rates can be lumped and represented through our nonlinear modules. Our results can be used both to represent cascades in computational models of differential equations and to fit data efficiently, by reducing the number of equations and parameters involved. In particular, the length of the cascade appears as a real-valued parameter and can thus be fitted in the same manner as Hill coefficients. Finally, we show how the obtained nonlinear modules can be used instead of delay differential equations to model delays in signal transduction.

Third-Kind Encounters in Biomedicine: Immunology Meets Mathematics and Informatics to Become Quantitative and Predictive

The understanding of the immune response is right now at the center of biomedical research. There are growing expectations that immune-based interventions will in the midterm provide new, personalized, and targeted therapeutic options for many severe and highly prevalent diseases, from aggressive cancers to infectious and autoimmune diseases. To this end, immunology should surpass its current descriptive and phenomenological nature, and become quantitative, and thereby predictive.Immunology is an ideal field for deploying the tools, methodologies, and philosophy of systems biology, an approach that combines quantitative experimental data, computational biology, and mathematical modeling. This is because, from an organism-wide perspective, the immunity is a biological system of systems, a paradigmatic instance of a multi-scale system. At the molecular scale, the critical phenotypic responses of immune cells are governed by large biochemical networks, enriched in nested regulatory motifs such as feedback and feedforward loops. This network complexity confers them the ability of highly nonlinear behavior, including remarkable examples of homeostasis, ultra-sensitivity, hysteresis, and bistability. Moving from the cellular level, different immune cell populations communicate with each other by direct physical contact or receiving and secreting signaling molecules such as cytokines. Moreover, the interaction of the immune system with its potential targets (e.g., pathogens or tumor cells) is far from simple, as it involves a number of attack and counterattack mechanisms that ultimately constitute a tightly regulated multi-feedback loop system. From a more practical perspective, this leads to the consequence that today's immunologists are facing an ever-increasing challenge of integrating massive quantities from multi-platforms.In this chapter, we support the idea that the analysis of the immune system demands the use of systems-level approaches to ensure the success in the search for more effective and personalized immune-based therapies.

Analyzing Th17 cell differentiation dynamics using a novel integrative modeling framework for time-course RNA sequencing data

Background

The differentiation of naive CD 4⁺ helper T (Th) cells into effector Th17 cells is steered by extracellular cytokines that activate and control the lineage specific transcriptional program. While the inducing cytokine signals and core transcription factors driving the differentiation towards Th17 lineage are well known, detailed mechanistic interactions between the key components are poorly understood.

Results

We develop an integrative modeling framework which combines RNA sequencing data with mathematical modeling and enables us to construct a mechanistic model for the core Th17 regulatory network in a data-driven manner.

Conclusions

Our results show significant evidence, for instance, for inhibitory mechanisms between the transcription factors and reveal a previously unknown dependency between the dosage of the inducing cytokine TGF β and the expression of the master regulator of competing (induced) regulatory T cell lineage. Further, our experimental validation approves this dependency in Th17 polarizing conditions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0223-6) contains supplementary material, which is available to authorized users.

A Mathematical Framework for Understanding Four-Dimensional Heterogeneous Differentiation of CD4+ T Cells

At least four distinct lineages of CD4+ T cells play diverse roles in the immune system. Both in vivo and in vitro, naïve CD4+ T cells often differentiate into a variety of cellular phenotypes. Previously, we developed a mathematical framework to study heterogeneous differentiation of two lineages governed by a mutual-inhibition motif. To understand heterogeneous differentiation of CD4+ T cells involving more than two lineages, we present here a mathematical framework for the analysis of multiple stable steady states in dynamical systems with multiple state variables interacting through multiple mutual-inhibition loops. A mathematical model for CD4+ T cells based on this framework can reproduce known properties of heterogeneous differentiation involving multiple lineages of this cell differentiation system, such as heterogeneous differentiation of TH1-TH2, TH1-TH17 and iTReg-TH17 under single or mixed types of differentiation stimuli. The model shows that high concentrations of differentiation stimuli favor the formation of phenotypes with co-expression of lineage-specific master regulators.

Differential control of CD4(+) T-cell subsets by the PD-1/PD-L1 axis in a mouse model of allergic asthma

Studies examining the role of PD-1 family members in allergic asthma have yielded conflicting results. Using a mouse model of allergic asthma, we demonstrate that blockade of PD-1/PD-L1 has distinct influences on different CD4(+) T-cell subsets. PD-1/PD-L1 blockade enhances airway hyperreactivity (AHR), not by altering the magnitude of the underlying Th2-type immune response, but by allowing the development of a concomitant Th17-type immune response. Supporting differential CD4(+) T-cell responsiveness to PD-1-mediated inhibition, naïve PD-1(-/-) mice displayed elevated Th1 and Th17 levels, but diminished Th2 cytokine levels, and ligation of PD-1 in WT cells limited cytokine production by in vitro polarized Th1 and Th17 cells, but slightly enhanced cytokine production by in vitro polarized Th2 cells. Furthermore, PD-1 ligation enhanced Th2 cytokine production by naïve T cells cultured under nonpolarizing conditions. These data demonstrate that different CD4(+) T-cell subsets respond differentially to PD-1 ligation and may explain some of the variable results observed in control of allergic asthma by the PD-1 family members. As the PD-1/PD-L1 axis limits asthma severity by constraining Th17 cell activity, this suggests that severe allergic asthma may be associated with a defective PD-1/PD-L1 regulatory axis in some individuals.

Constitutive IDO expression in human cancer is sustained by an autocrine signaling loop involving IL-6, STAT3 and the AHR

Indoleamine-2,3-dioxygenase (IDO) inhibitors have entered clinical trials based on their ability to restore anti-tumor immunity in preclinical studies. However, the mechanisms leading to constitutive expression of IDO in human tumors are largely unknown. Here we analyzed the pathways mediating constitutive IDO expression in human cancer. IDO-positive tumor cells and tissues showed basal phosphorylation and acetylation of STAT3 as evidenced by western blotting and immunoprecipitation. Inhibition of IL-6 or STAT3 using siRNA and/or pharmacological inhibitors reduced IDO mRNA and protein expression as well as kynurenine formation. In turn, IDO enzymatic activity activated the AHR as shown by the induction of AHR target genes. IDO-mediated AHR activation induced IL-6 expression, while inhibition or knockdown of the AHR reduced IL-6 expression. IDO activity thus sustains its own expression via an autocrine AHR–IL-6–STAT3 signaling loop. Inhibition of the AHR–IL-6–STAT3 signaling loop restored T-cell proliferation in mixed leukocyte reactions performed in the presence of IDO-expressing human cancer cells. Identification of the IDO-AHR-IL-6-STAT3 signaling loop maintaining IDO expression in human cancers reveals novel therapeutic targets for the inhibition of this core pathway promoting immunosuppression of human cancers. The relevance of the IDO-AHR-IL-6-STAT3 transcriptional circuit is underscored by the finding that high expression of its members IDO, STAT3 and the AHR target gene CYP1B1 is associated with reduced relapse-free survival in lung cancer patients.

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.

Nuclear factor of activated T cells regulates the expression of interleukin-4 in Th2 cells in an all-or-none fashion

Th2 memory lymphocytes have imprinted their Il4 genes epigenetically for expression in dependence of T cell receptor restimulation. However, in a given restimulation, not all Th cells with a memory for IL-4 expression express IL-4. Here, we show that in reactivated Th2 cells, the transcription factors NFATc2, NF-kB p65, c-Maf, p300, Brg1, STAT6, and GATA-3 assemble at the Il4 promoter in Th2 cells expressing IL-4 but not in Th2 cells not expressing it. NFATc2 is critical for assembly of this transcription factor complex. Because NFATc2 translocation into the nucleus occurs in an all-or-none fashion, dependent on complete dephosphorylation by calcineurin, NFATc2 controls the frequencies of cells reexpressing Il4, translates analog differences in T cell receptor stimulation into a digital decision for Il4 reexpression, and instructs all reexpressing cells to express the same amount of IL-4. This analog-to-digital conversion may be critical for the immune system to respond to low concentrations of antigens.

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.

A theoretical study of lipid accumulation in the liver-implications for nonalcoholic fatty liver disease

A hallmark of the nonalcoholic fatty liver disease is the accumulation of lipids. We developed a mathematical model of the hepatic lipid dynamics to simulate the fate of fatty acids in hepatocytes. Our model involves fatty acid uptake, lipid oxidation, and lipid export. It takes into account that storage of triacylglycerol within hepatocytes leads to cell enlargement reducing the sinusoids radius and impairing hepatic microcirculation. Thus oxygen supply is reduced, which impairs lipid oxidation. The analysis of our model revealed a bistable behavior (two stable steady states) of the system, in agreement with histological observations showing distinct areas of lipid accumulation in lobules. The first (healthy) state is characterized by intact lipid oxidation and a low amount of stored lipids. The second state in our model may correspond to the steatotic cell; it is marked by a high amount of stored lipids and a reduced lipid oxidation caused by impaired oxygen supply. Our model stresses the role of insufficient oxygen supply for the development of steatosis. We discuss implications of our results in regard to the experimental design aimed at exploring lipid metabolism reactions under steatotic conditions. Moreover, the model helps to understand the reversibility of lipid accumulation and predicts the reversible switch to show hysteresis. The system can switch from the steatotic state back to the healthy state by reduction of fatty acid uptake below the threshold at which steatosis started. The reversibility corresponds to the observation that caloric restriction can reduce the lipid content in the liver.

Stable T-bet(+)GATA-3(+) Th1/Th2 hybrid cells arise in vivo, can develop directly from naive precursors, and limit immunopathologic inflammation

The stable lineage commitment of naïve T helper cells to a hybrid Th1/2 phenotype reveals the cell-intrinsic reconciliation of two opposing T cell differentiation programs and provides a self-limiting mechanism to dampen immunopathology.

Differentiated T helper (Th) cell lineages are thought to emerge from alternative cell fate decisions. However, recent studies indicated that differentiated Th cells can adopt mixed phenotypes during secondary immunological challenges. Here we show that natural primary immune responses against parasites generate bifunctional Th1 and Th2 hybrid cells that co-express the lineage-specifying transcription factors T-bet and GATA-3 and co-produce Th1 and Th2 cytokines. The integration of Th1-promoting interferon (IFN)-γ and interleukin (IL)-12 signals together with Th2-favoring IL-4 signals commits naive Th cells directly and homogeneously to the hybrid Th1/2 phenotype. Specifically, IFN-γ signals are essential for T-bet⁺GATA-3⁺ cells to develop in vitro and in vivo by breaking the dominance of IL-4 over IL-12 signals. The hybrid Th1/2 phenotype is stably maintained in memory cells in vivo for months. It resists reprogramming into classic Th1 or Th2 cells by Th1- or Th2-promoting stimuli, which rather induce quantitative modulations of the combined Th1 and Th2 programs without abolishing either. The hybrid phenotype is associated with intermediate manifestations of both Th1 and Th2 cell properties. Consistently, hybrid Th1/2 cells support inflammatory type-1 and type-2 immune responses but cause less immunopathology than Th1 and Th2 cells, respectively. Thus, we propose the self-limitation of effector T cells based on the stable cell-intrinsic balance of two opposing differentiation programs as a novel concept of how the immune system can prevent excessive inflammation.

T helper (Th) cells, a subgroup of white blood cells important in the immune system, can differentiate into diverse lineages, for example Th1 and Th2, whose effector mechanisms target different types of pathogens but cause problems if not properly regulated. Lineage commitment is driven by cytokine signals that control the expression of distinct lineage-specifying “master regulator” transcription factor molecules. Lineage commitment is thought to reflect alternative cell-fate decisions because the initiated differentiation programs have self-amplifying and mutually repressive features. Here we show that the Th1 and Th2 differentiation programs are more compatible with each other than previously thought. Individual naive T cells can simultaneously integrate Th1- and Th2-polarizing signals and develop into hybrid Th1/2 cells that stably co-express both the Th1 master regulator T-bet and the Th2 master regulator GATA-3. We find that hybrid Th1/2 cells arise naturally during parasite infections and that the two opposing differentiation programs can stably co-exist in resting memory Th1/2 cells for periods of months. Th1- or Th2-polarizing stimuli induced quantitative modulations in the hybrid state but did not extinguish either program. The cell-intrinsic antagonism gives the hybrid Th1/2 cells properties that are quantitatively intermediate between those of Th1 and Th2 cells. Thus, in typical Th1 and Th2 immune responses, hybrid Th1/2 cells cause less immunopathology than their classic Th1 or Th2 counterparts, demonstrating a cell-intrinsic self-limiting mechanism that can prevent excessive inflammation.

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.

Stochastic cytokine expression induces mixed T helper cell States

During early differentiation of T helper cells, stochastic cytokine expression triggers the co-expression of antagonistic transcription factors at high levels, buffered by the interplay between extracellular and intracellular signaling components.

During eukaryotic development, the induction of a lineage-specific transcription factor typically drives differentiation of multipotent progenitor cells, while repressing that of alternative lineages. This process is often mediated by some extracellular signaling molecules, such as cytokines that can bind to cell surface receptors, leading to activation and/or repression of transcription factors. We explored the early differentiation of naive CD4 T helper (Th) cells into Th1 versus Th2 states by counting single transcripts and quantifying immunofluorescence in individual cells. Contrary to mutually exclusive expression of antagonistic transcription factors, we observed their ubiquitous co-expression in individual cells at high levels that are distinct from basal-level co-expression during lineage priming. We observed that cytokines are expressed only in a small subpopulation of cells, independent from the expression of transcription factors in these single cells. This cell-to-cell variation in the cytokine expression during the early phase of T helper cell differentiation is significantly larger than in the fully differentiated state. Upon inhibition of cytokine signaling, we observed the classic mutual exclusion of antagonistic transcription factors, thus revealing a weak intracellular network otherwise overruled by the strong signals that emanate from extracellular cytokines. These results suggest that during the early differentiation process CD4 T cells acquire a mixed Th1/Th2 state, instructed by extracellular cytokines. The interplay between extracellular and intracellular signaling components unveiled in Th1/Th2 differentiation may be a common strategy for mammalian cells to buffer against noisy cytokine expression.

During the development of a multicellular organism, the progenitor cells, which have the potential to become any of several different cell lineages with specialized functions, commit and differentiate into one particular lineage. This differentiation of progenitors is driven by the induction of lineage-specific transcription factors, molecules that regulate gene expression. This process is often mediated by extracellular signaling molecules, including a class of molecules called cytokines that can bind to cell surface receptors, activating and/or repressing transcription factors. Here we explored the early differentiation of naive T helper (Th) cells, an important class of T lymphocytes that help effector immune cells to defend the body against various pathogens. We measured both mRNA and protein levels of cytokines and transcription factors in individual cells. In particular, mRNA levels were measured with single-molecule resolution. Contrary to the expression of only one set of lineage-specific transcription factors, we observed ubiquitous high-level co-expression of antagonistic transcription factors in individual cells. We found that cytokines are expressed only in a small subpopulation of cells, independent from the expression of transcription factors in individual cells. When cytokine signaling is inhibited, each cell expressed only one of the antagonistic transcription factors at high levels. This reveals a weak intracellular network that is otherwise overruled by the strong signals that emanate from extracellular cytokines. These results suggest that during the early differentiation process T helper cells acquire a mixed Th1/Th2 state, instructed by extracellular cytokines. The interplay between extracellular and intracellular signaling components unveiled in Th1/Th2 differentiation may be a common strategy for mammalian cells to buffer against noisy cytokine expression.

Mathematical model of the roles of T cells in inflammatory bowel disease

Gut mucosal homeostasis depends on complex interactions among the microbiota, the intestinal epithelium, and the gut associated immune system. A breakdown in some of these interactions may precipitate inflammation. Inflammatory bowel diseases, Crohn's disease, and ulcerative colitis are chronic inflammatory disorders of the gastrointestinal tract. The initial stages of disease are marked by an abnormally high level of pro-inflammatory helper T cells, Th1. In later stages, Th2 helper cells may dominate while the Th1 response may dampen. The interaction among the T cells includes the regulatory T cells (Treg). The present paper develops a mathematical model by a system of differential equations with terms nonlocal in the space spanned by the concentrations of cytokines that represents the interaction among T cells through a cytokine signaling network. The model demonstrates how the abnormal levels of T cells observed in inflammatory bowel diseases can arise from abnormal regulation of Th1 and Th2 cells by Treg cells.

Asthma phenotyping, therapy, and prevention: what can we learn from systems biology?

Asthma has a high prevalence worldwide, and contributes significantly to the socioeconomic burden. According to a classical paradigm, asthma symptoms are attributable to an allergic, Th2-driven airway inflammation that causes airway hyperresponsiveness and results in reversible airway obstruction. Diagnosis and therapy are based mainly on these pathophysiologic concepts. However, these have increasingly been challenged by findings of recent studies, and the frequently observed failure in controlling asthma symptoms. Important recent findings are the protective "farm effect" in children, the possible prenatal mechanisms of this protection, the recognition of many different asthma phenotypes in children and adults, and the partly disappointing clinical effects of new targeted therapeutic approaches. Systems biology approaches may lead to a more comprehensive view of asthma pathophysiology and a higher success rate of new therapies. Systems biology integrates clinical and experimental data by means of bioinformatics and mathematical modeling. In general, the "-omics" approach, and the "mathematical modeling" approach can be described. Recently, several consortia have been attempting to bring together clinical and molecular data from large asthma cohorts, using novel experimental setups, biostatistics, bioinformatics, and mathematical modeling. This "systems medicine" approach to asthma will help address the different asthma phenotypes with adequate therapy and possibly preventive strategies.

Immunopathological patterns from EAE and Theiler's virus infection: Is multiple sclerosis a homogenous 1-stage or heterogenous 2-stage disease?

Multiple sclerosis (MS) is a disease which can presents in different clinical courses. The most common form of MS is the relapsing-remitting (RR) course, which in many cases evolves into secondary progressive (SP) disease. Autoimmune models such as experimental autoimmune encephalomyelitis (EAE) have been developed to represent the various clinical forms of MS. These models along with clinico-pathological evidence obtained from MS patients have allowed us to propose '1-stage' and '2-stage' disease theories to explain the transition in the clinical course of MS from RR to SP. Relapses in MS are associated with pro-inflammatory T helper (Th) 1/Th17 immune responses, while remissions are associated with anti-inflammatory Th2/regulatory T (Treg) immune responses. Based on the '1-stage disease' theory, the transition from RR to SP disease occurs when the inflammatory immune response overwhelms the anti-inflammatory immune response. The '2-stage disease' theory proposes that the transition from RR to SP-MS occurs when the Th2 response or some other responses overwhelm the inflammatory response resulting in the sustained production of anti-myelin antibodies, which cause continuing demyelination, neurodegeneration, and axonal loss. The Theiler's virus model is also a 2-stage disease, where axonal degeneration precedes demyelination during the first stage, followed by inflammatory demyelination during the second stage.

Compound stress response in stomatal closure: a mathematical model of ABA and ethylene interaction in guard cells

BACKGROUND

Stomata are tiny pores in plant leaves that regulate gas and water exchange between the plant and its environment. Abscisic acid and ethylene are two well-known elicitors of stomatal closure when acting independently. However, when stomata are presented with a combination of both signals, they fail to close.

RESULTS

Toshed light on this unexplained behaviour, we have collected time course measurements of stomatal aperture and hydrogen peroxide production in Arabidopsis thaliana guard cells treated with abscisic acid, ethylene, and a combination of both. Our experiments show that stomatal closure is linked to sustained high levels of hydrogen peroxide in guard cells. When treated with a combined dose of abscisic acid and ethylene, guard cells exhibit increased antioxidant activity that reduces hydrogen peroxide levels and precludes closure. We construct a simplified model of stomatal closure derived from known biochemical pathways that captures the experimentally observed behaviour.

CONCLUSIONS

Our experiments and modelling results suggest a distinct role for two antioxidant mechanisms during stomatal closure: a slower, delayed response activated by a single stimulus (abscisic acid 'or' ethylene) and another more rapid 'and' mechanism that is only activated when both stimuli are present. Our model indicates that the presence of this rapid 'and' mechanism in the antioxidant response is key to explain the lack of closure under a combined stimulus.

A simple theoretical framework for understanding heterogeneous differentiation of CD4+ T cells

BACKGROUND

CD4+ T cells have several subsets of functional phenotypes, which play critical yet diverse roles in the immune system. Pathogen-driven differentiation of these subsets of cells is often heterogeneous in terms of the induced phenotypic diversity. In vitro recapitulation of heterogeneous differentiation under homogeneous experimental conditions indicates some highly regulated mechanisms by which multiple phenotypes of CD4+ T cells can be generated from a single population of naïve CD4+ T cells. Therefore, conceptual understanding of induced heterogeneous differentiation will shed light on the mechanisms controlling the response of populations of CD4+ T cells under physiological conditions.

RESULTS

We present a simple theoretical framework to show how heterogeneous differentiation in a two-master-regulator paradigm can be governed by a signaling network motif common to all subsets of CD4+ T cells. With this motif, a population of naïve CD4+ T cells can integrate the signals from their environment to generate a functionally diverse population with robust commitment of individual cells. Notably, two positive feedback loops in this network motif govern three bistable switches, which in turn, give rise to three types of heterogeneous differentiated states, depending upon particular combinations of input signals. We provide three prototype models illustrating how to use this framework to explain experimental observations and make specific testable predictions.

CONCLUSIONS

The process in which several types of T helper cells are generated simultaneously to mount complex immune responses upon pathogenic challenges can be highly regulated, and a simple signaling network motif can be responsible for generating all possible types of heterogeneous populations with respect to a pair of master regulators controlling CD4+ T cell differentiation. The framework provides a mathematical basis for understanding the decision-making mechanisms of CD4+ T cells, and it can be helpful for interpreting experimental results. Mathematical models based on the framework make specific testable predictions that may improve our understanding of this differentiation system.

Cell division curtails helper phenotype plasticity and expedites helper T-cell differentiation

Following activation by antigen, helper T cells differentiate into one of many effector phenotypes. Formulating mechanistic mathematical models combining regulatory networks at the transcriptional, translational and epigenetic level, we study how individual helper T cells may adopt their different phenotypes. For each cytokine phenotype, for example, T helper type 1 (Th1) and type 2 (Th2) cells, we find that the intracellular molecular network allows a cell to adopt one of the three states, which we interpret as naive, active and memory states. Cell division markedly speeds up the differentiation into a particular memory state because of DNA demythelation. In a memory state, cells readily resume production of the same cytokine they produced before. Using stochastic models we show that helper T-cell plasticity (that is, the ability to switch phenotype) is low during clonal expansion. Although most memory cells rapidly secrete the original cytokine upon restimulation, some adopt another phenotype and produce different cytokines, allowing for considerable diversity in the phenotypes that are adopted during a memory response. In summary, we show that helper T-cell division expedites cell differentiation by increasing DNA demethylation. We also show that plasticity is low during the clonal expansion phase, but that helper T cells may adopt alternative phenotypes during a memory response.

The molecular mechanisms of regulatory T cell immunosuppression

CD4⁺CD25⁺Foxp3⁺ T lymphocytes, known as regulatory T cells or T(regs), have been proposed to be a lineage of professional immune suppressive cells that exclusively counteract the effects of the immunoprotective "helper" and "cytotoxic" lineages of T lymphocytes. Here we discuss new concepts on the mechanisms and functions of T(regs). There are several key points we emphasize: 1. Tregs exert suppressive effects both directly on effector T cells and indirectly through antigen-presenting cells; 2. Regulation can occur through a novel mechanism of cytokine consumption to regulate as opposed to the usual mechanism of cytokine/chemokine production; 3. In cases where CD4⁺ effector T cells are directly inhibited by T(regs), it is chiefly through a mechanism of lymphokine withdrawal apoptosis leading to polyclonal deletion; and 4. Contrary to the current view, we discuss new evidence that T(regs), similar to other T-cells lineages, can promote protective immune responses in certain infectious contexts (Chen et al., 2011; Pandiyan et al., 2011). Although these points are at variance to varying degrees with the standard model of T(reg) behavior, we will recount developing findings that support these new concepts.

A mathematical model for the reciprocal differentiation of T helper 17 cells and induced regulatory T cells

The reciprocal differentiation of T helper 17 (T_H17) cells and induced regulatory T (iT_reg) cells plays a critical role in both the pathogenesis and resolution of diverse human inflammatory diseases. Although initial studies suggested a stable commitment to either the T_H17 or the iT_reg lineage, recent results reveal remarkable plasticity and heterogeneity, reflected in the capacity of differentiated effectors cells to be reprogrammed among T_H17 and iT_reg lineages and the intriguing phenomenon that a group of naïve precursor CD4⁺ T cells can be programmed into phenotypically diverse populations by the same differentiation signal, transforming growth factor beta. To reconcile these observations, we have built a mathematical model of T_H17/iT_reg differentiation that exhibits four different stable steady states, governed by pitchfork bifurcations with certain degrees of broken symmetry. According to the model, a group of precursor cells with some small cell-to-cell variability can differentiate into phenotypically distinct subsets of cells, which exhibit distinct levels of the master transcription-factor regulators for the two T cell lineages. A dynamical control system with these properties is flexible enough to be steered down alternative pathways by polarizing signals, such as interleukin-6 and retinoic acid and it may be used by the immune system to generate functionally distinct effector cells in desired fractions in response to a range of differentiation signals. Additionally, the model suggests a quantitative explanation for the phenotype with high expression levels of both master regulators. This phenotype corresponds to a re-stabilized co-expressing state, appearing at a late stage of differentiation, rather than a bipotent precursor state observed under some other circumstances. Our simulations reconcile most published experimental observations and predict novel differentiation states as well as transitions among different phenotypes that have not yet been observed experimentally.

In order to perform complex functions upon pathogenic challenges, the immune system needs to efficiently deploy a repertoire of specialized cells by inducing the differentiation of precursor cells into effector cells. In a critical process of the adaptive immune system, one common type of precursor cell can give rise to both T helper 17 cells and regulatory T cells, which have distinct phenotypes and functions. Recent discoveries have revealed a certain heterogeneity in this reciprocal differentiation system. In particular, treating precursor cells with a single differentiation signal can result in a remarkably diverse population. An understanding of such variable responses is limited by a lack of quantitative models. Our mathematical model of this cell differentiation system reveals how the control system generates phenotypic diversity and how its final state can be regulated by various signals. The model suggests a new quantitative explanation for the scenario in which the master regulators of two different T cell lineages can be highly expressed in a single cell. The model provides a new framework for understanding the dynamic properties of this type of regulatory network and the mechanisms that help to maintain a balance of effector cells during the inflammatory response to infection.

A robust model to describe the differentiation of T-helper cells

There is a wealth of information regarding the differentiation of T-helper cells. Nevertheless, there is no general agreement on the topology and dynamical properties of the molecular network controlling the differentiation of these cells. This paper presents a continuous dynamical system to model the signaling network that controls the differentiation process of T-helper cells. The model is able to represent the differentiation from the precursor Th0 cell to any of the four effectors types (Th1, Th2, Th17, and Treg), as well as the phenotype of single null mutants. We present the first sensitivity analysis of the equations defining the Th model, showing that the qualitative dynamical behavior of the model is very robust against changes in three out of four tested parameters. The robustness of the model is in agreement with our claim that the qualitative behavior of the system is to a large extent independent of the methodological framework used for modeling.

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.

Short-term memory in gene induction reveals the regulatory principle behind stochastic IL-4 expression

Combining experiments on primary T cells and mathematical modeling, we characterized the stochastic expression of the interleukin-4 cytokine gene in its physiologic context, showing that a two-step model of transcriptional regulation acting on chromatin rearrangement and RNA polymerase recruitment accounts for the level, kinetics, and population variability of expression.

A rate-limiting step upstream of transcription initiation, but occurring at the level of an individual allele, controls whether the interleukin-4 gene is expressed during antigenic stimulation, suggesting that the observed stochasticity of expression is linked to the dynamics of chromatin rearrangement.

The computational analysis predicts that the probability to re-express an interleukin-4 gene that has been expressed once is transiently increased. In support, we experimentally demonstrate a short-term memory for interleukin-4 expression at the predicted time scale of several days.

The model provides a unifying framework that accounts for both graded and binary modes of gene regulation. Graded changes in expression level can be achieved by controlling transcription initiation, whereas binary regulation acts at the level of chromatin rearrangement and is targeted during the differentiation of T cells that specialize in interleukin-4 production.

Cell populations are typically heterogeneous with respect to protein expression even when clonally derived from a single progenitor. In bacteria and yeast, such heterogeneity has been shown to be due to intrinsically stochastic dynamics of gene expression (Raj and van Oudenaarden, 2008). Thus, cross-population heterogeneity may be an unavoidable by-product of random fluctuations in molecular interactions (Raser and O'Shea, 2004; Pedraza and van Oudenaarden, 2005). The phenotypic variability deriving from it may also be beneficial for cell function, differentiation, or adaptation to changing environments (Chang et al, 2008; Feinerman et al, 2008; Losick and Desplan, 2008). However, little is known about how gene-expression variability is caused in mammalian cells.

Two principal modes of gene regulation have been identified: graded and binary. In the graded mode, transcriptional regulators can tune the level of a gene product in a continuous manner (Hazzalin and Mahadevan, 2002). In the binary mode, the gene is expressed at an invariant level, whereas its probability of being expressed in a given cell is regulated, so that the gene has discrete ‘on' and ‘off' states (Walters et al, 1995; Hume, 2000; Biggar and Crabtree, 2001). In humans and mice, cytokine genes are expressed in a binary manner (Bix and Locksley, 1998; Riviere et al, 1998; Hu-Li et al, 2001; Apostolou and Thanos, 2008). A particularly well-studied case is the interleukin-4 (il4) gene that is critical for antibody-based immune responses. This gene is expressed by antigen-stimulated T cells initially with low probability, so that in most IL-4-positive cells only one allele is active (Bix and Locksley, 1998; Riviere et al, 1998). The expressed allele is not imprinted but chosen stochastically during each cell stimulation (Hu-Li et al, 2001).

Here, we have studied the dynamics of IL-4 expression quantitatively. Primary murine CD4⁺ T cells have been differentiated uniformly into type-2 T-helper (Th2) cells that express the lineage-specifying transcription factor (TF) Gata-3 and are competent to activate the il4 gene upon challenge with antigen. Using T cells heterozygous for an il4 wild-type allele and an il4 allele with GFP knock-in after the promoter, the alleles are found to be expressed stochastically and in an uncorrelated manner (Figure 2A; Hu-Li et al, 2001). To account for the observed stochastic dynamics of IL-4 expression, we considered a basic model of gene transcription, mRNA translation, turnover, and protein secretion (Figure 2B). However, our experimental estimates of the intracellular life times of IL-4 mRNA and protein (∼1 h) and their absolute numbers (mRNA∼10³, protein∼10⁵) rule out random fluctuations in transcription, translation as well as mRNA and protein turnover as an explanation for the observed stochastic properties of IL-4 expression (Thattai and van Oudenaarden, 2001; Paulsson, 2004).

As il4 is known to be strongly regulated at the chromatin level (Ansel et al, 2006), we included in the model a reversible step of chromatin opening that is permissive for transcription (Figure 2C and D). Both chromatin opening and transcription initiation are driven by TFs that are transiently activated during the antigen stimulus, with NFAT1 playing a prominent role (Agarwal et al, 2000; Avni et al, 2002; Guo et al, 2004). The model accounts for the kinetics of NFAT1 TF activity (Figure 2E) (Loh et al, 1996). Using a best-fit procedure for estimating the kinetics of the chromatin transition and TF activity from experimental data, we found that the model accurately reproduces the distribution of IL-4 expression within the cell population over the entire time course of a stimulation (Figure 3A). At the same time, it accounts for the measured kinetics of IL-4 mRNA, intracellular and secreted protein (Figure 3B). Additional data show that the model can also explain IL-4 expression at different stages of Th2 differentiation and upon pharmacological inhibition of NFAT1 activity. In each case, the model predicts a slow and stochastic chromatin opening (Step 1 in Figure 2C) that is the limiting step for the activation of the gene.

The slowness of chromatin opening inferred by the model implies an extended lifetime of the open chromatin state (several days), which lasts longer than TF activity during antigenic stimulation (several hours). This indicates that acute IL-4 expression is terminated by the cessation of TF activity (Step 2 in Figure 2C), rather than by the closing of the chromatin (Step 1). In support of this prediction, we observed an elevated fraction of IL-4-producing cells after secondary stimulations administered within a few days of the primary stimulus. Consistent with the model, this elevation disappeared with a half-life of ∼3 days (Figure 4B). To test whether this ‘short-term memory' for activation of the il4 gene is indeed due to the IL-4 producers in the primary stimulation, we sorted stimulated Th2 cells into viable IL-4-producing and non-producing fractions using the cytokine secretion assay (Ouyang et al, 2000) and cultured them separately for different resting periods. The probability of IL-4 re-expression in the positive-sorted cells was consistently larger than in negative-sorted cells and decreased progressively over several days (Figure 4C). By contrast, the sorted IL-4 negative cells exhibited a constant induction probability indistinguishable from the unsorted population. This behavior was not due to differential cell proliferation in the sorted populations or different success of Th2 differentiation. Moreover, using heterozygous il4-wild-type/il4-gfp cells, and sorting for expression of the wild-type allele, we observed that expression of the il4-gfp allele was similar in IL-4-positive and negative sorted fractions. Taken together, these findings imply that stochastic, slow chromatin changes at individual il4 genes govern the binary expression pattern of this cytokine.

In conclusion, we propose an experimentally based model of inducible gene expression where strong stochasticity arises from slow (hours to days) chromatin opening and closing transitions, rather than being due to small numbers of mRNA or protein molecules or transcriptional bursting (Raj et al, 2006). This rate-limiting step upstream of transcription initiation (which may entail several interacting epigenetic processes) naturally gives rise to a binary expression pattern of the gene. By contrast, regulation at the level of transcription initiation can have a graded effect on the expression level. We provide evidence that both binary and graded regulation can occur for the il4 gene. Physiological regulation of il4 seems to be mainly binary, thus enabling a dose–response within a population while producing an unequivocal all-or-none signal at the single-cell level.

Although cell-to-cell variability has been recognized as an unavoidable consequence of stochasticity in gene expression, it may also serve a functional role for tuning physiological responses within a cell population. In the immune system, remarkably large variability in the expression of cytokine genes has been observed in homogeneous populations of lymphocytes, but the underlying molecular mechanisms are incompletely understood. Here, we study the interleukin-4 gene (il4) in T-helper lymphocytes, combining mathematical modeling with the experimental quantification of expression variability and critical parameters. We show that a stochastic rate-limiting step upstream of transcription initiation, but acting at the level of an individual allele, controls il4 expression. Only a fraction of cells reaches an active, transcription-competent state in the transient time window determined by antigen stimulation. We support this finding by experimental evidence of a previously unknown short-term memory that was predicted by the model to arise from the long lifetime of the active state. Our analysis shows how a stochastic mechanism acting at the chromatin level can be integrated with transcriptional regulation to quantitatively control cell-to-cell variability.

Nonidentifiability of the source of intrinsic noise in gene expression from single-burst data

Over the last few years, experimental data on the fluctuations in gene activity between individual cells and within the same cell over time have confirmed that gene expression is a "noisy" process. This variation is in part due to the small number of molecules taking part in some of the key reactions that are involved in gene expression. One of the consequences of this is that protein production often occurs in bursts, each due to a single promoter or transcription factor binding event. Recently, the distribution of the number of proteins produced in such bursts has been experimentally measured, offering a unique opportunity to study the relative importance of different sources of noise in gene expression. Here, we provide a derivation of the theoretical probability distribution of these bursts for a wide variety of different models of gene expression. We show that there is a good fit between our theoretical distribution and that obtained from two different published experimental datasets. We then prove that, irrespective of the details of the model, the burst size distribution is always geometric and hence determined by a single parameter. Many different combinations of the biochemical rates for the constituent reactions of both transcription and translation will therefore lead to the same experimentally observed burst size distribution. It is thus impossible to identify different sources of fluctuations purely from protein burst size data or to use such data to estimate all of the model parameters. We explore methods of inferring these values when additional types of experimental data are available.

Positive feedback in cellular control systems

Feedback loops have been identified in a variety of regulatory systems and organisms. While feedback loops of the same type (negative or positive) tend to have properties in common, they can play distinctively diverse roles in different regulatory systems, where they can affect virulence in a pathogenic bacterium, maturation patterns of vertebrate oocytes and transitions through cell cycle phases in eukaryotic cells. This review focuses on the properties and functions of positive feedback in biological systems, including bistability, hysteresis and activation surges.

Positive receptor feedback during lineage commitment can generate ultrasensitivity to ligand and confer robustness to a bistable switch

Cytokines and lineage-specific transcription factors are critical molecular effectors for terminal differentiation during hematopoiesis. Intrinsic transcription factor activity is often believed to drive commitment and differentiation, whereas cytokine receptor signals have been implicated in the regulation of cell proliferation, survival, and differentiation. In erythropoiesis, recent experimental findings provide direct evidence that erythropoietin (Epo) can generate commitment cues via the erythropoietin receptor (EpoR); specifically, EpoR signaling leads to activation of the transcription factor GATA-1, which then triggers transcription of erythrocyte-specific genes. In particular, activated GATA-1 induces two positive feedback loops in the system through the enhanced expression of both inactive GATA-1 and EpoR, the latter of which is externally regulatable by Epo. Based upon this network architecture, we present a mathematical model of GATA-1 activation by EpoR, which bidirectionally links a lineage-specific receptor and transcription factor. Our deterministic model offers insight into stimulus-response relationships between Epo and several downstream effectors. In addition to the survival signals that EpoR provides, steady-state analysis of our model suggests that receptor upregulation during lineage commitment can also generate ultrasensitivity to Epo and bistability in GATA-1 activity. These system-level properties can induce a switch-like characteristic during differentiation and provide robustness to the mature state. The topology also suggests a novel mechanism for achieving robust bistability in a purely deterministic manner without molecular cooperativity. The analytical solution of a generalized, minimal model is provided and the significance of each of the two positive feedback loops is elucidated through bifurcation analysis. This network topology, or variations thereof, may link other receptor-transcription factor pairs and may therefore be of general relevance in cellular decision-making.