The duration of T cell stimulation is a critical determinant of cell fate and plasticity

The kinase ITK controls a Ca2+-mediated switch that balances TH17 and Treg cell differentiation

The balance of proinflammatory T helper type 17 (TH17) and anti-inflammatory T regulatory (Treg) cells is crucial for immune homeostasis in health and disease. The differentiation of naïve CD4+ T cells into TH17 and Treg cells depends on T cell receptor (TCR) signaling mediated, in part, by interleukin-2-inducible T cell kinase (ITK), which stimulates mitogen-activated protein kinases (MAPKs) and Ca2+ signaling. Here, we report that, in the absence of ITK activity, naïve murine CD4+ T cells cultured under TH17-inducing conditions expressed the Treg transcription factor Foxp3 and did not develop into TH17 cells. Furthermore, ITK inhibition in vivo during allergic inflammation increased the Treg:TH17 ratio in the lung. These switched Foxp3+ Treg-like cells had suppressive function, and their transcriptomic profile resembled that of differentiated, induced Treg (iTreg) cells, but their chromatin accessibility profiles were intermediate between TH17 and iTreg cells. Like iTreg cells, switched Foxp3+ Treg-like cells had reductions in the expression of genes involved in mitochondrial oxidative phosphorylation and glycolysis, in the activation of the mechanistic target of rapamycin (mTOR) signaling pathway, and in the abundance of the TH17 pioneer transcription factor BATF. This ITK-dependent switch between TH17 and Treg cells depended on Ca2+ signaling but not on MAPKs. These findings suggest potential strategies for fine-tuning TCR signal strength through ITK to control the balance of TH17 and Treg cells.

Dendritic cells and antigen-specific immunotherapy in autoimmune rheumatic diseases

Dendritic cells (DCs) are professional antigen-presenting cells and trigger downstream immune responses to antigen while integrating cellular pathogen and damage-associated molecular pattern (PAMP and DAMP) or immunomodulatory signals. In healthy individuals, resting and tolerogenic DCs draining skin and intestine facilitate expansion of regulatory T cells (Treg) to maintain peripheral antigen-specific immune tolerance. In patients with rheumatic diseases, however, DCs activated by PAMPs and DAMPs expand self-reactive effector T cells, including follicular helper T cells that promote the expansion of activated autoreactive B cells, chronic inflammation and end-organ damage. With the development of cellular and nanoparticle (NP)-based self-antigen-specific immunotherapies we here consider the new opportunities and the challenges for restoring immunoregulation in the treatment and prevention of autoimmune inflammatory rheumatic conditions through DCs.

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.

ATF4-SLC7A11-GSH axis mediates the acquisition of immunosuppressive properties by activated CD4+ T cells in low arginine condition

The tumor microenvironment (TME) is restricted in metabolic nutrients including the semi-essential amino acid arginine. While complete arginine deprivation causes T cell dysfunction, it remains unclear how arginine levels fluctuate in the TME to shape T cell fates. Here, we find that the 20-μM low arginine condition, representing the levels found in the plasma of patients with cancers, confers Treg-like immunosuppressive capacities upon activated T cells. In vivo mouse tumor models and human single-cell RNA-sequencing datasets reveal positive correlations between low arginine condition and intratumoral Treg accumulation. Mechanistically, low arginine-activated T cells engage in metabolic and transcriptional reprogramming, using the ATF4-SLC7A11-GSH axis, to preserve their suppressive function. These findings improve our understanding of the role of arginine in human T cell biology with potential applications for immunotherapy strategies.

Cellular and molecular signaling towards T cell immunological self-tolerance

The binding of a cognate antigen to T cell receptor (TCR) complex triggers a series of intracellular events controlling T cell activation, proliferation, and differentiation. Upon TCR engagement, different negative regulatory feedback mechanisms are rapidly activated to counterbalance T cell activation, thus preventing excessive signal propagation and promoting the induction of immunological self-tolerance. Both positive and negative regulatory processes are tightly controlled to ensure the effective elimination of foreign antigens while limiting surrounding tissue damage and autoimmunity. In this context, signals deriving from co-stimulatory molecules (i.e., CD80, CD86), co-inhibitory receptors (PD-1, CTLA-4), the tyrosine phosphatase CD45 and cytokines such as IL-2 synergize with TCR-derived signals to guide T cell fate and differentiation. The balance of these mechanisms is also crucial for the generation of CD4+ Foxp3+ regulatory T cells, a cellular subset involved in the control of immunological self-tolerance. This review provides an overview of the most relevant pathways induced by TCR activation combined with those derived from co-stimulatory and co-inhibitory molecules implicated in the cell-intrinsic modulation of T cell activation. In addition to the latter, we dissected mechanisms responsible for T cell-mediated suppression of immune cell activation through regulatory T cell generation, homeostasis, and effector functions. We also discuss how imbalanced signaling derived from TCR and accessory molecules can contribute to autoimmune disease pathogenesis.

Hsp70-Bim incoherent feedforward loop contributes to cell-fate heterogeneity and fractional killing

BACKGROUND AND PURPOSE

Although chemotherapeutics or molecular targeted drugs often elicit profound initial responses, fractional killing capable of driving acquired resistance can persist. Identifying stress-induced negative feedback or an incoherent feedforward loop (IFFL), which may contribute to fractional killing, is urgently needed.

EXPERIMENTAL APPROACH

Mathematical modelling was used to identify how and to what extent a recently reported Hsp70-Bim protein-protein interaction (PPI) contributes to the adaptation of the Bcl-2 network. Experimental validation was made by using a specific inhibitor of Hsp70-Bim PPI, S1g-2, as chemical tool. Bifurcation analysis and stochastic simulation were used for the theoretical study of the impact of Hsp70-Bim PPI on cell-fate heterogeneity and factional killing.

KEY RESULTS

The Hsp70-Bim-AKT circuit forms an IFFL that greatly contributes to the adaptation of the Bcl-2-regulated apoptosis network, thus leading to fractional killing. This adaptive programme enhances noise-induced cell-fate heterogeneity by shifting from a saddle-node to a saddle-collision transition scenario.

CONCLUSION AND IMPLICATIONS

Hsp70-Bim IFFL serves as a molecular pathway induced by DNA damaging drugs or tyrosine kinase inhibitors that enabled fractional killing, whereby acquired resistance emerges. A synergistic strategy is unveiled for overcoming fractional killing by suppressing Hsp70-Bim PPI.

Stimulation Strength Determined by Superantigen Dose Controls Subcellular Localization of FOXP3 Isoforms and Suppressive Function of CD4+CD25+FOXP3+ T Cells

Staphylococcal superantigens induce massive activation of T cells and inflammation, leading to toxic shock syndrome. Paradoxically, increasing evidence indicates that superantigens can also induce immunosuppression by promoting regulatory T cell (Treg) development. In this study, we demonstrate that stimulation strength plays a critical role in superantigen-mediated induction of immunosuppressive human CD4+CD25+FOXP3+ T cells. Suboptimal stimulation by a low dose (1 ng/ml) of staphylococcal enterotoxin C1 (SEC1) led to de novo generation of Treg-like CD4+CD25+FOXP3+ T cells with strong suppressive activity. In contrast, CD4+CD25+ T cells induced by optimal stimulation with high-dose SEC1 (1 µg/ml) were not immunosuppressive, despite high FOXP3 expression. Signal transduction pathway analysis revealed differential activation of the PI3K signaling pathway and expression of PTEN in optimal and suboptimal stimulation with SEC1. Additionally, we identified that FOXP3 isoforms in Treg-like cells from the suboptimal condition were located in the nucleus, whereas FOXP3 in nonsuppressive cells from the optimal condition localized in cytoplasm. Sequencing analysis of FOXP3 isoform transcripts identified five isoforms, including a FOXP3 isoform lacking partial exon 3. Overexpression of FOXP3 isoforms confirmed that both an exon 2-lacking isoform and a partial exon 3-lacking isoform confer suppressive activity. Furthermore, blockade of PI3K in optimal stimulation conditions led to induction of suppressive Treg-like cells with nuclear translocation of FOXP3, suggesting that PI3K signaling impairs induction of Tregs in a SEC1 dose-dependent manner. Taken together, these data demonstrate that the strength of activation signals determined by superantigen dose regulates subcellular localization of FOXP3 isoforms, which confers suppressive functionality.

Proofreading does not result in more reliable ligand discrimination in receptor signaling due to its inherent stochasticity

Kinetic proofreading (KPR) has been used as a paradigmatic explanation for the high specificity of ligand discrimination by cellular receptors. KPR enhances the difference in the mean receptor occupancy between different ligands compared to a nonproofread receptor, thus potentially enabling better discrimination. On the other hand, proofreading also attenuates the signal and introduces additional stochastic receptor transitions relative to a nonproofreading receptor. This increases the relative magnitude of noise in the downstream signal, which can interfere with reliable ligand discrimination. To understand the effect of noise on ligand discrimination beyond the comparison of the mean signals, we formulate the task of ligand discrimination as a problem of statistical estimation of the receptor affinity of ligands based on the molecular signaling output. Our analysis reveals that proofreading typically worsens ligand resolution compared to a nonproofread receptor. Furthermore, the resolution decreases further with more proofreading steps under most commonly biologically considered conditions. This contrasts with the usual notion that KPR universally improves ligand discrimination with additional proofreading steps. Our results are consistent across a variety of different proofreading schemes and metrics of performance, suggesting that they are inherent to the KPR mechanism itself rather than any particular model of molecular noise. Based on our results, we suggest alternative roles for KPR schemes such as multiplexing and combinatorial encoding in multi-ligand/multi-output pathways.

ITK signaling regulates a switch between T helper 17 and T regulatory cell lineages via a calcium-mediated pathway

The balance of pro-inflammatory T helper type 17 (Th17) and anti-inflammatory T regulatory (Treg) cells is crucial in maintaining immune homeostasis in health and disease conditions. Differentiation of naïve CD4+ T cells into Th17/Treg cells is dependent upon T cell receptor (TCR) activation and cytokine signaling, which includes the kinase ITK. Signals from ITK can regulate the differentiation of Th17 and Treg cell fate choice, however, the mechanism remains to be fully understood. We report here that in the absence of ITK activity, instead of developing into Th17 cells under Th17 conditions, naïve CD4+ T cells switch to cells expressing the Treg marker Foxp3 (Forkhead box P3). These switched Foxp3+ Treg like cells retain suppressive function and resemble differentiated induced Tregs in their transcriptomic profile, although their chromatin accessibility profiles are intermediate between Th17 and induced Tregs cells. Generation of the switched Foxp3+ Treg like cells was associated with reduced expression of molecules involved in mitochondrial oxidative phosphorylation and glycolysis, with reduced activation of the mTOR signaling pathway, and reduced expression of BATF. This ITK dependent switch between Th17 and Treg cells was reversed by increasing intracellular calcium. These findings suggest potential strategies for fine tune the TCR signal strength via ITK to regulate the balance of Th17/Treg cells.

T cell receptor signaling in the differentiation and plasticity of CD4+ T cells

CD4⁺ T cells are critical components of the adaptive immune system. The T cell receptor (TCR) and co-receptor signaling cascades shape the phenotype and functions of CD4⁺ T cells. TCR signaling plays a crucial role in T cell development, antigen recognition, activation, and differentiation upon recognition of foreign- or auto-antigens. In specific autoimmune conditions, altered TCR repertoire is reported and can predispose autoimmunity with organ-specific inflammation and tissue damage. TCR signaling modulates various signaling cascades and regulates epigenetic and transcriptional regulation during homeostasis and disease conditions. Understanding the mechanism by which coreceptors and cytokine signals control the magnitude of TCR signal amplification will aid in developing therapeutic strategies to treat inflammation and autoimmune diseases. This review focuses on the role of the TCR signaling cascade and its components in the activation, differentiation, and plasticity of various CD4⁺ T cell subsets.

Temporary and permanent control of partially specified Boolean networks

Boolean networks (BNs) are a well-accepted modelling formalism in computational systems biology. Nevertheless, modellers often cannot identify only a single BN that matches the biological reality. The typical reasons for this is insufficient knowledge or a lack of experimental data. Formally, this uncertainty can be expressed using partially specified Boolean networks (PSBNs), which encode the wide range of network candidates into a single structure. In this paper, we target the control of PSBNs. The goal of BN control is to find perturbations which guarantee stabilisation of the system in the desired state. Specifically, we consider variable perturbations (gene knock-out and over-expression) with three types of application time-window: one-step, temporary, and permanent. While the control of fully specified BNs is a thoroughly explored topic, control of PSBNs introduces additional challenges that we address in this paper. In particular, the unspecified components of the model cause a significant amount of additional state space explosion. To address this issue, we propose a fully symbolic methodology that can represent the numerous system variants in a compact form. In fully specified models, the efficiency of a perturbation is characterised by the count of perturbed variables (the perturbation size). However, in the case of a PSBN, a perturbation might work only for a subset of concrete BN models. To that end, we introduce and quantify perturbation robustness. This metric characterises how efficient the given perturbation is with respect to the model uncertainty. Finally, we evaluate the novel control methods using non-trivial real-world PSBN models. We inspect the method's scalability and efficiency with respect to the size of the state space and the number of unspecified components. We also compare the robustness metrics for all three perturbation types. Our experiments support the hypothesis that one-step perturbations are significantly less robust than temporary and permanent ones.

Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells

As a result of the development of experimental technologies and the accumulation of data, biological and molecular processes can be described as complex networks of signaling pathways. These networks are often directed and signed, where nodes represent entities (genes/proteins) and arrows interactions. They are translated into mathematical models by adding a dynamic layer onto them. Such mathematical models help to understand and interpret non-intuitive experimental observations and to anticipate the response to external interventions such as drug effects on phenotypes. Several frameworks for modeling signaling pathways exist. The choice of the appropriate framework is often driven by the experimental context. In this review, we present MaBoSS, a tool based on Boolean modeling using a continuous time approach, which predicts time-dependent probabilities of entities in different biological contexts. MaBoSS was initially built to model the intracellular signaling in non-interacting homogeneous cell populations. MaBoSS was then adapted to model heterogeneous cell populations (EnsembleMaBoSS) by considering families of models rather than a unique model. To account for more complex questions, MaBoSS was extended to simulate dynamical interacting populations (UPMaBoSS), with a precise spatial distribution (PhysiBoSS). To illustrate all these levels of description, we show how each of these tools can be used with a running example of a simple model of cell fate decisions. Finally, we present practical applications to cancer biology and studies of the immune response.

Structurally related (-)-epicatechin metabolites and gut microbiota derived metabolites exert genomic modifications via VEGF signaling pathways in brain microvascular endothelial cells under lipotoxic conditions: Integrated multi-omic study

Dysfunction of blood-brain barrier formed by endothelial cells of cerebral blood vessels, plays a key role in development of neurodegenerative disorders. Epicatechin exerts vasculo-protective effects through genomic modifications, however molecular mechanisms of action, particularly on brain endothelial cells, are largely unknow. This study aimed to use a multi-omic approach (transcriptomics of mRNA, miRNAs and lncRNAs, and proteomics), to provide novel in-depth insights into molecular mechanisms of how metabolites affect brain endothelial cells under lipid-stressed (as a model of BBB dysfunction) at physiological concentrations. We showed that metabolites can simultaneously modulate expression of protein-coding, non-coding genes and proteins. Integrative analysis revealed interactions between different types of RNAs and form functional groups of genes involved in regulation of processing like VEGF-related functions, cell signaling, cell adhesion and permeability. Molecular modeling of genomics data predicted that metabolites decrease endothelial cell permeability, increased by lipotoxic stress. Correlation analysis between genomic modifications observed and genomic signature of patients with vascular dementia and Alzheimer's diseases showed opposite gene expression changes. Taken together, this study describes for the first time a multi-omic mechanism of action by which (-)-epicatechin metabolites could preserve brain vascular endothelial cell integrity and reduce the risk of neurodegenerative diseases. SIGNIFICANCE: Dysfunction of the blood-brain barrier (BBB), characterized by dysfunction of endothelial cells of cerebral blood vessels, result in an increase in permeability and neuroinflammation which constitute a key factor in the development neurodegenerative disorders. Even though it is suggested that polyphenols can prevent or delay the development of these disorders, their impact on brain endothelial cells and underlying mechanisms of actions are unknow. This study aimed to use a multi-omic approach including analysis of expression of mRNA, microRNA, long non-coding RNAs, and proteins to provide novel global in-depth insights into molecular mechanisms of how (-)-epicatechin metabolites affect brain microvascular endothelial cells under lipid-stressed (as a model of BBB dysfunction) at physiological relevant conditions. The results provide basis of knowledge on the capacity of polyphenols to prevent brain endothelial dysfunction and consequently neurodegenerative disorders.

Guided assembly of cellular network models from knowledge in literature

Computational modeling is crucial for understanding and analyzing complex systems. In biology, model creation is a human dependent task that requires reading hundreds of papers and conducting wet lab experiments, which would take days or months. To overcome this hurdle, we propose a novel automated method, that utilizes the knowledge published in literature to suggest model extensions by selecting most relevant and useful information in few seconds. In particular, our novel approach organizes the events extracted from the literature as a collaboration graph with additional metric that relies on the event occurrence frequency in literature. Additionally, we show that common graph centrality metrics vary in the assessment of the extracted events. We have demonstrated the reliability of the proposed method using three different selected models, namely, T cell differentiation, T cell large granular lymphocyte, and pancreatic cancer cell. Our proposed method was able to find high percent of the desired new events with an average recall of 82%.

Strength and Numbers: The Role of Affinity and Avidity in the 'Quality' of T Cell Tolerance

The ability of T cells to identify foreign antigens and mount an efficient immune response while limiting activation upon recognition of self and self-associated peptides is critical. Multiple tolerance mechanisms work in concert to prevent the generation and activation of self-reactive T cells. T cell tolerance is tightly regulated, as defects in these processes can lead to devastating disease; a wide variety of autoimmune diseases and, more recently, adverse immune-related events associated with checkpoint blockade immunotherapy have been linked to a breakdown in T cell tolerance. The quantity and quality of antigen receptor signaling depend on a variety of parameters that include T cell receptor affinity and avidity for peptide. Autoreactive T cell fate choices (e.g., deletion, anergy, regulatory T cell development) are highly dependent on the strength of T cell receptor interactions with self-peptide. However, less is known about how differences in the strength of T cell receptor signaling during differentiation influences the 'function' and persistence of anergic and regulatory T cell populations. Here, we review the literature on this subject and discuss the clinical implications of how T cell receptor signal strength influences the 'quality' of anergic and regulatory T cell populations.

CLARINET: efficient learning of dynamic network models from literature

Motivation

Creating or extending computational models of complex systems, such as intra- and intercellular biological networks, is a time and labor-intensive task, often limited by the knowledge and experience of modelers. Automating this process would enable rapid, consistent, comprehensive and robust analysis and understanding of complex systems.

Results

In this work, we present CLARINET (CLARIfying NETworks), a novel methodology and a tool for automatically expanding models using the information extracted from the literature by machine reading. CLARINET creates collaboration graphs from the extracted events and uses several novel metrics for evaluating these events individually, in pairs, and in groups. These metrics are based on the frequency of occurrence and co-occurrence of events in literature, and their connectivity to the baseline model. We tested how well CLARINET can reproduce manually built and curated models, when provided with varying amount of information in the baseline model and in the machine reading output. Our results show that CLARINET can recover all relevant interactions that are present in the reading output and it automatically reconstructs manually built models with average recall of 80% and average precision of 70%. CLARINET is highly scalable, its average runtime is at the order of ten seconds when processing several thousand interactions, outperforming other similar methods.

Availability and implementation

The data underlying this article are available in Bitbucket at https://bitbucket.org/biodesignlab/clarinet/src/master/.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

TCR Affinity Controls the Dynamics but Not the Functional Specification of the Antimycobacterial CD4+ T Cell Response

The quality of T cell responses depends on the lymphocytes' ability to undergo clonal expansion, acquire effector functions, and traffic to the site of infection. Although TCR signal strength is thought to dominantly shape the T cell response, by using TCR transgenic CD4⁺ T cells with different peptide:MHC binding affinity, we reveal that TCR affinity does not control Th1 effector function acquisition or the functional output of individual effectors following mycobacterial infection in mice. Rather, TCR affinity calibrates the rate of cell division to synchronize the distinct processes of T cell proliferation, differentiation, and trafficking. By timing cell division-dependent IL-12R expression, TCR affinity controls when T cells become receptive to Th1-imprinting IL-12 signals, determining the emergence and magnitude of the Th1 effector pool. These findings reveal a distinct yet cooperative role for IL-12 and TCR binding affinity in Th1 differentiation and suggest that the temporal activation of clones with different TCR affinity is a major strategy to coordinate immune surveillance against persistent pathogens.

Modeling the Th17 and Tregs Paradigm: Implications for Cancer Immunotherapy

CD4 + T cell differentiation is governed by gene regulatory and metabolic networks, with both networks being highly interconnected and able to adapt to external stimuli. Th17 and Tregs differentiation networks play a critical role in cancer, and their balance is affected by the tumor microenvironment (TME). Factors from the TME mediate recruitment and expansion of Th17 cells, but these cells can act with pro or anti-tumor immunity. Tregs cells are also involved in tumor development and progression by inhibiting antitumor immunity and promoting immunoevasion. Due to the complexity of the underlying molecular pathways, the modeling of biological systems has emerged as a promising solution for better understanding both CD4 + T cell differentiation and cancer cell behavior. In this review, we present a context-dependent vision of CD4 + T cell transcriptomic and metabolic network adaptability. We then discuss CD4 + T cell knowledge-based models to extract the regulatory elements of Th17 and Tregs differentiation in multiple CD4 + T cell levels. We highlight the importance of complementing these models with data from omics technologies such as transcriptomics and metabolomics, in order to better delineate existing Th17 and Tregs bifurcation mechanisms. We were able to recompilate promising regulatory components and mechanisms of Th17 and Tregs differentiation under normal conditions, which we then connected with biological evidence in the context of the TME to better understand CD4 + T cell behavior in cancer. From the integration of mechanistic models with omics data, the transcriptomic and metabolomic reprograming of Th17 and Tregs cells can be predicted in new models with potential clinical applications, with special relevance to cancer immunotherapy.

An Integrated Analysis of miRNA and Gene Expression Changes in Response to an Obesogenic Diet to Explore the Impact of Transgenerational Supplementation with Omega 3 Fatty Acids

Insulin resistance decreases the ability of insulin to inhibit hepatic gluconeogenesis, a key step in the development of metabolic syndrome. Metabolic alterations, fat accumulation, and fibrosis in the liver are closely related and contribute to the progression of comorbidities, such as hypertension, type 2 diabetes, or cancer. Omega 3 (n-3) polyunsaturated fatty acids, such as eicosapentaenoic acid (EPA), were identified as potent positive regulators of insulin sensitivity in vitro and in animal models. In the current study, we explored the effects of a transgenerational supplementation with EPA in mice exposed to an obesogenic diet on the regulation of microRNAs (miRNAs) and gene expression in the liver using high-throughput techniques. We implemented a comprehensive molecular systems biology approach, combining statistical tools, such as MicroRNA Master Regulator Analysis pipeline and Boolean modeling to integrate these biochemical processes. We demonstrated that EPA mediated molecular adaptations, leading to the inhibition of miR-34a-5p, a negative regulator of Irs2 as a master regulatory event leading to the inhibition of gluconeogenesis by insulin during the fasting-feeding transition. Omics data integration provided greater biological insight and a better understanding of the relationships between biological variables. Such an approach may be useful for deriving innovative data-driven hypotheses and for the discovery of molecular-biochemical mechanistic links.

Understanding the Dynamics of T-Cell Activation in Health and Disease Through the Lens of Computational Modeling

T cells in the immune system are activated by binding to foreign peptides (from an external pathogen) or mutant peptide (derived from endogenous proteins) displayed on the surface of a diseased cell. This triggers a series of intracellular signaling pathways, which ultimately dictate the response of the T cell. The insights from computational models have greatly improved our understanding of the mechanisms that control T-cell activation. In this review, we focus on the use of ordinary differential equation–based mechanistic models to study T-cell activation. We highlight several examples that demonstrate the models’ utility in answering specific questions related to T-cell activation signaling, from antigen discrimination to the feedback mechanisms that initiate transcription factor activation. In addition, we describe other modeling approaches that can be combined with mechanistic models to bridge time scales and better understand how intracellular signaling events, which occur on the order of seconds to minutes, influence phenotypic responses of T-cell activation, which occur on the order of hours to days. Overall, through concrete examples, we emphasize how computational modeling can be used to enable the rational design and optimization of immunotherapies.

Molecular Mechanisms Controlling Foxp3 Expression in Health and Autoimmunity: From Epigenetic to Post-translational Regulation

The discovery of the transcription factor Forkhead box-p3 (Foxp3) has shed fundamental insights into the understanding of the molecular determinants leading to generation and maintenance of T regulatory (Treg) cells, a cell population with a key immunoregulatory role. Work over the past few years has shown that fine-tuned transcriptional and epigenetic events are required to ensure stable expression of Foxp3 in Treg cells. The equilibrium between phenotypic plasticity and stability of Treg cells is controlled at the molecular level by networks of transcription factors that bind regulatory sequences, such as enhancers and promoters, to regulate Foxp3 expression. Recent reports have suggested that specific modifications of DNA and histones are required for the establishment of the chromatin structure in conventional CD4⁺ T (Tconv) cells for their future differentiation into the Treg cell lineage. In this review, we discuss the molecular events that control Foxp3 gene expression and address the associated alterations observed in human diseases. Also, we explore how Foxp3 influences the gene expression programs in Treg cells and how unique properties of Treg cell subsets are defined by other transcription factors.

A Faster DiSH: Hardware Implementation of a Discrete Cell Signaling Simulator

Development of fast methods to conduct in silico experiments using computational models of cellular signaling is a promising approach toward advances in personalized medicine. However, software-based cellular network simulation has runtimes plagued by wasted CPU cycles and unnecessary processes. Hardware emulation affords substantial speedup, but prior attempts at hardware implementation of biological simulators have been limited in scope and have suffered from inaccuracies due to poor random number generation. In this work, we propose several simulation schemes utilizing novel random update index generation techniques for step-based and round-based stochastic simulations of cellular networks. Our results show improved runtimes while maintaining simulation accuracy compared to software implementations.

Cutting Edge: TCR Signal Strength Regulates Acetyl-CoA Metabolism via AKT

TCR signaling activates kinases including AKT/mTOR that engage metabolic networks to support the energetic demands of a T cell during an immune response. It is realized that CD4⁺ T cell subsets have different metabolic requirements. Yet, how TCR signaling is coupled to the regulation of intermediate metabolites and how changes in metabolite flux contribute to T cell differentiation are less established. We find that TCR signaling regulates acetyl-CoA metabolism via AKT in murine CD4⁺ T cells. Weak TCR signals promote AKT-catalyzed phosphorylation and inhibition of citrate synthase, elevated acetyl-CoA levels, and hyperacetylation of mitochondrial proteins. Genetic knockdown of citrate synthase promotes increased nuclear acetyl-CoA levels, increased histone acetylation at the FOXP3 promotor and induction of FOXP3 transcription. These data identify a circuit between AKT signaling and acetyl-CoA metabolism regulated via TCR signal strength and that transient fluctuations in acetyl-CoA levels function in T cell fate decisions.

NLRP3 negatively regulates Treg differentiation through Kpna2-mediated nuclear translocation

Naïve CD4⁺ T cells in the periphery differentiate into regulatory T cells (Tregs) in which Foxp3 is expressed for their suppressive function. NLRP3, a pro-inflammatory molecule, is known to be involved in inflammasome activation associated with several diseases. Recently, the expression of NLRP3 in CD4⁺ T cells, as well as in myeloid cells, has been described; however, a role of T cell-intrinsic NLRP3 in Treg differentiation remains unknown. Here, we report that NLRP3 impeded the expression of Foxp3 independent of inflammasome activation in Tregs. NLRP3-deficient mice elevate Treg generation in various organs in the de novo pathway. NLRP3 deficiency increased the amount and suppressive activity of Treg populations, whereas NLRP3 overexpression reduced Foxp3 expression and Treg abundance. Importantly, NLRP3 interacted with Kpna2 and translocated to the nucleus from the cytoplasm under Treg-polarizing conditions. Taken together, our results identify a novel role for NLRP3 as a new negative regulator of Treg differentiation, mediated via its interaction with Kpna2 for nuclear translocation.

Reduced Annexin A1 Expression Associates with Disease Severity and Inflammation in Multiple Sclerosis Patients

Chronic neuroinflammation is a key pathological hallmark of multiple sclerosis (MS) that suggests that resolution of inflammation by specialized proresolving molecules is dysregulated in the disease. Annexin A1 (ANXA1) is a protein induced by glucocorticoids that facilitates resolution of inflammation through several mechanisms that include an inhibition of leukocyte recruitment and activation. In this study, we investigated the ability of ANXA1 to influence T cell effector function in relapsing/remitting MS (RRMS), an autoimmune disease sustained by proinflammatory Th1/Th17 cells. Circulating expression levels of ANXA1 in naive-to-treatment RRMS subjects inversely correlated with disease score and progression. At the cellular level, there was an impaired ANXA1 production by CD4⁺CD25^- conventional T and CD4⁺RORγt⁺ T (Th17) cells from RRMS subjects that associated with an increased migratory capacity in an in vitro model of blood brain barrier. Mechanistically, ANXA1 impaired monocyte maturation secondarily to STAT3 hyperactivation and potently reduced T cell activation, proliferation, and glycolysis. Together, these findings identify impaired disease resolution pathways in RRMS caused by dysregulated ANXA1 expression that could represent new potential therapeutic targets in RRMS.

From Discrete to Continuous Modeling of Lymphocyte Development and Plasticity in Chronic Diseases

The molecular events leading to differentiation, development, and plasticity of lymphoid cells have been subject of intense research due to their key roles in multiple pathologies, such as lymphoproliferative disorders, tumor growth maintenance and chronic diseases. The emergent roles of lymphoid cells and the use of high-throughput technologies have led to an extensive accumulation of experimental data allowing the reconstruction of gene regulatory networks (GRN) by integrating biochemical signals provided by the microenvironment with transcriptional modules of lineage-specific genes. Computational modeling of GRN has been useful for the identification of molecular switches involved in lymphoid specification, prediction of microenvironment-dependent cell plasticity, and analyses of signaling events occurring downstream the activation of antigen recognition receptors. Among most common modeling strategies to analyze the dynamical behavior of GRN, discrete dynamic models are widely used for their capacity to capture molecular interactions when a limited knowledge of kinetic parameters is present. However, they are less powerful when modeling complex systems sensitive to biochemical gradients. To compensate it, discrete models may be transformed into regulatory networks that includes state variables and parameters varying within a continuous range. This approach is based on a system of differential equations dynamics with regulatory interactions described by fuzzy logic propositions. Here, we discuss the applicability of this method on modeling of development and plasticity processes of adaptive lymphocytes, and its potential implications in the study of pathological landscapes associated to chronic diseases.

Reprogramming of Th1 cells into regulatory T cells through rewiring of the metabolic status

T helper type 1 (Th1) cells form one of the most stable CD4 T-cell subsets, and direct conversion of fully differentiated Th1 to regulatory T (Treg) cells has been poorly investigated. Here, we established a culture method for inducing Foxp3 from Th1 cells of mice and humans. This is achieved simply by resting Th1 cells without T-cell receptor ligation before stimulation in the presence of transforming growth factor-beta (TGF-β). We named the resulting Th1-derived Foxp3+ cells Th1reg cells. Mouse Th1reg cells showed an inducible Treg-like phenotype and suppressive ability both in vitro and in vivo. Th1reg cells could also be induced from in vivo-developed mouse Th1 cells. Unexpectedly, the resting process enabled Foxp3 expression not through epigenetic changes at the locus, but through metabolic change resulting from reduced mammalian target of rapamycin complex 1 (mTORC1) activity. mTORC1 suppressed TGF-β-induced phosphorylation of Smad2/3 in Th1 cells, which was restored in rested cells. Our study warrants future research aiming at development of immunotherapy with Th1reg cells.

Spatial presentation of biological molecules to cells by localized diffusive transfer

Cellular decisions in human development, homeostasis, regeneration, and disease are coordinated in large part by signals that are spatially localized in tissues. These signals are often soluble, such that biomolecules produced by one cell diffuse to receiving cells. To recapitulate soluble factor patterning in vitro, several microscale strategies have been developed. However, these techniques often introduce new variables into cell culture experiments (e.g., fluid flow) or are limited in their ability to pattern diverse solutes in a user-defined manner. To address these challenges, we developed an adaptable method that facilitates spatial presentation of biomolecules across cells in traditional open cultures in vitro. This technique employs device inserts that are placed in standard culture wells, which support localized diffusive pattern transmission through microscale spaces between device features and adherent cells. Devices can be removed and cultures can be returned to standard media following patterning. We use this method to spatially control cell labeling with pattern features ranging in scale from several hundred microns to millimeters and with sequential application of multiple patterns. To better understand the method we investigate relationships between pattern fidelity, device geometry, and consumption and diffusion kinetics using finite element modeling. We then apply the method to spatially defining reporter cell heterogeneity by patterning a small molecule modulator of genetic recombination with the requisite sustained exposure. Finally, we demonstrate use of this method for patterning larger and more slowly diffusing particles by creating focal sites of gene delivery and infection with adenoviral, lentiviral, and Zika virus particles. Thus, our method leverages devices that interface with standard culture vessels to pattern diverse diffusible factors, geometries, exposure dynamics, and recipient cell types, making it well poised for adoption by researchers across various fields of biological research.

An enriched network motif family regulates multistep cell fate transitions with restricted reversibility

Multistep cell fate transitions with stepwise changes of transcriptional profiles are common to many developmental, regenerative and pathological processes. The multiple intermediate cell lineage states can serve as differentiation checkpoints or branching points for channeling cells to more than one lineages. However, mechanisms underlying these transitions remain elusive. Here, we explored gene regulatory circuits that can generate multiple intermediate cellular states with stepwise modulations of transcription factors. With unbiased searching in the network topology space, we found a motif family containing a large set of networks can give rise to four attractors with the stepwise regulations of transcription factors, which limit the reversibility of three consecutive steps of the lineage transition. We found that there is an enrichment of these motifs in a transcriptional network controlling the early T cell development, and a mathematical model based on this network recapitulates multistep transitions in the early T cell lineage commitment. By calculating the energy landscape and minimum action paths for the T cell model, we quantified the stochastic dynamics of the critical factors in response to the differentiation signal with fluctuations. These results are in good agreement with experimental observations and they suggest the stable characteristics of the intermediate states in the T cell differentiation. These dynamical features may help to direct the cells to correct lineages during development. Our findings provide general design principles for multistep cell linage transitions and new insights into the early T cell development. The network motifs containing a large family of topologies can be useful for analyzing diverse biological systems with multistep transitions.

The functions of cells are dynamically controlled in many biological processes including development, regeneration and disease progression. Cell fate transition, or the switch of cellular functions, often involves multiple steps. The intermediate stages of the transition provide the biological systems with the opportunities to regulate the transitions in a precise manner. These transitions are controlled by key regulatory genes of which the expression shows stepwise patterns, but how the interactions of these genes can determine the multistep processes was unclear. Here, we present a comprehensive analysis on the design principles of gene circuits that govern multistep cell fate transition. We found a large network family with common structural features that can generate systems with the ability to control three consecutive steps of the transition. We found that this type of networks is enriched in a gene circuit controlling the development of T lymphocyte, a crucial type of immune cells. We performed mathematical modeling using this gene circuit and we recapitulated the stepwise and irreversible loss of stem cell properties of the developing T lymphocytes. Our findings can be useful to analyze a wide range of gene regulatory networks controlling multistep cell fate transitions.

T cells transduce T-cell receptor signal strength by generating different phosphatidylinositols

T-cell receptor (TCR) signaling strength is a dominant factor regulating T-cell differentiation, thymic development, and cytokine signaling. The molecular mechanisms by which TCR signal strength is transduced to downstream signaling networks remains ill-defined. Using computational modeling, biochemical assays, and imaging flow cytometry, we found here that TCR signal strength differentially generates phosphatidylinositol species. Weak TCR signals generated elevated phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) and reduced phosphatidylinositol (3,4,5)-trisphosphate (PIP3) levels, whereas strong TCR signals reduced PI(4,5)P2 and elevated PIP3 levels. A proteomics screen revealed that focal adhesion kinase bound PI(4,5)P2, biochemical assays disclosed that focal adhesion kinase is preferentially activated by weak TCR signals and is required for optimal Treg induction, and further biochemical experiments revealed how TCR signaling strength regulates AKT activation. Low PIP3 levels generated by weak TCR signals were sufficient to activate phosphoinositide-dependent kinase-1 to phosphorylate AKT on Thr-308 but insufficient to activate mTOR complex 2 (mTORC2), whereas elevated PIP3 levels generated by a strong TCR signal were required to activate mTORC2 to phosphorylate Ser-473 on AKT. Our results provide support for a model that links TCR signaling to mTORC2 activation via phosphoinositide 3-kinase signaling. Together, the findings in this work establish that T cells measure TCR signal strength by generating different levels of phosphatidylinositol species that engage alternate signaling networks to control cell fate decisions.

A Step-by-Step Guide to Using BioNetFit

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

Automated Extension of Cell Signaling Models with Genetic Algorithm

The number of published results in biology and medicine is growing at an exceeding rate, and thus, extracting relevant information for building useful models is becoming very laborious. Furthermore, with the newly published information, previously built models need to be extended and updated, and with the voluminous literature, it is necessary to automate the model extension process. In this work, we introduce a methodology for extending logical models of cell signaling networks using a Genetic Algorithm (GA). The proposed procedure is developed to optimally search for a subset of biological interactions that extend logical models while preserving their desired behavior. To evaluate the effectiveness of the proposed methodology, we randomly removed a subset of elements from an existing T cell differentiation model, and mixed them with randomly created interactions to mimic the output of literature reading. We then used the GA to search for the extensions that optimally reconstructed the model. The simulation results showed that the GA was able to find a set of extensions that preserved the desired behavior of the model with fewer elements than the original model. The results demonstrate that the GA is an efficient tool for model extension, and suggest that it can be used for model reduction as well.

Mathematical modeling identifies Lck as a potential mediator for PD-1 induced inhibition of early TCR signaling

Programmed cell death-1 (PD-1) is an inhibitory immune checkpoint receptor that negatively regulates the functioning of T cell. Although the direct targets of PD-1 were not identified, its inhibitory action on the TCR signaling pathway was known much earlier. Recent experiments suggest that the PD-1 inhibits the TCR and CD28 signaling pathways at a very early stage ─ at the level of phosphorylation of the cytoplasmic domain of TCR and CD28 receptors. Here, we develop a mathematical model to investigate the influence of inhibitory effect of PD-1 on the activation of early TCR and CD28 signaling molecules. Proposed model recaptures several quantitative experimental observations of PD-1 mediated inhibition. Model simulations show that PD-1 imposes a net inhibitory effect on the Lck kinase. Further, the inhibitory effect of PD-1 on the activation of TCR signaling molecules such as Zap70 and SLP76 is significantly enhanced by the PD-1 mediated inhibition of Lck. These results suggest a critical role for Lck as a mediator for PD-1 induced inhibition of TCR signaling network. Multi parametric sensitivity analysis explores the effect of parameter uncertainty on model simulations.

mTOR Complex 1 Signaling Regulates the Generation and Function of Central and Effector Foxp3+ Regulatory T Cells

The mechanistic/mammalian target of rapamycin (mTOR) has emerged as a critical integrator of signals from the immune microenvironment capable of regulating T cell activation, differentiation, and function. The precise role of mTOR in the control of regulatory T cell (Treg) differentiation and function is complex. Pharmacologic inhibition and genetic deletion of mTOR promotes the generation of Tregs even under conditions that would normally promote generation of effector T cells. Alternatively, mTOR activity has been observed to be increased in Tregs, and the genetic deletion of the mTOR complex 1 (mTORC1)-scaffold protein Raptor inhibits Treg function. In this study, by employing both pharmacologic inhibitors and genetically altered T cells, we seek to clarify the role of mTOR in Tregs. Our studies demonstrate that inhibition of mTOR during T cell activation promotes the generation of long-lived central Tregs with a memory-like phenotype in mice. Metabolically, these central memory Tregs possess enhanced spare respiratory capacity, similar to CD8⁺ memory cells. Alternatively, the generation of effector Tregs (eTregs) requires mTOR function. Indeed, genetic deletion of Rptor leads to the decreased expression of ICOS and PD-1 on the eTregs. Overall, our studies define a subset of mTORC1^hi eTregs and mTORC1^lo central Tregs.

Differential T-cell receptor signals for T helper cell programming

Upon encounter with their cognate antigen, naive CD4 T cells become activated and are induced to differentiate into several possible T helper (Th) cell subsets. This differentiation depends on a number of factors including antigen-presenting cells, cytokines and co-stimulatory molecules. The strength of the T-cell receptor (TCR) signal, related to the affinity of TCR for antigen and antigen dose, has emerged as a dominant factor in determining Th cell fate. Recent studies have revealed that TCR signals of high or low strength do not simply induce quantitatively different signals in the T cells, but rather qualitatively distinct pathways can be induced based on TCR signal strength. This review examines the recent literature in this area and highlights important new developments in our understanding of Th cell differentiation and TCR signal strength.

Target Control in Logical Models Using the Domain of Influence of Nodes

Dynamical models of biomolecular networks are successfully used to understand the mechanisms underlying complex diseases and to design therapeutic strategies. Network control and its special case of target control, is a promising avenue toward developing disease therapies. In target control it is assumed that a small subset of nodes is most relevant to the system's state and the goal is to drive the target nodes into their desired states. An example of target control would be driving a cell to commit to apoptosis (programmed cell death). From the experimental perspective, gene knockout, pharmacological inhibition of proteins, and providing sustained external signals are among practical intervention techniques. We identify methodologies to use the stabilizing effect of sustained interventions for target control in Boolean network models of biomolecular networks. Specifically, we define the domain of influence (DOI) of a node (in a certain state) to be the nodes (and their corresponding states) that will be ultimately stabilized by the sustained state of this node regardless of the initial state of the system. We also define the related concept of the logical domain of influence (LDOI) of a node, and develop an algorithm for its identification using an auxiliary network that incorporates the regulatory logic. This way a solution to the target control problem is a set of nodes whose DOI can cover the desired target node states. We perform greedy randomized adaptive search in node state space to find such solutions. We apply our strategy to in silico biological network models of real systems to demonstrate its effectiveness.

A framework to find the logic backbone of a biological network

Background

Cellular behaviors are governed by interaction networks among biomolecules, for example gene regulatory and signal transduction networks. An often used dynamic modeling framework for these networks, Boolean modeling, can obtain their attractors (which correspond to cell types and behaviors) and their trajectories from an initial state (e.g. a resting state) to the attractors, for example in response to an external signal. The existing methods however do not elucidate the causal relationships between distant nodes in the network.

Results

In this work, we propose a simple logic framework, based on categorizing causal relationships as sufficient or necessary, as a complement to Boolean networks. We identify and explore the properties of complex subnetworks that are distillable into a single logic relationship. We also identify cyclic subnetworks that ensure the stabilization of the state of participating nodes regardless of the rest of the network. We identify the logic backbone of biomolecular networks, consisting of external signals, self-sustaining cyclic subnetworks (stable motifs), and output nodes. Furthermore, we use the logic framework to identify crucial nodes whose override can drive the system from one steady state to another. We apply these techniques to two biological networks: the epithelial-to-mesenchymal transition network corresponding to a developmental process exploited in tumor invasion, and the network of abscisic acid induced stomatal closure in plants. We find interesting subnetworks with logical implications in these networks. Using these subgraphs and motifs, we efficiently reduce both networks to succinct backbone structures.

Conclusions

The logic representation identifies the causal relationships between distant nodes and subnetworks. This knowledge can form the basis of network control or used in the reverse engineering of networks.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0482-5) contains supplementary material, which is available to authorized users.

Role of Metabolism in the Immunobiology of Regulatory T Cells

Intracellular metabolism is central to cell activity and function. CD4(+)CD25(+) regulatory T cells (Tregs) that express the transcription factor FOXP3 play a pivotal role in the maintenance of immune tolerance to self. Recent studies showed that the metabolism and function of Tregs are influenced significantly by local environmental conditions and the availability of certain metabolites. It also was reported that defined metabolic programs associate with Treg differentiation, expression of FOXP3, and phenotype stabilization. This article reviews how metabolism modulates FOXP3 expression and Treg function, what environmental factors are involved, and how metabolic manipulation could alter Treg frequency and function in physiopathologic conditions.

The CD4+ T cell regulatory network mediates inflammatory responses during acute hyperinsulinemia: a simulation study

Background

Obesity is frequently linked to insulin resistance, high insulin levels, chronic inflammation, and alterations in the behaviour of CD4+ T cells. Despite the biomedical importance of this condition, the system-level mechanisms that alter CD4+ T cell differentiation and plasticity are not well understood.

Results

We model how hyperinsulinemia alters the dynamics of the CD4+ T regulatory network, and this, in turn, modulates cell differentiation and plasticity. Different polarizing microenvironments are simulated under basal and high levels of insulin to assess impacts on cell-fate attainment and robustness in response to transient perturbations. In the presence of high levels of insulin Th1 and Th17 become more stable to transient perturbations, and their basin sizes are augmented, Tr1 cells become less stable or disappear, while TGFβ producing cells remain unaltered. Hence, the model provides a dynamic system-level framework and explanation to further understand the documented and apparently paradoxical role of TGFβ in both inflammation and regulation of immune responses, as well as the emergence of the adipose Treg phenotype. Furthermore, our simulations provide new predictions on the impact of the microenvironment in the coexistence of the different cell types, suggesting that in pro-Th1, pro-Th2 and pro-Th17 environments effector and regulatory cells can coexist, but that high levels of insulin severely diminish regulatory cells, especially in a pro-Th17 environment.

Conclusions

This work provides a first step towards a system-level formal and dynamic framework to integrate further experimental data in the study of complex inflammatory diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0436-y) contains supplementary material, which is available to authorized users.

TCR Signal Strength Regulates Akt Substrate Specificity To Induce Alternate Murine Th and T Regulatory Cell Differentiation Programs

The Akt/mTOR pathway is a key driver of murine CD4⁺ T cell differentiation, and induction of regulatory T (Treg) cells results from low TCR signal strength and low Akt/mTOR signaling. However, strong TCR signals induce high Akt activity that promotes Th cell induction. Yet, it is unclear how Akt controls alternate T cell fate decisions. We find that the strength of the TCR signal results in differential Akt enzymatic activity. Surprisingly, the Akt substrate networks associated with T cell fate decisions are qualitatively different. Proteomic profiling of Akt signaling networks during Treg versus Th induction demonstrates that Akt differentially regulates RNA processing and splicing factors to drive T cell differentiation. Interestingly, heterogeneous nuclear ribonucleoprotein (hnRNP) L or hnRNP A1 are Akt substrates during Treg induction and have known roles in regulating the stability and splicing of key mRNAs that code for proteins in the canonical TCR signaling pathway, including CD3ζ and CD45. Functionally, inhibition of Akt enzymatic activity results in the dysregulation of splicing during T cell differentiation, and knockdown of hnRNP L or hnRNP A1 results in the lower induction of Treg cells. Together, this work suggests that a switch in substrate specificity coupled to the phosphorylation status of Akt may lead to alternative cell fates and demonstrates that proteins involved with alternative splicing are important factors in T cell fate decisions.

Protein Kinase CK2 Controls the Fate between Th17 Cell and Regulatory T Cell Differentiation

CK2 is a highly conserved and pleiotropic serine/threonine kinase that promotes many prosurvival and proinflammatory signaling pathways, including PI3K/Akt/mTOR and JAK/STAT. These pathways are essential for CD4⁺ T cell activation and polarization, but little is known about how CK2 functions in T cells. In this article, we demonstrate that CK2 expression and kinase activity are induced upon CD4⁺ T cell activation. Targeting the catalytic activity of CK2 using the next-generation small molecule inhibitor CX-4945 in vitro significantly and specifically inhibited mouse and human Th17 cell differentiation while promoting the generation of Foxp3⁺ regulatory T cells (Tregs). These findings were associated with suppression of PI3K/Akt/mTOR activation and STAT3 phosphorylation upon CX-4945 treatment. Furthermore, we demonstrate that CX-4945 treatment inhibits the maturation of Th17 cells into inflammatory IFN-γ-coproducing effector cells. The Th17/Treg axis and maturation of Th17 cells are major contributing factors to the pathogenesis of many autoimmune disorders, including multiple sclerosis. Using a murine model of multiple sclerosis, experimental autoimmune encephalomyelitis, we demonstrate that in vivo administration of CX-4945 targets Akt/mTOR signaling in CD4⁺ T cells and the Th17/Treg axis throughout disease. Importantly, CX-4945 treatment after disease initiation significantly reduced disease severity, which was associated with a significant decrease in the frequency of pathogenic IFN-γ⁺ and GM-CSF⁺ Th17 cells in the CNS. Our data implicate CK2 as a regulator of the Th17/Treg axis and Th17 cell maturation and suggest that CK2 could be targeted for the treatment of Th17 cell-driven autoimmune disorders.

PKCθ links proximal T cell and Notch signaling through localized regulation of the actin cytoskeleton

Notch is a critical regulator of T cell differentiation and is activated through proteolytic cleavage in response to ligand engagement. Using murine myelin-reactive CD4 T cells, we demonstrate that proximal T cell signaling modulates Notch activation by a spatiotemporally constrained mechanism. The protein kinase PKCθ is a critical mediator of signaling by the T cell antigen receptor and the principal costimulatory receptor CD28. PKCθ selectively inactivates the negative regulator of F-actin generation, Coronin 1A, at the center of the T cell interface with the antigen presenting cell (APC). This allows for effective generation of the large actin-based lamellum required for recruitment of the Notch-processing membrane metalloproteinase ADAM10. Such enhancement of Notch activation is critical for efficient T cell proliferation and Th17 differentiation. We reveal a novel mechanism that, through modulation of the cytoskeleton, controls Notch activation at the T cell:APC interface thereby linking T cell receptor and Notch signaling pathways.

Adequate immune response ensured by binary IL-2 and graded CD25 expression in a murine transfer model

The IL-2/IL-2Ralpha (CD25) axis is of central importance for the interplay of effector and regulatory T cells. Nevertheless, the question how different antigen loads are translated into appropriate IL-2 production to ensure adequate responses against pathogens remains largely unexplored. Here we find that at single cell level, IL-2 is binary (digital) and CD25 is graded expressed whereas at population level both parameters show graded expression correlating with the antigen amount. Combining in vivo data with a mathematical model we demonstrate that only this binary IL-2 expression ensures a wide linear antigen response range for Teff and Treg cells under real spatiotemporal conditions. Furthermore, at low antigen concentrations binary IL-2 expression safeguards by its spatial distribution selective STAT5 activation only of closely adjacent Treg cells regardless of their antigen specificity. These data show that the mode of IL-2 secretion is critical to tailor the adaptive immune response to the antigen amount.

DOI:http://dx.doi.org/10.7554/eLife.20616.001

Compensatory interactions to stabilize multiple steady states or mitigate the effects of multiple deregulations in biological networks

Complex diseases can be modeled as damage to intracellular networks that results in abnormal cell behaviors. Network-based dynamic models such as Boolean models have been employed to model a variety of biological systems including those corresponding to disease. Previous work designed compensatory interactions to stabilize an attractor of a Boolean network after single node damage. We generalize this method to a multinode damage scenario and to the simultaneous stabilization of multiple steady state attractors. We classify the emergent situations, with a special focus on combinatorial effects, and characterize each class through simulation. We explore how the structural and functional properties of the network affect its resilience and its possible repair scenarios. We demonstrate the method's applicability to two intracellular network models relevant to cancer. This work has implications in designing prevention strategies for complex disease.

Solving Immunology?

Emergent responses of the immune system result from the integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for the systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the National Institute of Allergy and Infectious Disease (NIAID) workshop 'Complex Systems Science, Modeling and Immunity' and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling, and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies.

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.

Functionally Diverse NK-Like T Cells Are Effectors and Predictors of Successful Aging

The fundamental challenge of aging and long-term survivorship is maintenance of functional independence and compression of morbidity despite a life history of disease. Inasmuch as immunity is a determinant of individual health and fitness, unraveling novel mechanisms of immune homeostasis in late life is of paramount interest. Comparative studies of young and old persons have documented age-related atrophy of the thymus, the contraction of diversity of the T cell receptor (TCR) repertoire, and the intrinsic inefficiency of classical TCR signaling in aged T cells. However, the elderly have highly heterogeneous health phenotypes. Studies of defined populations of persons aged 75 and older have led to the recognition of successful aging, a distinct physiologic construct characterized by high physical and cognitive functioning without measurable disability. Significantly, successful agers have a unique T cell repertoire; namely, the dominance of highly oligoclonal αβT cells expressing a diverse array of receptors normally expressed by NK cells. Despite their properties of cell senescence, these unusual NK-like T cells are functionally active effectors that do not require engagement of their clonotypic TCR. Thus, NK-like T cells represent a beneficial remodeling of the immune repertoire with advancing age, consistent with the concept of immune plasticity. Significantly, certain subsets are predictors of physical/cognitive performance among older adults. Further understanding of the roles of these NK-like T cells to host defense, and how they integrate with other physiologic domains of function are new frontiers for investigation in Aging Biology. Such pursuits will require a research paradigm shift from the usual young-versus-old comparison to the analysis of defined elderly populations. These endeavors may also pave way to age-appropriate, group-targeted immune interventions for the growing elderly population.

Engineering dynamical control of cell fate switching using synthetic phospho-regulons

Many cells can sense and respond to time-varying stimuli, selectively triggering changes in cell fate only in response to inputs of a particular duration or frequency. A common motif in dynamically controlled cells is a dual-timescale regulatory network: although long-term fate decisions are ultimately controlled by a slow-timescale switch (e.g., gene expression), input signals are first processed by a fast-timescale signaling layer, which is hypothesized to filter what dynamic information is efficiently relayed downstream. Directly testing the design principles of how dual-timescale circuits control dynamic sensing, however, has been challenging, because most synthetic biology methods have focused solely on rewiring transcriptional circuits, which operate at a single slow timescale. Here, we report the development of a modular approach for flexibly engineering phosphorylation circuits using designed phospho-regulon motifs. By then linking rapid phospho-feedback with slower downstream transcription-based bistable switches, we can construct synthetic dual-timescale circuits in yeast in which the triggering dynamics and the end-state properties of the ON state can be selectively tuned. These phospho-regulon tools thus open up the possibility to engineer cells with customized dynamical control.

Impact of Enriched Environment on Murine T Cell Differentiation and Gene Expression Profile

T cells are known to be plastic and to change their phenotype according to the cellular and biochemical milieu they are embedded in. In this study, we transposed this concept at a macroscopic level assessing whether changes in the environmental housing conditions of C57/BL6 mice would influence the phenotype and function of T cells. Our study shows that exposure to 2 weeks in an enriched environment (EE) does not impact the T cell repertoire in vivo and causes no changes in the early TCR-driven activation events of these cells. Surprisingly, however, T cells from enriched mice showed a unique T helper effector cell phenotype upon differentiation in vitro. This was featured by a significant reduction in their ability to produce IFN-γ and by an increased release of IL-10 and IL-17. Microarray analysis of these cells also revealed a unique gene fingerprint with key signaling pathways involved in autoimmunity being modulated. Together, our results provide first evidence for a specific effect of EE on T cell differentiation and its associated changes in gene expression profile. In addition, our study sheds new light on the possible mechanisms by which changes in environmental factors can significantly influence the immune response of the host and favor the resolution of the inflammatory response.