Systems model of T cell receptor proximal signaling reveals emergent ultrasensitivity

Advances in adoptive cellular immunotherapy and therapeutic breakthroughs in multiple myeloma

The basic idea of modulating the immune system to better recognize and fight tumor cells has led to the successful introduction of adoptive cellular immunotherapy (ACT). ACT-based treatment regimens, in which the patient's own immune cells are isolated and subsequently expanded (ex vivo) and reinfused, have also contributed significantly to the development of a personalized treatment strategy. Complementing this, the unprecedented advances in ACTs as chimeric antigen receptor (CAR)-T cell therapies and their derivatives such as CAR-NK, CAR-macrophages, CAR-γδT and CAR-NKT have further maximized the therapeutic outcomes. Herein, we provide a comprehensive overview of the development of ACTs in multiple myeloma (MM) and outline how they have evolved from an experimental form to a mainstay of standard clinical settings. Besides, we provide insights into cytokine-induced killer cell (CIK) therapy, an alternative form of ACT that (as CIK or CAR-CIK) has enormous potential in the clinical spectrum of MM. We also summarize the results of the major preclinical and clinical studies of adoptive cell therapy in MM and address the current challenges (such as cytokine release syndrome (CRS) and neurotoxicity) that limit its complete success in the cancer landscape.

Cardiac myosin binding protein-C phosphorylation as a function of multiple protein kinase and phosphatase activities

Phosphorylation of cardiac myosin binding protein-C (cMyBP-C) is a determinant of cardiac myofilament function. Although cMyBP-C phosphorylation by various protein kinases has been extensively studied, the influence of protein phosphatases on cMyBP-C's multiple phosphorylation sites has remained largely obscure. Here we provide a detailed biochemical characterization of cMyBP-C dephosphorylation by protein phosphatases 1 and 2 A (PP1 and PP2A), and develop an integrated kinetic model for cMyBP-C phosphorylation using data for both PP1, PP2A and various protein kinases known to phosphorylate cMyBP-C. We find strong site-specificity and a hierarchical mechanism for both phosphatases, proceeding in the opposite direction of sequential phosphorylation by potein kinase A. The model is consistent with published data from human patients and predicts complex non-linear cMyBP-C phosphorylation patterns that are validated experimentally. Our results suggest non-redundant roles for PP1 and PP2A under both physiological and heart failure conditions, and emphasize the importance of phosphatases for cMyBP-C regulation.

Sensitive bispecific chimeric T cell receptors for cancer therapy

The expression of a synthetic chimeric antigen receptor (CAR) to redirect antigen specificity of T cells is transforming the treatment of hematological malignancies and autoimmune diseases [1-7]. In cancer, durable efficacy is frequently limited by the escape of tumors that express low levels or lack the target antigen [8-12]. These clinical results emphasize the need for immune receptors that combine high sensitivity and multispecificity to improve outcomes. Current mono- and bispecific CARs do not faithfully recapitulate T cell receptor (TCR) function and require high antigen levels on tumor cells for recognition [13-17]. Here, we describe a novel synthetic chimeric TCR (ChTCR) that exhibits superior antigen sensitivity and is readily adapted for bispecific targeting. Bispecific ChTCRs mimic TCR structure, form classical immune synapses, and exhibit TCR-like proximal signaling. T cells expressing Bi-ChTCRs more effectively eliminated tumors with heterogeneous antigen expression in vivo compared to T cells expressing optimized bispecific CARs. The Bi-ChTCR architecture is resilient and can be designed to target multiple B cell lineage and multiple myeloma antigens. Our findings identify a broadly applicable approach for engineering T cells to target hematologic malignancies with heterogeneous antigen expression, thereby overcoming the most frequent mechanism of relapse after current CAR T therapies.

CD3ζ ITAMs enable ligand discrimination and antagonism by inhibiting TCR signaling in response to low-affinity peptides

The T cell antigen receptor (TCR) contains ten immunoreceptor tyrosine-based activation motif (ITAM) signaling sequences distributed within six CD3 subunits; however, the reason for such structural complexity and multiplicity is unclear. Here we evaluated the effect of inactivating the three CD3ζ chain ITAMs on TCR signaling and T cell effector responses using a conditional 'switch' mouse model. Unexpectedly, we found that T cells expressing TCRs containing inactivated (non-signaling) CD3ζ ITAMs (6F-CD3ζ) exhibited reduced ability to discriminate between low- and high-affinity ligands, resulting in enhanced signaling and cytokine responses to low-affinity ligands because of a previously undetected inhibitory function of CD3ζ ITAMs. Also, 6F-CD3ζ TCRs were refractory to antagonism, as predicted by a new in silico adaptive kinetic proofreading model that revises the role of ITAM multiplicity in TCR signaling. Finally, T cells expressing 6F-CD3ζ displayed enhanced cytolytic activity against solid tumors expressing low-affinity ligands, identifying a new counterintuitive approach to TCR-mediated cancer immunotherapy.

A Systematic Review of the Efforts and Hindrances of Modeling and Simulation of CAR T-cell Therapy

Chimeric antigen receptor (CAR) T-cell therapy is an immunotherapy that has recently become highly instrumental in the fight against life-threatening diseases. A variety of modeling and computational simulation efforts have addressed different aspects of CAR T-cell therapy, including T-cell activation, T- and malignant cell population dynamics, therapeutic cost-effectiveness strategies, and patient survival. In this article, we present a systematic review of those efforts, including mathematical, statistical, and stochastic models employing a wide range of algorithms, from differential equations to machine learning. To the best of our knowledge, this is the first review of all such models studying CAR T-cell therapy. In this review, we provide a detailed summary of the strengths, limitations, methodology, data used, and data gap in currently published models. This information may help in designing and building better models for enhanced prediction and assessment of the benefit-risk balance associated with novel CAR T-cell therapies, as well as with the data need for building such models.

Intrinsic Disorder in the T Cell Receptor Creates Cooperativity and Controls ZAP70 Binding

Many immunoreceptors have cytoplasmic domains that are intrinsically disordered (i.e., have high configurational entropy), have multiple sites of posttranslational modification (e.g., tyrosine phosphorylation), and participate in nonlinear signaling pathways (e.g., exhibiting switch-like behavior). Several hypotheses to explain the origin of these nonlinearities fall under the broad hypothesis that modification at one site changes the immunoreceptor’s entropy, which in turn changes further modification dynamics. Here, we use coarse-grain simulation to study three scenarios, all related to the chains that constitute the T cell receptor (TCR). We find that first, if phosphorylation induces local changes in the flexibility of the TCR ζ-chain, this naturally leads to rate enhancements and cooperativity. Second, we find that TCR CD3ɛ can provide a switch by modulating its residence in the plasma membrane. By constraining our model to be consistent with the previous observation that both basic residues and phosphorylation control membrane residence, we find that there is only a moderate rate enhancement of 10% between first and subsequent phosphorylation events. Third, we find that volume constraints do not limit the number of ZAP70s that can bind the TCR but that entropic penalties lead to a 200-fold decrease in binding rate by the seventh ZAP70, potentially explaining the observation that each TCR has around six ZAP70 molecules bound after receptor triggering. In all three scenarios, our results demonstrate that phenomena that change an immunoreceptor chain’s entropy (stiffening, confinement to a membrane, and multiple simultaneous binding) can lead to nonlinearities (rate enhancement, switching, and negative cooperativity) in how the receptor participates in signaling. These polymer-entropy-driven nonlinearities may augment the nonlinearities that arise from, e.g., kinetic proofreading and cluster formation. They also suggest different design strategies for engineered receptors, e.g., whether or not to put signaling modules on one chain or multiple clustered chains.

Engineering Cytoplasmic Signaling of CD28ζ CARs for Improved Therapeutic Functions

Chimeric antigen receptor modified T cells (CAR-T) have yielded impressive clinical outcomes in treating hematopoietic malignancies. However, relapses have occurred in a substantial number of patients and limited the development of CAR-T therapy. Most underlying reasons for these relapses can be attributed to poor persistence and rapid exhaustion of CAR-T cells in vivo. Despite multiple strategies having been developed, how to improve CAR-T persistence or resist exhaustion while maintaining sufficient cytotoxic functions is still a great challenge. Here we discuss engineering cytoplasmic signaling as an important strategy for CAR optimization. This review summarizes recent advances showing that the anti-tumor function of CAR-T cells can be improved by optimizing the CD3ζ domain or downstream signaling of CD28ζ CAR.

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.

Clustering of the ζ-Chain Can Initiate T Cell Receptor Signaling

T cell activation is initiated when ligand binding to the T cell receptor (TCR) triggers intracellular phosphorylation of the TCR-CD3 complex. However, it remains unknown how biophysical properties of TCR engagement result in biochemical phosphorylation events. Here, we constructed an optogenetic tool that induces spatial clustering of ζ-chain in a light controlled manner. We showed that spatial clustering of the ζ-chain intracellular tail alone was sufficient to initialize T cell triggering including phosphorylation of ζ-chain, Zap70, PLCγ, ERK and initiated Ca²⁺ flux. In reconstituted COS-7 cells, only Lck expression was required to initiate ζ-chain phosphorylation upon ζ-chain clustering, which leads to the recruitment of tandem SH2 domain of Zap70 from cell cytosol to the newly formed ζ-chain clusters at the plasma membrane. Taken together, our data demonstrated the biophysical relevance of receptor clustering in TCR signaling.

Targeting Cancer Stem Cells by Genetically Engineered Chimeric Antigen Receptor T Cells

The term cancer stem cell (CSC) starts 25 years ago with the evidence that CSC is a subpopulation of tumor cells that have renewal ability and can differentiate into several distinct linages. Therefore, CSCs play crucial role in the initiation and the maintenance of cancer. Moreover, it has been proposed throughout several studies that CSCs are behind the failure of the conventional chemo-/radiotherapy as well as cancer recurrence due to their ability to resist the therapy and their ability to re-regenerate. Thus, the need for targeted therapy to eliminate CSCs is crucial; for that reason, chimeric antigen receptor (CAR) T cells has currently been in use with high rate of success in leukemia and, to some degree, in patients with solid tumors. This review outlines the most common CSC populations and their common markers, in particular CD133, CD90, EpCAM, CD44, ALDH, and EGFRVIII, the interaction between CSCs and the immune system, CAR T cell genetic engineering and signaling, CAR T cells in targeting CSCs, and the barriers in using CAR T cells as immunotherapy to treat solid cancers.

T Cell Reprogramming Against Cancer

Advances in academic and clinical studies during the last several years have resulted in practical outcomes in adoptive immune therapy of cancer. Immune cells can be programmed with molecular modules that increase their therapeutic potency and specificity. It has become obvious that successful immunotherapy must take into account the full complexity of the immune system and, when possible, include the use of multifactor cell reprogramming that allows fast adjustment during the treatment. Today, practically all immune cells can be stably or transiently reprogrammed against cancer. Here, we review works related to T cell reprogramming, as the most developed field in immunotherapy. We discuss factors that determine the specific roles of αβ and γδ T cells in the immune system and the structure and function of T cell receptors in relation to other structures involved in T cell target recognition and immune response. We also discuss the aspects of T cell engineering, specifically the construction of synthetic T cell receptors (synTCRs) and chimeric antigen receptors (CARs) and the use of engineered T cells in integrative multifactor therapy of cancer.

The Influence of Molecular Reach and Diffusivity on the Efficacy of Membrane-Confined Reactions

Signaling by surface receptors often relies on tethered reactions whereby an enzyme bound to the cytoplasmic tail of a receptor catalyzes reactions on substrates within reach. The overall length and stiffness of the receptor tail, the enzyme, and the substrate determine a biophysical parameter termed the molecular reach of the reaction. This parameter determines the probability that the receptor-tethered enzyme will contact the substrate in the volume proximal to the membrane when separated by different distances within the membrane plane. In this work, we develop particle-based stochastic reaction-diffusion models to study the interplay between molecular reach and diffusion. We find that increasing the molecular reach can increase reaction efficacy for slowly diffusing receptors, whereas for rapidly diffusing receptors, increasing molecular reach reduces reaction efficacy. In contrast, if reactions are forced to take place within the two-dimensional plasma membrane instead of the three-dimensional volume proximal to it or if molecules diffuse in three dimensions, increasing molecular reach increases reaction efficacy for all diffusivities. We show results in the context of immune checkpoint receptors (PD-1 dephosphorylating CD28), a standard opposing kinase-phosphatase reaction, and a minimal two-particle model. The work highlights the importance of the three-dimensional nature of many two-dimensional membrane-confined interactions, illustrating a role for molecular reach in controlling biochemical reactions.

PyBioNetFit and the Biological Property Specification Language

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

Molecular mechanisms of T cell sensitivity to antigen

T cells initiate and regulate adaptive immune responses that can clear infections. To do this, they use their T cell receptors (TCRs) to continually scan the surfaces of other cells for cognate peptide antigens presented on major histocompatibility complexes (pMHCs). Experimental work has established that as few 1-10 pMHCs are sufficient to activate T cells. This sensitivity is remarkable in light of a number of factors, including the observation that the TCR and pMHC are short molecules relative to highly abundant long surface molecules, such as CD45, that can hinder initial binding, and moreover, the TCR/pMHC interaction is of weak affinity with solution lifetimes of approximately 1 second. Here, we review experimental and mathematical work that has contributed to uncovering molecular mechanisms of T cell sensitivity. We organize the mechanisms by where they act in the pathway to activate T cells, namely mechanisms that (a) promote TCR/pMHC binding, (b) induce rapid TCR signaling, and (c) amplify TCR signaling. We discuss work showing that high sensitivity reduces antigen specificity unless molecular feedbacks are invoked. We conclude by summarizing a number of open questions.

Calibration of CAR activation potential directs alternative T cell fates and therapeutic potency

Chimeric antigen receptors (CARs) are synthetic receptors that target and reprogram T cells to acquire augmented antitumor properties¹. CD19-specific CARs that comprise CD28 and CD3ζ signaling motifs² have induced remarkable responses in patients with refractory leukemia^3-5 and lymphoma⁶ and were recently approved by the US Food and Drug Administration⁷. These CARs program highly performing effector functions that mediate potent tumor elimination^4,8 despite the limited persistence they confer on T cells^3-6,8. Extending their functional persistence without compromising their potency should improve current CAR therapies. Strong T cell activation drives exhaustion^9,10, which may be accentuated by the redundancy of CD28 and CD3ζ signaling^11,12 as well as the spatiotemporal constraints imparted by the structure of second-generation CARs². Thus, we hypothesized that calibrating the activation potential of CD28-based CARs would differentially reprogram T cell function and differentiation. Here, we show that CARs encoding a single immunoreceptor tyrosine-based activation motif direct T cells to different fates by balancing effector and memory programs, thereby yielding CAR designs with enhanced therapeutic profiles.

Computational Model of Chimeric Antigen Receptors Explains Site-Specific Phosphorylation Kinetics

Chimeric antigen receptors (CARs) have recently been approved for the treatment of hematological malignancies, but our lack of understanding of the basic mechanisms that activate these proteins has made it difficult to optimize and control CAR-based therapies. In this study, we use phosphoproteomic mass spectrometry and mechanistic computational modeling to quantify the in vitro kinetics of individual tyrosine phosphorylation on a variety of CARs. We show that each of the 10 tyrosine sites on the CD28-CD3ζ CAR is phosphorylated by lymphocyte-specific protein-tyrosine kinase (LCK) with distinct kinetics. The addition of CD28 at the N-terminal of CD3ζ increases the overall rate of CD3ζ phosphorylation. Our computational model identifies that LCK phosphorylates CD3ζ through a mechanism of competitive inhibition. This model agrees with previously published data in the literature and predicts that phosphatases in this system interact with CD3ζ through a similar mechanism of competitive inhibition. This quantitative modeling framework can be used to better understand CAR signaling and T cell activation.

Tuning ITAM multiplicity on T cell receptors can control potency and selectivity to ligand density

The T cell antigen receptor (TCR) recognizes peptides from pathogenic proteins bound in the major histocompatibility complex (MHC). To convert this binding event into downstream signaling, the TCR complex contains immunoreceptor tyrosine-based activation motifs (ITAMs) that act as docking sites for the cytoplasmic tyrosine kinase ZAP-70. Unique among antigen receptors, the TCR complex uses 10 ITAMs to transduce peptide-MHC binding to the cell interior. Using synthetic, drug-inducible receptor-ligand pairs, it was found that greater ITAM multiplicity primarily enhanced the efficiency with which ligand binding was converted into an intracellular signal. This manifested as an increase in the fraction of cells that became activated in response to antigen, and a more synchronous initiation of TCR-proximal signaling, rather than direct amplification of the intracellular signals. Exploiting these findings, the potency and selectivity of chimeric antigen receptors targeted against cancer were substantially enhanced by modulating the number of encoded ITAMs.

Selected signalling proteins recruited to the T-cell receptor-CD3 complex

The T-cell receptor (TCR)-CD3 complex, expressed on T cells, determines the outcome of a T-cell response. It consists of the TCR-αβ heterodimer and the non-covalently associated signalling dimers of CD3εγ, CD3εδ and CD3ζζ. TCR-αβ binds specifically to a cognate peptide antigen bound to an MHC molecule, whereas the CD3 subunits transmit the signal into the cytosol to activate signalling events. Recruitment of proteins to specialized localizations is one mechanism to regulate activation and termination of signalling. In the last 25 years a large number of signalling molecules recruited to the TCR-CD3 complex upon antigen binding to TCR-αβ have been described. Here, we review knowledge about five of those interaction partners: Lck, ZAP-70, Nck, WASP and Numb. Some of these proteins have been targeted in the development of immunomodulatory drugs aiming to treat patients with autoimmune diseases and organ transplants.

Dynamic regulation of CD28 conformation and signaling by charged lipids and ions

CD28 provides an essential costimulatory signal for T cell activation, and its function is critical in antitumor immunity. However, the molecular mechanism of CD28 transmembrane signaling remains elusive. Here we show that the conformation and signaling of CD28 are regulated by two counteractive charged factors, acidic phospholipids and Ca²⁺ ions. NMR spectroscopy analyses showed that acidic phospholipids can sequester CD28 signaling motifs within the membrane, thereby limiting CD28 basal signaling. T cell receptor (TCR) activation induced an increase in the local Ca²⁺ concentration around CD28, and Ca²⁺ directly disrupted CD28-lipid interaction, leading to opening and signaling of CD28. We observed that the TCR, Ca²⁺, and CD28 together form a dual-positive-feedback circuit that substantially amplifies T cell signaling and thus increases antigen sensitivity. This work unravels a new regulatory mechanism for CD28 signaling and thus contributes to the understanding of the dependence of costimulation signaling on TCR signaling and the high sensitivity of T cells.

Cutting Edge: CD3 ITAM Diversity Is Required for Optimal TCR Signaling and Thymocyte Development

For the αβ or γδTCR chains to integrate extracellular stimuli into the appropriate intracellular cellular response, they must use the 10 ITAMs found within the CD3 subunits (CD3γε, CD3δε, and ζζ) of the TCR signaling complex. However, it remains unclear whether each specific ITAM sequence of the individual subunit (γεδζ) is required for thymocyte development or whether any particular CD3 ITAM motif is sufficient. In this article, we show that mice utilizing a single ITAM sequence (γ, ε, δ, ζa, ζb, or ζc) at each of the 10 ITAM locations exhibit a substantial reduction in thymic cellularity and limited CD4^-CD8^- (double-negative) to CD4⁺CD8⁺ (double-positive) maturation because of low TCR expression and signaling. Together, the data suggest that ITAM sequence diversity is required for optimal TCR signal transduction and subsequent T cell maturation.

Multisite Phosphorylation Modulates the T Cell Receptor ζ-Chain Potency but not the Switchlike Response

Multisite phosphorylation is ubiquitous in cellular signaling and is thought to provide signaling proteins with additional regulatory mechanisms. Indeed, mathematical models have revealed a large number of mechanisms by which multisite phosphorylation can produce switchlike responses. The T cell antigen receptor (TCR) is a multisubunit receptor on the surface of T cells that is a prototypical multisite substrate as it contains 20 sites that are distributed on 10 conserved immunoreceptor tyrosine-based activation motifs (ITAMs). The TCR ζ-chain is a homodimer subunit that contains six ITAMs (12 sites) and exhibits a number of properties that are predicted to be sufficient for a switchlike response. We have used cellular reconstitution to systematically study multisite phosphorylation of the TCR ζ-chain. We find that multisite phosphorylation proceeds by a nonsequential random mechanism, and find no evidence that multiple ITAMs modulate a switchlike response but do find that they alter receptor potency and maximum phosphorylation. Modulation of receptor potency can be explained by a reduction in molecular entropy of the disordered ζ-chain upon phosphorylation. We further find that the tyrosine kinase ZAP-70 increases receptor potency but does not modulate the switchlike response. In contrast to other multisite proteins, where phosphorylations act in strong concert to modulate protein function, we suggest that the multiple ITAMs on the TCR function mainly to amplify subsequent signaling.

Predictive Model of Lymphocyte-Specific Protein Tyrosine Kinase (LCK) Autoregulation

Lymphocyte-specific protein tyrosine kinase (LCK) is a key activator of T cells; however, little is known about the specific autoregulatory mechanisms that control its activity. We have constructed a model of LCK autophosphorylation and phosphorylation by the regulating kinase CSK. The model was fit to existing experimental data in the literature that presents an in vitro reconstituted membrane system, which provides more physiologically relevant kinetic measurements than traditional solution-based systems. The model is able to predict a robust mechanism of LCK autoregulation. It provides insights into the molecular causes of key site-specific phosphorylation differences between distinct experimental conditions. Probing the model also provides new hypotheses regarding the influence of individual binding and catalytic rates, which can be tested experimentally. This minimal model is required to elucidate the mechanistic interactions of LCK and CSK and can be further expanded to better understand T cell activation from a systems perspective. Our computational model enables the evaluation of LCK protein interactions that mediate T cell activation on a more quantitative level, providing new insights and testable hypotheses.

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.

Temporal protein expression pattern in intracellular signalling cascade during T-cell activation: a computational study

Various T-cell co-receptor molecules and calcium channel CRAC play a pivotal role in the maintenance of cell's functional responses by regulating the production of effector molecules (mostly cytokines) that aids in immune clearance and also maintaining the cell in a functionally active state. Any defect in these co-receptor signalling pathways may lead to an altered expression pattern of the effector molecules. To study the propagation of such defects with time and their effect on the intracellular protein expression patterns, a comprehensive and largest pathway map of T-cell activation network is reconstructed manually. The entire pathway reactions are then translated using logical equations and simulated using the published time series microarray expression data as inputs. After validating the model, the effect of in silico knock down of co-receptor molecules on the expression patterns of their downstream proteins is studied and simultaneously the changes in the phenotypic behaviours of the T-cell population are predicted, which shows significant variations among the proteins expression and the signalling routes through which the response is propagated in the cytoplasm. This integrative computational approach serves as a valuable technique to study the changes in protein expression patterns and helps to predict variations in the cellular behaviour.

The catalytic activity of the kinase ZAP-70 mediates basal signaling and negative feedback of the T cell receptor pathway

T cell activation by antigens binding to the T cell receptor (TCR) must be properly regulated to ensure normal T cell development and effective immune responses to pathogens and transformed cells while avoiding autoimmunity. The Src family kinase Lck and the Syk family kinase ZAP-70 (ζ chain-associated protein kinase of 70 kD) are sequentially activated in response to TCR engagement and serve as critical components of the TCR signaling machinery that leads to T cell activation. We performed a mass spectrometry-based phosphoproteomic study comparing the quantitative differences in the temporal dynamics of phosphorylation in stimulated and unstimulated T cells with or without inhibition of ZAP-70 catalytic activity. The data indicated that the kinase activity of ZAP-70 stimulates negative feedback pathways that target Lck and thereby modulate the phosphorylation patterns of the immunoreceptor tyrosine-based activation motifs (ITAMs) of the CD3 and ζ chain components of the TCR and of signaling molecules downstream of Lck, including ZAP-70. We developed a computational model that provides a mechanistic explanation for the experimental findings on ITAM phosphorylation in wild-type cells, ZAP-70-deficient cells, and cells with inhibited ZAP-70 catalytic activity. This model incorporated negative feedback regulation of Lck activity by the kinase activity of ZAP-70 and predicted the order in which tyrosines in the ITAMs of TCR ζ chains must be phosphorylated to be consistent with the experimental data.

Protein kinase D2 is a digital amplifier of T cell receptor-stimulated diacylglycerol signaling in naïve CD8⁺ T cells

Protein kinase D2 (PKD2) is a serine and threonine kinase that is activated in T cells by diacylglycerol and protein kinase C in response to stimulation of the T cell receptor (TCR) by antigen. We quantified the activation of PKD2 at the single-cell level and found that this kinase acts as a sensitive digital amplifier of TCR engagement, enabling CD8(+) T cells to match the production of inflammatory cytokines to the quality and quantity of TCR ligands. There was a digital response pattern of PKD2 activation in response to TCR engagement, such that increasing the concentration and potency of TCR ligands increased the number of cells that exhibited activated PKD2. However, for each cell that responded to TCR stimulation, the entire cellular pool of PKD2 (~400,000 molecules) was activated. Moreover, PKD2 acted as an amplification checkpoint for antigen-stimulated digital cytokine responses and translated the differential strength of TCR signaling to determine the number of naïve CD8(+) T cells that became effector cells. Together, these results provide insights into PKD family kinases and how they act digitally to amplify signaling networks controlled by the TCR.

Phosphorylation site dynamics of early T-cell receptor signaling

In adaptive immune responses, T-cell receptor (TCR) signaling impacts multiple cellular processes and results in T-cell differentiation, proliferation, and cytokine production. Although individual protein-protein interactions and phosphorylation events have been studied extensively, we lack a systems-level understanding of how these components cooperate to control signaling dynamics, especially during the crucial first seconds of stimulation. Here, we used quantitative proteomics to characterize reshaping of the T-cell phosphoproteome in response to TCR/CD28 co-stimulation, and found that diverse dynamic patterns emerge within seconds. We detected phosphorylation dynamics as early as 5 s and observed widespread regulation of key TCR signaling proteins by 30 s. Development of a computational model pointed to the presence of novel regulatory mechanisms controlling phosphorylation of sites with central roles in TCR signaling. The model was used to generate predictions suggesting unexpected roles for the phosphatase PTPN6 (SHP-1) and shortcut recruitment of the actin regulator WAS. Predictions were validated experimentally. This integration of proteomics and modeling illustrates a novel, generalizable framework for solidifying quantitative understanding of a signaling network and for elucidating missing links.

Accumulation of dynamic catch bonds between TCR and agonist peptide-MHC triggers T cell signaling

TCR-pMHC interactions initiate adaptive immune responses, but the mechanism of how such interactions under force induce T cell signaling is unclear. We show that force prolongs lifetimes of single TCR-pMHC bonds for agonists (catch bonds) but shortens those for antagonists (slip bonds). Both magnitude and duration of force are important, as the highest Ca(2+) responses were induced by 10 pN via both pMHC catch bonds whose lifetime peaks at this force and anti-TCR slip bonds whose maximum lifetime occurs at 0 pN. High Ca(2+) levels require early and rapid accumulation of bond lifetimes, whereas short-lived bonds that slow early accumulation of lifetimes correspond to low Ca(2+) responses. Our data support a model in which force on the TCR induces signaling events depending on its magnitude, duration, frequency, and timing, such that agonists form catch bonds that trigger the T cell digitally, whereas antagonists form slip bonds that fail to activate.

Receptor Pre-Clustering and T cell Responses: Insights into Molecular Mechanisms

T cell activation, initiated by T cell receptor (TCR) mediated recognition of pathogen-derived peptides presented by major histocompatibility complex class I or II molecules (pMHC), shows exquisite specificity and sensitivity, even though the TCR-pMHC binding interaction is of low affinity. Recent experimental work suggests that TCR pre-clustering may be a mechanism via which T cells can achieve such high sensitivity. The unresolved stoichiometry of the TCR makes TCR-pMHC binding and TCR triggering, an open question. We formulate a mathematical model to characterize the pre-clustering of T cell receptors (TCRs) on the surface of T cells, motivated by the experimentally observed distribution of TCR clusters on the surface of naive and memory T cells. We extend a recently introduced stochastic criterion to compute the timescales of T cell responses, assuming that ligand-induced cross-linked TCR is the minimum signaling unit. We derive an approximate formula for the mean time to signal initiation. Our results show that pre-clustering reduces the mean activation time. However, additional mechanisms favoring the existence of clusters are required to explain the difference between naive and memory T cell responses. We discuss the biological implications of our results, and both the compatibility and complementarity of our approach with other existing mathematical models.

Fine specificity and molecular competition in SLAM family receptor signalling

SLAM family receptors regulate activation and inhibition in immunity through recruitment of activating and inhibitory SH2 domain containing proteins to immunoreceptor tyrosine based switch motifs (ITSMs). Binding of the adaptors, SAP and EAT-2 to ITSMs in the cytoplasmic regions of SLAM family receptors is important for activation. We analysed the fine specificity of SLAM family receptor phosphorylated ITSMs and the conserved tyrosine motif in EAT-2 for SH2 domain containing signalling proteins. Consistent with the literature describing dependence of CRACC (SLAMF7) on EAT-2, CRACC bound EAT-2 (K_D = 0.003 μM) with approximately 2 orders of magnitude greater affinity than SAP (K_D = 0.44 μM). RNA interference in cytotoxicity assays in NK92 cells showed dependence of CRACC on SAP in addition to EAT-2, indicating selectivity of SAP and EAT-2 may depend on the relative concentrations of the two adaptors. The concentration of SAP was four fold higher than EAT-2 in NK92 cells. Compared with SAP, the significance of EAT-2 recruitment and its downstream effectors are not well characterised. We identified PLCγ1 and PLCγ2 as principal binding partners for the EAT-2 tail. Both PLCγ1 and PLCγ2 are functionally important for cytotoxicity in NK92 cells through CD244 (SLAMF4), NTB-A (SLAMF6) and CRACC. Comparison of the specificity of SH2 domains from activating and inhibitory signalling mediators revealed a hierarchy of affinities for CD244 (SLAMF4) ITSMs. While binding of phosphatase SH2 domains to individual ITSMs of CD244 was weak compared with SAP or EAT-2, binding of tandem SH2 domains of SHP-2 to longer peptides containing tandem phosphorylated ITSMs in human CD244 increased the affinity ten fold. The concentration of the tyrosine phosphatase, SHP-2 was in the order of a magnitude higher than the adaptors, SAP and EAT-2. These data demonstrate a mechanism for direct recruitment of phosphatases in inhibitory signalling by ITSMs, while explaining competitive dominance of SAP and EAT-2.

In vitro membrane reconstitution of the T-cell receptor proximal signaling network

T-cell receptor (TCR) phosphorylation is controlled by a complex network that includes Lck, a Src family kinase (SFK), the tyrosine phosphatase CD45, and the Lck-inhibitory kinase Csk. How these competing phosphorylation and dephosphorylation reactions are modulated to produce T-cell triggering is not fully understood. Here we reconstituted this signaling network using purified enzymes on liposomes, recapitulating the membrane environment in which they normally interact. We demonstrate that Lck's enzymatic activity can be regulated over a ~10-fold range by controlling its phosphorylation state. By varying kinase and phosphatase concentrations, we constructed phase diagrams that reveal ultrasensitivity in the transition from the quiescent to the phosphorylated state and demonstrate that coclustering TCR-Lck or detaching Csk from the membrane can trigger TCR phosphorylation. Our results provide insight into the mechanism of TCR signaling as well as other signaling pathways involving SFKs.

Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and posttranslational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation).

Imaging early signaling events in T lymphocytes with fluorescent biosensors

Many recent advances in our understanding of T lymphocyte functions in adaptive immunity are derived from sophisticated imaging techniques used to visualize T lymphocyte behavior in vitro and in vivo. A current challenge is to couple such imaging techniques with methods that will allow researchers to visualize signaling phenomenon at the single-cell level. Fluorescent biosensors, either synthetic or genetically encoded, are emerging as important tools for revealing the spatio-temporal regulation of intracellular biochemical events, such as specific enzyme activities or fluctuations in metabolites. In this review, we revisit the development of fluorescent Ca(2+) sensors with which the first experiments visualizing T lymphocyte activation at the single-cell were performed, and which have since become routine tools in immunology. We then examine a number of examples of how fluorescence resonance energy transfer (FRET)-based biosensors have been developed and applied to T lymphocyte migration, adhesion and T-cell receptor (TCR)-mediated signal transduction. These include the study of small GTPases such as RhoA, Rac and Rap1, the tyrosine kinases Lck and ZAP-70, and metabolites such as cAMP and Ca(2+) . Future development and use of biosensors should allow immunologists to reconcile the vast wealth of existing biochemical data concerning T-cell functions with the power of dynamic live-cell imaging.

Cellular-level versus receptor-level response threshold hierarchies in T-cell activation

Peptide-MHC (pMHC) ligand engagement by T-cell receptors (TCRs) elicits a variety of cellular responses, some of which require substantially more TCR-mediated stimulation than others. This threshold hierarchy could reside at the receptor level, where different response pathways branch off at different stages of the TCR/CD3 triggering cascade, or at the cellular level, where the cumulative TCR signal registered by the T-cell is compared to different threshold values. Alternatively, dual-level thresholds could exist. In this study, we show that the cellular hypothesis provides the most parsimonious explanation consistent with data obtained from an in-depth analysis of distinct functional responses elicited in a clonal T-cell system by a spectrum of biophysically defined altered peptide ligands across a range of concentrations. Further, we derive a mathematical model that describes how ligand density, affinity, and off-rate all affect signaling in distinct ways. However, under the kinetic regime prevailing in the experiments reported here, the TCR/pMHC class I (pMHCI) dissociation rate was found to be the main governing factor. The CD8 coreceptor modulated the TCR/pMHCI interaction and altered peptide ligand potency. Collectively, these findings elucidate the relationship between TCR/pMHCI kinetics and cellular function, thereby providing an integrated mechanistic understanding of T-cell response profiles.