Modeling T cell antigen discrimination based on feedback control of digital ERK responses

Extracellular signal-regulated kinase (ERK) pathway control of CD8+ T cell differentiation

The integration of multiple signalling pathways that co-ordinate T cell metabolism and transcriptional reprogramming is required to drive T cell differentiation and proliferation. One key T cell signalling module is mediated by extracellular signal-regulated kinases (ERKs) which are activated in response to antigen receptor engagement. The activity of ERKs is often used to report antigen receptor occupancy but the full details of how ERKs control T cell activation is not understood. Accordingly, we have used mass spectrometry to explore how ERK signalling pathways control antigen receptor driven proteome restructuring in CD8⁺ T cells to gain insights about the biological processes controlled by ERKs in primary lymphocytes. Quantitative analysis of >8000 proteins identified 900 ERK regulated proteins in activated CD8⁺ T cells. The data identify both positive and negative regulatory roles for ERKs during T cell activation and reveal that ERK signalling primarily controls the repertoire of transcription factors, cytokines and cytokine receptors expressed by activated T cells. It was striking that a large proportion of the proteome restructuring that is driven by triggering of the T cell antigen receptor is not dependent on ERK activation. However, the selective targets of the ERK signalling module include the critical effector molecules and the cytokines that allow T cell communication with other immune cells to mediate adaptive immune responses.

Modeling the Dynamics of T-Cell Development in the Thymus

The thymus hosts the development of a specific type of adaptive immune cells called T cells. T cells orchestrate the adaptive immune response through recognition of antigen by the highly variable T-cell receptor (TCR). T-cell development is a tightly coordinated process comprising lineage commitment, somatic recombination of Tcr gene loci and selection for functional, but non-self-reactive TCRs, all interspersed with massive proliferation and cell death. Thus, the thymus produces a pool of T cells throughout life capable of responding to virtually any exogenous attack while preserving the body through self-tolerance. The thymus has been of considerable interest to both immunologists and theoretical biologists due to its multi-scale quantitative properties, bridging molecular binding, population dynamics and polyclonal repertoire specificity. Here, we review experimental strategies aimed at revealing quantitative and dynamic properties of T-cell development and how they have been implemented in mathematical modeling strategies that were reported to help understand the flexible dynamics of the highly dividing and dying thymic cell populations. Furthermore, we summarize the current challenges to estimating in vivo cellular dynamics and to reaching a next-generation multi-scale picture of T-cell development.

T cells: a dedicated effector kinase pathways for every trait?

Signaling pathways play critical roles in regulating the activation of T cells. Recognition of foreign peptide presented by MHC to the T cell receptor (TCR) triggers a signaling cascade of proximal kinases and adapter molecules that lead to the activation of Effector kinase pathways. These effector kinase pathways play pivotal roles in T cell activation, differentiation, and proliferation. RNA sequencing-based methods have provided insights into the gene expression programs that support the above-mentioned cell biological responses. The proteome is often overlooked. A recent study by Damasio et al. [Biochem. J. (2021) 478, 79-98. doi:10.1042/BCJ20200661] focuses on characterizing the effect of extracellular signal-regulated kinase (ERK) on the remodeling of the proteome of activated CD8+ T cells using Mass spectrometric analysis. Surprisingly, the Effector kinase ERK pathway is responsible for only a select proportion of the proteome that restructures during T cell activation. The primary targets of ERK signaling are transcription factors, cytokines, and cytokine receptors. In this commentary, we discuss the recent findings by Damasio et al. [Biochem. J. (2021) 478, 79-98. doi:10.1042/BCJ20200661] in the context of different Effector kinase pathways in activated T cells.

In search of lost time: Enhancers as modulators of timing in lymphocyte development and differentiation

Proper timing of gene expression is central to lymphocyte development and differentiation. Lymphocytes often delay gene activation for hours to days after the onset of signaling components, which act on the order of seconds to minutes. Such delays play a prominent role during the intricate choreography of developmental events and during the execution of an effector response. Though a number of mechanisms are sufficient to explain timing at short timescales, it is not known how timing delays are implemented over long timescales that may span several cell generations. Based on the literature, we propose that a class of cis-regulatory elements, termed "timing enhancers," may explain how timing delays are controlled over these long timescales. By considering chromatin as a kinetic barrier to state switching, the timing enhancer model explains experimentally observed dynamics of gene expression where other models fall short. In this review, we elaborate on features of the timing enhancer model and discuss the evidence for its generality throughout development and differentiation. We then discuss potential molecular mechanisms underlying timing enhancer function. Finally, we explore recent evidence drawing connections between timing enhancers and genetic risk for immunopathology. We argue that the timing enhancer model is a useful framework for understanding how cis-regulatory elements control the central dimension of timing in lymphocyte biology.

Perfect adaptation of CD8+ T cell responses to constant antigen input over a wide range of affinities is overcome by costimulation

Reduced T cell responses by contrast antigen stimulation can be rescued by signals from costimulatory receptors.

Signaling Dynamics Regulating Crosstalks between T-Cell Activation and Immune Checkpoints

Immune checkpoint inhibitors (ICIs) targeting cytotoxic T lymphocyte-associated protein-4 (CTLA-4) and programmed cell death protein-1 (PD-1) have been hailed as major advances in cancer therapeutics; however, in many cancers response rates remain low. Extensive research efforts are underway to improve the efficacy of ICIs. The signaling pathways regulated by immune checkpoints (ICs) may be an important lever as they interfere with T-cell activation when activated by ICIs. Here, we review the current understanding of T-cell receptor signaling and their intersection with IC signaling pathways. As these signaling processes are highly dynamic and controlled by intricate spatiotemporal mechanisms, we focus on aspects of kinetic regulation that are modulated by ICs. Recent advances in computational modeling and experimental methods that can resolve spatiotemporal dynamics provide insights that reveal molecular mechanisms and new potential approaches for improving the design and application of ICIs.

How the T cell signaling network processes information to discriminate between self and agonist ligands

T cells exhibit remarkable sensitivity and selectivity in detecting and responding to agonist peptides (p) bound to MHC molecules in a sea of self pMHC molecules. Despite much work, understanding of the underlying mechanisms of distinguishing such ligands remains incomplete. Here, we quantify T cell discriminatory capacity using channel capacity, a direct measure of the signaling network's ability to discriminate between antigen-presenting cells (APCs) displaying either self ligands or a mixture of self and agonist ligands. This metric shows how differences in information content between these two types of peptidomes are decoded by the topology and rates of kinetic proofreading signaling steps inside T cells. Using channel capacity, we constructed numerically substantiated hypotheses to explain the discriminatory role of a recently identified slow LAT Y132 phosphorylation step. Our results revealed that in addition to the number and kinetics of sequential signaling steps, a key determinant of discriminatory capability is spatial localization of a minimum number of these steps to the engaged TCR. Biochemical and imaging experiments support these findings. Our results also reveal the discriminatory role of early negative feedback and necessary amplification conferred by late positive feedback.

MicroRNA miR-181-A Rheostat for TCR Signaling in Thymic Selection and Peripheral T-Cell Function

The selection of T cells during intra-thymic d evelopment is crucial to obtain a functional and simultaneously not self-reactive peripheral T cell repertoire. However, selection is a complex process dependent on T cell receptor (TCR) thresholds that remain incompletely understood. In peripheral T cells, activation, clonal expansion, and contraction of the active T cell pool, as well as other processes depend on TCR signal strength. Members of the microRNA (miRNA) miR-181 family have been shown to be dynamically regulated during T cell development as well as dependent on the activation stage of T cells. Indeed, it has been shown that expression of miR-181a leads to the downregulation of multiple phosphatases, implicating miR-181a as ‘‘rheostat’’ of TCR signaling. Consistently, genetic models have revealed an essential role of miR-181a/b-1 for the generation of unconventional T cells as well as a function in tuning TCR sensitivity in peripheral T cells during aging. Here, we review these broad roles of miR-181 family members in T cell function via modulating TCR signal strength.

Human CD8+ T Cells Exhibit a Shared Antigen Threshold for Different Effector Responses

CD8⁺ T cells produce TNF-α, IL-2, and IFN-γ with similar Ag thresholds.

Costimulation decreases Ag thresholds similarly for different cytokines.

A common rate-limiting switch downstream of the TCR can explain these findings.

T cells recognizing cognate pMHC Ags become activated to elicit a myriad of cellular responses, such as target cell killing and the secretion of different cytokines, that collectively contribute to adaptive immunity. These effector responses have been hypothesized to exhibit different Ag dose and affinity thresholds, suggesting that pathogen-specific information may be encoded within the nature of the Ag. In this study, using systematic experiments in a reductionist system, in which primary human CD8⁺ T cell blasts are stimulated by recombinant peptides presented on MHC Ag alone, we show that different inflammatory cytokines have comparable Ag dose thresholds across a 25,000-fold variation in affinity. Although costimulation by CD28, CD2, and CD27 increased cytokine production in this system, the Ag threshold remained comparable across different cytokines. When using primary human memory CD8⁺ T cells responding to autologous APCs, equivalent thresholds were also observed for different cytokines and killing. These findings imply a simple phenotypic model of TCR signaling in which multiple T cell responses share a common rate-limiting threshold and a conceptually simple model of CD8⁺ T cell Ag recognition, in which Ag dose and affinity do not provide any additional response-specific information.

Relaxation Times of Ligand-Receptor Complex Formation Control T Cell Activation

One of the most important functions of immune T cells is to recognize the presence of the pathogen-derived ligands and to quickly respond to them while at the same time not responding to its own ligands. This is known as absolute discrimination, and it is one of the most challenging phenomena to explain. The effectiveness of pathogen detection by T cell receptor is limited by chemical similarity of foreign and self-peptides and very low concentrations of foreign ligands. We propose a new mechanism of how absolute discrimination by T cells might function. It is suggested that the decision to activate or not to activate the immune response is controlled by the time to reach the stationary concentration of the T-cell-receptor-ligand-activated complex, which transfers the signal to downstream cellular biochemical networks. Our theoretical method models T cell receptor phosphorylation events as a sequence of stochastic transitions between discrete biochemical states, and this allows us to explicitly describe the dynamical properties of the system. It is found that the proposed criterion on the relaxation times is able to explain available experimental observations. In addition, we suggest that the level of stochastic noise might be an additional factor in the activation mechanisms. Furthermore, our theoretical approach explicitly analyzes the relationships between speed, sensitivity, and specificity of T cell functioning, which are the main characteristics of the process. Thus, it clarifies the molecular picture of T cell activation in immune response.

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.

Manipulating the TCR signaling network for cellular immunotherapy: Challenges & opportunities

T cells can help confer protective immunity by eliminating infections and tumors or drive immunopathology by damaging host cells. Both outcomes require a series of steps from the activation of naïve T cells to their clonal expansion, differentiation and migration to tissue sites. In addition to specific recognition of the antigen via the T cell receptor (TCR), multiple accessory signals from costimulatory molecules, cytokines and metabolites also influence each step along the progression of the T cell response. Current efforts to modify effector T cell function in many clinical contexts focus on the latter - which encompass antigen-independent and broad, contextual regulators. Not surprisingly, such approaches are often accompanied by adverse events, as they also affect T cells not relevant to the specific treatment. In contrast, fine tuning T cell responses by precisely targeting antigen-specific TCR signals has the potential to radically alter therapeutic strategies in a focused manner. Development of such approaches, however, requires a better understanding of functioning of the TCR and the biochemical signaling network coupled to it. In this article, we review some of the recent advances which highlight important roles of TCR signals throughout the activation and differentiation of T cells during an immune response. We discuss how, an appreciation of specific signaling modalities and variant ligands that influence the function of the TCR has the potential to influence design principles for the next generation of pharmacologic and cellular therapies, especially in the context of tumor immunotherapies involving adoptive cell transfers.

Regulation of T Helper Cell Fate by TCR Signal Strength

T cells are critical in orchestrating protective immune responses to cancer and an array of pathogens. The interaction between a peptide MHC (pMHC) complex on antigen presenting cells (APCs) and T cell receptors (TCRs) on T cells initiates T cell activation, division, and clonal expansion in secondary lymphoid organs. T cells must also integrate multiple T cell-intrinsic and extrinsic signals to acquire the effector functions essential for the defense against invading microbes. In the case of T helper cell differentiation, while innate cytokines have been demonstrated to shape effector CD4⁺ T lymphocyte function, the contribution of TCR signaling strength to T helper cell differentiation is less understood. In this review, we summarize the signaling cascades regulated by the strength of TCR stimulation. Various mechanisms in which TCR signal strength controls T helper cell expansion and differentiation are also discussed.

Stimulation strength controls the rate of initiation but not the molecular organisation of TCR-induced signalling

Millions of naïve T cells with different TCRs may interact with a peptide-MHC ligand, but very few will activate. Remarkably, this fine control is orchestrated using a limited set of intracellular machinery. It remains unclear whether changes in stimulation strength alter the programme of signalling events leading to T cell activation. Using mass cytometry to simultaneously measure multiple signalling pathways during activation of murine CD8+ T cells, we found a programme of distal signalling events that is shared, regardless of the strength of TCR stimulation. Moreover, the relationship between transcription of early response genes Nr4a1 and Irf8 and activation of the ribosomal protein S6 is also conserved across stimuli. Instead, we found that stimulation strength dictates the rate with which cells initiate signalling through this network. These data suggest that TCR-induced signalling results in a coordinated activation program, modulated in rate but not organization by stimulation strength.

The early proximal αβ TCR signalosome specifies thymic selection outcome through a quantitative protein interaction network

During αβ T cell development, T cell antigen receptor (TCR) engagement transduces biochemical signals through a protein-protein interaction (PPI) network that dictates dichotomous cell fate decisions. It remains unclear how signal specificity is communicated, instructing either positive selection to advance cell differentiation or death by negative selection. Early signal discrimination might occur by PPI signatures differing qualitatively (customized, unique PPI combinations for each signal), quantitatively (graded amounts of a single PPI series), or kinetically (speed of PPI pathway progression). Using a novel PPI network analysis, we found that early TCR-proximal signals distinguishing positive from negative selection appeared to be primarily quantitative in nature. Furthermore, the signal intensity of this PPI network was used to find an antigen dose that caused a classic negative selection ligand to induce positive selection of conventional αβ T cells, suggesting that the quantity of TCR triggering was sufficient to program selection outcome. Because previous work had suggested that positive selection might involve a qualitatively unique signal through CD3δ, we reexamined the block in positive selection observed in CD3δ⁰ mice. We found that CD3δ⁰ thymocytes were inhibited but capable of signaling positive selection, generating low numbers of MHC-dependent αβ T cells that expressed diverse TCR repertoires and participated in immune responses against infection. We conclude that the major role for CD3δ in positive selection is to quantitatively boost the signal for maximal generation of αβ T cells. Together, these data indicate that a quantitative network signaling mechanism through the early proximal TCR signalosome determines thymic selection outcome.

ERK Activation in CAR T Cells Is Amplified by CD28-Mediated Increase in CD3ζ Phosphorylation

Chimeric antigen receptors (CARs) are engineered receptors that mediate T cell activation. CARs are comprised of activating and co-stimulatory intracellular signaling domains derived from endogenous T cells that initiate signaling required for T cell activation, including ERK activation through the MAPK pathway. Understanding the mechanisms by which co-stimulatory domains influence signaling can help guide the design of next-generation CARs. Therefore, we constructed an experimentally validated computational model of anti-CD19 CARs in T cells bearing the CD3ζ domain alone or in combination with CD28. We performed a systematic analysis to explore the different mechanisms of CD28 co-stimulation on the ERK response time. Comparing these model simulations with experimental data indicates that CD28 primarily influences ERK activation by enhancing the phosphorylation kinetics of CD3ζ. Overall, we present a mechanistic mathematical modeling framework that can be used to gain insights into the mechanism of CAR T cell activation and produce new testable hypotheses.

Data-driven analysis of a mechanistic model of CAR T cell signaling predicts effects of cell-to-cell heterogeneity

Due to the variability of protein expression, cells of the same population can exhibit different responses to stimuli. It is important to understand this heterogeneity at the individual level, as population averages mask these underlying differences. Using computational modeling, we can interrogate a system much more precisely than by using experiments alone, in order to learn how the expression of each protein affects a biological system. Here, we examine a mechanistic model of CAR T cell signaling, which connects receptor-antigen binding to MAPK activation, to determine intracellular modulations that can increase cellular response. CAR T cell cancer therapy involves removing a patient's T cells, modifying them to express engineered receptors that can bind to tumor-associated antigens to promote tumor cell killing, and then injecting the cells back into the patient. This population of cells, like all cell populations, would have heterogeneous protein expression, which could affect the efficacy of treatment. Thus, it is important to examine the effects of cell-to-cell heterogeneity. We first generated a dataset of simulated cell responses via Monte Carlo simulations of the mechanistic model, where the initial protein concentrations were randomly sampled. We analyzed the dataset using partial least-squares modeling to determine the relationships between protein expression and ERK phosphorylation, the output of the mechanistic model. Using this data-driven analysis, we found that only the expressions of proteins relating directly to the receptor and the MAPK cascade, the beginning and end of the network, respectively, are relevant to the cells' response. We also found, surprisingly, that increasing the amount of receptor present can actually inhibit the cell's ability to respond due to increasing the strength of negative feedback from phosphatases. Overall, we have combined data-driven and mechanistic modeling to generate detailed insight into CAR T cell signaling.

Order by chance: origins and benefits of stochasticity in immune cell fate control

To protect against diverse challenges, the immune system must continuously generate an arsenal of specialized cell types, each of which can mount a myriad of effector responses upon detection of potential threats. To do so, it must generate multiple differentiated cell populations with defined sizes and proportions, often from rare starting precursor cells. Here, we discuss the emerging view that inherently probabilistic mechanisms, involving rare, rate-limiting regulatory events in single cells, control fate decisions and population sizes and fractions during immune development and function. We first review growing evidence that key fate control points are gated by stochastic signaling and gene regulatory events that occur infrequently over decision-making timescales, such that initially homogeneous cells can adopt variable outcomes in response to uniform signals. We next discuss how such stochastic control can provide functional capabilities that are harder to achieve with deterministic control strategies, and may be central to robust immune system function.

Targeting STAT3 and STAT5 in Tumor-Associated Immune Cells to Improve Immunotherapy

Oncogene-induced STAT3-activation is central to tumor progression by promoting cancer cell expression of pro-angiogenic and immunosuppressive factors. STAT3 is also activated in infiltrating immune cells including tumor-associated macrophages (TAM) amplifying immune suppression. Consequently, STAT3 is considered as a target for cancer therapy. However, its interplay with other STAT-family members or transcription factors such as NF-κB has to be considered in light of their concerted regulation of immune-related genes. Here, we discuss new attempts at re-educating immune suppressive tumor-associated macrophages towards a CD8 T cell supporting profile, with an emphasis on the role of STAT transcription factors on TAM functional programs. Recent clinical trials using JAK/STAT inhibitors highlighted the negative effects of these molecules on the maintenance and function of effector/memory T cells. Concerted regulation of STAT3 and STAT5 activation in CD8 T effector and memory cells has been shown to impact their tumor-specific responses including intra-tumor accumulation, long-term survival, cytotoxic activity and resistance toward tumor-derived immune suppression. Interestingly, as an escape mechanism, melanoma cells were reported to impede STAT5 nuclear translocation in both CD8 T cells and NK cells. Ours and others results will be discussed in the perspective of new developments in engineered T cell-based adoptive therapies to treat cancer patients.

Quantitative analysis of T cell proteomes and environmental sensors during T cell differentiation

Quantitative mass spectrometry reveals how CD4+ and CD8+ T cells restructure proteomes in response to antigen and mammalian target of rapamycin complex 1 (mTORC1). Analysis of copy numbers per cell of >9,000 proteins provides new understanding of T cell phenotypes, exposing the metabolic and protein synthesis machinery and environmental sensors that shape T cell fate. We reveal that lymphocyte environment sensing is controlled by immune activation, and that CD4+ and CD8+ T cells differ in their intrinsic nutrient transport and biosynthetic capacity. Our data also reveal shared and divergent outcomes of mTORC1 inhibition in naïve versus effector T cells: mTORC1 inhibition impaired cell cycle progression in activated naïve cells, but not effector cells, whereas metabolism was consistently impacted in both populations. This study provides a comprehensive map of naïve and effector T cell proteomes, and a resource for exploring and understanding T cell phenotypes and cell context effects of mTORC1.

Mechanosensing through immunoreceptors

The immune response is orchestrated by a variety of immune cells. The function of each cell is determined by the collective signals from various immunoreceptors, whose expression and activity depend on the developmental stages of the cell and its environmental context. Recent studies have highlighted the presence of mechanical force on several immunoreceptor-ligand pairs and the important role of force in regulating their interaction and function. In this Perspective, we use the T cell antigen receptor as an example with which to review the current understanding of the mechanosensing properties of immunoreceptors. We discuss the types of forces that immunoreceptors may encounter and the effects of force on ligand bonding, conformational change and the triggering of immunoreceptors, as well as the effects of force on the downstream signal transduction, cell-fate decisions and effector function of immune cells.

TCR-pMHC bond conformation controls TCR ligand discrimination

A major unanswered question is how a TCR discriminates between foreign and self-peptides presented on the APC surface. Here, we used in situ fluorescence resonance energy transfer (FRET) to measure the distances of single TCR–pMHC bonds and the conformations of individual TCR–CD3ζ receptors at the membranes of live primary T cells. We found that a TCR discriminates between closely related peptides by forming single TCR–pMHC bonds with different conformations, and the most potent pMHC forms the shortest bond. The bond conformation is an intrinsic property that is independent of the binding affinity and kinetics, TCR microcluster formation, and CD4 binding. The bond conformation dictates the degree of CD3ζ dissociation from the inner leaflet of the plasma membrane via a positive calcium signaling feedback loop to precisely control the accessibility of CD3ζ ITAMs for phosphorylation. Our data revealed the mechanism by which a TCR deciphers the structural differences among peptides via the TCR–pMHC bond conformation.

The Design Principles of Biochemical Timers: Circuits that Discriminate between Transient and Sustained Stimulation

Many cellular responses for which timing is critical display temporal filtering-the ability to suppress response until stimulated for longer than a given minimal time. To identify biochemical circuits capable of kinetic filtering, we comprehensively searched the space of three-node enzymatic networks. We define a metric of "temporal ultrasensitivity," the steepness of activation as a function of stimulus duration. We identified five classes of core network motifs capable of temporal filtering, each with distinct functional properties such as rejecting high-frequency noise, committing to response (bistability), and distinguishing between long stimuli. Combinations of the two most robust motifs, double inhibition (DI) and positive feedback with AND logic (PF_AND), underlie several natural timer circuits involved in processes such as cell cycle transitions, T cell activation, and departure from the pluripotent state. The biochemical network motifs described in this study form a basis for understanding common ways cells make dynamic decisions.

Immunology of the ageing kidney

Immunosenescence involves a series of ageing-induced alterations in the immune system and is characterized by two opposing hallmarks: defective immune responses and increased systemic inflammation. The immune system is modulated by intrinsic and extrinsic factors and undergoes profound changes in response to the ageing process. Immune responses are therefore highly age-dependent. Emerging data show that immunosenescence underlies common mechanisms responsible for several age-related diseases and is a plastic state that can be modified and accelerated by non-heritable environmental factors and pharmacological intervention. In the kidney, resident macrophages and fibroblasts are continuously exposed to components of the external environment, and the effects of cellular reprogramming induced by local immune responses, which accumulate with age, might have a role in the increased susceptibility to kidney disease among elderly individuals. Additionally, because chronic kidney disease, especially end-stage renal disease, is often accompanied by immunosenescence, which affects these patients independently of age, and many kidney diseases are strongly age-associated, treatment approaches that target immunosenescence might be particularly clinically relevant.

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.

Multidomain Control Over TEC Kinase Activation State Tunes the T Cell Response

Signaling through the T cell antigen receptor (TCR) activates a series of tyrosine kinases. Directly associated with the TCR, the SRC family kinase LCK and the SYK family kinase ZAP-70 are essential for all downstream responses to TCR stimulation. In contrast, the TEC family kinase ITK is not an obligate component of the TCR cascade. Instead, ITK functions as a tuning dial, to translate variations in TCR signal strength into differential programs of gene expression. Recent insights into TEC kinase structure have provided a view into the molecular mechanisms that generate different states of kinase activation. In resting lymphocytes, TEC kinases are autoinhibited, and multiple interactions between the regulatory and kinase domains maintain low activity. Following TCR stimulation, newly generated signaling modules compete with the autoinhibited core and shift the conformational ensemble to the fully active kinase. This multidomain control over kinase activation state provides a structural mechanism to account for ITK's ability to tune the TCR signal.

Critical slowing down in biochemical networks with feedback

Near a bifurcation point, the response time of a system is expected to diverge due to the phenomenon of critical slowing down. We investigate critical slowing down in well-mixed stochastic models of biochemical feedback by exploiting a mapping to the mean-field Ising universality class. We analyze the responses to a sudden quench and to continuous driving in the model parameters. In the latter case, we demonstrate that our class of models exhibits the Kibble-Zurek collapse, which predicts the scaling of hysteresis in cellular responses to gradual perturbations. We discuss the implications of our results in terms of the tradeoff between a precise and a fast response. Finally, we use our mapping to quantify critical slowing down in T cells, where the addition of a drug is equivalent to a sudden quench in parameter space.

Constitutive Activation of the B Cell Receptor Underlies Dysfunctional Signaling in Chronic Lymphocytic Leukemia

In cancer biology, the functional interpretation of genomic alterations is critical to achieve the promise of genomic profiling in the clinic. For chronic lymphocytic leukemia (CLL), a heterogeneous disease of B-lymphocytes maturing under constitutive B cell receptor (BCR) stimulation, the functional role of diverse clonal mutations remains largely unknown. Here, we demonstrate that alterations in BCR signaling dynamics underlie the progression of B cells toward malignancy. We reveal emergent dynamic features—bimodality, hypersensitivity, and hysteresis—in the BCR signaling pathway of primary CLL B cells. These signaling abnormalities in CLL quantitatively derive from BCR clustering and constitutive signaling with positive feedback reinforcement, as demonstrated through single-cell analysis of phospho-responses, computational modeling, and super-resolution imaging. Such dysregulated signaling segregates CLL patients by disease severity and clinical presentation. These findings provide a quantitative framework and methodology to assess complex and heterogeneous leukemia pathology and to inform therapeutic strategies in parallel with genomic profiling.

Using phospho-flow cytometry and computational modeling, Ziegler et al. find that B cell receptor clustering and positive feedback through SYK and LYN drive signaling hypersensitivity, bistability, and hysteresis in chronic lymphocytic leukemic B cells. Super-resolution microscopy confirms membrane auto-aggregation in leukemic B cells, and variability in signaling dysfunction predicts disease severity.

A cell topography-based mechanism for ligand discrimination by the T cell receptor

The T cell receptor (TCR) initiates the elimination of pathogens and tumors by T cells. To avoid damage to the host, the receptor must be capable of discriminating between wild-type and mutated self and nonself peptide ligands presented by host cells. Exactly how the TCR does this is unknown. In resting T cells, the TCR is largely unphosphorylated due to the dominance of phosphatases over the kinases expressed at the cell surface. However, when agonist peptides are presented to the TCR by major histocompatibility complex proteins expressed by antigen-presenting cells (APCs), very fast receptor triggering, i.e., TCR phosphorylation, occurs. Recent work suggests that this depends on the local exclusion of the phosphatases from regions of contact of the T cells with the APCs. Here, we developed and tested a quantitative treatment of receptor triggering reliant only on TCR dwell time in phosphatase-depleted cell contacts constrained in area by cell topography. Using the model and experimentally derived parameters, we found that ligand discrimination likely depends crucially on individual contacts being ∼200 nm in radius, matching the dimensions of the surface protrusions used by T cells to interrogate their targets. The model not only correctly predicted the relative signaling potencies of known agonists and nonagonists but also achieved this in the absence of kinetic proofreading. Our work provides a simple, quantitative, and predictive molecular framework for understanding why TCR triggering is so selective and fast and reveals that, for some receptors, cell topography likely influences signaling outcomes.

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.

A temporal thymic selection switch and ligand binding kinetics constrain neonatal Foxp3+ Treg cell development

The neonatal thymus generates Foxp3⁺ regulatory T (tT_reg) cells that are critical in controlling immune homeostasis and preventing multiorgan autoimmunity. The role of antigen specificity on neonatal tT_reg cell selection is unresolved. Here we identify 17 self-peptides recognized by neonatal tT_reg cells, and reveal ligand specificity patterns that include self-antigens presented in an age- and inflammation-dependent manner. Fate-mapping studies of neonatal peptidyl arginine deiminase type IV (Padi4)-specific thymocytes reveal disparate fate choices. Neonatal thymocytes expressing T cell receptors that engage IA^b-Padi4 with moderate dwell times within a conventional docking orientation are exported as tT_reg cells. In contrast, Padi4-specific T cell receptors with short dwell times are expressed on CD4⁺ T cells, while long dwell times induce negative selection. Temporally, Padi4-specific thymocytes are subject to a developmental stage-specific change in negative selection, which precludes tT_reg cell development. Thus, a temporal switch in negative selection and ligand binding kinetics constrains the neonatal tT_reg selection window.

Universality of biochemical feedback and its application to immune cells

We map a class of well-mixed stochastic models of biochemical feedback in steady state to the mean-field Ising model near the critical point. The mapping provides an effective temperature, magnetic field, order parameter, and heat capacity that can be extracted from biological data without fitting or knowledge of the underlying molecular details. We demonstrate this procedure on fluorescence data from mouse T cells, which reveals distinctions between how the cells respond to different drugs. We also show that the heat capacity allows inference of the absolute molecule number from fluorescence intensity. We explain this result in terms of the underlying fluctuations, and we demonstrate the generality of our work.

Optogenetic control shows that kinetic proofreading regulates the activity of the T cell receptor

The immune system distinguishes between self and foreign antigens. The kinetic proofreading (KPR) model proposes that T cells discriminate self from foreign ligands by the different ligand binding half-lives to the T cell receptor (TCR). It is challenging to test KPR as the available experimental systems fall short of only altering the binding half-lives and keeping other parameters of the interaction unchanged. We engineered an optogenetic system using the plant photoreceptor phytochrome B (PhyB) as a ligand to selectively control the dynamics of ligand binding to the TCR by light. This opto-ligand-TCR system was combined with the unique property of PhyB to continuously cycle between the binding and non-binding states under red light, with the light intensity determining the cycling rate and thus the binding duration. Mathematical modeling of our experimental datasets showed that indeed the ligand-TCR interaction half-life is the decisive factor for activating downstream TCR signaling, substantiating KPR.

In Silico Evolution of Biochemical Log-Response

Numerous biological systems are known to harbor a form of logarithmic behavior, from Weber's law to bacterial chemotaxis. Such a log-response allows for sensitivity to small relative variations of biochemical inputs over a large range of concentration values. Here we use a genetic algorithm to evolve biochemical networks displaying a logarithmic response. A quasi-perfect log-response implemented by the same core network evolves in a convergent way across our different in silico replications. The best network is able to fit a logarithm over 4 orders of magnitude with an accuracy of the order of 1%. At the heart of this network, we show that a logarithmic approximation may be implemented with one single nonlinear interaction, that can be interpreted either as multisite phosphorylations or as a ligand induced multimerization. We provide an analytical explanation for the effect and exhibit constraints on parameters. Biological log-response might thus be easier to implement than usually assumed.

Is TCR/pMHC Affinity a Good Estimate of the T-cell Response? An Answer Based on Predictions From 12 Phenotypic Models

On the T-cell surface the TCR is the only molecule that senses antigen, and the engagement of TCR with its specific antigenic peptide (agonist)/MHC complex (pMHC) is determined by the biochemical parameters of the TCR-pMHC interaction. This interaction is the keystone of the adaptive immune response by triggering intracellular signaling pathways that induce the expression of genes required for T cell-mediated effector functions, such as T cell proliferation, cytokine secretion and cytotoxicity. To study the TCR-pMHC interaction one of its properties most extensively analyzed has been TCR-pMHC affinity. However, and despite of intensive experimental research, the results obtained are far from conclusive. Here, to determine if TCR-pMHC affinity is a reliable parameter to characterize T-cell responses, a systematic study has been performed based on the predictions of 12 phenotypic models. This approach has the advantage that allow us to study the response of a given system as a function of only those parameters in which we are interested while other system parameters remain constant. A little surprising, only the simple occupancy model predicts a direct relationship between affinity and response so that an increase in affinity always leads to larger responses. Conversely, in the others more elaborate models this clear situation does not occur, i.e., that a general positive correlation between affinity and immune response does not exist. This is mainly because affinity values are given by the quotient k _on/k _off where k _on and k _off are the rate constants of the binding process (i.e., affinity is in fact the quotient of two parameters), so that different sets of these rate constants can give the same value of affinity. However, except in the occupancy model, the predicted T-cell responses depend on the individual values of k _on and k _off rather than on their quotient k _on/k _off. This allows: a) that systems with the same affinity can show quite different responses; and b) that systems with low affinity may exhibit larger responses than systems with higher affinities. This would make affinity a poor estimate of T-cell responses and, as a result, data correlations between affinity and immune response should be interpreted and used with caution.

Towards multiscale modeling of the CD8+ T cell response to viral infections

The CD8⁺ T cell response is critical to the control of viral infections. Yet, defining the CD8⁺ T cell response to viral infections quantitatively has been a challenge. Following antigen recognition, which triggers an intracellular signaling cascade, CD8⁺ T cells can differentiate into effector cells, which proliferate rapidly and destroy infected cells. When the infection is cleared, they leave behind memory cells for quick recall following a second challenge. If the infection persists, the cells may become exhausted, retaining minimal control of the infection while preventing severe immunopathology. These activation, proliferation and differentiation processes as well as the mounting of the effector response are intrinsically multiscale and collective phenomena. Remarkable experimental advances in the recent years, especially at the single cell level, have enabled a quantitative characterization of several underlying processes. Simultaneously, sophisticated mathematical models have begun to be constructed that describe these multiscale phenomena, bringing us closer to a comprehensive description of the CD8⁺ T cell response to viral infections. Here, we review the advances made and summarize the challenges and opportunities ahead. This article is categorized under: Analytical and Computational Methods > Computational Methods Biological Mechanisms > Cell Fates Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models.

Cheating the Hunger Games; Mechanisms Controlling Clonal Diversity of CD8 Effector and Memory Populations

Effector and memory CD8 T cells have an intrinsic difference in the way they must approach antigen; effector cells need to address the pathogen at hand and therefore favor outgrowth of only high-affinity clones. In contrast, the memory pool benefits from greater clonal diversity to recognize and eliminate pathogens with mutations in their immunogenic epitopes. Effector and memory fates are ultimately the result of the same three signals that control T cell activation; T cell receptor (TCR) engagement together with co-stimulation and cytokines. Great progress has been made in our understanding of the transcriptional programs that drive effector or memory differentiation. However, how these two different programs result from the same initial cues is still a matter of debate. An emerging image is that not only the classical three signals determine T cell differentiation, but also the ability of cells to access these signals relative to that of other activated clones. Inter-clonal competition is therefore not only a selective force, but also a mediator of CD8 T cell fate. How this is regulated on a transcriptional level, especially in the context of a selective “hunger game” based on antigen-affinity in which only cells of high-affinity are supposed to survive, is still poorly defined. In this review, we discuss recent literature that illustrates how antigen-affinity dependent inter-clonal competition shapes effector and memory populations in an environment of antigen affinity-driven selection. We argue that fine-tuning of TCR signal intensity presents an attractive target for regulating the scope of CD8 T cell vaccines.

Role of Zinc Signaling in the Regulation of Mast Cell-, Basophil-, and T Cell-Mediated Allergic Responses

Zinc is essential for maintaining normal structure and physiological function of cells. Its deficiency causes growth retardation, immunodeficiency, and neuronal degeneration. Zinc homeostasis is tightly regulated by zinc transporters and metallothioneins that control zinc concentration and its distribution in individual cells and contributes to zinc signaling. The intracellular zinc signaling regulates immune reactions. Although many molecules involved in these processes have zinc-binding motifs, the molecular mechanisms and the role of zinc in immune responses have not been elucidated. We and others have demonstrated that zinc signaling plays diverse and specific roles in vivo and in vitro in studies using knockout mice lacking zinc transporter function and metallothionein function. In this review, we discuss the impact of zinc signaling focusing particularly on mast cell-, basophil-, and T cell-mediated inflammatory and allergic responses. We also describe zinc signaling dysregulation as a leading health problem in inflammatory disease and allergy.

Single-cell RNA-Seq analysis identifies a noncoding interleukin 4 (IL-4) RNA that post-transcriptionally up-regulates IL-4 production in T helper cells

High-throughput sequencing has revealed a tremendous complexity of cellular transcriptomes, which is partly due to the generation of multiple alternative transcripts from a single gene locus. Because alternative transcripts often have low abundance in bulk cells, the functions of most of these transcripts and their relationship with their canonical counterparts remain unclear. Here we applied single-cell RNA-Seq to analyze the transcriptome complexity of in vitro-differentiated, murine type 2 T helper (Th2) cells. We found that cytokine gene transcripts contribute most of the intercellular heterogeneity, with a group of universal cytokines, including interleukins 1a, 2, 3, and 16, being bimodally expressed. At the single-cell level, use of alternative promoters prevalently generated alternative transcripts. For instance, although undetectable in bulk cells, a noncoding RNA isoform of IL-4 (IL4nc), which was driven by an intronic promoter in the IL-4 locus, was predominantly expressed in a subset of Th2 cells. IL4nc displayed distinct temporal expression patterns compared with the canonical IL-4 mRNA and post-transcriptionally promoted the production of IL-4 protein in Th2 cells. In conclusion, our findings reveal a mechanism whereby minor noncanonical transcripts post-transcriptionally regulate expression of their cognate canonical genes.

Protein Tyrosine Phosphatases: Regulators of CD4 T Cells in Inflammatory Bowel Disease

Protein tyrosine phosphatases (PTPs) play a critical role in co-ordinating the signaling networks that maintain lymphocyte homeostasis and direct lymphocyte activation. By dephosphorylating tyrosine residues, PTPs have been shown to modulate enzyme activity and both mediate and disrupt protein-protein interactions. Through these molecular mechanisms, PTPs ultimately impact lymphocyte responses to environmental cues such as inflammatory cytokines and chemokines, as well as antigenic stimulation. Mouse models of acute and chronic intestinal inflammation have been shown to be exacerbated in the absence of PTPs such as PTPN2 and PTPN22. This increase in disease severity is due in part to hyper-activation of lymphocytes in the absence of PTP activity. In accordance, human PTPs have been linked to intestinal inflammation. Genome wide association studies (GWAS) identified several PTPs within risk loci for inflammatory bowel disease (IBD). Therapeutically targeting PTP substrates and their associated signaling pathways, such as those implicated in CD4⁺ T cell responses, has demonstrated clinical efficacy. The current review focuses on the role of PTPs in controlling CD4⁺ T cell activity in the intestinal mucosa and how disruption of PTP activity in CD4⁺ T cells can contribute to intestinal inflammation.

BRAF and MEK inhibitors differentially affect nivolumab-induced T cell activation by modulating the TCR and AKT signaling pathways

Immune checkpoint inhibitors (ICIs) such as the anti-PD-1 antibody Nivolumab, achieve remarkable clinical efficacy in patients with late stage cancers. However, only a small subset of patients benefit from this therapy. Numerous clinical trials are underway testing whether combining ICIs with other anti-cancer therapies can increase this response rate. For example, anti-PD-1/PD-L1 therapy combined with MAP kinase inhibition using BRAF inhibitors (BRAFi) and/or MEK inhibitors (MEKi) are in development for treatment of late stage melanomas. However, the benefits and underlying mechanisms of these combinatorial therapies remain unclear. In the current study, we assess the effects of MAPK inhibition on Nivolumab-induced T cell responses. Using an in vitro mixed lymphocyte reaction assay, we demonstrate that Nivolumab-induced T cell activation is highly heterogeneous. While BRAFi inhibits Nivolumab-induced cytokine production, T cell proliferation, activation markers (CD69, CD25), and Granzyme B in a substantial proportion of donor pairs, a small subset of donor pairs shows an additive effect. MEKi alone significantly inhibits Nivolumab-induced T cell activation; the addition of BRAFi significantly enhances this inhibitory effect. Mechanistically, the effects of BRAFi and/or MEKi on Nivolumab-induced T cell activation may be due to alteration of the activation of the AKT and T cell receptor (TCR) signaling pathways. Our results suggest that MAPK inhibition may not provide a clinical benefit for most melanoma patients being treated with anti-PD-1 therapy.

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.

Emergent memory in cell signaling: Persistent adaptive dynamics in cascades can arise from the diversity of relaxation time-scales

The mitogen-activated protein kinase (MAPK) signaling cascade, an evolutionarily conserved motif present in all eukaryotic cells, is involved in coordinating crucial cellular functions. While the asymptotic dynamical behavior of the pathway stimulated by a time-invariant signal is relatively well-understood, we show using a computational model that it exhibits a rich repertoire of transient adaptive responses to changes in stimuli. When the signal is switched on, the response is characterized by long-lived modulations in frequency as well as amplitude. On withdrawing the stimulus, the activity decays over long timescales, exhibiting reverberations characterized by repeated spiking in the activated MAPK concentration. The long-term persistence of such post-stimulus activity suggests that the cascade retains memory of the signal for a significant duration following its removal. The molecular mechanism underlying the reverberatory activity is related to the existence of distinct relaxation rates for the different cascade components. This results in the imbalance of fluxes between different layers of the cascade, with the reuse of activated kinases as enzymes when they are released from sequestration in complexes. The persistent adaptive response, indicative of a cellular “short-term” memory, suggests that this ubiquitous signaling pathway plays an even more central role in information processing by eukaryotic cells.

Drug treatment efficiency depends on the initial state of activation in nonlinear pathways

An accurate prediction of the outcome of a given drug treatment requires quantitative values for all parameters and concentrations involved as well as a detailed characterization of the network of interactions where the target molecule is embedded. Here, we present a high-throughput in silico screening of all potential networks of three interacting nodes to study the effect of the initial conditions of the network in the efficiency of drug inhibition. Our study shows that most network topologies can induce multiple dose-response curves, where the treatment has an enhanced, reduced or even no effect depending on the initial conditions. The type of dual response observed depends on how the potential bistable regimes interplay with the inhibition of one of the nodes inside a nonlinear pathway architecture. We propose that this dependence of the strength of the drug on the initial state of activation of the pathway may be affecting the outcome and the reproducibility of drug studies and clinical trials.

Peptide Antigen Concentration Modulates Digital NFAT1 Activation in Primary Mouse Naive CD8+ T Cells as Measured by Flow Cytometry of Isolated Cell Nuclei

Circulating naive T cells exist in a quiescent state. After TCR contact with the cognate peptide presented by APCs in secondary lymphoid structures, T cells undergo a period of rapid transcriptional changes that set the stage for fate-determining effector or memory programming. We describe a novel method to analyze TCR signaling pathway activation in nuclei isolated from primary mouse naive T cells after stimulation with natural peptide Ags. We prelabeled cells with cell tracking dye to easily distinguish CD8⁺ T cell nuclei from APC nuclei by conventional flow cytometry. Using this approach, we observed clear digital activation of NFAT1 transcription factor in OT-I T cells stimulated with OVA peptide presented by bulk splenocytes. OVA concentration had discrete control over the fraction of the cells that translocated NFAT1, indicating that a distinct threshold amount of TCR signaling is required to switch on NFAT1 in naive T cells. This behavior was cell contact dependent and qualitatively more exact than the NFAT1 response in ionomycin-stimulated naive T cells. These data contribute to our understanding of the digital behavior of TCR signaling components documented in other studies and indicate how T cells might discriminate log-fold changes in Ag availability during an actual infection. Overall, these results highlight the potential of this coculture nuclei isolation protocol to address stimulation-dependent translocation of proteins in primary lymphocytes.

Will Systems Biology Deliver Its Promise and Contribute to the Development of New or Improved Vaccines? What Really Constitutes the Study of "Systems Biology" and How Might Such an Approach Facilitate Vaccine Design

A dichotomy exists in the field of vaccinology about the promise versus the hype associated with application of "systems biology" approaches to rational vaccine design. Some feel it is the only way to efficiently uncover currently unknown parameters controlling desired immune responses or discover what elements actually mediate these responses. Others feel that traditional experimental, often reductionist, methods for incrementally unraveling complex biology provide a more solid way forward, and that "systems" approaches are costly ways to collect data without gaining true insight. Here I argue that both views are inaccurate. This is largely because of confusion about what can be gained from classical experimentation versus statistical analysis of large data sets (bioinformatics) versus methods that quantitatively explain emergent properties of complex assemblies of biological components, with the latter reflecting what was previously called "physiology." Reductionist studies will remain essential for generating detailed insight into the functional attributes of specific elements of biological systems, but such analyses lack the power to provide a quantitative and predictive understanding of global system behavior. But by employing (1) large-scale screening methods for discovery of unknown components and connections in the immune system (omics), (2) statistical analysis of large data sets (bioinformatics), and (3) the capacity of quantitative computational methods to translate these individual components and connections into models of emergent behavior (systems biology), we will be able to better understand how the overall immune system functions and to determine with greater precision how to manipulate it to produce desired protective responses.

T cell cytolytic capacity is independent of initial stimulation strength

How cells respond to myriad stimuli with finite signaling machinery is central to immunology. In naive T cells, the inherent effect of ligand strength on activation pathways and endpoints has remained controversial, confounded by environmental fluctuations and intercellular variability within populations. Here we studied how ligand potency affected the activation of CD8+ T cells in vitro, through the use of genome-wide RNA, multi-dimensional protein and functional measurements in single cells. Our data revealed that strong ligands drove more efficient and uniform activation than did weak ligands, but all activated cells were fully cytolytic. Notably, activation followed the same transcriptional pathways regardless of ligand potency. Thus, stimulation strength did not intrinsically dictate the T cell-activation route or phenotype; instead, it controlled how rapidly and simultaneously the cells initiated activation, allowing limited machinery to elicit wide-ranging responses.