Comparing antigen-independent mechanisms of T cell regulation

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.

HIV-1-infected T-cells dynamics and prognosis: An evolutionary game model

BACKGROUND AND OBJECTIVE

Understanding the dynamics of human immunodeficiency virus (HIV) is essential for depicting, developing, and investigating effective treatment strategies. HIV infects several types of immune cells, but its main target is to destroy helper T-cells. In the lymph nodes, the infected T-cells interact with each other and their environment to obtain more resources. According to infectivity and replicative capacity of T-cells in the HIV infection process, they can be divided into four phenotypes. Although genetic mutations in the reverse transcription that beget these phenotypes are random, the framework by which a phenotype become favored is affected by the environment and neighboring phenotypes. Moreover, the HIV disease has all components of an evolutionary process, including replication, mutation, and selection.

METHODS

We propose a novel structure-based game-theoretic model for the evolution of HIV-1-Infected CD4+T-cells and invasion of the immune system. We discuss the theoretical basis of the stable equilibrium states of the evolutionary dynamics of four T-cells types as well as its significant results to understand and control HIV infection. The results include the importance of genetic variations and the process of establishing evolutionary dynamics of the virus quasispecies.

RESULTS

Our results show that there is a direct dependency between some parameters such as mutation rates and the stability of equilibrium states in the HIV infection. This is an interesting result because these parameters can be changed by some pharmacotherapies and alternative treatments. Our model indicates that in an appropriate treatment the relative frequency of the wild type of virus quasispecies can be decreased in the population. Consequently, this can cause delaying the emergence of the AIDS phase. To assess the model, we investigate two new treatments for HIV. The results show that our model can predict the treatment results.

CONCLUSIONS

The paper shows that a structured-based evolutionary game theory can model the evolutionary dynamics of the infected T-cells and virus quasispecies. The model predicts certain aspects of the HIV infection process under several treatments.

Integrating Antimicrobial Therapy with Host Immunity to Fight Drug-Resistant Infections: Classical vs. Adaptive Treatment

Antimicrobial resistance of infectious agents is a growing problem worldwide. To prevent the continuing selection and spread of drug resistance, rational design of antibiotic treatment is needed, and the question of aggressive vs. moderate therapies is currently heatedly debated. Host immunity is an important, but often-overlooked factor in the clearance of drug-resistant infections. In this work, we compare aggressive and moderate antibiotic treatment, accounting for host immunity effects. We use mathematical modelling of within-host infection dynamics to study the interplay between pathogen-dependent host immune responses and antibiotic treatment. We compare classical (fixed dose and duration) and adaptive (coupled to pathogen load) treatment regimes, exploring systematically infection outcomes such as time to clearance, immunopathology, host immunization, and selection of resistant bacteria. Our analysis and simulations uncover effective treatment strategies that promote synergy between the host immune system and the antimicrobial drug in clearing infection. Both in classical and adaptive treatment, we quantify how treatment timing and the strength of the immune response determine the success of moderate therapies. We explain key parameters and dimensions, where an adaptive regime differs from classical treatment, bringing new insight into the ongoing debate of resistance management. Emphasizing the sensitivity of treatment outcomes to the balance between external antibiotic intervention and endogenous natural defenses, our study calls for more empirical attention to host immunity processes.

Feedback regulation of proliferation vs. differentiation rates explains the dependence of CD4 T-cell expansion on precursor number

The mechanisms regulating clonal expansion and contraction of T cells in response to immunization remain to be identified. A recent study established that there was a log-linear relation between CD4 T-cell precursor number (PN) and factor of expansion (FE), with a slope of ∼-0.5 over a range of 3-30,000 precursors per mouse. The results suggested inhibition of precursor expansion either by competition for specific antigen-presenting cells or by the action of other antigen-specific cells in the same microenvironment as the most likely explanation. Several molecular mechanisms potentially accounting for such inhibition were examined and rejected. Here we adopt a previously proposed concept, "feedback-regulated balance of growth and differentiation," and show that it can explain the observed findings. We assume that the most differentiated effectors (or memory cells) limit the growth of less differentiated effectors, locally, by increasing the rate of differentiation of the latter cells in a dose-dependent manner. Consequently, expansion is blocked and reversed after a delay that depends on initial PN, accounting for the dependence of the peak of the response on that number. We present a parsimonious mathematical model capable of reproducing immunization response kinetics. Model definition is achieved in part by requiring consistency with available BrdU-labeling and carboxyfluorescein diacetate succinimidyl ester (CFSE)-dilution data. The calibrated model correctly predicts FE as a function of PN. We conclude that feedback-regulated balance of growth and differentiation, although awaiting definite experimental characterization of the hypothetical cells and molecules involved in regulation, can explain the kinetics of CD4 T-cell responses to antigenic stimulation.

Emergent group dynamics governed by regulatory cells produce a robust primary T cell response

The currently accepted paradigm for the primary T cell response is that effector T cells commit to autonomous developmental programs. This concept is based on several experiments that have demonstrated that the dynamics of a T cell response is largely determined shortly after antigen exposure and that T cell dynamics do not depend on the level and duration of antigen stimulation. Another experimental study has also shown that T cell responses are robust to variations in antigen-specific precursor frequency. Various mathematical models have corroborated the first result that programmed T cell responses are insensitive to the level of antigen stimulation. However, this paper proposes that programmed responses do not entirely explain the robustness of T cell dynamics to variations in precursor frequency. This work studies the hypothesis that the dynamics of a T cell response may also be governed by a feedback loop involving adaptive regulatory cells rather than by intrinsic developmental programs. We formulate two mathematical models based on T cell developmental programs. In one model, effector cells undergo a fixed number of divisions before dying. In the second model, effector cells live for a fixed time during which they may divide. The study of these models suggests that developmental programs are not sufficiently robust as they produce an immune response that directly scales with precursor frequencies. Consequently, we derive a third model based on the principle that adaptive regulatory T cells develop in the course of an immune response and suppress effector cells. Our simulations show that this feedback mechanism responds robustly over a range of at least four orders of magnitude of precursor frequencies. We conclude that the proliferation program paradigm does not entirely capture the observed robustness of T cell responses to variations in precursor frequency. We propose an alternative mechanism by which the primary T cell response is governed by an emergent group dynamic and not by individual T cell programs.

Modelling pathways of CD8+ T-cell differentiation

In an immune response to infection, naïve T lymphocytes proliferate and give rise to a heterogeneous population of effector and memory cells. How is this diversity generated, and how can it be manipulated? Answering these questions requires an understanding of the lineage relationships between different effector and memory-cell subsets, but these relationships remain to be identified definitively. In this issue of the European Journal of Immunology, a study moves us closer to this goal by combining a mathematical model and data from influenza infections in mice to support the hypothesis that CD8(+) T-cell differentiation is strongly coupled to cell division.

Low dose IL-15 induces snap arming of CD44(low) T lymphocytes in the absence of antigen

It is widely accepted that naïve T cells require two signals, antigen recognition and co-simulation, to become cytotoxic over the course of 3-5days. However, we observed that freshly isolated murine splenocytes without exposure to antigen become cytotoxic within 24h after culture with IL-15. IL-15 is a cytokine that promotes homeostatic proliferation, maintenance and activation of memory T cells. The induced cytotoxicity, measured by anti-CD3 redirected (51)Cr release, represented the combined activity of T cells regardless of their antigen specificity, and proceeded even when CD44(hi) (memory-associated phenotype) CD8(+) T cells were depleted. Cytotoxic capacity was perforin-dependent and occurred without detectable up-regulation of granzyme B or cell division. After induction, the phenotypic markers for the memory subset and for activation remained unchanged from the expression of resting T cells. Our work suggests that T cells may gain cytotoxic potential earlier than currently thought and even without TCR stimulation.

Molecular networks of brain and immunity

Exciting complexity of natural phenomena can be based on rather simple biophysical principles. For example, the genetic code is based on a double-helix of DNA formed by planar geometry of weak hydrogen bounds. On the examples of cytokine networks, immune synapse, psychoneuroimmunology and systems biology, this review paper attempts to show how molecular networks both in brain and immunity can be studied using common principles of protein-protein interactions.

Modeling T- and B-cell growth and differentiation

The control of T- and B-cell proliferation following antigen stimulation lies at the heart of the adaptive immune response. The outcome of a response depends on the number of cells that are activated to go into cycle, the rates at which the cells divide and die, and the number of division cycles the cells undergo. Each of these processes may be under independent control, and the precise outcome of T- or B-cell responses to antigen will depend on how the signals controlling the different events are integrated. In this article, the way different mathematical models in combination with data from carboxyfluorescein diacetate succinamidyl ester (CFSE) experiments can be used to investigate the mechanisms controlling T- and B-cell proliferation is reviewed.

How do adaptive immune systems control pathogens while avoiding autoimmunity?

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

The role of models in understanding CD8+ T-cell memory

Immunological memory - the ability to 'remember' previously encountered pathogens and respond faster on re-exposure - is a central feature of the immune response of vertebrates. We outline how mathematical models have contributed to our understanding of CD8(+) T-cell memory. Together with experimental data, models have helped to quantitatively describe and to further our understanding of both the generation of memory after infection with a pathogen and the maintenance of this memory throughout the life of an individual.