The effects of thymic selection on the range of T cell cross-reactivity

Is the exquisite specificity of lymphocytes generated by thymic selection or due to evolution?

We have previously argued that the antigen receptors of T and B lymphocytes evolved to be sufficiently specific to avoid massive deletion of clonotypes by negative selection. Their optimal 'specificity' level, i.e., probability of binding any particular epitope, was shown to be inversely related to the number of self-antigens that the cells have to be tolerant to. Experiments have demonstrated that T lymphocytes also become more specific during negative selection in the thymus, because cells expressing the most crossreactive receptors have the highest likelihood of binding a self-antigen, and hence to be tolerized (i.e., deleted, anergized, or diverted into a regulatory T cell phenotype). Thus, there are two -not mutually exclusive- explanations for the exquisite specificity of T cells, one involving evolution and the other thymic selection. To better understand the impact of both, we extend a previously developed mathematical model by allowing for T cells with very different binding probabilities in the pre-selection repertoire. We confirm that negative selection tends to tolerize the most crossreactive clonotypes. As a result, the average level of specificity in the functional post-selection repertoire depends on the number of self-antigens, even if there is no evolutionary optimization of binding probabilities. However, the evolutionary optimal range of binding probabilities in the pre-selection repertoire also depends on the number of self-antigens. Species with more self antigens need more specific pre-selection repertoires to avoid excessive loss of T cells during thymic selection, and hence mount protective immune responses. We conclude that both evolution and negative selection are responsible for the high level of specificity of lymphocytes.

Quantitative approaches for decoding the specificity of the human T cell repertoire

T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method's mathematical approach, predictive performance, and limitations.

Broad cross-reactivity of the T-cell repertoire achieves specific and sufficiently rapid target searching

The molecular recognition of T-cell receptors is the hallmark of the adaptive immunity. Given the finiteness of the T-cell repertoire, individual T-cell receptors are necessary to be cross-reactive to multiple antigenic peptides. In this study, we quantify the variability of the cross-reactivity by using a string model that estimates the binding affinity between two sequences of amino acids. We examine sequences of 10,000 human T-cell receptors and 10,000 antigenic peptides, and obtain a full spectrum of cross-reactivity of the receptor-peptide binding. Then, we find that the cross-reactivity spectrum is broad. Some T-cells are reactive to 1000 peptides, but some T-cells are reactive to only one or two peptides. Since the degree of cross-reactivity has a correlation with the (un)binding affinity of receptors, we further investigate how the broad cross-reactivity affects the target searching of T-cells. High cross-reactive T-cells may not require many trials for searching correct targets, but they may spend long time to unbind from incorrect targets. In contrast, low cross-reactive T-cells may not spend long time to ignore incorrect targets, but they require many trials for screening correct targets. We evaluate this hypothesis, and show that the broad cross-reactivity of the natural T-cell repertoire can balance the trade-off between the rapid screening and unbinding penalty.

Effects of thymic selection on T cell recognition of foreign and tumor antigenic peptides

The advent of cancer immunotherapy has generated renewed hope for the treatment of many malignancies by introducing a number of novel strategies that exploit various properties of the immune system. These therapies are based on the idea that cytotoxic T lymphocytes (CTLs) directly recognize and respond to tumor-associated neoantigens (TANs) in much the same way as they would to foreign peptides presented on cell surfaces. To date, however, nearly all attempts to optimize immunotherapeutic strategies have been empirical. Here, we develop a model of T cell selection based on the assumption of random interaction strengths between a self-peptide and the various T cell receptors. The model enables the analytical study of the effects of selection on the CTL recognition of TANs and completely foreign peptides and can estimate the number of CTLs that can detect donor-matched transplants. We show that negative selection thresholds chosen to reflect experimentally observed thymic survival rates result in near-optimal production of T cells that are capable of surviving selection and recognizing foreign antigen. These analytical results are confirmed by simulation.

A Perspective on the Role of Computational Models in Immunology

This is an exciting time for immunology because the future promises to be replete with exciting new discoveries that can be translated to improve health and treat disease in novel ways. Immunologists are attempting to answer increasingly complex questions concerning phenomena that range from the genetic, molecular, and cellular scales to that of organs, whole animals or humans, and populations of humans and pathogens. An important goal is to understand how the many different components involved interact with each other within and across these scales for immune responses to emerge, and how aberrant regulation of these processes causes disease. To aid this quest, large amounts of data can be collected using high-throughput instrumentation. The nonlinear, cooperative, and stochastic character of the interactions between components of the immune system as well as the overwhelming amounts of data can make it difficult to intuit patterns in the data or a mechanistic understanding of the phenomena being studied. Computational models are increasingly important in confronting and overcoming these challenges. I first describe an iterative paradigm of research that integrates laboratory experiments, clinical data, computational inference, and mechanistic computational models. I then illustrate this paradigm with a few examples from the recent literature that make vivid the power of bringing together diverse types of computational models with experimental and clinical studies to fruitfully interrogate the immune system.

A signal integration model of thymic selection and natural regulatory T cell commitment

The extent of TCR self-reactivity is the basis for selection of a functional and self-tolerant T cell repertoire and is quantified by repeated engagement of TCRs with a diverse pool of self-peptides complexed with self-MHC molecules. The strength of a TCR signal depends on the binding properties of a TCR to the peptide and the MHC, but it is not clear how the specificity to both components drives fate decisions. In this study, we propose a TCR signal-integration model of thymic selection that describes how thymocytes decide among distinct fates, not only based on a single TCR-ligand interaction, but taking into account the TCR stimulation history. These fates are separated based on sustained accumulated signals for positive selection and transient peak signals for negative selection. This spans up the cells into a two-dimensional space where they are either neglected, positively selected, negatively selected, or selected as natural regulatory T cells (nTregs). We show that the dynamics of the integrated signal can serve as a successful basis for extracting specificity of thymocytes to MHC and detecting the existence of cognate self-peptide-MHC. It allows to select a self-MHC-biased and self-peptide-tolerant T cell repertoire. Furthermore, nTregs in the model are enriched with MHC-specific TCRs. This allows nTregs to be more sensitive to activation and more cross-reactive than conventional T cells. This study provides a mechanistic model showing that time integration of TCR-mediated signals, as opposed to single-cell interaction events, is needed to gain a full view on the properties emerging from thymic selection.

Theories and quantification of thymic selection

The peripheral T cell repertoire is sculpted from prototypic T cells in the thymus bearing randomly generated T cell receptors (TCR) and by a series of developmental and selection steps that remove cells that are unresponsive or overly reactive to self-peptide–MHC complexes. The challenge of understanding how the kinetics of T cell development and the statistics of the selection processes combine to provide a diverse but self-tolerant T cell repertoire has invited quantitative modeling approaches, which are reviewed here.

Models of self-peptide sampling by developing T cells identify candidate mechanisms of thymic selection

Conventional and regulatory T cells develop in the thymus where they are exposed to samples of self-peptide MHC (pMHC) ligands. This probabilistic process selects for cells within a range of responsiveness that allows the detection of foreign antigen without excessive responses to self. Regulatory T cells are thought to lie at the higher end of the spectrum of acceptable self-reactivity and play a crucial role in the control of autoimmunity and tolerance to innocuous antigens. While many studies have elucidated key elements influencing lineage commitment, we still lack a full understanding of how thymocytes integrate signals obtained by sampling self-peptides to make fate decisions. To address this problem, we apply stochastic models of signal integration by T cells to data from a study quantifying the development of the two lineages using controllable levels of agonist peptide in the thymus. We find two models are able to explain the observations; one in which T cells continually re-assess fate decisions on the basis of multiple summed proximal signals from TCR-pMHC interactions; and another in which TCR sensitivity is modulated over time, such that contact with the same pMHC ligand may lead to divergent outcomes at different stages of development. Neither model requires that T(conv) and T(reg) are differentially susceptible to deletion or that the two lineages need qualitatively different signals for development, as have been proposed. We find additional support for the variable-sensitivity model, which is able to explain apparently paradoxical observations regarding the effect of partial and strong agonists on T(conv) and T(reg) development.

Positive and negative selection, self-nonself discrimination and the roles of costimulation and anergy

It is still unclear whether the adaptive immune system can perform accurate self-nonself discrimination and what could influence its performance. Starting from simple cellular interaction rules we show that it is possible to achieve perfect self-nonself discrimination in a consistent framework provided positive and negative selection operate during repertoire education, and costimulation and anergy are also considered during T cell activation. In this theory T cell receptors diversity is required for cells to sense differently different peptides; positive selection is needed to guarantee maximal lymphocyte's interactivity and to allow negative selection to reduce conjugation lifetimes maximally; costimulation is necessary to signal that an antigen presenting cell established an uncommon rate of long lived conjugations when presenting foreign peptides; anergy is required to guarantee that these stable contacts involved different T cells and not always the same. These results suggest that accurate self-nonself discrimination can have shaped the adaptive immune system.

Mapping the landscape of host-pathogen coevolution: HLA class I binding and its relationship with evolutionary conservation in human and viral proteins

The high diversity of HLA binding preferences has been driven by the sequence diversity of short segments of relevant pathogenic proteins presented by HLA molecules to the immune system. To identify possible commonalities in HLA binding preferences, we quantify these using a novel measure termed "targeting efficiency," which captures the correlation between HLA-peptide binding affinities and the conservation of the targeted proteomic regions. Analysis of targeting efficiencies for 95 HLA class I alleles over thousands of human proteins and 52 human viruses indicates that HLA molecules preferentially target conserved regions in these proteomes, although the arboviral Flaviviridae are a notable exception where nonconserved regions are preferentially targeted by most alleles. HLA-A alleles and several HLA-B alleles that have maintained close sequence identity with chimpanzee homologues target conserved human proteins and DNA viruses such as Herpesviridae and Adenoviridae most efficiently, while all HLA-B alleles studied efficiently target RNA viruses. These patterns of host and pathogen specialization are both consistent with coevolutionary selection and functionally relevant in specific cases; for example, preferential HLA targeting of conserved proteomic regions is associated with improved outcomes in HIV infection and with protection against dengue hemorrhagic fever. Efficiency analysis provides a novel perspective on the coevolutionary relationship between HLA class I molecular diversity, self-derived peptides that shape T-cell immunity through ontogeny, and the broad range of viruses that subsequently engage with the adaptive immune response.

Predicting interactions between T cell receptors and MHC-peptide complexes

Conserved interactions between T cell receptors (TCRs) and major histocompatibility complex (MHC) proteins with bound peptide antigens are not well understood. In order to gain a better understanding of the interaction modes of human TCR variable (V) regions, we have performed a structural analysis of the TCRs bound to their MHC-peptide ligands in human, using the available structural models determined by X-ray crystallography. We identified important differences to previous studies in which such interactions were evaluated. Based on the interactions found in the actual experimental structures we developed the first rule-based approach for predicting the ability of TCR residues in the complementarity-determining region (CDR) 1, CDR2, and CDR3 loops to interact with the MHC-peptide antigen complex. Two relatively simple algorithms show good performance under cross validation.

Statistical mechanical concepts in immunology

Higher organisms, such as humans, have an adaptive immune system that usually enables them to successfully combat diverse (and evolving) microbial pathogens. The adaptive immune system is not preprogrammed to respond to prescribed pathogens. Yet it mounts pathogen-specific responses against diverse microbes and establishes memory of past infections (the basis of vaccination). Although major advances have been made in understanding pertinent molecular and cellular phenomena, the mechanistic principles that govern many aspects of an immune response are not known. We illustrate how complementary approaches from the physical and life sciences can help confront this challenge. Specifically, we describe work that brings together statistical mechanics and cell biology to shed light on how key molecular/cellular components of the adaptive immune system are selected to enable pathogen-specific responses. We hope these examples encourage physical chemists to work at this crossroad of disciplines where fundamental discoveries with implications for human health might be made.

Effects of thymic selection of the T-cell repertoire on HLA class I-associated control of HIV infection

Without therapy, most people infected with human immunodeficiency virus (HIV) ultimately progress to AIDS. Rare individuals ('elite controllers') maintain very low levels of HIV RNA without therapy, thereby making disease progression and transmission unlikely. Certain HLA class I alleles are markedly enriched in elite controllers, with the highest association observed for HLA-B57 (ref. 1). Because HLA molecules present viral peptides that activate CD8(+) T cells, an immune-mediated mechanism is probably responsible for superior control of HIV. Here we describe how the peptide-binding characteristics of HLA-B57 molecules affect thymic development such that, compared to other HLA-restricted T cells, a larger fraction of the naive repertoire of B57-restricted clones recognizes a viral epitope, and these T cells are more cross-reactive to mutants of targeted epitopes. Our calculations predict that such a T-cell repertoire imposes strong immune pressure on immunodominant HIV epitopes and emergent mutants, thereby promoting efficient control of the virus. Supporting these predictions, in a large cohort of HLA-typed individuals, our experiments show that the relative ability of HLA-B alleles to control HIV correlates with their peptide-binding characteristics that affect thymic development. Our results provide a conceptual framework that unifies diverse empirical observations, and have implications for vaccination strategies.

Thymic selection of T-cell receptors as an extreme value problem

T lymphocytes (T cells) orchestrate adaptive immune responses upon activation. T-cell activation requires sufficiently strong binding of T-cell receptors on their surface to short peptides (p) derived from foreign proteins, which are bound to major histocompatibility gene products (displayed on antigen-presenting cells). A diverse and self-tolerant T-cell repertoire is selected in the thymus. We map thymic selection processes to an extreme value problem and provide an analytic expression for the amino acid compositions of selected T-cell receptors (which enable its recognition functions).

How the thymus designs antigen-specific and self-tolerant T cell receptor sequences

T lymphocytes (T cells) orchestrate adaptive immune responses that clear pathogens from infected hosts. T cells recognize short peptides (p) derived from antigenic proteins bound to protein products of the MHC genes. Recognition occurs when T cell receptor (TCR) proteins expressed on T cells bind sufficiently strongly to antigen-derived pMHC complexes on the surface of antigen-presenting cells. A diverse repertoire of self-pMHC-tolerant TCR sequences is shaped during development of T cells in the thymus by processes called positive and negative selection. Combining computational models and analysis of experimental data, we parsed the contributions of positive and negative selection to the design of TCR sequences that recognize antigenic peptides with specificity, yet also exhibit cross-reactivity. A dominant role for negative selection in mediating antigen specificity of mature T cells and a molecular mechanism for TCR recognition of antigen are described.

Computer immunology

This review describes a body of work on computational immune systems that behave analogously to the natural immune system. These artificial immune systems (AIS) simulate the behavior of the natural immune system and in some cases have been used to solve practical engineering problems such as computer security. AIS have several strengths that can complement wet lab immunology. It is easier to conduct simulation experiments and to vary experimental conditions, for example, to rule out hypotheses; it is easier to isolate a single mechanism to test hypotheses about how it functions; agent-based models of the immune system can integrate data from several different experiments into a single in silico experimental system.