Discriminating self from nonself with short peptides from large proteomes

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.

Peptide absent sequences emerging in human cancers

Early diagnosis of cancer can significantly improve survival of cancer patients; however sensitive and highly specific biomarkers for cancer detection are currently lacking for most cancer types. Nullpeptides are short peptides that are absent from the human proteome. Here, we examined the emergence of nullpeptides during cancer development. We analyzed 3,600,964 somatic mutations across 10,064 whole exome sequencing tumor samples spanning 32 cancer types. We analyze RNA-seq data from primary tumor samples to identify the subset of nullpeptides that emerge in highly expresed genes. We show that nullpeptides, and particularly the subset that is highly recurrent across cancer patients, can be identified in tumor biopsy samples. We find that cancer genes show an excess of nullpeptides and detect nullpeptide hotspots in specific loci of oncogenes and tumor suppressors. We also observe that recurrent nullpeptides are more likely to be found in neoantigens, which have been shown to be effective targets for immunotherapy, suggesting that they can be used to prioritize candidates. Our findings provide evidence for the utility of nullpeptides as cancer detection and therapeutic biomarkers.

HLA variants have different preferences to present proteins with specific molecular functions which are complemented in frequent haplotypes

Human leukocyte antigen (HLA) genes are the most polymorphic loci in the human genome and code for proteins that play a key role in guiding adaptive immune responses by presenting foreign and self peptides (ligands) to T cells. Each person carries up to 6 HLA class I variants (maternal and paternal copies of HLA-A, HLA-B and HLA-C genes) and also multiple HLA class II variants, which cumulatively define the landscape of peptides presented to T cells. Each HLA variant has its own repertoire of presented peptides with a certain sequence motif which is mainly defined by peptide anchor residues (typically the second and the last positions for HLA class I ligands) forming key interactions with the peptide-binding groove of HLA. In this study, we aimed to characterize HLA binding preferences in terms of molecular functions of presented proteins. To focus on the ligand presentation bias introduced specifically by HLA-peptide interaction we performed large-scale in silico predictions of binding of all peptides from human proteome for a wide range of HLA variants and established which functions are characteristic for proteins that are more or less preferentially presented by different HLA variants using statistical calculations and gene ontology (GO) analysis. We demonstrated marked distinctions between HLA variants in molecular functions of preferentially presented proteins (e.g. some HLA variants preferentially present membrane and receptor proteins, while others - ribosomal and DNA-binding proteins) and reduced presentation of extracellular matrix and collagen proteins by the majority of HLA variants. To explain these observations we demonstrated that HLA preferentially presents proteins enriched in amino acids which are required as anchor residues for the particular HLA variant. Our observations can be extrapolated to explain the protective effect of certain HLA alleles in infectious diseases, and we hypothesize that they can also explain susceptibility to certain autoimmune diseases and cancers. We demonstrate that these differences lead to differential presentation of HIV, influenza virus, SARS-CoV-1 and SARS-CoV-2 proteins by various HLA alleles. Taking into consideration that HLA alleles are inherited in haplotypes, we hypothesized that haplotypes composed of a combination of HLA variants with different presentation preferences should be more advantageous as they allow presenting a larger repertoire of peptides and avoiding holes in immunopeptidome. Indeed, we demonstrated that HLA-A/HLA-B and HLA-A/HLA-C haplotypes which have a high frequency in the human population are comprised of HLA variants that are more distinct in terms of functions of preferentially presented proteins than the control pairs.

An immunological glimpse of human virus peptides: distance from self, MHC class I binding, Proteasome Cleveage, TAP Transport and sequence composition entropy

Adaptive immune response is triggered when specific pathogen peptides called epitopes are recognised as exogenous according to the paradigm of self/non-self. To be recognized by immune cells, epitopes have to be exposed (presented) on the surface of the cell. Predicting if a peptide is exposed is important to shed light on the rules that govern immune response, and thus to identify potential targets, and to design vaccine and drugs. We focused on peptides exposed on cell surface and made accessible to immune system through the MHC Class I complex. Before this can happen, three successive selection steps have to take place: a) Proteasome cleveage, b) TAP Transport, and c) binding to MHC-class I. Starting from a set of 211 host human reference viruses, we computed the set of unique peptides occurring in the correspondent proteomes. Then, we obtained the probability values of Proteasome Cleveage, TAP Transport and Binding to MHC Class I associated to those peptides through established prediction software tools. Such values were analysed in conjunction with two other features that could play a major role: the distance from self, strictly linked to the concept of nullomers, and the sequence entropy, measuring the complexity of the peptide amino acid composition. The analysis confirmed and extended previous results on a larger, more significant and consistent data set; we showed that the higher the distances from self, the higher the score of TAP Transport and binding to MHC class I; no significant association was instead found between distance from self and Proteasome Cleveage. Additionally, amino acid peptide composition entropy was significantly associated with the other features. In particular, higher entropies were linked with higher scores of Proteasome Cleveage, TAP Transport, Binding to MHC Class I, and higher distance from self. The relationship among the three selection steps provided evidence of a tight correlation among them, clearly suggesting it could be the product of a co-evolutive process. We believe that these results give new insights on the complex processes that regulate peptide presentation through MHC class I, and unveil the mechanisms the allow the immune system to distinguish self and viral non-self peptides.

Self-mediated positive selection of T cells sets an obstacle to the recognition of nonself

Adaptive immune recognition is mediated by the binding of peptide-human leukocyte antigen complexes by T cells. Positive selection of T cells in the thymus is a fundamental step in the generation of a responding T cell repertoire: only those T cells survive that recognize human peptides presented on the surface of cortical thymic epithelial cells. We propose that while this step is essential for optimal immune function, the process results in a defective T cell repertoire because it is mediated by self-peptides. To test our hypothesis, we focused on amino acid motifs of peptides in contact with T cell receptors. We found that motifs rarely or not found in the human proteome are unlikely to be recognized by the immune system just like the ones that are not expressed in cortical thymic epithelial cells or not presented on their surface. Peptides carrying such motifs were especially dissimilar to human proteins. Importantly, we present our main findings on two independent T cell activation datasets and directly demonstrate the absence of naïve T cells in the repertoire of healthy individuals. We also show that T cell cross-reactivity is unable to compensate for the absence of positively selected T cells. Additionally, we show that the proposed mechanism could influence the risk for different infectious diseases. In sum, our results suggest a side effect of T cell positive selection, which could explain the nonresponsiveness to many nonself peptides and could improve the understanding of adaptive immune recognition.

Unique pathogen peptidomes facilitate pathogen-specific selection and specialization of MHC alleles

A key component of pathogen-specific adaptive immunity in vertebrates is the presentation of pathogen-derived antigenic peptides by major histocompatibility complex (MHC) molecules. The excessive polymorphism observed at MHC genes is widely presumed to result from the need to recognize diverse pathogens, a process called pathogen-driven balancing selection. This process assumes that pathogens differ in their peptidomes—the pool of short peptides derived from the pathogen’s proteome—so that different pathogens select for different MHC variants with distinct peptide-binding properties. Here, we tested this assumption in a comprehensive data set of 51.9 Mio peptides, derived from the peptidomes of 36 representative human pathogens. Strikingly, we found that 39.7% of the 630 pairwise comparisons among pathogens yielded not a single shared peptide and only 1.8% of pathogen pairs shared more than 1% of their peptides. Indeed, 98.8% of all peptides were unique to a single pathogen species. Using computational binding prediction to characterize the binding specificities of 321 common human MHC class-I variants, we investigated quantitative differences among MHC variants with regard to binding peptides from distinct pathogens. Our analysis showed signatures of specialization toward specific pathogens especially by MHC variants with narrow peptide-binding repertoires. This supports the hypothesis that such fastidious MHC variants might be maintained in the population because they provide an advantage against particular pathogens. Overall, our results establish a key selection factor for the excessive allelic diversity at MHC genes observed in natural populations and illuminate the evolution of variable peptide-binding repertoires among MHC variants.

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.

Significant non-existence of sequences in genomes and proteomes

Minimal absent words (MAWs) are minimal-length oligomers absent from a genome or proteome. Although some artificially synthesized MAWs have deleterious effects, there is still a lack of a strategy for the classification of non-occurring sequences as potentially malicious or benign. In this work, by using Markovian models with multiple-testing correction, we reveal significant absent oligomers, which are statistically expected to exist. This suggests that their absence is due to negative selection. We survey genomes and proteomes covering the diversity of life and find thousands of significant absent sequences. Common significant MAWs are often mono- or dinucleotide tracts, or palindromic. Significant viral MAWs are often restriction sites and may indicate unknown restriction motifs. Surprisingly, significant mammal genome MAWs are often present, but rare, in other mammals, suggesting that they are suppressed but not completely forbidden. Significant human MAWs are frequently present in prokaryotes, suggesting immune function, but rarely present in human viruses, indicating viral mimicry of the host. More than one-fourth of human proteins are one substitution away from containing a significant MAW, with the majority of replacements being predicted harmful. We provide a web-based, interactive database of significant MAWs across genomes and proteomes.

HLA binding of self-peptides is biased towards proteins with specific molecular functions

Human leukocyte antigen (HLA) is highly polymorphic and plays a key role in guiding adaptive immune responses by presenting foreign and self peptides to T cells. Each HLA variant selects a minor fraction of peptides that match a certain motif required for optimal interaction with the peptide-binding groove. These restriction rules define the landscape of peptides presented to T cells. Given these limitations, one might suggest that the choice of peptides presented by HLA is non-random and there is preferential presentation of an array of peptides that is optimal for distinguishing self and foreign proteins. In this study we explore these preferences with a comparative analysis of self peptides enriched and depleted in HLA ligands. We show that HLAs exhibit preferences towards presenting peptides from certain proteins while disfavoring others with specific functions, and highlight differences between various HLA genes and alleles in those preferences. We link those differences to HLA anchor residue propensities and amino acid composition of preferentially presented proteins. The set of proteins that peptides presented by a given HLA are most likely to be derived from can be used to distinguish between class I and class II HLAs and HLA alleles. Our observations can be extrapolated to explain the protective effect of certain HLA alleles in infectious diseases, and we hypothesize that they can also explain susceptibility to certain autoimmune diseases and cancers. We demonstrate that these differences lead to differential presentation of HIV, influenza virus, SARS-CoV-1 and SARS-CoV-2 proteins by various HLA alleles. Finally, we show that the reported self peptidome preferences of distinct HLA variants can be compensated by combinations of HLA-A/HLA-B and HLA-A/HLA-C alleles in frequent haplotypes.

The farther the better: Investigating how distance from human self affects the propensity of a peptide to be presented on cell surface by MHC class I molecules, the case of Trypanosoma cruzi

More than twenty years ago the reverse vaccinology paradigm came to light trying to design new vaccines based on the analysis of genomic information in order to select those pathogen peptides able to trigger an immune response. In this context, focusing on the proteome of Trypanosoma cruzi, we investigated the link between the probabilities for pathogen peptides to be presented on a cell surface and their distance from human self. We found a reasonable but, as far as we know, undiscovered property: the farther the distance between a peptide and the human-self the higher the probability for that peptide to be presented on a cell surface. We also found that the most distant peptides from human self bind, on average, a broader collection of HLAs than expected, implying a potential immunological role in a large portion of individuals. Finally, introducing a novel quantitative indicator for a peptide to measure its potential immunological role, we proposed a pool of peptides that could be potential epitopes and that can be suitable for experimental testing. The software to compute peptide classes according to the distance from human self is free available at http://www.iasi.cnr.it/~dsantoni/nullomers.

Viral peptides-MHC interaction: Binding probability and distance from human peptides

Identification of peptides binding to MHC class I complex can play a crucial role in retrieving potential targets able to trigger an immune response. Affinity binding of viral peptides can be estimated through effective computational methods that in the most of cases are based on machine learning approach. Achieving a better insight into peptide features that impact on the affinity binding rate is a challenging issue. In the present work we focused on 9-mer peptides of Human immunodeficiency virus type 1 and Human herpes simplex virus 1, studying their binding to MHC class I. Viral 9-mers were partitioned into different classes, where each class is characterized by how far (in terms of mutation steps) the peptides belonging to that class are from human 9-mers. Viral 9-mers were partitioned in different classes, based on the number of mutation steps they are far from human 9-mers. We showed that the overall binding probability significantly differs among classes, and it typically increases as the distance, computed in terms of number of mutation steps from the human set of 9-mers, increases. The binding probability is particularly high when considering viral 9-mers that are far from all human 9-mers more than three mutation steps. A further evidence, providing significance to those special viral peptides and suggesting a potential role they can play, comes from the analysis of their distribution along viral genomes, as it revealed they are not randomly located, but they preferentially occur in specific genes.

Can the Immune System Perform a t-Test?

The self-nonself discrimination hypothesis remains a landmark concept in immunology. It proposes that tolerance breaks down in the presence of nonself antigens. In strike contrast, in statistics, occurrence of nonself elements in a sample (i.e., outliers) is not obligatory to violate the null hypothesis. Very often, what is crucial is the combination of (self) elements in a sample. The two views on how to detect a change seem challengingly different and it could seem difficult to conceive how immunological cellular interactions could trigger responses with a precision comparable to some statistical tests. Here it is shown that frustrated cellular interactions reconcile the two views within a plausible immunological setting. It is proposed that the adaptive immune system can be promptly activated either when nonself ligands are detected or self-ligands occur in abnormal combinations. In particular we show that cellular populations behaving in this way could perform location statistical tests, with performances comparable to t or KS tests, or even more general data mining tests such as support vector machines or random forests. In more general terms, this work claims that plausible immunological models should provide accurate detection mechanisms for host protection and, furthermore, that investigation on mechanisms leading to improved detection in “in silico” models can help unveil how the real immune system works.

How many TCR clonotypes does a body maintain?

We consider the lifetime of a T cell clonotype, the set of T cells with the same T cell receptor, from its thymic origin to its extinction in a multiclonal repertoire. Using published estimates of total cell numbers and thymic production rates, we calculate the mean number of cells per TCR clonotype, and the total number of clonotypes, in mice and humans. When there is little peripheral division, as in a mouse, the number of cells per clonotype is small and governed by the number of cells with identical TCR that exit the thymus. In humans, peripheral division is important and a clonotype may survive for decades, during which it expands to comprise many cells. We therefore devise and analyse a computational model of homeostasis of a multiclonal population. Each T cell in the model competes for self pMHC stimuli, cells of any one clonotype only recognising a small fraction of the many subsets of stimuli. A constant mean total number of cells is maintained by a balance between cell division and death, and a stable number of clonotypes by a balance between thymic production of new clonotypes and extinction of existing ones. The number of distinct clonotypes in a human body may be smaller than the total number of naive T cells by only one order of magnitude.

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The number of T cells of one clonotype is an integer.

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The history of a clonotype starts with release from the thymus, and ends with extinction.

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Competition and cross-reactivity are included in a natural way.

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The average number of cells per clonotype, in a human body, is only of order 10.

Optimal T cell cross-reactivity and the role of regulatory T cells

The T lymphocytes of the adaptive immune system constitute a highly diverse repertoire of clones expressing a unique T cell receptor (TCR). It has been argued that TCRs are cross-reactive, meaning that one receptor can recognize a multitude of epitopes. Cross-reactivity between self and foreign epitopes can potentially lead to autoimmune responses. Regulatory T cells (Tregs) down-regulate immune reactions, and play an important role in the avoidance of autoimmunity. We use a probabilistic modeling approach to investigate how suppression of antigen-presenting dendritic cells (DCs) by Tregs influences the probability of mounting a successful immune response against a pathogen while remaining self-tolerant. For T cell cross-reactivity values close to experimental estimates, we find that the presence of Tregs increases this success probability somewhat. However, the probability of a successful immune response remains relatively low for these cross-reactivity values, and the probability of success is optimized when T cells are more specific and no Tregs are formed. We conclude that DC suppression on its own is insufficient to provide an evolutionary benefit of regulatory T cells. Rejecting one intuitively likely hypothesis for the function of Tregs thus narrows down the search for the mechanisms by which they are suppressing inappropriate immune responses.

TCR contact residue hydrophobicity is a hallmark of immunogenic CD8+ T cell epitopes

Despite the availability of major histocompatibility complex (MHC)-binding peptide prediction algorithms, the development of T-cell vaccines against pathogen and tumor antigens remains challenged by inefficient identification of immunogenic epitopes. CD8(+) T cells must distinguish immunogenic epitopes from nonimmunogenic self peptides to respond effectively against an antigen without endangering the viability of the host. Because this discrimination is fundamental to our understanding of immune recognition and critical for rational vaccine design, we interrogated the biochemical properties of 9,888 MHC class I peptides. We identified a strong bias toward hydrophobic amino acids at T-cell receptor contact residues within immunogenic epitopes of MHC allomorphs, which permitted us to develop and train a hydrophobicity-based artificial neural network (ANN-Hydro) to predict immunogenic epitopes. The immunogenicity model was validated in a blinded in vivo overlapping epitope discovery study of 364 peptides from three HIV-1 Gag protein variants. Applying the ANN-Hydro model on existing peptide-MHC algorithms consistently reduced the number of candidate peptides across multiple antigens and may provide a correlate with immunodominance. Hydrophobicity of TCR contact residues is a hallmark of immunogenic epitopes and marks a step toward eliminating the need for empirical epitope testing for vaccine development.

In silico predicted mycobacterial epitope elicits in vitro T-cell responses

Epitope-based vaccines permit the selection of only a specific subset of epitopes to induce the necessary immune response, thus providing a rational alternative to conventional design approaches. Using a range of immunoinformatics tools, we identified a novel, contiguous 28 amino acid multi-epitope cluster within the highly conserved secretory protein Ag85B of Mycobacterium tuberculosis, the causative agent of TB. This cluster, named Ep85B, is composed of epitopes which bind to three HLA Class I and 15 Class II molecules, and harbors the potential to generate 99% population coverage in TB-endemic regions. We experimentally evaluated the capacity of Ep85B to elicit T-cell immune responses using whole blood cells and, as predicted, observed significant increases in populations of both CD4+ and memory CD4+ CD45RO+ T-cells. Our results demonstrate the practical utility of an epitope-based design methodology - a strategy that, following further evaluation, may serve as an additional tool for the development of novel vaccine candidates against TB and other diseases.

Anergy in self-directed B lymphocytes: A statistical mechanics perspective

Self-directed lymphocytes may evade clonal deletion at ontogenesis but still remain harmless due to a mechanism called clonal anergy. For B-lymphocytes, two major explanations for anergy developed over the last decades: according to Varela theory, anergy stems from a proper orchestration of the whole B-repertoire, such that self-reactive clones, due to intensive feed-back from other clones, display strong inertia when mounting a response. Conversely, according to the model of cognate response, self-reacting cells are not stimulated by helper lymphocytes and the absence of such signaling yields anergy. Through statistical mechanics we show that helpers do not prompt activation of a sub-group of B-cells: remarkably, the latter are just those broadly interacting in the idiotypic network. Hence Varela theory can finally be reabsorbed into the prevailing framework of the cognate response model. Further, we show how the B-repertoire architecture may emerge, where highly connected clones are self-directed as a natural consequence of ontogenetic learning.

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.

Social discrimination by quantitative assessment of immunogenetic similarity

Genes of the major histocompatibility complex (MHC) that underlie the adaptive immune system may allow vertebrates to recognize their kin. True kin-recognition genes should produce signalling products to which organisms can respond. Allelic variation in the peptide-binding region (PBR) of MHC molecules determines the pool of peptides that can be presented to trigger an immune response. To examine whether these MHC peptides also might underlie assessments of genetic similarity, we tested whether Xenopus laevis tadpoles socially discriminate between pairs of siblings with which they differed in PBR amino acid sequences. We found that tadpoles (four sibships, n = 854) associated preferentially with siblings with which they were more similar in PBR amino acid sequence. Moreover, the strength of their preference for a conspecific was directly proportional to the sequence similarity between them. Discrimination was graded, and correlated more closely with functional sequence differences encoded by MHC class I and class II alleles than with numbers of shared haplotypes. Our results thus suggest that haplotype analyses may fail to reveal fine-scale behavioural responses to divergence in functionally expressed sequences. We conclude that MHC-PBR gene products mediate quantitative social assessment of immunogenetic similarity that may facilitate kin recognition in vertebrates.

Degenerate T-cell recognition of peptides on MHC molecules creates large holes in the T-cell repertoire

The cellular immune system screens peptides presented by host cells on MHC molecules to assess if the cells are infected. In this study we examined whether the presented peptides contain enough information for a proper self/nonself assessment by comparing the presented human (self) and bacterial or viral (nonself) peptides on a large number of MHC molecules. For all MHC molecules tested, only a small fraction of the presented nonself peptides from 174 species of bacteria and 1000 viral proteomes ([Formula: see text]0.2%) is shown to be identical to a presented self peptide. Next, we use available data on T-cell receptor-peptide-MHC interactions to estimate how well T-cells distinguish between similar peptides. The recognition of a peptide-MHC by the T-cell receptor is flexible, and as a result, about one-third of the presented nonself peptides is expected to be indistinguishable (by T-cells) from presented self peptides. This suggests that T-cells are expected to remain tolerant for a large fraction of the presented nonself peptides, which provides an explanation for the "holes in the T-cell repertoire" that are found for a large fraction of foreign epitopes. Additionally, this overlap with self increases the need for efficient self tolerance, as many self-similar nonself peptides could initiate an autoimmune response. Degenerate recognition of peptide-MHC-I complexes by T-cells thus creates large and potentially dangerous overlaps between self and nonself.

SUFFICIENT TO RECOGNIZE SELF TO ATTACK NON-SELF: BLUEPRINT FOR A ONE-SIGNAL T CELL MODEL

Current consensus postulates that the class I-antigen processing system is evolved to present microbial antigens to specific T cells. Since such cells are rare and short-lived, they require three to five days to attain fighting strength. During this critical period he innate immune system holds back the briskly multiplying pathogens. Nevertheless, a T cell response is measurable in the lymph nodes draining the infection site within 12 to 18 h. In order to explain this paradox here we suggest a new T cell model. This is based on the observation that T cells require continuous contact of the T cell receptor (TCR) with selecting self-peptide–major histocompatibility complex (MHC) molecules in the periphery for their survival. We postulate that a dynamic steady state, a so-called coupled system is formed through low affinity complementary TCR–MHC interactions between T cells and host cells. Under such condition it is sufficient to recognize what is self in order to attack what is not self. A coupled system is regulated via soluble forms of peptide–MHC and TCR molecules by the law of mass action. In a coupled system one signal is sufficient for T cell activation. The new model implies that a significant fraction of the naive polyclonal T cells are recruited into the first line of defense from the very outset of an infection, so the number of activated T cells is increased by several orders of magnitude compared to conventional models. The one-signal model also predicts that therapeutic administration of soluble agonist or antagonist T cell receptor ligands may be able to fine tune the homeostatic physiological regulatory mechanism and thus improve the treatment of some chronic diseases such as metastatic cancer, HIV/AIDS, and transplantation.

Specific T-cell activation in an unspecific T-cell repertoire

T-cells are a vital type of white blood cell that circulate around our bodies, scanning for cellular abnormalities and infections. They recognise disease-associated antigens via a surface receptor called the T-cell antigen receptor (TCR). If there were a specific TCR for every single antigen, no mammal could possibly contain all the T-cells it needs. This is clearly absurd and suggests that T-cell recognition must, to the contrary, be highly degenerate. Yet highly promiscuous TCRs would appear to be equally impossible: they are bound to recognise self as well as non-self antigens. We review how contributions from mathematical analysis have helped to resolve the paradox of the promiscuous TCR. Combined experimental and theoretical work shows that TCR degeneracy is essentially dynamical in nature, and that the T-cell can differentially adjust its functional sensitivity to the salient epitope, "tuning up" sensitivity to the antigen associated with disease and "tuning down" sensitivity to antigens associated with healthy conditions. This paradigm of continual modulation affords the TCR repertoire, despite its limited numerical diversity, the flexibility to respond to almost any antigenic challenge while avoiding autoimmunity.

Quantifying how MHC polymorphism prevents pathogens from adapting to the antigen presentation pathway

The classical antigen presentation pathway consists of two monomorphic (proteasome and TAP) and one polymorphic components (MHC Class I). Viruses can escape CTL responses by mutating an epitope so that it is no longer correctly processed by the pathway. Whereas escape mutations that affect MHC binding are typically no longer under selection pressure in the next host of the virus (as hosts differ in their MHC alleles), escape mutations that affect the antigen processing of epitope precursors prevent the use of those epitope precursors by any of the MHC alleles in a host population. Viruses might therefore be under selection pressure to adapt to the monomorphic proteasome and TAP. We designed an agent-based model of a host population, in which an HIV-1 like virus adapts to the antigen presentation pathway of individual hosts, as the virus spreads through the population. We studied how the polymorphism of the MHC and the monomorphism of the proteasome and TAP affected the level of adaptation to the host population that the virus could reach. We found that due to the polymorphism and high specificity of the MHC class I molecules, the CTL epitopes that are targeted by the CTL responses of different hosts do not share many epitope precursors. Therefore, escape mutations in epitope precursors are frequently released from immune selection pressure, and can revert back to the virus wildtype sequence. As a result, the selection pressure on the virus to adapt to the proteasome and TAP is relatively small, which explains the low level of adaptation of the virus to the monomorphic steps in the antigen presentation pathway.

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.

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).

The distribution of CTL epitopes in HIV-1 appears to be random, and similar to that of other proteomes

Background

HIV-1 viruses are highly capable of mutating their proteins to escape the presentation of CTL epitopes in their current host. Upon transmission to another host, some escape mutations revert, but other remain stable in the virus sequence for at least several years. Depending on the rate of accumulation and reversion of escape mutations, HIV-1 could reach a high level of adaptation to the human population. Yusim et. al. hypothesized that the apparent clustering of CTL epitopes in the conserved regions of HIV-1 proteins could be an evolutionary signature left by large-scale adaptation of HIV-1 to its human/simian host.

Results

In this paper we quantified the distribution of CTL epitopes in HIV-1 and found that that in 99% of the HIV-1 protein sequences, the epitope distribution was indistinguishable from random. Similar percentages were found for HCV, Influenza and for three eukaryote proteomes (Human, Drosophila, Yeast).

Conclusion

We conclude that CTL epitopes in HIV-1 are randomly distributed, and that this distribution is similar to the distribution of CTL epitopes in proteins from other proteomes. Therefore, the visually apparent clustering of CTL epitopes in epitope maps should not be interpreted as a signature of a past large-scale adaptation of HIV-1 to the human cellular immune response.

A comparative study of HLA binding affinity and ligand diversity: implications for generating immunodominant CD8+ T cell responses

Conventional CD8(+) T cell responses against intracellular infectious agents are initiated upon recognition of pathogen-derived peptides presented at the cell surface of infected cells in the context of MHC class I molecules. Among the major MHC class I loci, HLA-B is the swiftest evolving and the most polymorphic locus. Additionally, responses restricted by HLA-B molecules tend to be dominant, and most associations with susceptibility or protection against infectious diseases have been assigned to HLA-B alleles. To assess whether the differences in responses mediated via two major HLA class I loci, HLA-B and HLA-A, may already begin at the Ag presentation level, we have analyzed the diversity and binding affinity of their peptide repertoire by making use of curated pathogen-derived epitope data retrieved from the Immune Epitope Database and Analysis Resource, as well as in silico predicted epitopes. In contrast to our expectations, HLA-B alleles were found to have a less diverse peptide repertoire, which points toward a more restricted binding motif, and the respective average peptide binding affinity was shown to be lower than that of HLA-A-restricted epitopes. This unexpected observation gives rise to new hypotheses concerning the mechanisms underlying immunodominance of CD8(+) T cell responses.

The specificity and polymorphism of the MHC class I prevents the global adaptation of HIV-1 to the monomorphic proteasome and TAP

The large diversity in MHC class I molecules in a population lowers the chance that a virus infects a host to which it is pre-adapted to escape the MHC binding of CTL epitopes. However, viruses can also lose CTL epitopes by escaping the monomorphic antigen processing components of the pathway (proteasome and TAP) that create the epitope precursors. If viruses were to accumulate escape mutations affecting these monomorphic components, they would become pre-adapted to all hosts regardless of the MHC polymorphism. To assess whether viruses exploit this apparent vulnerability, we study the evolution of HIV-1 with bioinformatic tools that allow us to predict CTL epitopes, and quantify the frequency and accumulation of antigen processing escapes. We found that within hosts, proteasome and TAP escape mutations occur frequently. However, on the population level these escapes do not accumulate: the total number of predicted epitopes and epitope precursors in HIV-1 clade B has remained relatively constant over the last 30 years. We argue that this lack of adaptation can be explained by the combined effect of the MHC polymorphism and the high specificity of individual MHC molecules. Because of these two properties, only a subset of the epitope precursors in a host are potential epitopes, and that subset differs between hosts. We estimate that upon transmission of a virus to a new host 39%–66% of the mutations that caused epitope precursor escapes are released from immune selection pressure.

Widespread and ample peptide overlapping between HCV and Homo sapiens proteomes

Alignment of protein sequences is fundamental in analyzing homology, evolutionary events and functional relationships. Searching for the epitopic peptide platform underlying hepatitis C virus (HCV) infection and autoimmune phenomena, we have used sequence-sequence peptide matching to compare the HCV polyprotein sequence to the human proteome. The following results were obtained: (1) pentamers from HCV polyprotein have a widespread and high level of similarity to a large number of human proteins (19,605 human proteins, that is 57.6% of the human proteome); (2) remarkable similarity between the two proteomes persists even using longer peptide motifs as probes for identity scanning; (3) only a limited number of HCV pentameric fragments have no similarity to the human host, so representing molecular sequence signatures of the virus. We conclude that the widespread sharing of numerous perfect exact matches between HCV and human proteomes might explain HCV persistence in humans.

T Cells Survey the Stability of the Self: A Testable Hypothesis on the Homeostatic Role of TCR-MHC Interactions

In the lifetime of an individual, every single gene will have undergone mutation on about 10(10) separate occasions. Nevertheless, cancer occurs mainly with advancing age. Here, we hypothesize that the evolutionary pressure driving the creation of the T cell receptor (TCR) repertoire was primarily the homeostatic surveillance of the genome. The subtly variable T cells may in fact constitute an evolutionary link between the invariable innate and hypervariable B cell systems. The new model is based on the homeostatic role of T cells, suggesting that molecular complementarity between the positively selected TCR and the self peptide-presenting major histocompatibility complex molecules establishes and regulates homeostasis, strictly limiting variations of its components. Notwithstanding, the 'homeostatic role of T cells' model offers a more realistic explanation as to how a naïve clonal immune system can cope with the much faster replicating pathogens, despite a limited repertoire that is capable of facing only a small fraction of the vast antigenic universe at a time.

Quantitative theories of T-cell responsiveness

We review recent advances toward a comprehensive mathematical theory of T-cell immunity. A key insight is that the efficacy of the T-cell response is best analyzed in terms of T-cell receptor (TCR) avidity and the distribution of this avidity across the TCR repertoire (the 'avidity spectrum'). Modification of this avidity spectrum by a wide range of tuning and tolerance mechanisms allows the system to adapt cross-reactivity and specificity to the challenge at hand while avoiding inappropriate responses against non-pathogenic cells and tissues. Theoretical models relate molecular kinetic parameters and cellular properties to systemic level statistics such as avidity spectra. Such bridge equations are crucial for rational clinical manipulation of T cells at the molecular level.

Polyspecificity of T cell and B cell receptor recognition

A recent workshop discussed the recognition of multiple distinct ligands by individual T cell and B cell receptors and the implications of this discovery for lymphocyte biology. The workshop recommends general use of the term polyspecificity because it emphasizes two fundamental aspects, the inherent specificity of receptor recognition and the ability to recognize multiple ligands. Many different examples of polyspecificity and the structural mechanisms were discussed, and the group concluded that polyspecificity is a general, inherent feature of TCR and antibody recognition. This review summarizes the relevance of polyspecificity for lymphocyte development, activation and disease processes.

Modelling the human immune system by combining bioinformatics and systems biology approaches

Over the past decade a number of bioinformatics tools have been developed that use genomic sequences as input to predict to which parts of a microbe the immune system will react, the so-called epitopes. Many predicted epitopes have later been verified experimentally, demonstrating the usefulness of such predictions. At the same time, simulation models have been developed that describe the dynamics of different immune cell populations and their interactions with microbes. These models have been used to explain experimental findings where timing is of importance, such as the time between administration of a vaccine and infection with the microbe that the vaccine is intended to protect against. In this paper, we outline a framework for integration of these two approaches. As an example, we develop a model in which HIV dynamics are correlated with genomics data. For the first time, the fitness of wild type and mutated virus are assessed by means of a sequence-dependent scoring matrix, derived from a BLOSUM matrix, that links protein sequences to growth rates of the virus in the mathematical model. A combined bioinformatics and systems biology approach can lead to a better understanding of immune system-related diseases where both timing and genomic information are of importance.