Evolution of SARS-CoV-2-specific CD4+ T cell epitopes.

Vaccination clearly decreases coronavirus disease 2019 (COVID-19) mortality; however, they also impose selection pressure on the virus, which promotes the evolution of immune escape variants. For example, despite the high vaccination level in especially Western countries, the Omicron variant caused millions of breakthrough infections, suggesting that the highly mutated spike protein in the Omicron variant can escape antibody immunity much more efficiently than the other variants of concern (VOCs). In this study, we investigated the resistance/susceptibility of T helper cell responses that are necessary for generating efficient long-lasting antibody immunity, in several VOCs. By predicting T helper cell epitopes on the spike protein for most common HLA-DRB1 alleles worldwide, we found that although most of high frequency HLA-DRB1 alleles have several potential T helper cell epitopes, few alleles like HLA-DRB1 13:01 and 11:01 are not predicted to have any significant T helper cell responses after vaccination. Using these predictions, a population based on realistic human leukocyte antigen-II (HLA-II) frequencies were simulated to visualize the T helper cell immunity on the population level. While a small fraction of this population had alarmingly little predicted CD4 T cell epitopes, the majority had several epitopes that should be enough to generate efficient B cell responses. Moreover, we show that VOC spike mutations hardly affect T helper epitopes and mainly occur in other residues of the spike protein. These results suggest that lack of long-lasting antibody responses is not likely due to loss of T helper cell epitopes in new VOCs.

Role of T cells in severe COVID-19 disease, protection, and long term immunity.

Infection with SARS-CoV-2 causes wide range of disease severities from asymptomatic to life-threatening disease. Understanding the contribution of immunological traits in immunity against SARS-CoV-2 and in protection against severe COVID-19 could result in effective measures to prevent development of severe disease. While the role of cytokines and antibodies has been thoroughly studied, this is not the case for T cells. In this review, the association between T cells and COVID-19 disease severity and protection upon reexposure is discussed. While infiltration of overactivated cytotoxic T cells might be harmful in the infected tissue, fast responding T cells are important in the protection against severe COVID-19. This protection could even be viable in the long term as long-living memory T cells seem to be stabilized and mutations do not appear to have a large impact on T cell responses. Thus, after vaccination and infections, memory T cells should be able to help prevent onset of severe disease for most cases. Considering this, it would be useful to add N or M proteins in vaccinations, alongside the S protein which is currently used, as this results in a broader T cell response.

VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium.

Here, we report an update of the VDJdb database with a substantial increase in the number of T-cell receptor (TCR) sequences and their cognate antigens. The update further provides a new database infrastructure featuring two additional analysis modes that facilitate database querying and real-world data analysis. The increased yield of TCR specificity identification methods and the overall increase in the number of studies in the field has allowed us to expand the database more than 5-fold. Furthermore, several new analysis methods are included. For example, batch annotation of TCR repertoire sequencing samples allows for annotating large datasets on-line. Using recently developed bioinformatic methods for TCR motif mining, we have built a reduced set of high-quality TCR motifs that can be used for both training TCR specificity predictors and matching against TCRs of interest. These additions enhance the versatility of the VDJdb in the task of exploring T-cell antigen specificities. The database is available at https://vdjdb.cdr3.net.

Revealing factors determining immunodominant responses against dominant epitopes.

Upon recognition of peptide-MHC complexes by T cell receptors (TCR), the cognate T cells expand and differentiate into effector T cells to generate protective immunity. Despite the fact that any immune response generates a diverse set of TCR clones against a particular epitope, only a few clones are highly expanded in any immune response. Previous studies observed that the highest frequency clones usually control viral infections better than subdominant clones, but the reasons for this dominance among T cell clones are still unclear. Here, we used publicly available TCR amino acid sequences to study which factors determine whether a response becomes immunodominance (ID) per donor; we classified the largest T cell clone as the epitope-specific dominant clone and all the other clones as subdominant responses (SD). We observed a distinctively hydrophobic CDR3 in ID responses against a dominant epitope from influenza A virus, compared to the SD responses. The common V-J combinations were shared between ID and SD responses, suggesting that the biased V-J recombination events are restricted by epitope specificity; thus, the immunodominance is not directly determined by a bias combination of V and J genetic segments. Our findings reveal a close similarity of global sequence properties between dominant and subdominant clones of epitope-specific responses but detectable distinctive amino acid enrichments in ID. Taken together, we believe this first comparative study of immunodominant and subdominant TCR sequences can guide further studies to resolve factors determining the immunodominance of antiviral as well as tumor-specific T cell responses.

VDJdb: a curated database of T-cell receptor sequences with known antigen specificity.

The ability to decode antigen specificities encapsulated in the sequences of rearranged T-cell receptor (TCR) genes is critical for our understanding of the adaptive immune system and promises significant advances in the field of translational medicine. Recent developments in high-throughput sequencing methods (immune repertoire sequencing technology, or RepSeq) and single-cell RNA sequencing technology have allowed us to obtain huge numbers of TCR sequences from donor samples and link them to T-cell phenotypes. However, our ability to annotate these TCR sequences still lags behind, owing to the enormous diversity of the TCR repertoire and the scarcity of available data on T-cell specificities. In this paper, we present VDJdb, a database that stores and aggregates the results of published T-cell specificity assays and provides a universal platform that couples antigen specificities with TCR sequences. We demonstrate that VDJdb is a versatile instrument for the annotation of TCR repertoire data, enabling a concatenated view of antigen-specific TCR sequence motifs. VDJdb can be accessed at https://vdjdb.cdr3.net and https://github.com/antigenomics/vdjdb-db.

Immune biomarkers for predicting response to adoptive cell transfer as cancer treatment.

Adoptive cell transfer (ACT) is a form of personalised immunotherapy which has shown promising results in metastasised cancer. For this treatment, autologous T lymphocytes are selected and stimulated in vitro before re-administration in large numbers. However, only a fraction of patients benefit from ACT, and it is not yet known what biomarkers can predict treatment outcome. In this review, we describe what tumour characteristics are associated with response to ACT. Based on the current knowledge, the best candidate biomarker for a good anti-tumour response seems to be a large number of neoantigens with a homogeneous distribution across the tumour in combination with sufficient MHC-I expression level. Additionally, it is necessary to be able to isolate a diverse population of T cells reactive to these neoantigens from tumour tissue or peripheral blood. Additional promising candidate biomarkers shared with other cancer immunotherapies are a large number of tumour-infiltrating cytotoxic and memory T cells, normal levels of glycolysis, and a pro-inflammatory cytokine profile within the tumour. Intense research in this field will hopefully result in identification of more biomarkers for cancers with low mutational load.

Specificity of inhibitory KIRs enables NK cells to detect changes in an altered peptide environment.

The activity of natural killer (NK) cells is tightly regulated by inhibitory and activating receptors. Inhibitory killer immunoglobulin-like receptors (iKIRs) survey the surface of target cells by monitoring the expression of human leukocyte antigen (HLA) class I. The binding of iKIRs has been shown to be sensitive to the peptides presented by HLA class I, implying that iKIRs have the ability to detect the changes in the repertoire of peptide-HLA class I complexes (pHLA), a process occurring during viral infection and in tumor cells. To study how the pHLA repertoire changes upon infection, and whether an iKIR is able to detect these changes, we study peptides eluted from cells prior and after infection with measles virus (MV). Remarkably, most changes in the repertoire of potential iKIR ligands are predicted to be caused by the altered expression of self-peptides. We show that an iKIR can detect these changes in the presented peptides only if it is sufficiently specific, e.g., if iKIRs can distinguish between different amino acids in the contact residues (e.g., position 7 and 8). Our analysis further indicates that one single iKIR per host is not sufficient to detect changes in the peptide repertoire, suggesting that a multigene family encoding for different iKIRs is required for successful peptide recognition.

CD8+ TCR Bias and Immunodominance in HIV-1 Infection.

Immunodominance describes a phenomenon whereby the immune system consistently targets only a fraction of the available Ag pool derived from a given pathogen. In the case of CD8(+) T cells, these constrained epitope-targeting patterns are linked to HLA class I expression and determine disease progression. Despite the biological importance of these predetermined response hierarchies, little is known about the factors that control immunodominance in vivo. In this study, we conducted an extensive analysis of CD8(+) T cell responses restricted by a single HLA class I molecule to evaluate the mechanisms that contribute to epitope-targeting frequency and antiviral efficacy in HIV-1 infection. A clear immunodominance hierarchy was observed across 20 epitopes restricted by HLA-B*42:01, which is highly prevalent in populations of African origin. Moreover, in line with previous studies, Gag-specific responses and targeting breadth were associated with lower viral load set-points. However, peptide-HLA-B*42:01 binding affinity and stability were not significantly linked with targeting frequencies. Instead, immunodominance correlated with epitope-specific usage of public TCRs, defined as amino acid residue-identical TRB sequences that occur in multiple individuals. Collectively, these results provide important insights into a potential link between shared TCR recruitment, immunodominance, and antiviral efficacy in a major human infection.

A Coevolutionary Arms Race between Hosts and Viruses Drives Polymorphism and Polygenicity of NK Cell Receptors.

Natural killer cell receptors (NKRs) monitor the expression of major histocompatibility class I (MHC-I) and stress molecules to detect unhealthy tissue, such as infected or tumor cells. The NKR gene family shows a remarkable genetic diversity, containing several genes encoding receptors with activating and inhibiting signaling, and varying in gene content and allelic polymorphism. The expansion of the NKR genes is species-specific, with different species evolving alternative expanded NKR genes, which encode structurally different proteins, yet perform comparable functions. So far, the biological function of this expansion within the NKR cluster has remained poorly understood. To study the evolution of NKRs, we have developed an agent-based model implementing a coevolutionary scenario between hosts and herpes-like viruses that are able to evade the immune response by downregulating the expression of MHC-I on the cell surface. We show that hosts evolve specific inhibitory NKRs, specialized to particular MHC-I alleles in the population. Viruses in our simulations readily evolve proteins mimicking the MHC molecules of their host, even in the absence of MHC-I downregulation. As a result, the NKR locus becomes polygenic and polymorphic, encoding both specific inhibiting and activating receptors to optimally protect the hosts from coevolving viruses.

The evolution of natural killer cell receptors.

Natural killer (NK) cells are immune cells that play a crucial role against viral infections and tumors. To be tolerant against healthy tissue and simultaneously attack infected cells, the activity of NK cells is tightly regulated by a sophisticated array of germline-encoded activating and inhibiting receptors. The best characterized mechanism of NK cell activation is "missing self" detection, i.e., the recognition of virally infected or transformed cells that reduce their MHC expression to evade cytotoxic T cells. To monitor the expression of MHC-I on target cells, NK cells have monomorphic inhibitory receptors which interact with conserved MHC molecules. However, there are other NK cell receptors (NKRs) encoded by gene families showing a remarkable genetic diversity. Thus, NKR haplotypes contain several genes encoding for receptors with activating and inhibiting signaling, and that vary in gene content and allelic polymorphism. But if missing-self detection can be achieved by a monomorphic NKR system why have these polygenic and polymorphic receptors evolved? Here, we review the expansion of NKR receptor families in different mammal species, and we discuss several hypotheses that possibly underlie the diversification of the NK cell receptor complex, including the evolution of viral decoys, peptide sensitivity, and selective MHC-downregulation.

Complex T-cell receptor repertoire dynamics underlie the CD8+ T-cell response to HIV-1.

UNLABELLED: Although CD8(+) T cells are important for the control of HIV-1 in vivo, the precise correlates of immune efficacy remain unclear. In this study, we conducted a comprehensive analysis of viral sequence variation and T-cell receptor (TCR) repertoire composition across multiple epitope specificities in a group of antiretroviral treatment-naive individuals chronically infected with HIV-1. A negative correlation was detected between changes in antigen-specific TCR repertoire diversity and CD8(+) T-cell response magnitude, reflecting clonotypic expansions and contractions related to alterations in cognate viral epitope sequences. These patterns were independent of the individual, as evidenced by discordant clonotype-specific transitions directed against different epitopes in single subjects. Moreover, long-term asymptomatic HIV-1 infection was characterized by evolution of the TCR repertoire in parallel with viral replication. Collectively, these data suggest a continuous bidirectional process of adaptation between HIV-1 and virus-specific CD8(+) T-cell clonotypes orchestrated at the TCR-antigen interface. IMPORTANCE: We describe a relation between viral epitope mutation, antigen-specific T-cell expansion, and the repertoire of responding clonotypes in chronic HIV-1 infection. This work provides insights into the process of coadaptation between the human immune system and a rapidly evolving lentivirus.

Zooming into the binding groove of HLA molecules: which positions and which substitutions change peptide binding most?

Human leukocyte antigen (HLA) genes are the most polymorphic genes in the human genome. Almost all polymorphic residues are located in the peptide-binding groove, resulting in different peptide-binding preferences. Whether a single amino acid change can alter the peptide-binding repertoire of an HLA molecule has never been shown. To experimentally quantify the contribution of a single amino acid change to the peptide repertoire of even a single HLA molecule requires an immense number of HLA peptide-binding measurements. Therefore, we used an in silico method to study the effect of single mutations on the peptide repertoires. We predicted the peptide-binding repertoire of a large set of HLA molecules and used the overlap of the peptide-binding repertoires of each pair of HLA molecules that differ on a single position to measure how much single substitutions change the peptide binding. We found that the effect of a single substitution in the peptide-binding groove depends on the substituted position and the amino acids involved. The positions that alter peptide binding most are the most polymorphic ones, while those that are hardly variable among HLA molecules have the lowest effect on the peptide repertoire. Although expected, the relationship between functional divergence and polymorphism of HLA molecules has never been shown before. Additionally, we show that a single substitution in HLA-B molecules has more effect on the peptide-binding repertoire compared to that in HLA-A molecules. This provides an (alternative) explanation for the larger polymorphism of HLA-B molecules compared to HLA-A molecules.

Role of peptide processing predictions in T cell epitope identification: contribution of different prediction programs.

Proteolysis is the general term to describe the process of protein degradation into peptides. Proteasomes are the main actors in cellular proteolysis, and their activity can be measured in in vitro digestion experiments. However, in vivo proteolysis can be different than what is measured in these experiments if other proteases participate or if proteasomal activity is different in vivo. The in vivo proteolysis can be measured only indirectly, by the analysis of peptides presented on MHC-I molecules. MHC-I presented peptides are protected from further degradation, thus enabling an indirect view on the underlying in vivo proteolysis. The ligands presented on different MHC-I molecules enable different views on this process; in combination, they might give a complete picture. Based on in vitro proteasome-only digestions and MHC-I ligand data, different proteolysis predictors have been developed. With new in vitro digestion and MHC-I ligand data sets, we benchmarked how well these predictors capture in vitro proteasome-only activity and in vivo whole-cell proteolysis, respectively. Even though the in vitro proteasome digestion patterns were best captured by methods trained on such data (ProteaSMM and NetChop 20S), the in vivo whole-cell proteolysis was best predicted by a method trained on MHC-I ligand data (NetChop Cterm). Follow-up analysis showed that the likely source of this difference is the activity from proteases other than the proteasome, such as TPPII. This non-proteasomal in vivo activity is captured by NetChop Cterm and should be taken into account in MHC-I ligand predictions.

CD8+ TCR repertoire formation is guided primarily by the peptide component of the antigenic complex.

CD8(+) T cells recognize infected or dysregulated cells via the clonotypically expressed αß TCR, which engages Ag in the form of peptide bound to MHC class I (MHC I) on the target cell surface. Previous studies have indicated that a diverse Ag-specific TCR repertoire can be beneficial to the host, yet the determinants of clonotypic diversity are poorly defined. To better understand the factors that govern TCR repertoire formation, we conducted a comprehensive clonotypic analysis of CD8(+) T cell populations directed against epitopes derived from EBV and CMV. Neither pathogen source nor the restricting MHC I molecule were linked with TCR diversity; indeed, both HLA-A and HLA-B molecules were observed to interact with an overlapping repertoire of expressed TRBV genes. Peptide specificity, however, markedly impacted TCR diversity. In addition, distinct peptides sharing HLA restriction and viral origin mobilized TCR repertoires with distinct patterns of TRBV gene usage. Notably, no relationship was observed between immunodominance and TCR diversity. These findings provide new insights into the forces that shape the Ag-specific TCR repertoire in vivo and highlight a determinative role for the peptide component of the peptide-MHC I complex on the molecular frontline of CD8(+) T cell-mediated immune surveillance.

Virus encoded MHC-like decoys diversify the inhibitory KIR repertoire.

Natural killer (NK) cells are circulating lymphocytes that play an important role in the control of viral infections and tumors. Their functions are regulated by several activating and inhibitory receptors. A subset of these receptors in human NK cells are the killer immunoglobulin-like receptors (KIRs), which interact with the highly polymorphic MHC class I molecules. One important function of NK cells is to detect cells that have down-regulated MHC expression (missing-self). Because MHC molecules have non polymorphic regions, their expression could have been monitored with a limited set of monomorphic receptors. Surprisingly, the KIR family has a remarkable genetic diversity, the function of which remains poorly understood. The mouse cytomegalovirus (MCMV) is able to evade NK cell responses by coding "decoy" molecules that mimic MHC class I. This interaction was suggested to have driven the evolution of novel NK cell receptors. Inspired by the MCMV system, we develop an agent-based model of a host population infected with viruses that are able to evolve MHC down-regulation and decoy molecules. Our simulations show that specific recognition of MHC class I molecules by inhibitory KIRs provides excellent protection against viruses evolving decoys, and that the diversity of inhibitory KIRs will subsequently evolve as a result of the required discrimination between host MHC molecules and decoy molecules.

Properties of MHC class I presented peptides that enhance immunogenicity.

T-cells have to recognize peptides presented on MHC molecules to be activated and elicit their effector functions. Several studies demonstrate that some peptides are more immunogenic than others and therefore more likely to be T-cell epitopes. We set out to determine which properties cause such differences in immunogenicity. To this end, we collected and analyzed a large set of data describing the immunogenicity of peptides presented on various MHC-I molecules. Two main conclusions could be drawn from this analysis: First, in line with previous observations, we showed that positions P4-6 of a presented peptide are more important for immunogenicity. Second, some amino acids, especially those with large and aromatic side chains, are associated with immunogenicity. This information was combined into a simple model that was used to demonstrate that immunogenicity is, to a certain extent, predictable. This model (made available at http://tools.iedb.org/immunogenicity/) was validated with data from two independent epitope discovery studies. Interestingly, with this model we could show that T-cells are equipped to better recognize viral than human (self) peptides. After the past successful elucidation of different steps in the MHC-I presentation pathway, the identification of variables that influence immunogenicity will be an important next step in the investigation of T-cell epitopes and our understanding of cellular immune responses.

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.

Proteome sampling by the HLA class I antigen processing pathway.

The peptide repertoire that is presented by the set of HLA class I molecules of an individual is formed by the different players of the antigen processing pathway and the stringent binding environment of the HLA class I molecules. Peptide elution studies have shown that only a subset of the human proteome is sampled by the antigen processing machinery and represented on the cell surface. In our study, we quantified the role of each factor relevant in shaping the HLA class I peptide repertoire by combining peptide elution data, in silico predictions of antigen processing and presentation, and data on gene expression and protein abundance. Our results indicate that gene expression level, protein abundance, and rate of potential binding peptides per protein have a clear impact on sampling probability. Furthermore, once a protein is available for the antigen processing machinery in sufficient amounts, C-terminal processing efficiency and binding affinity to the HLA class I molecule determine the identity of the presented peptides. Having studied the impact of each of these factors separately, we subsequently combined all factors in a logistic regression model in order to quantify their relative impact. This model demonstrated the superiority of protein abundance over gene expression level in predicting sampling probability. Being able to discriminate between sampled and non-sampled proteins to a significant degree, our approach can potentially be used to predict the sampling probability of self proteins and of pathogen-derived proteins, which is of importance for the identification of autoimmune antigens and vaccination targets.

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.