T-cell recognition is shaped by epitope sequence conservation in the host proteome and microbiome

Next-generation IEDB tools: a platform for epitope prediction and analysis

The Next-Generation (NG) IEDB Tools website (https://nextgen-tools.iedb.org) provides users with a redesigned interface to many of the algorithms for epitope prediction and analysis that were originally released on the legacy IEDB Tools website. The initial release focuses on consolidation of all tools related to HLA class I epitopes (MHC binding, elution, immunogenicity, and processing), making all of these predictions accessible from a single application and allowing for their simultaneous execution with minimal user inputs. Additionally, the PEPMatch tool for identifying highly similar epitopes in a set of curated proteomes, as well as a tool for epitope clustering, are available on the site. The NG Tools site allows users to build data pipelines by sending the output of one tool as input for the next. Over the next several years, all pre-existing IEDB Tools, and any newly developed tools, will be integrated into this new site. Here we describe the philosophy behind the redesign and demonstrate the utility and productivity enhancements that are enabled by the new interface.

A journey to your self: The vague definition of immune self and its practical implications

The identification of immunogenic peptides has become essential in an increasing number of fields in immunology, ranging from tumor immunotherapy to vaccine development. The nature of the adaptive immune response is shaped by the similarity between foreign and self-protein sequences, a concept extensively applied in numerous studies. Can we precisely define the degree of similarity to self? Furthermore, do we accurately define immune self? In the current work, we aim to unravel the conceptual and mechanistic vagueness hindering the assessment of self-similarity. Accordingly, we demonstrate the remarkably low consistency among commonly employed measures and highlight potential avenues for future research.

Identification of cow milk epitopes to characterize and quantify disease-specific T cells in allergic children

BACKGROUND

Cow milk (CM) allergy is the most prevalent food allergy in young children in the United States and Great Britain. Current diagnostic tests are either unreliable (IgE test and skin prick test) or resource-intensive with risks (food challenges).

OBJECTIVE

We sought to determine whether allergen-specific T cells in CM-allergic (CMA) patients have a distinct quality and/or quantity that could potentially be used as a diagnostic marker.

METHODS

Using PBMCs from 147 food-allergic pediatric subjects, we mapped T-cell responses to a set of reactive epitopes in CM that we compiled in a peptide pool. This pool induced cytokine responses in in vitro cultured cells distinguishing subjects with CMA from subjects without CMA. We further used the pool to isolate and characterize antigen-specific CD4 memory T cells using flow cytometry and single-cell RNA/TCR sequencing assays.

RESULTS

We detected significant changes in the transcriptional program and clonality of CM antigen-specific (CM+) T cells elicited by the pool in subjects with CMA versus subjects without CMA ex vivo. CM+ T cells from subjects with CMA had increased percentages of FOXP3+ cells over FOXP3- cells. FOXP3+ cells are often equated with regulatory T cells that have suppressive activity, but CM+ FOXP3+ cells from subjects with CMA showed significant expression of interferon-responsive genes and dysregulated chemokine receptor expression compared with subjects without CMA, suggesting that these are not conventional regulatory T cells. The CM+ FOXP3+ cells were also more clonally expanded than the FOXP3- population. We were further able to use surface markers (CD25, CD127, and CCR7) in combination with our peptide pool stimulation to quantify these CM+ FOXP3+ cells by a simple flow-cytometry assay. We show increased percentages of CM+ CD127-CD25+ cells from subjects with CMA in an independent cohort, which could be used for diagnostic purposes. Looking specifically for TH2 cells normally associated with allergic diseases, we found a small population of clonally expanded CM+ cells that were significantly increased in subjects with CMA and that had high expression of TH2 cytokines and pathogenic TH2/T follicular helper markers.

CONCLUSIONS

Overall, these findings suggest that there are several differences in the phenotypes of CM+ T cells with CM allergy and that the increase in CM+ FOXP3+ cells is a potential diagnostic marker of an allergic state. Such markers have promising applications in monitoring natural disease outgrowth and/or the efficacy of immunotherapy that will need to be validated in future studies.

A robust deep learning workflow to predict CD8 + T-cell epitopes

BACKGROUND

T-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes.

METHODS

We developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to 'dissimilarity to self' from cancer studies.

RESULTS

TRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP .

CONCLUSIONS

This study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.

Differential activation of innate and adaptive lymphocytes during latent or active infection with Mycobacterium tuberculosis

Most individuals infected with Mycobacterium tuberculosis (Mtb) have latent tuberculosis (TB), which can be diagnosed with tests (like the QuantiFERON test, QFT) that detect the production of IFN-γ by memory T cells in response to the Mtb-specific antigens ESAT-6, CFP-10 and TB7.7. However, the immunological mechanisms that determine if an individual will develop latent or active TB remain incompletely understood. Here we compared the response of innate and adaptive peripheral blood lymphocytes from healthy individuals without Mtb infection (QFT-negative) and from individuals with latent (QFT-positive) or active TB infection, in order to determine the characteristics of these cells that correlate with each condition. In active TB patients, the levels of IFN-γ that were produced in response to Mtb-specific antigens had high positive correlations with IL-1β, TNF-α, MCP-1, IL-6, IL-12p70 and IL-23, while the pro-inflammatory cytokines had high positive correlations between themselves and with IL-12p70 and IL-23. These correlations were not observed in QFT-negative or QFT-positive healthy volunteers. Activation with Mtb soluble extract (a mixture of Mtb antigens and pathogen-associated molecular patterns [PAMPs]) increased the percentage of IFN-γ/IL-17-producing NK cells and of IL-17-producing ILC3 in the peripheral blood of active TB patients, but not of QFT-negative or QFT-positive healthy volunteers. Thus, active TB patients have both adaptive and innate lymphocyte subsets that produce characteristic cytokine profiles in response to Mtb-specific antigens or PAMPs. These profiles are not observed in uninfected individuals or in individuals with latent TB, suggesting that they are a response to active TB infection. This article is protected by copyright. All rights reserved.

Outer Membrane Vesicles From The Gut Microbiome Contribute to Tumor Immunity by Eliciting Cross-Reactive T Cells

A growing body of evidence supports the notion that the gut microbiome plays an important role in cancer immunity. However, the underpinning mechanisms remain to be fully elucidated. One attractive hypothesis envisages that among the T cells elicited by the plethora of microbiome proteins a few exist that incidentally recognize neo-epitopes arising from cancer mutations (“molecular mimicry (MM)” hypothesis). To support MM, the human probiotic Escherichia coli Nissle was engineered with the SIINFEKL epitope (OVA-E.coli Nissle) and orally administered to C57BL/6 mice. The treatment with OVA-E.coli Nissle, but not with wild type E. coli Nissle, induced OVA-specific CD8⁺ T cells and inhibited the growth of tumors in mice challenged with B16F10 melanoma cells expressing OVA. The microbiome shotgun sequencing and the sequencing of TCRs from T cells recovered from both lamina propria and tumors provide evidence that the main mechanism of tumor inhibition is mediated by the elicitation at the intestinal site of cross-reacting T cells, which subsequently reach the tumor environment. Importantly, the administration of Outer Membrane Vesicles (OMVs) from engineered E. coli Nissle, as well as from E. coli BL21(DE3)ΔompA, carrying cancer-specific T cell epitopes also elicited epitope-specific T cells in the intestine and inhibited tumor growth. Overall, our data strengthen the important role of MM in tumor immunity and assign a novel function of OMVs in host-pathogen interaction. Moreover, our results pave the way to the exploitation of probiotics and OMVs engineered with tumor specific-antigens as personalized mucosal cancer vaccines.

HLA-dependent variation in SARS-CoV-2 CD8 + T cell cross-reactivity with human coronaviruses

The conditions and extent of cross-protective immunity between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and common-cold human coronaviruses (HCoVs) remain open despite several reports of pre-existing T cell immunity to SARS-CoV-2 in individuals without prior exposure. Using a pool of functionally evaluated SARS-CoV-2 peptides, we report a map of 126 immunogenic peptides with high similarity to 285 MHC-presented peptides from at least one HCoV. Employing this map of SARS-CoV-2-non-homologous and homologous immunogenic peptides, we observe several immunogenic peptides with high similarity to human proteins, some of which have been reported to have elevated expression in severe COVID-19 patients. After combining our map with SARS-CoV-2-specific TCR repertoire data from COVID-19 patients and healthy controls, we show that public repertoires for the majority of convalescent patients are dominated by TCRs cognate to non-homologous SARS-CoV-2 peptides. We find that for a subset of patients, >50% of their public SARS-CoV-2-specific repertoires consist of TCRs cognate to homologous SARS-CoV-2-HCoV peptides. Further analysis suggests that this skewed distribution of TCRs cognate to homologous or non-homologous peptides in COVID-19 patients is likely to be HLA-dependent. Finally, we provide 10 SARS-CoV-2 peptides with known cognate TCRs that are conserved across multiple coronaviruses and are predicted to be recognized by a high proportion of the global population. These findings may have important implications for COVID-19 heterogeneity, vaccine-induced immune responses, and robustness of immunity to SARS-CoV-2 and its variants.

Assay format diversity in pre-clinical immunogenicity risk assessment: Toward a possible harmonization of antigenicity assays

A major impediment to successful use of therapeutic protein drugs is their ability to induce anti-drug antibodies (ADA) that can alter treatment efficacy and safety in a significant number of patients. To this aim, in silico, in vitro, and in vivo tools have been developed to assess sequence and other liabilities contributing to ADA development at different stages of the immune response. However, variability exists between similar assays developed by different investigators due to the complexity of assays, a degree of uncertainty about the underlying science, and their intended use. The impact of protocol variations on the outcome of the assays, i.e., on the immunogenicity risk assigned to a given drug candidate, cannot always be precisely assessed. Here, the Non-Clinical Immunogenicity Risk Assessment working group of the European Immunogenicity Platform (EIP) reviews currently used assays and protocols and discusses feasibility and next steps toward harmonization and standardization.

Commensal Bifidobacterium Strains Enhance the Efficacy of Neo-Epitope Based Cancer Vaccines

A large body of data both in animals and humans demonstrates that the gut microbiome plays a fundamental role in cancer immunity and in determining the efficacy of cancer immunotherapy. In this work, we have investigated whether and to what extent the gut microbiome can influence the antitumor activity of neo-epitope-based cancer vaccines in a BALB/c-CT26 cancer mouse model. Similarly to that observed in the C57BL/6-B16 model, Bifidobacterium administration per se has a beneficial effect on CT26 tumor inhibition. Furthermore, the combination of Bifidobacterium administration and vaccination resulted in a protection which was superior to vaccination alone and to Bifidobacterium administration alone, and correlated with an increase in the frequency of vaccine-specific T cells. The gut microbiome analysis by 16S rRNA gene sequencing and shotgun metagenomics showed that tumor challenge rapidly altered the microbiome population, with Muribaculaceae being enriched and Lachnospiraceae being reduced. Over time, the population of Muribaculaceae progressively reduced while the Lachnospiraceae population increased—a trend that appeared to be retarded by the oral administration of Bifidobacterium. Interestingly, in some Bacteroidales, Prevotella and Muribaculacee species we identified sequences highly homologous to immunogenic neo-epitopes of CT26 cells, supporting the possible role of “molecular mimicry” in anticancer immunity. Our data strengthen the importance of the microbiome in cancer immunity and suggests a microbiome-based strategy to potentiate neo-epitope-based cancer vaccines.

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.

The Cancer Epitope Database and Analysis Resource: A Blueprint for the Establishment of a New Bioinformatics Resource for Use by the Cancer Immunology Community

Recent years have witnessed a dramatic rise in interest towards cancer epitopes in general and particularly neoepitopes, antigens that are encoded by somatic mutations that arise as a consequence of tumorigenesis. There is also an interest in the specific T cell and B cell receptors recognizing these epitopes, as they have therapeutic applications. They can also aid in basic studies to infer the specificity of T cells or B cells characterized in bulk and single-cell sequencing data. The resurgence of interest in T cell and B cell epitopes emphasizes the need to catalog all cancer epitope-related data linked to the biological, immunological, and clinical contexts, and most importantly, making this information freely available to the scientific community in a user-friendly format. In parallel, there is also a need to develop resources for epitope prediction and analysis tools that provide researchers access to predictive strategies and provide objective evaluations of their performance. For example, such tools should enable researchers to identify epitopes that can be effectively used for immunotherapy or in defining biomarkers to predict the outcome of checkpoint blockade therapies. We present here a detailed vision, blueprint, and work plan for the development of a new resource, the Cancer Epitope Database and Analysis Resource (CEDAR). CEDAR will provide a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and provide easily accessible epitope and T cell/B cell target prediction and analysis tools. The curated cancer epitope data will provide a transparent benchmark dataset that can be used to assess how well prediction tools perform and to develop new prediction tools relevant to the cancer research community.

IgE-Mediated Peanut Allergy: Current and Novel Predictive Biomarkers for Clinical Phenotypes Using Multi-Omics Approaches

Food allergy is a collective term for several immune-mediated responses to food. IgE-mediated food allergy is the best-known subtype. The patients present with a marked diversity of clinical profiles including symptomatic manifestations, threshold reactivity and reaction kinetics. In-vitro predictors of these clinical phenotypes are evasive and considered as knowledge gaps in food allergy diagnosis and risk management. Peanut allergy is a relevant disease model where pioneer discoveries were made in diagnosis, immunotherapy and prevention. This review provides an overview on the immune basis for phenotype variations in peanut-allergic individuals, in the light of future patient stratification along emerging omic-areas. Beyond specific IgE-signatures and basophil reactivity profiles with established correlation to clinical outcome, allergenomics, mass spectrometric resolution of peripheral allergen tracing, might be a fundamental approach to understand disease pathophysiology underlying biomarker discovery. Deep immune phenotyping is thought to reveal differential cell responses but also, gene expression and gene methylation profiles (eg, peanut severity genes) are promising areas for biomarker research. Finally, the study of microbiome-host interactions with a focus on the immune system modulation might hold the key to understand tissue-specific responses and symptoms. The immune mechanism underlying acute food-allergic events remains elusive until today. Deciphering this immunological response shall enable to identify novel biomarker for stratification of patients into reaction endotypes. The availability of powerful multi-omics technologies, together with integrated data analysis, network-based approaches and unbiased machine learning holds out the prospect of providing clinically useful biomarkers or biomarker signatures being predictive for reaction phenotypes.

Epitope prediction and identification- adaptive T cell responses in humans

Epitopes, in the context of T cell recognition, are short peptides typically derived by antigen processing, and presented on the cell surface bound to MHC molecules (HLA molecules in humans) for TCR scrutiny. The identification of epitopes is a context-dependent process, with consideration given to, for example, the source pathogen and protein, the host organism, and state of the immune reaction (e.g., following natural infection, vaccination, etc.). In the following review, we consider the various approaches used to define T cell epitopes, including both bioinformatic and experimental approaches, and discuss the concepts of immunodominance and immunoprevalence. We also discuss HLA polymorphism and epitope restriction, and the resulting impact on the identification of, and potential population coverage afforded by, epitopes or epitope-based vaccines. Finally, some examples of the practical application of T cell epitope identification are provided, showing how epitopes have been valuable for deriving novel immunological insights in the context of the immune response to various pathogens and allergens.

Immunoinformatics: Predicting Peptide-MHC Binding

Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.

T Cell Epitope Predictions

Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.

Better Epitope Discovery, Precision Immune Engineering, and Accelerated Vaccine Design Using Immunoinformatics Tools

Computational vaccinology includes epitope mapping, antigen selection, and immunogen design using computational tools. Tools that facilitate the in silico prediction of immune response to biothreats, emerging infectious diseases, and cancers can accelerate the design of novel and next generation vaccines and their delivery to the clinic. Over the past 20 years, vaccinologists, bioinformatics experts, and advanced programmers based in Providence, Rhode Island, USA have advanced the development of an integrated toolkit for vaccine design called iVAX, that is secure and user-accessible by internet. This integrated set of immunoinformatic tools comprises algorithms for scoring and triaging candidate antigens, selecting immunogenic and conserved T cell epitopes, re-engineering or eliminating regulatory T cell epitopes, and re-designing antigens to induce immunogenicity and protection against disease for humans and livestock. Commercial and academic applications of iVAX have included identifying immunogenic T cell epitopes in the development of a T-cell based human multi-epitope Q fever vaccine, designing novel influenza vaccines, identifying cross-conserved T cell epitopes for a malaria vaccine, and analyzing immune responses in clinical vaccine studies. Animal vaccine applications to date have included viral infections of pigs such as swine influenza A, PCV2, and African Swine Fever. “Rapid-Fire” applications for biodefense have included a demonstration project for Lassa Fever and Q fever. As recent infectious disease outbreaks underscore the significance of vaccine-driven preparedness, the integrated set of tools available on the iVAX toolkit stand ready to help vaccine developers deliver genome-derived, epitope-driven vaccines.

Two signal half-century: From negative selection of self-reactivity to positive selection of near-self-reactivity

With the emergence of clonal selection ideas in the 1950s, the development of immune cell repertoires was seen to require the negative selection of self-reacting cells, with surviving cells exhibiting a broad range of specificities. Thus, confronting a universe of not-self-antigens, a potential host organism spread its resources widely. In the 1960s, the two signal hypothesis showed how this might work. However, in the 1970s an affinity/avidity model further proposed that anticipating a pathogen strategy of exploiting "holes" in the repertoire created by negative selection, hosts should also positively select near-self-reacting cells. A microbe mutating an antigen from a form foreign to its host to a form resembling that host should prevail over host defences with respect to that antigen. By mutating a step towards host self, along the path from non-self to self, it should come to dominate the microbe population. By progressive stepwise mutations, such microbes would become better adapted, to the detriment of their hosts. But they would lose this advantage if, as they mutated closer to host self, they encountered progressively stiffer host defences. Thus, as described in the affinity/avidity model, positive selection of lymphocytes for specificities that were very close to, but not quite, anti-self (ie, "anti-near-self") should be an important host adaptation. While positive selection affects both B and T cells, mechanisms are uncertain. Converging evidence from studies of lymphocyte activation, either polyclonally (with lectins as "antigen-analogs") or monoclonally (by specific antigen), supports the original generic affinity/avidity model for countering mutations towards host self.

A Review on T Cell Epitopes Identified Using Prediction and Cell-Mediated Immune Models for Mycobacterium tuberculosis and Bordetella pertussis

In the present review, we summarize work from our as well as other groups related to the characterization of bacterial T cell epitopes, with a specific focus on two important pathogens, namely, Mycobacterium tuberculosis (Mtb), the bacterium that causes tuberculosis (TB), and Bordetella pertussis (BP), the bacterium that causes whooping cough. Both bacteria and their associated diseases are of large societal significance. Although vaccines exist for both pathogens, their efficacy is incomplete. It is widely thought that defects and/or alteration in T cell compartments are associated with limited vaccine effectiveness. As discussed below, a full genome-wide map was performed in the case of Mtb. For BP, our focus has thus far been on the antigens contained in the acellular vaccine; a full genome-wide screen is in the planning stage. Nevertheless, the sum-total of the results in the two different bacterial systems allows us to exemplify approaches and techniques that we believe are generally applicable to the mapping and characterization of human immune responses to bacterial pathogens. Finally, we add, as a disclaimer, that this review by design is focused on the work produced by our laboratory as an illustration of approaches to the study of T cell responses to Mtb and BP, and is not meant to be comprehensive, nor to detract from the excellent work performed by many other groups.

Predicting T cell recognition of MHC class I restricted neoepitopes

Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope's immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used. Abbreviations: SNV: single nucleotide variant; nsSNV: nonsynonymous single nucleotide variant; ROC: receiver operating characteristic; AUC: area under ROC curve; HLA: human leukocyte antigen; MHC: major histocompatibility complex; PD-1: Programmed cell death protein 1; PD-L1 or CTLA-4: cytotoxic T-lymphocyte associated protein 4.

Microbiota epitope similarity either dampens or enhances the immunogenicity of disease-associated antigenic epitopes

The microbiome influences adaptive immunity and molecular mimicry influences T cell reactivity. Here, we evaluated whether the sequence similarity of various antigens to the microbiota dampens or increases immunogenicity of T cell epitopes. Sets of epitopes and control sequences derived from 38 antigenic categories (infectious pathogens, allergens, autoantigens) were retrieved from the Immune Epitope Database (IEDB). Their similarity to microbiome sequences was calculated using the BLOSUM62 matrix. We found that sequence similarity was associated with either dampened (tolerogenic; e.g. most allergens) or increased (inflammatory; e.g. Dengue and West Nile viruses) likelihood of a peptide being immunogenic as a function of epitope source category. Ten-fold cross-validation and validation using sets of manually curated epitopes and non-epitopes derived from allergens were used to confirm these initial observations. Furthermore, the genus from which the microbiome homologous sequences were derived influenced whether a tolerogenic versus inflammatory modulatory effect was observed, with Fusobacterium most associated with inflammatory influences and Bacteroides most associated with tolerogenic influences. We validated these effects using PBMCs stimulated with various sets of microbiome peptides. "Tolerogenic" microbiome peptides elicited IL-10 production, "inflammatory" peptides elicited mixed IL-10/IFNγ production, while microbiome epitopes homologous to self were completely unreactive for both cytokines. We also tested the sequence similarity of cockroach epitopes to specific microbiome sequences derived from households of cockroach allergic individuals and non-allergic controls. Microbiomes from cockroach allergic households were less likely to contain sequences homologous to previously defined cockroach allergens. These results are compatible with the hypothesis that microbiome sequences may contribute to the tolerization of T cells for allergen epitopes, and lack of these sequences might conversely be associated with increased likelihood of T cell reactivity against the cockroach epitopes. Taken together this study suggests that microbiome sequence similarity influences immune reactivity to homologous epitopes encoded by pathogens, allergens and auto-antigens.

Differential Recognition of Mycobacterium tuberculosis-Specific Epitopes as a Function of Tuberculosis Disease History

RATIONALE

Individuals with a history of tuberculosis (TB) disease are at elevated risk of disease recurrence. The underlying cause is not known, but one explanation is that previous disease results in less-effective immunity against Mycobacterium tuberculosis (Mtb).

OBJECTIVES

We hypothesized that the repertoire of Mtb-derived epitopes recognized by T cells from individuals with latent Mtb infection differs as a function of previous diagnosis of active TB disease.

METHODS

T-cell responses to peptide pools in samples collected from an adult screening and an adolescent validation cohort were measured by IFN-γ enzyme-linked immunospot assay or intracellular cytokine staining.

MEASUREMENTS AND MAIN RESULTS

We identified a set of "type 2" T-cell epitopes that were recognized at 10-fold-lower levels in Mtb-infected individuals with a history of TB disease less than 6 years ago than in those without previous TB. By contrast, "type 1" epitopes were recognized equally well in individuals with or without previous TB. The differential epitope recognition was not due to differences in HLA class II binding, memory phenotypes, or gene expression in the responding T cells. Instead, "TB disease history-sensitive" type 2 epitopes were significantly (P < 0.0001) more homologous to sequences from bacteria found in the human microbiome than type 1 epitopes.

CONCLUSIONS

Preferential loss of T-cell reactivity to Mtb epitopes that are homologous to bacteria in the microbiome in persons with previous TB disease may reflect long-term effects of antibiotic TB treatment on the microbiome.

Protein nanovaccine confers robust immunity against Toxoplasma

We designed and produced a self-assembling protein nanoparticle. This self-assembling protein nanoparticle contains five CD8+ HLA-A03-11 supertypes-restricted epitopes from antigens expressed during Toxoplasma gondii's lifecycle, the universal CD4+ T cell epitope PADRE, and flagellin as a scaffold and TLR5 agonist. These CD8+ T cell epitopes were separated by N/KAAA spacers and optimized for proteasomal cleavage. Self-assembling protein nanoparticle adjuvanted with TLR4 ligand-emulsion GLA-SE were evaluated for their efficacy in inducing IFN-γ responses and protection of HLA-A*1101 transgenic mice against T. gondii. Immunization, using self-assembling protein nanoparticle-GLA-SE, activated CD8+ T cells to produce IFN-γ. Self-assembling protein nanoparticle-GLA-SE also protected HLA-A*1101 transgenic mice against subsequent challenge with Type II parasites. Hence, combining CD8+ T cell-eliciting peptides and PADRE into a multi-epitope protein that forms a nanoparticle, administered with GLA-SE, leads to efficient presentation by major histocompatibility complex Class I and II molecules. Furthermore, these results suggest that activation of TLR4 and TLR5 could be useful for development of vaccines that elicit T cells to prevent toxoplasmosis in humans.

TANTIGEN: a comprehensive database of tumor T cell antigens

Tumor T cell antigens are both diagnostically and therapeutically valuable molecules. A large number of new peptides are examined as potential tumor epitopes each year, yet there is no infrastructure for storing and accessing the results of these experiments. We have retroactively cataloged more than 1000 tumor peptides from 368 different proteins, and implemented a web-accessible infrastructure for storing and accessing these experimental results. All peptides in TANTIGEN are labeled as one of the four categories: (1) peptides measured in vitro to bind the HLA, but not reported to elicit either in vivo or in vitro T cell response, (2) peptides found to bind the HLA and to elicit an in vitro T cell response, (3) peptides shown to elicit in vivo tumor rejection, and (4) peptides processed and naturally presented as defined by physical detection. In addition to T cell response, we also annotate peptides that are naturally processed HLA binders, e.g., peptides eluted from HLA in mass spectrometry studies. TANTIGEN provides a rich data resource for tumor-associated epitope and neoepitope discovery studies and is freely available at http://cvc.dfci.harvard.edu/tantigen/ or http://projects.met-hilab.org/tadb (mirror).

Immunoproteomic analysis of house dust mite antigens reveals distinct classes of dominant T cell antigens according to function and serological reactivity

BACKGROUND

House dust mite (HDM) allergens are a common cause of allergy and allergic asthma. A comprehensive analysis of proteins targeted by T cells, which are implicated in the development and regulation of allergic disease independent of their antibody reactivity, is still lacking.

OBJECTIVE

To comprehensively analyse the HDM-derived protein targets of T cell responses in HDM-allergic individuals, and investigate their correlation with IgE/IgG responses and protein function.

METHODS

Proteomic analysis (liquid chromatography-tandem mass spectrometry) of HDM extracts identified 90 distinct protein clusters, corresponding to 29 known allergens and 61 novel proteins. Peripheral blood mononuclear cells (PBMC) from 20 HDM-allergic individuals were stimulated with HDM extracts and assayed with a set of ~2500 peptides derived from these 90 protein clusters and predicted to bind the most common HLA class II types. 2D immunoblots were made in parallel to elucidate IgE and IgG reactivity, and putative function analyses were performed in silico according to Gene Ontology annotations.

RESULTS

Analysis of T cell reactivity revealed a large number of T cell epitopes. Overall response magnitude and frequency was comparable for known and novel proteins, with 15 antigens (nine of which were novel) dominating the total T cell response. Most of the known allergens that were dominant at the T cell level were also IgE reactive, as expected, while few novel dominant T cell antigens were IgE reactive. Among known allergens, hydrolase activity and detectable IgE/IgG reactivity are strongly correlated, while no protein function correlates with immunogenicity of novel proteins. A total of 106 epitopes accounted for half of the total T cell response, underlining the heterogeneity of T cell responses to HDM allergens.

CONCLUSIONS AND CLINICAL RELEVANCE

Herein, we define the T cell targets for both known allergens and novel proteins, which may inform future diagnostics and immunotherapeutics for allergy to HDM.

Agility in adversity: Vaccines on Demand

Is the US ready for a biological attack using Ebola virus or Anthrax? Will vaccine developers be able to produce a Zika virus vaccine, before the epidemic spreads around the world? A recent report by The Blue Ribbon Study Panel on Biodefense argues that the US is not ready for these challenges, however, technologies and capabilities that could address these deficiencies are within reach. Vaccine technologies have advanced and readiness has improved in recent years, due to advances in sequencing technology and computational power making the 'vaccines on demand' concept a reality. Building a robust strategy to design effective biodefense vaccines from genome sequences harvested by real-time biosurveillance will benefit from technologies that are being brought to bear on the cancer cure 'moonshot'. When combined with flexible vaccine production platforms, vaccines on demand will relegate expensive and, in some cases, insufficiently effective vaccine stockpiles to the dust heap of history.

Pan-Specific Prediction of Peptide-MHC Class I Complex Stability, a Correlate of T Cell Immunogenicity

Binding of peptides to MHC class I (MHC-I) molecules is the most selective event in the processing and presentation of Ags to CTL, and insights into the mechanisms that govern peptide-MHC-I binding should facilitate our understanding of CTL biology. Peptide-MHC-I interactions have traditionally been quantified by the strength of the interaction, that is, the binding affinity, yet it has been shown that the stability of the peptide-MHC-I complex is a better correlate of immunogenicity compared with binding affinity. In this study, we have experimentally analyzed peptide-MHC-I complex stability of a large panel of human MHC-I allotypes and generated a body of data sufficient to develop a neural network-based pan-specific predictor of peptide-MHC-I complex stability. Integrating the neural network predictors of peptide-MHC-I complex stability with state-of-the-art predictors of peptide-MHC-I binding is shown to significantly improve the prediction of CTL epitopes. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCstabpan.