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.

Neoantigen-specific stem cell memory-like CD4+ T cells mediate CD8+ T cell-dependent immunotherapy of MHC class II-negative solid tumors.

CD4+ T cells play key roles in a range of immune responses, either as direct effectors or through accessory cells, including CD8+ T lymphocytes. In cancer, neoantigen (NeoAg)-specific CD8+ T cells capable of direct tumor recognition have been extensively studied, whereas the role of NeoAg-specific CD4+ T cells is less well understood. We have characterized the murine CD4+ T cell response against a validated NeoAg (CLTCH129>Q) expressed by the MHC-II-deficient squamous cell carcinoma tumor model (SCC VII) at the level of single T cell receptor (TCR) clonotypes and in the setting of adoptive immunotherapy. We find that the natural CLTCH129>Q-specific repertoire is diverse and contains TCRs with distinct avidities as measured by tetramer-binding assays and CD4 dependence. Despite these differences, CD4+ T cells expressing high or moderate avidity TCRs undergo comparable in vivo proliferation to cross-presented antigen from growing tumors and drive similar levels of therapeutic immunity that is dependent on CD8+ T cells and CD40L signaling. Adoptive cellular therapy (ACT) with NeoAg-specific CD4+ T cells is most effective when TCR-engineered cells are differentiated ex vivo with IL-7 and IL-15 rather than IL-2 and this was associated with both increased expansion as well as the acquisition and stable maintenance of a T stem cell memory (TSCM)-like phenotype in tumor-draining lymph nodes (tdLNs). ACT with TSCM-like CD4+ T cells results in lower PD-1 expression by CD8+ T cells in the tumor microenvironment and an increased frequency of PD-1+CD8+ T cells in tdLNs. These findings illuminate the role of NeoAg-specific CD4+ T cells in mediating antitumor immunity via providing help to CD8+ T cells and highlight their therapeutic potential in ACT.

Targets of T Cell Responses to SARS-CoV-2 Coronavirus in Humans with COVID-19 Disease and Unexposed Individuals.

Understanding adaptive immunity to SARS-CoV-2 is important for vaccine development, interpreting coronavirus disease 2019 (COVID-19) pathogenesis, and calibration of pandemic control measures. Using HLA class I and II predicted peptide "megapools," circulating SARS-CoV-2-specific CD8+ and CD4+ T cells were identified in ∼70% and 100% of COVID-19 convalescent patients, respectively. CD4+ T cell responses to spike, the main target of most vaccine efforts, were robust and correlated with the magnitude of the anti-SARS-CoV-2 IgG and IgA titers. The M, spike, and N proteins each accounted for 11%-27% of the total CD4+ response, with additional responses commonly targeting nsp3, nsp4, ORF3a, and ORF8, among others. For CD8+ T cells, spike and M were recognized, with at least eight SARS-CoV-2 ORFs targeted. Importantly, we detected SARS-CoV-2-reactive CD4+ T cells in ∼40%-60% of unexposed individuals, suggesting cross-reactive T cell recognition between circulating "common cold" coronaviruses and SARS-CoV-2.

Antigen-Specific Adaptive Immunity to SARS-CoV-2 in Acute COVID-19 and Associations with Age and Disease Severity.

Limited knowledge is available on the relationship between antigen-specific immune responses and COVID-19 disease severity. We completed a combined examination of all three branches of adaptive immunity at the level of SARS-CoV-2-specific CD4+ and CD8+ T cell and neutralizing antibody responses in acute and convalescent subjects. SARS-CoV-2-specific CD4+ and CD8+ T cells were each associated with milder disease. Coordinated SARS-CoV-2-specific adaptive immune responses were associated with milder disease, suggesting roles for both CD4+ and CD8+ T cells in protective immunity in COVID-19. Notably, coordination of SARS-CoV-2 antigen-specific responses was disrupted in individuals ≥ 65 years old. Scarcity of naive T cells was also associated with aging and poor disease outcomes. A parsimonious explanation is that coordinated CD4+ T cell, CD8+ T cell, and antibody responses are protective, but uncoordinated responses frequently fail to control disease, with a connection between aging and impaired adaptive immune responses to SARS-CoV-2.

Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression.

While many genetic variants have been associated with risk for human diseases, how these variants affect gene expression in various cell types remains largely unknown. To address this gap, the DICE (database of immune cell expression, expression quantitative trait loci [eQTLs], and epigenomics) project was established. Considering all human immune cell types and conditions studied, we identified cis-eQTLs for a total of 12,254 unique genes, which represent 61% of all protein-coding genes expressed in these cell types. Strikingly, a large fraction (41%) of these genes showed a strong cis-association with genotype only in a single cell type. We also found that biological sex is associated with major differences in immune cell gene expression in a highly cell-specific manner. These datasets will help reveal the effects of disease risk-associated genetic polymorphisms on specific immune cell types, providing mechanistic insights into how they might influence pathogenesis (https://dice-database.org).

Epigenomic analysis of primary human T cells reveals enhancers associated with TH2 memory cell differentiation and asthma susceptibility.

A characteristic feature of asthma is the aberrant accumulation, differentiation or function of memory CD4(+) T cells that produce type 2 cytokines (TH2 cells). By mapping genome-wide histone modification profiles for subsets of T cells isolated from peripheral blood of healthy and asthmatic individuals, we identified enhancers with known and potential roles in the normal differentiation of human TH1 cells and TH2 cells. We discovered disease-specific enhancers in T cells that differ between healthy and asthmatic individuals. Enhancers that gained the histone H3 Lys4 dimethyl (H3K4me2) mark during TH2 cell development showed the highest enrichment for asthma-associated single nucleotide polymorphisms (SNPs), which supported a pathogenic role for TH2 cells in asthma. In silico analysis of cell-specific enhancers revealed transcription factors, microRNAs and genes potentially linked to human TH2 cell differentiation. Our results establish the feasibility and utility of enhancer profiling in well-defined populations of specialized cell types involved in disease pathogenesis.

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.

The Cancer Epitope Database and Analysis Resource (CEDAR).

We established The Cancer Epitope Database and Analysis Resource (CEDAR) to catalog all epitope data in the context of cancer. The specific molecular targets of adaptive T cell and B cell immune responses are referred to as epitopes. Epitopes derived from cancer antigens are of high relevance as they are recognized by anti-cancer immune cells. Detailed knowledge of the molecular characteristic of cancer epitopes and associated metadata is relevant to understanding and planning prophylactic and therapeutic applications and accurately characterizing naturally occurring immune responses and cancer immunopathology. CEDAR provides a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and serves as a companion site to the Immune Epitope Database (IEDB), which is focused on infectious, autoimmune, and allergic diseases. CEDAR is freely accessible at https://cedar.iedb.org/.

A comprehensive analysis of the IEDB MHC class-I automated benchmark.

In 2014, the Immune Epitope Database automated benchmark was created to compare the performance of the MHC class I binding predictors. However, this is not a straightforward process due to the different and non-standardized outputs of the methods. Additionally, some methods are more restrictive regarding the HLA alleles and epitope sizes for which they predict binding affinities, while others are more comprehensive. To address how these problems impacted the ranking of the predictors, we developed an approach to assess the reliability of different metrics. We found that using percentile-ranked results improved the stability of the ranks and allowed the predictors to be reliably ranked despite not being evaluated on the same data. We also found that given the rate new data are incorporated into the benchmark, a new method must wait for at least 4 years to be ranked against the pre-existing methods. The best-performing tools with statistically indistinguishable scores in this benchmark were NetMHCcons, NetMHCpan4.0, ANN3.4, NetMHCpan3.0 and NetMHCpan2.8. The results of this study will be used to improve the evaluation and display of benchmark performance. We highly encourage anyone working on MHC binding predictions to participate in this benchmark to get an unbiased evaluation of their predictors.

Immunodominant MHC-II (Major Histocompatibility Complex II) Restricted Epitopes in Human Apolipoprotein B.

BACKGROUND: CD (cluster of differentiation) 4+ T-cell responses to APOB (apolipoprotein B) are well characterized in atherosclerotic mice and detectable in humans. CD4+ T cells recognize antigenic peptides displayed on highly polymorphic HLA (human leukocyte antigen)-II. Immunogenicity of individual APOB peptides is largely unknown in humans. Only 1 HLA-II-restricted epitope was validated using the DRB1*07:01-APOB3036-3050 tetramer. We hypothesized that human APOB may contain discrete immunodominant CD4+ T-cell epitopes that trigger atherosclerosis-related autoimmune responses in donors with diverse HLA alleles. METHODS: We selected 20 APOB-derived peptides (APOB20) from an in silico screen and experimentally validated binding to the most commonly occurring human HLA-II alleles. We optimized a restimulation-based workflow to evaluate antigenicity of multiple candidate peptides in HLA-typed donors. This included activation-induced marker assay, intracellular cytokine staining, IFNγ (interferon gamma) enzyme-linked immunospot and cytometric bead array. High-throughput sequencing revealed TCR (T-cell receptor) clonalities of APOB-reactive CD4+ T cells. RESULTS: Using stringent positive, negative, and crossover stimulation controls, we confirmed specificity of expansion-based protocols to detect CD4+ T cytokine responses to the APOB20 pool. Ex vivo assessment of AIM+CD4+ T cells revealed a statistically significant autoimmune response to APOB20 but not to a ubiquitously expressed negative control protein, actin. Resolution of CD4+ T responses to the level of individual peptides using IFNγ enzyme-linked immunospot led to the discovery of 6 immunodominant epitopes (APOB6) that triggered robust CD4+ T activation in most donors. APOB6-specific responding CD4+ T cells were enriched in unique expanded TCR clonotypes and preferentially expressed memory markers. Cytometric bead array analysis detected APOB6-induced secretion of both proinflammatory and regulatory cytokines. In clinical samples from patients with angiographically verified coronary artery disease, APOB6 stimulation induced higher activation and memory phenotypes and augmented secretion of proinflammatory cytokines TNF (tumor necrosis factor) and IFNγ, compared with patients with low coronary artery disease. CONCLUSIONS: Using 3 cohorts, each with ≈20 donors, we discovered and validated 6 immunodominant, HLA-II-restricted APOB epitopes. The immune response to these APOB epitopes correlated with coronary artery disease severity.

Minimal Information about MHC Multimers (MIAMM).

With the goal of improving the reproducibility and annotatability of MHC multimer reagent data, we present the establishment of a new data standard: Minimal Information about MHC Multimers (https://miamm.lji.org/). Multimers are engineered reagents composed of a ligand and a MHC, which can be represented in a standardized format using ontology terminology. We provide an online Web site to host the details of the standard, as well as a validation tool to assist with the adoption of the standard. We hope that this publication will bring increased awareness of Minimal Information about MHC Multimers and drive acceptance, ultimately improving the quality and documentation of multimer data in the scientific literature.

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.

T-cell epitope discovery and single-cell technologies to advance food allergy research.

There is good evidence for a role of T cells in food allergy, but there is a lack of mechanistic understanding and phenotypic markers of the specific T cells contributing to pathology. Recent technologic advancements have allowed for a new experimental paradigm where we can find and pull out rare antigen-specific T cells and characterize them at the single-cell level. However, studies in infectious disease and broader allergy have shown that these techniques benefit greatly from precisely defined T-cell epitopes. Food allergens have fewer epitopes currently available, but it is growing and promises to overcome this gap. With growing use of this experimental design, it will be important to unbiasedly map T-cell phenotypes across food allergy and look for commonalities and contrasts to other allergic and infectious diseases. Once a pathologic phenotype for T cells has been established, the frequencies of these cells can be monitored with simpler techniques that could be applied to the clinic and used in diagnosis, prediction of treatment responsiveness, and discovery of targets for new treatments.

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.

PEPMatch: a tool to identify short peptide sequence matches in large sets of proteins.

BACKGROUND: Numerous tools exist for biological sequence comparisons and search. One case of particular interest for immunologists is finding matches for linear peptide T cell epitopes, typically between 8 and 15 residues in length, in a large set of protein sequences. Both to find exact matches or matches that account for residue substitutions. The utility of such tools is critical in applications ranging from identifying conservation across viral epitopes, identifying putative epitope targets for allergens, and finding matches for cancer-associated neoepitopes to examine the role of tolerance in tumor recognition. RESULTS: We defined a set of benchmarks that reflect the different practical applications of short peptide sequence matching. We evaluated a suite of existing methods for speed and recall and developed a new tool, PEPMatch. The tool uses a deterministic k-mer mapping algorithm that preprocesses proteomes before searching, achieving a 50-fold increase in speed over methods such as the Basic Local Alignment Search Tool (BLAST) without compromising recall. PEPMatch's code and benchmark datasets are publicly available. CONCLUSIONS: PEPMatch offers significant speed and recall advantages for peptide sequence matching. While it is of immediate utility for immunologists, the developed benchmarking framework also provides a standard against which future tools can be evaluated for improvements. The tool is available at https://nextgen-tools.iedb.org , and the source code can be found at https://github.com/IEDB/PEPMatch .

Disruption of mitochondrial quality control genes promotes caspase-resistant cell survival following apoptotic stimuli.

In cells undergoing cell-intrinsic apoptosis, mitochondrial outer membrane permeabilization (MOMP) typically marks an irreversible step in the cell death process. However, in some cases, a subpopulation of treated cells can exhibit a sublethal response, termed "minority MOMP." In this phenomenon, the affected cells survive, despite a low level of caspase activation and subsequent limited activation of the endonuclease caspase-activated DNase (DNA fragmentation factor subunit beta). Consequently, these cells can experience DNA damage, increasing the probability of oncogenesis. However, little is known about the minority MOMP response. To discover genes that affect the MOMP response in individual cells, we conducted an imaging-based phenotypic siRNA screen. We identified multiple candidate genes whose downregulation increased the heterogeneity of MOMP within single cells, among which were genes related to mitochondrial dynamics and mitophagy that participate in the mitochondrial quality control (MQC) system. Furthermore, to test the hypothesis that functional MQC is important for reducing the frequency of minority MOMP, we developed an assay to measure the clonogenic survival of caspase-engaged cells. We found that cells deficient in various MQC genes were indeed prone to aberrant post-MOMP survival. Our data highlight the important role of proteins involved in mitochondrial dynamics and mitophagy in preventing apoptotic dysregulation and oncogenesis.

In vivo RNA interference screens identify regulators of antiviral CD4(+) and CD8(+) T cell differentiation.

Classical genetic approaches to examine the requirements of genes for T cell differentiation during infection are time consuming. Here we developed a pooled approach to screen 30-100+ genes individually in separate antigen-specific T cells during infection using short hairpin RNAs in a microRNA context (shRNAmir). Independent screens using T cell receptor (TCR)-transgenic CD4(+) and CD8(+) T cells responding to lymphocytic choriomeningitis virus (LCMV) identified multiple genes that regulated development of follicular helper (Tfh) and T helper 1 (Th1) cells, and short-lived effector and memory precursor cytotoxic T lymphocytes (CTLs). Both screens revealed roles for the positive transcription elongation factor (P-TEFb) component Cyclin T1 (Ccnt1). Inhibiting expression of Cyclin T1, or its catalytic partner Cdk9, impaired development of Th1 cells and protective short-lived effector CTL and enhanced Tfh cell and memory precursor CTL formation in vivo. This pooled shRNA screening approach should have utility in numerous immunological studies.

Towards the prediction of non-peptidic epitopes.

In-silico methods for the prediction of epitopes can support and improve workflows for vaccine design, antibody production, and disease therapy. So far, the scope of B cell and T cell epitope prediction has been directed exclusively towards peptidic antigens. Nevertheless, various non-peptidic molecular classes can be recognized by immune cells. These compounds have not been systematically studied yet, and prediction approaches are lacking. The ability to predict the epitope activity of non-peptidic compounds could have vast implications; for example, for immunogenic risk assessment of the vast number of drugs and other xenobiotics. Here we present the first general attempt to predict the epitope activity of non-peptidic compounds using the Immune Epitope Database (IEDB) as a source for positive samples. The molecules stored in the Chemical Entities of Biological Interest (ChEBI) database were chosen as background samples. The molecules were clustered into eight homogeneous molecular groups, and classifiers were built for each cluster with the aim of separating the epitopes from the background. Different molecular feature encoding schemes and machine learning models were compared against each other. For those models where a high performance could be achieved based on simple decision rules, the molecular features were then further investigated. Additionally, the findings were used to build a web server that allows for the immunogenic investigation of non-peptidic molecules (http://tools-staging.iedb.org/np_epitope_predictor). The prediction quality was tested with samples from independent evaluation datasets, and the implemented method received noteworthy Receiver Operating Characteristic-Area Under Curve (ROC-AUC) values, ranging from 0.69-0.96 depending on the molecule cluster.