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).

Late-rising CD4 T cells resolve mouse cytomegalovirus persistent replication in the salivary gland.

Conventional antiviral memory CD4 T cells typically arise during the first two weeks of acute infection. Unlike most viruses, cytomegalovirus (CMV) exhibits an extended persistent replication phase followed by lifelong latency accompanied with some gene expression. We show that during mouse CMV (MCMV) infection, CD4 T cells recognizing an epitope derived from the viral M09 protein only develop after conventional memory T cells have already peaked and contracted. Ablating these CD4 T cells by mutating the M09 genomic epitope in the MCMV Smith strain, or inducing them by introducing the epitope into the K181 strain, resulted in delayed or enhanced control of viral persistence, respectively. These cells were shown to be unique compared to their conventional memory counterparts; producing higher IFNγ and IL-2 and lower IL-10 levels. RNAseq analyses revealed them to express distinct subsets of effector genes as compared to classical CD4 T cells. Additionally, when M09 cells were induced by epitope vaccination they significantly enhanced protection when compared to conventional CD4 T cells alone. These data show that late-rising CD4 T cells are a unique memory subset with excellent protective capacities that display a development program strongly differing from the majority of memory T cells.

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.

Developmentally distinct CD4+ Treg lineages shape the CD8+ T cell response to acute Listeria infection.

SignificanceThe CD4+ Treg response following acute Listeria infection is heterogeneous and deploys two distinct modes of suppression coinciding with initial pathogen exposure and resolution of infection. This bimodal suppression of CD8+ T cells during priming and contraction is mediated by separate Treg lineages. These findings make a significant contribution to our understanding of the functional plasticity inherent within Tregs, which allows these cells to serve as a sensitive and dynamic cellular rheostat for the immune system to prevent autoimmune pathology in the face of inflammation attendant to acute infection, enable expansion of the pathogen-specific response needed to control the infection, and reestablish immune homeostasis after the threat has been contained.

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.

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 .

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.

Distinguishing cell-cell complexes from dual lineage cells using single-cell transcriptomics is not trivial.

In their recent correspondence, Jie et al. strongly defend that the DE cell population they discovered are always dual lineage co-expressing cells and not complexes of B cells and T cells, which we have previously described as frequently present in single-cell RNA sequencing data. Here, we respond to the specific arguments made in their correspondence. Specifically, we demonstrate that the presence of a gene signature in a given cell population is not enough to ascertain that it does not contain cell-cell complexes, or that it represents a biologically distinct cell type. We also show that the gene signature of DE cells contains several genes from the myeloid lineage, suggesting either that their DE cells are a triple-lineage co-expressing cell, or a three-component cell aggregate. Finally, we identify multiple transcriptomic features of DE cells that correspond to B cell-T cell complexes, namely the presence of lower average expression of B- and T-cell specific genes, and a higher number of detected genes per cell. Taken together, our results demonstrate that solely based on their scRNAseq profile, it is not possible to ascertain that DE cells are dual expressing cells and not cell-cell complexes.

IEDB-AR: immune epitope database-analysis resource in 2019.

The Immune Epitope Database Analysis Resource (IEDB-AR, http://tools.iedb.org/) is a companion website to the IEDB that provides computational tools focused on the prediction and analysis of B and T cell epitopes. All of the tools are freely available through the public website and many are also available through a REST API and/or a downloadable command-line tool. A virtual machine image of the entire site is also freely available for non-commercial use and contains most of the tools on the public site. Here, we describe the tools and functionalities that are available in the IEDB-AR, focusing on the 10 new tools that have been added since the last report in the 2012 NAR webserver edition. In addition, many of the tools that were already hosted on the site in 2012 have received updates to newest versions, including NetMHC, NetMHCpan, BepiPred and DiscoTope. Overall, this IEDB-AR update provides a substantial set of updated and novel features for epitope prediction and analysis.

A behind-the-scenes tour of the IEDB curation process: an optimized process empirically integrating automation and human curation efforts.

The Immune Epitope Database and Analysis Resource (IEDB) provides the scientific community with open access to epitope data, as well as epitope prediction and analysis tools. The IEDB houses the most extensive collection of experimentally validated B-cell and T-cell epitope data, sourced primarily from published literature by expert curation. The data procurement requires systematic identification, categorization, curation and quality-checking processes. Here, we provide insights into these processes, with particular focus on the dividends they have paid in terms of attaining project milestones, as well as how objective analyses of our processes have identified opportunities for process optimization. These experiences are shared as a case study of the benefits of process implementation and review in biomedical big data, as well as to encourage idea-sharing among players in this ever-growing space.

Transcriptomic Analysis of CD4+ T Cells Reveals Novel Immune Signatures of Latent Tuberculosis.

In the context of infectious diseases, cell population transcriptomics are useful to gain mechanistic insight into protective immune responses, which is not possible using traditional whole-blood approaches. In this study, we applied a cell population transcriptomics strategy to sorted memory CD4 T cells to define novel immune signatures of latent tuberculosis infection (LTBI) and gain insight into the phenotype of tuberculosis (TB)-specific CD4 T cells. We found a 74-gene signature that could discriminate between memory CD4 T cells from healthy latently Mycobacterium tuberculosis-infected subjects and noninfected controls. The gene signature presented a significant overlap with the gene signature of the Th1* (CCR6+CXCR3+CCR4-) subset of CD4 T cells, which contains the majority of TB-specific reactivity and is expanded in LTBI. In particular, three Th1* genes (ABCB1, c-KIT, and GPA33) were differentially expressed at the RNA and protein levels in memory CD4 T cells of LTBI subjects compared with controls. The 74-gene signature also highlighted novel phenotypic markers that further defined the CD4 T cell subset containing TB specificity. We found the majority of TB-specific epitope reactivity in the CD62L-GPA33- Th1* subset. Thus, by combining cell population transcriptomics and single-cell protein-profiling techniques, we identified a CD4 T cell immune signature of LTBI that provided novel insights into the phenotype of TB-specific CD4 T cells.

Development of Asthma in Inner-City Children: Possible Roles of MAIT Cells and Variation in the Home Environment.

Humans have populations of innate-like T lymphocytes with an invariant TCR α-chain that recognize nonpeptide Ags, including invariant NKT (iNKT) cells and mucosal-associated invariant T (MAIT) cells. iNKT cell involvement in human asthma is controversial, whereas there has been little analysis of MAIT cells. Using peripheral blood cells from 110 participants from the Urban Environment and Childhood Asthma (URECA) birth cohort study, these cells were analyzed for number and function. We determined whether iNKT cell or MAIT cell frequency at 1 y is correlated with the cytokine polarization of mainstream CD4+ T cells and/or the development of asthma by age 7 y. Dust samples from 300 houses were tested for iNKT cell antigenic activity. Our results show that a higher MAIT cell frequency at 1 y of age was associated with a decreased risk of asthma by age 7 y. The frequency of MAIT cells was associated with increased production of IFN-γ by activated CD4+ T cells from the URECA cohort. iNKT cell antigenic activity in bedroom dust samples was associated with higher endotoxin concentration and also with reduced risk of asthma. In conclusion, MAIT cell frequency at 1 y may reflect the tendency of the immune system toward Th1 responses and is associated with protection from asthma. Additionally, iNKT cell antigenic activity may be a marker of houses with increased microbial exposures and therefore also with protection from asthma.

An automated benchmarking platform for MHC class II binding prediction methods.

Motivation: Computational methods for the prediction of peptide-MHC binding have become an integral and essential component for candidate selection in experimental T cell epitope discovery studies. The sheer amount of published prediction methods-and often discordant reports on their performance-poses a considerable quandary to the experimentalist who needs to choose the best tool for their research. Results: With the goal to provide an unbiased, transparent evaluation of the state-of-the-art in the field, we created an automated platform to benchmark peptide-MHC class II binding prediction tools. The platform evaluates the absolute and relative predictive performance of all participating tools on data newly entered into the Immune Epitope Database (IEDB) before they are made public, thereby providing a frequent, unbiased assessment of available prediction tools. The benchmark runs on a weekly basis, is fully automated, and displays up-to-date results on a publicly accessible website. The initial benchmark described here included six commonly used prediction servers, but other tools are encouraged to join with a simple sign-up procedure. Performance evaluation on 59 data sets composed of over 10 000 binding affinity measurements suggested that NetMHCIIpan is currently the most accurate tool, followed by NN-align and the IEDB consensus method. Availability and implementation: Weekly reports on the participating methods can be found online at: http://tools.iedb.org/auto_bench/mhcii/weekly/. Contact: mniel@bioinformatics.dtu.dk. Supplementary information: Supplementary data are available at Bioinformatics online.

Improved methods for predicting peptide binding affinity to MHC class II molecules.

Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC-II-peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC-peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2.

Immunological consequences of intragenus conservation of Mycobacterium tuberculosis T-cell epitopes.

A previous unbiased genome-wide analysis of CD4 Mycobacterium tuberculosis (MTB) recognition using peripheral blood mononuclear cells from individuals with latent MTB infection (LTBI) or nonexposed healthy controls (HCs) revealed that certain MTB sequences were unexpectedly recognized by HCs. In the present study, it was found that, based on their pattern of reactivity, epitopes could be divided into LTBI-specific, mixed reactivity, and HC-specific categories. This pattern corresponded to sequence conservation in nontuberculous mycobacteria (NTMs), suggesting environmental exposure as an underlying cause of differential reactivity. LTBI-specific epitopes were found to be hyperconserved, as previously reported, whereas the opposite was true for NTM conserved epitopes, suggesting that intragenus conservation also influences host pathogen adaptation. The biological relevance of this observation was demonstrated further by several observations. First, the T cells elicited by MTB/NTM cross-reactive epitopes in HCs were found mainly in a CCR6(+)CXCR3(+) memory subset, similar to findings in LTBI individuals. Thus, both MTB and NTM appear to elicit a phenotypically similar T-cell response. Second, T cells reactive to MTB/NTM-conserved epitopes responded to naturally processed epitopes from MTB and NTMs, whereas T cells reactive to MTB-specific epitopes responded only to MTB. Third, cross-reactivity could be translated to antigen recognition. Several MTB candidate vaccine antigens were cross-reactive, but others were MTB-specific. Finally, NTM-specific epitopes that elicit T cells that recognize NTMs but not MTB were identified. These epitopes can be used to characterize T-cell responses to NTMs, eliminating the confounding factor of MTB cross-recognition and providing insights into vaccine design and evaluation.

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.

The immune epitope database (IEDB) 3.0.

The IEDB, www.iedb.org, contains information on immune epitopes--the molecular targets of adaptive immune responses--curated from the published literature and submitted by National Institutes of Health funded epitope discovery efforts. From 2004 to 2012 the IEDB curation of journal articles published since 1960 has caught up to the present day, with >95% of relevant published literature manually curated amounting to more than 15,000 journal articles and more than 704,000 experiments to date. The revised curation target since 2012 has been to make recent research findings quickly available in the IEDB and thereby ensure that it continues to be an up-to-date resource. Having gathered a comprehensive dataset in the IEDB, a complete redesign of the query and reporting interface has been performed in the IEDB 3.0 release to improve how end users can access this information in an intuitive and biologically accurate manner. We here present this most recent release of the IEDB and describe the user testing procedures as well as the use of external ontologies that have enabled it.

T-cell epitope conservation across allergen species is a major determinant of immunogenicity.

BACKGROUND: Patients with pollen allergies are frequently polysensitized. Pollens contain epitopes that are conserved across multiple species. OBJECTIVE: We sought to demonstrate that cross-reactive T cells that recognize conserved epitopes show higher levels of expansion than T cells recognizing monospecific epitopes because of more frequent stimulation. METHOD: RNA was sequenced from 9 pollens, and the reads were assembled de novo into more than 50,000 transcripts. T-cell epitopes from timothy grass (Phleum pratense) were examined for conservation in these transcripts, and this was correlated to their ability to induce T-cell responses. T cells were expanded in vitro with P pratense-derived peptides and tested for cross-reactivity to pollen extracts in ELISpot assays. RESULTS: We found that antigenic proteins are more conserved than nonimmunogenic proteins in P pratense pollen. Additionally, P pratense epitopes that were highly conserved across pollens elicited more T-cell responses in donors with grass allergy than less conserved epitopes. Moreover, conservation of a P pratense peptide at the transcriptomic level correlated with the ability of that peptide to trigger T cells that were cross-reactive with other non-P pratense pollen extracts. CONCLUSION: We found a correlation between conservation of peptides in plant pollens and their T-cell immunogenicity within P pratense, as well as their ability to induce cross-reactive T-cell responses. T cells recognizing conserved epitopes might be more prominent because they can be stimulated by a broader range of pollens and thereby drive polysensitization in allergic donors. We propose that conserved peptides could potentially be used in diagnostic or immunomodulatory approaches that address the issue of polysensitization and target multiple pollen allergies.