Adaptive immunity is mediated by T- and B-cells, which are immune cells capable of developing pathogen-specific memory that confers immunological protection. Memory and effector functions of B- and T-cells are predicated on the recognition through specialized receptors of specific targets (antigens) in pathogens. More specifically, B- and T-cells recognize portions within their cognate antigens known as epitopes. There is great interest in identifying epitopes in antigens for a number of practical reasons, including understanding disease etiology, immune monitoring, developing diagnosis assays, and designing epitope-based vaccines. Epitope identification is costly and time-consuming as it requires experimental screening of large arrays of potential epitope candidates. Fortunately, researchers have developed in silico prediction methods that dramatically reduce the burden associated with epitope mapping by decreasing the list of potential epitope candidates for experimental testing. Here, we analyze aspects of antigen recognition by T- and B-cells that are relevant for epitope prediction. Subsequently, we provide a systematic and inclusive review of the most relevant B- and T-cell epitope prediction methods and tools, paying particular attention to their foundations.

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Motivation In eukaryotes, proteins targeted for secretion contain a signal peptide, which allows them to proceed through the conventional ER/Golgi-dependent pathway. However, an important number of proteins lacking a signal peptide can be secreted through unconventional routes, including that mediated by exosomes. Currently, no method is available to predict protein secretion via exosomes. Results Here, we first assembled a dataset including the sequences of 2992 proteins secreted by exosomes and 2961 proteins that are not secreted by exosomes. Subsequently, we trained different random forests models on feature vectors derived from the sequences in this dataset. In tenfold cross-validation, the best model was trained on dipeptide composition, reaching an accuracy of 69.88% ± 2.08 and an area under the curve (AUC) of 0.76 ± 0.03. In an independent dataset, this model reached an accuracy of 75.73% and an AUC of 0.840. After these results, we developed ExoPred, a web-based tool that uses random forests to predict protein secretion by exosomes. Conclusion ExoPred is available for free public use at http://imath.med.ucm.es/exopred/. Datasets are available at http://imath.med.ucm.es/exopred/datasets/.

The oral mucosa is a first line of defense against pathogenic organisms and yet tolerates food antigens and resident bacteria. Mucosal epithelial cells are emerging as important regulators of innate and adaptive immune responses. However, the contribution of oral epithelial cells (OECs) determining oral immunity is understudied. Here, we evaluated the ability of H413 and TR146 cells, two OEC lines derived from human oral squamous cell carcinomas, and primary OECs to modulate immune responses to a cocktail of Gram + and Gram − bacteria known as MV130. OECs expressed CD40 constitutively and class II major histocompatibility complex (MHC II) molecules when stimulated with IFNγ, but not CD80 or CD86. Dendritic cells (DCs) treated with bacteria in co-culture with OECs did not fully mature, as judged by the expression of MHC II, CD80 and CD86, and barely released IL-12 and TNFα, compared to control DCs. Furthermore, in the presence of OECs, DCs were unable to stimulate allogenic naive CD4 T cells to produce IFNγ and TNFα. Similarly, OECs in culture with total CD4 T cells or Th1 cells stimulated with anti-CD3 and anti-CD28 antibodies abrogated CD25 and CD69 expression, T cell proliferation and the release of IFNγ and TNFα. The inhibition on T cell activation by OECs was cell-contact dependent, TGFβ independent and largely irreversible. Overall, this behavior of OECs is likely key to avoid immune system over-reaction against resident bacteria.

Background We previously introduced PCPS (Proteasome Cleavage Prediction Server), a web-based tool to predict proteasome cleavage sites using n-grams. Here, we evaluated the ability of PCPS immunoproteasome cleavage model to discriminate CD8+ T cell epitopes. Results We first assembled an epitope dataset consisting of 844 unique virus-specific CD8+ T cell epitopes and their source proteins. We then analyzed cleavage predictions by PCPS immunoproteasome cleavage model on this dataset and compared them with those provided by a related method implemented by NetChop web server. PCPS was clearly superior to NetChop in term of sensitivity (0.89 vs. 0.79) but somewhat inferior with regard to specificity (0.55 vs. 0.60). Judging by the Mathew’s Correlation Coefficient, PCPS predictions were overall superior to those provided by NetChop (0.46 vs. 0.39). We next analyzed the power of C-terminal cleavage predictions provided by the same PCPS model to discriminate CD8+ T cell epitopes, finding that they could be discriminated from random peptides with an accuracy of 0.74. Following these results, we tuned the PCPS web server to predict CD8+ T cell epitopes and predicted the entire SARS-CoV-2 epitope space. Conclusions We report an improved version of PCPS named iPCPS for predicting proteasome cleavage sites and peptides with CD8+ T cell epitope features. iPCPS is available for free public use at https://imed.med.ucm.es/Tools/pcps/.

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Type 1 diabetes (T1D) results from the destruction of pancreatic β-cells by the immune system, and CD8 + T lymphocytes are critical actors in this autoimmune response. Pancreatic islets are surrounded by a mesh of nervous cells, the peri-insular Schwann cells, which are also targeted by autoreactive T lymphocytes and express specific antigens, such as the neurotrophic factor S100-β. Previous work has shown increased proliferative responses to whole S100-β in both human T1D patients and the nonobese diabetic (NOD) mouse model. We describe for the first time naturally processed and presented epitopes (NPPEs) presented by class I human leukocyte antigen–A*02:01 (A2.1) molecules derived from S100-β. These NPPEs triggered IFN-γ responses more frequently in both newly diagnosed and long-term T1D patients compared with healthy donors. Furthermore, the same NPPEs are recognized during the autoimmune response leading to diabetes in A2.1-transgenic NOD mice as early as 4 wk of age. Interestingly, when these NPPEs are used to prevent diabetes in this animal model, an acceleration of the disease is observed together with an exacerbation in insulitis and an increase in S100-β–specific cytotoxicity in vaccinated animals. Whether these can be used in diabetes prevention needs to be carefully evaluated in animal models before use in future clinical assays.—Calviño-Sampedro, C., Gomez-Tourino, I., Cordero, O. J., Reche, P. A., Gómez-Perosanz, M., Sánchez-Trincado, J. L., Rodríguez, M. Á., Sueiro, A. M., Viñuela, J. E., Calviño, R. V. Naturally presented HLA class I–restricted epitopes from the neurotrophic factor S100-β are targets of the autoimmune response in type 1 diabetes.

Background The diagnosis of coeliac disease (CD) in individuals that have started a gluten-free diet (GFD) without an adequate previous diagnostic work-out is a challenge. Several immunological assays such as IFN-γ ELISPOT have been developed to avoid the need of prolonged gluten challenge to induce the intestinal damage. We aimed to evaluate the diagnostic accuracy of activated gut-homing CD8+ and TCRγδ+ T cells in blood after a 3-day gluten challenge and to compare it with the performance of IFN-γ ELISPOT in a HLA-DQ2.5 subsample. Methods A total of 22 CD patients and 48 non-CD subjects, all of them following a GFD, underwent a 3-day 10-g gluten challenge. The percentage of two T cell subsets (CD8+ CD103+ β7hi CD38+/total CD8+ and TCRγδ+ CD103+ β7hi CD38+/total TCRγδ+) in fresh peripheral blood drawn baseline and 6 days after the challenge was determined by flow cytometry. IFN-γ ELISPOT assays were also performed in HLA-DQ2.5 participants. ROC curve analysis was used to assess the diagnostic performance of the CD8+ T cell response and IFN-γ ELISPOT. Results Significant differences between the percentage of the two studied subsets of CD8+ and TCRγδ+ cells at days 0 and 6 were found only when considering CD patients (p < 10−3 vs. non-CD subjects). Measuring activated CD8+ T cells provided accurate CD diagnosis with 95% specificity and 97% sensitivity, offering similar results than IFN-γ ELISPOT. Conclusions The results provide a highly accurate blood test for CD diagnosis in patients on a GFD of easy implementation in daily clinical practice.