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Background & Aims Noroviruses (NoVs) are the leading cause of acute gastroenteritis worldwide and are associated with significant morbidity and mortality. Moreover, an asymptomatic carrier state can persist following acute infection, promoting NoV spread and evolution. Thus, defining immune correlates of NoV protection and persistence is needed to guide the development of future vaccines and limit viral spread. Whereas antibody responses following NoV infection or vaccination have been studied extensively, cellular immunity has received less attention. Data from the mouse NoV model suggest that T cells are critical for preventing persistence and achieving viral clearance, but little is known about NoV-specific T-cell immunity in humans, particularly at mucosal sites. Methods We screened peripheral blood mononuclear cells from 3 volunteers with an overlapping NoV peptide library. We then used HLA-peptide tetramers to track virus-specific CD8+ T cells in peripheral, lymphoid, and intestinal tissues. Tetramer+ cells were further characterized using markers for cellular trafficking, exhaustion, cytotoxicity, and proliferation. Results We defined 7 HLA-restricted immunodominant class I epitopes that were highly conserved across pandemic strains from genogroup II.4. NoV-specific CD8+ T cells with central, effector, or tissue-resident memory phenotypes were present at all sites and were especially abundant in the intestinal lamina propria. The properties and differentiation states of tetramer+ cells varied across donors and epitopes. Conclusions Our findings are an important step toward defining the breadth, distribution, and properties of human NoV T-cell immunity. Moreover, the molecular tools we have developed can be used to evaluate future vaccines and engineer novel cellular therapeutics., Graphical abstract

A common strategy for predicting candidate human leukocyte antigen class I T-cell epitopes is to use an affinity-based threshold of 500 nM. Although a 500 nM threshold across alleles effectively identifies most epitopes across alleles, findings showing that major histocompatibility complex repertoire sizes vary by allele indicate that using thresholds specific to individual alleles may improve epitope identification. In this work, we compare different strategies utilizing common and allele-specific thresholds to identify an optimal approach for T-cell epitope prediction. First, we confirmed previous observations that different human leukocyte antigen class I alleles correspond with varying repertoire sizes. Here, we define general and allele-specific thresholds that capture 80% of eluted ligands and a different set of thresholds associated with capturing 9-mer T-cell epitopes at 80% sensitivity. Our analysis revealed that allele-specific threshold performance was roughly equivalent to that of a common threshold when considering a relatively large number of alleles. However, when predicting epitopes for only a few alleles, the use of allele-specific thresholds would be preferable. Finally, we present here for public use a set of allele-specific thresholds that may be used to identify T-cell epitopes at 80% sensitivity., Graphical Abstract, Highlights • Confirmed findings that different HLA class I alleles have varying repertoire sizes. • Defined common and allele-specific thresholds that capture 80% of eluted ligands. • Defined common and allele-specific thresholds that capture 80% of T-cell epitopes. • Allele-specific thresholds perform more consistently when analyzing a few alleles., In Brief Candidate HLA class I T-cell epitopes are frequently identified using a single threshold across multiple alleles. However, previous studies have demonstrated that different HLA class I alleles have varying repertoire sizes. In this study, we investigated the performance of different strategies to perform epitope-binding prediction for multiple HLA class I alleles simultaneously. We identified and present here a set of general and allele-specific thresholds that may be used to identify eluted ligands and T-cell epitopes at 80% sensitivity.

Type 1 diabetes (T1D) is a T cell–mediated autoimmune disease that affects the insulin-producing beta cells of the pancreatic islets. The nonobese diabetic mouse is a widely studied spontaneous model of the disease that has contributed greatly to our understanding of T1D pathogenesis. This is especially true in the case of antigen discovery. Upon review of existing knowledge concerning the antigens and peptide epitopes that are recognized by T cells in this model, good concordance is observed between mouse and human antigens. A fascinating recent illustration of the contribution of the nonobese diabetic mouse in the area of epitope identification is the discovery of noncontiguous CD4+ T cell epitopes. This novel epitope class is characterized by the linkage of an insulin-derived peptide to, most commonly, a fragment of a natural cleavage product of another beta cell secretory granule constituent. These so-called hybrid insulin peptides are also recognized by T cells in patients with T1D, although the precise mechanism for their generation has yet to be defined and is the subject of active investigation. Although evidence from the tumor immunology arena documented the existence of noncontiguous CD8+ T cell epitopes, generated by proteasome-mediated peptide splicing involving transpeptidation, such CD8+ T cell epitopes were thought to be a rare immunological curiosity. However, recent advances in bioinformatics and mass spectrometry have challenged this view. These developments, coupled with the discovery of hybrid insulin peptides, have spurred a search for noncontiguous CD8+ T cell epitopes in T1D, an exciting frontier area still in its infancy.

Highlights • Epitope-based vaccine design facilitates laboratory research and save valuable time and money. • Immunoinformatics tools were used to predict B and T-cell epitopes within the E2 protein. • Highest population coverage was recorded 84.94% in Papua New Guinea. • Docking study revealed that E2 binds with lowest possible binding energy. • Pharmacophore study suggested MAN as the most effective inhibitor of the CHIKV E2., Recent concerning facts of Chikungunya virus (CHIKV); a Togaviridae family alphavirus has proved this as a worldwide emerging threat which causes Chikungunya fever and devitalizing arthritis. Despite severe outbreaks and lack of antiviral drug, a mere progress has been made regarding to an epitope-based vaccine designed for CHIKV. In this study, we aimed to design an epitope-based vaccine that can trigger a significant immune response as well as to prognosticate inhibitor that can bind with potential drug target sites by using various immunoinformatics and docking simulation tools. Initially, whole proteome of CHIKV was retrieved from database and perused to identify the most immunogenic protein. Structural properties of the selected protein were analyzed. The capacity to induce both humoral and cell-mediated immunity by T cell and B cell were checked for the selected protein. The peptide region spanning 9 amino acids from 397 to 405 and the sequence YYYELYPTM were found as the most potential B cell and T cell epitopes respectively. This peptide could interact with as many as 19 HLAs and showed high population coverage ranging from 69.50% to 84.94%. By using in silico docking techniques the epitope was further assessed for binding against HLA molecules to verify the binding cleft interaction. In addition with this, the allergenicity of the epitopes was also evaluated. In the post therapeutic strategy, three dimensional structure was predicted along with validation and verification that resulted in molecular docking study to identify the potential drug binding sites and suitable therapeutic inhibitor against targeted protein. Finally, pharmacophore study was also performed in quest of seeing potent drug activity. However, this computational epitope-based peptide vaccine designing and target site prediction against CHIKV opens up a new horizon which may be the prospective way in Chikungunya virus research; the results require validation by in vitro and in vivo experiments.

Cancer cell lines are a tremendous resource for cancer biology and therapy development. These multipurpose tools are commonly used to examine the genetic origin of cancers, to identify potential novel tumor targets, such as tumor antigens for vaccine devel-opment, and utilized to screen potential therapies in preclinical studies. Mutations, gene expression, and drug sensitivity have been determined for many cell lines using next-generation sequencing (NGS). However, the human leukocyte antigen (HLA) type and HLA expression of tumor cell lines, characterizations necessary for the development of cancer vaccines, have remained largely incomplete and, such information, when available, has been distributed in many publications. Here, we determine the 4-digit HLA type and HLA expression of 167 cancer and 10 non-cancer cell lines from publically available RNA-Seq data. We use standard NGS RNA-Seq short reads from "whole transcriptome" sequencing, map reads to known HLA types, and statistically determine HLA type, heterozygosity, and expression. First, we present previously unreported HLA Class I and II genotypes. Second, we determine HLA expression levels in each cancer cell line, providing insights into HLA downregulation and loss in cancer. Third, using these results, we provide a fundamental cell line "barcode" to track samples and prevent sample annotation swaps and contamination. Fourth, we integrate the cancer cell-line specific HLA types and HLA expression with available cell-line specific mutation information and existing HLA binding prediction algorithms to make a catalog of predicted antigenic mutations in each cell line. The compilation of our results are a fundamental resource for all researchers selecting specific cancer cell lines based on the HLA type and HLA expression, as well as for the development of immunotherapeutic tools for novel cancer treatment modalities.