While individuals infected with coronavirus disease 2019 (COVID-19) manifested a broad range in susceptibility and severity to the disease, the pre-existing immune memory to related pathogens cross-reactive against SARS-CoV-2 can influence the disease outcome in COVID-19. Here, we investigated the potential extent of T cell cross-reactivity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can be conferred by other coronaviruses and influenza virus, and generated an in silico map of public and private CD8+ T cell epitopes between coronaviruses. We observed 794 predicted SARS-CoV-2 epitopes of which 52% were private and 48% were public. Ninety-nine percent of the public epitopes were shared with SARS-CoV and 5.4% were shared with either one of four common coronaviruses, 229E, HKU1, NL63, and OC43. Moreover, to assess the potential risk of self-reactivity and/or diminished T cell response for peptides identical or highly similar to the host, we identified predicted epitopes with high sequence similarity with human proteome. Lastly, we compared predicted epitopes from coronaviruses with epitopes from influenza virus deposited in IEDB, and found only a small number of peptides with limited potential for cross-reactivity between the two virus families. We believe our comprehensive in silico profile of private and public epitopes across coronaviruses would facilitate design of vaccines, and provide insights into the presence of pre-existing coronavirus-specific memory CD8+ T cells that may influence immune responses against SARS-CoV-2.

T cell recognition of a cognate peptide–major histocompatibility complex (pMHC) presented on the surface of infected or malignant cells is of the utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T cell receptors would greatly facilitate identification of vaccine targets for both pathogenic diseases and personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the center of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as severe acute respiratory syndrome coronavirus 2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in the training data of the models seems to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen versus cancer peptides. Overall, we demonstrate that accurate and reliable predictions of immunogenic CD8+ T cell targets remain unsolved; thus, we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors.

No abstract

T cell recognition of a cognate peptide-MHC complex (pMHC) presented on the surface of infected or malignant cells are of utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T Cell Receptors (TCR) would greatly facilitate identification of vaccine targets for both pathogenic diseases as well as personalized cancer immunotherapies. Predicting immunogenic epitopes therefore has been at the centre of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes for humans has not been measured and compared.In this study, we curate and present a ‘standard-dataset’ of class I MHC epitopes for evaluating the performance of immunogenicity classifiers. Using this data, we present a systematic evaluation of performance of several publicly available models which are commonly used to predict CD8+ T cell epitopes in the context of both pathogens and cancers. The benchmarking is performed in two different settings: a pan-HLA setting in which all models are trained and evaluated by a pool of peptides from multiple HLA types, and a HLA-specific setting in which models are trained and evaluated by peptides from HLA-A02:01. In the pan-HLA setting, we observe that the best performing model achieves 75.6% accuracy suggesting considerable room for improvement. In the HLA-specific setting we observe a substantial reduction in performance with a mean of −11.9%. Our work shows that existing models exhibit suboptimal performance for predicting immunogenic cancer neoantigens. We then further interrogate the underlying problems of model predictions and highlight HLA-bias as a main source of variation amongst other issues associated with the design of models and/or to their training data.

The aim of this project was to study the efficacy of current methods of quality control and quality assurance for ultra-high molecular weight polyethylene (UHMWPE) products, and find improvements where possible. Intrinsic viscosity (IV) tests were performed on three grades of polyethylene with weight average relative molar masses $̅{M}$w of about 6 × 105, 5.0 × 106 and 9.0 × 106. Results from three laboratories showed substantial scatter, probably because different methods were used to make and test solutions. Tensile tests were carried out to 600 % extension at 150 °C under both constant applied load and constant Hencky strain rate, on compression mouldings made by a leading manufacturer of ultra-high molecular weight polyethylene. They gave low values of $̅{M}$w, suggesting incomplete entanglement at ‘grain boundaries’ between powder particles. Results from conventional melt-rheology tests are presented, and their relevance to quality control and assurance is discussed. Attempts to calculate molecular weights from these data met with limited success because of extended relaxation times. Suggestions are made for improving international standards for IV testing of UHMWPE, by investigating the various factors that can cause significant errors, and by introducing methods for checking the homogeneity (and hence validity) of the solutions tested. Part 2 addresses characterization of crystallinity and structure. Part 3 covers mechanical properties, and Part 4 focuses on the sporadic crack propagation behaviour exhibited by all three grades of UHMWPE in fatigue tests on 10 mm thick compact tension specimens.

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.

While individuals infected with coronavirus disease 2019 (COVID-19) manifested a broad range in susceptibility and severity to the disease, the pre-existing immune memory of related pathogens can influence the disease outcome. Here, we investigated the potential extent of T cell cross-reactivity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can be conferred by other coronaviruses and influenza virus, and generated a map of public and private predicted CD8+ T cell epitopes between coronaviruses. Moreover, to assess the potential risk of self-reactivity and/or diminished T cell response for peptides identical or highly similar to the host, we identified predicted epitopes with high sequence similarity with human proteome. Lastly, we compared predicted epitopes from coronaviruses with epitopes from influenza virus deposited in IEDB to support vaccine development against different virus strains. We believe the comprehensive in silico profile of private and public predicted epitopes across coronaviruses and influenza viruses will facilitate design of vaccines capable of protecting against various viral infections.

Novel adjuvant technologies have a key role in the development of next-generation vaccines, due to their capacity to modulate the duration, strength and quality of the immune response. The AS01 adjuvant is used in the malaria vaccine RTS,S/AS01 and in the licensed herpes-zoster vaccine (Shingrix) where the vaccine has proven its ability to generate protective responses with both robust humoral and T-cell responses. For many years, animal models have provided insights into adjuvant mode-of-action (MoA), generally through investigating individual genes or proteins. Furthermore, modeling and simulation techniques can be utilized to integrate a variety of different data types; ranging from serum biomarkers to large scale “omics” datasets. In this perspective we present a framework to create a holistic integration of pre-clinical datasets and immunological literature in order to develop an evidence-based hypothesis of AS01 adjuvant MoA, creating a unified view of multiple experiments. Furthermore, we highlight how holistic systems-knowledge can serve as a basis for the construction of models and simulations supporting exploration of key questions surrounding adjuvant MoA. Using the Systems-Biology-Graphical-Notation, a tool for graphical representation of biological processes, we have captured high-level cellular behaviors and interactions, and cytokine dynamics during the early immune response, which are substantiated by a series of diagrams detailing cellular dynamics. Through explicitly describing AS01 MoA we have built a consensus of understanding across multiple experiments, and so we present a framework to integrate modeling approaches into exploring adjuvant MoA, in order to guide experimental design, interpret results and inform rational design of vaccines.