A robust deep learning workflow to predict CD8 + T-cell epitopes.

BACKGROUND: T-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes. METHODS: We developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to 'dissimilarity to self' from cancer studies. RESULTS: TRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP . CONCLUSIONS: This study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.

A systems approach evaluating the impact of SARS-CoV-2 variant of concern mutations on CD8+ T cell responses.

T cell recognition of SARS-CoV-2 antigens after vaccination and/or natural infection has played a central role in resolving SARS-CoV-2 infections and generating adaptive immune memory. However, the clinical impact of SARS-CoV-2-specific T cell responses is variable and the mechanisms underlying T cell interaction with target antigens are not fully understood. This is especially true given the virus' rapid evolution, which leads to new variants with immune escape capacity. In this study, we used the Omicron variant as a model organism and took a systems approach to evaluate the impact of mutations on CD8+ T cell immunogenicity. We computed an immunogenicity potential score for each SARS-CoV-2 peptide antigen from the ancestral strain and Omicron, capturing both antigen presentation and T cell recognition probabilities. By comparing ancestral vs. Omicron immunogenicity scores, we reveal a divergent and heterogeneous landscape of impact for CD8+ T cell recognition of mutated targets in Omicron variants. While T cell recognition of Omicron peptides is broadly preserved, we observed mutated peptides with deteriorated immunogenicity that may assist breakthrough infection in some individuals. We then combined our scoring scheme with an in silico mutagenesis, to characterise the position- and residue-specific theoretical mutational impact on immunogenicity. While we predict many escape trajectories from the theoretical landscape of substitutions, our study suggests that Omicron mutations in T cell epitopes did not develop under cell-mediated pressure. Our study provides a generalisable platform for fostering a deeper understanding of existing and novel variant impact on antigen-specific vaccine- and/or infection-induced T cell immunity.

Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens.

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.

HLA-dependent variation in SARS-CoV-2 CD8 + T cell cross-reactivity with human coronaviruses.

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.

Potential CD8+ T Cell Cross-Reactivity Against SARS-CoV-2 Conferred by Other Coronavirus Strains.

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.

Communicating marine climate change impacts in the Caribbean and Pacific regions.

The scientific literature on marine and coastal climate change has proliferated in recent decades. Translating and communicating this evidence in a timely, and accessible manner, is critical to support adaptation, but little is being done to summarise the latest science for decision makers. For Small Island Developing States (SIDS), which are highly vulnerable to marine and coastal climate change impacts, there is an urgent need to make the latest science readily available to inform national policy, leverage climate funding and highlight their vulnerability for international reports and climate negotiations. Climate change report cards are a proven successful way of presenting climate change information in an easily accessible and informative manner. Here we compare the development of marine climate change report cards for Caribbean and Pacific Commonwealth SIDS as a means of translating the latest science for decision makers. Regional engagement, priority issues and lessons learnt in these regions are compared, and future opportunities identified.

Application of Modeling Approaches to Explore Vaccine Adjuvant Mode-of-Action.

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.

Characterization of Primary Organic Aerosol from Domestic Wood, Peat, and Coal Burning in Ireland.

An aerosol chemical speciation monitor (ACSM) was deployed to study the primary nonrefractory submicron particulate matter emissions from the burning of commercially available solid fuels (peat, coal, and wood) typically used in European domestic fuel stoves. Organic mass spectra (MS) from burning wood, peat, and coal were characterized and intercompared for factor analysis against ambient data. The reference profiles characterized in this study were used to estimate the contribution of solid fuel sources, along with oil combustion, to ambient pollution in Galway, Ireland using the multilinear engine (ME-2). During periods influenced by marine air masses, local source contribution had dominant impact and nonsea-spray primary organic emissions comprised 88% of total organic aerosol mass, with peat burning found to be the greatest contributor (39%), followed by oil (21%), coal (17%), and wood (11%). In contrast, the resolved oxygenated organic aerosol (OOA) dominated the aerosol composition in continental air masses, with contributions of 50%, compared to 12% in marine air masses. The source apportionment results suggest that the use of domestic solid fuels (peat, wood, and coal) for home heating is the major source of evening and night-time particulate pollution events despite their small use.

Public awareness, concerns, and priorities about anthropogenic impacts on marine environments.

Numerous international bodies have advocated the development of strategies to achieve the sustainability of marine environments. Typically, such strategies are based on information from expert groups about causes of degradation and policy options to address them, but these strategies rarely take into account assessed information about public awareness, concerns, and priorities. Here we report the results of a pan-European survey of public perceptions about marine environmental impacts as a way to inform the formation of science and policy priorities. On the basis of 10,106 responses to an online survey from people in 10 European nations, spanning a diversity of socioeconomic and geographical areas, we examine the public's informedness and concern regarding marine impacts, trust in different information sources, and priorities for policy and funding. Results show that the level of concern regarding marine impacts is closely associated with the level of informedness and that pollution and overfishing are two areas prioritized by the public for policy development. The level of trust varies greatly among different information sources and is highest for academics and scholarly publications but lower for government or industry scientists. Results suggest that the public perceives the immediacy of marine anthropogenic impacts and is highly concerned about ocean pollution, overfishing, and ocean acidification. Eliciting public awareness, concerns, and priorities can enable scientists and funders to understand how the public relates to marine environments, frame impacts, and align managerial and policy priorities with public demand.

An automated algorithm for consolidating dates of diagnosis from multiple sources.

BACKGROUND: Multiple dates of diagnosis are often received from different reporting sources at a central cancer registry. Resolving these inconsistencies can be a labor-intensive task. To our knowledge, no algorithms for the consolidation of diagnosis dates have been published. We present such an algorithm here. METHODS: The algorithm uses a "take the best" heuristic approach, incorporating the reported dates of diagnosis, class of case, service type (a New York-specific item similar to type of reporting source), and the date of first contact. The algorithm was evaluated by comparing results to those obtained with manual review by experienced certified tumor registrars (CTRs). RESULTS: From a sample of 209,907 tumors with multiple diagnosis dates reported to the New York State Cancer Registry (NYSCR), the algorithm determined a single date for 94.7 percent of these, with the balance designated for manual review. Of a sample of 636 tumors that were manually reviewed to evaluate the algorithm, the algorithm obtained the same year as the CTRs for 621 tumors (97.6 percent), the same month and year for 572 tumors (89.9 percent) and the same month, year, and day for 518 tumors (81.4 percent). There was much lower agreement between the manually derived dates and the originally consolidated dates. CONCLUSION: The algorithm presented here is accurate, efficient, and reliable, and hopefully will help the cancer registry community move toward standard practices for record consolidation.

Genetic basis of resistance to fall armyworm (Lepidoptera: Noctuidae) and southwestern corn borer (Lepidoptera: Crambidae) leaf-feeding damage in maize.

Leaf-feeding damage by first generation larvae of fall armyworm, Spodopter frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), and southwestern corn borer, Diatraea grandiosella Dyar (Lepidoptera: Crambidae), cause major economic losses each year in maize, Zea mays L. A previous study identified quantitative trait loci (QTL) contributing to reduced leaf-feeding damage by these insects in the maize line Mp704. This study was initiated to identify QTL and their interactions associated with first generation leaf-feeding damage by fall armyworm and southwestern corn borer. QTL associated with fall armyworm and southwestern corn borer resistance in resistant line Mp708 were identified and compared with Mp704. Multiple trait analysis (MTA) of both data sets was then used to identify the most important genetic regions affecting resistance to fall armyworm and southwestern corn borer leaf-feeding damage. Genetic models containing four and seven QTL explained southwestern corn borer and fall armyworm resistance, respectively, in Mp708. Key genomic regions on chromosomes 1, 5, 7, and 9 were identified by MTA in Mp704 and Mp708 that confer resistance to both fall armyworm and southwestern corn borer. QTL regions on chromosomes 1, 5, 7, and 9 contained resistance to both insects and were present in both resistant lines. These regions correspond with previously identified QTL related to resistance to other lepidopteran insects, suggesting that broad-spectrum resistance to leaf feeding is primarily controlled by only a few genetic regions in this germplasm.

Broncholithiasis: a case report.

A case is reported of broncholithiasis in a 29-year-old female factory worker presenting with cough and lithoptysis. Broncholithiasis is a rare disorder characterized by calcified perihilar and mediastinal lymph nodes eroding into the tracheobronchial tree. Although cough, hemoptysis, lithoptysis, pneumonia and bronchoesophageal fistula formation have been reported, broncholithiasis may also result in potentially life-threatening conditions such as airway obstruction from endobronchial polypoid granulation masses, and massive hemorrhage from an aorto-tracheal fistula or erosion of a pulmonary artery branch.

Southwestern corn borer (Lepidoptera: Crambidae) damage and aflatoxin accumulation in maize.

Aflatoxin, a potent carcinogen, is produced by the fungus Aspergillus flavus Link: Fr. Drought, high temperatures, and insect damage contribute to increased levels of aflatoxin contamination in corn, Zea mays L. Plant resistance is widely considered a desirable method of reducing aflatoxin contamination. Germplasm lines with aflatoxin resistance have been developed. This investigation was undertaken to determine whether crosses among these lines exhibited resistance to southwestern corn borer, Diatraea grandiosella Dyar, and to assess the effects of southwestern corn borer feeding on aflatoxin accumulation. Differences in ear damage among southwestern corn borer infested hybrids were significant. Estimates of general combining ability effects indicated that the lines Mp80:04, Mp420, and Mp488 contributed to reduced ear damage, and SC213 and T165 contributed to greater damage when used in hybrids. Mean aflatoxin levels were 254 ng/g for hybrids infested with southwestern corn borer larvae and 164 ng/g for noninfested hybrids in 2000 when environmental conditions were conducive to aflatoxin production. In contrast, the overall mean aflatoxin level for southwestern corn borer infested hybrids was only 5 ng/g in 1999 when environmental conditions did not favor aflatoxin accumulation. Crosses that included lines selected for aflatoxin resistance as parents (Mp80:04 and Mp313E) exhibited lower levels of aflatoxin contamination both with and without southwestern corn borer infestation in 2000. Only the experimental line Mp80:04 contributed significantly to both reduced southwestern corn borer damage and reduced aflatoxin contamination.