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      In silico identification of vaccine targets for 2019-nCoV

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          Abstract

          Background: The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China.

          Methods: The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0.

          Results: We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential.

          Conclusions: Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.

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          NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.

          Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide-MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.
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            2019-nCoV in context: lessons learned?

            The emergence of a new coronavirus (2019-nCoV) in Wuhan creates a sense of déjà vu with the severe acute respiratory syndrome coronavirus (SARS-CoV) epidemic in China in 2003. Coronaviruses are enveloped, positive-stranded RNA viruses of mammals and birds. These viruses have high mutation and gene recombination rates, making them ideal for pathogen evolution. 1 In humans, coronavirus is usually associated with mild disease, the common cold. Previous emerging novel coronaviruses, such as SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), which emerged in the Middle East in 2012, were associated with severe and sometimes fatal disease. MERS-CoV was less pathogenic than SARS-CoV, with the most severe infections mainly in individuals with underlying illnesses. Clinically and epidemiologically, the contemporary 2019-nCoV in China seems to resemble SARS-CoV. The genome of 2019-nCoV also appears most closely related to SARS-CoV and related bat coronaviruses. 2 The infection has now spread widely, with phylogenetic analysis of the emerging viruses suggesting an initial single-locus zoonotic spillover event in November, 2019, 3 and subsequent human-to-human transmission. The SARS epidemic in 2003 was followed soon after by avian influenza H5N1 in 2006, centred on the Asian continent and Middle East. Other surprising viral zoonoses that have caused serious disease include Nipah encephalitic virus in pigs and humans in southeast and south Asia in 1999–2014, and large-scale Ebola virus epidemics in 2014–16 and 2018–19 in west and central Africa. Taken together, these events ring alarm bells about disease emergence in the 21st century, and the importance of human diseases originating from indiscriminate contacts with infected animals. There is an increasing focus on the human-animal-environment disease interface, as encompassed in the One Health concept. Mortalities, disability-adjusted life-years, and billions of dollars of economic losses from these infections demand action and investment in prevention to face novel challenges to human and animal health. Research has led to better understanding of the nature and drivers of cross-species viral jumps, but the detail is still elusive. No reservoir population of bats for SARS and MERS-CoV or Ebola virus have been definitively identified, despite considerable searching, possibly because of the source virus circulating in small and isolated populations. Forensic examination has clarified the human infection sources and multispecies involvement in these diseases, with some species confirmed as competent hosts (eg, camels for MERS-CoV 4 ), bridge (or amplifying) hosts (eg, pigs for Nipah virus, non-human primates for Ebola virus 5 ), or dead-end hosts. The crucial checkpoint is the jump and bridging of the viruses to humans, which occurs most frequently through animal-based food systems. In the case of SARS, markets with live and dead animals of wild and domestic origins were the crucible for virus evolution and emergence in the human population. Once the viruses' functional proteins enabled cell entry in civets (Paguma larvata) and racoon dogs (Nyctereutes procyonoides), the bridge was established and it was only a matter of time before the jump to humans occurred. 6 Sequence comparison of civet viruses suggested evolution was ongoing; this was further supported by high seroprevalence of antibodies against SARS-CoV among civet sellers, suggesting previous cross-species transmission events without necessarily human-to-human transmission.7, 8 Similarly, early Ebola virus was mostly associated with bushmeat and its consumption in Africa; Nipah virus is associated with date palm sap, fruit, and domestic pig farms; MERS is associated with the camel livestock industry; and H5N1 arose from viral evolution in domestic and wild birds, to ultimately bring all these cases to humans. The 2019-nCoV is another virus in the pipeline that originated from contact with animals, in this case a seafood and animal market in Wuhan, China. Inevitably, the health sectors are primarily reactive to these events, acting to save lives as well as undertake surveillance and control. The drama and panic typically fade into history with the substantial costs being absorbed by ordinary people, international financial systems, and tax bases, making life go on as normal, but not quite. The frequency, severity, and financial impacts of these events are growing, and the world can no longer afford to just wait and see, especially because prevention of these threats is in theory relatively simple and where addressed has resulted in a cessation of risk. The best example being Nipah virus, where separation of pig farms from fruit agriculture, and by the same measure, fruit bats, has substantially reduced the potential for Nipah virus spill over. Bats have always had viral populations, and despite close association with humans for millennia, this has not resulted in pandemics until recent times. In conclusion, have we learned lessons? Yes and no. These events are of global public health and economic importance and need collective societal response. But governments and civil society are not heeding these warnings, as the 2019-nCoV attests. 9 Concerns have been repeatedly raised and voiced since the idea of One Health was first expressed in around 2000. 10 What we need to learn and communicate is that the zoonotic or agricultural bridging of novel pathogens from domestic and captive wildlife needs urgent attention, along with attention to the human appetite for meat. This approach is easily achieved for coronavirus threats—eg, by substantially reducing the trade of risky species of wild caught animals for food or other purposes, and a culturally sensitive ban on the sale of these animals in wet markets. Vaccines and therapeutic alternatives might be possible and are needed, but they are a response, because the emerging strain is unpredictable and a vaccine is unlikely to prevent the initial events. In some parts of Africa, prevention of Ebola virus and future coronavirus threats require shifts in food habits, a transition from bushmeat being a cultural norm or primary source of protein, and by discouraging agricultural development that brings bats into increased contact with humans or livestock. In the Middle East, re-evaluating and improving infection prevention and control measures for camel farms, a recent introduction coincident with the emergence of MERS-CoV, would be a positive step forward.
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              Exploring the pre-immune landscape of antigen-specific T cells

              Background Adaptive immune responses to newly encountered pathogens depend on the mobilization of antigen-specific clonotypes from a vastly diverse pool of naive T cells. Using recent advances in immune repertoire sequencing technologies, models of the immune receptor rearrangement process, and a database of annotated T cell receptor (TCR) sequences with known specificities, we explored the baseline frequencies of T cells specific for defined human leukocyte antigen (HLA) class I-restricted epitopes in healthy individuals. Methods We used a database of TCR sequences with known antigen specificities and a probabilistic TCR rearrangement model to estimate the baseline frequencies of TCRs specific to distinct antigens epitopespecificT-cells. We verified our estimates using a publicly available collection of TCR repertoires from healthy individuals. We also interrogated a database of immunogenic and non-immunogenic peptides is used to link baseline T-cell frequencies with epitope immunogenicity. Results Our findings revealed a high degree of variability in the prevalence of T cells specific for different antigens that could be explained by the physicochemical properties of the corresponding HLA class I-bound peptides. The occurrence of certain rearrangements was influenced by ancestry and HLA class I restriction, and umbilical cord blood samples contained higher frequencies of common pathogen-specific TCRs. We also identified a quantitative link between specific T cell frequencies and the immunogenicity of cognate epitopes presented by defined HLA class I molecules. Conclusions Our results suggest that the population frequencies of specific T cells are strikingly non-uniform across epitopes that are known to elicit immune responses. This inference leads to a new definition of epitope immunogenicity based on specific TCR frequencies, which can be estimated with a high degree of accuracy in silico, thereby providing a novel framework to integrate computational and experimental genomics with basic and translational research efforts in the field of T cell immunology. Electronic supplementary material The online version of this article (10.1186/s13073-018-0577-7) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Role: Data CurationRole: Formal AnalysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: Data CurationRole: Funding AcquisitionRole: InvestigationRole: SupervisionRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Journal
                F1000Res
                F1000Res
                F1000Research
                F1000Research
                F1000 Research Limited (London, UK )
                2046-1402
                25 February 2020
                2020
                : 9
                : 145
                Affiliations
                [1 ]MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK, Oxford, UK
                [1 ]DIMI, Department of Internal Medicine, University of Genova, Genova, Italy
                [2 ]IBBC (Istituto di Biochimica e Biologia Cellulare), CNR-Napoli, Naples, Italy
                [1 ]Tuberculosis Laboratory, The Francis Crick Institute, London, UK
                [2 ]Institute of Infectious Disease and Molecular Medicine, University of Cape Town, South Africa
                Author notes

                No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Author information
                https://orcid.org/0000-0002-3640-7043
                Article
                10.12688/f1000research.22507.1
                7111504
                32269766
                97e154b4-e0b3-4a1c-b42b-1685047eb596
                Copyright: © 2020 Hyun-Jung Lee C and Koohy H

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 February 2020
                Funding
                Funded by: Medical Research Council
                This study was funded by the Medical Research Council Human Immunology Unit.
                Categories
                Research Article
                Articles

                coronavirus,adaptive immunity,immunogenicity,t cell cross-reactivity,vaccine development

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