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      Vaccination with recombinant adenovirus expressing peste des petits ruminants virus-F or -H proteins elicits T cell responses to epitopes that arises during PPRV infection

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          Abstract

          Peste des petits ruminants virus (PPRV) causes an economically important disease that limits productivity in small domestic ruminants and often affects the livestock of the poorest populations in developing countries. Animals that survive PPRV develop strong cellular and humoral responses, which are probably necessary for protection. Vaccination should thus aim at mimicking these natural responses. Immunization strategies against this morbillivirus using recombinant adenoviruses expressing PPRV-F or -H proteins can protect PPRV-challenged animals and permit differentiation of infected from vaccinated animals. Little is known of the T cell repertoire these recombinant vaccines induce. In the present work, we identified several CD4 + and CD8 + T cell epitopes in sheep infected with PPRV. We also show that recombinant adenovirus vaccination induced T cell responses to the same epitopes, and led to memory T cell differentiation. T cells primed by these recombinant adenovirus vaccines expanded after PPRV challenge and probably contributed to protection. These data validate the use of recombinant adenovirus expressing PPRV genes as DIVA strategies to control this highly contagious disease.

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          The online version of this article (10.1186/s13567-017-0482-x) contains supplementary material, which is available to authorized users.

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          Multifunctional TH1 cells define a correlate of vaccine-mediated protection against Leishmania major.

          CD4+ T cells have a crucial role in mediating protection against a variety of pathogens through production of specific cytokines. However, substantial heterogeneity in CD4+ T-cell cytokine responses has limited the ability to define an immune correlate of protection after vaccination. Here, using multiparameter flow cytometry to assess the immune responses after immunization, we show that the degree of protection against Leishmania major infection in mice is predicted by the frequency of CD4+ T cells simultaneously producing interferon-gamma, interleukin-2 and tumor necrosis factor. Notably, multifunctional effector cells generated by all vaccines tested are unique in their capacity to produce high amounts of interferon-gamma. These data show that the quality of a CD4+ T-cell cytokine response can be a crucial determinant in whether a vaccine is protective, and may provide a new and useful prospective immune correlate of protection for vaccines based on T-helper type 1 (TH1) cells.
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            Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.

            In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.
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              SYFPEITHI: database for MHC ligands and peptide motifs.

              The first version of the major histocompatibility complex (MHC) databank SYFPEITHI: database for MHC ligands and peptide motifs, is now available to the general public. It contains a collection of MHC class I and class II ligands and peptide motifs of humans and other species, such as apes, cattle, chicken, and mouse, for example, and is continuously updated. All motifs currently available are accessible as individual entries. Searches for MHC alleles, MHC motifs, natural ligands, T-cell epitopes, source proteins/organisms and references are possible. Hyperlinks to the EMBL and PubMed databases are included. In addition, ligand predictions are available for a number of MHC allelic products. The database content is restricted to published data only.
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                Author and article information

                Contributors
                rojas.jose@inia.es
                avia.jose@inia.es
                elena.pascual@ufv.es
                sevilla@inia.es
                veronica.martin@inia.es
                Journal
                Vet Res
                Vet. Res
                Veterinary Research
                BioMed Central (London )
                0928-4249
                1297-9716
                21 November 2017
                21 November 2017
                2017
                : 48
                : 79
                Affiliations
                ISNI 0000 0001 2300 669X, GRID grid.419190.4, Centro de Investigación en Sanidad Animal (CISA-INIA), , Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, ; Valdeolmos, Madrid, Spain
                Author information
                http://orcid.org/0000-0003-1486-1656
                Article
                482
                10.1186/s13567-017-0482-x
                5697415
                29157291
                83863e65-ab4f-4730-9ad1-9eba3897a6ea
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 25 July 2017
                : 26 October 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003329, Ministerio de Economía y Competitividad;
                Award ID: BES-2013-066406
                Award ID: RyC2010-06516
                Award ID: AGL2011-25025
                Award ID: AGL2012-33289
                Award ID: AGL2015-64290R
                Award ID: AGL2015-64290R
                Award ID: BIO2015-68990-REDT
                Award ID: BIO2015-68990-REDT
                Award Recipient :
                Funded by: Comunidad de Madrid (ES) y FEDER
                Award ID: S2013/ABI-2906-PLATESA
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Veterinary medicine
                Veterinary medicine

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