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      Immunogenicity of a Multi-Epitope DNA Vaccine Encoding Epitopes from Cu–Zn Superoxide Dismutase and Open Reading Frames of Brucella abortus in Mice

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          Abstract

          Brucellosis is a bacterial zoonotic disease affecting several mammalian species that is transmitted to humans by direct or indirect contact with infected animals or their products. In cattle, brucellosis is almost invariably caused by Brucella abortus. Live, attenuated Brucella vaccines are commonly used to prevent illness in cattle, but can cause abortions in pregnant animals. It is, therefore, desirable to design an effective and safer vaccine against Brucella. We have used specific Brucella antigens that induce immunity and protection against B. abortus. A novel recombinant multi-epitope DNA vaccine specific for brucellosis was developed. To design the vaccine construct, we employed bioinformatics tools to predict epitopes present in Cu–Zn superoxide dismutase and in the open reading frames of the genomic island-3 (BAB1_0260, BAB1_0270, BAB1_0273, and BAB1_0278) of Brucella. We successfully designed a multi-epitope DNA plasmid vaccine chimera that encodes and expresses 21 epitopes. This DNA vaccine induced a specific humoral and cellular immune response in BALB/c mice. It induced a typical T-helper 1 response, eliciting production of immunoglobulin G2a and IFN-γ particularly associated with the Th1 cell subset of CD4 + T cells. The production of IL-4, an indicator of Th2 activation, was not detected in splenocytes. Therefore, it is reasonable to suggest that the vaccine induced a predominantly Th1 response. The vaccine induced a statistically significant level of protection in BALB/c mice when challenged with B. abortus 2308. This is the first use of an in silico strategy to a design a multi-epitope DNA vaccine against B. abortus.

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          Most cited references47

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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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            The immune epitope database (IEDB) 3.0

            The IEDB, www.iedb.org, contains information on immune epitopes—the molecular targets of adaptive immune responses—curated from the published literature and submitted by National Institutes of Health funded epitope discovery efforts. From 2004 to 2012 the IEDB curation of journal articles published since 1960 has caught up to the present day, with >95% of relevant published literature manually curated amounting to more than 15 000 journal articles and more than 704 000 experiments to date. The revised curation target since 2012 has been to make recent research findings quickly available in the IEDB and thereby ensure that it continues to be an up-to-date resource. Having gathered a comprehensive dataset in the IEDB, a complete redesign of the query and reporting interface has been performed in the IEDB 3.0 release to improve how end users can access this information in an intuitive and biologically accurate manner. We here present this most recent release of the IEDB and describe the user testing procedures as well as the use of external ontologies that have enabled it.
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              Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method

              Background Many processes in molecular biology involve the recognition of short sequences of nucleic-or amino acids, such as the binding of immunogenic peptides to major histocompatibility complex (MHC) molecules. From experimental data, a model of the sequence specificity of these processes can be constructed, such as a sequence motif, a scoring matrix or an artificial neural network. The purpose of these models is two-fold. First, they can provide a summary of experimental results, allowing for a deeper understanding of the mechanisms involved in sequence recognition. Second, such models can be used to predict the experimental outcome for yet untested sequences. In the past we reported the development of a method to generate such models called the Stabilized Matrix Method (SMM). This method has been successfully applied to predicting peptide binding to MHC molecules, peptide transport by the transporter associated with antigen presentation (TAP) and proteasomal cleavage of protein sequences. Results Herein we report the implementation of the SMM algorithm as a publicly available software package. Specific features determining the type of problems the method is most appropriate for are discussed. Advantageous features of the package are: (1) the output generated is easy to interpret, (2) input and output are both quantitative, (3) specific computational strategies to handle experimental noise are built in, (4) the algorithm is designed to effectively handle bounded experimental data, (5) experimental data from randomized peptide libraries and conventional peptides can easily be combined, and (6) it is possible to incorporate pair interactions between positions of a sequence. Conclusion Making the SMM method publicly available enables bioinformaticians and experimental biologists to easily access it, to compare its performance to other prediction methods, and to extend it to other applications.
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                Author and article information

                Contributors
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                09 February 2017
                2017
                : 8
                : 125
                Affiliations
                [1] 1Laboratory of Molecular Immunology, Department of Microbiology, Faculty of Biological Sciences, Universidad de Concepción , Concepción, Chile
                Author notes

                Edited by: Clarisa B. Palatnik-de-Sousa, Federal University of Rio de Janeiro, Brazil

                Reviewed by: Daniel Olive, Institut national de la santé et de la recherche médicale, France; Pablo Penaloza, Northwestern University, USA

                *Correspondence: Angel Oñate, aonate@ 123456udec.cl

                Specialty section: This article was submitted to Vaccines and Molecular Therapeutics, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2017.00125
                5298974
                28232837
                5190a079-ef46-4c81-a047-de66b9f1066d
                Copyright © 2017 Escalona, Sáez and Oñate.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 14 November 2016
                : 25 January 2017
                Page count
                Figures: 6, Tables: 2, Equations: 0, References: 59, Pages: 10, Words: 7226
                Funding
                Funded by: Fondo Nacional de Desarrollo Científico y Tecnológico 10.13039/501100002850
                Award ID: 1130093
                Categories
                Immunology
                Original Research

                Immunology
                brucellosis,brucella abortus,multi-epitope dna vaccine,genomic island 3,cu–zn sod
                Immunology
                brucellosis, brucella abortus, multi-epitope dna vaccine, genomic island 3, cu–zn sod

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