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      Chimeric yellow fever 17D-Zika virus (ChimeriVax-Zika) as a live-attenuated Zika virus vaccine

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          Abstract

          Zika virus (ZIKV) is an emerging mosquito-borne pathogen representing a global health concern. It has been linked to fetal microcephaly and other birth defects and neurological disorders in adults. Sanofi Pasteur has engaged in the development of an inactivated ZIKV vaccine, as well as a live chimeric vaccine candidate ChimeriVax-Zika (CYZ) that could become a preferred vaccine depending on future ZIKV epidemiology. This report focuses on the CYZ candidate that was constructed by replacing the pre-membrane and envelope (prM-E) genes in the genome of live attenuated yellow fever 17D vaccine virus (YF 17D) with those from ZIKV yielding a viable CYZ chimeric virus. The replication rate of CYZ in the Vero cell substrate was increased by using a hybrid YF 17D-ZIKV signal sequence for the prM protein. CYZ was highly attenuated both in mice and in human in vitro models (human neuroblastoma and neuronal progenitor cells), without the need for additional attenuating modifications. It exhibited significantly reduced viral loads in organs compared to a wild-type ZIKV and a complete lack of neuroinvasion following inoculation of immunodeficient A129 mice. A single dose of CYZ elicited high titers of ZIKV-specific neutralizing antibodies in both immunocompetent and A129 mice and protected animals from ZIKV challenge. The data indicate that CYZ is a promising vaccine candidate against ZIKV.

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          Most cited references 43

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          A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood.

          The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximum- likelihood principle, which clearly satisfies these requirements. The core of this method is a simple hill-climbing algorithm that adjusts tree topology and branch lengths simultaneously. This algorithm starts from an initial tree built by a fast distance-based method and modifies this tree to improve its likelihood at each iteration. Due to this simultaneous adjustment of the topology and branch lengths, only a few iterations are sufficient to reach an optimum. We used extensive and realistic computer simulations to show that the topological accuracy of this new method is at least as high as that of the existing maximum-likelihood programs and much higher than the performance of distance-based and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximum-likelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting of 500 rbcL sequences with 1,428 base pairs from plant plastids, thus reaching a speed of the same order as some popular distance-based and parsimony algorithms. This new method is implemented in the PHYML program, which is freely available on our web page: http://www.lirmm.fr/w3ifa/MAAS/.
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            Improved prediction of signal peptides: SignalP 3.0.

            We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the cleavage site position and the amino acid composition of the signal peptide are correlated, new features have been included as input to the neural network. This addition, combined with a thorough error-correction of a new data set, have improved the performance of the predictor significantly over SignalP version 2. In version 3, correctness of the cleavage site predictions has increased notably for all three organism groups, eukaryotes, Gram-negative and Gram-positive bacteria. The accuracy of cleavage site prediction has increased in the range 6-17% over the previous version, whereas the signal peptide discrimination improvement is mainly due to the elimination of false-positive predictions, as well as the introduction of a new discrimination score for the neural network. The new method has been benchmarked against other available methods. Predictions can be made at the publicly available web server
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              A Mouse Model of Zika Virus Pathogenesis.

              The ongoing Zika virus (ZIKV) epidemic and unexpected clinical outcomes, including Guillain-Barré syndrome and birth defects, has brought an urgent need for animal models. We evaluated infection and pathogenesis with contemporary and historical ZIKV strains in immunocompetent mice and mice lacking components of the antiviral response. Four- to six-week-old Irf3(-/-)Irf5(-/-)Irf7(-/-) triple knockout mice, which produce little interferon α/β, and mice lacking the interferon receptor (Ifnar1(-/-)) developed neurological disease and succumbed to ZIKV infection, whereas single Irf3(-/-), Irf5(-/-), and Mavs(-/-) knockout mice exhibited no overt illness. Ifnar1(-/-) mice sustained high viral loads in the brain and spinal cord, consistent with evidence that ZIKV causes neurodevelopmental defects in human fetuses. The testes of Ifnar1(-/-) mice had the highest viral loads, which is relevant to sexual transmission of ZIKV. This model of ZIKV pathogenesis will be valuable for evaluating vaccines and therapeutics as well as understanding disease pathogenesis.
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                Author and article information

                Contributors
                konstantin.pugachev@sanofi.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                4 September 2018
                4 September 2018
                2018
                : 8
                Affiliations
                [1 ]Sanofi Pasteur Research & Development, Cambridge, MA USA
                [2 ]GRID grid.417924.d, Sanofi Pasteur Research & Development, ; Marcy-l’Étoile, France
                [3 ]Sanofi Pasteur Research & Development, Swiftwater, PA USA
                [4 ]Present Address: VL46 Inc., Cambridge, MA USA
                [5 ]Present Address: Kanyos Bio, Cambridge, MA USA
                31375
                10.1038/s41598-018-31375-9
                6123396
                30181550
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                Funding
                Funded by: The study was funded by Sanofi Pasteur
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