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      Arboviruses and symbiotic viruses cooperatively hijack insect sperm-specific proteins for paternal transmission

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          Abstract

          Arboviruses and symbiotic viruses can be paternally transmitted by male insects to their offspring for long-term viral persistence in nature, but the mechanism remains largely unknown. Here, we identify the sperm-specific serpin protein HongrES1 of leafhopper Recilia dorsalis as a mediator of paternal transmission of the reovirus Rice gall dwarf virus (RGDV) and a previously undescribed symbiotic virus of the Virgaviridae family, Recilia dorsalis filamentous virus (RdFV). We show that HongrES1 mediates the direct binding of virions to leafhopper sperm surfaces and subsequent paternal transmission via interaction with both viral capsid proteins. Direct interaction of viral capsid proteins mediates simultaneously invasion of two viruses into male reproductive organs. Moreover, arbovirus activates HongrES1 expression to suppress the conversion of prophenoloxidase to active phenoloxidase, potentially producing a mild antiviral melanization defense. Paternal virus transmission scarcely affects offspring fitness. These findings provide insights into how different viruses cooperatively hijack insect sperm-specific proteins for paternal transmission without disturbing sperm functions.

          Abstract

          Arboviruses and symbiotic viruses can be paternally transmitted by male insects to their offspring, but the mechanism remains largely unknown. Here, the authors identify the sperm-specific serpin protein HongrES1 of the leafhopper Recilia dorsalis as a mediator of paternal transmission of the reovirus rice gall dwarf virus and of a previously undescribed symbiotic virus of the Virgaviridae family, Recilia dorsalis filamentous virus, via direct binding of virions to leafhopper sperm surfaces and subsequent paternal transmission via interaction with both viral capsid proteins.

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          MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

          Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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            ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates

            Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates. The improvement is achieved by incorporating a model of rate-heterogeneity across sites not previously considered in this context, and by allowing concurrent searches of model-space and tree-space.
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              Heparan Sulfate Proteoglycans and Viral Attachment: True Receptors or Adaptation Bias?

              Heparan sulfate proteoglycans (HSPG) are composed of unbranched, negatively charged heparan sulfate (HS) polysaccharides attached to a variety of cell surface or extracellular matrix proteins. Widely expressed, they mediate many biological activities, including angiogenesis, blood coagulation, developmental processes, and cell homeostasis. HSPG are highly sulfated and broadly used by a range of pathogens, especially viruses, to attach to the cell surface. In this review, we summarize the current knowledge on HSPG–virus interactions and distinguish viruses with established HS binding, viruses that bind HS only after intra-host or cell culture adaptation, and finally, viruses whose dependence on HS for infection is debated. We also provide an overview of the antiviral compounds designed to interfere with HS binding. Many questions remain about the true importance of these receptors in vivo, knowledge that is critical for the design of future antiviral therapies.
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                Author and article information

                Contributors
                weitaiyun@fafu.edu.cn
                chenqian@fafu.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                9 March 2023
                9 March 2023
                2023
                : 14
                : 1289
                Affiliations
                [1 ]GRID grid.256111.0, ISNI 0000 0004 1760 2876, Vector-Borne Virus Research Center, , Fujian Agriculture and Forestry University, ; Fuzhou, Fujian 350002 China
                [2 ]GRID grid.256111.0, ISNI 0000 0004 1760 2876, State Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops, , Fujian Agriculture and Forestry University, ; Fuzhou, Fujian 350002 China
                Author information
                http://orcid.org/0000-0001-8850-8754
                http://orcid.org/0000-0002-0732-9752
                http://orcid.org/0000-0002-8442-6089
                Article
                36993
                10.1038/s41467-023-36993-0
                9998617
                36894574
                313f3b2c-4aa4-43a1-9bf4-d1191e1c8f2f
                © The Author(s) 2023

                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/.

                History
                : 14 September 2022
                : 23 February 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 31730071
                Award ID: 31920103014
                Award ID: U21A20221
                Award Recipient :
                Categories
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                © The Author(s) 2023

                Uncategorized
                virology,pathogens
                Uncategorized
                virology, pathogens

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