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      Polyamine-metabolizing enzymes are activated to promote the proper assembly of rice stripe mosaic virus in insect vectors

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          Abstract

          Both viruses and host cells compete for intracellular polyamines for efficient propagation. Currently, how the key polyamine-metabolizing enzymes, including ornithine decarboxylase 1 (ODC1) and its antizyme 1 (OAZ1), are activated to co-ordinate viral propagation and polyamine biosynthesis remains unknown. Here, we report that the matrix protein of rice stripe mosaic virus (RSMV), a cytorhabdovirus, directly hijacks OAZ1 to ensure the proper assembly of rigid bacilliform non-enveloped virions in leafhopper vector. Viral matrix protein effectively competes with ODC1 to bind to OAZ1, and thus, the ability of OAZ1 to target and mediate the degradation of ODC1 is significantly inhibited during viral propagation, which finally promotes polyamines production. Thus, OAZ1 and ODC1 are activated to synergistically promote viral persistent propagation and polyamine biosynthesis in viruliferous vectors. Our data suggest that it is a novel mechanism for rhabdovirus to exploit OAZ1 for facilitating viral assembly.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s44154-021-00032-z.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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              MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

              Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.
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                Author and article information

                Contributors
                weitaiyun@fafu.edu.cn
                Journal
                Stress Biol
                Stress Biol
                Stress Biology
                Springer Nature Singapore (Singapore )
                2731-0450
                15 April 2022
                15 April 2022
                December 2022
                : 2
                : 1
                : 10
                Affiliations
                GRID grid.256111.0, ISNI 0000 0004 1760 2876, Fujian Province Key Laboratory of Plant Virology, State Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops, , Fujian Agriculture and Forestry University, ; Fuzhou, Fujian People’s Republic of China
                Article
                32
                10.1007/s44154-021-00032-z
                10441986
                37676339
                3704a02f-9007-4d5e-a8a2-311854088d4b
                © The Author(s) 2022

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 October 2021
                : 30 December 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31920103014
                Award ID: 31970160
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100007219, Natural Science Foundation of Shanghai;
                Award ID: 31871931
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003392, Natural Science Foundation of Fujian Province;
                Award ID: 2020J06015
                Award Recipient :
                Categories
                Original Paper
                Custom metadata
                © The Author(s) 2022

                rice stripe mosaic virus,rhabdovirus,insect vector,polyamines,oaz1,odc1,viral assembly

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