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      Microbiome of Zoophytophagous Biological Control Agent Nesidiocoris tenuis

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          Abstract

          Many insects are associated with endosymbionts that influence the feeding, reproduction, and distribution of their hosts. Although the small green mirid, Nesidiocoris tenuis (Reuter) (Hemiptera: Miridae), a zoophytophagous predator that feeds on plants as well as arthropods, is a globally important biological control agent, its microbiome has not been sufficiently studied. In the present study, we assessed the microbiome variation in 96 N. tenuis individuals from 14 locations throughout Japan, based on amplicon sequencing of the 16S ribosomal RNA gene. Nine major bacteria associated with N. tenuis were identified: Rickettsia, two strains of Wolbachia, Spiroplasma, Providencia, Serratia, Pseudochrobactrum, Lactococcus, and Stenotrophomonas. Additionally, a diagnostic PCR analysis for three typical insect reproductive manipulators, Rickettsia, Wolbachia, and Spiroplasma, was performed on a larger sample size ( n = 360) of N. tenuis individuals; the most prevalent symbiont was Rickettsia (69.7%), followed by Wolbachia (39.2%) and Spiroplasma (6.1%). Although some symbionts were co-infected, their prevalence did not exhibit any specific tendency, such as a high frequency in specific infection combinations. The infection frequency of Rickettsia was significantly correlated with latitude and temperature, while that of Wolbachia and Spiroplasma was significantly correlated with host plants. The predominance of these bacteria and the absence of obligate symbionts suggested that the N. tenuis microbiome is typical for predatory arthropods rather than sap-feeding insects. Rickettsia and Wolbachia were vertically transmitted rather than horizontally transmitted from the prey. The functional validation of each symbiont would be warranted to develop N. tenuis as a biological control agent.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00248-023-02290-y.

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          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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            Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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              A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences.

              Some simple formulae were obtained which enable us to estimate evolutionary distances in terms of the number of nucleotide substitutions (and, also, the evolutionary rates when the divergence times are known). In comparing a pair of nucleotide sequences, we distinguish two types of differences; if homologous sites are occupied by different nucleotide bases but both are purines or both pyrimidines, the difference is called type I (or "transition" type), while, if one of the two is a purine and the other is a pyrimidine, the difference is called type II (or "transversion" type). Letting P and Q be respectively the fractions of nucleotide sites showing type I and type II differences between two sequences compared, then the evolutionary distance per site is K = -(1/2) ln [(1-2P-Q) square root of 1-2Q]. The evolutionary rate per year is then given by k = K/(2T), where T is the time since the divergence of the two sequences. If only the third codon positions are compared, the synonymous component of the evolutionary base substitutions per site is estimated by K'S = -(1/2) ln (1-2P-Q). Also, formulae for standard errors were obtained. Some examples were worked out using reported globin sequences to show that synonymous substitutions occur at much higher rates than amino acid-altering substitutions in evolution.
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                Author and article information

                Contributors
                kagyamad@affrc.go.jp
                tadachi@cc.miyazaki-u.ac.jp
                Journal
                Microb Ecol
                Microb Ecol
                Microbial Ecology
                Springer US (New York )
                0095-3628
                1432-184X
                2 September 2023
                2 September 2023
                2023
                : 86
                : 4
                : 2923-2933
                Affiliations
                [1 ]GRID grid.416835.d, ISNI 0000 0001 2222 0432, Institute of Agrobiological Sciences, , National Agriculture and Food Research Organization (NARO), ; Tsukuba, Ibaraki, Japan
                [2 ]Laboratory of Applied Entomology, University of Miyazaki, ( https://ror.org/0447kww10) Miyazaki, Japan
                [3 ]Frontier Science Research Center, University of Miyazaki, ( https://ror.org/0447kww10) Miyazaki, Japan
                [4 ]Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, ( https://ror.org/057zh3y96) Tokyo, Japan
                [5 ]Shizuoka Prefectural Research Institute of Agriculture and Forestry, ( https://ror.org/024285019) Shizuoka, Japan
                Author information
                https://orcid.org/0000-0003-0785-8694
                https://orcid.org/0000-0003-2759-9167
                https://orcid.org/0000-0002-8405-2937
                https://orcid.org/0000-0002-9026-9825
                https://orcid.org/0000-0003-4868-7772
                Article
                2290
                10.1007/s00248-023-02290-y
                10640431
                37658881
                9c2d9bd1-07c3-4542-8d30-7b9e188cefae
                © 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 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
                : 3 July 2023
                : 16 August 2023
                Categories
                Research
                Custom metadata
                © Springer Science+Business Media, LLC, part of Springer Nature 2023

                Microbiology & Virology
                symbiotic bacteria,biological control,rickettsia,wolbachia,spiroplasma,mirid
                Microbiology & Virology
                symbiotic bacteria, biological control, rickettsia, wolbachia, spiroplasma, mirid

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