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      A study on Xenorhabdus and Photorhabdus isolates from Northeastern Thailand: Identification, antibacterial activity, and association with entomopathogenic nematode hosts

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          Abstract

          Xenorhabdus and Photorhabdus are gram negative bacteria that can produce several secondary metabolites, including antimicrobial compounds. They have a symbiotic association with entomopathogenic nematodes (EPNs). The aim of this study was to isolate and identify Xenorhabdus and Photorhabdus species and their associated nematode symbionts from Northeastern region of Thailand. We also evaluated the antibacterial activity of these symbiotic bacteria. The recovery rate of EPNs was 7.82% (113/1445). A total of 62 Xenorhabdus and 51 Photorhabdus strains were isolated from the EPNs. Based on recA sequencing and phylogeny, Xenorhabdus isolates were identified as X. stockiae (n = 60), X. indica (n = 1) and X. eapokensis (n = 1). Photorhabdus isolates were identified as P. luminescens subsp. akhurstii (n = 29), P. luminescens subsp. hainanensis (n = 18), P. luminescens subsp. laumondii (n = 2), and P. asymbiotica subsp. australis (n = 2). The EPNs based on 28S rDNA and internal transcribed spacer (ITS) analysis were identified as Steinernema surkhetense (n = 35), S. sangi (n = 1), unidentified Steinernema (n = 1), Heterorhabditis indica (n = 39), H. baujardi (n = 1), and Heterorhabditis sp. SGmg3 (n = 3). Antibacterial activity showed that X. stockiae (bMSK7.5_TH) extract inhibited several antibiotic-resistant bacterial strains. To the best of our knowledge, this is the first report on mutualistic association between P. luminescens subsp. laumondii and Heterorhabditis sp. SGmg3. This study could act as a platform for future studies focusing on the discovery of novel antimicrobial compounds from these bacterial isolates.

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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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            MEGA6: Molecular Evolutionary Genetics Analysis version 6.0.

            We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements the RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from www.megasoftware.net free of charge.
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              CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Methodology
                Role: MethodologyRole: Writing – original draft
                Role: Methodology
                Role: Methodology
                Role: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                12 August 2021
                2021
                : 16
                : 8
                : e0255943
                Affiliations
                [1 ] Department of Microbiology and Parasitology, Faculty of Medical Science, Naresuan University, Phitsanulok, Thailand
                [2 ] Department of Microbiology and Immunology, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
                [3 ] Department of Biology, Faculty of Science, Naresuan University, Phitsanulok, Thailand
                [4 ] Center of Excellence for Biodiversity, Faculty of Science, Naresuan University, Phitsanulok, Thailand
                Universidade Catolica Portuguesa, PORTUGAL
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-7842-2850
                Article
                PONE-D-20-35264
                10.1371/journal.pone.0255943
                8360611
                34383819
                a2157263-78d6-4497-b61d-edee430d68cf
                © 2021 Yimthin et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 9 November 2020
                : 27 July 2021
                Page count
                Figures: 5, Tables: 4, Pages: 21
                Funding
                Funded by: Center of Excellence on Biodiversity (BDC), Office of Higher Education Commission
                Award ID: BDC-PG4-161011
                Award Recipient :
                This work was supported by Center of Excellence on Biodiversity (BDC), Office of Higher Education Commission (BDC-PG4-161011). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Research Article
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                All relevant data are within the paper and its Supporting Information files.

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