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      Antibacterial Activity of Endophytic Actinomycetes Isolated from the Medicinal Plant Vochysia divergens (Pantanal, Brazil)

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          Abstract

          Endophytic actinomycetes from medicinal plants produce a wide diversity of secondary metabolites (SM). However, to date, the knowledge about endophytes from Brazil remains scarce. Thus, we analyzed the antimicrobial potential of 10 actinomycetes isolated from the medicinal plant Vochysia divergens located in the Pantanal sul-mato-grossense, an unexplored wetland in Brazil. Strains were classified as belonging to the Aeromicrobium, Actinomadura, Microbacterium, Microbispora, Micrococcus, Sphaerisporangium, Streptomyces , and Williamsia genera, through morphological and 16S rRNA phylogenetic analyzes. A susceptibility analysis demonstrated that the strains were largely resistant to the antibiotics oxacillin and nalidixic acid. Additionally, different culture media (SG and R5A), and temperatures (28 and 36°C) were evaluated to select the best culture conditions to produce the active SM. All conditions were analyzed for active metabolites, and the best antibacterial activity was observed from metabolites produced with SG medium at 36°C. The LGMB491 (close related to Aeromicrobium ponti) extract showed the highest activity against methicillin-resistant Staphylococcus aureus (MRSA), with a MIC of 0.04 mg/mL, and it was selected for SM identification. Strain LGMB491 produced 1-acetyl-β-carboline ( 1), indole-3-carbaldehyde ( 2), 3-(hydroxyacetyl)-indole ( 4), brevianamide F ( 5), and cyclo-(L-Pro-L-Phe) ( 6) as major compounds with antibacterial activity. In this study, we add to the knowledge about the endophytic community from the medicinal plant V. divergens and report the isolation of rare actinomycetes that produce highly active metabolites.

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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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            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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              The antibiotic resistance crisis: part 1: causes and threats.

              Decades after the first patients were treated with antibiotics, bacterial infections have again become a threat because of the rapid emergence of resistant bacteria-a crisis attributed to abuse of these medications and a lack of new drug development.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                06 September 2017
                2017
                : 8
                : 1642
                Affiliations
                [1] 1Department of Basic Pathology, Federal University of Paraná Curitiba, Brazil
                [2] 2Department of Genetics, Federal University of Paraná Curitiba, Brazil
                [3] 3Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky Lexington, KY, United States
                [4] 4Center for Pharmaceutical Research and Innovation, College of Pharmacy, University of Kentucky Lexington, KY, United States
                [5] 5Department of Biology, Federal University of Mato Grosso do Sul Campo Grande, Brazil
                Author notes

                Edited by: Elizabeth M. H. Wellington, University of Warwick, United Kingdom

                Reviewed by: D. Ipek Kurtboke, University of the Sunshine Coast, Australia; Atte Von Wright, University of Eastern Finland, Finland

                *Correspondence: Jürgen Rohr jrohr2@ 123456email.uky.edu

                This article was submitted to Antimicrobials, Resistance and Chemotherapy, a section of the journal Frontiers in Microbiology

                †These authors have contributed equally to this work.

                Article
                10.3389/fmicb.2017.01642
                5592219
                28932210
                104303f7-b947-455c-84c9-3c56f89906a8
                Copyright © 2017 Gos, Savi, Shaaban, Thorson, Aluizio, Possiede, Rohr and Glienke.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 12 May 2017
                : 14 August 2017
                Page count
                Figures: 9, Tables: 5, Equations: 0, References: 82, Pages: 17, Words: 9609
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: CA 091091
                Award ID: GM 105977
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico 10.13039/501100003593
                Award ID: 486016/2011-0
                Funded by: Fundação Araucária 10.13039/501100004612
                Award ID: 441/2012 – 23510
                Funded by: National Center for Advancing Translational Sciences 10.13039/100006108
                Award ID: UL1TR001998
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                actinomycetes,endophytes,vochysia divergens,pantanal,mrsa,secondary metabolites

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