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      The Space-Exposed Kombucha Microbial Community Member Komagataeibacter oboediens Showed Only Minor Changes in Its Genome After Reactivation on Earth

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      1 , 2 , 1 , 3 , 1 , 2 , 2 , 2 , 2 , 2 , 2 , 2 , 4 , 5 , 5 , 5 , 6 , 7 , 2 , 8 , 9 , 10 , 5 , 2 , 11 , * , , 1 , * ,
      Frontiers in Microbiology
      Frontiers Media S.A.
      comparative genomics, Acetobacteraceae, metabolic reconstruction, single nucleotide variation, nitrogen fixation, hopanoids, cellulose biosynthesis

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          Abstract

          Komagataeibacter is the dominant taxon and cellulose-producing bacteria in the Kombucha Microbial Community (KMC). This is the first study to isolate the K. oboediens genome from a reactivated space-exposed KMC sample and comprehensively characterize it. The space-exposed genome was compared with the Earth-based reference genome to understand the genome stability of K. oboediens under extraterrestrial conditions during a long time. Our results suggest that the genomes of K. oboediens IMBG180 (ground sample) and K. oboediens IMBG185 (space-exposed) are remarkably similar in topology, genomic islands, transposases, prion-like proteins, and number of plasmids and CRISPR-Cas cassettes. Nonetheless, there was a difference in the length of plasmids and the location of cas genes. A small difference was observed in the number of protein coding genes. Despite these differences, they do not affect any genetic metabolic profile of the cellulose synthesis, nitrogen-fixation, hopanoid lipids biosynthesis, and stress-related pathways. Minor changes are only observed in central carbohydrate and energy metabolism pathways gene numbers or sequence completeness. Altogether, these findings suggest that K. oboediens maintains its genome stability and functionality in KMC exposed to the space environment most probably due to the protective role of the KMC biofilm. Furthermore, due to its unaffected metabolic pathways, this bacterial species may also retain some promising potential for space applications.

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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            UCSF Chimera--a visualization system for exploratory research and analysis.

            The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale molecular assemblies such as viral coats, and Collaboratory, which allows researchers to share a Chimera session interactively despite being at separate locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and associated structures; ViewDock, for screening docked ligand orientations; Movie, for replaying molecular dynamics trajectories; and Volume Viewer, for display and analysis of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/. Copyright 2004 Wiley Periodicals, Inc.
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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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                Author and article information

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                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                11 March 2022
                2022
                : 13
                : 782175
                Affiliations
                [1] 1Laboratory of Molecular and Computational Biology of Fungi, Department of Microbiology, Department of Genetics, Ecology and Evolution, Institute of Biological Sciences, Federal University of Minas Gerais , Belo Horizonte, Brazil
                [2] 2Laboratory of Cellular and Molecular Genetics, Department of Genetics, Ecology and Evolution, Universidade Federal de Minas Gerais , Belo Horizonte, Brazil
                [3] 3Postgraduate Program in Biology and Biotechnology of Microorganisms, Department of Biological Sciences, State University of Santa Cruz , Ilhéus, Brazil
                [4] 4Computational Biology and Biotechnological Information Management Center (NBCGIB), State University of Santa Cruz , Ilhéus, Brazil
                [5] 5Institute of Molecular Biology and Genetics of NASU , Kyiv, Ukraine
                [6] 6Department of Biochemistry, Genetics and Microbiology, Centre for Bioinformatics and Computational Biology, University of Pretoria , Pretoria, South Africa
                [7] 7Center for Data and Knowledge Integration for Health (CIDACS), Institute Gonçalo Moniz, Oswaldo Cruz Foundation (FIOCRUZ-Bahia) , Salvador, Brazil
                [8] 8Institute of Veterinary Medicine, Burckhardtweg, University of Göttingen , Göttingen, Germany
                [9] 9Laboratory of Bioinformatics and Computational Chemistry, Department of Biological Sciences, State University of Southwest Bahia (UESB) , Jequié, Brazil
                [10] 10German Aerospace Center (DLR) Berlin, Institute of Planetary Research, Planetary Laboratories, Astrobiological Laboratories , Berlin, Germany
                [11] 11Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology , Purba Medinipur, India
                Author notes

                Edited by: Elena Perrin, University of Florence, Italy

                Reviewed by: Stefano Amalfitano, Water Research Institute, National Research Council, Italy; Niamh O’Hara, SUNY Downstate Medical Center, United States

                *Correspondence: Debmalya Barh, dr.barh@ 123456gmail.com
                Aristóteles Góes-Neto, arigoesneto@ 123456icb.ufmg.br

                These authors have contributed equally to this work

                This article was submitted to Evolutionary and Genomic Microbiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2022.782175
                8970348
                35369445
                9bad7ed8-fa50-4a75-adfb-7b568a9bb854
                Copyright © 2022 Santana de Carvalho, Trovatti Uetanabaro, Kato, Aburjaile, Jaiswal, Profeta, De Oliveira Carvalho, Tiwar, Cybelle Pinto Gomide, Almeida Costa, Kukharenko, Orlovska, Podolich, Reva, Ramos, De Carvalho Azevedo, Brenig, Andrade, de Vera, Kozyrovska, Barh and Góes-Neto.

                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) and the copyright owner(s) 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
                : 23 September 2021
                : 01 February 2022
                Page count
                Figures: 8, Tables: 6, Equations: 0, References: 81, Pages: 17, Words: 11553
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                comparative genomics,acetobacteraceae,metabolic reconstruction,single nucleotide variation,nitrogen fixation,hopanoids,cellulose biosynthesis

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