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      Genome Investigation of Urinary Gardnerella Strains and Their Relationship to Isolates of the Vaginal Microbiota

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          Abstract

          Prior research into the bacterium Gardnerella vaginalis has largely focused on its association with bacterial vaginosis (BV). However, G. vaginalis is also frequently found within the urinary microbiota of women with and without lower urinary tract symptoms as well as individuals with chronic kidney disease, interstitial cystitis, and BV.

          ABSTRACT

          Gardnerella is a frequent member of the urogenital microbiota. Given the association between Gardnerella vaginalis and bacterial vaginosis (BV), significant efforts have been focused on characterizing this species in the vaginal microbiota. However, Gardnerella also is a frequent member of the urinary microbiota. In an effort to characterize the bacterial species of the urinary microbiota, we present here 10 genomes of urinary Gardnerella isolates from women with and without lower urinary tract symptoms. These genomes complement those of 22 urinary Gardnerella strains previously isolated and sequenced by our team. We included these genomes in a comparative genome analysis of all publicly available Gardnerella genomes, which include 33 urinary isolates, 78 vaginal isolates, and 2 other isolates. While once this genus was thought to consist of a single species, recent comparative genome analyses have revealed 3 new species and an additional 9 groups within Gardnerella. Based upon our analysis, we suggest a new group for the species. We also find that distinction between these Gardnerella species/groups is possible only when considering the core or whole-genome sequence, as neither the sialidase nor vaginolysin genes are sufficient for distinguishing between species/groups despite their clinical importance. In contrast to the vaginal microbiota, we found that only five Gardnerella species/groups have been detected within the lower urinary tract. Although we found no association between a particular Gardnerella species/group(s) and urinary symptoms, further sequencing of urinary Gardnerella isolates is needed for both comprehensive taxonomic characterization and etiological classification of Gardnerella in the urinary tract.

          IMPORTANCE Prior research into the bacterium Gardnerella vaginalis has largely focused on its association with bacterial vaginosis (BV). However, G. vaginalis is also frequently found within the urinary microbiota of women with and without lower urinary tract symptoms as well as individuals with chronic kidney disease, interstitial cystitis, and BV. This prompted our investigation into Gardnerella from the urinary microbiota and all publicly available Gardnerella genomes from the urogenital tract. Our work suggests that while some Gardnerella species can survive in both the urinary tract and vagina, others likely cannot. This study provides the foundation for future studies of Gardnerella within the urinary tract and its possible contribution to lower urinary tract symptoms.

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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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            FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

            Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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              CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes

              Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions in sequencing costs. Although this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes. Making robust biological inferences from draft genomes requires accurate estimates of their completeness and contamination. Current methods for assessing genome quality are ad hoc and generally make use of a limited number of “marker” genes conserved across all bacterial or archaeal genomes. Here we introduce CheckM, an automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes. We demonstrate the effectiveness of CheckM using synthetic data and a wide range of isolate-, single-cell-, and metagenome-derived genomes. CheckM is shown to provide accurate estimates of genome completeness and contamination and to outperform existing approaches. Using CheckM, we identify a diverse range of errors currently impacting publicly available isolate genomes and demonstrate that genomes obtained from single cells and metagenomic data vary substantially in quality. In order to facilitate the use of draft genomes, we propose an objective measure of genome quality that can be used to select genomes suitable for specific gene- and genome-centric analyses of microbial communities.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mSphere
                mSphere
                msph
                msph
                mSphere
                mSphere
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2379-5042
                12 May 2021
                May-Jun 2021
                : 6
                : 3
                : e00154-21
                Affiliations
                [a ]Department of Microbiology and Immunology, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA
                [b ]Bioinformatics Program, Loyola University Chicago, Chicago, Illinois, USA
                [c ]Department of Biology, Loyola University Chicago, Chicago, Illinois, USA
                [d ]Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA
                University of Michigan-Ann Arbor
                Author notes
                Address correspondence to Catherine Putonti, cputonti@ 123456luc.edu .
                [*]

                Present address: Evann E. Hilt and Travis K. Price, Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, California, USA.

                Citation Putonti C, Thomas-White K, Crum E, Hilt EE, Price TK, Wolfe AJ. 2021. Genome investigation of urinary Gardnerella strains and their relationship to isolates of the vaginal microbiota. mSphere 6:e00154-21. https://doi.org/10.1128/mSphere.00154-21.

                Author information
                https://orcid.org/0000-0003-3049-5991
                https://orcid.org/0000-0003-4532-0545
                Article
                mSphere00154-21
                10.1128/mSphere.00154-21
                8125048
                33980674
                3ed050d1-3e14-4130-ad4a-4c1777567fc7
                Copyright © 2021 Putonti et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 21 February 2021
                : 13 April 2021
                Page count
                supplementary-material: 6, Figures: 4, Tables: 3, Equations: 0, References: 61, Pages: 12, Words: 6499
                Funding
                Funded by: HHS | National Institutes of Health (NIH), https://doi.org/10.13039/100000002;
                Award ID: R01 DK104718
                Award Recipient :
                Funded by: Loyola University Chicago (LUC), https://doi.org/10.13039/100007656;
                Award ID: Mulcahy Research Fellowship
                Award Recipient :
                Funded by: National Science Foundation (NSF), https://doi.org/10.13039/100000001;
                Award ID: 1661357
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                May/June 2021

                gardnerella,urinary microbiome,phylogenomics,lower urinary tract symptoms

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