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      Genomic epidemiology of syphilis reveals independent emergence of macrolide resistance across multiple circulating lineages

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          Abstract

          Syphilis is a sexually transmitted infection caused by Treponema pallidum subspecies pallidum and may lead to severe complications. Recent years have seen striking increases in syphilis in many countries. Previous analyses have suggested one lineage of syphilis, SS14, may have expanded recently, indicating emergence of a single pandemic azithromycin-resistant cluster. Here we use direct sequencing of T. pallidum combined with phylogenomic analyses to show that both SS14- and Nichols-lineages are simultaneously circulating in clinically relevant populations in multiple countries. We correlate the appearance of genotypic macrolide resistance with multiple independently evolved SS14 sub-lineages and show that genotypically resistant and sensitive sub-lineages are spreading contemporaneously. These findings inform our understanding of the current syphilis epidemic by demonstrating how macrolide resistance evolves in Treponema subspecies and provide a warning on broader issues of antimicrobial resistance.

          Abstract

          Syphilis is caused by the bacterium Treponema pallidum subspecies pallidum (TPA), and incidence has risen recently in many countries. Here, Beale et al. provide whole-genome TPA sequences from 73 clinical samples and show how antimicrobial resistance emerged independently in circulating lineages.

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          Most cited references73

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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

                Contributors
                mathew.beale@sanger.ac.uk
                nrt@sanger.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                22 July 2019
                22 July 2019
                2019
                : 10
                : 3255
                Affiliations
                [1 ]ISNI 0000 0004 0606 5382, GRID grid.10306.34, Parasites and Microbes, Wellcome Sanger Institute, Wellcome Genome Campus, ; Hinxton, Cambridgeshire UK
                [2 ]ISNI 0000 0004 0425 469X, GRID grid.8991.9, Clinical Research Department, Faculty of Infectious and Tropical Diseases, , London School of Hygiene & Tropical Medicine, ; London, UK
                [3 ]GRID grid.439634.f, Hospital for Tropical Diseases, ; London, UK
                [4 ]ISNI 0000000122986657, GRID grid.34477.33, Department of Neurology, , University of Washington, ; Seattle, WA 98195 USA
                [5 ]GRID grid.420545.2, Guy’s & St Thomas’ NHS Foundation Trust, ; London, UK
                [6 ]GRID grid.450578.b, The Mortimer Market Centre CNWL, Camden Provider Services, ; London, UK
                [7 ]ISNI 0000000122986657, GRID grid.34477.33, Departments of Medicine and Global Health, , University of Washington, ; Seattle, WA 98195 USA
                [8 ]ISNI 0000 0004 0425 469X, GRID grid.8991.9, Department of Pathogen Molecular Biology, Faculty of Infectious and Tropical Diseases, , London School of Hygiene & Tropical Medicine, ; London, UK
                Author information
                http://orcid.org/0000-0002-4740-3187
                http://orcid.org/0000-0002-7585-4743
                Article
                11216
                10.1038/s41467-019-11216-7
                6646400
                31332179
                605d2993-ed81-4194-b66b-35780c864bbb
                © The Author(s) 2019

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 September 2018
                : 1 July 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: 098051
                Award ID: 102807
                Award Recipient :
                Funded by: Core Faculty funding to the Wellcome Sanger Institute
                Funded by: FundRef https://doi.org/10.13039/100000060, U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID);
                Award ID: AI 34616
                Award ID: AI 42143
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000065, U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS);
                Award ID: NS34235
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                phylogenetics,bacterial genetics,bacterial infection
                Uncategorized
                phylogenetics, bacterial genetics, bacterial infection

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