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      Five decades of genome evolution in the globally distributed, extensively antibiotic-resistant Acinetobacter baumannii global clone 1

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          Abstract

          The majority of Acinetobacter baumannii isolates that are multiply, extensively and pan-antibiotic resistant belong to two globally disseminated clones, GC1 and GC2, that were first noticed in the 1970s. Here, we investigated microevolution and phylodynamics within GC1 via analysis of 45 whole-genome sequences, including 23 sequenced for this study. The most recent common ancestor of GC1 arose around 1960 and later diverged into two phylogenetically distinct lineages. In the 1970s, the main lineage acquired the AbaR resistance island, conferring resistance to older antibiotics, via a horizontal gene transfer event. We estimate a mutation rate of ∼5 SNPs genome − 1 year − 1 and detected extensive recombination within GC1 genomes, introducing nucleotide diversity into the population at >20 times the substitution rate (the ratio of SNPs introduced by recombination compared with mutation was 22). The recombination events were non-randomly distributed in the genome and created significant diversity within loci encoding outer surface molecules (including the capsular polysaccharide, the outer core lipooligosaccharide and the outer membrane protein CarO), and spread antimicrobial resistance-conferring mutations affecting the gyrA and parC genes and insertion sequence insertions activating the ampC gene. Both GC1 lineages accumulated resistance to newer antibiotics through various genetic mechanisms, including the acquisition of plasmids and transposons or mutations in chromosomal genes. Our data show that GC1 has diversified into multiple successful extensively antibiotic-resistant subclones that differ in their surface structures. This has important implications for all avenues of control, including epidemiological tracking, antimicrobial therapy and vaccination.

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          Most cited references 65

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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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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              Bayesian Phylogenetics with BEAUti and the BEAST 1.7

              Computational evolutionary biology, statistical phylogenetics and coalescent-based population genetics are becoming increasingly central to the analysis and understanding of molecular sequence data. We present the Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7, which implements a family of Markov chain Monte Carlo (MCMC) algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses. This package includes an enhanced graphical user interface program called Bayesian Evolutionary Analysis Utility (BEAUti) that enables access to advanced models for molecular sequence and phenotypic trait evolution that were previously available to developers only. The package also provides new tools for visualizing and summarizing multispecies coalescent and phylogeographic analyses. BEAUti and BEAST 1.7 are open source under the GNU lesser general public license and available at http://beast-mcmc.googlecode.com and http://beast.bio.ed.ac.uk
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                Author and article information

                Journal
                Microb Genom
                Microb Genom
                MGen
                Microbial Genomics
                Microbiology Society
                2057-5858
                February 2016
                23 February 2016
                : 2
                : 2
                Affiliations
                [ 1]Department of Biochemistry & Molecular Biology, The University of Melbourne , Royal Parade, Parkville, Victoria, Australia
                [ 2]School of Biomedical Science, Queensland University of Technology , Brisbane, Queensland, Australia
                [ 3]School of Molecular Bioscience, The University of Sydney , Sydney, New South Wales, Australia
                [ 4]Centre for Systems Genomics, The University of Melbourne , Parkville, Victoria, Australia
                [ 5]Wellcome Sanger Trust Institute , Hinxton, Cambridge, UK
                Author notes
                Correspondence Kathryn Holt ( kholt@ 123456unimelb.edu.au ) and Ruth Hall ( ruth.hall@ 123456sydney.edu.au )
                [†]

                These authors contributed equally to this paper.

                All supporting data, code and protocols have been provided within the article or through supplementary data files.

                Article
                mgen000052
                10.1099/mgen.0.000052
                5320584
                © 2016 The Authors

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/).

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                Categories
                Research Paper
                Microbial evolution and epidemiology: Population Genomics

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