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      Vibrio cholerae genomic diversity within and between patients

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          Abstract

          Cholera is a severe, water-borne diarrhoeal disease caused by toxin-producing strains of the bacterium Vibrio cholerae. Comparative genomics has revealed ‘waves’ of cholera transmission and evolution, in which clones are successively replaced over decades and centuries. However, the extent of V. cholerae genetic diversity within an epidemic or even within an individual patient is poorly understood. Here, we characterized V. cholerae genomic diversity at a micro-epidemiological level within and between individual patients from Bangladesh and Haiti. To capture within-patient diversity, we isolated multiple (8 to 20) V. cholerae colonies from each of eight patients, sequenced their genomes and identified point mutations and gene gain/loss events. We found limited but detectable diversity at the level of point mutations within hosts (zero to three single nucleotide variants within each patient), and comparatively higher gene content variation within hosts (at least one gain/loss event per patient, and up to 103 events in one patient). Much of the gene content variation appeared to be due to gain and loss of phage and plasmids within the V. cholerae population, with occasional exchanges between V. cholerae and other members of the gut microbiota. We also show that certain intra-host variants have phenotypic consequences. For example, the acquisition of a Bacteroides plasmid and non-synonymous mutations in a sensor histidine kinase gene both reduced biofilm formation, an important trait for environmental survival. Together, our results show that V. cholerae is measurably evolving within patients, with possible implications for disease outcomes and transmission dynamics.

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          The RAST Server: Rapid Annotations using Subsystems Technology

          Background The number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them. Description We describe a fully automated service for annotating bacterial and archaeal genomes. The service identifies protein-encoding, rRNA and tRNA genes, assigns functions to the genes, predicts which subsystems are represented in the genome, uses this information to reconstruct the metabolic network and makes the output easily downloadable for the user. In addition, the annotated genome can be browsed in an environment that supports comparative analysis with the annotated genomes maintained in the SEED environment. The service normally makes the annotated genome available within 12–24 hours of submission, but ultimately the quality of such a service will be judged in terms of accuracy, consistency, and completeness of the produced annotations. We summarize our attempts to address these issues and discuss plans for incrementally enhancing the service. Conclusion By providing accurate, rapid annotation freely to the community we have created an important community resource. The service has now been utilized by over 120 external users annotating over 350 distinct genomes.
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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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              Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

              F Tajima (1989)
              The relationship between the two estimates of genetic variation at the DNA level, namely the number of segregating sites and the average number of nucleotide differences estimated from pairwise comparison, is investigated. It is found that the correlation between these two estimates is large when the sample size is small, and decreases slowly as the sample size increases. Using the relationship obtained, a statistical method for testing the neutral mutation hypothesis is developed. This method needs only the data of DNA polymorphism, namely the genetic variation within population at the DNA level. A simple method of computer simulation, that was used in order to obtain the distribution of a new statistic developed, is also presented. Applying this statistical method to the five regions of DNA sequences in Drosophila melanogaster, it is found that large insertion/deletion (greater than 100 bp) is deleterious. It is suggested that the natural selection against large insertion/deletion is so weak that a large amount of variation is maintained in a population.
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                Author and article information

                Journal
                Microb Genom
                Microb Genom
                MGen
                Microbial Genomics
                Microbiology Society
                2057-5858
                December 2017
                7 December 2017
                : 3
                : 12
                : e000142
                Affiliations
                [ 1]Department of Biological Sciences, University of Montreal , Montreal, Quebec, Canada
                [ 2]Division of Infectious Diseases, Massachusetts General Hospital , Boston, MA, USA
                [ 3]Department of Medicine, Harvard Medical School , Boston, MA, USA
                [ 4]Center for Vaccine Sciences, International Centre for Diarrhoeal Disease Research , Dhaka, Bangladesh
                [ 5]National Public Health Laboratory, Ministry of Public Health and Population , Port-au-Prince, Haiti
                [ 6]Division of Global Health Equity, Brigham and Women’s Hospital , Boston, MA, USA
                [ 7]Department of Global Health and Social Medicine, Harvard Medical School , Boston, MA, USA
                [ 8]Department of Immunology and Infectious Diseases, Harvard School of Public Health , Boston, MA, USA
                [ 9]Department of Microbiology and Immunobiology, Harvard Medical School , Boston, MA, USA
                [ 10]Department of Pediatrics, Harvard Medical School , Boston, MA, USA
                Author notes
                *Correspondence: B. Jesse Shapiro, jesse.shapiro@ 123456umontreal.ca

                All supporting data, code and protocols have been provided within the article or through supplementary data files. Supplementary material is available with the online version of this article.

                Article
                mgen000142
                10.1099/mgen.0.000142
                5761273
                29306353
                85781f69-b558-41d5-9819-313acd84b06d
                © 2017 The Authors

                This is an open access article under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

                History
                : 01 September 2017
                : 12 November 2017
                Funding
                Funded by: Canadian Institutes of Health Research
                Funded by: Canada Research Chairs
                Funded by: National Institutes of Health
                Award ID: AI099243
                Funded by: National Institutes of Health
                Award ID: AI099243
                Funded by: National Institutes of Health
                Award ID: AI103055
                Funded by: National Institutes of Health
                Award ID: AI103055
                Funded by: National Institutes of Health
                Award ID: AI106878
                Funded by: National Institutes of Health
                Award ID: AI106878
                Funded by: National Institutes of Health
                Award ID: AI106878
                Funded by: National Institutes of Health
                Award ID: T32A1070611976
                Funded by: National Institutes of Health
                Award ID: K08AI123494
                Funded by: Robert Wood Johnson Foundation
                Categories
                Research Article
                Microbial Evolution and Epidemiology: Population Genomics
                Custom metadata
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                vibrio cholerae,comparative genomics,within-host evolution,horizontal gene transfer,biofilm

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