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      Evolutionary Genomics of Staphylococcus aureus Reveals Insights into the Origin and Molecular Basis of Ruminant Host Adaptation

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          Abstract

          Phenotypic biotyping has traditionally been used to differentiate bacteria occupying distinct ecological niches such as host species. For example, the capacity of Staphylococcus aureus from sheep to coagulate ruminant plasma, reported over 60 years ago, led to the description of small ruminant and bovine S. aureus ecovars. The great majority of small ruminant isolates are represented by a single, widespread clonal complex (CC133) of S. aureus, but its evolutionary origin and the molecular basis for its host tropism remain unknown. Here, we provide evidence that the CC133 clone evolved as the result of a human to ruminant host jump followed by adaptive genome diversification. Comparative whole-genome sequencing revealed molecular evidence for host adaptation including gene decay and diversification of proteins involved in host–pathogen interactions. Importantly, several novel mobile genetic elements encoding virulence proteins with attenuated or enhanced activity in ruminants were widely distributed in CC133 isolates, suggesting a key role in its host-specific interactions. To investigate this further, we examined the activity of a novel staphylococcal pathogenicity island (SaPIov2) found in the great majority of CC133 isolates which encodes a variant of the chromosomally encoded von Willebrand-binding protein (vWbp Sov2), previously demonstrated to have coagulase activity for human plasma. Remarkably, we discovered that SaPIov2 confers the ability to coagulate ruminant plasma suggesting an important role in ruminant disease pathogenesis and revealing the origin of a defining phenotype of the classical S. aureus biotyping scheme. Taken together, these data provide broad new insights into the origin and molecular basis of S. aureus ruminant host specificity.

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          Most cited references64

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          Dating of the human-ape splitting by a molecular clock of mitochondrial DNA.

          A new statistical method for estimating divergence dates of species from DNA sequence data by a molecular clock approach is developed. This method takes into account effectively the information contained in a set of DNA sequence data. The molecular clock of mitochondrial DNA (mtDNA) was calibrated by setting the date of divergence between primates and ungulates at the Cretaceous-Tertiary boundary (65 million years ago), when the extinction of dinosaurs occurred. A generalized least-squares method was applied in fitting a model to mtDNA sequence data, and the clock gave dates of 92.3 +/- 11.7, 13.3 +/- 1.5, 10.9 +/- 1.2, 3.7 +/- 0.6, and 2.7 +/- 0.6 million years ago (where the second of each pair of numbers is the standard deviation) for the separation of mouse, gibbon, orangutan, gorilla, and chimpanzee, respectively, from the line leading to humans. Although there is some uncertainty in the clock, this dating may pose a problem for the widely believed hypothesis that the pipedal creature Australopithecus afarensis, which lived some 3.7 million years ago at Laetoli in Tanzania and at Hadar in Ethiopia, was ancestral to man and evolved after the human-ape splitting. Another likelier possibility is that mtDNA was transferred through hybridization between a proto-human and a proto-chimpanzee after the former had developed bipedalism.
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            Evaluation of the maximum likelihood estimate of the evolutionary tree topologies from DNA sequence data, and the branching order in hominoidea.

            A maximum likelihood method for inferring evolutionary trees from DNA sequence data was developed by Felsenstein (1981). In evaluating the extent to which the maximum likelihood tree is a significantly better representation of the true tree, it is important to estimate the variance of the difference between log likelihood of different tree topologies. Bootstrap resampling can be used for this purpose (Hasegawa et al. 1988; Hasegawa and Kishino 1989), but it imposes a great computation burden. To overcome this difficulty, we developed a new method for estimating the variance by expressing it explicitly. The method was applied to DNA sequence data from primates in order to evaluate the maximum likelihood branching order among Hominoidea. It was shown that, although the orangutan is convincingly placed as an outgroup of a human and African apes clade, the branching order among human, chimpanzee, and gorilla cannot be determined confidently from the DNA sequence data presently available when the evolutionary rate constancy is not assumed.
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              Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models.

              Q. Z. Yang (2000)
              Approximate methods for estimating the numbers of synonymous and nonsynonymous substitutions between two DNA sequences involve three steps: counting of synonymous and nonsynonymous sites in the two sequences, counting of synonymous and nonsynonymous differences between the two sequences, and correcting for multiple substitutions at the same site. We examine complexities involved in those steps and propose a new approximate method that takes into account two major features of DNA sequence evolution: transition/transversion rate bias and base/codon frequency bias. We compare the new method with maximum likelihood, as well as several other approximate methods, by examining infinitely long sequences, performing computer simulations, and analyzing a real data set. The results suggest that when there are transition/transversion rate biases and base/codon frequency biases, previously described approximate methods for estimating the nonsynonymous/synonymous rate ratio may involve serious biases, and the bias can be both positive and negative. The new method is, in general, superior to earlier approximate methods and may be useful for analyzing large data sets, although maximum likelihood appears to always be the method of choice.
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                Author and article information

                Journal
                Genome Biol Evol
                gbe
                gbe
                Genome Biology and Evolution
                Oxford University Press
                1759-6653
                2010
                9 June 2010
                2010
                9 June 2010
                : 2
                : 454-466
                Affiliations
                [1 ]The Roslin Institute and Centre for Infectious Diseases, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, Scotland, United Kingdom
                [2 ]Centro de Investigación y Tecnología Animal, Instituto Valenciano de Investigaciones Agrarias, Castellón, Spain
                [3 ]Institute of Evolutionary Biology, Ashworth Laboratories, University of Edinburgh, Edinburgh, Scotland, United Kingdom
                [4 ]University of Mississippi Medical Center, Jackson, Mississippi
                [5 ]Department of Microbiology, School of Genetics and Microbiology, Moyne Institute of Preventive Medicine, University of Dublin, Trinity College, Dublin, Ireland
                [6 ]Department of Cellular & Molecular Medicine, St George’s Hospital Medical School, University of London, London, United Kingdom
                Author notes
                [* ]Corresponding author: E-mail: ross.fitzgerald@ 123456ed.ac.uk .

                Associate editor: Hidemi Watanabe

                ED133 genome sequence: accession no CP001996.

                Article
                10.1093/gbe/evq031
                2997551
                20624747
                cd61c1f7-cd61-43cd-97ff-31723e32ea36
                © The Author(s) 2010. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 4 June 2010
                Categories
                Research Articles

                Genetics
                bacteria,niche adaptation,mobile genetic elements,population genetics,genome diversification

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