43
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Phylogenetic relationships among Staphylococcus species and refinement of cluster groups based on multilocus data

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Estimates of relationships among Staphylococcus species have been hampered by poor and inconsistent resolution of phylogenies based largely on single gene analyses incorporating only a limited taxon sample. As such, the evolutionary relationships and hierarchical classification schemes among species have not been confidently established. Here, we address these points through analyses of DNA sequence data from multiple loci (16S rRNA gene, dnaJ, rpoB, and tuf gene fragments) using multiple Bayesian and maximum likelihood phylogenetic approaches that incorporate nearly all recognized Staphylococcus taxa.

          Results

          We estimated the phylogeny of fifty-seven Staphylococcus taxa using partitioned-model Bayesian and maximum likelihood analysis, as well as Bayesian gene-tree species-tree methods. Regardless of methodology, we found broad agreement among methods that the current cluster groups require revision, although there was some disagreement among methods in resolution of higher order relationships. Based on our phylogenetic estimates, we propose a refined classification for Staphylococcus with species being classified into 15 cluster groups (based on molecular data) that adhere to six species groups (based on phenotypic properties).

          Conclusions

          Our findings are in general agreement with gene tree-based reports of the staphylococcal phylogeny, although we identify multiple previously unreported relationships among species. Our results support the general importance of such multilocus assessments as a standard in microbial studies to more robustly infer relationships among recognized and newly discovered lineages.

          Related collections

          Most cited references45

          • Record: found
          • Abstract: found
          • Article: not found

          MRBAYES: Bayesian inference of phylogenetic trees.

          The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo. MRBAYES, including the source code, documentation, sample data files, and an executable, is available at http://brahms.biology.rochester.edu/software.html.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Bayesian phylogenetic analysis of combined data.

            The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5% of the characters in the data set but nevertheless influenced the combined-data tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria (Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as among-site rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more parameter-rich models, but the best model overall is also the most complex and Bayes factors do not support exclusion of apparently weak parameters from this model. Thus, Bayes factors appear to be useful for selecting among complex models, but it is still unclear whether their use strikes a reasonable balance between model complexity and error in parameter estimates.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Bayesian selection of continuous-time Markov chain evolutionary models.

              We develop a reversible jump Markov chain Monte Carlo approach to estimating the posterior distribution of phylogenies based on aligned DNA/RNA sequences under several hierarchical evolutionary models. Using a proper, yet nontruncated and uninformative prior, we demonstrate the advantages of the Bayesian approach to hypothesis testing and estimation in phylogenetics by comparing different models for the infinitesimal rates of change among nucleotides, for the number of rate classes, and for the relationships among branch lengths. We compare the relative probabilities of these models and the appropriateness of a molecular clock using Bayes factors. Our most general model, first proposed by Tamura and Nei, parameterizes the infinitesimal change probabilities among nucleotides (A, G, C, T/U) into six parameters, consisting of three parameters for the nucleotide stationary distribution, two rate parameters for nucleotide transitions, and another parameter for nucleotide transversions. Nested models include the Hasegawa, Kishino, and Yano model with equal transition rates and the Kimura model with a uniform stationary distribution and equal transition rates. To illustrate our methods, we examine simulated data, 16S rRNA sequences from 15 contemporary eubacteria, halobacteria, eocytes, and eukaryotes, 9 primates, and the entire HIV genome of 11 isolates. We find that the Kimura model is too restrictive, that the Hasegawa, Kishino, and Yano model can be rejected for some data sets, that there is evidence for more than one rate class and a molecular clock among similar taxa, and that a molecular clock can be rejected for more distantly related taxa.
                Bookmark

                Author and article information

                Contributors
                Journal
                BMC Evol Biol
                BMC Evol. Biol
                BMC Evolutionary Biology
                BioMed Central
                1471-2148
                2012
                6 September 2012
                : 12
                : 171
                Affiliations
                [1 ]Burnett School of Biomedical Sciences, University of Central Florida College of Medicine, 4000 Central Florida Boulevard, Orlando, FL, 32816, USA
                [2 ]Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, 12801 17th Avenue, Aurora, CO, 80045, USA
                [3 ]Stokes Advanced Research Computing Center, Institute for Simulation and Training, University of Central Florida, 3100 Technology Parkway, Orlando, FL, 32826, USA
                [4 ]Department of Biology, University of Central Florida, 4000 Central Florida Boulevard, Orlando, FL, 32816, USA
                [5 ]Current affiliation: Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada
                Article
                1471-2148-12-171
                10.1186/1471-2148-12-171
                3464590
                22950675
                5635380c-9450-4438-b5da-9a65c4a53556
                Copyright ©2012 Lamers et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 1 September 2011
                : 30 August 2012
                Categories
                Research Article

                Evolutionary Biology
                Evolutionary Biology

                Comments

                Comment on this article