Blog
About

  • Record: found
  • Abstract: found
  • Article: found
Is Open Access

Bacterial DNA Sifted from the Trichoplax adhaerens (Animalia: Placozoa) Genome Project Reveals a Putative Rickettsial Endosymbiont

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

      Eukaryotic genome sequencing projects often yield bacterial DNA sequences, data typically considered as microbial contamination. However, these sequences may also indicate either symbiont genes or lateral gene transfer (LGT) to host genomes. These bacterial sequences can provide clues about eukaryote–microbe interactions. Here, we used the genome of the primitive animal Trichoplax adhaerens (Metazoa: Placozoa), which is known to harbor an uncharacterized Gram-negative endosymbiont, to search for the presence of bacterial DNA sequences. Bioinformatic and phylogenomic analyses of extracted data from the genome assembly (181 bacterial coding sequences [CDS]) and trace read archive (16S rDNA) revealed a dominant proteobacterial profile strongly skewed to Rickettsiales (Alphaproteobacteria) genomes. By way of phylogenetic analysis of 16S rDNA and 113 proteins conserved across proteobacterial genomes, as well as identification of 27 rickettsial signature genes, we propose a Rickettsiales endosymbiont of T. adhaerens (RETA). The majority (93%) of the identified bacterial CDS belongs to small scaffolds containing prokaryotic-like genes; however, 12 CDS were identified on large scaffolds comprised of eukaryotic-like genes, suggesting that T. adhaerens might have recently acquired bacterial genes. These putative LGTs may coincide with the placozoan’s aquatic niche and symbiosis with RETA. This work underscores the rich, and relatively untapped, resource of eukaryotic genome projects for harboring data pertinent to host–microbial interactions. The nature of unknown (or poorly characterized) bacterial species may only emerge via analysis of host genome sequencing projects, particularly if these species are resistant to cell culturing, as are many obligate intracellular microbes. Our work provides methodological insight for such an approach.

      Related collections

      Most cited references 118

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

      MUSCLE: multiple sequence alignment with high accuracy and high throughput.

       Robert Edgar (2004)
      We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
        Bookmark
        • Record: found
        • Abstract: found
        • Article: found
        Is Open Access

        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.
          Bookmark
          • Record: found
          • Abstract: found
          • Article: not found

          Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.

          We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at http://www.cbs.dtu.dk/services/TMHMM/. Copyright 2001 Academic Press.
            Bookmark

            Author and article information

            Affiliations
            1Virginia Bioinformatics Institute at Virginia Polytechnic Institute and State University
            2Department of Microbiology and Immunology, University of Maryland School of Medicine
            3Present address: Nestlé Institute of Health Sciences SA, Campus EPFL, Quartier de L'innovation, Lausanne, Switzerland
            Author notes
            *Corresponding author: E-mail: jgille@123456vbi.vt.edu.

            These authors contributed equally to this work.

            Associate editor: Shu-Miaw Chaw

            Journal
            Genome Biol Evol
            Genome Biol Evol
            gbe
            gbe
            Genome Biology and Evolution
            Oxford University Press
            1759-6653
            2013
            7 March 2013
            April 2013
            7 March 2013
            : 5
            : 4
            : 621-645
            23475938
            3641634
            10.1093/gbe/evt036
            evt036
            © The Author(s) 2013. 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/3.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

            Counts
            Pages: 25
            Categories
            Research Article

            Comments

            Comment on this article