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      Large scale variation in Enterococcus faecalis illustrated by the genome analysis of strain OG1RF

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          Abstract

          A comparison of two strains of the hospital pathogen Enterococcus faecalis suggests that mediators of virulence differ between strains and that virulence does not depend on mobile gene elements

          Abstract

          Background

          Enterococcus faecalis has emerged as a major hospital pathogen. To explore its diversity, we sequenced E. faecalis strain OG1RF, which is commonly used for molecular manipulation and virulence studies.

          Results

          The 2,739,625 base pair chromosome of OG1RF was found to contain approximately 232 kilobases unique to this strain compared to V583, the only publicly available sequenced strain. Almost no mobile genetic elements were found in OG1RF. The 64 areas of divergence were classified into three categories. First, OG1RF carries 39 unique regions, including 2 CRISPR loci and a new WxL locus. Second, we found nine replacements where a sequence specific to V583 was substituted by a sequence specific to OG1RF. For example, the iol operon of OG1RF replaces a possible prophage and the vanB transposon in V583. Finally, we found 16 regions that were present in V583 but missing from OG1RF, including the proposed pathogenicity island, several probable prophages, and the cpsCDEFGHIJK capsular polysaccharide operon. OG1RF was more rapidly but less frequently lethal than V583 in the mouse peritonitis model and considerably outcompeted V583 in a murine model of urinary tract infections.

          Conclusion

          E. faecalis OG1RF carries a number of unique loci compared to V583, but the almost complete lack of mobile genetic elements demonstrates that this is not a defining feature of the species. Additionally, OG1RF's effects in experimental models suggest that mediators of virulence may be diverse between different E. faecalis strains and that virulence is not dependent on the presence of mobile genetic elements.

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          Most cited references65

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          Phage response to CRISPR-encoded resistance in Streptococcus thermophilus.

          Clustered regularly interspaced short palindromic repeats (CRISPR) and their associated genes are linked to a mechanism of acquired resistance against bacteriophages. Bacteria can integrate short stretches of phage-derived sequences (spacers) within CRISPR loci to become phage resistant. In this study, we further characterized the efficiency of CRISPR1 as a phage resistance mechanism in Streptococcus thermophilus. First, we show that CRISPR1 is distinct from previously known phage defense systems and is effective against the two main groups of S. thermophilus phages. Analyses of 30 bacteriophage-insensitive mutants of S. thermophilus indicate that the addition of one new spacer in CRISPR1 is the most frequent outcome of a phage challenge and that the iterative addition of spacers increases the overall phage resistance of the host. The added new spacers have a size of between 29 to 31 nucleotides, with 30 being by far the most frequent. Comparative analysis of 39 newly acquired spacers with the complete genomic sequences of the wild-type phages 2972, 858, and DT1 demonstrated that the newly added spacer must be identical to a region (named proto-spacer) in the phage genome to confer a phage resistance phenotype. Moreover, we found a CRISPR1-specific sequence (NNAGAAW) located downstream of the proto-spacer region that is important for the phage resistance phenotype. Finally, we show through the analyses of 20 mutant phages that virulent phages are rapidly evolving through single nucleotide mutations as well as deletions, in response to CRISPR1.
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            GeneMark.hmm: new solutions for gene finding.

            The number of completely sequenced bacterial genomes has been growing fast. There are computer methods available for finding genes but yet there is a need for more accurate algorithms. The GeneMark. hmm algorithm presented here was designed to improve the gene prediction quality in terms of finding exact gene boundaries. The idea was to embed the GeneMark models into naturally derived hidden Markov model framework with gene boundaries modeled as transitions between hidden states. We also used the specially derived ribosome binding site pattern to refine predictions of translation initiation codons. The algorithm was evaluated on several test sets including 10 complete bacterial genomes. It was shown that the new algorithm is significantly more accurate than GeneMark in exact gene prediction. Interestingly, the high gene finding accuracy was observed even in the case when Markov models of order zero, one and two were used. We present the analysis of false positive and false negative predictions with the caution that these categories are not precisely defined if the public database annotation is used as a control.
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              Solexa Ltd.

              Solexa Ltd is developing an integrated system, based on a breakthrough single molecule sequencing technology, to address a US$2 billion market that is expected to grow exponentially alongside and as a consequence of further technological enhancements. The system, software and consumables will initially be sold to research organizations, pharmaceutical companies and diagnostic companies that will sequence large regions of genomic DNA, including whole genomes, at costs several orders of magnitude below current levels. Solexa expects to launch its first product in 2006, and as it continues to make time and cost efficiencies, additional products will be launched into the expanding markets that will have broad applications in basic research through to healthcare management.
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                Author and article information

                Journal
                Genome Biol
                Genome Biology
                BioMed Central
                1465-6906
                1465-6914
                2008
                8 July 2008
                : 9
                : 7
                : R110
                Affiliations
                [1 ]Division of Infectious Diseases, Department of Medicine, University of Texas Medical School, Houston, Texas 77030, USA
                [2 ]Center for the Study of Emerging and Re-emerging Pathogens, University of Texas Medical School, Houston, Texas 77030, USA
                [3 ]Department of Microbiology and Molecular Genetics, University of Texas Medical School, Houston, Texas 77030, USA
                [4 ]Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA
                [5 ]Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
                [6 ]Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas 77030, USA
                Article
                gb-2008-9-7-r110
                10.1186/gb-2008-9-7-r110
                2530867
                18611278
                37573e52-f352-4676-99af-d7037df05a97
                Copyright © 2008 Bourgogne 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
                : 14 February 2008
                : 8 May 2008
                : 8 July 2008
                Categories
                Research

                Genetics
                Genetics

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