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Genome characterization and population genetic structure of the zoonotic pathogen, Streptococcus canis

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      Abstract

      Background

      Streptococcus canis is an important opportunistic pathogen of dogs and cats that can also infect a wide range of additional mammals including cows where it can cause mastitis. It is also an emerging human pathogen.

      Results

      Here we provide characterization of the first genome sequence for this species, strain FSL S3-227 (milk isolate from a cow with an intra-mammary infection). A diverse array of putative virulence factors was encoded by the S. canis FSL S3-227 genome. Approximately 75% of these gene sequences were homologous to known Streptococcal virulence factors involved in invasion, evasion, and colonization. Present in the genome are multiple potentially mobile genetic elements (MGEs) [plasmid, phage, integrative conjugative element (ICE)] and comparison to other species provided convincing evidence for lateral gene transfer (LGT) between S. canis and two additional bovine mastitis causing pathogens ( Streptococcus agalactiae, and Streptococcus dysgalactiae subsp. dysgalactiae), with this transfer possibly contributing to host adaptation. Population structure among isolates obtained from Europe and USA [bovine = 56, canine = 26, and feline = 1] was explored. Ribotyping of all isolates and multi locus sequence typing (MLST) of a subset of the isolates ( n = 45) detected significant differentiation between bovine and canine isolates (Fisher exact test: P = 0.0000 [ribotypes], P = 0.0030 [sequence types]), suggesting possible host adaptation of some genotypes. Concurrently, the ancestral clonal complex (54% of isolates) occurred in many tissue types, all hosts, and all geographic locations suggesting the possibility of a wide and diverse niche.

      Conclusion

      This study provides evidence highlighting the importance of LGT in the evolution of the bacteria S. canis, specifically, its possible role in host adaptation and acquisition of virulence factors. Furthermore, recent LGT detected between S. canis and human bacteria ( Streptococcus urinalis) is cause for concern, as it highlights the possibility for continued acquisition of human virulence factors for this emerging zoonotic pathogen.

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      Most cited references 94

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      Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study.

      The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.
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        Inference of population structure using multilocus genotype data.

        We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci-e.g. , seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/ approximately pritch/home. html.
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          Velvet: algorithms for de novo short read assembly using de Bruijn graphs.

          We have developed a new set of algorithms, collectively called "Velvet," to manipulate de Bruijn graphs for genomic sequence assembly. A de Bruijn graph is a compact representation based on short words (k-mers) that is ideal for high coverage, very short read (25-50 bp) data sets. Applying Velvet to very short reads and paired-ends information only, one can produce contigs of significant length, up to 50-kb N50 length in simulations of prokaryotic data and 3-kb N50 on simulated mammalian BACs. When applied to real Solexa data sets without read pairs, Velvet generated contigs of approximately 8 kb in a prokaryote and 2 kb in a mammalian BAC, in close agreement with our simulated results without read-pair information. Velvet represents a new approach to assembly that can leverage very short reads in combination with read pairs to produce useful assemblies.
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            Author and article information

            Affiliations
            [1 ]Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, 14853, USA
            [2 ]Quality Milk Production Services, College of Veterinary Medicine, Cornell University, Ithaca, NY, 14853, USA
            [3 ]Università degli Studi di Milano, Department of Health, Animal Science and Food Safety, Via Celoria 10, 20133, Milan, Italy
            [4 ]Current address: Moredun Research Institute, Pentlands Science Park, Bush Loan, Penicuik and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Scotland, UK
            [5 ]Current address: Université de Lyon, Université Lyon 1, Centre National de la Recherche Scientifique, Ecologie des Hydrosystèmes Naturels et Anthropisés, Villeurbanne, France
            [6 ]Current address: Department of Plant Pathology & Plant-Microbe Biology, Cornell University, Ithaca, NY, 14853, USA
            [7 ]Current address: 4055 McIntyre Road, Trumansburg, NY, 14886, USA
            Contributors
            Journal
            BMC Microbiol
            BMC Microbiol
            BMC Microbiology
            BioMed Central
            1471-2180
            2012
            18 December 2012
            : 12
            : 293
            23244770
            3541175
            1471-2180-12-293
            10.1186/1471-2180-12-293
            Copyright ©2012 Richards 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.

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            Research Article

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