6 research outputs found

    MLST majority consensus phylogeny.

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    <p>A majority consensus phylogeny of 77 <i>V. parahaemolyticus</i> isolates based on 7 concatenated housekeeping loci (<i>dna</i>E, <i>gyr</i>B, <i>rec</i>A, <i>dtd</i>S, <i>pnt</i>A, <i>pry</i>C and <i>tna</i>A) and representing 3,682 total nucleotides was constructed using the Bayesian Markov chain Monte Carlo (MCMC) method as implemented in MrBayes v3.2. The 77 isolates included in this phylogeny were separated into three major clusters (I, II, III) and 12 distinct clades (1–12). Sequence typing (ST) designations for MLST analysis describe the 24 MLST sequence types comprising each of the 12 clades. Distinct clades clearly highlighted by alternating blue and gray shading. Nodes are labeled with posterior probabilities (0–1) while cladogram shading is indicative of branches with weak support (red) and strong support (black).</p

    Results of the Pairwise Homoplasy Index (φ<sub>w</sub>)<sup>a</sup> and Sawyer’s Run Test<sup>b</sup> for homologous recombination performed on individual loci included in the MLST scheme (<i>dna</i>E, <i>gyr</i>B, <i>rec</i>A, <i>dtd</i>S, <i>pnt</i>A, <i>pry</i>C and <i>tna</i>A).

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    a<p>mean Pairwise Homoplasy Index, see reference <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055726#pone.0055726-Bruen1" target="_blank">[48]</a>.</p>b<p>sum of squared lengths of condensed fragments (SSCF) and sum of squared lengths of uncondensed fragments (SSUF), see reference <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055726#pone.0055726-Sawyer1" target="_blank">[49]</a>.</p>*<p>significance declared at P<0.05.</p

    REP-PCR patterns and representative dendrogram.

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    <p>The electrophoresis banding patterns of 167 <i>V. parahaemolyticus</i> isolates assayed by REP-PCR is shown. BioNumerics analysis of patterns revealed 39 unique REP-PCR groups comprised of N isolates. The corresponding BioNumerics dendrogram illustrates the genetic relatedness between REP-PCR groups, which we grouped into three major clusters (I, II, III). Groups 27, 28 and 3 comprise cluster I while groups 11, 29 and 34 comprise cluster III and all remaining groups comprise cluster II. Electrophoresis banding patterns shown with scale indicating fragment size in base pairs (bp).</p

    NeighborNet analysis.

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    <p>SplitsTree v4 NeigborNet analysis of 77 <i>V. parahaemolyticus</i> isolates based on 7 concatenated housekeeping loci (<i>dna</i>E, <i>gyr</i>B, <i>rec</i>A, <i>dtd</i>S, <i>pnt</i>A, <i>pry</i>C and <i>tna</i>A) representing a total 3,682 nucleotides. Sequence typing (ST) designations for MLST analysis and phylogenetic clades (1–12) included for reference. Regions of the network showing extensive reticulation (e.g., clades 8 and 10), consistent with higher rates of recombination, contrast with the less reticulated nature of clade 12. Highlights in blue distinguish groups of isolates sharing ST and clade designations and function to facilitate comparison with <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055726#pone-0055726-g002" target="_blank">Figure 2</a>.</p

    In Situ Strain-Level Detection and Identification of <i>Vibrio parahaemolyticus</i> Using Surface-Enhanced Raman Spectroscopy

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    The outer membrane of a bacterium is composed of chemical and biological components that carry specific molecular information related to strains, growth stages, expressions to stimulation, and maybe even geographic differences. In this work, we demonstrate that the biochemical information embedded in the outer membrane can be used for rapid detection and identification of pathogenic bacteria using surface-enhanced Raman spectroscopy (SERS). We used seven different strains of the marine pathogen <i>Vibrio parahaemolyticus</i> as a model system. The strains represent four genetically distinct clades isolated from clinical and environmental sources in Washington, U.S.A. The unique quasi-3D (Q3D) plasmonic nanostructure arrays, optimized using finite-difference time-domain (FDTD) calculations, were used as SERS-active substrates for sensitive and reproducible detection of these bacteria. SERS barcodes were generated on the basis of SERS spectra and were used to successfully detect individual strains in both blind samples and mixtures. The sensing and detection methods developed in this work could have broad applications in the areas of environmental monitoring, biomedical diagnostics, and homeland security
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