12
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Accessory Traits and Phylogenetic Background Predict Escherichia coli Extraintestinal Virulence Better Than Does Ecological Source

      Read this article at

      ScienceOpenPublisherPMC
          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

          The distinguishing characteristics of extraintestinal pathogenic Escherichia coli (ExPEC) strains are incompletely defined. We characterized 292 diverse-source human Escherichia coli isolates (116 from fecal specimens, 79 from urine specimens [of which 39 were from patients with cystitis and 40 were from patients with pyelonephritis], and 97 from blood specimens) for phylogenetic group, sequence type complex (STc), and 49 putative extraintestinal pathogenic E. coli (ExPEC)–associated virulence genes. We then assessed these traits and ecological source as predictors of illness severity in a murine sepsis model. The study isolates exhibited a broad range of virulence in mice. Most of the studied bacterial characteristics corresponded significantly with experimental virulence, as did ecological source and established molecular definitions of ExPEC and uropathogenic E. coli (UPEC). Multivariable modeling identified the following bacterial traits as independent predictors of illness severity both overall and among the fecal and clinical (ie, urine and blood) isolates separately: fyuA (yersiniabactin receptor), kpsM K1 (K1 capsule), and kpsM II (group 2 capsules). Molecular UPEC status predicted virulence independently only among fecal isolates. Neither ecological source (ie, clinical vs fecal) nor molecular ExPEC status added predictive power to these traits, which accounted collectively for up to 49% of the observed variation in virulence. Among human-source E. coli isolates, specific accessory traits and phylogenetic/clonal backgrounds predict experimental virulence in a murine sepsis model better than does ecological source. Among human-source Escherichia coli isolates, specific accessory traits and phylogenetic/clonal backgrounds predicted experimental illness severity in a murine sepsis model better than did ecological source (ie, fecal vs clinical origin).

          Related collections

          Most cited references40

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

          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
            • Record: found
            • Abstract: found
            • Article: not found

            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins

              The emergence of new sequencing technologies has facilitated the use of bacterial whole genome alignments for evolutionary studies and outbreak analyses. These datasets, of increasing size, often include examples of multiple different mechanisms of horizontal sequence transfer resulting in substantial alterations to prokaryotic chromosomes. The impact of these processes demands rapid and flexible approaches able to account for recombination when reconstructing isolates’ recent diversification. Gubbins is an iterative algorithm that uses spatial scanning statistics to identify loci containing elevated densities of base substitutions suggestive of horizontal sequence transfer while concurrently constructing a maximum likelihood phylogeny based on the putative point mutations outside these regions of high sequence diversity. Simulations demonstrate the algorithm generates highly accurate reconstructions under realistically parameterized models of bacterial evolution, and achieves convergence in only a few hours on alignments of hundreds of bacterial genome sequences. Gubbins is appropriate for reconstructing the recent evolutionary history of a variety of haploid genotype alignments, as it makes no assumptions about the underlying mechanism of recombination. The software is freely available for download at github.com/sanger-pathogens/Gubbins, implemented in Python and C and supported on Linux and Mac OS X.

                Author and article information

                Journal
                The Journal of Infectious Diseases
                Oxford University Press (OUP)
                0022-1899
                1537-6613
                August 02 2018
                August 02 2018
                Affiliations
                [1 ]Veterans Affairs Medical Center, Minneapolis, Minnesota
                [2 ]University of Minnesota, Minneapolis, Minnesota
                [3 ]George Washington University, Washington, D. C
                Article
                10.1093/infdis/jiy459
                6284546
                30085181
                4e38700f-064d-44fa-b434-58330f811efa
                © 2018
                History

                Comments

                Comment on this article

                Related Documents Log