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      Molecular concordance of methicillin-resistant Staphylococcus aureus isolates from healthcare workers and patients

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          Abstract

          Background:

          Methicillin-resistant Staphylococcus aureus (MRSA) is a significant nosocomial pathogen in the ICU. MRSA contamination of healthcare personnel (HCP) gloves and gowns after providing care to patients with MRSA occurs at a rate of 14%–16% in the ICU setting. Little is known about whether the MRSA isolates identified on HCP gown and gloves following patient care activities are the same as MRSA isolates identified as colonizing or infecting the patient.

          Methods:

          From a multisite cohort of 388 independent patient MRSA isolates and their corresponding HCP gown and glove isolates, we selected 91 isolates pairs using a probability to proportion size (PPS) sampling method. To determine whether the patient and HCP gown or gloves isolates were genetically similar, we used 5 comparative genomic typing methods: phylogenetic analysis, spa typing, multilocus sequence typing (MLST), large-scale BLAST score ratio (LSBSR), and single-nucleotide variant (SNV) analysis.

          Results:

          We identified that 56 (61.5%) of isolate pairs were genetically similar at least by 4 of the methods. Comparably, the spa typing and the LSBSR analyses revealed that >75% of the examined isolate pairs were concordant, with the thresholds established for each analysis.

          Conclusions:

          Many of the patient MRSA isolates were genetically similar to those on the HCP gown or gloves following a patient care activity. This finding indicates that the patient is often the primary source of the MRSA isolates transmitted to the HCP, which can potentially be spread to other patients or hospital settings through HCP vectors. These results have important implications because they provide additional evidence for hospitals considering ending the use of contact precautions (gloves and gowns) for MRSA patients.

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          Most cited references39

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          RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

          Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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            FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix

            Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement Neighbor-Joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N 2) space and O(N 2 L) time, but FastTree requires just O(NLa + N ) memory and O(N log (N)La) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 h and 2.4 GB of memory. Just computing pairwise Jukes–Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 h and 50 GB of memory. In simulations, FastTree was slightly more accurate than Neighbor-Joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.
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              Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications

              The PubMLST.org website hosts a collection of open-access, curated databases that integrate population sequence data with provenance and phenotype information for over 100 different microbial species and genera.  Although the PubMLST website was conceived as part of the development of the first multi-locus sequence typing (MLST) scheme in 1998 the software it uses, the Bacterial Isolate Genome Sequence database (BIGSdb, published in 2010), enables PubMLST to include all levels of sequence data, from single gene sequences up to and including complete, finished genomes.  Here we describe developments in the BIGSdb software made from publication to June 2018 and show how the platform realises microbial population genomics for a wide range of applications.  The system is based on the gene-by-gene analysis of microbial genomes, with each deposited sequence annotated and curated to identify the genes present and systematically catalogue their variation.  Originally intended as a means of characterising isolates with typing schemes, the synthesis of sequences and records of genetic variation with provenance and phenotype data permits highly scalable (whole genome sequence data for tens of thousands of isolates) means of addressing a wide range of functional questions, including: the prediction of antimicrobial resistance; likely cross-reactivity with vaccine antigens; and the functional activities of different variants that lead to key phenotypes.  There are no limitations to the number of sequences, genetic loci, allelic variants or schemes (combinations of loci) that can be included, enabling each database to represent an expanding catalogue of the genetic variation of the population in question.  In addition to providing web-accessible analyses and links to third-party analysis and visualisation tools, the BIGSdb software includes a RESTful application programming interface (API) that enables access to all the underlying data for third-party applications and data analysis pipelines.
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                Author and article information

                Journal
                8804099
                4791
                Infect Control Hosp Epidemiol
                Infect Control Hosp Epidemiol
                Infection control and hospital epidemiology
                0899-823X
                1559-6834
                6 February 2023
                April 2023
                30 September 2022
                14 April 2023
                : 44
                : 4
                : 578-588
                Affiliations
                [1 ]Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland
                [2 ]Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, Maryland
                [3 ]Department of Pathology, University of Maryland School of Medicine, Baltimore, Maryland
                [4 ]Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland
                Author notes
                Authors for correspondence: David A. Rasko, drasko@ 123456som.umaryland.edu . Or Anthony D. Harris, aharris@ 123456som.umaryland.edu
                Author information
                http://orcid.org/0000-0002-7872-4338
                http://orcid.org/0000-0003-3974-3752
                http://orcid.org/0000-0002-4565-6439
                http://orcid.org/0000-0001-7409-3391
                http://orcid.org/0000-0002-7337-7154
                Article
                NIHMS1868895
                10.1017/ice.2022.159
                10060437
                36177884
                bb3961d9-3f2e-4db1-9993-55653705a3fa

                This is an Open Access article, distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike licence ( http://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is used to distribute the re-used or adapted article and the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use.

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