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      Diverse Fluoroquinolone Resistance Plasmids From Retail Meat E. coli in the United States

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          Abstract

          Fluoroquinolones are used to treat serious bacterial infections, including those caused by Escherichia coli and Salmonella enterica. The emergence of plasmid-mediated quinolone resistance (PMQR) represent a new challenge to the successful treatment of Gram-negative infections. As part of a long-term strategy to generate a reference database of closed plasmids from antimicrobial resistant foodborne bacteria, we performed long-read sequencing of 11 E. coli isolates from retail meats that were non-susceptible to ciprofloxacin. Each of the isolates had PMQR genes, including qnrA1, qnrS1, and qnrB19. The four qnrB19 genes were carried on two distinct ColE-type plasmids among isolates from pork chop and ground turkey and were identical to plasmids previously identified in Salmonella. Seven other plasmids differed from any other sequences in GenBank and comprised IncF and IncR plasmids that ranged in size from 48 to 180 kb. These plasmids also contained different combinations of resistance genes, including those conferring resistance to beta-lactams, macrolides, sulfonamides, tetracycline, and heavy metals. Although relatively few isolates have PMQR genes, the identification of diverse plasmids in multiple retail meat sources suggests the potential for further spread of fluoroquinolone resistance, including through co-selection. These results highlight the value of long-read sequencing in characterizing antimicrobial resistance genes of public health concern.

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          Most cited references31

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          Multilocus sequence typing of total-genome-sequenced bacteria.

          Accurate strain identification is essential for anyone working with bacteria. For many species, multilocus sequence typing (MLST) is considered the "gold standard" of typing, but it is traditionally performed in an expensive and time-consuming manner. As the costs of whole-genome sequencing (WGS) continue to decline, it becomes increasingly available to scientists and routine diagnostic laboratories. Currently, the cost is below that of traditional MLST. The new challenges will be how to extract the relevant information from the large amount of data so as to allow for comparison over time and between laboratories. Ideally, this information should also allow for comparison to historical data. We developed a Web-based method for MLST of 66 bacterial species based on WGS data. As input, the method uses short sequence reads from four sequencing platforms or preassembled genomes. Updates from the MLST databases are downloaded monthly, and the best-matching MLST alleles of the specified MLST scheme are found using a BLAST-based ranking method. The sequence type is then determined by the combination of alleles identified. The method was tested on preassembled genomes from 336 isolates covering 56 MLST schemes, on short sequence reads from 387 isolates covering 10 schemes, and on a small test set of short sequence reads from 29 isolates for which the sequence type had been determined by traditional methods. The method presented here enables investigators to determine the sequence types of their isolates on the basis of WGS data. This method is publicly available at www.cbs.dtu.dk/services/MLST.
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            Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates

            Antimicrobial resistance (AMR) is a major public health problem that requires publicly available tools for rapid analysis. To identify AMR genes in whole-genome sequences, the National Center for Biotechnology Information (NCBI) has produced AMRFinder, a tool that identifies AMR genes using a high-quality curated AMR gene reference database. The Bacterial Antimicrobial Resistance Reference Gene Database consists of up-to-date gene nomenclature, a set of hidden Markov models (HMMs), and a curated protein family hierarchy. Currently, it contains 4,579 antimicrobial resistance proteins and more than 560 HMMs. Here, we describe AMRFinder and its associated database. To assess the predictive ability of AMRFinder, we measured the consistency between predicted AMR genotypes from AMRFinder and resistance phenotypes of 6,242 isolates from the National Antimicrobial Resistance Monitoring System (NARMS). This included 5,425 Salmonella enterica , 770 Campylobacter spp., and 47 Escherichia coli isolates phenotypically tested against various antimicrobial agents. Of 87,679 susceptibility tests performed, 98.4% were consistent with predictions. To assess the accuracy of AMRFinder, we compared its gene symbol output with that of a 2017 version of ResFinder, another publicly available resistance gene detection system. Most gene calls were identical, but there were 1,229 gene symbol differences (8.8%) between them, with differences due to both algorithmic differences and database composition. AMRFinder missed 16 loci that ResFinder found, while ResFinder missed 216 loci that AMRFinder identified. Based on these results, AMRFinder appears to be a highly accurate AMR gene detection system.
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              WGS accurately predicts antimicrobial resistance in Escherichia coli.

              The objective of this study was to determine the effectiveness of WGS in identifying resistance genotypes of MDR Escherichia coli and whether these correlate with observed phenotypes.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                05 December 2019
                2019
                : 10
                : 2826
                Affiliations
                Office of Research, Center for Veterinary Medicine, U.S. Food and Drug Administration , Laurel, MD, United States
                Author notes

                Edited by: Ludmila Chistoserdova, University of Washington, United States

                Reviewed by: Liang Li, The Lundquist Institute, United States; Xiang-Dang Du, Henan Agricultural University, China

                *Correspondence: Gregory H. Tyson, Gregory.Tyson@ 123456fda.hhs.gov

                This article was submitted to Evolutionary and Genomic Microbiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2019.02826
                6906146
                31866986
                84cc0434-69bd-4b61-8d2c-3c4df937c874
                Copyright © 2019 Tyson, Li, Hsu, Bodeis-Jones and McDermott.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 04 September 2019
                : 21 November 2019
                Page count
                Figures: 3, Tables: 2, Equations: 0, References: 38, Pages: 7, Words: 0
                Funding
                Funded by: U.S. Food and Drug Administration 10.13039/100000038
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                pacbio,antimicrobial resistance,escherichia coli,plasmids,fluoroquinolones
                Microbiology & Virology
                pacbio, antimicrobial resistance, escherichia coli, plasmids, fluoroquinolones

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