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      Predicting Antibiotic Resistance in Gram-Negative Bacilli from Resistance Genes

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          Abstract

          We developed a rapid high-throughput PCR test and evaluated highly antibiotic-resistant clinical isolates of Escherichia coli ( n = 2,919), Klebsiella pneumoniae ( n = 1,974), Proteus mirabilis ( n = 1,150), and Pseudomonas aeruginosa ( n = 1,484) for several antibiotic resistance genes for comparison with phenotypic resistance across penicillins, cephalosporins, carbapenems, aminoglycosides, trimethoprim-sulfamethoxazole, fluoroquinolones, and macrolides. The isolates originated from hospitals in North America (34%), Europe (23%), Asia (13%), South America (12%), Africa (7%), or Oceania (1%) or were of unknown origin (9%).

          ABSTRACT

          We developed a rapid high-throughput PCR test and evaluated highly antibiotic-resistant clinical isolates of Escherichia coli ( n = 2,919), Klebsiella pneumoniae ( n = 1,974), Proteus mirabilis ( n = 1,150), and Pseudomonas aeruginosa ( n = 1,484) for several antibiotic resistance genes for comparison with phenotypic resistance across penicillins, cephalosporins, carbapenems, aminoglycosides, trimethoprim-sulfamethoxazole, fluoroquinolones, and macrolides. The isolates originated from hospitals in North America (34%), Europe (23%), Asia (13%), South America (12%), Africa (7%), or Oceania (1%) or were of unknown origin (9%). We developed statistical methods to predict phenotypic resistance from resistance genes for 49 antibiotic-organism combinations, including gentamicin, tobramycin, ciprofloxacin, levofloxacin, trimethoprim-sulfamethoxazole, ertapenem, imipenem, cefazolin, cefepime, cefotaxime, ceftazidime, ceftriaxone, ampicillin, and aztreonam. Average positive predictive values for genotypic prediction of phenotypic resistance were 91% for E. coli, 93% for K. pneumoniae, 87% for P. mirabilis, and 92% for P. aeruginosa across the various antibiotics for this highly resistant cohort of bacterial isolates.

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          Most cited references 10

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          The comprehensive antibiotic resistance database.

          The field of antibiotic drug discovery and the monitoring of new antibiotic resistance elements have yet to fully exploit the power of the genome revolution. Despite the fact that the first genomes sequenced of free living organisms were those of bacteria, there have been few specialized bioinformatic tools developed to mine the growing amount of genomic data associated with pathogens. In particular, there are few tools to study the genetics and genomics of antibiotic resistance and how it impacts bacterial populations, ecology, and the clinic. We have initiated development of such tools in the form of the Comprehensive Antibiotic Research Database (CARD; http://arpcard.mcmaster.ca). The CARD integrates disparate molecular and sequence data, provides a unique organizing principle in the form of the Antibiotic Resistance Ontology (ARO), and can quickly identify putative antibiotic resistance genes in new unannotated genome sequences. This unique platform provides an informatic tool that bridges antibiotic resistance concerns in health care, agriculture, and the environment.
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            ARDB—Antibiotic Resistance Genes Database

             Bo Liu,  Mihai Pop (2009)
            The treatment of infections is increasingly compromised by the ability of bacteria to develop resistance to antibiotics through mutations or through the acquisition of resistance genes. Antibiotic resistance genes also have the potential to be used for bio-terror purposes through genetically modified organisms. In order to facilitate the identification and characterization of these genes, we have created a manually curated database—the Antibiotic Resistance Genes Database (ARDB)—unifying most of the publicly available information on antibiotic resistance. Each gene and resistance type is annotated with rich information, including resistance profile, mechanism of action, ontology, COG and CDD annotations, as well as external links to sequence and protein databases. Our database also supports sequence similarity searches and implements an initial version of a tool for characterizing common mutations that confer antibiotic resistance. The information we provide can be used as compendium of antibiotic resistance factors as well as to identify the resistance genes of newly sequenced genes, genomes, or metagenomes. Currently, ARDB contains resistance information for 13 293 genes, 377 types, 257 antibiotics, 632 genomes, 933 species and 124 genera. ARDB is available at http://ardb.cbcb.umd.edu/.
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              Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis

              The rise of antibiotic-resistant bacteria has led to an urgent need for rapid detection of drug resistance in clinical samples, and improvements in global surveillance. Here we show how de Bruijn graph representation of bacterial diversity can be used to identify species and resistance profiles of clinical isolates. We implement this method for Staphylococcus aureus and Mycobacterium tuberculosis in a software package (‘Mykrobe predictor') that takes raw sequence data as input, and generates a clinician-friendly report within 3 minutes on a laptop. For S. aureus, the error rates of our method are comparable to gold-standard phenotypic methods, with sensitivity/specificity of 99.1%/99.6% across 12 antibiotics (using an independent validation set, n=470). For M. tuberculosis, our method predicts resistance with sensitivity/specificity of 82.6%/98.5% (independent validation set, n=1,609); sensitivity is lower here, probably because of limited understanding of the underlying genetic mechanisms. We give evidence that minor alleles improve detection of extremely drug-resistant strains, and demonstrate feasibility of the use of emerging single-molecule nanopore sequencing techniques for these purposes.
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                Author and article information

                Journal
                Antimicrob Agents Chemother
                Antimicrob. Agents Chemother
                aac
                aac
                AAC
                Antimicrobial Agents and Chemotherapy
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                0066-4804
                1098-6596
                28 January 2019
                27 March 2019
                April 2019
                27 March 2019
                : 63
                : 4
                Affiliations
                [a ]OpGen, Inc., Gaithersburg, Maryland, USA
                [b ]Intermountain Medical Center, Murray, Utah, USA
                [c ]University of Utah, Salt Lake City, Utah, USA
                [d ]Merck & Co., Inc., Whitehouse Station, New Jersey, USA
                [e ]IHMA, Inc., Schaumburg, Illinois, USA
                Author notes
                Address correspondence to G. Terrance Walker, twalker@ 123456opgen.com .
                [*]

                Present address: Natalie Whitfield, GenMark Diagnostics, Inc., Carlsbad, California, USA.

                Citation Walker GT, Quan J, Higgins SG, Toraskar N, Chang W, Saeed A, Sapiro V, Pitzer K, Whitfield N, Lopansri BK, Motyl M, Sahm D. 2019. Predicting antibiotic resistance in Gram-negative bacilli from resistance genes. Antimicrob Agents Chemother 63:e02462-18. https://doi.org/10.1128/AAC.02462-18.

                Article
                02462-18
                10.1128/AAC.02462-18
                6496154
                30917985
                Copyright © 2019 Walker et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                Page count
                Figures: 0, Tables: 5, Equations: 0, References: 13, Pages: 9, Words: 5041
                Product
                Funding
                Funded by: OpGen, Inc.;
                Award Recipient :
                Categories
                Mechanisms of Resistance
                Custom metadata
                April 2019

                Infectious disease & Microbiology

                pcr, antibiotic resistance, resistance genes

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