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      Evolution of carbapenem resistance in Acinetobacter baumannii during a prolonged infection

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          Acinetobacter baumannii is a common causative agent of hospital-acquired infections and a leading cause of infection in burns patients. Carbapenem-resistant A. baumannii is considered a major public-health threat and has been identified by the World Health Organization as the top priority organism requiring new antimicrobials. The most common mechanism for carbapenem resistance in A. baumannii is via horizontal acquisition of carbapenemase genes. In this study, we sampled 20 A. baumannii isolates from a patient with extensive burns, and characterized the evolution of carbapenem resistance over a 45 day period via Illumina and Oxford Nanopore sequencing. All isolates were multidrug resistant, carrying two genomic islands that harboured several antibiotic-resistance genes. Most isolates were genetically identical and represented a single founder genotype. We identified three novel non-synonymous substitutions associated with meropenem resistance: F136L and G288S in AdeB (part of the AdeABC efflux pump) associated with an increase in meropenem MIC to ≥8 µg ml −1; and A515V in FtsI (PBP3, a penicillin-binding protein) associated with a further increase in MIC to 32 µg ml −1. Structural modelling of AdeB and FtsI showed that these mutations affected their drug-binding sites and revealed mechanisms for meropenem resistance. Notably, one of the adeB mutations arose prior to meropenem therapy but following ciprofloxacin therapy, suggesting exposure to one drug whose resistance is mediated by the efflux pump can induce collateral resistance to other drugs to which the bacterium has not yet been exposed.

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          Most cited references 40

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          Acinetobacter baumannii: epidemiology, antimicrobial resistance, and treatment options.

          Multidrug-resistant Acinetobacter baumannii is recognized to be among the most difficult antimicrobial-resistant gram-negative bacilli to control and treat. Increasing antimicrobial resistance among Acinetobacter isolates has been documented, although definitions of multidrug resistance vary in the literature. A. baumannii survives for prolonged periods under a wide range of environmental conditions. The organism causes outbreaks of infection and health care-associated infections, including bacteremia, pneumonia, meningitis, urinary tract infection, and wound infection. Antimicrobial resistance greatly limits the therapeutic options for patients who are infected with this organism, especially if isolates are resistant to the carbapenem class of antimicrobial agents. Because therapeutic options are limited for multidrug-resistant Acinetobacter infection, the development or discovery of new therapies, well-controlled clinical trials of existing antimicrobial regimens and combinations, and greater emphasis on the prevention of health care-associated transmission of multidrug-resistant Acinetobacter infection are essential.
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            Acinetobacter spp. as nosocomial pathogens: microbiological, clinical, and epidemiological features.

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              Is Open Access

              DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach

              Cancer genome and other sequencing initiatives are generating extensive data on non-synonymous single nucleotide polymorphisms (nsSNPs) in human and other genomes. In order to understand the impacts of nsSNPs on the structure and function of the proteome, as well as to guide protein engineering, accurate in silicomethodologies are required to study and predict their effects on protein stability. Despite the diversity of available computational methods in the literature, none has proven accurate and dependable on its own under all scenarios where mutation analysis is required. Here we present DUET, a web server for an integrated computational approach to study missense mutations in proteins. DUET consolidates two complementary approaches (mCSM and SDM) in a consensus prediction, obtained by combining the results of the separate methods in an optimized predictor using Support Vector Machines (SVM). We demonstrate that the proposed method improves overall accuracy of the predictions in comparison with either method individually and performs as well as or better than similar methods. The DUET web server is freely and openly available at

                Author and article information

                Microb Genom
                Microb Genom
                Microbial Genomics
                Microbiology Society
                March 2018
                16 March 2018
                16 March 2018
                : 4
                : 3
                [ 1]Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne , Parkville, Victoria 3010, Australia
                [ 2]Infection and Immunity Program, Monash Biomedicine Discovery Institute and Department of Microbiology, Monash University , Clayton, Victoria 3800, Australia
                [ 3]Victorian Adult Burns Service, The Alfred Hospital , Melbourne, Victoria 3004, Australia
                [ 4]Department of Surgery, Central Clinical School, Monash University , Melbourne, Victoria 3004, Australia
                [ 5]Department of Infectious Diseases, The Alfred Hospital , Melbourne, Victoria 3004, Australia
                [ 6]Central Clinical School, Monash University , Melbourne, Victoria 3004, Australia
                Author notes
                *Correspondence: Jane Hawkey, jane.hawkey@

                These authors contributed equally to this work.

                © 2018 The Authors

                This is an open access article under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

                Funded by: National Health and Medical Research Council
                Award ID: APP1072476
                Funded by: National Health and Medical Research Council
                Award ID: APP1117940
                Funded by: National Health and Medical Research Council
                Award ID: APP1061409
                Funded by: Jack Brockhoff Foundation
                Award ID: JBF 4186
                Research Article
                Responses to Human Interventions: Antibiotics
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