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      Enhanced antibacterial effect of the novel T4-like bacteriophage KARL-1 in combination with antibiotics against multi-drug resistant Acinetobacter baumannii

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          Abstract

          The continuing rise of infections caused by multi-drug resistant bacteria has led to a renewed interest in bacteriophage therapy. Here we characterize phage vB_AbaM-KARL-1 with lytic activity against multi-drug resistant clinical isolates of Acinetobacter baumannii (AB). Besides genomic and phenotypic phage analysis, the objective of our study was to investigate the antibacterial outcome when the phage acts in concert with distinct antibiotics. KARL-1 belongs to the family of Myoviridae and is able to lyse 8 of 20 (40%) tested clinical isolates. Its double-stranded DNA genome consists of 166,560 bp encoding for 253 open reading frames. Genome wide comparison suggests that KARL-1 is a novel species within the subfamily Tevenvirinae, sharing 77% nucleotide identity (coverage 58%) with phage ZZ1. The antibacterial efficacy at various multiplicities of infection (MOI) was monitored either alone or in combination with meropenem, ciprofloxacin, and colistin. A complete clearance of liquid cultures was achieved with KARL-1 at an MOI of 10 −1 and meropenem (>128 mg/l). KARL-1 was still effective at an MOI of 10 −7, but antibacterial activity was significantly augmented with meropenem. While ciprofloxacin did generally not support phage activity, the application of KARL-1 at an MOI of 10 −7 and therapeutic doses of colistin significantly elevated bacterial suppression. Hence, KARL-1 represents a novel candidate for use against multi-drug resistant AB and the therapeutic outcome may be positively influenced by the addition of traditional antibiotics.

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          Most cited references55

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          Antibiotic resistance-the need for global solutions.

          The causes of antibiotic resistance are complex and include human behaviour at many levels of society; the consequences affect everybody in the world. Similarities with climate change are evident. Many efforts have been made to describe the many different facets of antibiotic resistance and the interventions needed to meet the challenge. However, coordinated action is largely absent, especially at the political level, both nationally and internationally. Antibiotics paved the way for unprecedented medical and societal developments, and are today indispensible in all health systems. Achievements in modern medicine, such as major surgery, organ transplantation, treatment of preterm babies, and cancer chemotherapy, which we today take for granted, would not be possible without access to effective treatment for bacterial infections. Within just a few years, we might be faced with dire setbacks, medically, socially, and economically, unless real and unprecedented global coordinated actions are immediately taken. Here, we describe the global situation of antibiotic resistance, its major causes and consequences, and identify key areas in which action is urgently needed. Copyright © 2013 Elsevier Ltd. All rights reserved.
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            GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions.

            J Besemer (2001)
            Improving the accuracy of prediction of gene starts is one of a few remaining open problems in computer prediction of prokaryotic genes. Its difficulty is caused by the absence of relatively strong sequence patterns identifying true translation initiation sites. In the current paper we show that the accuracy of gene start prediction can be improved by combining models of protein-coding and non-coding regions and models of regulatory sites near gene start within an iterative Hidden Markov model based algorithm. The new gene prediction method, called GeneMarkS, utilizes a non-supervised training procedure and can be used for a newly sequenced prokaryotic genome with no prior knowledge of any protein or rRNA genes. The GeneMarkS implementation uses an improved version of the gene finding program GeneMark.hmm, heuristic Markov models of coding and non-coding regions and the Gibbs sampling multiple alignment program. GeneMarkS predicted precisely 83.2% of the translation starts of GenBank annotated Bacillus subtilis genes and 94.4% of translation starts in an experimentally validated set of Escherichia coli genes. We have also observed that GeneMarkS detects prokaryotic genes, in terms of identifying open reading frames containing real genes, with an accuracy matching the level of the best currently used gene detection methods. Accurate translation start prediction, in addition to the refinement of protein sequence N-terminal data, provides the benefit of precise positioning of the sequence region situated upstream to a gene start. Therefore, sequence motifs related to transcription and translation regulatory sites can be revealed and analyzed with higher precision. These motifs were shown to possess a significant variability, the functional and evolutionary connections of which are discussed.
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              tRNAscan-SE: A Program for Improved Detection of Transfer RNA Genes in Genomic Sequence

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                Author and article information

                Contributors
                hhorz@ukaachen.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                20 September 2018
                20 September 2018
                2018
                : 8
                : 14140
                Affiliations
                [1 ]ISNI 0000 0000 8653 1507, GRID grid.412301.5, Institute of Medical Microbiology, , RWTH Aachen University Hospital, ; Aachen, Germany
                [2 ]Third Medical Department for Haematology and Oncology, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
                [3 ]ISNI 0000 0000 8653 1507, GRID grid.412301.5, Department of Infection Control and Infectious Diseases, , RWTH Aachen University Hospital, ; Aachen, Germany
                [4 ]ISNI 0000 0000 8653 1507, GRID grid.412301.5, Electron Microscopy Facility, Institute of Pathology, , RWTH Aachen University Hospital, ; Aachen, Germany
                Article
                32344
                10.1038/s41598-018-32344-y
                6147977
                30237558
                b91de1ac-a3ad-4a74-b023-6dd626f5d111
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 May 2018
                : 6 September 2018
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