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      Phylogenetic barriers to horizontal transfer of antimicrobial peptide resistance genes in the human gut microbiota

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          Abstract

          The human gut microbiota has adapted to the presence of antimicrobial peptides (AMPs) that are ancient components of immune defence. Despite its medical importance, it has remained unclear whether AMP resistance genes in the gut microbiome are available for genetic exchange between bacterial species. Here we show that AMP- and antibiotic-resistance genes differ in their mobilization patterns and functional compatibilities with new bacterial hosts. First, whereas AMP resistance genes are widespread in the gut microbiome, their rate of horizontal transfer is lower than that of antibiotic resistance genes. Second, gut microbiota culturing and functional metagenomics revealed that AMP resistance genes originating from phylogenetically distant bacteria have only a limited potential to confer resistance in Escherichia coli, an intrinsically susceptible species. Taken together, functional compatibility with the new bacterial host emerges as a key factor limiting the genetic exchange of AMP resistance genes. Finally, our results suggest that AMPs induce highly specific changes in the composition of the human microbiota with implications for disease risks.

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          Most cited references36

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Is Open Access

            ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data

            The Environment for Tree Exploration (ETE) is a computational framework that simplifies the reconstruction, analysis, and visualization of phylogenetic trees and multiple sequence alignments. Here, we present ETE v3, featuring numerous improvements in the underlying library of methods, and providing a novel set of standalone tools to perform common tasks in comparative genomics and phylogenetics. The new features include (i) building gene-based and supermatrix-based phylogenies using a single command, (ii) testing and visualizing evolutionary models, (iii) calculating distances between trees of different size or including duplications, and (iv) providing seamless integration with the NCBI taxonomy database. ETE is freely available at http://etetoolkit.org
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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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                Author and article information

                Journal
                101674869
                44774
                Nat Microbiol
                Nat Microbiol
                Nature microbiology
                2058-5276
                13 November 2018
                17 December 2018
                March 2019
                17 June 2019
                : 4
                : 3
                : 447-458
                Affiliations
                [1 ]Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, 6726 Szeged, Hungary
                [2 ]Department of Genetics, Eötvös Loránd University, 1117 Budapest, Hungary
                [3 ]Doctoral School in Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary
                [4 ]SeqOmics Biotechnology Ltd., 6782 Mórahalom, Hungary
                [5 ]Sequencing Platform, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, 6726 Szeged, Hungary
                [6 ]Hereditary Endocrine Tumors Research Group, Hungarian Academy of Sciences and Semmelweis University, 1088 Budapest, Hungary
                [7 ]1 st Department of Internal Medicine, Albert Szent-Györgyi Health Centre, University of Szeged, 6720 Szeged, Hungary
                Author notes
                [* ]Correspondence to Csaba Pál, Balázs Papp or Bálint Kintses. cpal@ 123456brc.hu , pappb@ 123456brc.hu or kintses.balint@ 123456brc.mta.hu
                Article
                EMS80358
                10.1038/s41564-018-0313-5
                6387620
                30559406
                7e54627c-58d2-4c13-8481-c543c5ad73b6

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