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      Human gut resistome can be country-specific

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      PeerJ
      PeerJ Inc.
      Resistome, Antibiotic resistance gene, Metagenomics, Machine learning

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          Abstract

          The emergence and spread of antibiotic resistance have become emerging threats to human health. The human gut is a large reservoir for antibiotic resistance genes. The gut resistome may be influenced by many factors, but the consumption of antibiotics at both individual and country level should be one of the most significant factors. Previous studies have suggested that the gut resistome of different populations may vary, but lack quantitative characterization supported with relatively large datasets. In this study, we filled the gap by analyzing a large gut resistome dataset of 1,267 human gut samples of America, China, Denmark, and Spain. We built a stacking machine-learning model to determine whether the gut resistome can act as the sole feature to identify the nationality of an individual reliably. It turned out that the machine learning method could successfully identify American, Chinese, Danish, and Spanish populations with F1 score of 0.964, 0.987, 0.971, and 0.986, respectively. Our finding does highlight the significant differences in the composition of the gut resistome among different nationalities. Our study should be valuable for policy-makers to look into the influences of country-specific factors of the human gut resistome.

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          Most cited references28

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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

            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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              Review of Antimicrobial Resistance in the Environment and Its Relevance to Environmental Regulators

              The environment is increasingly being recognized for the role it might play in the global spread of clinically relevant antibiotic resistance. Environmental regulators monitor and control many of the pathways responsible for the release of resistance-driving chemicals into the environment (e.g., antimicrobials, metals, and biocides). Hence, environmental regulators should be contributing significantly to the development of global and national antimicrobial resistance (AMR) action plans. It is argued that the lack of environment-facing mitigation actions included in existing AMR action plans is likely a function of our poor fundamental understanding of many of the key issues. Here, we aim to present the problem with AMR in the environment through the lens of an environmental regulator, using the Environment Agency (England’s regulator) as an example from which parallels can be drawn globally. The issues that are pertinent to environmental regulators are drawn out to answer: What are the drivers and pathways of AMR? How do these relate to the normal work, powers and duties of environmental regulators? What are the knowledge gaps that hinder the delivery of environmental protection from AMR? We offer several thought experiments for how different mitigation strategies might proceed. We conclude that: (1) AMR Action Plans do not tackle all the potentially relevant pathways and drivers of AMR in the environment; and (2) AMR Action Plans are deficient partly because the science to inform policy is lacking and this needs to be addressed.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ
                PeerJ Inc. (San Diego, USA )
                2167-8359
                21 March 2019
                2019
                : 7
                : e6389
                Affiliations
                [1 ]Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming, China
                [2 ]Kunming College of Life Science, University of Chinese Academy of Sciences , Kunming, China
                [3 ]School of Public Health, Sun Yat-sen University , Guangzhou, China
                [4 ]School of Mathematics, Sun Yat-sen University , Guangzhou, China
                [5 ]One Health Center of Excellence for Research & Training, Sun Yat-Sen University , Guangzhou, China
                [6 ]Key Laboratory for Tropical Disease Control of Ministry of Education, Sun Yat-Sen University , Guangzhou, China
                Article
                6389
                10.7717/peerj.6389
                6431545
                30923648
                3aa28d17-e55a-4321-bd3c-5f42963c8a10
                © 2019 Xia et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 28 June 2018
                : 4 January 2019
                Funding
                Funded by: National Natural Science Fund of China
                Award ID: 31872499
                Funded by: National Key Research and Development Program of China
                Award ID: 2018YFD0500505
                Funded by: National Science and Technology Major Project
                Award ID: 2018ZX10101002
                This work was supported by the National Natural Science Fund of China (31872499), the National Key Research and Development Program of China (2018YFD0500505), and the National Science and Technology Major Project (2018ZX10101002). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Bioinformatics
                Microbiology
                Epidemiology
                Global Health
                Data Mining and Machine Learning

                resistome,antibiotic resistance gene,metagenomics,machine learning

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