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      Metagenomics-Based Approach to Source-Attribution of Antimicrobial Resistance Determinants – Identification of Reservoir Resistome Signatures

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          Abstract

          Metagenomics can unveil the genetic content of the total microbiota in different environments, such as food products and the guts of humans and livestock. It is therefore considered of great potential to investigate the transmission of foodborne hazards as part of source-attribution studies. Source-attribution of antimicrobial resistance (AMR) has traditionally relied on pathogen isolation, while metagenomics allows investigating the full span of AMR determinants. In this study, we hypothesized that the relative abundance of fecal resistome components can be associated with specific reservoirs, and that resistomes can be used for AMR source-attribution. We used shotgun-sequences from fecal samples of pigs, broilers, turkeys- and veal calves collected across Europe, and fecal samples from humans occupationally exposed to livestock in one country (pig slaughterhouse workers, pig and broiler farmers). We applied both hierarchical and flat forms of the supervised classification ensemble algorithm Random Forests to classify resistomes into corresponding reservoir classes. We identified country-specific and -independent AMR determinants, and assessed the impact of country-specific determinants when attributing AMR resistance in humans. Additionally, we performed a similarity percentage analysis with the full spectrum of AMR determinants to identify resistome signatures for the different reservoirs. We showed that the number of AMR determinants necessary to attribute a resistome into the correct reservoir increases with a larger reservoir heterogeneity, and that the impact of country-specific resistome signatures on prediction varies between countries. We predicted a higher occupational exposure to AMR determinants among workers exposed to pigs than among those exposed to broilers. Additionally, results suggested that AMR exposure on pig farms was higher than in pig slaughterhouses. Human resistomes were more similar to pig and veal calves’ resistomes than to those of broilers and turkeys, and the majority of these resistome dissimilarities can be explained by a small set of AMR determinants. We identified resistome signatures for each individual reservoir, which include AMR determinants significantly associated with on-farm antimicrobial use. We attributed human resistomes to different livestock reservoirs using Random Forests, which allowed identifying pigs as a potential source of AMR in humans. This study thus demonstrates that it is possible to apply metagenomics in AMR source-attribution.

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          Most cited references41

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            A Coefficient of Agreement for Nominal Scales

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              CD-HIT: accelerated for clustering the next-generation sequencing data

              Summary: CD-HIT is a widely used program for clustering biological sequences to reduce sequence redundancy and improve the performance of other sequence analyses. In response to the rapid increase in the amount of sequencing data produced by the next-generation sequencing technologies, we have developed a new CD-HIT program accelerated with a novel parallelization strategy and some other techniques to allow efficient clustering of such datasets. Our tests demonstrated very good speedup derived from the parallelization for up to ∼24 cores and a quasi-linear speedup for up to ∼8 cores. The enhanced CD-HIT is capable of handling very large datasets in much shorter time than previous versions. Availability: http://cd-hit.org. Contact: liwz@sdsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                15 January 2021
                2020
                : 11
                : 601407
                Affiliations
                [1] 1Division of Genomic Epidemiology, National Food Institute, Technical University of Denmark , Kgs Lyngby, Denmark
                [2] 2Institute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University , Utrecht, Netherlands
                [3] 3Intomics A/S , Lyngby, Denmark
                [4] 4Wageningen Bioveterinary Research , Lelystad, Netherlands
                [5] 5Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University , Utrecht, Netherlands
                Author notes

                Edited by: Jean-christophe Augustin, INRA École Nationale Vétérinaire d’Alfort (ENVA), France

                Reviewed by: Pierre-Emmanuel Douarre, Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail (ANSES), France; Héctor Argüello, University of Córdoba, Spain

                *Correspondence: Ana Sofia Ribeiro Duarte, asrd@ 123456food.dtu.dk

                This article was submitted to Food Microbiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2020.601407
                7843941
                33519742
                7c2afac8-172a-471a-96f6-5257043f0a94
                Copyright © 2021 Duarte, Röder, Van Gompel, Petersen, Hansen, Hansen, Bossers, Aarestrup, Wagenaar and Hald.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 31 August 2020
                : 14 December 2020
                Page count
                Figures: 8, Tables: 1, Equations: 0, References: 46, Pages: 17, Words: 0
                Funding
                Funded by: Seventh Framework Programme 10.13039/501100004963
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                metagenomics,source-attribution,antimicrobial resistance,resistome,random forests,machine learning

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